Digital Commons Network^™

Towards Modeling Human Attention From Eye Movements For Neural Source Code Summarization, Aakash Bansal, Bonita Sharif, Collin Mcmillan

School of Computing: Faculty Publications

Neural source code summarization is the task of generating natural language descriptions of source code behavior using neural networks. A fundamental component of most neural models is an attention mechanism. The attention mechanism learns to connect features in source code to specific words to use when generating natural language descriptions. Humans also pay attention to some features in code more than others. This human attention reflects experience and high-level cognition well beyond the capability of any current neural model. In this paper, we use data from published eye-tracking experiments to create a model of this human attention. The model predicts …

Co-Existence With Ieee 802.11 Networks In The Ism Band Without Channel Estimation, Muhammad Naveed Aman, Muhammad Ishfaq, Biplab Sikdar

School of Computing: Faculty Publications

Any new deployment of networks in the industrial, scientific, and medical (ISM) band, even though it is license-free, has to co-exist with IEEE 802.11 networks. IoT devices are typically deployed in the ISM band, creating a spectrum bottleneck for competing networks. This paper investigates the issue of co-existence of wireless networks with WiFi networks. In our scenario, we consider WiFi as the “primary” or higher priority network co-existing with multiple “secondary” networks that may be used for low priority devices, with both networks operating in the ISM band. Towards this end, we first develop an analytical model for a metric …

A Light-Weight Technique To Detect Gps Spoofing Using Attenuated Signal Envelopes, Xiao Wei, Muhammad Naveed Aman, Biplab Sikdar

School of Computing: Faculty Publications

Global Positioning System (GPS) spoofing attacks have attracted more attention as one of the most effective GPS attacks. Since the signals from an authentic satellite and the spoofer undergo different attenuation, the captured envelope of fake GPS signals exhibits distinctive transmission characteristics due to short transmission paths. This can be utilized for GPS spoofing detection. The existing technique for GPS spoofing are either computationally too expensive, require specialize hardware/ software updates, or are not accurate enough. To solve these issues, we propose a light-weight GPS spoofing detection method based on a dynamic threshold and captured signal envelope. We validate the …

Realizing Molecular Machine Learning Through Communications For Biological Ai: Future Directions And Challenges, Sasitharan Balasubramaniam, Samitha Somathilaka, Sehee Sun, Adrian Ratwatte, Massimiliano Pierobon

School of Computing: Faculty Publications

Artificial Intelligence (AI) and Machine Learning (ML) are weaving their way into the fabric of society, where they are playing a crucial role in numerous facets of our lives. As we witness the increased deployment of AI and ML in various types of devices, we benefit from their use into energy-efficient algorithms for low powered devices. In this paper, we investigate a scale and medium that is far smaller than conventional devices as we move towards molecular systems that can be utilized to perform machine learning functions, i.e., Molecular Machine Learning (MML). Fundamental to the operation of MML is the …

Room-Temperature Polariton Quantum Fluids In Halide Perovskites, Kai Peng, Renjie Tao, Louis Haeberlé, Quanwei Li, Dafei Jin, Graham R. Fleming, Stéphane Kéna-Cohen, Xiang Zhang, Wei Bao

School of Computing: Faculty Publications

Quantum fluids exhibit quantum mechanical effects at the macroscopic level, which contrast strongly with classical fluids. Gain-dissipative solid-state exciton-polaritons systems are promising emulation platforms for complex quantum fluid studies at elevated temperatures. Recently, halide perovskite polariton systems have emerged as materials with distinctive advantages over other room-temperature systems for future studies of topological physics, non-Abelian gauge fields, and spin-orbit interactions. However, the demonstration of nonlinear quantum hydrodynamics, such as superfluidity and Čerenkov flow, which is a consequence of the renormalized elementary excitation spectrum, remains elusive in halide perovskites. Here, using homogenous halide perovskites single crystals, we report, in both one- …

Incoherent And Online Dictionary Learning Algorithm For Motion Prediction, Farrukh Hafeez, Usman Ullah Sheikh, Asif Iqbal, Muhammad Naveed Aman

School of Computing: Faculty Publications

Accurate model development and efficient representations of multivariate trajectories are crucial to understanding the behavioral patterns of pedestrian motion. Most of the existing algorithms use offline learning approaches to learn such motion behaviors. However, these approaches cannot take advantage of the streams of data that are available after training has concluded, and typically are not generalizable to data that they have not seen before. To solve this problem, this paper proposes two algorithms for learning incoherent dictionaries in an offline and online manner by extending the offline augmented semi-non-negative sparse coding (ASNSC) algorithm. We do this by adding a penalty …

Icebar: Feedback-Driven Iterative Repair Of Alloy Specifications, Simón Gutiérrez Brida, Germán Regis, Guolong Zheng, Hamid Bagheri, Thanhvu Nguyen, Nazareno Aguirre, Marcelo Frias

School of Computing: Faculty Publications

Automated program repair (APR) techniques have shown great success in automatically finding fixes for programs in programming languages such as C or Java. In this work, we focus on repairing formal specifications, in particular for the Alloy specification language. As opposed to most APR tools, our approach to repair Alloy specifications, named ICEBAR, does not use test-based oracles for patch assessment. Instead, ICEBAR relies on the use of property-based oracles, commonly found in Alloy specifications as predicates and assertions. These property-based oracles define stronger conditions for patch assessment, thus reducing the notorious overfitting issue caused by using test-based oracles, …

The Road Not Taken: Exploring Alias Analysis Based Optimizations Missed By The Compiler, Khushboo Chitre, Piyus Kedia, Rahul Purandare

School of Computing: Faculty Publications

Context-sensitive inter-procedural alias analyses are more precise than intra-procedural alias analyses. However, context-sensitive inter-procedural alias analyses are not scalable. As a consequence, most of the production compilers sacrifice precision for scalability and implement intra-procedural alias analysis. The alias analysis is used by many compiler optimizations, including loop transformations. Due to the imprecision of alias analysis, the program’s performance may suffer, especially in the presence of loops.

Previous work proposed a general approach based on code-versioning with dynamic checks to disambiguate pointers at runtime. However, the overhead of dynamic checks in this approach is 𝑂(𝑙𝑜𝑔 𝑛), which is substantially high to …

Deja Vu: Semantics-Aware Recording And Replay Of High-Speed Eye Tracking And Interaction Data To Support Cognitive Studies Of Software Engineering Tasks—Methodology And Analyses, Vlas Zyrianov, Cole S. Peterson, Drew T. Guarnera, Joshua Behler, Praxis Weston, Bonita Sharif Ph.D., Jonathan I. Maletic

School of Computing: Faculty Publications

The paper introduces a fundamental technological problem with collecting high-speed eye tracking data while studying software engineering tasks in an integrated development environment. The use of eye trackers is quickly becoming an important means to study software developers and how they comprehend source code and locate bugs. High quality eye trackers can record upwards of 120 to 300 gaze points per second. However, it is not always possible to map each of these points to a line and column position in a source code file (in the presence of scrolling and file switching) in real time at data rates over …

Neural Network Repair With Reachability Analysis, Xiaodong Yang, Tom Yamaguchi, Tran Hoang-Dung, Bardh Hoxha, Taylor T. Johnson, Danil Prokhorov

School of Computing: Faculty Publications

Safety is a critical concern for the next generation of autonomy that is likely to rely heavily on deep neural networks for perception and control. Formally verifying the safety and robustness of well-trained DNNs and learning-enabled cyber-physical systems (Le-CPS) under adversarial attacks, model uncertainties, and sensing errors is essential for safe autonomy. This research proposes a framework to repair unsafe DNNs in safety-critical systems with reachability analysis. The repair process is inspired by adversarial training which has demonstrated high effectiveness in improving the safety and robustness of DNNs. Different from traditional adversarial training approaches where adversarial examples are utilized from …

Pitfalls And Guidelines For Using Time-Based Git Data, Samuel W. Flint, Jigyasa Chauhan, Robert Dyer

School of Computing: Faculty Publications

Many software engineering research papers rely on time-based data (e.g., commit timestamps, issue report creation/update/close dates, release dates). Like most real-world data however, time-based data is often dirty. To date, there are no studies that quantify how frequently such data is used by the software engineering research community, or investigate sources of and quantify how often such data is dirty. Depending on the research task and method used, including such dirty data could aect the research results. This paper presents an extended survey of papers that utilize time-based data, published in the Mining Software Repositories (MSR) conference series. Out of …

Decipherment Challenges Due To Tamga And Letter Mix-Ups In An Old Hungarian Runic Inscription From The Altai Mountains, Peter Revesz

School of Computing: Faculty Publications

An Old Hungarian Runic inscription from the Altai Mountains with 40 signs has posed some special challenges for decipherment due to several letter mix-ups and the use of a tamga sign, which is the first reported use of a tamga within this type of script. This paper gives a complete and correct translation and draws some lessons that can be learned about decipherment. It introduces sign similarity matrices as a method of detecting accidental misspellings and shows that sign similarity matrices can be efficiently computed. It also explains the importance of simultaneously achieving the three criteria for a valid decipherment: …

What Makes The Article “Condition Monitoring And Fault Diagnosis Of Electrical Motors—A Review” So Popular?, Wei Qiao

School of Computing: Faculty Publications

Electric motors are widely used in the industrial, commercial, residential, and transportation sectors to power the systems that provide goods and services to end users. The failure of electric motors may cause significant production or service interruption and financial losses. To improve the quality of service of systems driven by electric motors, it is vital to continuously improve the reliability of electric motors. Driven by this demand, various condition monitoring and fault diagnostic techniques for electric motors have been developed by academia and industry over the past decades.

The article “Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review,” written …

Nanomechanical Resonators: Toward Atomic Scale, Bo Xu, Pengcheng Zhang, Jiankai Zhu, Zuheng Liu, Alexander Eichler, Xu-Qian Zheng, Jaesung Lee, Aneesh Dash, Swapnil More, Song Wu, Yanan Wang, Hao Jia, Akshay Naik, Adrian Bachtold, Rui Yang, Philip X.-L. Feng, Zenghui Wang

School of Computing: Faculty Publications

The quest for realizing and manipulating ever smaller man-made movable structures and dynamical machines has spurred tremendous endeavors, led to important discoveries, and inspired researchers to venture to previously unexplored grounds. Scientific feats and technological milestones of miniaturization of mechanical structures have been widely accomplished by advances in machining and sculpturing ever shrinking features out of bulk materials such as silicon. With the flourishing multidisciplinary field of low-dimensional nanomaterials, including one-dimensional (1D) nanowires/nanotubes and two-dimensional (2D) atomic layers such as graphene/ phosphorene, growing interests and sustained effort have been devoted to creating mechanical devices toward the ultimate limit of miniaturization--genuinely …

Enabling Intelligent Iots For Histopathology Image Analysis Using Convolutional Neural Networks, Mohammed H. Alali, Arman Roohi, Shaahin Angizi, Jitender S. Deogun

School of Computing: Faculty Publications

Medical imaging is an essential data source that has been leveraged worldwide in healthcare systems. In pathology, histopathology images are used for cancer diagnosis, whereas these images are very complex and their analyses by pathologists require large amounts of time and effort. On the other hand, although convolutional neural networks (CNNs) have produced near-human results in image processing tasks, their processing time is becoming longer and they need higher computational power. In this paper, we implement a quantized ResNet model on two histopathology image datasets to optimize the inference power consumption. We analyze classification accuracy, energy estimation, and hardware utilization …

Deep Reinforcement Learning For End-To-End Network Slicing: Challenges And Solutions, Qiang Liu, Nakjung Choi, Tao Han

School of Computing: Faculty Publications

5G and beyond is expected to enable various emerging use cases with diverse performance requirements from vertical industries. To serve these use cases cost-effectively, network slicing plays a key role in dynamically creating virtual end-to-end networks according to specific resource demands. A network slice may have hundreds of configurable parameters over multiple technical domains that define the performance of the network slice, which makes it impossible to use traditional model-based solutions to orchestrate resources for network slices. In this article, we discuss how to design and deploy deep reinforcement learning (DRL), a model-free approach, to address the network slicing problem. …

Profiling A Community-Specific Function Landscape For Bacterial Peptides Through Protein-Level Meta-Assembly And Machine Learning, Mitra Vajjala, Brady Johnson, Lauren Kasparek, Michael Leuze, Qiuming Yao

School of Computing: Faculty Publications

Small proteins, encoded by small open reading frames, are only beginning to emerge with the current advancement of omics technology and bioinformatics. There is increasing evidence that small proteins play roles in diverse critical biological functions, such as adjusting cellular metabolism, regulating other protein activities, controlling cell cycles, and affecting disease physiology. In prokaryotes such as bacteria, the small proteins are largely unexplored for their sequence space and functional groups. For most bacterial species from a natural community, the sample cannot be easily isolated or cultured, and the bacterial peptides must be better characterized in a metagenomic manner. The bacterial …

Internal Model Control (Imc)-Based Active And Reactive Power Control Of Brushless Double-Fed Induction Generator With Notch Filter, Ahsanullah Memon, Mohd Wazir Bin Mustafa, Zohaib Hussain Laghari, Touqeer Ahmed Jumani, Waqas Anjum, Shafi Ullah, Muhammad Naveed Aman

School of Computing: Faculty Publications

The increase in demand for electricity and, in particular, green energy has put renewable energy systems at the focal point of energy policy worldwide. The higher reliability of brushless doubly fed induction generators (BDFIGs) makes them suitable for offshore and remote wind energy generation (WEG) applications. Besides, controlling the active and reactive powers in an electrical power system is critical for optimal voltage regulation, reduced power losses, and enhanced utilization of installed equipment. However, the existing literature on BDFIG’s active and reactive power control highlights the poor dynamic response and high transients with harmonic generation during inductive load insertion. It …

On Approximating Total Variation Distance, Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel, Dimitrios Myrisiotis, A. Pavan, N. V. Vinodchandran

School of Computing: Faculty Publications

Total variation distance (TV distance) is a fundamental notion of distance between probability distributions. In this work, we introduce and study the computational problem of determining the TV distance between two product distributions over the domain {0, 1}ⁿ. We establish the following results.

1. Exact computation of TV distance between two product distributions is #P-complete. This is in stark contrast with other distance measures such as KL, Chi-square, and Hellinger which tensorize over the marginals.

2. Given two product distributions P and Q with marginals of P being at least 1/2 and marginals of Q being at most …

Inter-Cell Slicing Resource Partitioning Via Coordinated Multi-Agent Deep Reinforcement Learning, Tianlun Hu, Qi Liao, Qiang Liu, Dan Wellington, Georg Carle

School of Computing: Faculty Publications

Network slicing enables the operator to configure virtual network instances for diverse services with specific requirements. To achieve the slice-aware radio resource scheduling, dynamic slicing resource partitioning is needed to orchestrate multi-cell slice resources and mitigate inter-cell interference. It is, however, challenging to derive the analytical solutions due to the complex inter-cell interdependencies, interslice resource constraints, and service-specific requirements. In this paper, we propose a multi-agent deep reinforcement learning (DRL) approach that improves the max-min slice performance while maintaining the constraints of resource capacity. We design two coordination schemes to allow distributed agents to coordinate and mitigate inter-cell interference. The …

Systems-Based Approach For Optimization Of Assembly-Free Bacterial Mlst Mapping, Natasha Pavlovikj, Joao Carlos Gomes-Neto, Jitender Deogun, Andrew Benson

School of Computing: Faculty Publications

Epidemiological surveillance of bacterial pathogens requires real-time data analysis with a fast turnaround, while aiming at generating two main outcomes: (1) species-level identification and (2) variant mapping at different levels of genotypic resolution for population-based tracking and surveillance, in addition to predicting traits such as antimicrobial resistance (AMR). Multilocus sequence typing (MLST) aids this process by identifying sequence types (ST) based on seven ubiquitous genome-scattered loci. In this paper, we selected one assembly-dependent and one assembly-free method for ST mapping and applied them with the default settings and ST schemes they are distributed with, and systematically assessed their accuracy and …

Ubjective Information And Survival In A Simulated Biological System, Tyler S. Barker, Massimiliano Pierobon, Peter J. Thomas

School of Computing: Faculty Publications

Information transmission and storage have gained traction as unifying concepts to characterize biological systems and their chances of survival and evolution at multiple scales. Despite the potential for an information-based mathematical framework to offer new insights into life processes and ways to interact with and control them, the main legacy is that of Shannon’s, where a purely syntactic characterization of information scores systems on the basis of their maximum information efficiency. The latter metrics seem not entirely suitable for biological systems, where transmission and storage of different pieces of information (carrying different semantics) can result in different chances of survival. …

Pitfalls And Guidelines For Using Time-Based Git Data, Samuel W. Flint, Jigyasa Chauhan, Robert Dyer

School of Computing: Faculty Publications

Many software engineering research papers rely on time-based data (e.g., commit timestamps, issue report creation/update/close dates, release dates). Like most real-world data however, time-based data is often dirty. To date, there are no studies that quantify how frequently such data is used by the software engineering research community, or investigate sources of and quantify how often such data is dirty. Depending on the research task and method used, including such dirty data could affect the research results. This paper presents an extended survey of papers that utilize time-based data, published in the Mining Software Repositories (MSR) conference series. Out of …

Autonomous, Long-Range, Sensor Emplacement Using Unmanned Aircraft Systems, Adam Plowcha, Justin Bradley, Jacob Hoberg, Thomas Ammon, Mark Nail, Brittany Duncan, Carrick Detweiler

School of Computing: Faculty Publications

Automated, in-ground sensor emplacement can significantly improve remote, terrestrial, data collection capabilities. Utilizing a multicopter, unmanned aircraft system (UAS) for this purpose allows sensor insertion with minimal disturbance to the target site or surrounding area. However, developing an emplacement mechanism for a small multicopter, autonomy to manage the target selection and implantation process, as well as long-range deployment are challenging to address. We have developed an autonomous, multicopter UAS that can implant subsurface sensor devices. We enhanced the UAS autopilot with autonomy for target and landing zone selection, as well as ensuring the sensor is implanted properly in the ground. …

Rethinking Sampled-Data Control For Unmanned Aircraft Systems, Xinkai Zhang, Justin M. Bradley

School of Computing: Faculty Publications

Unmanned aircraft systems are expected to provide both increasingly varied functionalities and outstanding application performances, utilizing the available resources. In this paper, we explore the recent advances and challenges at the intersection of real-time computing and control and show how rethinking sampling strategies can improve performance and resource utilization. We showcase a novel design framework, cyber-physical co-regulation, which can efficiently link together computational and physical characteristics of the system, increasing robust performance and avoiding pitfalls of event-triggered sampling strategies. A comparison experiment of different sampling and control strategies was conducted and analyzed. We demonstrate that co-regulation has resource savings similar …

Security, Trust And Privacy For Cloud, Fog And Internet Of Things, Chien-Ming Chen, Shehzad Ashraf Chaudhry, Kuo-Hui Yeh, Muhammad Naveed Aman

School of Computing: Faculty Publications

No abstract provided.

High Throughput Analysis Of Leaf Chlorophyll Content In Sorghum Using Rgb, Hyperspectral, And Fluorescence Imaging And Sensor Fusion, Huichun Zhang, Yufeng Ge, Xinyan Xie, Abbas Atefi, Nuwan Wijewardane,, Suresh Thapa

School of Computing: Faculty Publications

Leaf chlorophyll content plays an important role in indicating plant stresses and nutrient status. Traditional approaches for the quantification of chlorophyll content mainly include acetone ethanol extraction, spectrophotometry and high-performance liquid chromatography. Such destructive methods based on laboratory procedures are time consuming, expensive, and not suitable for high-throughput analysis. High throughput imaging techniques are now widely used for non-destructive analysis of plant phenotypic traits. In this study three imaging modules (RGB, hyperspectral, and fluorescence imaging) were, separately and in combination, used to estimate chlorophyll content of sorghum plants in a greenhouse environment. Color features, spectral indices, and chlorophyll fluorescence intensity …

Using Deep Learning To Detect Digitally Encoded Dna Trigger For Trojan Malware In Bio‑Cyber Attacks, M. S. Islam, S. Ivanov, H. Awan, J. Drohan, Sasitharan Balasubramaniam, L. Coffey, Srivatsan Kidambi, W. Sri-Saan

School of Computing: Faculty Publications

This article uses Deep Learning technologies to safeguard DNA sequencing against Bio-Cyber attacks. We consider a hybrid attack scenario where the payload is encoded into a DNA sequence to activate a Trojan malware implanted in a software tool used in the sequencing pipeline in order to allow the perpetrators to gain control over the resources used in that pipeline during sequence analysis. The scenario considered in the paper is based on perpetrators submitting synthetically engineered DNA samples that contain digitally encoded IP address and port number of the perpetrator’s machine in the DNA. Genetic analysis of the sample’s DNA will …

Real-Time Dynamic Map With Crowdsourcing Vehicles In Edge Computing, Qiang Liu, Tao Han, Jiang (Linda) Xie, Baekgyu Kim

School of Computing: Faculty Publications

Autonomous driving perceives surroundings with line-of-sight sensors that are compromised under environmental uncertainties. To achieve real time global information in high definition map, we investigate to share perception information among connected and automated vehicles. However, it is challenging to achieve real time perception sharing under varying network dynamics in automotive edge computing. In this paper, we propose a novel real time dynamic map, named LiveMap to detect, match, and track objects on the road. We design the data plane of LiveMap to efficiently process individual vehicle data with multiple sequential computation components, including detection, projection, extraction, matching and combination. We …

Quasi-Spherical Absorbing Receiver Model Of Glioblastoma Cells For Exosome-Based Molecular Communications, Caio Fonseca, Michael Taynan Barros, Andreani Odysseos, Srivatsan Kidambi, Sasitharan Balasubramaniam

School of Computing: Faculty Publications

In this paper, we propose a mathematical and computational model for the GBM initiating of recurring focus as a quasi-spherical absorbing receiver considering the irregular shape as a Bernoulli trial process that accounts for the uncontrollable tumor growth over an initial spherical surface. Our results show that when GBM grow to irregular quasi-sphere shapes, they will increase the channel capacity, which is fully aligned with the evolution and configuration of GSC niches in GBM cultures.