Miracle-dz/UniFAR-FTUs huggingface dataset:
Ad–hoc Workflows for Higher Education

It is commonplace in higher education institutions, for a large number of informal business processes to be handled through e-mail. However, e-mail fails to properly support traceability, and it fosters artifact duplication. This paper introduces GEARS: a solution to the e-mail overuse phenomenon. GEARS is a new approach to ad-hoc workflow systems that focuses on keeping the simplicity of the email user experience while implementing a participation-driven process execution.
Computer Aided Diagnostics of Digital Evidence Tampering (CADDET)

The tampering of the digital crime scene has become more common. When tampering behaviour is successful, it does not leave a trace of either the incriminating evidence or the act of tampering and the digital evidence that digital investigators seek will be absent. The research into the automatic detection of digital evidence tampering has been ongoing for over one decade. Many approaches had been proposed, but the practical tools for automatic detection of evidence tampering are still missing. Automatic analysis is hard due to the complexity of real-world computers and differences between software installed on different computers. A similar problem exists in medical imaging. Despite the common grand design, every human is unique and complex, and it is hard to come up with the exact rules for detecting lesions in medical images. Visualization for forensic analysis of the data stored on a specific device has received much less attention, while the use of visualization for detection of digital evidence tampering is virtually unexplored. This paper proposes, for the first time, a semi-automated approach based on visualization of relevant data properties, helping human investigators to detect digital evidence tampering and anomaly. This is analogous to computer aided processing of medical X-Ray images that enhance the visibility of lesions facilitating easier detection by a doctor. The aim of this paper is to identify data tampered features on the digital devices, then find suitable visualization to display identified data tampered features for investigators. One of the outstanding features of the approach proposed in this paper for detecting digital evidence tampering is its malleability. It can easily apply to any specific or whole part of data in the digital devices, visualize, and reveal offender concealment behaviour in relation to detection of evidence tampering. CADDET sheds new light and explores new insight to contribute investigators to fill a gap of detecting digital evidence tampering. This semi-automated approach has never been studied before and is novel in the context of digital forensics.
Multi-mission selective maintenance modelling for multistate systems over a finite time horizon

In the life cycle of a long-life structure, multiple maintenances are often required to ensure safety or normal performance. This article developed a multi-mission selective maintenance optimization model for a system with several multistate components over a finite time horizon. The model aims to minimize the total cost by determining the optimal intervention time and the corresponding maintenance option of each component. The system reliability requirement is taken as the constraint. The degradation process of each component is characterized by a homogeneous continuous time Markov chain in this model. Considering the uncertainties that exist during the deterioration–maintenance process, the condition state of each component at any time is described by a state probability vector. In addition, the components in the system are considered to be structurally dependent during the maintenance process, and a directed graph is used to represent the precedence relations among these components. A differential evolution algorithm is utilized to solve this complicated optimization model. A numerical example and an engineering application are presented to illustrate the availability of the proposed model.
Apache Nemo: A Framework for Building Distributed Dataflow Optimization Policies

Optimizing scheduling and communication of distributed data processing for resource and data characteristics is crucial for achieving high performance. Existing approaches to such optimizations largely fall into two categories. First, distributed runtimes provide low-level policy interfaces to apply the optimizations, but do not ensure the maintenance of correct application semantics and thus often require significant effort to use. Second, policy interfaces that extend a high-level application programming model ensure correctness, but do not provide sufficient fine control. We describe Apache Nemo, an optimization framework for distributed dataflow processing that provides fine control for high performance, and also ensures correctness for ease of use. We combine several techniques to achieve this, including an intermediate representation, optimization passes, and runtime extensions. Our evaluation results show that Nemo enables composable and reusable optimizations that bring performance improvements on par with existing specialized runtimes tailored for a specific deployment scenario.
Building An Autonomous Indoor Drone System

This thesis presents an Indoor Autonomous Drone System using a self assembled drone which uses a companion computer as well as external sensors for autonomous flight. While autonomous drone systems have been around for some time, originating with the military, the vast majority of them are designed for outdoor use, because of their heavy reliance on GPS for their positioning systems. In order to achieve autonomous flight indoors we choose to use Simultaneous Localization and Mapping (SLAM) as our positioning system. The contributions of this thesis is an in depth guide to the hardware and assembly of a drone system, the software required for a basic drone set-up as well as additional software needed for autonomous operations, and finally the results of the project along with the difficulties and set backs that were encountered.
Generalized Hybrid Beamforming for Vehicular Connectivity Using THz Massive MIMO

Hybrid beamforming (HBF) array structure has been extensively demonstrated as the practically feasible architecture for massive multiple-input multiple-output (MIMO). From the perspectives of spectral efficiency (SE), energy efficiency (EE), cost, and hardware complexity, HBF strikes a balanced performance tradeoff when compared with the fully analog and the fully digital implementations. Using the HBF architecture, it is possible to realize three different subarray structures, specifically the fully connected, the subconnected and the overlapped subarray structures. This paper presents a novel generalized framework for the design and performance analysis of the HBF architecture. A parameter, known as the subarray spacing, is introduced such that varying its value leads to the different subarray configurations and the consequent changes in the system performance. Using a realistic power consumption model, we investigate the performance of the generalized HBF array structure in a cellular infrastructure-to-everything application scenario (involving pedestrian and vehicular users) using the single-path terahertz (THz) channel model. Simulation results are provided for the comparative performance analysis of the different subarray structures. The results show that the overlapped subarray implementation maintains a balanced tradeoff in terms of SE, EE, and hardware cost when compared with the popular fully connected and the subconnected structures. The overlapped subarray structure, therefore, offers promising potentials for the beyond-5G networks employing THz massive MIMO to deliver ultrahigh data rates whilst maintaining a balance in the EE of the network.
AKUPM: Attention-Enhanced Knowledge-Aware User Preference Model for Recommendation

Recently, much attention has been paid to the usage of knowledge graph within the context of recommender systems to alleviate the data sparsity and cold-start problems. However, when incorporating entities from a knowledge graph to represent users, most existing works are unaware of the relationships between these entities and users. As a result, the recommendation results may suffer a lot from some unrelated entities. In this paper, we investigate how to explore these relationships which are essentially determined by the interactions among entities. Firstly, we categorize the interactions among entities into two types: inter-entity-interaction and intra-entity-interaction. Inter-entity-interaction is the interactions among entities that affect their importances to represent users. And intra-entity-interaction is the interactions within an entity that describe the different characteristics of this entity when involved in different relations. Then, considering these two types of interactions, we propose a novel model named Attention-enhanced Knowledge-aware User Preference Model (AKUPM) for click-through rate (CTR) prediction. More specifically, a self-attention network is utilized to capture the inter-entity-interaction by learning appropriate importance of each entity w.r. t the user. Moreover, the intra-entity-interaction is modeled by projecting each entity into its connected relation spaces to obtain the suitable characteristics. By doing so, AKUPM is able to figure out the most related part of incorporated entities (i.e., filter out the unrelated entities). Extensive experiments on two real-world public datasets demonstrate that AKUPM achieves substantial gains in terms of common evaluation metrics (e.g., AUC, ACC and Recall@top-K) over several state-of-the-art baselines.
Light touch based virtual cane for balance assistance during standing

Can additional information about one’s body kinematics provided through hands improve human balance? Light-Touch (LT) through hands helps improve balance in a wide range of populations, both healthy and impaired. The force is too small to provide any meaningful mechanical assistance – rather, it is suggested that the additional sensory information through hands helps the body improve balance. To investigate the potential for improving human balance through biofeedback through hands, we developed a Virtual Cane (VC) for balance assistance during standing. The VC mimics the physical cane’s function of providing information about one’s body in space. Balance experiments on 10 healthy young adults are conducted, where the evidence of improved standing balance with VC is collected and analyzed in terms of both, mediolateral & anterior-posterior accelerations of the trunk. The results showed that VC improved balance in both X & Y directions as compared to no cane and in some cases, balance improvement was almost as good as physical cane condition. This shows that standing balance can be improved by even a simple binary information on one’s hand position with respect to the ground. This work furthers the concept of biofeedback from using virtual devices for balance assistance using virtual LT through hands. Specifically, this work investigates a novel case where information that otherwise cannot be provided by any of the sensory organs (i.e., accurate distance from one’s hand to an external object), improves human standing balance. This research will propagate and give a boost to inspect and analyze similar or supplementary improvement effects during walking.
Neural Network Alignment for Sentential Paraphrases

We present a monolingual alignment system for long, sentence- or clause-level alignments, and demonstrate that systems designed for word- or short phrase-based alignment are ill-suited for these longer alignments. Our system is capable of aligning semantically similar spans of arbitrary length. We achieve significantly higher recall on aligning phrases of four or more words and outperform state-of-the- art aligners on the long alignments in the MSR RTE corpus.
ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R

We introduce the C++ application and R package ranger. The software is a fast implementation of random forests for high dimensional data. Ensembles of classification, regression and survival trees are supported. We describe the implementation, provide examples, validate the package with a reference implementation, and compare runtime and memory usage with other implementations. The new software proves to scale best with the number of features, samples, trees, and features tried for splitting. Finally, we show that ranger is the fastest and most memory efficient implementation of random forests to analyze data on the scale of a genome-wide association study.
A simulation model of DYMO for ad hoc routing in OMNeT

Mobile Ad Hoc Networks (MANETs) have evolved in the last years into standards in the communication world. By definition, they do not need any network infrastructure to facilitate communication between participating nodes. Therefore, they are dealing with new challenges in the context of ad hoc routing. In this paper, we present our new implementation of the ad hoc routing protocol Dynamic MANET On Demand (DYMO) for the popular discrete event simulation environment OMNeT++. DYMO offers adaptation to changing network topology and determines unicast routes between nodes within the network on demand. Based on the developed model, we performed several simulation experiments to analyze the performance of DYMO with respect to changing network size and traffic conditions. The implementation of DYMO and the results of the simulation experiments are described in this paper.
Evolving Legacy System's Features into Fine-grained Components Using Regression Test-Cases

Because many software systems used for business today are considered legacy systems, the need for software evolution techniques has never been greater. We propose a novel evolution methodology for legacy systems that integrates the concepts of features, regression testing, and Component-Based Software Engineering (CBSE). Regression test suites are untapped resources that contain important information about the features of a software system. By exercising each feature with its associated test cases using code profilers and similar tools, code can be located and refactored to create components. The unique combination of Feature Engineering and CBSE makes it possible for a legacy system to be modernized quickly and affordably. We develop a new framework to evolve legacy software that maps the features to fine-grained software components refactored from their feature implementation. In this dissertation, we make the following contributions: First, a new methodology to evolve legacy code is developed that improves the maintainability of evolved legacy systems. Second, the technique describes a clear understanding between features and functionality, and relationships among features using our feature model. Third, the methodology provides guidelines to construct feature-based reusable components using our fine-grained component model. Fourth, we bridge the complexity gap by identifying feature-based test cases and developing feature-based reusable components. We show how to reuse existing tools to aid the evolution of legacy systems rather than re-writing special purpose tools for program slicing and requirement management. We have validated our approach on the evolution of a real-world legacy system. By applying this methodology, American Financial Systems, Inc. (AFS), has successfully restructured its enterprise legacy system and reduced the costs of future maintenance.
Evolutionary approaches for statistical modelling

In this paper, we describe some evolutionary approaches based on genetic algorithms to deal with the statistical model selection problem using completely data-driven algorithms. First, we propose an approach to select multivariate linear regression models as well as to build ARMA time-series models. Then we introduce a methodology to tackle the clustering problem in a model-based framework. We report the results from several applications and from simulated data sets, and we compare the evolutionary approaches with some classical ones.
Novel Side-channel Attacks On Emerging Cryptographic Algorithms And Computing Systems

of the Dissertation Novel Side-Channel Attacks on Emerging Cryptographic Algorithms and Computing Systems by Chao Luo Doctor of Philosophy in Computer Engineering Northeastern University, December 2018 Dr. Yunsi Fei, Advisor After more than 20 year’s research and development, side-channel attacks are constantly posing serious threats to various computing systems. When targeting crypto-implementations to retrieve the secret, side-channel attacks utilize the peculiarity of the specific implementations, and achieve much better efficiency than brute force attacks and traditional cryptanalysis which attacks the weakness of the cryptographic algorithms themselves. Typical side channels include power consumption, electromagnetic emanation, and execution time. With inherent correlation between these side-channel information and the secret, statistic analysis can be employed to find the secret. However, there are still many challenges presented for side-channel research driven by two trends: new ciphers and emerging computing platforms. New ciphers or variants are being developed to provide higher level of security or get tailored to different applications. For example, XTSAES (XEX-based tweaked-codebook mode with ciphertext stealing AES) is a security-hardened mode of AES for storage systems, which increases the algorithm complexity and hides more systemdependent parameters to users (attackers). Meanwhile, we see more emerging computing platforms, for general purpose computing or specific algorithm acceleration. Graphic Processing Unit (GPU) has been used to run a range of cryptographic algorithms for higher performance. However, the security of GPU when processing sensitive data, especially the highly relevant side-channel vulnerabilities, has received little attention and is vastly unexplored. Yet GPU differs from other computing platforms distinctly in terms of the hardware structure and software programming model, making side-channel attacks on GPU much more challenging. In this dissertation, I propose several novel side-channel attacks, targeting new ciphers including XTS-AES and ECC and also popular accelerators GPUs. Some of our vulnerabilities analysis and
Hardware Implementation Analysis of Min-Sum Decoders

The objective of this work is to propose a modified Min-Sum decoding Low Density Parity Check (LDPC) algorithm and perform the hardware implementation analysis of Min-Sum, optimized Min-Sum and modified Min-Sum decoders. The Min-Sum algorithm mainly uses the process of finding the minimum and addition. Hence the number of multiplications is drastically reduced which helps in reducing the complexity of implementation. Adding an optimisation factor to the decoder increases the accuracy and reduces the number of iterations required to compute the decoded message. Hence the process of optimisation reduces the overall decoding time required. Modified Min-Sum algorithm is proposed to further improve the performance by decreasing the number of stages in the decoding process which reduces the complexity in Field Programmable Gate Array (FPGA) implementation.
A COMPREHENSIVE FRAMEWORK FOR MANAGING CONFLICTS/ANOMALIES BETWEEN XACML POLICIES: MATHEMATICAL BASIS AND ARCHITECTURE

In this work we address the problem of detection and resolution of conflicts/anomalies between XACML (eXtensible Access Control Markup Language) policies of access control. We mean here by conflict/Anomaly the case where several policies give conflicting answers (deny, allow) to a same access request. Indeed, this problem is foreseeable in access control systems based on policies in general. We give more attention to the mathematical formalism of the problem. We introduce the notion of the canonical representation of the query space. This is a partition of the query space formed by authorization classes. Each authorization class regroups queries that are intercepted by the same policies. This classification provides a natural way to handle interferences between policy targets (in other words conflicts/anomalies). Then we bring the study of the problem from the whole query space to elements of its canonical representation. The final result of this work is a Framework for detection and resolution of conflicts/anomalies between XACML policies. This Framework, which is located in the PAP (Policy Administration Point), is responsible for generating a conflicts-free representation from the initially provided policies. This representation is dynamically maintained and updated by the Framework following the addition, deletion or modification of policies.
Pragmatic and Metacognitive Applications of Metaphor and Metonymy in Dialogic Professional Genres: the Case of the Job Interview

This article brings into focus the notion of metaphorical competence (MC) and posits the inclusion of its explicit training in LSP syllabi, stressing its important function in dialogic self-evaluative genres like the job/selection interview. A perspective of the different approaches to MC is given prior to discussing the features and specific difficulties intrinsic to the interview, as well as to analysing the meta-cognitive and pragmatic roles of metaphor and metonymy and the constraints governing them. Both tropes prove efficacious lexical activators and mnemonics and valid politeness strategies, especially as conflict neutralizers, intimacy builders, hedges, persuasive devices and topic-closing formulas. Key words: metaphorical compe- tence, metaphor, metonymy, metacognition, pragmatic impact, LSP
A Novel Method for Detection of Internet Worm Malcodes using Principal Component Analysis and Multiclass Support Vector Machine

Internet worms are malware programs that imitate themselves and spread around the network. Internet worm, a wide spreading malcode exploits vulnerability in the operating system, hard disk, software and web browsers. This paper analyzes and classifies the Internet worm, depending on the training signatures. This work presents the Internet worm detection mechanism, using Principal Component Analysis (PCA) and Support Vector Machine (SVM). A Selective sampling technique is applied to maximize the performance of the classifier and to reduce misleading data instances. The results obtained show improved memory utilization, detection time and detection accuracy for Internet worms. Keyword - Malcode, Selective sampling, Multiclass SVM and PCA
Local minima and back propagation

It is shown that, in a feedforward net of logistic units, if there are as many hidden nodes as patterns to learn then almost certainly a solution exists, and the error function has no local minima. A large enough feedforward net can reproduce almost any finite set of targets for almost any set of input patterns, and will almost certainly not be trapped in a local minimum while learning to do so. <<ETX>>
A Module-Based Skill Ontology for Industrial Robots

With industrial robots ready to take the next step in mastering manufacturing tasks new approaches to reduce the programing effort are needed. This is achieved by introducing skills as robot "know-how" and using them as a higher abstraction level of robot instructions during programming. The skills are reusable items providing motion control and rich declarative descriptions of complex robot capabilities. Storing the skills requires an adequate knowledge representation model that enables reuse and reasoning on skills and simplifies knowledge management. In this thesis we develop a skill representation model and implement it in the LTH knowledge base. It was developed by studying, normalizing, extending and modularizing the current skill concepts in the LTH knowledge base. The developed model is effectively a class hierarchy of the skill concepts implemented in a modularized ontology structure. The resulting model clarifies the intrinsic concepts of a skill and presents a module structure that enables the future development and reuse of skills in general.