Reviewerly/exHarmony huggingface dataset:
A Study in SHMEM: Parallel Graph Algorithm Acceleration with Distributed Symmetric Memory

Over the last few decades, the Message Passing Interface (MPI) has become the parallel-communication standard for distributed algorithms on high-performance CPUs. MPI’s minimal setup overhead and simple API calls give it a low barrier of entry, while still providing support for more complex communication patterns. Communication schemes that use physically or logically shared memory provide a number of improvements to HPC-algorithm parallelization by reducing synchronization calls between processors and overlapping communication and computation via strategic programming techniques. The OpenSHMEM specification developed in the last decade applies these benefits to distributed-memory computing systems by leveraging a Partitioned Global Address Space (PGAS) model and remote memory access (RMA) operations. Paired with non-blocking communication patterns, these technologies enable increased parallelization of existing apps. This research studies the impact of these techniques on the Multi-Node Parallel Boruvka’s Minimum Spanning Tree Algorithm (MND-MST), which uses distributed programming for inter-processor communication. This research also provides a foundation for applying complex communication libraries like OpenSHMEM to large-scale apps. To provide further context for the comparison of MPI to the OpenSHMEM specification, this work presents a baseline comparison of relevant API calls as well as a productivity analysis for both implementations of the MST algorithm. Through experiments performed on the National Energy Research Scientific Computing Center (NERSC), it is found that the OpenSHMEM-based app has an average of 33.9% improvement in overall app execution time scaled up to 16 nodes and 64 processes. The program complexity, measured as a combination of lines of code and API calls, increases from MPI to OpenSHMEM implementations by $$\sim $$ 25%. These findings encourage further study into the use of distributed symmetric-memory architectures and RMA-communication models applied to both additional hardware systems and scalable HPC apps.
Explainable Software Fault Localization Model: From Blackbox to Whitebox

The most resource-intensive and laborious part of debugging is finding the exact location of the fault from the more significant number of code snippets. Plenty of machine intelligence models has offered the effective localization of defects. Some models can precisely locate the faulty with more than 95% accuracy, resulting in demand for trustworthy models in fault localization. Confidence and trustworthiness within machine intelligence-based software models can only be achieved via explainable artificial intelligence in Fault Localization (XFL). The current study presents a model for generating counterfactual interpretations for the fault localization model's decisions. Neural system approximations and disseminated presentation of input information may be achieved by building a nonlinear neural network model. That demonstrates a high level of proficiency in transfer learning, even with minimal training data. The proposed XFL would make the decision-making transparent simultaneously without impacting the model's performance. The proposed XFL ranks the software program statements based on the possible vulnerability score approximated from the training data. The model's performance is further evaluated using various metrics like the number of assessed statements, confidence level of fault localization, and Top-N evaluation strategies.
Deepfake Detection of Occluded Images Using a Patch-Based Approach

The DeepFake involves the use of deep learning and artificial intelligence techniques to produce or change video and image contents typically generated by GANs. It can be performed by programs, and used by users without a special programming skill. Moreover, it can be misused and leads to fictitious news, ethical and financial crimes, and also affects the performance of facial recognition systems.Thus, detection of real or fake images is significant specially to authenticate originality of people's images or videos. One of the most important challenges in this topic is obstruction that decreases the system precision.In this study, we present a deep learning approach using the entire face and face patches to distinguish real/fake images in the presence of obstruction with a three-path decision: first entire-face reasoning, second a decision based on the concatenation of feature vectors of face patches, and third a majority vote decision based on these features.To test our approach, new datasets including real and fake images are created. For producing fake images, StyleGAN and StyleGAN2 are trained by FFHQ images and also StarGAN and PGGAN are trained by CelebA images. The CelebA and FFHQ datasets are used as real images.The proposed approach reaches higher results in early epochs than other methods and increases the SoTA results by 0.4\%-7.9\% in the different built data-sets. Also, we have shown in experimental results that weighing the patches may improve accuracy.
Analogies Between Short Sentences: A Semantico-Formal Approach

The present article proposes a method to solve analogies between sentences by combining existing techniques to solve formal analogies between strings and semantic analogies between words. The method is applied on sentences from the Tatoeba corpus. Two datasets of more than five thousand semantico-formal analogies, in English and French, are released.
Digital Inclusion and Academic Performance

As ICT continues to change the educational landscape in ways beyond many of our imaginations, how digital inclusion affects adolescent students’ learning and their academic achievement have also become an important topic for scholars. Using 2018 PISA data, this chapter examines the effects of ICT use at home and in school on adolescents’ reading performance, with a focus on 21 developed countries and societies. Results from multilevel regression analyses suggest that: (1) ICT use in school has no positive effects on students’ reading achievement in most developed countries. In a small number of countries (e.g., Australia, New Zealand, the United States, and Scandinavian nations), a moderate level of school ICT use has positive effects on reading, but the effects become negative when students use ICT in school at a high level. When school ICT use shows positive effects, the effects on low-SES students are often greater than the effects on high-SES students, especially when they are using ICT for core subjects in school. (2) Inverted U-shaped curvilinear relationships between ICT use at home and reading performance are apparent in most countries. These curvilinear effects suggest that a moderate level of home ICT use would help improve adolescents’ reading performance. As ICT use at home increases, however, its positive effect diminishes, and may have negative impacts on academic achievement if students use ICT at home at a high level. Taken together, these findings suggest that the relationships between adolescents’ ICT use and academic achievement vary both across countries and by students’ socioeconomic background within countries.
on the 2-adic complexity of cyclotomic binary sequences of order three

<p style='text-indent:20px;'>Let <inline-formula><tex-math id="M1">\begin{document}$ p\equiv 1\pmod 3 $\end{document}</tex-math></inline-formula> be a prime. In this paper, we determine the 2-adic complexity of all non-trivial balanced cyclotomic binary sequences of order three with period <inline-formula><tex-math id="M2">\begin{document}$ 2p $\end{document}</tex-math></inline-formula> and the 2-adic complexity of several non-balanced cyclotomic binary sequences of order three with period <inline-formula><tex-math id="M3">\begin{document}$ 2p $\end{document}</tex-math></inline-formula>.
Two Families of Optimal Ternary Cyclic Codes With Two Zeros

Cyclic codes are an important subclass of linear codes and have wide applications in consumer electronics, data storage systems and communication systems as they have efficient encoding and decoding algorithms. In this paper, two families of optimal ternary cyclic codes with parameters [3<sup><i>m</i></sup> -1, 3<sup><i>m</i></sup> -2<i>m</i> -1, 4] are presented. The first family of cyclic codes with two zeros &#x03C0; and &#x03C0;<sup><i>v</i></sup> is constructed by using multivariate method. The second family of cyclic codes with two zeros &#x03C0;<sup>2</sup> and &#x03C0;<sup><i>v</i></sup> is obtained by analyzing irreducible factors of certain polynomials with finite degrees over the finite field F<sub>3<sup><i>m</i></sup></sub>, where &#x03C0; is a generator of F*<sub>3<sup><i>m</i></sup></sub>.
The Impact of Computational Complexity on the Learning of Long-Distance Linguistic and Non-Linguistic Harmony Patterns

This article examines the psychological reality of the Subsequential Hypothesis by Gainor, Lai and Heinz (2012) and Heinz & Lai (2013), which states that only subsequential phonological patterns are humanly learnable, and can therefore be attested in natural language phonology. This article compared the learnability of three types of long-distance vowel harmony patterns, dominant-recessive, sour-grapes and an unattested pattern that only spreads within a limited distance. These patterns differ typologically and computationally. The patterns were presented as linguistic stimuli and non-linguistic stimuli to test whether the computational limitations are only present in the linguistic domain, or in both domains. The results show that only the attested subsequential dominant-recessive pattern was learned in both domains, and the unattested non-subsequential sour grapes were not learned in either. The unattested but subsequential pattern was learned only in the linguistic domain but not in the non-linguistic domain. These findings suggest that an overall learning bias towards the attested subsequential pattern was found in adults when they were faced with both linguistic and non-linguistic stimuli. The two unattested patterns showed different levels of learnability when they were instantiated in different domains suggest that the learning mechanisms of the linguistic and non-linguistic domains are not identical.
Origins and Development of Formal Methods

This chapter offers an historical perspective on the development of Formal Methods for software engineering. It surveys some of the problems and solution methods that have shaped and become our theoretical understanding and practical capability for making software. Starting in the 1950s, the history is organised by the topics of programming, data, reasoning, and concurrency, and concludes with a selection of notes on application areas relevant to the book. Although the account emphasizes some contributions and neglects others, it provides a starting point for studying the development of the challenging and ongoing enterprise that is software engineering.
From Handshakes to Blockchains: Economic Analysis of Contracts in Agriculture

This chapter integrates discussion of historically separate literatures regarding the analysis of agricultural contracts as farmgate contracts between producers and first handlers, contracts between private parties at other stages of the agro-food chain, and policy contracts that involve the government. It discusses different approaches economists have taken to modeling agricultural contracts and how the use of contracts in agriculture and their market implications have evolved over time. It concludes with an exploration of the next stage of contract use in agriculture through the emergence of blockchain technology as a means of supply chain management and the implications for future research.
One Light, One App: Tackling a Common Misperception Causing Breach of User Privacy

Built-in and computer-connected web cameras can be hacked with malware that aim in activating the camera without setting on the green led indicator (in systems that support this feature). A simple countermeasure to at least preserve the user privacy, until the security incident is contained, is to cover the camera up when not in use. One could also argue that there is a sense of security when an application (e.g. zoom, WebEx, Skype) is using the web camera and the light is on. The user trusts that there is one-to-one relationship between the web-camera (and its light indicator) and an application. In this paper, we tackle this common misperception by demonstrating that the aforementioned relationship could be one-to-many, allowing many applications accessing the web camera stream simultaneously, posing a serious privacy threat that could go undetected.
Semi-Supervised Learning Applied to Mobile App Reviews

Semi-supervised learning, using both labelled and unlabelled data can be helpful in situations where a large amount of unlabelled data is readily available and labelled data is expensive to create, such as with app review classification.Although semi-supervised learning has been proven to provide meaningful results for a variety of applications, it has not been heavily researched in the field of app review analysis. This research analysed the suitability of semi-supervised classification through self-training when a low proportion of data is labelled. It also experimented with how adding metadata changes the performance of semi-supervised classification and analysed a large dataset of reviews to find how the differences between free and paid apps, app categories, apps with different popularities and the version releases of apps.In this paper we assess the viability of using semi-supervised classification to classify app reviews by analysing a large dataset of reviews extracted from the Apple App Store. We classify the data as either \textit{bugs}, \textit{feature requests} or \textit{other}. We found that semi-supervised learning can be effective even when a low proportion of data is labelled and can perform similarly to a base classifier with 100% labelled data.
Ensemble Model of Machine Learning for Integrating Risk in Software Effort Estimation

The development of software involves expending a significant quantum of time, effort, cost, and other resources, and effort estimation is an important aspect. Though there are many software estimation models, risks are not adequately considered in the estimation process leading to wide gap between the estimated and actual efforts. Higher the level of accuracy of estimated effort, better would be the compliance of the software project in terms of completion within the budget and schedule. This study has been undertaken to integrate risk in effort estimation process so as to minimize the gap between the estimated and the actual efforts. This is achieved through consideration of risk score as an effort driver in the computation of effort estimates and formulating a machine learning model. It has been identified that risk score reveals feature importance and the predictive model with integration of risk score in the effort estimates indicated an enhanced fit.
Future Work and Conclusion

Although much research effort has been devoted to CSS over large HINs over the past several decades, there are still many issues that are not well addressed, thus there is still much room to perform further study on CSS over large HINs in the future, from the perspectives of effective CSMs, computational efficiency, parameter optimization, tools, etc. In this chapter, we discuss several important future research directions about CSS over HINs, including novel application-driven CSMs, efficient search algorithms, parameter optimization, and an online repository for collecting HIN datasets, tools, and algorithm codes, which can provide researchers some good starting points to work in this area. In addition, we draw a brief conclusion for the book.
A Comparative Study of Existing Knowledge Based Techniques for Word Sense Disambiguation

Word Sense Disambiguation refers to the process of determining the correct sense of a given word in a given sentence. It is a difficult problem to solve because it necessitates gathering information from various sources. The human mind can use cognition and world knowledge to resolve word sense ambiguities. However, machine translation systems are in high demand today, and because machines cannot use cognition and world knowledge to resolve such ambiguities, they make semantic errors and generate incorrect interpretations. Handling sense ambiguity is one of the most difficult challenges in natural language processing and understanding; such words result in erroneous machine translation. WSD techniques were used and implemented on various corpora for almost all languages some Lexical knowledge sources are available used as machine readable dictionaries. WSD algorithms were classified into two broad categories: knowledge-based, machine learning based approaches. In this paper we have tried to compare various used algorithms and methods like overlap based, selection preference, semantic approach and heuristic approach under knowledge-based approach for WSD. By comparison, we have concluded that Knowledge based resource plays a vital role in processing of any language. Some knowledge-based techniques give high accuracy while some give low results for WSD for various languages. In this paper we try to focus on Knowledge based Techniques or algorithms used and their benefits and drawbacks in terms of execution speed or accuracy level with different languages. Some results are low due to low resource languages used for WSD needs a work to prepare knowledge resource.
Architecture and Security Analysis of Federated Learning-Based Automatic Modulation Classification

Conducting effective deep learning for automatic modulation recognition (AMR) plays a key role in non-cooperative communication systems. Applying federated learning (FL) to train AMR-oriented deep neural networks (ADNN) models is promising in achieving high recognition accuracy while maintaining the data privacy and saving precious wireless transmission bandwidth. In this paper, a FL architecture for ADNN training is presented, and four ADNN models are implemented under the FL architecture. Moreover, to evaluate the security vulnerability of the FL architecture facing ADNN models, multiple adversarial attacks are deployed at the clients to attack the central model training. Besides, label poisoning attacks targeting the FL architecture are investigated. To evaluate the efficiency and vulnerability of the FL architecture for ADNN training, a series of experiments are conducted on an open RF dataset. Results show that ADNN models can achieve high recognition accuracy. Moreover, data or label poisoning attacks are more effective in degrading recognition accuracy than adversarial attacks under the FL architecture.
Temporal and Geographic Oriented Event Retrieval for Historical Analogy

There are many benefits to studying history. Recently, the study of support for learning history has emphasized the development of the ability to use knowledge of the past in an analogous manner when considering solutions to problems that arise in the present day, a process called historical analogy. Although previous studies have developed the ability of historical analogy using card games or datasets prepared in advance by experts, the situation is not ready for anyone to learn about a subject that is in line with their interests. In this study, we propose a Twitter chatbot that presents past events recorded on Wikipedia. When this chatbot receives a reply from a Twitter user, it analyzes the user's past tweets to collect geographic and temporal information of interest and returns past events that are close to those. We conducted experiments to confirm the effectiveness of our algorithm, and confirmed that the accuracy of our algorithm was approximately 30% points higher than that of the methods used in previous studies.
Implementation and Evaluation of Ring Oscillator-based True Random Number Generator

A true random number generator (TRNG) is suitable for generating secure keys and nonces. TRNGs implemented in IoT devices must be small in scale, have low power consumption, and be feasible. The random number sequence generated by TRNG needs to have high entropy immediately after startup and a stable state. This paper implements three types of ring oscillator type TRNGs, TERO-based, COSO-based, and STR-based TRNG, on Zynq-7010. When these TRNGs are implemented as a single entropy source, it is challenging to implement them because evaluating the layout and wiring for each FPGA is necessary. This paper evaluates a TRNG configuration, which exclusively ORs the outputs of multiple entropy sources. We show that this configuration can reduce the implementing difficulty and realize high entropy. For the random number sequence evaluation, we use the statistical test of NIST SP800-90B, SP800-22, and BSI AIS 20/31. In addition, the random number sequence immediately after the startup is also statistically evaluated. As a result, our evaluated TRNGs generate high entropy random numbers. They are easy to implement on FPGA when we implement TRNGs with eight single entropy sources for TERO-based TRNG, 48 for COSO-based TRNG, and two for STR-based TRNG, respectively.
Imbalanced Learning for Insurance Using Modified Loss Functions in Tree-Based Models

Tree-based models have gained momentum in insurance claim loss modeling; however, the point mass at zero and the heavy tail of insurance loss distribution pose the challenge to apply conventional methods directly to claim loss modeling. With a simple illustrative dataset, we first demonstrate how the traditional tree-based algorithm's splitting function fails to cope with a large proportion of data with zero responses. To address the imbalance issue presented in such loss modeling, this paper aims to modify the traditional splitting function of Classification and Regression Tree (CART). In particular, we propose two novel modified loss functions, namely, the weighted sum of squared error and the sum of squared Canberra error. These modified loss functions impose a significant penalty on grouping observations of non-zero response with those of zero response at the splitting procedure, and thus significantly enhance their separation. Finally, we examine and compare the predictive performance of such modified tree-based models to the traditional model on synthetic datasets that imitate insurance loss. The results show that such modification leads to substantially different tree structures and improved prediction performance.
Rgb-T Tracking by Modality Difference Reduction and Feature Re-Selection

RGB-T tracking has attracted increasing attention, since visible and thermal data have strong complementary advantages to improve the robustness of trackers. Most existing models focus on investigating efficient ways of fusing the complementary information from RGB and thermal images for better tracking performance. However, the modality differences caused by different imaging mechanisms may degrade the discriminability of the fused features. Meanwhile, compared with the unimodal features, the fused features may not always improve the tracking performance, especially when one of the input images contain much noisy information. In view of this, we propose a novel RGB-T tracking model by simultaneously reducing modality difference and re-selecting discriminative features from the fused features as well as from the unimodal features. To this end, a Modality Difference Compensation module (MDC) and a Feature Re-selection module (FRS) are presented. The former one reduces the modality differences between RGB and thermal features and obtains the fused features. The latter one adaptively selects such discriminative features from the unimodal features and the fused features for the subsequent classification and regression. Exhausted experiments are conducted on three RGB-T tracking benchmark datasets, which verify that our proposed tracker performs favorably against some state-of-the-art tracking algorithms.