Reviewerly/exHarmony huggingface dataset:
Comparison of Information Retrieval Techniques for Traceability Link Recovery

Requirements traceability supports many software engineering activities such as change impact analysis and requirements validation, providing benefits to the overall quality of software systems. Factors such as lack of communication, time pressure problems, and unsuccessfully implemented traceability practices result in developers losing track of requirements. Requirements traceability is a primary means to address completeness and accuracy of requirements. It is an active research topic for software engineers. Textual analysis and information retrieval techniques have been applied to the requirements traceability recovery problem for many years, due to the textual components of requirements and source code. Information retrieval techniques are semiautomatic techniques for recovering traceability links and on occasion, they have become the baseline for automatic methods applied to requirements traceability recovery. We evaluate the performance of IR techniques applied to the requirement traceability recovery process. The most popular information retrieval techniques applied to the requirements traceability recovery problem are the IR Probabilistic, Vector Space Model, and Latent Semantic Index approach. All three approaches rank documents by using one of the documents for extracting queries and the other as the documents being search using those extracted queries; however, they apply different internal logics for establishing similarities. We compared IR Probabilistic, Vector Space Model, and Latent Semantic Index approaches to evaluate their performance for requirement traceability recovery using the metrics of precision and recall. Experimental results indicate a low precision and recall for the LSI technique and high precision and low recall for both the IR probabilistic and the VSM techniques.
Cloud SLA negotiation and re‐negotiation: An ontology‐based context‐aware approach

Summary Service Level Agreement (SLA) has recently garnered increasing attention in cloud computing due to its capability in the definition and the monitoring of the Quality of Service (QoS). SLA is generally generated following a process of negotiation in which the client chooses a subset of clauses among choices predefined by the provider on a “take it or leave it” basis. However, the heterogeneity between client requests and provider offers may result in inappropriate SLA. In addition, the cloud service context may change over the time, which causes a client requirement adjustment, and therefore, the SLA becomes unsatisfactory. In this paper, we propose a cloud SLA negotiation and re‐negotiation approach. Our aim is to ameliorate the SLA negotiation phase by adding the ability to integrate a semantic mapping between client requests and provider offers to generate a suitable SLA document. Moreover, our approach includes a context‐aware system to adapt the SLA throughout reasoning techniques and automatically ensure the re‐negotiation. We develop a prototype, and we test its performance. The results of a case study also show the efficiency of our approach.
Instruction level program tracking using electromagnetic emanations

Monitoring computer system activities on the instruction level provides more resilience to malware attacks because these attacks can be analyzed better by observing the changes on the instruction level. Assuming the source code is available, many training signals can be collected to track the instruction sequence to detect whether a malware is injected or the system works properly. However, training signals have to be collected with high sampling rate to ensure that the significant features of these signals do not vanish. Since the clock frequencies of the current computer systems are extremely high, we need to have a commercial device with high sampling rate, i.e. 10GHz, which either costs remarkably high, or does not exist. To eliminate the deficiencies regarding the insufficient sampling rate, we propose a method to increase the sampling rate with the moderate commercial devices for training symbols. In that respect, we first generate some random instruction sequences which exist in the inspected source code. Then, these sequences are executed in a for-loop, and emanated electromagnetic (EM) signals from the processor are collected by a commercially available device with moderate sampling rate, i.e. sampling rate is much smaller than the clock frequency. Lastly, we apply a mapping of the gathered samples by utilizing modulo of their timings with respect to execution time of overall instruction sequence. As the final step, we provide some experimental results to illustrate that we successfully track the instruction sequence by applying the proposed approach.
AYNEC: All You Need for Evaluating Completion Techniques in Knowledge Graphs

The popularity of knowledge graphs has led to the development of techniques to refine them and increase their quality. One of the main refinement tasks is completion (also known as link prediction for knowledge graphs), which seeks to add missing triples to the graph, usually by classifying potential ones as true or false. While there is a wide variety of graph completion techniques, there is no standard evaluation setup, so each proposal is evaluated using different datasets and metrics. In this paper we present AYNEC, a suite for the evaluation of knowledge graph completion techniques that covers the entire evaluation workflow. It includes a customisable tool for the generation of datasets with multiple variation points related to the preprocessing of graphs, the splitting into training and testing examples, and the generation of negative examples. AYNEC also provides a visual summary of the graph and the optional exportation of the datasets in an open format for their visualisation. We use AYNEC to generate a library of datasets ready to use for evaluation purposes based on several popular knowledge graphs. Finally, it includes a tool that computes relevant metrics and uses significance tests to compare each pair of techniques. These open source tools, along with the datasets, are freely available to the research community and will be maintained.
UAV pursuit using reinforcement learning

Over the past few years, UAVs have known and increase in popularity and are now widely used in many applications. Today, the use of multiple UAVs and UAV swarms are attracting more interest from the research community leading to the exploration of topics such as UAV cooperation, multi-drones autonomous navigation, etc. In this work, we are interested in UAVs tracking and pursuit. The goal here, is to use deep learning and the captured images from one of the UAVs to detect and track the second moving UAV. The proposed approach uses deep reinforcement learning for UAV pursuit. The input is the current frame cropped using the last target pose, and the output is a probabilistic distribution between a set of possible actions. The experimental results are promising and show that the proposed algorithm achieves high performances in challenging outdoor scenarios.
Mining Regular High Utility Sequential Patterns in Static and Dynamic Databases

Regular pattern mining has been emerged as one of the important sub-domains of data mining with its numerous applications. Although patterns that occur at a regular interval throughout the whole database can lead to interesting knowledge, examining the utility values of these patterns can unveil more interesting useful information. In a sequence database, the task of mining regular high utility patterns can be more challenging. In this paper, we first propose a new algorithm for mining regular high utility sequential patterns from static databases. As handling of the incremental nature of big data brings useful results in many applications in the recent era of big data, we then extend our algorithm to mine regular high utility sequential patterns from dynamic databases. Evaluation results on several real-life datasets show the effectiveness of our two algorithms.
Investigation of incremental hybrid genetic algorithm with subgraph isomorphism problem

Graph pattern matching is a key problem in many applications which data is represented in the form of a graph, and this problem is generally defined as a subgraph isomorphism. In this paper, we analyze an incremental hybrid genetic algorithm for the subgraph isomorphism problem considering various design issues to improve the performance of the algorithm. An incremental hybrid genetic algorithm was previously suggested to solve the subgraph isomorphism problem and have shown good performance. It decomposes the problem into a sequence of consecutive subproblems which has an optimal substructure. Each subproblem is solved by the hybrid genetic algorithm and the solutions obtained are extended to be applied to the next subproblem as initial solutions. We examine a wide range of schemes that determine the overall performance of the incremental process and make a number of experiments to verify the effectiveness of each scheme with the synthetic dataset of random graphs. We show that the performance of incremental approach can be significantly improved compared to the previous representative studies by applying appropriate schemes found by the experiments. In addition, we also investigate the effect of different genetic parameters and identify the scalability of our method by conducting experiments using real world dataset with large-sized graphs.
A new axiomatic methodology for the image similarity

In this paper, a new theoretical framework is introduced which explores the notion of image similarity in two axiomatic conditions. The first one resembles to the concept of frequency in matching image patches. The second one formalizes the high-level strategy for computing the image similarity. A realization of this theoretical framework is a new image similarity measure based on approximate matching of patches shared between the images. The approximate match is defined in terms of normalized 2D Hamming distance, and verified within a given neighbourhood from the position of the patches in the images. The similarity measure is computed as the average area of these shared patches. The proposed approach is tested on well-known benchmark datasets. The obtained results show that the proposed method overcomes, in terms of retrieval precision and computational complexity, other competing measures adopted in image retrieval.
Improved Look-Ahead Approaches for Nearest Neighbor Synthesis of 1D Quantum Circuits

In the present era of computation, quantum computing may offer a new direction as it allows to solve certain problems significantly faster than classical solutions. But it also has been found that there are several constrains in performing a successful realization of quantum circuits. One such constraint is the nearest neighbor (NN) criterion which states that qubits which interact with each other have to be adjacent. Motivated by this objective, in this work we propose a linear qubit placement technique that effectively rearranges the qubits and transforms quantum circuits to improved NN-based designs by inserting SWAPs. Furthermore, for placing these SWAPs in appropriate positions, we implemented a look-ahead strategy that considers the effect of the rest of the gates and computes a corresponding impact value which guides the insertion of the SWAP gates. To this extent, we consider three different strategies to evaluate the corresponding "look-ahead effects" and their influence on existing gates. At the end of this work, we have evaluated the developed methodology over a wide range of benchmarks and compared the results with existing related works. In this comparison, we have seen that the proposed technique outperforms the related works and provides substantial reductions in SWAP overhead.
Improving Data Analytics with Fast and Adaptive Regularization

Deep Learning and Machine Learning models have recently been shown to be effective in many real world applications. While these models achieve increasingly better predictive performance, their structures have also become much more complex. A common and difficult problem for complex models is overfitting. Regularization is used to penalize the complexity of the model in order to avoid overfitting. However, in most learning frameworks, regularization function is usually set with some hyper-parameters where the best setting is difficult to find. In this paper, we propose an adaptive regularization method, as part of a large end-to-end healthcare data analytics software stack, which effectively addresses the above difficulty. First, we propose a general adaptive regularization method based on Gaussian Mixture (GM) to learn the best regularization function according to the observed parameters. Second, we develop an effective update algorithm which integrates Expectation Maximization (EM) with Stochastic Gradient Descent (SGD). Third, we design a lazy update and sparse update algorithm to reduce the computational cost by 4x and 20x, respectively. The overall regularization framework is fast, adaptive, and easy-to-use. We validate the effectiveness of our regularization method through an extensive experimental study over 14 standard benchmark datasets and three kinds of deep learning/machine learning models. The results illustrate that our proposed adaptive regularization method achieves significant improvement over state-of-the-art regularization methods.
An Attention-based Model for Joint Extraction of Entities and Relations with Implicit Entity Features

Extracting entities and relations is critical to the understanding of massive text corpora. Recently, neural joint models have shown promising results for this task. However, the entity features are not effectively used in these joint models. In this paper, we propose an approach to utilize the implicit entity features in the joint model and show these features can facilitate the joint extraction task. Particularly, we use the hidden-layer vectors extracted from a pre-trained named entity recognition model as the entity features. Thus, our method does not need to design the entity features by hand and can benefit from the new development of named entity recognition task. In addition, we introduce an attention mechanism in our model which can select the informative parts of the input sentence to the prediction. We conduct a series of experiments on a public dataset and the results show the effectiveness of our model.
IDCam: Precise Item Identification for AR Enhanced Object Interactions

Augmented reality (AR) promises to revolutionize the way people interact with their surroundings by seamlessly overlaying virtual information onto the physical world. To improve the quality of such information, AR systems need to identify the object with which the user is interacting. AR systems today heavily rely on computer vision for object identification; however, state-of-the-art computer vision systems can only identify the general object categories, rather than their precise identity. In this work, we propose IDCam, a system that fuses RFID and computer vision for precise item identification in AR object-oriented interactions. IDCam simultaneously tracks users' hands using a depth camera and generates motion traces for RFID-tagged objects. The system then correlates traces from vision and RFID to match item identities with user interactions. We tested our system through a simulated retail scenario where 5 participants interacted with a clothing rack simultaneously. In our evaluation study deployed in a lab environment, IDCam identified item interactions with an accuracy of 82.0% within 2 seconds.
Ontology-based conversational recommender system for recommending laptop

Currently there are many recommender systems for various products, but the recommender system still uses questions that refer to product specifications. For example, on laptop, the question is how much hard drive capacity is needed, what type of processor, etc. With the recommender systems previously described, customers who are not familiar with product's specifications confused in choosing product to match with their needs. To solve these problems, a recommender system is required to prioritizes the functional requirements (high level requirement). Previously, we have developed a multi-domain framework for developing a conversational recommender systems (CRS) based on functional requirements. This framework comprises interaction generate method and ontology. The ontology aims to map functional requirements with technical features of the product. In this paper, we operationalize that framework and propose an ontology based on that framework for developing a CRS in a laptop domain. This CRS interacts with an iterative conversation to find out what the customer needs (e.g., customers need a laptop to watch video). This recommender system does the same conversation as a customer with a professional seller. The users involved in this test show that a recommender system that prioritizes functional requirements is more helpful in product selection than the recommender system commonly used in e-commerce.
An Experimental Investigation of Human Presence and Mobile Technologies on College Students’ Sun Protection Intentions: Between-Subjects Study

Health promotion and education programs are increasingly being adapted and developed for delivery through digital technologies. With this shift toward digital health approaches, it is important to identify design strategies in health education and promotion programs that enhance participant engagement and promote behavior change.This study aimed to examine the impact of an experiment testing various mobile health (mHealth) skin cancer prevention messages on sun protection intentions and message perceptions among American college students.A sample of 134 college students aged 18 years or older participated in a 2×2×2 between-subjects experimental study, designed to examine the individual and combinatory effects of multiple dimensions (human presence, screen size, and interactivity) of digital technologies. The primary study outcome was intention to use sun protection; secondary outcomes included attitudes toward the information, two dimensions of trust, and information processing.Generally, intention to use sun protection was positively associated with the presence of human characters in the health educational messages (P<.001), delivering educational health messages on a large screen (ie, iPad; P<.001), and higher interactivity (P<.001). Only human presence produced more favorable attitudes (P=.02). Affective trust was positively associated with human presence (P=.006) and large screen size (P<.001), whereas cognitive trust was positively associated with human presence (P<.001) and small screen size (P=.007). Moreover, large screen size led to more heuristic processing (P=.03), whereas small screen size led to more systematic processing (P=.04).This experimental study demonstrates that the impact of mHealth skin cancer prevention messages differs based on platform and delivery design features. Effects on behavioral intentions, attitudes, and trust were found for conditions with human presence, highlighting the importance of including this feature in mHealth programs. Results from this experimental study can be used to optimize the design of mHealth educational interventions that promote sun protection.
Transfer and Online Reinforcement Learning in STT-MRAM Based Embedded Systems for Autonomous Drones

In this paper we present an algorithm-hardware co-design for camera-based autonomous flight in small drones. We show that the large write-latency and write-energy for nonvolatile memory (NVM) based embedded systems makes them unsuitable for real-time reinforcement learning (RL). We address this by performing transfer learning (TL) on meta-environments and RL on the last few layers of a deep convolutional network. While the NVM stores the meta-model from TL, an on-die SRAM stores the weights of the last few layers. Thus all the real-time updates via RL are carried out on the SRAM arrays. This provides us with a practical platform with comparable performance as end-to-end RL and 83.4% lower energy per image frame.
Critical Information Infrastructures Security Modeling

The paper discusses the modeling of various aspects of the security of critical information infrastructures (CII) in the assumption of creating a reference model of CII security in the future. The features of CII in terms of goals and safety criteria based on the analysis of various regulatory and methodically established definitions and descriptions of CII are established. The contradictions arising in the attempts to use the traditional methodology of information security in relation to CII are shown. The problems of using the methods and models of classical risk analysis are discussed, in particular, the impossibility of applying the concept of residual risk to the formation of CII safety objectives. The conclusion is made about the expediency of basing these goals on the exhaustion of possible protective measures (controls and activities), the concept of asymptotic safety management of CII, which guarantees the trend of security growth without its current assessment. Changes in the role and place of the threat model in ensuring the security of CII related to the lack of evidence of the completeness of this model are considered. The attractiveness of using the SDL technique for forming elements of the threat model in the conditions of a specific CII is indicated. The structure of the future reference model of safety of the CII including definition of the purposes and criteria of safety (including functional), multilevel static model of functioning of the CII (including security factors), a dynamic model of the spread of security incidents within the CII, the typology of the result of aggressive manifestations of the CII functioning environment (threat model) and the model (methodology) of the spread of protective activities within the information infrastructure.
Segmenting Brain Tumour Regions with Fuzzy Integrated Active Contours

Magnetic resonance imaging used for diagnosis, localisation, and volume quantisation of tumours helps radiologists set treatment plans. Medical image segmentation is vital in detecting tumours. We propose a segmentation algorithm for identifying brain tumour regions using the fuzzy integrated active contour model. Fuzzy energy alterations define the contour and eliminate partial volume effects. We evaluated the algorithm on the BRATS 2012 and 2015 database and achieved an average dice score of 81% for the total abnormal region, including oedema, and 67% for an active tumour. This model is more robust than the classical snake methods based on the gradient.
Blind image deblurring by game theory

In this paper, we present a novel blind deconvolution technique for the restoration of linearly degraded images without explicit knowledge of either the original image or the point spread function (PSF). We propose to determine the optimal image deblurring as a Nash equilibrium, we use two criteria associated with two players. In this case, we suggest applying a concurrent optimization realized by an algorithm simulating a Nash game between two the players. Indeed, The aim of the first player is to minimize objective function using the first strategy image deblurring. Furthermore, a point spread function (PSF) is used by the second player. Accordingly, we present a mathematical proof of the existence of a discrete valued optimal solution for the second player, and it is concluded that no regularization of the sub optimization problem is needed. Finally, We present some numerical examples which illustrate the proposed methodology.