Reviewerly/exHarmony huggingface dataset:
On the use of C# Unsafe Code Context

Background. C# maintains type safety and security by not allowing direct dangerous pointer arithmetic. To improve performance for special cases, pointer arithmetic is provided via an unsafe context. Programmers can use the C# unsafe keyword to encapsulate a code block, which can use pointer arithmetic. In the Common Language Runtime (CLR), unsafe code is referred to as unverifiable code. It then becomes the responsibility of the programmer to ensure the encapsulated code snippet is not dangerous. Naturally, this raises concern on whether such trust is misused by programmers when they promote the use of C# unsafe context. Aim. We aim to analyze the prevalence and vulnerabilities of share code examples using C# unsafe keyword in Stack Overflow (SO) code sharing platform. Method. By using some regular expressions and manual checks, we extracted C# unsafe code relevant posts from SO and categorized them into some software development scenarios. Results. In the entire SO data dump of September 2018, we find 2,283 C# snippets with the unsafe keyword. Among those posts, 27% of posts are about Image processing, where unsafe codes are mainly used for performance reasons. The second most popular category by 21% of the codes in the posts is used for 'Interoperability' reasons. That is 'unsafe' is used to enable 'Interoperability' between C# managed codes and unmanaged codes. The 'stackalloc' operator is the third category with 9% of unsafe code posts. The stackalloc operator allocates a block of memory on the stack. Since C# 7.2, Microsoft recommends against using 'stackalloc' in unsafe context whenever possible. Manual inspection shows 67 code snippets with dangerous functions that can introduce vulnerability if not used with caution (e.g., buffer overflow). Finally, 35% of 'Interoperability' posts have 'P/Invoke' tag were used outside NativeMethods class, which is in contrast to Microsoft design suggestion. Conclusion. Our study leads to 7 main findings, and these findings show the importance of cautiously using this feature.
Formalizing Security and Safety Requirements by Mapping Attack-Fault Trees on Obstacle Models with Constraint Programming Semantics

Requirements Engineering (RE) covers not only the capture and structuring of various properties the system should achieve but also the identification of high-level choices on how to achieve such goals or to avoid related obstacles. Generic RE frameworks support simple formalisation of alternatives using AND/OR refinements while more specialised fields such as safety and security engineering have richer analysis capabilities respectively through fault and attack trees. In this paper, we review the various constructs proposed in those domains and state their semantics at RE level to support safety and security co-engineering. As a supplementary step, we propose a mapping on the semantics provided by Constraint Programming in order to search for optimal configurations in the design space of a RE model. We consider multiple objectives stated as non-functional requirements and formalised using quantified attributes over goal models. Our work is validated on the complex design of an oil pipe system mixing safety and security critical properties.
Minimax Modifications of Linear Discriminant Analysis for Classification with Rare Classes

We consider the problem of classification for imbalanced samples with rare classes. A common problem for machine learning methods in such setting is that a rare class would have extremely high classification error compared to more widespread classes. In general, this problem could be mitigated with re-sampling or fitting additional weights to control the classification errors in classes, though those methods are computationally expensive for large datasets and sometimes fail to attain appropriate results. It this paper we present cost-efficient modifications of Linear Discriminant Analysis allowing to mitigate the problem by minimizing maximal classification error among the classes. For example, this allows achieving more robust machinery malfunction detection algorithms where our expectations on recall would be more consistent among different malfunction types.
An Active Learning Based Hybrid Neural Network for Joint Information Extraction

Joint information extraction with high quality and low annotation costs plays an important role in many natural language processing (NLP) scenarios. To tackle this challenging problem, we firstly propose a joint machine extraction method based on a hybrid neural network which takes three common NLP tasks—named entity recognition (NER), relation extraction (RE) and event extraction (EE) into consideration. Then, based on the joint model, we propose an efficient active learning algorithm to select the most beneficial sentences to be annotated for further improving the model quality in a batch mode. Experimental results show that the proposed joint framework achieves better performance than state-of-the-art information extraction approaches on standard datasets, and our active algorithm surpasses all baseline methods with just 25% of the original training data and saves more than 70% annotation costs in testing data.
A Siamese Adversarial Anonymizer for Data Minimization in Biometric Applications

A wealth of sensors measure our day to day activities. Sharing these biometric data with cloud-enabled applications provides users with useful insights about their health status. However, due to their high re-identification potential, granting medical and fitness applications access to sensors data raises serious privacy concerns.In this work, we aim to find a data representation that sup-presses identity while retaining utility, i.e. data minimization. To this end, we define a component that is plugged into a machine learning pipeline and trained following a min-max adversarial optimization strategy. By using our component during training, we limit the need to rely on third parties and, therefore, reduce the risks of leakages and unintended processing. We evaluate privacy vs. utility trade-off on a real-world sharing scenario involving motion data, i.e. accelerometer and gyroscope measurements. Privacy is estimated by analyzing if an honest-but-curious attacker can effectively link two traces to the same user. In our experiments, we observe a decrease in test verification accuracy, i.e. the identifiability of the users, from 85% to 57%. This leaves an attacker with a representation that he can match only slightly better compared to a random guess. On top of that, the privatized gait representation is accompanied by an increase in performance w.r.t. the main tasks.
Informatical Analysis of Go, Part 1: Evolutionary Changes of Board Size

This paper explores the nature of the Go game. Go has been played on different size of the board in its long history where it was born in 2356 BC and played on 9×9 board, and later played on 13×13, 15×15, 17×17, 19×19, and 21×21 board. Self-play experiments using an open-source implementation of AlphaZero, namely LeelaZero, as the artificial intelligence (AI) player, are conducted for data collection, whereas game refinement (GR) measures had been employed for the assessment. An informational analysis indicates that Go game have been searching not only for a harmonic balance between deterministic and stochastic aspects found in the similar board games (Chess and Mahjong) but also the expected fairness when playing Go. Three aspects of the game have been analyzed: the perspective of game designer (using the GR measure), the perspective of Go evolution relative to Chess and Mahjong evolutionary histories, and other perspectives that closely related to the cultural and strategic development of Go game. Based on such aspects, the impact of komi values and the reasons for 19×19 board size to the contemporary Go popularity had been identified.
Passive Copy-Move Forgery Detection using Localized Intensity Feature

This paper presents an approach for the detection and localization of copy-move forgery (CMF) in digital images using feature based on the localized intensity patterns of pixels. Initially, the test image is partitioned into overlapping blocks and local intensity feature descriptors are extracted from each of these blocks or frames. The matching of similar local frames is then performed in feature space using generalized 2NN (g2NN) test. The detection gets refined in the next stage by the application of agglomerative hierarchal clustering followed by RANdom SAmple Consensus (RANSAC) algorithm with the removal of unwanted outliers. Simulation results are demonstrated to show the efficacy of proposed technique even after numerous pre- and post-processing operations.
How Does the Digital Transformation Affect the Job of University Teachers?

The use of digital devices, which is exponentially growing among students, requires universities rethinking teaching practices. Digital acceleration in the field of higher education is very important especially with the appearance of modular object-oriented dynamic learning environment, massive open online course, and small private online courses phenomena. Thus, digital pedagogy transforms the required digital skills of university teachers' teaching. Determining in detail the digital skills of university teachers was difficult. This chapter is based on some research works as well as on the repository of skills defined by the Virtual University of Tunis, which provides training of university teachers to enhance their e-skills. This theoretical chapter presents a synthesis of works dealing with the skills required by university teachers in a context of digital transformation. Such a synthesis will allow a good preparation of training programs for university teachers.
Towards an order-aware object comparison

As several studies show, code that compares objects is a known source of bugs in object-oriented languages. One of the causes is that multiple methods handle comparison. Another cause is more insidious and has its roots in how these languages control method invocation. Both the literature and the industry have proposals to deal with the problem. They either enforce hard constraints or do not handle all situations. Moreover, none of them deal with the ordering aspect of comparison. We propose a declarative approach to object comparison that handles ordering, and is language agnostic. We created a prototype in Java and evaluated its impacts on the software quality of applications in the DaCapo benchmark suite. The benefits were measured based on the ISO/IEC 25010 product quality model. Results suggest that the overall quality of the software improved, despite the deterioration of performance.
Stance Classification with Improved Elementary Classifiers Using Lemmatization (Grand Challenge)

Twitter, a microblogging and social networking service, gives us access to a large scale social data. In this report, we try to obtain sentiments of the tweets in context of the #metoo movement. We try to derive a model that classifies tweets into categories of different linguistic aspects like hate, sarcasm, allegations and support/opposition. This helps analyze the tweets and flag them according to relevance. Here we explore elementary machine learning algorithms, multinomial naive Bayes and random forest classifier with ratio selection classification to improve stance classification at greater efficiency than baseline models.
An Improved Particle Filter Based on UKF and Weight Optimization

Aiming at the problem of limited efficiency and accuracy of state estimation in the case of non-linear and non-Gaussian systems, this paper proposes an improved particle filtering algorithm based on edge unscented Kalman filtering and weight optimization for the existing efficiency problems of UPF. Compared with traditional particle filtering, the improved filtering algorithm generates a suggested distribution function in order to avoid excessive variance of particle weights and combines the latest observation information to calculate a more efficient edgeless trace Kalman filter; during the resampling process The weight-optimized resampling method is introduced to solve the problem of particle depletion and improve particle diversity. It can be verified through theoretical derivation and simulation analysis that the improved algorithm effectively improves the calculation efficiency and has better estimation accuracy.
Multiscale Convolutional Generative Adversarial Network for Anchorage Grout Defect Detection

Grout is an important part of the bolt anchorage system; once having the damage, the cohesive force will not reach the requirement, which may affect the support effect. Moreover, as buried in the rock, the defects that happened in the grout have unknown characteristics. In order to identify the grout defect, a multiscale convolutional generative adversarial network (MSCGAN) method is proposed in this article. The generator based on supervised learning is designed in the MSCGAN framework, which can generate data for enriching the diversity of the system characteristics. Together with adopting the convolutional neural network, which can realize end-to-end learning as a discriminator, MSCGAN also draws on the idea of multiscale to improve the ability of feature extraction. The proposed method was experimentally verified on the bolt anchorage system experimental platform. Through the comprehensive analysis of the experimental results of different methods, the effectiveness of the proposed method is demonstrated.
Optimization Scheduling of Cloud Service Resources Based on Beetle Antennae Search Algorithm

As cloud computing systems have the characteristics of a large number of users, large task scales, and distributed storage of massive data, reasonable scheduling system resources has become a difficult problem in cloud computing research, and it is particularly important to design an efficient cloud computing task scheduling algorithm. Although most traditional optimization algorithms can achieve better results in cloud resource scheduling problems, there is still much room for improvement. On the basis of summarizing the existing research work, this paper aims at optimizing task execution time and algorithm execution efficiency, and proposes a cloud resource scheduling optimization strategy based on the Beetle Antennae Search (BAS). This article first analyzes the principle of BAS algorithm and discusses its mathematical model. Secondly, the application of BAS algorithm in cloud computing resource scheduling problem is analyzed, fitness calculation function is designed, and individual coding schemes are studied. Finally, in order to verify the effectiveness of the scheduling strategy, experiments were conducted on the optimization effect of the algorithm by building a CloudSim cloud computing simulation platform. The experimental results show that the BAS algorithm has a better optimization effect than the PSO algorithm, and can significantly reduce the iteration time of the algorithm.
Obstacle Tower Without Human Demonstrations: How Far a Deep Feed-Forward Network Goes with Reinforcement Learning

The Obstacle Tower Challenge is the task to master a procedurally generated chain of levels that subsequently get harder to complete. Whereas the most top performing entries of last year's competition used human demonstrations or reward shaping to learn how to cope with the challenge, we present an approach that performed competitively (placed 7th) but starts completely from scratch by means of Deep Reinforcement Learning with a relatively simple feed-forward deep network structure. We especially look at the generalization performance of the taken approach concerning different seeds and various visual themes that have become available after the competition, and investigate where the agent fails and why. Note that our approach does not possess a short-term memory like employing recurrent hidden states. With this work, we hope to contribute to a better understanding of what is possible with a relatively simple, flexible solution that can be applied to learning in environments featuring complex 3D visual input where the abstract task structure itself is still fairly simple.
Anonymized person re-identification in surveillance cameras

Person re-identification (Re-ID) is a valuable technique because it can assist in finding suspects after a terrorist attack. However, the machine learning algorithms for person Re-ID are usually trained on large datasets with images of many different people in a public space. This could pose privacy concerns for the people involved. One way to alleviate this concern is to anonymize the people in the dataset. Anonymization is important to minimize the storage and processing of personal information, such as facial information in a surveillance video. However, anonymization typically leads to loss of information and could lead to severe deterioration of the Re-ID quality. In this paper, we show that it is possible to store only anonymized person detections while still achieving a high quality person Re-ID. This leads to the conclusion that for the development of re-identification algorithms in situations where privacy is of great importance it is not necessary to store facial information in person re-identification datasets.
Terrorist Video Detection System Based on Faster R-CNN and LightGBM

Nowadays the mobile phone has become an indispensable tool in the lives of many people. While facilitating people's lives, it also provides criminals with a very important tool for spreading the terrorist video. Traditional manual detection of the terrorist video has the problem of low accuracy and inefficiency. To address the issue, this paper proposes a terrorist video detection system based on Light Gradient Boosting Machine (LightGBM) and Faster Region-based Convolutional Neural Network (Faster R-CNN) for mobile phone forensics system, which is used to quickly detect whether there is a terrorist video in the suspect's mobile phone. The system uses a multi-model method for detection, which includes preliminary detection and deep detection in two stages. Experimental research shows that it can effectively and accurately detect terrorist videos in mobile phones, thereby helping criminal investigation personnel to quickly grasp criminal evidence and provide some clues for the detection of the case.
Dissipative dynamics of an entangled three-qubit system via non-Hermitian Hamiltonian: Its correspondence with Markovian and non-Markovian regimes

We investigate an entangled three-qubit system in which only one of the qubits experiences the decoherence effect by considering a non-Hermitian Hamiltonian, while the other two qubits are isolated, i.e., do not interact with environment, directly. Then, the time evolution of the density matrix (for the pure as well as mixed initial density matrix) and the corresponding reduced density matrices are obtained, by which we are able to utilize the dissipative non-Hermitian Hamiltonian model with Markovian and non-Markovian regimes via adjusting the strange of the non-Hermitian term of the total Hamiltonian of the under-considered system.
The Development Of Interactive Lego For Kids

Lego is a colorful plastic toy that can be disassembled to the liking accordingly. In general, lego forms on the market already, with interactive Lego for Kids can create new rides for children, which can make children more creative and imagination high in making Lego shapes according to the wishes of children. Interactive Lego can also execute forward and backward using an infrared remote control connected to the Arduino inside Lego. This research aims to learn learning outcomes and describe children's intelligent through interactive games can be used as a medium to develop cognitive abilities and motor abilities in children.
Supply Chain Management for Agri Foods Using Blockchain Technology

The agricultural supply chain consists of many participants like producer (generally farmer), consumer (people who buy the product and consume them), wholesalers, and retailers. This system consists of many levels of mediator parties as well which have different policies of the commission. Due to the difference in these policies, the producers do not get their fair share of price. Due to the varying prices, consumers also suffer as they do not get the right quality of the product for the right price. There are no central records maintained regarding the transactions between the participants which could lead to many serious problems. To tackle the above-mentioned issues, we need a holistic approach that can provide solutions to most of the above issues. Here, blockchain-based solution can be used to achieve traceability (we can trace the whereabouts of the product, the origin of the product, etc.), transparency (so that a sense of trust is achieved), fairness (by removing the intermediaries), assurance of food safety and pricing (so that nobody has to bear the loss).
Building Computationally Intensive Internet-of-Everything through Synergy of Engineering and Computer Science

An ad-hoc creation of situations where interconnected and movable devices, often empowered with computational capabilities, create instances of Internet-of-Everything, (IoE) has become common. The examples range from autonomous vehicles on our roads and unmanned movable objects, designed for a variety of tasks, to internet of biomedical devices, wearable robotics and digital therapeutics. In all these examples, we create computational models and solutions, which enable inter-operation between devices, and secure the accomplishment of various tasks. However, computational models in such environments are highly influenced by physical devices, which either generate data and feed our computations, or perform "action(s)" according to results of computing. Computations and devices do not exist in isolation; they are interwoven and influence each other, even if we see them as belonging to two different worlds. Design decision and manufacturing of a device may have implications on types of computing we could perform with them, and thus affect the performance of an instance of IoE, as a cyber-physical space. The same is true for software abstractions and models. We expect that data should be available or delivered by devices, and computations should be accommodated by at least partially, if not completely by these devices. In this study, we illustrate the interference or an impact of engineering practices and design of wearable devices, including wearable robotics, with/on computational power these devices may or should have. We debate the way of enhancing the both: device manufacturing and creation of software where these wearable devices perform a crucial role. The purpose of this study is to open discussions towards intelligent engineering, where the purpose and usefulness of devices are jointly decided, during their design, by both engineers and computer scientists. By understanding the impact of engineering design decisions to potential computational power of cyber physical spaces, such as IoE, we should be able to create more efficient operational IoE, which will be more manageable and might meet our expectations.
Hybrid-Trusted Party Contract Agrees on Clients Input

Modern applications implemented on distributed ledgers and blockchain platforms serve a wide variety of purposes in automating business and end-user processes. Smart contracts, which are immutable records of functions, are used to mitigate and reduce the administration cost of these implementations. This paper describes a hybrid-trusted multi-party contract to identify the input of a client and validate a transaction. A policy is defined to create the initial relationship, using trusted parties to authorize a particular input and verify that an owner initially owns that input. This ties the reality into the blockchain and requires that trusted parties set up the initial blockchain database. After that, transactions are performed without trusted third parties, reducing costs.