Reviewerly/exHarmony huggingface dataset:
Factors’ Influencing Driving Demand of Cryptocurrency and its Impact on Behavioral Intention: An Indian Perspective

Investors are gearing up to invest their money into wide range of cryptocurrency choices. However, investors are still somewhat hesitant about the adoption of this virtual currency. Therefore, there is a strong need to Önd out the reasons by identifying the factors that a§ect the investorsí perceptions towards adoption decisions for investing in cryptocur- rencies. New constructs have been identiÖed that will provide value and utility for users of cryptocurrencies. The factors proposed in the model have the greatest ináuence on the behavioral intention of the investors. Findings highlighted the concerns regarding trust, risk factors, ease of use, and supportive technologies.
The Impacts of Leadership Style on Innovative Work Behaviour During Covid-19 Pandemic in Oman

Pandemics present different challenges for leaders, organizations, and subordinates. During the Covid19 pandemic, all organizations faced challenges, including constant regulatory changes, for social, business, and economic conversions to protection and safety. The important challenge highlighted by the pandemic is how leaders can inspire and retain creative work behaviour when all employees work from home, and what leadership style will help the company succeed and survive. On the other hand, the researcher looks to be in a unique position to explore the influence of the Covid19 pandemic on leadership style and innovative work behaviour. In the meanwhile, the earlier research explored the significant role of leadership on innovativeness and sustainability. By hiring managers from several industries across the Sultanate of Oman, this study explores the influence of leadership style on innovative work behaviour during the covid19 epidemic. For data collection, a quantitative method was adopted, with a structured questionnaire sent out through email to a randomly chosen sample of managers in two large cities. The researchers employed descriptive statistics and statistical inference to analyze the correlation and multiple regression using the Statistical Package for Social Sciences version 25.0. The key results of the research were that transformational leadership has a considerable beneficial influence on employee innovative work behaviour, but transactional leadership has no such effect.
Roles of technologies for future teaching in a pandemic: activity, agency, and humans-with-media

Research literature on the role of mathematics teachers during the COVID-19 crisis shows that teacher preparation for emergency situations is required. In reporting on this exploratory study, we present and analyse lesson plans created by seven future teachers for mathematics classes during the pandemic. Data were collected between April and October 2021 from 16 four-hour class sessions in a Mathematics Degree Program at a public university in Medellín, Colombia. The notion of Humans-with-Media and the Learning by Expanding theory were used as frameworks to understand what roles prospective mathematics teacher (PMTs) assign to technologies for teaching in pandemic conditions. The PMTs' uses of technology for teaching mathematics during a pandemic were categorized. The results show that technology was used to reorganize and reproduce mathematics teaching practices. This report addresses the impact of technology on the activity system, and we conclude with a discussion of opportunities and limitations of students' conceptions about teaching and technology during a pandemic.
Continuous Dynamic Update of Fuzzy Random Forests

Abstract Fuzzy random forests are well-known machine learning classification mechanisms based on a collection of fuzzy decision trees. An advantage of using fuzzy rules is the possibility to manage uncertainty and to work with linguistic scales. Fuzzy random forests achieve a good classification performance in many problems, but their quality decreases when they face a classification problem with imbalanced data between classes. In some applications, e.g., in medical diagnosis, the classifier is used continuously to classify new instances. In that case, it is possible to collect new examples during the use of the classifier, which can later be taken into account to improve the set of fuzzy rules. In this work, we propose a new iterative method to update the set of trees in the fuzzy random forest by considering trees generated from small sets of new examples. Experiments have been done with a dataset of diabetic patients to predict the risk of developing diabetic retinopathy, and with a dataset about occupancy of an office room. With the proposed method, it has been possible to improve the results obtained when using only standard fuzzy random forests.
GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU

High-performance implementations of graph algorithms are challenging to implement on new parallel hardware such as GPUs because of three challenges: (1) the difficulty of coming up with graph building blocks, (2) load imbalance on parallel hardware, and (3) graph problems having low arithmetic intensity. To address some of these challenges, GraphBLAS is an innovative, on-going effort by the graph analytics community to propose building blocks based on sparse linear algebra, which will allow graph algorithms to be expressed in a performant, succinct, composable and portable manner. In this paper, we examine the performance challenges of a linear-algebra-based approach to building graph frameworks and describe new design principles for overcoming these bottlenecks. Among the new design principles is exploiting input sparsity, which allows users to write graph algorithms without specifying push and pull direction. Exploiting output sparsity allows users to tell the backend which values of the output in a single vectorized computation they do not want computed. Load-balancing is an important feature for balancing work amongst parallel workers. We describe the important load-balancing features for handling graphs with different characteristics. The design principles described in this paper have been implemented in "GraphBLAST", the first high-performance linear algebra-based graph framework on NVIDIA GPUs that is open-source. The results show that on a single GPU, GraphBLAST has on average at least an order of magnitude speedup over previous GraphBLAS implementations SuiteSparse and GBTL, comparable performance to the fastest GPU hardwired primitives and shared-memory graph frameworks Ligra and Gunrock, and better performance than any other GPU graph framework, while offering a simpler and more concise programming model.
Multi-Objective Fault Tolerance Model for Scientific Workflow Scheduling on Cloud Computing

Cloud computing is used for large-scale applications.Therefore, a lot of organizations and industries are moving their data to the cloud.Nevertheless, cloud computing might have maximum failure rates because of the great number of servers and parts with a high workload.Reducing the false in scheduling is a challenging task.Hence, in this study, an efficient multiobjective fault detector strategy using an improved Squirrel Optimization Algorithm (ISOA) in cloud computing is proposed.This method can effectively reduce energy consumption, makespan, and total cost, while also tolerating errors when planning scientific workflows.To increase the detection accuracy of failures, the Active Fault Tolerance Mechanism (PFTM) is used.Similarly, the reactive fault tolerance mechanism (RFTM) is used for processor failures.The efficiency of the proposed approach is analysed based on various measurements and performance compared to other approaches.
Real-Time Image-Based Vibration Extraction with Memory-Efficient Optical Flow and Block-Based Adaptive Filter

In this paper, we propose a real-time vibration extraction system, which extracts vibration component within a given frequency range from videos in real time, for realizing tremor suppression used in microsurgery assistance systems. To overcome the problems in our previous system based on the mean Lucas-Kanade (LK) optical flow of the whole frame, we have introduced a new architecture combining dense optical flow calculated with simple feature matching and block-based band-pass filtering using band-limited multiple Fourier linear combiner (BMFLC). As a feature of optical flow calculation, we use the simplified rotation-invariant histogram of oriented gradients (RIHOG) based on a gradient angle quantized to 1, 2, or 3 bits, which greatly reduces the usage of memory resources for a frame buffer. An obtained optical flow map is then divided into multiple blocks, and BMFLC is applied to the mean optical flow of each block independently. By using the L1-norm of adaptive weight vectors in BMFLC as a criterion, blocks belonging to vibrating objects can be isolated from background at low cost, leading to better extraction accuracy compared to the previous system. The whole system for 480p and 720p resolutions can be implemented on a single Xilinx Zynq-7000 XC7Z020 FPGA without any external memory, and can process a video stream supplied directly from a camera at 60fps.
Is NLP-based Test Automation Cheaper Than Programmable and Capture &amp;Replay?

Nowadays, there is a growing interest in the use of Natural-Language Processing (NLP) for supporting software test automation. This paper investigates the adoption of NLP in web testing. To this aim, a case study has been conducted to compare the cost of the adoption of a NLP testing approach, with respect to more consolidated approaches, i.e., programmable testing and capture and replay testing, in two testing tasks: test cases development and test case evolution/maintenance. Even if preliminary, results show that NLP testing is quite competitive with respect to the more consolidated approaches since the cumulative testing effort of a NLP testing approach, computed considering both development and evolution efforts, is almost always lower than the one of programmable testing and capture &replay testing.
New Insights and methods of vocabulary acquisition in Latin classes

Learning a historical language is different from learning a modern language in view of the emphasis on the work on texts instead of everyday communication. Therefore, not only the expectations and motivation differ, but also the teaching methodology. Whereas learners of modern languages focus on language production, learners of Latin read or translate their texts. Because of the overall low frequency of occurrence of a Latin word or a phrase in this kind of learning environment, most students are often unfamiliar with a given word and therefore finally unable to translate the texts. To tackle this underlying problem of Latin classes, an interdisciplinary research project conducted different studies using a data-driven learning (DDL) approach. So far, the findings are very multifaceted and sometimes even surprising: the majority of students fail to lemmatize words correctly even though they have learned Latin for four years or more.
CONTENT ANALYSIS OF NEWS COMPLETENESS ELEMENTS TEXT FOR STUDENTS OF SMP NEGERI 24 PADANG

This research is motivated by the presentation of news in the news text of students of SMPN 24 Padang who ignore the elements of completeness of the 5W + 1H news. Technically, news writing must meet the 5W + 1H elements, namely, what, who, where, when, why, and how. News as an introduction to information published through the mass media. Events in the news are something that happens in human life and are observed and then reported. Events that will be developed into news must be developed through aspects of the story, data, and facts. This study aims to describe the elements of completeness of the news in the news text of students of SMPN 24 Padang. The method used was quantitative content analysis with simple random sampling technique. The results of this study are that the news texts of the students of SMPN 24 Padang tend not to contain elements of the completeness of the 5W + 1H news. From the data for 5 items (16.67%) there was no when element, 8 items (26.67%) had nowhere element, 16 items (53.33%) had no why element, 20 items (66.67%) did not there is an element of how. The elements of what and who are not found incomplete in the news. In general, it can be concluded that the news text of SMPN 24 Padang students is incomplete using the 5W + 1H elements
Petals: Collaborative Inference and Fine-tuning of Large Models

Many NLP tasks benefit from using large language models (LLMs) that often have more than 100 billion parameters. With the release of BLOOM-176B and OPT-175B, everyone can download pretrained models of this scale. Still, using these models requires high-end hardware unavailable to many researchers. In some cases, LLMs can be used more affordably via RAM offloading or hosted APIs. However, these techniques have innate limitations: offloading is too slow for interactive inference, while APIs are not flexible enough for research that requires access to weights, attention or logits. In this work, we propose Petals - a system for inference and fine-tuning of large models collaboratively by joining the resources of multiple parties. We demonstrate that this strategy outperforms offloading for very large models, running inference of BLOOM-176B on consumer GPUs with $\approx$ 1 step per second, which is enough for many interactive LLM applications. Unlike most inference APIs, Petals also natively exposes hidden states of served models, allowing to train and share custom model extensions based on efficient fine-tuning methods.
Research on intelligent question answering model of hazardous chemicals knowledge based on ALBERT

Port dangerous goods have the characteristics of wide variety and wide distribution. Port safety production is facing great pressure. The management of port cargo safety storage needs to be analyzed and developed by the third-party software. In view of the above situation, taking the field of hazardous chemicals as an example, this paper proposes a question and answer model of risk knowledge in the field of hazardous chemicals. It is composed of question named entity recognition module, question intention recognition module and answer query module. The question entity recognition model based on ALBERT-BiLSTM-CRF and the question intention recognition module based on ALBERT-TextCNN are adopted. Compared with the traditional single structure and classical models, better model recognition results are achieved.In the answer query module, the intention classification template of hazardous chemical risk problems is designed, Cypher query statements are designed according to the classification template, the mapping relationship between the questions and the knowledge map is established, and the graph database Neo4j is used to complete the complex query tasks
Binsec/Rel: Symbolic Binary Analyzer for Security with Applications to Constant-Time and Secret-Erasure

This paper tackles the problem of designing efficient binary-level verification for a subset of information flow properties encompassing constant-time and secret-erasure. These properties are crucial for cryptographic implementations, but are generally not preserved by compilers. Our proposal builds on relational symbolic execution enhanced with new optimizations dedicated to information flow and binary-level analysis, yielding a dramatic improvement over prior work based on symbolic execution. We implement a prototype, Binsec/Rel, for bug-finding and bounded-verification of constant-time and secret-erasure, and perform extensive experiments on a set of 338 cryptographic implementations, demonstrating the benefits of our approach. Using Binsec/Rel, we also automate two prior manual studies on preservation of constant-time and secret-erasure by compilers for a total of 4148 and 1156 binaries respectively. Interestingly, our analysis highlights incorrect usages of volatile data pointer for secret erasure and shows that scrubbing mechanisms based on volatile function pointers can introduce additional register spilling which might break secret-erasure. We also discovered that gcc -O0 and backend passes of clang introduce violations of constant-time in implementations that were previously deemed secure by a state-of-the-art constant-time verification tool operating at LLVM level, showing the importance of reasoning at binary-level.
Word Embeddings with Fuzzy Ontology Reasoning for Feature Learning in Aspect Sentiment Analysis

In this paper, a hybrid feature learning approach is proposed to employ aspect sentiment analysis on sentence level through contextual discovery of unstructured text data. The proposed approach joins sentiment lexicon with pre-trained BERT (Bidirectional Encoder Representations from Transformers) word embeddings model for feature deep learning and prediction of context words. In addition, fuzzy ontology reasoning is employed for supporting more in-depth feature extraction through representing semantic knowledge by forming relationships between aspects. Subsequently, the extracted sentiment indicators in online user reviews are classified using Bi-LSTM (Bi-directional Long Short-Term Memory) deep learning model so that the context around words are learned and the corresponding meanings are captured both syntactically and semantically. According to the obtained results, the proposed approach outperforms other related feature learning approaches through improving sentence aspect sentiment analysis and accordingly boosting the overall accuracy of sentiment classification. An average accuracy of 96%, AUC score of 94.5%, and F-score of 95% are achieved by the proposed approach considering five public social media datasets of online reviews. The significance of this study is investigating enrichment of extracted features through using BERT transformer with fuzzy ontology in order to improve the performance of aspect-based sentiment analysis while adding the contextual meanings to the prediction task, and extracting the indirect relationships embedded in social data of user reviews.
Mitigate Gender Bias using Negative Multi-Task Learning

Abstract Deep learning models have shown their great performances in natural language processing tasks. While much attention has been paid towards improvements in utility, privacy leakage and social bias are two major concerns arising in trained models. In this paper, we protect individual privacy and mitigate gender bias on classification models simultaneously. First, we propose a selective privacy-preserving method that only obscure individuals' sensitive information by adding noise on word embeddings. Then we propose a negative multi-task learning framework to mitigate the gender bias which contains a main task and a gender prediction task. The main task uses a positive loss constraint to ensure utility while the gender prediction task applies a negative loss constraint to remove gender-specific features. We analyze two existing word embeddings and evaluate them on sentiment analysis and medical text classification tasks. Our experimental results show that our negative multi-task learning framework can mitigate the gender bias while keeping models’ utility on both sentiment analysis and medical text classification.
Performance Evaluation of Action Recognition Models on Low Quality Videos

In the design of action recognition models, the quality of videos is an important issue; however, the trade-off between the quality and performance is often ignored.In general, action recognition models are trained on high-quality videos, hence it is not known how the model performance degrades when tested on low-quality videos, and how much the quality of training videos affects the performance.The issue of video quality is important, however, it has not been studied so far.The goal of this study is to show the trade-off between the performance and the quality of training and test videos by quantitative performance evaluation of several action recognition models for transcoded videos in different qualities.First, we show how the video quality affects the performance of pre-trained models.We transcode the original validation videos of Kinetics400 by changing quality control parameters of JPEG (compression strength) and H.264/AVC (CRF).Then we use the transcoded videos to validate the pre-trained models.Second, we show how the models perform when trained on transcoded videos.We transcode the original training videos of Kinetics400 by changing the quality parameters of JPEG and H.264/AVC.Then we train the models on the transcoded training videos and validate them with the original and transcoded validation videos.Experimental results with JPEG transcoding show that there is no severe performance degradation (up to -1.5%) for compression strength smaller than 70 where no quality degradation is visually observed, and for larger than 80 the performance degrades linearly with respect to the quality index.Experiments with H.264/AVC transcoding show that there is no significant performance loss (up to -1%) with CRF30 while the total size of video files is reduced to 30%.In summary, the video quality doesn't have a large impact on the performance of action recognition models unless the quality degradation is severe and visible.This enables us to transcode the training and validation videos and reduce the file sizes to one-third of the original videos.
Blockchain in energy systems: From centralized to decentralized

The increasingly distributed energy system poses a serious challenge to traditional centralized management schemes. Blockchain is a promising technique for enabling trusted collaboration without a trusted central authority. Still, blockchains are limited to being applied in the energy system as they are not specialized for mathematical optimization. To fill this gap, Chen et al. propose a blockchain consensus mechanism (proof of solution, PoSo) tailored to support mathematical optimization problems in energy systems, which replaces the meaningless mathematical puzzle in proof of work (PoW) with a meaningful optimization problem. The inspiration comes from the fact that both the solutions to the puzzle and to an optimization problem are hard to find but easy to verify.
Cryptographic Pseudorandom Noise Generators for Lattice-based Cryptography and Differential Privacy

The classical use of a pseudorandom number gen-erator (PRNG) is in the stream cipher encryption where the PRNG is the core for the security of the encryption scheme. The use of PRNGs is also in modern security and privacy techniques such as fully homomorphic encryption (FHE) and differential privacy (DP). When such techniques are employed in many real-world applications such as traditional analytics computing, embedded systems, smart metering and mobile sys-tems, generating a large amount of random numbers and random noises following certain distributions is challenging. In this paper, we propose a construction of a cryptographic pseudorandom noise generator (CPNG) that can also be used for resource-constrained applications. As two use cases, we show how the CPNG can generate Laplace and Gaussian noises for FHE and DP applications. Experimental results on the performance of the proposed generators are presented, along with a comparison.