Reviewerly/exHarmony huggingface dataset:
A Novel Optimized Method for Feature Selection Using Non-linear Kernel-Free Twin Quadratic Surface Support Vector Machine

Feature Selection (FS) is an important topic in the domain of machine learning. Support Vector Machine (SVM) is one of the most popular ML models for classification tasks. Efficient feature selection may ensure enhanced classification accuracy. Although there are several feature selection algorithms in practice, they are either separately used, combinedly used with linear SVM or, used with kernel-based SVM. Additionally, there exists another problem in classification methods, which is the selection of correct kernel function. There seems to be as such no general rule for selecting a kernel that maximizes the model's performance. To handle the issues together, we propose a model called non-linear kernel-free twin quadratic surface SVM with optimized feature selection (LTQSSVM-OFS) which can eventually tackle both feature selection and classification tasks efficiently. We are doing feature selection in a kernel-free way by applying optimization method on a Laplacian Twin Quadratic Surface SVM classifier. We have validated our proposed model using (i) min-max approach-based SVM without FS, (ii) linear SVM, and (iii) radial basis function-based SVM models applied on four datasets, namely 'Star3642balanced', 'Diabetes', 'Health care: Heart attack possibility', and 'Blood Transfusion'. Experimental results reveal that the proposed model outperforms the other models. Additional worth noting benefit of our model is that it yields better results in case of working with fewer features.
Video anomaly detection using CycleGan based on skeleton features

Anomaly detection is a challenging task in the field of intelligent video surveillance. It aims to identify anomalous events by monitoring the video captured by visual sensors. The main difficulty of this task is that the definition of anomalies is ambiguous. In recent years, most anomaly detection methods use a two-stage learning strategy, i.e., feature extraction and model building. In this paper, with the idea of refactoring, we propose an end-to-end anomaly detection framework using cyclic consistent adversarial networks (CycleGAN). Dynamic skeleton features are used as network constraints to alleviate the inaccuracy of feature extraction algorithms of a single generative adversarial network. In the training phase, only normal video frames and the corresponding skeleton features are used to train the generator and discriminator. In the testing phase, anomalous behaviors with high reconstruction errors can be filtered out by manually set thresholds. To the best of our knowledge, this is the first time CycleGAN has been used for video anomaly detection. Experimental results on challenging datasets show that our method can accurately detect anomalous behaviors in videos collected by video surveillance systems and is comparable to the current state-of-the-art methods.
Toward an Effective Bug Triage System Using Transformers to Add New Developers

As defects become more widespread in software development and advancement, bug triaging has become imperative for software testing and maintenance. The bug triage process assigns an appropriate developer to a bug report. Many automated and semiautomated systems have been proposed in the last decade, and some recent techniques have provided direction for developing an effective triage system. However, these techniques still require improvement. Another open challenge related to this problem is adding new developers to the existing triage system, which is challenging because the developers have no listed triage history. This paper proposes a transformer-based bug triage system that uses bidirectional encoder representation from transformers (BERT) for word representation. The proposed model can add a new developer to the existing system without building a training model from scratch. To add new developers, we assumed that new developers had a triage history created by a manual triager or human triage manager after learning their skills from the existing developer history. Then, the existing model was fine-tuned to add new developers using the manual triage history. Experiments were conducted using datasets from well-known large-scale open-source projects, such as Eclipse and Mozilla, and top-k accuracy was used as a criterion for assessment. The experimental outcome suggests that the proposed triage system is better than other word-embedding-based triage methods for the bug triage problem. Additionally, the proposed method performs the best for adding new developers to an existing bug triage system without requiring retraining using a whole dataset.
Experiencing the structure and features of a machine tool with mixed reality

Augmented and mixed reality (AR/MR) enable a completely new way of visualizing the connections between components and assemblies of a complex machine tool. By means of AR/MR, users can immerse themselves in areas of interest of a machine or an assembly process and not only observe the connections from outside a real machine or a process. Experiencing virtual contents in the real surroundings is completely different from virtually seeing a design or an assembly process on a screen by means of a conventional CAD application or a simulation. Therefore, this paper presents a mixed reality app for the visualization of information about components and assemblies in the holograms of a complex machine tool. Within a research project on the improvement of teaching methods by augmented reality (AR), virtual reality (VR) and mixed reality (MR), it is planned to concept, develop and test teaching units in studies. At the Institute for Machine Tools (IfW) of the University of Stuttgart, the Index R200 high-end turning/milling centre has been chosen as demonstration model for an MR app. It is a 10-axis machining centre with several driven spindles and tool holders in the working space. It can be used both as real machine tool for complex experiments and as virtual machine for simulations or visualizations of CNC programs, among other things for developing new teaching methods and contents.
From Big Data to Blockchain: Promises and Challenges of an All-Encompassing Technology in Education

Blockchain[aut]Park, Jae is regarded as the next technology-mediated mega-transformation after the ongoing 'Google's System of the World' (Gilder, 2018). This chapter critically reviews the extant studies and theories on blockchain technology and its influence on education. Blockchain in education aims to enhance objectivity, validity and full control of a specific information without being compromised by economic, health or political instabilities. Education blockchain promises, among others, a greater control over financing and investing in education, implementing instructional projects, a certification system and learning. Blockchain with its distributed ledgers would set the standards to 'crypto-instruction' and 'crypto-educational administration' that would be recognized across organizational or national borders, yet, neither transparent nor clearly sustainable. Therefore, it is argued, blockchain technologies in and for education are a credible augur of an upcoming paradigm shift in human experience, yet, they are no exception to the general principle of magnification and the reduction of technology (Ihde, 1993).
Learning Dual-Routing Capsule Graph Neural Network for Few-Shot Video Classification

Few-shot video classification (video FSL), which learns classifiers for novel concepts, has gained increasing attention in the last few years from only a few samples. The existing methods rarely consider the local-global relation for video feature learning, which would ultimately result in low discriminative ability. Recently, the capsule network (CapsNet) has shown considerable potential in local-global relation learning in the image analysis field. However, CapsNet cannot be directly applied in video FSL since it ignores the interaction between videos and has high computational complexity. In this paper, a dual-routing capsule graph neural network (DR-CapsGNN) is proposed to solve the above issues. The DR-CapsGNN leverages CapsNet and a graph neural network (GNN) to explore local-global relations and to preserve the detailed properties. Specifically, the CapsGNN is used to learn video relations and structural information to generate high-quality hierarchical capsules. Furthermore, a novel dual-routing mechanism is designed to filter low-discriminative capsules from a holistic perspective and achieves high efficiency, which consists of inter-video and intra-video routing. Extensive experimental results demonstrate that our proposed approach performs favorably compared to state-of-the-art methods on two popular benchmarks.
On the evolution and impact of architectural smells—an industrial case study

Abstract Architectural smells (AS) are notorious for their long-term impact on the Maintainability and Evolvability of software systems. The majority of research work has investigated this topic by mining software repositories of open source Java systems, making it hard to generalise and apply them to an industrial context and other programming languages. To address this research gap, we conducted an embedded multiple-case case study, in collaboration with a large industry partner, to study how AS evolve in industrial embedded systems. We detect and track AS in 9 C/C++ projects with over 30 releases for each project that span over two years of development, with over 20 millions lines of code in the last release only. In addition to these quantitative results, we also interview 12 among the developers and architects working on these projects, collecting over six hours of qualitative data about the usefulness of AS analysis and the issues they experienced while maintaining and evolving artefacts affected by AS. Our quantitative findings show how individual smell instances evolve over time, how long they typically survive within the system, how they overlap with instances of other smell types, and finally what the introduction order of smell types is when they overlap. Our qualitative findings, instead, provide insights on the effects of AS on the long-term maintainability and evolvability of the system, supported by several excerpts from our interviews. Practitioners also mention what parts of the AS analysis actually provide actionable insights that they can use to plan refactoring activities.
The Future of Payments in a DLT-Based European Economy: A Roadmap

Distributed ledger technology (DLT) hasDistributed ledger technologies (DLTs) the potential to address long-standing industrial challenges, remove frictions, build trust, and unlock new value across businesses and industries. It enables decentralization, the immutability of data, transparency, and the automation of business processes. Thereby, it creates a multitude of use cases ranging from energy and manufacturing to mobility and logistics. However, a digitized economy based on DLT can flourish only if it does not merely enable the exchange of assets, goods, and services but also the exchange of money. In other words, there is a need for a payment solution that is compatible with DLT-based decentralized networks and enables transactions denominated in euro. This is particulary relevant in the currently evolving geopolitical environment.
Explaining Face Presentation Attack Detection Using Natural Language

A large number of deep neural network based techniques have been developed to address the challenging problem of face presentation attack detection (PAD). Whereas such techniques' focus has been on improving PAD performance in terms of classification accuracy and robustness against unseen attacks and environmental conditions, there exists little attention on the explainability of PAD predictions. In this paper, we tackle the problem of explaining PAD predictions through natural language. Our approach passes feature representations of a deep layer of the PAD model to a language model to generate text describing the reasoning behind the PAD prediction. Due to the limited amount of annotated data in our study, we apply a light-weight LSTM network as our natural language generation model. We investigate how the quality of the generated explanations is affected by different loss functions, including the commonly used word-wise cross entropy loss, a sentence discriminative loss, and a sentence semantic loss. We perform our experiments using face images from a dataset consisting of 1,105 bona-fide and 924 presentation attack samples. Our quantitative and qualitative results show the effectiveness of our model for generating proper PAD explanations through text as well as the power of the sentence-wise losses. To the best of our knowledge, this is the first introduction of a joint biometrics-NLP task. Our dataset can be obtained through our GitHub page <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/ISICV/PADISI_USC_Dataset .
The Digital Estate in the Conflict Between the Right of Inheritance and the Protection of Personality Rights

Online shopping via Amazon or Zalando, email communication, posting and sharing videos, photos and messages on Facebook, Youtube or Instagram, domain rights, Bitcoin transfers, copyright and trademark issues: the free development of the individual in the online environment produces a large bulk of data. In a series of decisions between 2015 and 2018, German civil courts for the first time had to deal with aspects of “digital estate”: The parents of a teenage girl who had died in an accident sued Facebook for access to the full social media account of their daughter because they hoped, i.a., to obtain background information about the death of the teenager. However, the actual significance of “digital estate” goes way beyond this case. This chapter deals with questions arising from constitutional law. It outlines in a first step a differentiated notion of “digital estate” and then introduces the facts and arguments of the case before the German courts. In its main parts, it analyses in detail the legal interests of the people concerned: the right to inherit on the one hand and the secrecy of telecommunications as well as the protection of personal data on the other hand, all guaranteed by fundamental rights of the German Constitution and further regulated in statutory law. It arrives at the result that neither the secrecy of telecommunications nor data protection in favour of the testator’s communication partners create obstacles to the heirs’ right to access the testator’s digital account data. Therefore, apart from providing legal certainty and as regards the cases discussed, already existing rules offer sufficient ground for convincing solutions.
Learning Accurate Long-term Dynamics for Model-based Reinforcement Learning

Accurately predicting the dynamics of robotic systems is crucial for model-based control and reinforcement learning. The most common way to estimate dynamics is by fitting a one-step ahead prediction model and using it to recursively propagate the predicted state distribution over long horizons. Unfortunately, this approach is known to compound even small prediction errors, making long-term predictions inaccurate. In this paper, we propose a new parametrization to supervised learning on state-action data to stably predict at longer horizons – that we call a trajectory-based model. This trajectory-based model takes an initial state, a future time index, and control parameters as inputs, and directly predicts the state at the future time index. Experimental results in simulated and real-world robotic tasks show that trajectory-based models yield significantly more accurate long term predictions, improved sample efficiency, and the ability to predict task reward. With these improved prediction properties, we conclude with a demonstration of methods for using the trajectory-based model for control.
Intuitionistic Fuzzy Representation of Uncertainty in Biomedical Operations

The paper studies the problem of representation of the uncertainty in biomedical operations. Each operation is considered as a request (e.g., a patient) offered to a virtual service device, representing service of a manual, semiautomatic or automatic action. Some concepts of Service Systems Theory are justified and/or extended, especially traffic characterization in the International Telecommunication Union recommendations [4]. This allows three service qualifications to be made: (i) traffic characterization (e.g., requests’ flow intensity (rate), probability of the next service direction, service duration, traffic intensity and volume); (ii) causal classification (offered, blocked, repeated, served, carried, lost, parasitic [14], effective, etc.); (iii) proposed intuitionistic fuzzy service characterization (successful (belonging to a fully completed successful service), uncertain (uncompleted, due to any reason, target service) and unsuccessful (a service not belonging to the desired successful service)). A conceptual model of the causal service structure of a virtual service system is presented, including graphical representation. A notation system of the devices and the devices’ parameters is proposed based on the qualifiers, corresponding to each of the traffic types considered. For every virtual service device, using the causal conceptual representation, an intuitionistic fuzzy pair [7] is defined which characterizes the uncertainty in the process of servicing of the requests. The degrees of membership, non-membership and uncertainty [6] are expressed as ratios of the parameters of the virtual devices. The problem of representing the uncertainty in a more complex system consisting of two consecutively composed services is also studied. The qualitative difference between the causal classification and the proposed intuitionistic fuzzy characterization is shown.
Integration of Quantum Computing and Blockchain Technology: A Cryptographic Perspective

Blockchain is a computational data structure that provides open and distributed and decentralized public ledger technology that has many promising applications. Blockchain applies block structure linked with each other to store and verify data and provides trustworthy consensus mechanism for the synchronization of changes in data which results in a tamperproof digital platform. Blockchain has many approaches for security services which includes integrity assurance, confidentiality, resource provenance and access control list. Blockchain is an emerging technology and believed to be employed in diverse interactive system of the Internet. The goal of this paper is to give a detailed overview on the blockchain technology such as its background, architecture and properties. Further, it describes the quantum-level vulnerabilities of different popular blockchains in-use and the different cryptographic concepts that are used in blockchain, and then it highlights the concept of quantum computing along with blockchain technology. In the end, it gives an insight of pre-quantum to post-quantum blockchain.
Property Rights, Knowledge Commons, and Blockchain Governance

Knowledge about property rights is a commons that facilitates market exchange and economic coordination. The governance of that shared knowledge resource—the various formal and informal rules that maintain ledgers of property rights—ranges from community norms to formal state registries. In this chapter we make three contributions. First, we use the lens of knowledge commons theory to argue that knowledge about property rights is a shared resource. Second, we explore how that knowledge commons is governed—particularly relating to “rules in use”—might shift due to technological advances in distributed ledgers. Third, we argue that as blockchain augments and complements existing governance structures, it creates a more robust political economy.
Targeted High-Utility Itemset Querying

Traditional high-utility itemset mining (HUIM) aims to determine all high-utility itemsets (HUIs) that satisfy the minimum utility threshold in transaction databases. However, in most applications, not all HUIs are interesting because only specific parts are required. Thus, targeted mining based on user preferences is more important than traditional mining tasks. This article is the first to propose a target-based HUIM problem and to provide a clear formulation of the targeted utility mining task in a quantitative transaction database. A tree-based algorithm known as <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</b> arget-based high- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">U</b> tility ite <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> set querying (TargetUM) is proposed. The algorithm uses a lexicographic querying tree and three effective pruning strategies to improve the mining efficiency. We implemented experimental validation on several real and synthetic databases, and the results demonstrate that the performance of TargetUM is satisfactory, complete, and correct. Finally, owing to the lexicographic querying tree, the database no longer needs to be scanned repeatedly for multiple queries.
Consistent Sub-Decision Network for Low-Quality Masked Face Recognition

The COVID-19 pandemic makes wearing masks mandatory in supermarkets, pharmacies, public transport, etc. Existing facial recognition systems encounter severe performance degradation as the masks occlude key facial regions. Recently, simulation-based methods are proposed to generate masked faces from unmasked faces. However, among simulated faces, there are low-quality samples with negative occlusion, which leads to ambiguous or absent facial features. In this paper, we propose a consistent sub-decision network to obtain sub-decisions that correspond to different facial regions and constrain sub-decisions by weighted bidirectional KL divergence to make the network concentrate on the upper faces without occlusion. In addition, we perform knowledge distillation to drive the masked face embeddings towards an approximation of the original data distribution to mitigate the information loss. Experiments show that the proposed method performs better than the baseline on public masked face recognition datasets, i.e., RMFD, MFR2, and MLFW.
Foundations for Entailment Checking in Quantitative Separation Logic

Abstract Quantitative separation logic () is an extension of separation logic () for the verification of probabilistic pointer programs. In , formulae evaluate to real numbers instead of truth values, e.g., the probability of memory-safe termination in a given symbolic heap. As with , one of the key problems when reasoning with is entailment : does a formula f entail another formula g ? We give a generic reduction from entailment checking in to entailment checking in . This allows to leverage the large body of research for the automated verification of probabilistic pointer programs. We analyze the complexity of our approach and demonstrate its applicability. In particular, we obtain the first decidability results for the verification of such programs by applying our reduction to a quantitative extension of the well-known symbolic-heap fragment of separation logic.