Reviewerly/exHarmony huggingface dataset:
6 Counterfactual conditionals: Linguistic variation in Italian and beyond

The aim of this paper is to integrate the analysis of the intra-linguistic variation attested in the coding of counterfactual conditionals in spoken Italian with a larger cross-linguistic perspective. We start by providing a sociolinguistically- informed discussion of the counterfactual conditional strategies attested in spoken Italian, through a corpus-based analysis. After describing the corpus and the parameters of analysis, we provide a typology of the attested constructions, based on the verbal forms employed for protasis and apodosis, showing that the observed variation goes beyond the representations available in the literature. In particular, we describe the spread of counterfactual imperfective past indicative forms, which are especially frequent in symmetrical constructions and are argued to be associated to lower educational achievements and, to a lesser extent, to informal contexts. We then consider the picture of Italian counterfactual conditionals within a wider perspective, to verify whether the patterns described for Italian follow more general tendencies. After addressing some crucial methodological issues, concerning the problems raised by the integration of a sociolinguistic and a cross-linguistic approach, we argue that Italian data are probably to be analyzed as manifestations of a widespread trend towards the use of symmetrical countefactual constructions on the one hand and the use of past habituals for functions connected to the irrealis domain on the other hand.
An Improved Chinese Pause Fillers Prediction Module Based on RoBERTa

The prediction of pause fillers plays a crucial role in enhancing the naturalness of synthesized speech. In recent years, neural networks, including LSTM, BERT, and XLNet, have been employed for pause fillers prediction modules. However, these methods have exhibited relatively lower accuracy in predicting pause fillers. This paper introduces the utilization of the RoBERTa model for predicting Chinese pause fillers and presents a novel approach to training the RoBERTa model, effectively enhancing the accuracy of Chinese pause fillers prediction. Our proposed approach involves categorizing text from different speakers into four distinct style groups based on the frequency and position of Chinese pause fillers. The RoBERTa model is trained on these four groups of data, which incorporate different styles of fillers, thereby ensuring a more natural synthesis of speech. The Chinese pause fillers prediction module is evaluated on systems such as Parallel Tacotron2, FastPitch, and Deep Voice3, achieving a notable 26.7% improvement in word-level prediction accuracy compared to the BERT model, along with a 14% enhancement in position-level prediction accuracy. This substantial improvement results in a significant enhancement of the naturalness of the generated speech.
Transform-Based Feature Map Compression Method for Video Coding for Machines (VCM)

The burgeoning field of machine vision has led to the development by the Moving Picture Experts Group (MPEG) of a new type of compression technology called video coding for machines (VCM), to enhance machine recognition through video information compression. This research proposes a principal component analysis (PCA)-based compression methodology for multi-level feature maps extracted from the feature pyramid network (FPN) structure. Unlike current PCA-based studies that independently carry out PCA for each feature map, our approach employs a generalized basis matrix and mean vector derived from channel correlations by a generalized PCA process to eliminate the need for a PCA process. Further compression is achieved by amalgamating high-dimensional feature maps, capitalizing on the spatial redundancy within these multi-level feature maps. As a result, the proposed VCM encoder forgoes the PCA process, and the generalized data do not incur any compression loss. It only requires compressing the coefficients for each feature map using versatile video coding (VVC). Experimental results demonstrate superior performance by our method over all feature anchors for each machine vision task, as specified by the MPEG-VCM common test conditions, outperforming previous PCA-based feature map compression methods. Notably, it achieved an 89.3% BD-rate reduction for instance segmentation tasks.
Improving Performance of Differential Evolution Using Multi-Population Ensemble Concept

Differential evolution (DE) stands out as a straightforward yet remarkably powerful evolutionary algorithm employed for real-world problem-solving purposes. In the DE algorithm, few parameters are used, and the population is evolved by applying various operations. It is difficult in evolutionary computation algorithms to maintain population diversity. The main issue is the sub-population of the DE algorithm that helps improve convergence speed and escape from the local optimum. Evolving sub-populations by maintaining diversity is an important issue in the literature that is considered in this research. A solution is proposed that uses sub-populations to promote greater diversity within the population and improve the algorithm performance. DE, heterogeneous distributed differential evolution (HDDE), multi-population ensemble differential evolution (MPEDE), and the proposed improved multi-population ensemble differential evolution (IMPEDE) are implemented using parameter settings; population sizes of 100 NP, 150 NP, and 200 NP; and dimensions of 10D, 30D, and 50D for performance comparison. Different combinations of mutations are used to generate the simulated results. The simulation results are generated using 1000, 3000, and 5000 iterations. Experimental outcomes show the superior results of the proposed IMPEDE over existing algorithms. The non-parametric significance Friedman test confirms that there is a significant difference in the performance of the proposed algorithm and other algorithms used in this study by considering a 0.05 level of significance using six benchmark functions.
Generating Structurally Complete Stylish Chinese Font Based on Semi-Supervised Model

Stylish fonts are widely utilized in social networks, driving research interest in stylish Chinese font generation. Most existing methods for generating Chinese characters rely on GAN-based deep learning approaches. However, persistent issues of mode collapse and missing structure in CycleGAN-generated results make font generation a formidable task. This paper introduces a unique semi-supervised model specifically designed for generating stylish Chinese fonts. By incorporating a small amount of paired data and stroke encoding as information into CycleGAN, our model effectively captures both the global structural features and local characteristics of Chinese characters. Additionally, an attention module has been added and refined to improve the connection between strokes and character structures. To enhance the model’s understanding of the global structure of Chinese characters and stroke encoding reconstruction, two loss functions are included. The integration of these components successfully alleviates mode collapse and structural errors. To assess the effectiveness of our approach, comprehensive visual and quantitative assessments are conducted, comparing our method with benchmark approaches on six diverse datasets. The results clearly demonstrate the superior performance of our method in generating stylish Chinese fonts.
A Logic for Desire Based on Causal Inference

Abstract Reasoning about desire plays a significant role in logic, artificial intelligence and philosophy, etc. In this paper, we propose an interpretation of desire that takes into account causal inference. To formalize this idea, we build a desire-causality model by combing the betterness model in preference logic and the causal model in the logic for causal reasoning. We then develop a logic for desire based on this semantics, and an axiomatization for our formal system is given.
The provability logic of all provability predicates

Abstract We prove that the provability logic of all provability predicates is exactly Fitting, Marek, and Truszczyński’s pure logic of necessitation $\textsf{N}$. Moreover, we introduce three extensions $\textsf{N4}$, $\textsf{NR}$ and $\textsf{NR4}$ of $\textsf{N}$ and investigate the arithmetical semantics of these logics. In fact, we prove that $\textsf{N4}$, $\textsf{NR}$ and $\textsf{NR4}$ are the provability logics of all provability predicates satisfying the third condition $\textbf{D3}$ of the derivability conditions, all Rosser provability predicates and all Rosser provability predicates satisfying $\textbf{D3}$, respectively.
A holistic qualitative exploration on the perception of scams, scam techniques and effectiveness of anti-scam campaigns in Malaysia

Purpose Scams are indeed malicious attempts to influence people and can take many forms, including online scams. With the increasing availability of technology, scammers have more tools at their disposal to create convincing and sophisticated communications that appear to come from legitimate sources, such as emails, text messages or social media posts. These scams can be designed to trick individuals into clicking on malicious links, downloading malware or ransomware or providing sensitive information such as login information, financial information or personal details. Scammers often use social engineering techniques to persuade their targets to take specific actions, such as creating a sense of urgency, offering a reward or prize or posing as a trusted authority. These tactics can be highly effective, particularly if the target is unaware of the warning signs of a scam or is unfamiliar with how to protect themselves from online fraud. This paper aims to explore the extent and nature of this problem and evokes the concern that the general public is vulnerable and susceptible to scams if they are not resilient and aware. This paper also explores why victims fall for online scams and uncovers preventive measures to enable a direction in tacitly strategizing ways to create more impactful and effective awareness campaigns. Design/methodology/approach This study explores these aspects through a holistic qualitative approach. Using in-depth interview techniques with six victims, six non-victims, four law enforcement officers, four scammers and seven stakeholders from various agencies such as banks, telecommunication agencies and the Malaysian Communications and Multimedia Commission involved in combating the issue of scams. Findings The findings generally revealed that participants who were victims of scams felt Malaysians were susceptible to scams, easily fooled and had a nonchalant attitude. Most participants also highlighted that public safety is important for ensuring a high quality of life for citizens that should work closely between the government and non-government agencies, including effective law enforcement and crime prevention strategies. Originality/value The uniqueness of this study is the feedback from scammers themselves and their input towards authority and victims. Overall, the respondents provide their views drawing strength from the ever-changing technological background as well as the susceptibility of security features and vulnerability of human engagement.
Algorithm for fast evaluation of in-plane fiber orientation in reinforced plastics using light microscopy images

Abstract Methods for evaluating the orientation of carbon fibers in reinforced plastics vary in complexity and are application specific. Here, we report an algorithm that quickly evaluates in-plane fiber orientation based on determining the correlation coefficient of adjacent regions in microscopy images. The result is not the fiber orientation of individual fibers, but the principal fiber orientation of small image areas. This method is applicable to large areas due to its low computation time and captures varying fiber orientations, making it suitable for the study of injection molded samples with complex geometries. A great advantage is that no information about the fibers or the matrix, or their combination, is needed prior to the analysis. This approach is also suitable for samples with surface defects. Demonstrations of this technique are given for Polyamide 6 and Polypropylene with 30 weight % carbon fibers of different fiber lengths.
An empirical study on configuration-related branch statement in database management system

Database management systems introduce thousands of configuration options to provide flexibility. However, configuration complexity and configuration dependencies have inevitably affect the system reliability and become one of the major causes of system failure. Previous research neither specifically detects configuration-related bugs nor studies the characteristics of configuration-related code in DBMSs. In this paper, we undertake one of the first attempts to conduct a real-world configuration-related branch statements characteristic study in DBMSs. We first use a taint analysis tool to construct a data set of 347 real world configuration-related branch statements including 100 in MySQL, 98 in SQLite and 149 in PostgreSQL, which are very popular. Based on our study, we have obtained some findings: (1) More than half (53. 6%) of configuration-related branch statements contain only configuration variables, 93. 5% of which contain only one configuration variable. (2) 46. 4% of configuration-related branch statements contain not only configuration variables but also program variables. (3) Complex configuration-related branch statements (which contain function call) account for 17. 9%.
Analyzing Indo-European Language Similarities Using Document Vectors

The evaluation of similarities between natural languages often relies on prior knowledge of the languages being studied. We describe three methods for building phylogenetic trees and clustering languages without the use of language-specific information. The input to our methods is a set of document vectors trained on a corpus of parallel translations of the Bible into 22 Indo-European languages, representing 4 language families: Indo-Iranian, Slavic, Germanic, and Romance. This text corpus consists of a set of 532,092 Bible verses, with 24,186 identical verses translated into each language. The methods are (A) hierarchical clustering using distance between language vector centroids, (B) hierarchical clustering using a network-derived distance measure, and (C) Deep Embedded Clustering (DEC) of language vectors. We evaluate our methods using a ground-truth tree and language families derived from said tree. All three achieve clustering F-scores above 0.9 on the Indo-Iranian and Slavic families; most confusion is between the Germanic and Romance families. The mean F-scores across all families are 0.864 (centroid clustering), 0.953 (network partitioning), and 0.763 (DEC). This shows that document vectors can be used to capture and compare linguistic features of multilingual texts, and thus could help extend language similarity and other translation studies research.
Compression schemes for concept classes induced by three types of discrete undirected graphical models

Sample compression schemes were first proposed by Littlestone and Warmuth in 1986. Undirected graphical model is a powerful tool for classification in statistical learning. In this paper, we consider labelled compression schemes for concept classes induced by discrete undirected graphical models. For the undirected graph of two vertices with no edge, where one vertex takes two values and the other vertex can take any finite number of values, we propose an algorithm to establish a labelled compression scheme of size VC dimension of associated concept class. Further, we extend the result to other two types of undirected graphical models and show the existence of labelled compression schemes of size VC dimension for induced concept classes. The work of this paper makes a step forward in solving sample compression problem for concept class induced by a general discrete undirected graphical model.
Optimizing Generative Adversarial Network (GAN) Models for Non-Pneumatic Tire Design

Pneumatic tires are used in diverse industries. However, their design is difficult, as it relies on the knowledge of experienced designers. In this paper, we generate images of non-pneumatic tire designs with patterns based on shapes and lines for different generative adversarial network (GAN) models and test the performance of the models. Using OpenCV, 2000 training images were generated, corresponding to spoke, curve, triangle, and honeycomb non-pneumatic tires. The images created for training were used after removing highly similar images by applying mean squared error (MSE) and structural similarity index (SSIM). To identify the best model for generating patterns of regularly shaped non-pneumatic tires, GAN, deep convolutional generative adversarial network (DCGAN), StarGAN v2, StyleGAN v2-ADA, and ProjectedGAN were compared and analyzed. In the qualitative evaluation, the GAN, DCGAN, StarGAN v2, and StyleGAN v2-ADA models distorted the circle shape and did not maintain a consistent pattern, but ProjectedGAN retained consistency in the circle, and the pattern was less distorted than in the other GAN models. When evaluating quantitative metrics, ProjectedGAN performed the best among several techniques when the difference between the generated and actual image distributions was measured.
Constructing the dataset for valid-configuration-related vulnerability

As the scale and complexity of software continue to increase, software security vulnerabilities are also growing. Among these vulnerabilities, configuration vulnerabilities account for a considerable proportion. These configuration vulnerabilities can only be triggered under specific configuration environments, making them harder to debug. However, existing work on vulnerability mining or vulnerability reproduction has overlooked the influence of configuration environment factors. In this paper, we investigated configuration vulnerabilities in five mainstream software products. We collected a total of 200 configuration vulnerabilities in which the related configuration changes are valid, and analyzed these vulnerabilities to construct a valid-configuration vulnerability database.
Efficient Two-Stage Max-Pooling Engines for an FPGA-Based Convolutional Neural Network

This paper proposes two max-pooling engines, named the RTB-MAXP engine and the CMB-MAXP engine, with a scalable window size parameter for FPGA-based convolutional neural network (CNN) implementation. The max-pooling operation for the CNN can be decomposed into two stages, i.e., a horizontal axis max-pooling operation and a vertical axis max-pooling operation. These two one-dimensional max-pooling operations are performed by tracking the rank of the values within the window in the RTB-MAXP engine and cascading the maximum operations of the values in the CMB-MAXP engine. Both the RTB-MAXP engine and the CMB-MAXP engine were implemented using VHSIC hardware description language (VHDL) and verified by simulations. The implementation results demonstrate that the 16 CMB-MAXP engines achieved a remarkable throughput of about 9 GBPS (gigabytes per second) while utilizing only about 3% of the available resources on the Xilinx Virtex UltraScale+ FPGA XCVU9P. On the other hand, the 16 RTB-MAXP engines exhibited somewhat lower throughput and resource utilization, although they did offer a slightly better latency when compared to the CMB-MAXP engines. In the comparison with existing techniques, the CMB-MAXP engine exhibited comparable implementation results in terms of the resource utilization and maximum operating frequency. It is crucial to note that only the proposed engines provide the features of runtime window scalability and boundary padding capability, which are essential requirements for CNN accelerators. The proposed max-pooling engines were employed and tested in our CNN accelerator targeting the CNN model YOLOv4-CSP-S-Leaky for object detection.
Fast and Efficient Entropy Coding Architectures for Massive Data Compression

The compression of data is fundamental to alleviating the costs of transmitting and storing massive datasets employed in myriad fields of our society. Most compression systems employ an entropy coder in their coding pipeline to remove the redundancy of coded symbols. The entropy-coding stage needs to be efficient, to yield high compression ratios, and fast, to process large amounts of data rapidly. Despite their widespread use, entropy coders are commonly assessed for some particular scenario or coding system. This work provides a general framework to assess and optimize different entropy coders. First, the paper describes three main families of entropy coders, namely those based on variable-to-variable length codes (V2VLC), arithmetic coding (AC), and tabled asymmetric numeral systems (tANS). Then, a low-complexity architecture for the most representative coder(s) of each family is presented—more precisely, a general version of V2VLC, the MQ, M, and a fixed-length version of AC and two different implementations of tANS. These coders are evaluated under different coding conditions in terms of compression efficiency and computational throughput. The results obtained suggest that V2VLC and tANS achieve the highest compression ratios for most coding rates and that the AC coder that uses fixed-length codewords attains the highest throughput. The experimental evaluation discloses the advantages and shortcomings of each entropy-coding scheme, providing insights that may help to select this stage in forthcoming compression systems.
Management in Enhancing Students' Reading Interest at SMA Negeri 1 Secanggang, Langkat Regency

&#x0D; &#x0D; &#x0D; Service management is the process of optimizing human contributions, materials, and budgets to achieve the goals of a library or library management process based on management principles and theories. On the other hand, reading interest refers to the ability to read and promote a reading culture and reading habits. Through reading, students can broaden their horizons, sharpen their ideas, and enhance their creativity. This study aims to explore the planning, implementation, and evaluation of library service activities at SMA Negeri 1 Secanggang, Langkat Regency. This research is a descriptive qualitative method. Data collection was carried out through observation, interviews, and documentation. The primary data sources of the study were interviews with the school principal, library head, library staff, teachers, and students. The secondary data consisted of documents related to library service management in enhancing students' reading interest at SMA Negeri 1 Secanggang, Langkat Regency. Data analysis techniques included data reduction, data presentation, drawing conclusions, and data validity checking by comparing interview data with the research objects. The results of the study showed that library service management in enhancing reading interest at SMA Negeri 1 Secanggang, Langkat Regency, was functioning well. The planning activities started with a meeting attended by the school principal, library head, library staff, teachers, and students. The library service implementation was carried out by two library staff members who had their respective schedules and were supervised by the library head. Evaluation was conducted on the school principal, library head, library staff, teachers, and students.&#x0D; &#x0D; &#x0D;