Reviewerly/exHarmony huggingface dataset:
Model‐based source code refactoring with interaction and visual cues

Abstract Refactoring source code involves the developer in a myriad of program detail that can obscure the design changes that they actually wish to bring about. On the other hand, refactoring a UML model of the code makes it easier to focus on the program design, but the burdensome task of applying the refactorings to the source code is left to the developer. In an attempt to obtain the advantages of both approaches, we propose a refactoring approach where the interaction with the developer takes place at the model level, but the actual refactoring occurs on the source code itself. We call this approach model‐based source code refactoring and implement it in this paper using two tools: (1) Design‐Imp enables the developer to use interactive search‐based design exploration to create a UML‐based desired design from an initial design extracted from the source code. It also provides visual cues to improve developer comprehension during the design‐level refactoring process and to help the developer to discern between promising and poor refactoring solutions. (2) Code‐Imp then refactors the original source so that it has the same functional behavior as the original program, and a design close to the one produced in the design exploration phase, that is, a design that has been confirmed as “desirable” by the developer. We evaluated our approach involving interaction and visual cues with industrial developers refactoring three Java projects, comparing it with an approach using interaction without visual cues and a fully automated approach. The results show that our approach yields refactoring sequences that are more acceptable both to the individual developer and to a set of independent expert refactoring evaluators. Furthermore, our approach removed more code smells and was evaluated very positively by the experiment participants.
Blockchain in the Global South: Key Issues and Current Status

This introductory chapter offers an analysis of blockchain’s potential to bring economic, political, and social transformations in the Global South. It highlights some of the key applications and uses of this technology in developing countries and takes a look at some of the visible effects. It argues that in many ways, blockchain has a much higher value proposition for the Global South than for their rich counterparts in the developed world. It also gives an overview of the uses of blockchain in promoting transparency and reducing frauds and corruption, reducing friction and costs of property registration, promoting efficiency in international B2B trade, and increasing access to trade and supply chain finance, reducing costs, and increasing efficiency in international payment systems. Other key uses in finance, banking, Insurance, and blockchain-based digital identity are also discussed.
21st-Century Skills Implementation in Teaching English at Primary School

This study aimed to analyze the implementation of 21st-century skills in teaching English at primary schools, such as in Public and Private Schools. This study used mixed method research design, especially the exploratory mixed methods designs. This study was taken place in primary schools in Karangasem Regency. There were 3 schools as the representative of Public Primary schools in Karangasem Regency, such as SDN 1 Kubu, SDN 4 Dukuh and SDN 4 Tulamben. Besides, the representative of Private Primary school was SD Insan Mandiri. Eight teachers were selected using a convenient sampling method. The instruments used in this study were questionnaires, observation, and interview guides. It was found the English teachers in the primary schools in Karangasem Regency were not ready in implementing 21st-century skills in the schools since there were several skills have not been implemented in the lesson plan and assessment. Meanwhile, all skills have been implemented in the teaching and learning process. It can be concluded that 21st-century skills in the primary schools were not implemented well.
Enhancing User Experience through Adaptive Human-Computer Interaction

Human-Computer Interaction (HCI) plays a crucial role in designing intuitive and user-friendly interfaces, ensuring efficient and effective interaction between humans and computer systems. Traditional HCI approaches focus on general usability criteria, but with the increasing complexity of modern computer systems and the diverse needs of users, a more personalized and contextually relevant approach is necessary. This research paper explores adaptive approaches in HCI and their impact on enhancing the user experience. The paper begins by providing a background on HCI, emphasizing its significance in today's technology-driven society. It highlights the importance of designing interfaces that meet user expectations and discusses the evolution of HCI from usability- focused approaches to the need for adaptive strategies that cater to individual user needs and preferences. The research objectives are outlined, focusing on investigating the benefits and challenges of adaptive HCI in enhancing user satisfaction and engagement. The paper addresses specific research questions, including the impact of adaptive HCI strategies on user satisfaction, the accommodation of diverse user needs and preferences, and the technical and implementation challenges associated with adaptive HCI
Role of Digital Forensics in Combating Financial Crimes in the Computer Era

Abstract Information and communication technologies (ICT) have changed every area of our lives. Cyberspace-related areas have reflected these shifts. Cyberspace has an undeniable positive impact on information, trade, industry, and communication. On the other hand, cybercrime is a dark side of the Internet that degrades its peaceful use. Any illegal activity carried out by or via cyberspace and its electronic environment is characterized as cybercrime. Unlike traditional crimes, cybercrimes present a real dilemma because the identities of criminals may be hidden in the virtual domain. Digital forensics has emerged to formulate possible ways for cybercrime investigation and analysis process. In this paper, we deplore the idea of digital forensics in the context of cybercrimes. An investigation of the positive impact of digital forensics in combating cybercrimes is discussed. In today’s world of computers, any information can be made available within a few clicks for different endeavors. The information may be tampered with by changing the statistical properties and can be further used for criminal activities.
Quantum attacks on Beyond-Birthday-Bound MACs

In this paper, we investigate the security of several recent MAC constructions with provable security beyond the birthday bound (called BBB MACs) in the quantum setting. On the one hand, we give periodic functions corresponding to targeted MACs (including PMACX, PMAC with parity, HPxHP, and HPxNP), and we can recover secret states using Simon algorithm, leading to forgery attacks with complexity O(n). This implies our results realize an exponential speedup compared with the classical algorithm. Note that our attacks can even break some optimally secure MACs, such as mPMAC+-f, mPMAC+-p1, mPMAC+-p2, mLightMAC+-f, etc. On the other hand, we construct new hidden periodic functions based on SUM-ECBC-like MACs: SUM-ECBC, PolyMAC, GCM-SIV2, and 2K-ECBC−Plus, where periods reveal the information of the secret key. Then, by applying Grover-meets-Simon algorithm to specially constructed functions, we can recover full keys with O(2n/2n) or O(2m/2n) quantum queries, where n is the message block size and m is the length of the key. Considering the previous best quantum attack, our key-recovery attacks achieve a quadratic speedup.
Approaching feature matrix: To solve two issues in link prediction

With the increase of graph-structure data, link prediction has become an active research topic. Recent progress in link prediction involves learning graph embeddings through graph neural networks. Graph autoencoders(GAE), variational graph autoencoders(VGAE), and their variants such as graph normalized autoencoders(GNAE) try to learn graph embeddings in an unsupervised setting and show promising results in link prediction. However, they suffer from degraded performance with two issues; they tend to assign close-to-zero embedding on nodes with zero degree (i.e., isolated node problem) and the issue caused by random negative sampling has not been addressed in link prediction experiments. As the two issues affect only links and not feature information, we propose a generic learning method based on the property of feature information that alleviates both issues and outperforms strong baselines across benchmark datasets.
Fixed-time convergence integral-enhanced ZNN for calculating complex-valued flow matrix Drazin inverse

The complex-valued flow matrix Drazin inverse has recently attracted considerable interest from researchers due to its great academic value. In this paper, a fixed-time convergence integral-enhanced zeroing neural network (FTCIEZNN) model is proposed and investigated for calculating the Drazin inverse of complex-valued flow matrix. Since the FTCIEZNN model possesses fixed-time convergence, its upper limit of convergence time is irrelevant to initial conditions and can be adjusted by specified system parameters. Meanwhile, by adopting the newly designed reformed nonlinear activation function (RNAF) and variable parameters, the FTCIEZNN model converges rapidly in a relatively fast fixed-time and its robustness is dramatically strengthened. In addition, the upper limit of the convergence time in the absence of noise and the upper limit of the steady-state error in the presence of time-varying bounded noise are given by a scrupulous mathematical logic calculation. Furthermore, the outcomes of the numerical simulations demonstrate that the FTCIEZNN model outshines existing zeroing neural network models in calculating complex-valued flow matrix Drazin inverse. Finally, an application based on the FTCIEZNN model in image encryption fully illustrates the practical value of the FCIEZNN model.
Penalizing proposals using classifiers for semi-supervised object detection

Obtaining gold standard annotated data for object detection is often costly, involving human-level effort. Semi-supervised object detection algorithms solve the problem with a small amount of gold-standard labels and a large unlabeled dataset used to generate silver-standard labels. But training on the silver standard labels does not produce good results, because they are machine-generated annotations. In this work, we design a modified loss function to train on large silver standard annotated sets generated by a weak annotator. We include a confidence metric associated with the annotation as an additional term in the loss function, signifying the quality of the annotation. We test the effectiveness of our approach on various test sets and use numerous variations to compare the results with some of the current approaches to object detection. In comparison with the baseline where no confidence metric is used, we achieved a 4% gain in mAP with 25% labeled data and 10% gain in mAP with 50% labeled data by using the proposed confidence metric.
Emoticons

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsAurora AriasAurora Arias (Santo Domingo, 1962) is a poet and short-story writer based in Michigan. She has published Vivienda de pájaros (1986; Bird Housing) and Piano lila (1994; Purple Piano), poetry; and Invi’s Paradise (1998), Fin de mundo (2000; End of the World), and Emoticons (2007), stories.
An S4 extension of the λμμ-calculus

In the context of the Curry-Howard correspondence, it is well-known that a typed calculus with control operator(s) corresponds to a formal system of classical logic, and various concrete examples have been discovered and studied since the early 90s. Among such work, Curien and Herbelin proposed the lambda-bar-mu-mu-tilde-calculus to exhibit the so-called duality of computation such as the (program/continuation)-duality and the (call-by-value/call-by-name)-duality. In their calculus, programs and continuations are first-class citizens formalized in a symmetric manner; and the call-by-value and call-by-name evaluation disciplines are characterized in some appropriate subcalculi, which are dual to each other. This work aims to introduce an extension of the lambda-bar-mu-mu-tilde-calculus with necessity and possibility modalities, which logically corresponds to classical S4 modal logic. In this paper, we show that the calculus is defined as a combination of existing calculi, and show that it satisfies desirable properties such as the subject reduction and the strong normalization. Two well-behaved subcalculi that realize the call-by-value and call-by-name disciplines are also discussed, following the work of Curien and Herbelin. As an application, we demonstrate that our calculus can be used to prove the strong normalization of the S4 modal lambda-mu-calculus of Kimura and Kakutani.
Unleashing the potential of chatbots in business: A bibliometric analysis

Chatbots are increasingly becoming a fundamental part of social life. They allow us to communicate in more sophisticated ways using the internet. It is crucial to study chatbots to gain comprehensive insights into their developments, trends, impact, and advancement in the field. This paper presents a bibliometric analysis of chatbot research conducted using the R bibliometrix tool and SPAR-4 protocol. The analysis includes 805 scholarly articles published between 2005 and 2021, collected from the Web of Science and Scopus databases. A conceptual, intellectual, and social structure analysis was conducted to analyse impactful authors, journals, and documents. The study reveals that adoption of chatbots in business accelerated during the pandemic period. The findings also highlight a plethora of opportunities for research in areas like chatbot design cues, user experience, chatbots in collaboration with human agents, and chatbot ethics and privacy. This study's findings provide valuable insights for practitioners, policymakers, developers and researchers to understand the current state of the chatbot research landscape and to identify promising future research directions.
Fintech and Digital Transformation: Accelerating Innovation in Financial Services

This paper focuses on the progress made in the field of financial technology as a result of digital development. The study highlights several major areas of financial technology and analyzes its risk level and future prospects. The research indicates that the areas of online payment and P2P lending are advancing successfully, and their respective legal and regulatory systems are relatively well-established. However, the development of Robo-Advisor and Blockchain faces significant challenges due to uncertainty and a lack of clarity regarding regulatory frameworks. The emergence of ChatGPT has disrupted the traditional understanding of Robo-Advisor, presenting new opportunities for development. On the other hand, the development of products based on blockchain technology poses a great risk as it is challenging to accurately determine their intrinsic value. Overall, digital development has greatly impacted the financial technology industry, and the study emphasizes the importance of understanding the risks associated with new technologies. The research recommends that regulators should work towards creating clear and comprehensive regulatory frameworks to promote responsible innovation in the financial technology sector.
Gammatonegram Representation for End-to-End Dysarthric Speech Processing Tasks: Speech Recognition, Speaker Identification, and Intelligibility Assessment

Dysarthria is a disability that causes a disturbance in the human speech system and reduces the quality and intelligibility of a person's speech. Because of this effect, the normal speech processing systems can not work properly on impaired speech. This disability is usually associated with physical disabilities. Therefore, designing a system that can perform some tasks by receiving voice commands in the smart home can be a significant achievement. In this work, we introduce gammatonegram as an effective method to represent audio files with discriminative details, which is used as input for the convolutional neural network. On the other word, we convert each speech file into an image and propose image recognition system to classify speech in different scenarios. Proposed CNN is based on the transfer learning method on the pre-trained Alexnet. In this research, the efficiency of the proposed system for speech recognition, speaker identification, and intelligibility assessment is evaluated. According to the results on the UA dataset, the proposed speech recognition system achieved 91.29% accuracy in speaker-dependent mode, the speaker identification system acquired 87.74% accuracy in text-dependent mode, and the intelligibility assessment system achieved 96.47% accuracy in two-class mode. Finally, we propose a multi-network speech recognition system that works fully automatically. This system is located in a cascade arrangement with the two-class intelligibility assessment system, and the output of this system activates each one of the speech recognition networks. This architecture achieves an accuracy of 92.3% WRR.
RGB pixel n-grams: A texture descriptor

This article proposes the “RGB Pixel N-grams” descriptor, which uses a sequence of n pixels to represent RGB color texture images. We conducted classification experiments with three different classifiers and five color texture image databases to evaluate the descriptor’s performance, using accuracy as the evaluation metric. These databases include various textures from different surfaces, sometimes under different lighting, scale, or rotation conditions. The proposed descriptor proved to be robust and competitive compared to other state-of-the-art descriptors, as it has better accuracy in classification results in most databases and classifiers.
'Zero Cost' Majority Attacks on Permissionless Blockchains

Permissionless blockchains were constructed with a view to being sustainably secure. At the heart of blockchain consensus mechanisms was an explicit cost (whether it be work or stake) for participation in the network and the opportunity to propose blocks that would be added to the blockchain. A key rationale for that cost was to make attacks on the network, which could be theoretically carried out if a majority of nodes were controlled by a single entity, too expensive to be worthwhile. Here we demonstrate that a majority attacker can successfully attack with a negative cost, which shows that explicit participation requirements do not necessarily result in a sustainably secure network. This suggests that sustainable security arises from outside the network protocol itself that regulation the benefits of an attack.
The CodeSparks Framework – Augmenting Source Code with Glyph-based Visualizations

The daily work of a software developer typically includes source-code-related tasks, such as testing, debugging, refactoring and optimizing. They can be facilitated by the use of software metrics, which are mainly available through external analysis tools. In this work, we present the CodeSparks framework, a Java API for IDE plugin development, that aims to facilitate the integration of software analysis tools into the IDE such that their results are displayed as glyph-based visualizations in the source code. In particular, it enables to combine multiple visualizations, each of which can represent different metrics simultaneously. We present the architecture of the framework and the underlying design goals. A total of thirteen plugins realized by different developers using the CodeSparks framework are presented to demonstrate the versatility of the framework. We conducted focus group interviews to investigate how the developers experienced the implementation of their plugins. Based on the plugins, deliberate design decisions and the interview results, we discuss to what extent we achieved our design goals. We found that the framework is largely independent of the target programming-language and that it supports the development of plugins for a wide range of source-code-related tasks while hiding most of the details of the underlying plugin development API.
Computing Short Films Using Language-Guided Diffusion and Vocoding Through Virtual Timelines of Summaries

Language-guided generative models are increasingly used in audiovisual production. Image diffusion allows for the development of video sequences and some of its coordination can be established by text prompts. This research automates a video production pipeline leveraging CLIP-guidance with longform text inputs and a separate text-to-speech system. We introduce a method for producing frame-accurate video and audio summaries using a virtual timeline and document a set of video outputs with diverging parameters. Our approach was applied in the production of the film Irreplaceable Biography and contributes to a future where multimodal generative architectures are set as underlying mechanisms to establish visual sequences in time. We contribute to a practice where language modelling is part of a shared and learned representation which can support professional video production, specifically used as a vehicle throughout the composition process as potential videography in physical space.
Reshaping the Musical Experience: A Critical Examination of Musique Concrète and the Notion of ‘Music of Technology’

The critical examination in this paper should point to a shift in a perspective in understanding musique concrète (concrete music) in relation to its historical evaluation, viewing it as a music of technology. The paper is based on the hypothesis that each technology in its materiality and functionality shapes the outcomes that arise from it, which is ultimately aimed at looking for the ‘human’ in the ‘composer-technology’ dichotomy. That is where the reshaping of the musical experience takes place. But it should be noted that the technology share will be questioned through only a few aspects of concrete music, which is why there is an open possibility for further research problems.
Active Learning of Deterministic Timed Automata with Myhill-Nerode Style Characterization

Abstract We present an algorithm to learn a deterministic timed automaton (DTA) via membership and equivalence queries. Our algorithm is an extension of the L* algorithm with a Myhill-Nerode style characterization of recognizable timed languages, which is the class of timed languages recognizable by DTAs. We first characterize the recognizable timed languages with a Nerode-style congruence. Using it, we give an algorithm with a smart teacher answering symbolic membership queries in addition to membership and equivalence queries. With a symbolic membership query, one can ask the membership of a certain set of timed words at one time. We prove that for any recognizable timed language, our learning algorithm returns a DTA recognizing it. We show how to answer a symbolic membership query with finitely many membership queries. We also show that our learning algorithm requires a polynomial number of queries with a smart teacher and an exponential number of queries with a normal teacher. We applied our algorithm to various benchmarks and confirmed its effectiveness with a normal teacher.