Reviewerly/exHarmony huggingface dataset:
A Generalized Optimization Embedded Framework of Undersampling Ensembles for Imbalanced Classification

Imbalanced classification exists commonly in practical applications, and it has always been a challenging issue. Traditional classification methods have poor performance on imbalanced data, especially, on the minority class. However, the minority class is usually of our interest, and its misclassification cost is higher. The critical factor is the intrinsic complicated distribution characteristics in imbalanced data itself. Resampling ensemble learning achieves promising results and is a research focus recently. However, some resampling ensembles do not consider complicated distribution characteristics, thus limiting the performance improvement. In this paper, a generalized optimization embedded framework (GOEF) is proposed based on undersampling bagging. The GOEF aims to pay more attention to the learning of local regions to handle the complicated distribution characteristics. Specifically, the GOEF utilizes out-of-bag data to explore heterogeneous local areas and chooses misclassified examples to optimize base classifiers. The optimization can focus on a single class or both classes. Extensive experiments over synthetic and real datasets demonstrate that GOEF with the minority class optimization performs the best in terms of AUC, G-mean, and sensitivity, compared with five resampling ensemble methods.
Completeness and Complexity of Reasoning about Call-by-Value in Hoare Logic

We provide a sound and relatively complete Hoare logic for reasoning about partial correctness of recursive procedures in presence of local variables and the call-by-value parameter mechanism and in which the correctness proofs support contracts and are linear in the length of the program. We argue that in spite of the fact that Hoare logics for recursive procedures were intensively studied, no such logic has been proposed in the literature.
Constrained Adversarial Attacks Against Image-Based Malware Classification System

In this paper, a multi-pixels attack is proposed to demonstrate the vulnerability of the image-based malware classifier. We adopt the self-adaptive differential evolutionary algorithm to reinforce the power of adversarial attack. The proposed scheme has the following advantages: 1) as a semi-black-box attack, it can fool the neural network without any knowledge about the gradients or model structure. 2) the transformation from binary files to images can train a classifier with higher performance. 3) the outside group is added while implementing the DE algorithm to avoid being trapped in the local optimal value. The experimental results show that our attack can make the classifier misjudge with high confidence rate and more false negatives under the restriction of not destroying the files' function. Besides, further defensive mechanisms are explored such as local median smoother, color depth reducing and adversarial training to show that the \(\ell _0\) norm attacks can be relieved.
Cybercrime in Selected European Countries

Abstract This study does not claim to exhaust all the abundant comparative legal issues. Therefore, it has been limited to discussing the penal provisions of eight European countries, without going into detailed considerations on issues related to e.g. form of offences committed or the liability of legal persons. The overarching intention was to present as diverse regulations as possible. All countries whose regulations were discussed have signed and ratified the Convention on Cybercrime and are members (except United Kingdom) of the European Union, which resulted in their obligation to implement Framework Decision 2005/222 on attacks against information systems, and the need to adapt their regulations to the provisions of Directive 2013/40, which has replaced this decision.
On the Value of CrowdRE in Research and Practice

Crowd-based Requirements Engineering, or CrowdRE, is an emerging requirements engineering paradigm in which a crowd of users plays a central role in carrying out activities such as elicitation and prioritization. In this keynote, I analyze the value of CrowdRE, focusing on its effectiveness and impact in research and in practice. Starting from research carried out in collaboration with industrial partners, I make the case for the need of conducting in vivo research for CrowdRE. This type of research is crucial to gather evidence for the practical effectiveness and to ultimately establish CrowdRE as a prominent paradigm for the software industry.
A Comprehensive Review of Investigations of Suspects of Cyber Crimes

Interrogations of suspects of cyber crimes after logs of Intrusion Detection Systems (IDSs) have been analyzed, and suspects have been apprehended are contestable issues over the years. Some studies submit that it is the prerogative and responsibility of governments’ agencies like Criminal Justice, Police, and FBI to interrogate suspects of cyber crimes. However, some experts maintain reservations on these issues for the fact that significant numbers of suspects of cyber crimes may not necessarily be accused in some cases once the issues are professionally investigated. Rather than subjecting innocent suspects to social problems like embarrassment, traumatic stigmatization, interpersonal and relationship problems in the society, this chapter submits those private and corporate organizations that have apprehended suspects of should conduct preliminary investigations of the suspects on their own to ascertain the depth of involvement of the suspects in the allegations against him/her. The chapter uses C++ programs and intrusive logs to pragmatically enumerate a number of arguable issues, including charges against suspects of cyber crimes and the limits of conventional methods of interrogations by the Police. The chapter proposes a model for the questioning and classification of suspects and witnesses of cyber crimes. Finally, the above models provide invaluable guidelines to the students of computer forensics and interrogators in conducting preliminary investigations of suspects of cyber crimes in accordance with best corporate and legal standards.
Rethinking on Pawar and Charak’s Priority Model of Employee Value Proposition: Development and Implications for Future Agenda

Today’s market is becoming more global, dynamic and competitive hence organisations needs better organisational attractiveness, brand and reputation for sustainability which can be effectively managed through the compelling employee value proposition. The employee value proposition represents the perceived overall deal between employer and employee. The employer makes an offer to the employee (or the give) and expects contributions (or the get) from the employee in return. It is the balance of the offerings and benefits that are received by employees in return for their performance at the workplace. Employee Value Proposition is at the core of all other organizational processes. This paper have adopted the qualitative approach and further reviewed and rethought on Pawar and Charak’s Priority Model of Employee Value Proposition as a framework with its implications in the organisations. The outcome of the study highlights the influences of this framework on employers and employees.
Employability Enhancement Network Construction Based on Multimedia Technology

At present, higher education institutions with colleges and universities as the main body, while undertaking teaching, scientific research, and serving social functions, the construction of campus culture in universities is also an important part of the construction and development of education. The multimedia era is developing rapidly, the new challenges to the employability and the construction of campus culture are presented. The main problems existing in the construction of university campus culture are: insufficient awareness of each construction subject and insufficient attention. These problems must be taken seriously and effective measures taken to solve them. The purpose of this article is to study the sustainable development of campus culture construction based on the improvement of employ ability. Based on the improvement of employ ability, this study combines the new requirements and trends of university campus culture construction and sustainable development, and builds a university campus culture analysis framework including five dimensions: material, spiritual, system, behavior, and ecology. Use this as a basis for the questionnaire design. Experimental research shows that on the basis of the improvement of employ ability, the construction of a sustainable university campus culture has an important role in improving the moral and cultural quality of college students, and provides a driving force for colleges and universities to cultivate cultural awareness and cultural self-confidence. Cultural strength.
Challenges of Adoption of Blockchain Technology in Supply Chain: An Overview

Blockchain Technology (BT) has grown noteworthy attention in recent years. Adoption of these technologies reforms supply chains (SC), produce an improved product quality within a lead time, and provide the customized products to the customer. BT has a characteristic of decentralized transactions and data management that has significant impact on traditional supply chain management. Integration of BT with SC is still in its early stages. Industries and academic research need to know the challenges in the adoption of BT in SC. It requires huge investments to investigate various challenges in adoption of BT in SC. Therefore, the aim of this study is to provide an overview about the challenges in adoption of BT in SC. A literature review has been conducted to find challenges in adoption of BT in SC. This paper recognizes some main challenges. Those challenges are to build trust among the SC members, prepare a policies that governs the implementation of BT in SC, maintain the balance between the transparency and confidentiality of the transaction data, make a hackproof system, enhance the communication within SC system, select a proper product in the early stage of adoption to recover the investment as early as possible, prove provenance of the product and train the people involved in implementing BT in SC. This work discussed potential implications based on challenges and provide platform for adoption of BT in SC.
Adaptation of Spiral Radius and Angle in Hypotrochoid Spiral Dynamic Algorithm

This paper focuses on developing an adaptation algorithm for hypotrochoid spiral dynamic optimization using linear adaptive spiral radius and angle by dynamically varying the value for each iteration based on the fitness function value. The adaptive algorithm will help obtain the optimal search point compared to fixed radius and angle in conventional methods. The spiral radius and angle effect show that the spiral movement with a negative radius will boost the process of escaping from converging at local optima. The statistical results on various benchmark functions show that the proposed optimization has performed better than spiral dynamic, adaptive spiral dynamic, and hypotrochoid spiral dynamic algorithms.
Unified Discriminant and Distribution Alignment for Visual Domain Adaptation

In visual understandings, images taken from different cameras usually have different resolutions, illumination, poses, and background views that lead to domain shift. Besides labeling these data is an expensive operation. These problems lead to the need for unsupervised domain adaptation (UDA), in which training and testing data are not drawn from the same distribution, and labels are not available in the target domain. This paper presents an improvement for unsupervised domain adaptation in transfer learning using a unified discriminant and distribution alignment (UDDA). The existing method of UDA only utilized unsupervised PCA as the dimensionality reduction process before being added to the joint objective function consisting of distribution discrepancy minimization and regularization. However, the label in the source domain has been utilized by some works (i.e., joint geometrical and statistical (JGSA)) to use the supervised method LDA and show good improvement. Nevertheless, LDA has some drawbacks that is sensitive to noise and outlier in square operations and only take part in global information. The contribution of this paper is to add local discriminant information to the proposed UDA model by adopting locality-sensitive discriminant analysis (LSDA) that strengthens the between-class and within-class discriminant in the source domain. In this method, within-class and between-class graphs will be computed, and summed up with within-class and between-class scatter matrices before being embedded into the joint domain adaptation framework. Comparing the proposed method with the state-of-the-art techniques in objects and digital datasets, it shows our UDDA improved the average accuracy by 3.43% and 7.13% compare with the second and third highest results respectively.
DPSGD Strategies for Cross-Silo Federated Learning

As federated learning (FL) grows and new techniques are created to improve its efficiency and robustness, differential privacy (DP) proved to be a good ally for protecting users’ information. The differentially private version of stochastic gradient descent (DPSGD) is one of the most promising methods for enforcing privacy in machine learning algorithms. The noise added in DPSGD plays an important role in the convergence and performance of a model but also in the resulting privacy guarantee and must thus be chosen carefully. This paper reviews the effects of either selecting fixed or adaptive noise when training federated models under the cross-silo setting. We highlight their strengths and weaknesses and propose a hybrid approach, getting the best of both worlds.
Graphs, Entities, and Step Mixture for Enriching Graph Representation

Graph neural networks have shown promising results on representing and analyzing diverse graph-structured data, such as social networks, traffic flow, drug discovery, and recommendation systems. Existing approaches for graph neural networks typically suffer from the oversmoothing issue that results in indistinguishable node representation, as recursive and simultaneous neighborhood aggregation deepens. Also, most methods focus on transductive scenarios that are limited to fixed graphs, which do not generalize properly to unseen graphs. To address these issues, we propose a novel graph neural network that considers both edge-based neighborhood relationships and node-based entity features with multiple steps, i.e. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> raph <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> ntities with <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> tep <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> ixture via <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">random walk</i> (GESM). GESM employs a mixture of various steps through random walk to alleviate the oversmoothing problem, an attention mechanism to dynamically reflect interrelations depending on node information, and structure-based regularization to enhance embedding representation. With intensive experiments, we show that our proposed GESM achieves state-of-the-art or comparable performances on eight benchmark graph datasets in both transductive and inductive learning tasks. Furthermore, we empirically demonstrate the superiority of our method on the oversmoothing issue with rich graph representations. Our source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ShinKyuY/GESM</uri> .
Comparison of Anomaly Detection and Solution Strategies for Household Service Robotics using Knowledge Graphs

The concept of anomaly detection is a majorly investigated problem in the service robotics domain. The motivation of this work is to enable household service robots to detect abnormalities in the environment and solve them. This paper investigates two approaches using knowledge-based systems to detect and solve anomalies in a household environment. Both methods use knowledge graphs as a knowledge representation format. The first approach is a classical approach that records absolute positions of objects and performs clustering to solve positional anomalies. In the second approach, we perform deep learning methods on knowledge graphs using graph neural networks to detect and solve anomalies. These approaches fall at the intersection between anomaly detection and problem-solution strategies for the service robotics domain. Finally, the paper also presents a comparison between the two approaches highlighting their advantages and disadvantages.
Document Language Classification: Hierarchical Model with Deep Learning Approach

Optical character recognition (OCR) refers to the task of recognizing the characters or text from digital document images. OCR is a widely researched area for the past many years due to its applications in various fields. It helps in the natural language processing of the documents, convert the document text to speech, semantic analysis of the text, searching in the documents etc. Multilingual OCR works with documents having more than one language. Different OCR models have been created and optimized for a particular language. However, while dealing with multiple languages or translation of documents, one needs to detect the language of the document first and then give it as input to a model-specific to that language. Most of the researched work in this area focuses on identifying scripts, but considering that the Convolutional Neural Network (CNN) can learn appropriate features, our work focuses on language detection using learned features. We use a hierarchical based method in which a binary classification followed by the multiclass classification is used to improve detection accuracy. Largely, the current approaches do not use hierarchy and hence fail to identify the language correctly. The proposed hierarchical approach is used to detect six Indian languages namely: Tamil, Telugu, Kannada, Hindi, Marathi, Gujarati, using the CNN from printed documents based on the text content in a page. Experiments are performed on scanned government documents, and results indicate that the proposed approach performs better than the other similar methods. Advantage of our approach is that it is based on features extracted from the entire page rather than the words or characters, and it can also be applied to handwritten documents.
Antagonistic Procedural Content Generation Of Sparse Reward Game

With the development of the game industry, procedural content generation technology has been widely used in game automatic content generation. And with the help of this technology, designers can quickly generate unlimited levels, implement real-time level generation, preset difficulty level generation, and so on. However, due to the difficulty of providing real-time and continuous feedback from game elements, sparse reward game is difficult to meet the expected results in content generation. This paper uses search-based procedural content generation combined with auxiliary task reinforcement learning to implement automatic content generation of sparse bonus games by means of confrontation generation. In this method, hierarchical reinforcement learning can smoothly evaluate the fitness of the generated candidate individuals, and screen individual populations according to the obtained fitness. This generation method is based on agent simulation, and generates runnable levels through free exploration of agents, which is more novel than traditional rule-based generation. In the end, we successfully implemented the procedural content generation of a typical sparse reward game, proving that our method is feasible. Different sparse reward games have different task complexity. By modifying the levels and tasks of hierarchical reinforcement learning, we can extend this method to other sparse reward games.
A Data Model Construction Method Based on the Questionnaire Survey

A model and application based on the questionnaire survey are presented. After a series of pre-processing of the original survey data, such as de-duplication, missing value and outlier value, the data from the two different data sources are combined and imported into SPSS software. Then, various research variables are extracted according to the research objectives, and the reliability and validity are tested. A structural equation model (SEM) for training willingness is constructed using AMOS software. This model integrates two statistical methods, factor analysis and path analysis, and can well express the direct or indirect effects of other variables on the latent variables of willingness. The results show that individual endeavor expectation is linearly related to subjective norms, which are also the two main factors influencing willingness. Therefore, more publicity and encouragement should be given.
Adoption of Best Practices in Drafting Patents for Innovative Models of Modern Artificial Intelligence Solutions

Recently, Machine learning models gaining more popularity and can be considered as a significant investment and valuable assets for companies. These models can be used in a wide variety of tasks and in different fields of medicine, automation and robotics in factories, FinTech for banking and financial sectors as well as in modern e-commerce and marketing platforms. If these machine learning models are not well protected by intellectual property (IP), competitor can copy and steal these models and they can tweak these models to avoid detection with very little time and efforts and claiming these models as their own products. There are special considerations should be highlighted to properly draft and publish successful machine learning model patent. This paper aims to highlight the best practices of drafting patents of newly innovative machine learning models. Special focuses on the possible directions of these innovations where machine learning models can be considered as the modern solutions that need to be protected as valuable investments. This paper focused on the increased recent importance of artificial intelligence-related inventions and attempting to clarify doubts about AI-patents. The definition and understanding of functional claiming is critical step toward successful AI-patents. These efforts help researchers and developers to overcome the challenges of ambiguity of AI-patents as these patents totally different in aspects than traditional hardware circuitry patents for example. Therefore, with these guidance of the best practices the building blocks functions can be easily identified for innovative methods that can AI patentable.
R-Calculi for First-Order Logic

Original R-calculus \(\mathbf{R}\) [] is a Gentzen-typed deduction system with cut-elimination rule. In this chapter we will give R-calculi \(\mathbf{S}^\mathrm{FOL}, \mathbf{T}^\mathrm{FOL}, \mathbf{U}^\mathrm{FOL},\) which we call alternative R-calculi for first-order logic, where \(\mathbf{S}^\mathrm{FOL},\mathbf{T}^\mathrm{FOL},\mathbf{U}^\mathrm{FOL}\) are sound and complete with respect to subset-minimal change, pseudo-subformula minimal change, deduction-based minimal change, respectively. For first-order logic [, , ], because the undecidability of deduction relation we will give R-calculi without deciding whether \(\Delta \not \vdash \lnot A,\) which are sound and complete with respect to \(\subseteq /\preceq /\vdash _\preceq \)-minimal change, respectively [].