Reviewerly/exHarmony huggingface dataset:
Technical Debt Prioritization: Taxonomy, Methods Results, and Practical Characteristics

Technical debt is the metaphor for shortcuts in software development that bring short-term benefits, but long-term consequences hinder the process of maintaining and developing software. It is important to manage these technical debt items, as not all of them need to be paid. Having a list of prioritized debts is an essential step in decision-making in the management process. This work aims at finding technical debt prioritization methods, providing a classification of them. That is, methods to identify whether and when a technical debt should be paid off. We performed a systematic mapping review to find and analyze the main papers of the area, covering the main bases. We selected 112 studies, resulting in 51 unique papers. We classified the methods in a two-level taxonomy containing 10 categories according to their different possible outcomes. In addition, we have identified three methods results: boolean, category and ordered list. Finally, we have also identified practical technical characteristics and requirements for a method to prioritize technical debt items in real projects. Although several methods have been found in literature, none of them are adaptive to the context and are language-independent, nor cover several technical debt types. Moreover, there is a clear lack of tools to use them. So, in conclusion, the research on technical debt prioritization is still wide open. From this study, a combination of the techniques used in these methods can be tested and automated to assist in the decision-making process on which debts should be paid.
The Causal Relationships Between Corruption, Investments and Economic Growth in GCC Countries

This paper investigates the impact of corruption on economic growth and investments in the Gulf Cooperation Council countries over the period 2003 to 2016 using the panel vector error correction model and the panel fully modified ordinary least squares method developed by Pedroni. We find that there is at least one cointegration relationship between the variables. The results of Granger causality tests indicate that corruption does not cause economic growth, foreign and domestic investments in the short run. Moreover, we find that there is strong long run unidirectional causality running from corruption to economic growth, foreign direct investment, domestic investment, and domestic credit. Estimation of the panel fully modified ordinary least squares model indicates that corruption has a negative impact on economic growth whereas a positive influence on domestic investment. Hence, the policy implication resulting from this study is that the improvement of corruption perception index in Gulf Cooperation Council countries is not without a positive impact on economic growth in the long run for these countries even not for domestic investments.
Emotional Conversation Generation With Bilingual Interactive Decoding

The perception and expression of emotion, as well as the content of a text, are key factors in the success of conversational agents. However, previous models for conversation generation handled single-language pairs during training and testing, neglecting the complementary information from different languages. In this article, we propose a bilingual-aided interactive approach that can simultaneously and interactively generate bilingual emotional replies to monolingual posts. Specifically, the generation of one emotional reply relies on the output of the encoder, the generated tokens, and the interactive information from the other language decoder. The interactive approach includes 1) internal interaction to capture the change in implicit contextual information and 2) external interaction to balance grammaticality and the expression of emotion. Qualitative and quantitative experiments with NLPCC2017 show that our model performs better in terms of the content and emotion of replies than several state-of-the-art approaches.
WARA-PS: a research arena for public safety demonstrations and autonomous collaborative rescue robotics experimentation

Abstract A research arena (WARA-PS) for sensing, data fusion, user interaction, planning and control of collaborative autonomous aerial and surface vehicles in public safety applications is presented. The objective is to demonstrate scientific discoveries and to generate new directions for future research on autonomous systems for societal challenges. The enabler is a computational infrastructure with a core system architecture for industrial and academic collaboration. This includes a control and command system together with a framework for planning and executing tasks for unmanned surface vehicles and aerial vehicles. The motivating application for the demonstration is marine search and rescue operations. A state-of-art delegation framework for the mission planning together with three specific applications is also presented. The first one concerns model predictive control for cooperative rendezvous of autonomous unmanned aerial and surface vehicles. The second project is about learning to make safe real-time decisions under uncertainty for autonomous vehicles, and the third one is on robust terrain-aided navigation through sensor fusion and virtual reality tele-operation to support a GPS-free positioning system in marine environments. The research results have been experimentally evaluated and demonstrated to industry and public sector audiences at a marine test facility. It would be most difficult to do experiments on this large scale without the WARA-PS research arena. Furthermore, these demonstrator activities have resulted in effective research dissemination with high public visibility, business impact and new research collaborations between academia and industry.
Anonymity: A Secure Identity Management Using Smart Contracts

Identity managements have different ways of managing identities right from traditional to modern age. In modern age internet serves as a platform to share it whenever required in the digital form which has its own disadvantages. Blockchain has now introduced the concept of a self-sovereign identity which enables to have one common identity for users. However, due to the public and decentralized nature of Blockchain, security of this self-sovereign identity has become the need of the hour. To solve this problem, various solutions have been proposed. In this paper, a zero knowledge protocol of Interval membership using the Pedersen commitment scheme is used to provide privacy of certain attributes of a user’s identity. This paper gives a detail about how the user’s attributes can be selectively kept anonymous over the blockchain.
Exploiting News Article Structure for Automatic Corpus Generation of Entailment Datasets

Transformers represent the state-of-the-art in Natural Language Processing (NLP) in recent years, proving effective even in tasks done in low-resource languages. While pretrained transformers for these languages can be made, it is challenging to measure their true performance and capacity due to the lack of hard benchmark datasets, as well as the difficulty and cost of producing them. In this paper, we present three contributions: First, we propose a methodology for automatically producing Natural Language Inference (NLI) benchmark datasets for low-resource languages using published news articles. Through this, we create and release NewsPH-NLI, the first sentence entailment benchmark dataset in the low-resource Filipino language. Second, we produce new pretrained transformers based on the ELECTRA technique to further alleviate the resource scarcity in Filipino, benchmarking them on our dataset against other commonly-used transfer learning techniques. Lastly, we perform analyses on transfer learning techniques to shed light on their true performance when operating in low-data domains through the use of degradation tests.
An Asymptotic Analysis of Probabilistic Logic Programming, with Implications for Expressing Projective Families of Distributions

Abstract Probabilistic logic programming is a major part of statistical relational artificial intelligence, where approaches from logic and probability are brought together to reason about and learn from relational domains in a setting of uncertainty. However, the behaviour of statistical relational representations across variable domain sizes is complex, and scaling inference and learning to large domains remains a significant challenge. In recent years, connections have emerged between domain size dependence, lifted inference and learning from sampled subpopulations. The asymptotic behaviour of statistical relational representations has come under scrutiny, and projectivity was investigated as the strongest form of domain size dependence, in which query marginals are completely independent of the domain size. In this contribution we show that every probabilistic logic program under the distribution semantics is asymptotically equivalent to an acyclic probabilistic logic program consisting only of determinate clauses over probabilistic facts. We conclude that every probabilistic logic program inducing a projective family of distributions is in fact everywhere equivalent to a program from this fragment, and we investigate the consequences for the projective families of distributions expressible by probabilistic logic programs.
Explainable clustering with multidimensional bounding boxes

Explainable Artificial Intelligence (XAI) aims at introducing transparency and intelligibility into decision-making process of AI systems. Most of the work in this area is focused on supervised machine learning tasks such as classification and regression. Unsupervised algorithms such as clustering can also be explained with existing approaches. This is most often achieved by explaining a classifier trained on cluster data with cluster labels as a dependant variable. However, with such a transformation the information about cluster shape and distribution is lost, which may lead to wrong interpretation of explanations. In this paper, we introduce a method that aids end experts in cluster analysis with human-readable rule-based explanations. We use state-of-the-art explanation mechanism on the multidimensional bounding boxes that represent arbitrarily-shaped clusters. We demonstrate our approach on reproducible synthetic datasets.
Render U-Net: A Unique Perspective on Render to Explore Accurate Medical Image Segmentation

Organ lesions have a high mortality rate, and pose a serious threat to people’s lives. Segmenting organs accurately is helpful for doctors to diagnose. There is a demand for the advanced segmentation model for medical images. However, most segmentation models directly migrated from natural image segmentation models. These models usually ignore the importance of the boundary. To solve this difficulty, in this paper, we provided a unique perspective on rendering to explore accurate medical image segmentation. We adapt a subdivision-based point-sampling method to get high-quality boundaries. In addition, we integrated the attention mechanism and nested U-Net architecture into the proposed network Render U-Net.Render U-Net was evaluated on three public datasets, including LiTS, CHAOS, and DSB. This model obtained the best performance on five medical image segmentation tasks.
Scheduling tasks based on branch and bound algorithm in cloud computing environment

Cloud computing is an ideal way for large-scale distributed computing and parallel processing. Cloud computing supports a vast number of services which covers a large number of consumer services like the cloud backup of the images, videos in the smartphone, etc. The performance and efficiency of services provided by cloud computing are dependent on the execution time of user tasks presented to the cloud system. Efficient scheduling of user tasks plays a significant role in managing the physical and virtual resources with a better performance in cloud services. Task Scheduling is one of the main types of scheduling performed in a cloud environment that aim to minimize the makespan for the task processing. Makespan means the total time taken by the virtual machines to complete the tasks allocated to them. In heterogeneous system scheduling the different size tasks of different significance is a complex problem that has been tried to resolve with various approaches e.g. FCFS, SJF, Min-Min, Max-Min, etc. In this work, the Branch and Bound (B&B) algorithm has been implemented and tested for assigning these heterogeneous tasks to virtual machines to reduce the makespan. It has configured the environment in the CloudSim simulator and obtained the results mainly about makespan. Results are compared with other general scheduling algorithms i.e. FCFS, MIN-MIN, MAX-MIN, and SJF.
Tackling the Background Bias in Sparse Object Detection via Cropped Windows

Object detection on Unmanned Aerial Vehicles (UAVs) is still a challenging task. The recordings are mostly sparse and contain only small objects. In this work, we propose a simple tiling method that improves the detection capability in the remote sensing case. We identified one core component of many tiling approaches and extracted an easy to implement preprocessing step.By reducing the background bias and enabling the usage of higher image resolutions during training, our method can improve the performance of models substantially. The procedure was validated on three different data sets and outperformed similar approaches in performance and speed.
Human Activity Recognition based on LSTM Neural Network Optimized by PSO Algorithm

Human activity recognition (HAR) approaches play significant roles in understanding behavioral activities. These tools allow personalized services and support for users in varied circumstances. Hence accurate prediction of user activity is valuable for several applications such as elderly care, healthcare, sports, and smart homes. In recent times, machine learning with deep learning models, particularly Convolutional Neural Networks (CNN), are becoming popular for human activity recognition. However, our earlier studies have proved that Recurrent Neural Networks (RNN) produce superior results to CNN. In the current study, a new approach, Long Short Term Memory(LSTM) neural network and Particle Swarm Optimization (PSO) algorithm are proposed. Though LSTM models produce good results in HAR, there are few parameters to consider for tracking down the optimal LSTM model. Therefore PSO algorithm is utilized for the optimization of the parameters. It possesses a quick convergence rate and the ability to enhance the prediction accuracy of LSTM in contrast to popular methods. After the training of the LSTM model, the weights are further optimized by using the PSO algorithm and are substituted into the model during the verification stage. This study investigates the performance of the LSTM-PSO model compared with few recurrent neural networks. For the implementation of the model, a benchmark WISDM dataset is utilized. The simulation results indicate that the proposed approach refines the recognition rate up to 97% which exceeds those of the compared models.
A necessary and sufficient condition for a class of nonsingular Galois NFSRs

Let n be a positive integer. An n-stage Galois NFSR is nonsingular if and only if the output sequences of each bit register are purely periodic. It is well known that a Galois NFSR used to build a stream cipher should be nonsingular. Recently, a useful concept that is the simplified feedback function is proposed for a Galois NFSR, which is a vectorial Boolean function. Generally, for a Galois NFSR, its simplified feedback function has less nonlinear terms than its traditional feedback function, and so is easier to analyze. Moreover, it has been shown that the nonsingularity of a Galois NFSR is decided by the invertibility of its simplified feedback function. Based on this observation, in this paper, we present a necessary and sufficient condition for the nonsingularity of a class of Galois NFSRs such that every component of its simplified feedback is linear or has a common nonlinear term.
Surface code design for asymmetric error channels

Surface codes are quantum error correcting codes normally defined on 2D arrays of qubits. In this paper, we introduce a surface code design based on the fact that the severity of bit flip and phase flip errors in the physical quantum systems is asymmetric. For our proposed surface code design for asymmetric error channels, we present pseudo-threshold and threshold values in the presence of various degrees of asymmetry of Pauli $\hat{X}$, $\hat{Y}$, and $\hat{Z}$ errors in a depolarization channel. We show that, compared to symmetric surface codes, our asymmetric surface codes can provide almost double the pseudo-threshold rates while requiring less than half the number of physical qubits in the presence of increasing asymmetry in the error channel. We also demonstrate that as the asymmetry of the surface code increases, the advantage in the pseudo-threshold rates begins to saturate for any degree of asymmetry in the channel.
Robust clustering based on finite mixture of multivariate fragmental distributions

A flexible class of multivariate distributions called scale mixtures of fragmental normal (SMFN) distributions, is introduced. Its extension to the case of a finite mixture of SMFN (FM-SMFN) distributions is also proposed. The SMFN family of distributions is convenient and effective for modelling data with skewness, discrepant observations and population heterogeneity. It also possesses some other desirable properties, including an analytically tractable density and ease of computation for simulation and estimation of parameters. A stochastic representation of the SMFN distribution is given and then a hierarchical representation is described, the latter aids in parameter estimation, derivation of statistical properties and simulations. Maximum likelihood estimation of the FM-SMFN distribution via the expectation–maximization (EM) algorithm is outlined before the clustering performance of the proposed mixture model is illustrated using simulated and real datasets. In particular, the ability of FM-SMFN distributions to model data generated from various well-known families is demonstrated.
Convolutional Neural Network - Long Short Term Memory based IOT Node for Violence Detection

Violence detection has been investigated extensively in the literature. Recently, IOT based violence video surveillance is an intelligent component integrated in security system of smart buildings. Violence video detector is a specific kind of detection models that should be highly accurate to increase the model's sensitivity and reduce the false alarm rate. This paper proposes a novel architecture of end-to-end CNN-LSTM (Convolutional Neural Network - Long Short-Term Memory) model that can run on low-cost Internet of Things (IOT) device such as raspberry pi board. The paper utilized CNN to learn spatial features from video's frames that were applied to LSTM for video classification into violence/non-violence classes. A complex dataset including two public datasets: RWF-2000 and RLVS-2000 was used for model training and evaluation. The challenging video content includes crowds and chaos, small object at far distance, low resolution, and transient action. Additionally, the videos were captured in various environments such as street, prison, and schools with several human actions such as eating, playing basketball, football, tennis, and swimming. The experimental results show good performance of the proposed violence detection model in terms of average metrics having an accuracy of 73.35 %, recall of 76.90 %, precision of 72.53 %, F1 score of 74.01 %, false negative rate of 23.10 %, false positive rate of 30.20 %, and AUC of 82.0 %. The proposed CNN-LSTM can balance good performance with low number of parameters and thus can be implemented on low-cost IOT node.
DeepGroup: Group Recommendation with Implicit Feedback

We focus on making recommendations for a new group of users whose preferences are unknown, but we are given the decisions of other groups. By formulating this problem as group recommendation from group implicit feedback, we focus on two of its practical instances: group decision prediction and reverse social choice. Given a set of groups and their observed decisions, group decision prediction intends to predict the decision of a new group of users, whereas reverse social choice aims to infer the preferences of those users involved in observed group decisions. These two problems are of interest to not only group recommendation, but also to personal privacy when the users intend to conceal their personal preferences but have participated in group decisions. To tackle these two problems, we propose and study DeepGroup---a deep learning approach for group recommendation with group implicit data. We empirically assess the predictive power of DeepGroup on various real-world datasets and group decision rules. Our extensive experiments not only demonstrate the efficacy of DeepGroup but also shed light on the privacy-leakage concerns of some decision-making processes.
Storing, learning and retrieving biased patterns

The formal equivalence between the Hopfield network (HN) and the Boltzmann Machine (BM) has been well established in the context of random, unstructured and unbiased patterns to be retrieved and recognised. Here we extend this equivalence to the case of “biased” patterns, that is patterns which display an unbalanced count of positive neurons/pixels: starting from previous results of the bias paradigm for the HN, we construct the BM’s equivalent Hamiltonian introducing a constraint parameter for the bias correction. We show analytically and numerically that the parameters suggested by equivalence are fixed points under contrastive divergence evolution when exposed to a dataset of blurred examples of each pattern, also enjoying large basins of attraction when the model suffers of a noisy initialisation. These results are also shown to be robust against increasing storage of the models, and increasing bias in the reference patterns. This picture, together with analytical derivation of HN’s phase diagram via self-consistency equations, allows us to enhance our mathematical control on BM’s performance when approaching more realistic datasets.
Stronger Notions and a More Efficient Construction of Threshold Ring Signatures

We consider threshold ring signatures (introduced by Bresson et al. []), where any \(t \) signers can sign a message while anonymizing themselves within a larger (size-\(n \)) group. The signature proves that \(t \) members of the group signed, without revealing anything else about their identities.