Miracle-dz/UniFAR-FTUs huggingface dataset:
Subtype polymorphism à la carte via machine learning on dependent types

The essential rationale for subtype polymorphism is adherence to the 'Open/Closed Principle' [12]: the ability to write framework code in terms of superclasses and subsequently invoke it with any subclass that exhibits 'proper subtyping' via the Liskov Substitution Principle (LSP) [11]. Formally, the LSP states that if ø(t : T) is a provable property of objects t of type T, then ø(s) should be true for objects s of subtype S of T. In practice, such properties have typically been those expressible via 'Design by Contract' [12], specifically preconditions, postconditions and invariants. Such abstraction via subtype polymorphism is intended to insulate against requirements change. However, when new requirements do necessitate a change of contract, the maintenance consequences can be severe. In the (typical) absence of explicit language or tool support, enforcement of proper subtyping is laborious and error-prone: contractual changes typically require manual inspection/repair of the class hierarchy to determine/address violations of the LSP.
Assessing spectral estimation methods for electric network frequency extraction

The Electric Network Frequency (ENF) criterion provides useful forensic evidence for multimedia authentication. In this paper, a systematic study of non-parametric and parametric spectral estimation methods is conducted for ENF extraction. Fast implementations of the Capon method and the Iterative Adaptive Approach, which exploit the Gohberg-Semencul factorization of the inverse covariance matrix, are included as well. When long segments are used, a very high matching accuracy is achieved. That is, the maximum correlation-coefficient between the extracted ENF and the ground truth may exceed 99%. Similarly, the standard deviation of error maybe as small as 1.069 · 10-3. Non-parametric spectral estimation techniques are shown to be able to detect an alteration in an audio recording, where a short utterance recorded in Europe is replaced by the same content recorded in the US.
Intrusion Detection and Mitigation System Using Blockchain Analysis for Bitcoin Exchange

Bitcoin exchanges rely heavily on traditional intrusion detection system to secure their system. However, this reliance has proven to be high risk, since Bitcoin and other blockchain-based transactions are not easily reversible. Many of the attacks have shown that the traditional intrusion detection system is not enough to safeguard against all possible attacks, and most importantly, in some cases, it takes a long time to assess the damage. In this paper, we first describe three types of intrusion models in Bitcoin exchanges and propose a detection and mitigation system using blockchain analysis for each. The proposed detection and mitigation system exploit the decentralized and public nature of Bitcoin blockchain to complement the existing traditional intrusion detection system as a fail-safe. The proposed method provides real-time intrusion detection capability that the existing work cannot provide. Although the proposed method is specifically for Bitcoin blockchain, similar ideas can be extended to other proof-of-work based blockchain cryptocurrencies.
Is this the lifecycle we really want?: an automated black-box testing approach for Android activities

Android is today the world's most popular mobile operating system and the demand for quality to Android mobile apps has grown together with their spread. Testing is a well-known approach for assuring the quality of software applications but Android apps have several peculiarities compared to traditional software applications that have to be taken into account by testers. Several studies have pointed out that mobile apps suffer from issues that can be attributed to Activity lifecycle mishandling, e.g. crashes, hangs, waste of system resources. Therefore the lifecycle of the Activities composing an app should be properly considered by testing approaches. In this paper we propose ALARic, a fully automated Black-Box Event-based testing technique that explores an application under test for detecting issues tied to the Android Activity lifecycle. ALARic has been implemented in a tool. We conducted an experiment involving 15 real Android apps that showed the effectiveness of ALARic in finding GUI failures and crashes tied to the Activity lifecycle. In the study, ALARic proved to be more effective in detecting crashes than Monkey, the state-of-the practice automated Android testing tool.
Software Engineering for Smart Cyber-Physical Systems: Models, System-Environment Boundary, and Social Aspects

Smart Cyber-Physical Systems (sCPS) are a novel kind of Cyber- Physical Systems engineered to take advantage of large-scale cooperation between devices, users and environment to achieve added value in face of uncertainty and various situations in their environment. Examples of sCPS include modern traffic systems, Industry 4.0 systems, systems for smart-buildings, smart energy grids, etc. The uniting aspect of all these systems is that to achieve their high-level of intelligence, adaptivity and ability to optimize and learn, they heavily rely on software. This makes them software-intensive systems, where software becomes their most complex part. Engineering sCPS thus becomes a recognized software engineering discipline, which however, due to specifics of sCPS, can only partially rely on the existing body of knowledge in software engineering. In fact, it turns out that many of the traditional approaches to architecture modeling and software development fail to cope with the high dynamicity and uncertainty of sCPS. This calls for innovative approaches that jointly reflect and address the specifics of such systems. This paper maps the discussions and results of the Third International Workshop on Software Engineering for Smart Cyber-Physical Systems (SEsCPS 2017), which specifically focuses on challenges and promising solutions in the area of software engineering for sCPS.
Enabling Privacy with Transfer Learning for Image Classification DNNs on Mobile Devices

More people could benefit of Machine Learning (ML) as an increasingly important technology and service, if state-of-the-art ML techniques with training capability were accessible on personal devices. To this end, we report details on how to deploy Tensor-Flow on off-the-shelf mobile and embedded devices and retrain current deep neural networks for image recognition on-device. Our motivation is to both grant privacy and allow users to efficiently personalize image classifiers for their own needs and purposes, and thus contribute towards turning ML into a "social good", which benefits the largest number of people in the greatest possible way.
A Remote Mobile Video Surveillance System Based on Smart Car

With the development of security technology and people's attention to individual and property safety, video surveillance system has been widelyused in many fields. The traditional video surveillance mode has intensive demands on improving the flexibility of monitoring mode, the capability of storing massive video data, and the efficiency of intelligent analysis for video content. A remote mobile video surveillance system based on smart car and cloud platform for indoor surveillance applications is designed in this paper. In the system, camera is mounted on a mobile car for video capturing. The video or image data is uploaded to the cloud server. Users can access the cloud server by intelligent terminals to watch the video online. At the same time, with the benefit of online image processing technology, the system can detect moving target against dynamicbackground to find suspicious target in real time. Experimental results show that the system performs well.
Thai Music Emotion Recognition by Linear Regression

Music emotion recognition has rapidly grown in the present because the listener's behavior is changing. Therefore, music emotion recognition helps to understand the emotion of music, listener, and support many careers in the music industry including general people. Many researchers have studied western music, but a few studied Thai music. In this work, we are interested to use 155 popular Thai music for explore emotion and use valence-arousal(energy) values from Spotify API to investigate the results. We select multiple linear regression (MLR) and support vector regression (SVR), kinds of 'Linear' function for valence-arousal values prediction. Experimental results demonstrate that the multiple linear regression provides highest accuracy (61.29%), precision (65%), recall (61%) and f-measure (60%) which is more than support vector regression.
Refinement of path expressions for static analysis

Algebraic program analyses compute information about a program’s behavior by first (a) computing a valid path expression—i.e., a regular expression that recognizes all feasible execution paths (and usually more)—and then (b) interpreting the path expression in a semantic algebra that defines the analysis. There are an infinite number of different regular expressions that qualify as valid path expressions, which raises the question “Which one should we choose? ” While any choice yields a sound result, for many analyses the choice can have a drastic effect on the precision of the results obtained. This paper investigates the following two questions: (1) What does it mean for one valid path expression to be “better” than another? (2) Can we compute a valid path expression that is “better,” and if so, how? We show that it is not satisfactory to compare two path expressions E1 and E2 solely by means of the languages that they generate. Counter to one’s intuition, it is possible for L(E2) ⊊ L(E1), yet for E2 to produce a less-precise analysis result than E1—and thus we would not want to perform the transformation E1 → E2. However, the exclusion of paths so as to analyze a smaller language of paths is exactly the refinement criterion used by some prior methods. In this paper, we develop an algorithm that takes as input a valid path expression E, and returns a valid path expression E′ that is guaranteed to yield analysis results that are at least as good as those obtained using E. While the algorithm sometimes returns E itself, it typically does not: (i) we prove a no-degradation result for the algorithm’s base case—for transforming a leaf loop (i.e., a most-deeply-nested loop); (ii) at a non-leaf loop L, the algorithm treats each loop L′ in the body of L as an indivisible atom, and applies the leaf-loop algorithm to L; the no-degradation result carries over to (ii), as well. Our experiments show that the technique has a substantial impact: the loop-refinement algorithm allows the implementation of Compositional Recurrence Analysis to prove over 25% more assertions for a collection of challenging loop micro-benchmarks.
A review of machine learning prediction methods for anxiety disorders

Anxiety disorders are a type of mental disorders characterized by important feelings of fear and anxiety. Recently the evolution of machine learning techniques has helped greatly to develop tools assisting doctors to predict mental disorders and support patient care. In this work, a comparative literature search was conducted on research for the prediction of specific types of anxiety disorders, using machine learning techniques. Sixteen (16) studies were selected and examined, revealing that machine learning techniques can be used for effectively predicting anxiety disorders. The accuracy of the results varies according to the type of anxiety disorder and the type of methods utilized for predicting the disorder. We can deduce that significant work has been done on the prediction of anxiety using machine learning techniques. However, in the future we may achieve higher accuracy scores and that could lead to a better treatment support for patients.
Studying human-computer interaction for social good: the case of digital government evaluation and re-design project

Inclusion of social good in computer science education has been proposed as an approach that demonstrates computing's social relevance and potential for positive societal impact, thus providing students with a motivating vision over the subject. This is particularly important in the context of human-computer interaction courses that can relate to real life situations more easily than other theoretical or more technical subjects. This paper presents a teaching intervention in the context of a master's level course of Human-Computer Interaction and Design of Interactive Systems, during which a group of students were asked to address a critical problem, i.e. to evaluate and re-design typical Greek e-government sites. Given the importance of efficient and effective public sector services for the society's cohesion and citizens' inclusion, and the demand for improving their usability, in particular for the Greek case, it is argued here that this approach of teaching human-computer interaction, situates the theory and methods of the course in a socially relevant context, motivating the students and increasing the learning effect of the teaching intervention.
The BigKClustering approach for document clustering using Hadoop MapReduce

Clustering is an efficient data mining as well as machine-learning method when we need to get an insight of the objects of a dataset that could be grouped together. K-Means is one of the most commonly used methods of clustering, due to its high quality results and low time cost. However, using the K-Means algorithm in document clustering over large-scale collections can lead to unpredictable time costs, since duration of a K-Means iteration tends to grow as the number of iterations grows. In this paper we first present some of the most promising alternatives for document clustering over such 'big data' (large-scale) collections. We also present our variation of an existing K-Means-based algorithm, known as BigKClustering (BKC) so that it can be applied in document clustering. The proposed adjustment of BKC is then implemented using Hadoop MapReduce to handle big (text) data collections efficiently and experimentally tested over a real cluster environment. As it comes out of the experiments, it leads to acceptable clustering quality as well as significant execution time improvements (compared to K-Means), thus constituting a promising clustering approach for big document collections.
Summary of the 1st IEEE Workshop on the Next Level of Test Automation (NEXTA 2018)

NEXTA is a new workshop on test automation that provides a meeting point for academic researchers and industry practitioners. While test automation already is an established practice in industry, the concept needs to evolve to go beyond its current state to support the ever faster release cycles of tomorrow's software engineering. NEXTA implications for research and practice will include test case generation, automated test result analysis, test suite assessment and maintenance, and infrastructure for the future of test automation. The first instance of NEXTA was co-located with the 11th IEEE Conference on Software Testing, Verification and Validation (ICST 2018) in Vasterfias, Sweden on April 9, 2018. NEXTA 2018 offered an interactive setting with a keynote and paper presentations, stimulated by two novel awards to incentivize interaction and dissemination: a Best Questions Award and a Most Viral Tweet Award. The workshop attracted 15 paper submissions and about 50 participants. Based on the positive feedback, we plan to organize the workshop again next year.
Catch and release patterns for target and bycatch species in the Northeast Atlantic deep-water shrimp fishery: Effect of using a sieve panel and a Nordmøre grid

The Nordmøre grid and the sieve panel are two of the main devices used to reduce fish bycatch in trawl fisheries targeting shrimp species. However, even when using such devices, some small-sized fish enter the codend of the trawl together with the targeted shrimp. Therefore, bycatch reduction remains a problem in some shrimp fisheries. One such fishery is the Northeast Atlantic deep-water shrimp (Pandalus borealis) fishery where it is mandatory to use a Nordmøre grid. In this study, the bycatch reduction efficiencies and patterns for several fish species using the standard Nordmøre grid and an experimental sieve panel were investigated and compared. The effect of combining these devices was also explored. The bycatch reduction patterns differed significantly between the two devices and a more efficient bycatch reduction was obtained by combining them. However, while the loss of commercial-sized shrimp was only between 0 and 2% for the Nordmøre grid, it was between 37 and 56% for the tested sieve panel, making this completely unacceptable for commercial fishing. Therefore, before a sieve panel can be considered as an option for the fishery, other sieve panel designs that have a significantly lower loss of shrimp catches need to be identified.
Protective effects of voltage-gated calcium channel antagonists against zinc toxicity in SN56 neuroblastoma cholinergic cells

One of the pathological site effects in excitotoxic activation is Zn2+ overload to postsynaptic neurons. Such an effect is considered to be equivalent to the glutamate component of excitotoxicity. Excessive uptake of Zn2+ by active voltage-dependent transport systems in these neurons may lead to significant neurotoxicity. The aim of this study was to investigate whether and which antagonists of the voltage gated calcium channels (VGCC) might modify this Zn2+-induced neurotoxicity in neuronal cells. Our data demonstrates that depolarized SN56 neuronal cells may take up large amounts of Zn2+ and store these in cytoplasmic and mitochondrial sub-fractions. The mitochondrial Zn2+ excess suppressed pyruvate uptake and oxidation. Such suppression was caused by inhibition of pyruvate dehydrogenase complex, aconitase and NADP-isocitrate dehydrogenase activities, resulting in the yielding of acetyl-CoA and ATP shortages. Moreover, incoming Zn2+ increased both oxidized glutathione and malondialdehyde levels, known parameters of oxidative stress. In depolarized SN56 cells, nifedipine treatment (L-type VGCC antagonist) reduced Zn2+ uptake and oxidative stress. The treatment applied prevented the activities of PDHC, aconitase and NADP-IDH enzymes, and also yielded the maintenance of acetyl-CoA and ATP levels. Apart from suppression of oxidative stress, N- and P/Q-type VGCCs presented a similar, but weaker protective influence. In conclusion, our data shows that in the course of excitotoxity, impairment to calcium homeostasis is tightly linked with an excessive neuronal Zn2+ uptake. Hence, the VGCCs types L, N and P/Q share responsibility for neuronal Zn2+ overload followed by significant energy-dependent neurotoxicity. Moreover, Zn2+ affects the target tricarboxylic acid cycle enzymes, yields acetyl-CoA and energy deficits as well.
Drug-Target Interaction Prediction via Dual Laplacian Graph Regularized Matrix Completion

Drug-target interactions play an important role for biomedical drug discovery and development. However, it is expensive and time-consuming to accomplish this task by experimental determination. Therefore, developing computational techniques for drug-target interaction prediction is urgent and has practical significance. In this work, we propose an effective computational model of dual Laplacian graph regularized matrix completion, referred to as DLGRMC briefly, to infer the unknown drug-target interactions. Specifically, DLGRMC transforms the task of drug-target interaction prediction into a matrix completion problem, in which the potential interactions between drugs and targets can be obtained based on the prediction scores after the matrix completion procedure. In DLGRMC, the drug pairwise chemical structure similarities and the target pairwise genomic sequence similarities are fully exploited to serve the matrix completion by using a dual Laplacian graph regularization term; i.e., drugs with similar chemical structure are more likely to have interactions with similar targets and targets with similar genomic sequence similarity are more likely to have interactions with similar drugs. In addition, during the matrix completion process, an indicator matrix with binary values which indicates the indices of the observed drug-target interactions is deployed to preserve the experimental confirmed interactions. Furthermore, we develop an alternative iterative strategy to solve the constrained matrix completion problem based on Augmented Lagrange Multiplier algorithm. We evaluate DLGRMC on five benchmark datasets and the results show that DLGRMC outperforms several state-of-the-art approaches in terms of 10-fold cross validation based AUPR values and PR curves. In addition, case studies also demonstrate that DLGRMC can successfully predict most of the experimental validated drug-target interactions.
The impact of health on economic and social outcomes in the United Kingdom: A scoping literature review

This is the first review of the evidence, based on longitudinal studies in the United Kingdom, on the association of ill health at any life stage and later social and economic outcomes. The review included a wide range of physical and mental health exposures, both self-reported and objectively measured, as well as social (e.g. life satisfaction) and economic (e.g. employment) outcomes. We searched the Web of Science, key longitudinal datasets based in the UK, major economic journals, Google Scholar and reference lists of relevant publications. The review includes 80 studies. There was strong evidence for the association between early mental health, mainly attention deficit hyperactivity disorder, and lifetime educational, occupational and various social outcomes. Also, both poor physical and mental health in early and middle adulthood, tended to be associated with unemployment and lower socioeconomic status. Among older adults, the evidence quite consistently indicated an association between mental health, chronic conditions, disability/functional limitations, self-rated general health and quality of life, life satisfaction and early retirement. Overall, mental health was consistently found to be associated with a range of social and economic outcomes throughout the lifespan. The evidence for the association between physical health and later outcomes is more inconsistent. A number of methodological challenges need to be addressed, particularly related to causal inference, to produce robust evidence with potential to inform public health policy.
Pancreaticoduodenectomy complicated by Budd-Chiari syndrome: A case report and review of literature

BACKGROUND Pancreaticoduodenectomy (PD)-induced morbidity, consisting mainly of the pancreatic fistula and its hemorrhagic and infectious consequences, is well described in the literature, in terms of its definition, risk factors, preventive measures, and standardized management of complications. However, some life-threatening complications remain atypical and undescribed. CASE SUMMARY We report here the case of a 69-year-old patient with Budd-Chiari syndrome that occurred after arterial embolization of postpancreatectomy hemorrhage. Diagnosis was established with biological findings (i.e., acute liver failure) and radiological findings (i.e., compressive hematoma of the retrohepatic vena cava). Emergency surgical revision was performed to evacuate the hematoma. The postoperative course was uneventful, with rapid recovery of liver function. To our knowledge, post-PD Budd-Chiari syndrome has never been described in the literature. CONCLUSION Acute liver failure in early post-PD should prompt investigation to rule out Budd-Chiari syndrome.