Computer Sciences Commons^™

Amazon Alexa + Linked Open Data: Theorizing Concerning Relationships Between (Surveillant) Smart-Home Voice Assistants And Linked Open Data, Michelle Nitto

Publications and Research

No abstract provided.

Trajtim Krahasues Ndërmjet Rest Api Dhe Graphql Api, Petrit Kabashi

Theses and Dissertations

Zhvillimi i webit po evoulon çdo ditë, dhe nevoja për zhvillim të web aplikacioneve është në rritje te sipërm. Ky hulumtim i jonë ka për synim krahasimin e shërbimeve që përdoren në zhvillim të web aplikacioneve, si dhe teknologjitë dhe gjuhët programuese të cilat mund të përdoren gjatë zhvillimit. Krahasimet tona ofrojnë mundësinë e zgjedhjes së mënyrës më të shpejtë dhe më efektive për zhvillim te web aplikacioneve.

Në këtë punimin tonë kemi zhvilluar një aplikacion i cili është bazuar në dy shërbimet që përdoren për zhvillim të API si REST API dhe GraphQL. Këto dy teknologji janë krahasuar me …

Fast Forensic Triage Using Centralised Thumbnail Caches On Windows Operating Systems, Sean Mckeown, Gordon Russell, Petra Leimich

Journal of Digital Forensics, Security and Law

A common investigative task is to identify known contraband images on a device, which typically involves calculating cryptographic hashes for all the files on a disk and checking these against a database of known contraband. However, modern drives are now so large that it can take several hours just to read this data from the disk, and can contribute to the large investigative backlogs suffered by many law enforcement bodies. Digital forensic triage techniques may thus be used to prioritise evidence and effect faster investigation turnarounds. This paper proposes a new forensic triage method for investigating disk evidence relating to …

Fkrr-Mvsf: A Fuzzy Kernel Ridge Regression Model For Identifying Dna-Binding Proteins By Multi-View Sequence Features Via Chou's Five-Step Rule, Yi Zou, Yije Ding, Jijun Tang, Fei Guo, Li Peng

Faculty Publications

DNA-binding proteins play an important role in cell metabolism. In biological laboratories, the detection methods of DNA-binding proteins includes yeast one-hybrid methods, bacterial singles and X-ray crystallography methods and others, but these methods involve a lot of labor, material and time. In recent years, many computation-based approachs have been proposed to detect DNA-binding proteins. In this paper, a machine learning-based method, which is called the Fuzzy Kernel Ridge Regression model based on Multi-View Sequence Features (FKRR-MVSF), is proposed to identifying DNA-binding proteins. First of all, multi-view sequence features are extracted from protein sequences. Next, a Multiple Kernel Learning (MKL) algorithm …

Dimensions Of 'Socio' Vulnerabilities Of Advanced Persistent Threats, Mathew Nicho, Christopher D. Mcdermott

All Works

© 2019 University of Split, FESB. Advanced Persistent Threats (APT) are highly targeted and sophisticated multi-stage attacks, utilizing zero day or near zero-day malware. Directed at internetworked computer users in the workplace, their growth and prevalence can be attributed to both socio (human) and technical (system weaknesses and inadequate cyber defenses) vulnerabilities. While many APT attacks incorporate a blend of socio-technical vulnerabilities, academic research and reported incidents largely depict the user as the prominent contributing factor that can weaken the layers of technical security in an organization. In this paper, our objective is to explore multiple dimensions of socio factors …

Symmetric Inkball Alignment With Loopy Models, Nicholas Howe, Ji Won Chung

Computer Science: Faculty Publications

Alignment tasks generally seek to establish a spatial correspondence between two versions of a text, for example between a set of manuscript images and their transcript. This paper examines a different form of alignment problem, namely pixel-scale alignment between two renditions of a handwritten word or phrase. Using loopy inkball graph models, the proposed technique finds spatial correspondences between two text images such that similar parts map to each other. The method has applications to word spotting and signature verification, and can provide analytical tools for the study of handwriting variation.

Reoptimization Of The Consensus Pattern Problem Under Pattern Length Modification, Jhoirene B. Clemente, Proceso L. Fernandez Jr, Richelle Ann B. Juayong, Jasmine A. Malinao, Ivy Ordanel, Henry N. Adorna

Department of Information Systems & Computer Science Faculty Publications

In Bioinformatics, finding conserved regions in genomic sequences remains to be a challenge not just because of the increasing size of genomic data collected but because of the hardness of the combinatorial model of the problem. One problem formulation is called the Consensus Pattern Problem (CPP). Given a set of t n-length strings S = {S₁,..., S_t} defined over some constant size alphabet Σ and an integer l, where l ≤ n, the objective of CPP is to find an l-length string v and a set of l-length substrings si of each Si in S such …

Using Vibrations From A Smartring As An Out-Of-Band Channel For Sharing Secret Keys, Sougata Sen, Varun Mishra, David Kotz

Dartmouth Scholarship

With the rapid growth in the number of Internet of Things (IoT) devices with wireless communication capabilities, and sensitive information collection capabilities, it is becoming increasingly necessary to ensure that these devices communicate securely with only authorized devices. A major requirement of this secure communication is to ensure that both the devices share a secret, which can be used for secure pairing and encrypted communication. Manually imparting this secret to these devices becomes an unnecessary overhead, especially when the device interaction is transient. In this work, we empirically investigate the possibility of using an out-of-band communication channel – vibration, generated …

Learning Meaningful Representations Of Surgical Motion Without Annotations, Robert Dipietro

Link Foundation Modeling, Simulation and Training Fellowship Reports

Superior technical skill in the operating room is associated with better patient outcomes [3,18], and at the core of surgical education is the belief that technical skill is improved through deliberate practice and appropriate feedback [10,17]. However, current standards for providing technical skills training are constrained by time [2], and most available methods for surgical skill assessment are subjective and global [14]. This has motivated interest in delivering targeted and automated assessment and feedback with machines, especially in robot-assisted surgery, during which high-quality surgical motion data can be captured transparently for analysis. Automated surgical activity recognition is an important precursor …

Improved Decay Tolerant Inference Of Previously Uninstalled Computer Applications, Oluwaseun Adegbehingbe, James Jones

Journal of Digital Forensics, Security and Law

When an application is uninstalled from a computer system, the application’s deleted file contents are overwritten over time, depending on factors such as operating system, available unallocated disk space, user activity, etc. As this content decays, the ability to infer the application’s prior presence, based on the remaining digital artifacts, becomes more difficult. Prior research inferring previously installed applications by matching sectors from a hard disk of interest to a previously constructed catalog of labeled sector hashes showed promising results. This prior work used a white list approach to identify relevant artifacts, resulting in no irrelevant artifacts but incurring the …

An Evolutionary Perspective On Foraging Strategies And Group Dynamics In Bio-Social Inspired Cognitive Radio Networks, Anna Wisniewska

Dissertations, Theses, and Capstone Projects

Saturation of wireless channels is inevitable as the number of wireless devices grows exponentially in an environment of limited radio spectrum capacity. Cognitive radio technology has been proposed to relieve overcrowded spectrum resources by allowing licensed channels to be opportunistically accessed by unlicensed users (cognitive radio devices) during periods of time when the license holder (primary user) is absent from its channel. Uncoordinated competition over limited resources among cognitive radio devices poses complex co-existence challenges. We propose novel bio-social inspired behavioral models and map out plausible evolutionary trajectories of co-use strategies in the cognitive radio ecosystem. By drawing parallels between …

Semi-Supervised Regression With Generative Adversarial Networks Using Minimal Labeled Data, Greg Olmschenk

Dissertations, Theses, and Capstone Projects

This work studies the generalization of semi-supervised generative adversarial networks (GANs) to regression tasks. A novel feature layer contrasting optimization function, in conjunction with a feature matching optimization, allows the adversarial network to learn from unannotated data and thereby reduce the number of labels required to train a predictive network. An analysis of simulated training conditions is performed to explore the capabilities and limitations of the method. In concert with the semi-supervised regression GANs, an improved label topology and upsampling technique for multi-target regression tasks are shown to reduce data requirements. Improvements are demonstrated on a wide variety of vision …

Do It Like A Syntactician: Using Binary Gramaticality Judgements To Train Sentence Encoders And Assess Their Sensitivity To Syntactic Structure, Pablo Gonzalez Martinez

Dissertations, Theses, and Capstone Projects

The binary nature of grammaticality judgments and their use to access the structure of syntax are a staple of modern linguistics. However, computational models of natural language rarely make use of grammaticality in their training or application. Furthermore, developments in modern neural NLP have produced a myriad of methods that push the baselines in many complex tasks, but those methods are typically not evaluated from a linguistic perspective. In this dissertation I use grammaticality judgements with artificially generated ungrammatical sentences to assess the performance of several neural encoders and propose them as a suitable training target to make models learn …

Extract Transform And Loading Tool For Email, Amit Rajiv Lawanghare

Electronic Theses, Projects, and Dissertations

This project focuses on applying Extract, Transform and Load (ETL) operations on the relational data exchanged via emails. An Email is an important form of communication by both personal and corporate means as it enables reliable and quick exchange. Many useful files are shared as a form of attachments which contains transactional/ relational data. This tool allows a user to write the filter conditions and lookup conditions on attachments; define the attribute map for attachments to the database table. The Data Cleansing for each attribute can be performed writing rules and their matching state. A user can add custom functions …

Preference Learning And Similarity Learning Perspectives On Personalized Recommendation, Duy Dung Le

Dissertations and Theses Collection (Open Access)

Personalized recommendation, whose objective is to generate a limited list of items (e.g., products on Amazon, movies on Netflix, or pins on Pinterest, etc.) for each user, has gained extensive attention from both researchers and practitioners in the last decade. The necessity of personalized recommendation is driven by the explosion of available options online, which makes it difficult, if not downright impossible, for each user to investigate every option. Product and service providers rely on recommendation algorithms to identify manageable number of the most likely or preferred options to be presented to each user. Also, due to the limited screen …

Exploiting Approximation, Caching And Specialization To Accelerate Vision Sensing Applications, Nguyen Loc Huynh

Dissertations and Theses Collection (Open Access)

Over the past few years, deep learning has emerged as state-of-the-art solutions for many challenging computer vision tasks such as face recognition, object detection, etc. Despite of its outstanding performance, deep neural networks (DNNs) are computational intensive, which prevent them to be widely adopted on billions of mobile and embedded devices with scarce resources. To address that limitation, we
focus on building systems and optimization algorithms to accelerate those models, making them more computational-efficient.
First, this thesis explores the computational capabilities of different existing processors (or co-processors) on modern mobile devices. It recognizes that by leveraging the mobile Graphics Processing …

A Low-Cost Soft Robotic Hand Exoskeleton For Use In Therapy Of Limited Hand–Motor Function, Grant Rudd, Liam Daly, Vukica Jovanovic, Filip Cukov

Engineering Technology Faculty Publications

We present the design and validation of a low-cost, customizable and 3D-printed anthropomorphic soft robotic hand exoskeleton for rehabilitation of hand injuries using remotely administered physical therapy regimens. The design builds upon previous work done on cable actuated exoskeleton designs by implementing the same kinematic functionality, but with the focus shifted to ease of assembly and cost effectiveness as to allow patients and physicians to manufacture and assemble the hardware necessary to implement treatment. The exoskeleton was constructed solely from 3D-printed and widely available of-the-shelf components. Control of the actuators was realized using an Arduino microcontroller, with a custom-designed shield …

A Case Study On Automated Fuzz Target Generation For Large Codebases, Matthew Kelly, Christoph Treude, Alex Murray

Research Collection School Of Computing and Information Systems

Fuzz Testing is a largely automated testing technique that provides random and unexpected input to a program in attempt to trigger failure conditions. Much of the research conducted thus far into Fuzz Testing has focused on developing improvements to available Fuzz Testing tools and frameworks in order to improve efficiency. In this paper however, we instead look at a way in which we can reduce the amount of developer time required to integrate Fuzz Testing to help maintain an existing codebase. We accomplish this with a new technique for automatically generating Fuzz Targets, the modified versions of programs on which …

Enhancing Python Compiler Error Messages Via Stack Overflow, Emillie Thiselton, Christoph Treude

Research Collection School Of Computing and Information Systems

Background: Compilers tend to produce cryptic and uninformative error messages, leaving programmers confused and requiring them to spend precious time to resolve the underlying error. To find help, programmers often take to online question-and-answer forums such as Stack Overflow to start discussion threads about the errors they encountered.Aims: We conjecture that information from Stack Overflow threads which discuss compiler errors can be automatically collected and repackaged to provide programmers with enhanced compiler error messages, thus saving programmers' time and energy.Method: We present Pycee, a plugin integrated with the popular Sublime Text IDE to provide enhanced compiler error messages for the …

Lightweight Fine-Grained Search Over Encrypted Data In Fog Computing, Yinbin Miao, Jianfeng Ma, Ximeng Liu, Jian Weng, Hongwei Li, Hui Li

Research Collection School Of Computing and Information Systems

Fog computing, as an extension of cloud computing, outsources the encrypted sensitive data to multiple fog nodes on the edge of Internet of Things (IoT) to decrease latency and network congestion. However, the existing ciphertext retrieval schemes rarely focus on the fog computing environment and most of them still impose high computational and storage overhead on resource-limited end users. In this paper, we first present a Lightweight Fine-Grained ciphertexts Search (LFGS) system in fog computing by extending Ciphertext-Policy Attribute-Based Encryption (CP-ABE) and Searchable Encryption (SE) technologies, which can achieve fine-grained access control and keyword search simultaneously. The LFGS can shift …

Self-Refining Deep Symmetry Enhanced Network For Rain Removal, Hong Liu, Hanrong Ye, Xia Li, Wei Shi, Mengyuan Liu, Qianru Sun

Research Collection School Of Computing and Information Systems

Rain removal aims to remove the rain streaks on rain images. Traditional methods based on convolutional neural network (CNN) have achieved impressive results. However, these methods are under-performed when dealing with tilted rain streaks, because CNN is not equivariant to object rotations. To tackle this problem, we propose the Deep Symmetry Enhanced Network (DSEN) that explicitly extracts and learns from rotation-equivariant features from rain images. In addition, we design a self-refining strategy to remove rain streaks in a coarse-to-fine manner. The key idea is to reuse DSEN with an information link which passes the gradient flow to the finer stage. …

Confusion And Information Triggered By Photos In Persona Profiles, Joni Salminen, Soon-Gyo Jung, Jisun An, Haewoon Kwak, Lene Nielsen, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

We investigate whether additional photos beyond a single headshot makes a persona profile more informative without confusing the end user. We conduct an eye-tracking experiment and qualitative interviews with digital content creators after varying the persona in photos via a single headshot, a headshot and photo of the persona in different contexts, and a headshot with photos of different people with key persona attributes the gender and age. Findings show that contextual photos provide significantly more persona information to end users; however, showing photos of multiple people engenders confusion and lowers informativeness. Also, as anticipated, viewing additional photos requires more …

Generating Expensive Relationship Features From Cheap Objects, Xiaogang Wang, Qianru Sun, Tat-Seng Chua, Marcelo Ang

Research Collection School Of Computing and Information Systems

We investigate the problem of object relationship classification of visual scenes. For a relationship object1-predicate-object2 that captures the object interaction, its representation is composed by the combination of object1 and object2 features. As a result, relationship classification models usually bias to the frequent objects, leading to poor generalization to rare or unseen objects. Inspired by the data augmentation methods, we propose a novel Semantic Transform Generative Adversarial Network (ST-GAN) that synthesizes relationship features for rare objects, conditioned on the features from random instances of the objects. Specifically, ST-GAN essentially offers a semantic transform function from cheap object features to expensive …

A Light Weight Smartphone Based Human Activity Recognition System With High Accuracy, Md. O. Gani, Taskina Fayezeen, Richard J. Povinelli, Roger O. Smith, Muhammad Arif, Ahmed Kattan, Sheikh Iqbal Ahamed

Computer Science Faculty Research and Publications

With the pervasive use of smartphones, which contain numerous sensors, data for modeling human activity is readily available. Human activity recognition is an important area of research because it can be used in context-aware applications. It has significant influence in many other research areas and applications including healthcare, assisted living, personal fitness, and entertainment. There has been a widespread use of machine learning techniques in wearable and smartphone based human activity recognition. Despite being an active area of research for more than a decade, most of the existing approaches require extensive computation to extract feature, train model, and recognize activities. …

Efficient Distributed Reachability Querying Of Massive Temporal Graphs, Tianming Zhang, Yunjun Gao, Chen Lu, Wei Guo, Shiliang Pu, Baihua Zheng, Christian S. Jensen

Research Collection School Of Computing and Information Systems

Reachability computation is a fundamental graph functionality with a wide range of applications. In spite of this, little work has as yet been done on efficient reachability queries over temporal graphs, which are used extensively to model time-varying networks, such as communication networks, social networks, and transportation schedule networks. Moreover, we are faced with increasingly large real-world temporal networks that may be distributed across multiple data centers. This state of affairs motivates the paper's study of efficient reachability queries on distributed temporal graphs. We propose an efficient index, called Temporal Vertex Labeling (TVL), which is a labeling scheme for distributed …

Finding Flaws From Password Authentication Code In Android Apps, Siqi Ma, Elisa Bertino, Surya Nepal, Jianru Li, Ostry Diethelm, Robert H. Deng, Sanjay Jha

Research Collection School Of Computing and Information Systems

Password authentication is widely used to validate users’ identities because it is convenient to use, easy for users to remember, and simple to implement. The password authentication protocol transmits passwords in plaintext, which makes the authentication vulnerable to eavesdropping and replay attacks, and several protocols have been proposed to protect against this. However, we find that secure password authentication protocols are often implemented incorrectly in Android applications (apps). To detect the implementation flaws in password authentication code, we propose GLACIATE, a fully automated tool combining machine learning and program analysis. Instead of creating detection templates/rules manually, GLACIATE automatically and accurately …

Spatio-Temporal Analysis And Prediction Of Cellular Traffic In Metropolis, Xu Wang, Zimu Zhou, Fu Xiao, Kai Xing, Zheng Yang, Yunhao Liu, Chunyi Peng

Research Collection School Of Computing and Information Systems

Understanding and predicting cellular traffic at large-scale and fine-granularity is beneficial and valuable to mobile users, wireless carriers and city authorities. Predicting cellular traffic in modern metropolis is particularly challenging because of the tremendous temporal and spatial dynamics introduced by diverse user Internet behaviours and frequent user mobility citywide. In this paper, we characterize and investigate the root causes of such dynamics in cellular traffic through a big cellular usage dataset covering 1.5 million users and 5,929 cell towers in a major city of China. We reveal intensive spatiotemporal dependency even among distant cell towers, which is largely overlooked in …

Efficient Oblivious Transfer With Membership Verification, Weiwei Liu, Dazhi Sun, Yangguang Tian

Research Collection School Of Computing and Information Systems

In this article, we introduce a new concept of oblivious transfer with membership verification that allows any legitimate group users to obtain services from a service provider in an oblivious manner. We present two oblivious transfer with membership verification schemes, differing in design. In the first scheme, a trusted group manager issues credentials for a pre-determined group of users so that the group of users with a valid group credential can obtain services from the service provider, while the choices made by group users remain oblivious to the service provider. The second scheme avoids the trusted group manager, which allows …

Can Earables Support Effective User Engagement During Weight-Based Gym Exercises?, Meeralakshmi Radhakrishnan, Archan Misra

Research Collection School Of Computing and Information Systems

We explore the use of personal ‘earable’ devices (widely used by gym-goers) in providing personalized, quantified insights and feedback to users performing gym exercises. As in-ear sensing by itself is often too weak to pick up exercise-driven motion dynamics, we propose a novel, low-cost system that can monitor multiple concurrent users by fusing data from (a) wireless earphones, equipped with inertial and physiological sensors and (b) inertial sensors attached to exercise equipment. We share preliminary findings from a small-scale study to demonstrate the promise of this approach, as well as identify open challenges.

Detecting Toxicity Triggers In Online Discussions, Hamad Bin Khalifa University, Haewoon Kwak

Research Collection School Of Computing and Information Systems

Despite the considerable interest in the detection of toxic comments, there has been little research investigating the causes -- i.e., triggers -- of toxicity. In this work, we first propose a formal definition of triggers of toxicity in online communities. We proceed to build an LSTM neural network model using textual features of comments, and then, based on a comprehensive review of previous literature, we incorporate topical and sentiment shift in interactions as features. Our model achieves an average accuracy of 82.5% of detecting toxicity triggers from diverse Reddit communities.