Reviewerly/exHarmony huggingface dataset:
A Performance Study of Multiobjective Particle Swarm Optimization Algorithms for Market Timing

Market timing is the issue of deciding when to buy or sell a given asset on a financial market. As one of the core issues of algorithmic trading systems, designers of such systems have turned to computational intelligence methods to aid them in this task. In our previous work, we introduced a number of Particle Swarm Optimization (PSO) algorithms to compose strategies for market timing using a novel training and testing methodology that reduced the likelihood of overfitting and tackled market timing as a multiobjective optimization problem. In this paper, we provide a detailed analysis of these multiobjective PSO algorithms and address two limitations in the results presented previously. The first limitation is that the PSO algorithms have not been compared to well-known algorithms or market timing techniques. This is addressed by comparing the results obtained against NSGA-II and MACD, a technique commonly used in market timing strategies. The second limitation is that we have no insight regarding diversity of the Pareto sets returned by the algorithms. We address this by using RadViz to visualize the Pareto sets returned by all the algorithms, including NSGA-II and MACD. The results show that the multiobjective PSO algorithms return statistically significantly better results than NSGA-II and MACD. We also observe that the multiobjective PSO <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SP</sup> algorithm consistently displayed the best spread in its returned Pareto sets despite not having any explicit diversity promoting measures.
Status, Challenges, and Future Views of DeepFake Techniques and Datasets

Deep counterfeiting is a term that has become popular in recent years and carries many risks in violation of privacy and other dangers, which were addressed by several research studies that used different methods and methods in counterfeiting and anti-counterfeiting detection methods, we will address in this paper one of the methods that help in detecting image manipulation specifically in a modified manner on the GAN algorithm and we will call it ADVGAN and it includes the process of selecting and configuring a specific database Several initial operations and training by learning the machine and relying on the proposed system in detecting image manipulation with excellent efficiency.
BERT Probe: A python package for probing attention based robustness evaluation of BERT models

Transformer models based on attention-based architectures have been significantly successful in establishing state-of-the-art results in natural language processing (NLP). However, recent work about adversarial robustness of attention-based models show that their robustness is susceptible to adversarial inputs causing spurious outputs thereby raising questions about trustworthiness of such models. In this paper, we present BERT Probe which is a python-based package for evaluating robustness to attention attribution based on character-level and word-level evasion attacks and empirically quantifying potential vulnerabilities for sequence classification tasks. Additionally, BERT Probe also provides two out-of-the-box defenses against character-level attention attribution-based evasion attacks.
Suggesting method names based on graph neural network with salient information modelling

Abstract Descriptive method names have a great impact on improving program readability and facilitating software maintenance. Recently, due to high similarity between the task of method naming and text summarization, large amount of research based on natural language processing has been conducted to generate method names. However, method names are much shorter compared to long source code sequences. The salient information of the whole code snippet account for an relatively small part. Additionally, unlike natural language, source code has complicated structure information. Thus, modelling the salient information from highly structured input presents a great challenge. To tackle this problem, we propose a graph neural network (GNN)‐based model with a novel salient information selection layer. Specifically, to comprehensively encode the tokens of the source code, we employ a GNN‐based encoder, which can be directly applied to the code graph to ensure that the syntactic information of code structure and semantic information of code sequence can be modelled sufficiently. To effectively discriminate the salient information, we introduce an information selection layer which contains two parts: a global filter gate used to filter irrelevant information, and a semantic‐aware convolutional layer used to focus on the semantic information contained in code sequence. To improve the precision of the copy mechanism when decoding, we introduce a salient feature enhanced attention mechanism to facilitate the accuracy of copying tokens from input. Experimental results on an open source dataset indicate that our proposed model, equipped with the salient information selection layer, can effectively improve method naming performance compared to other state‐of‐the‐art models.
Work in Progress – Augmented Reality Smart Tele-Assisting Technology for Enhancing Engineering Education

The laboratories play an important phase in the educational learning process by providing an interactive learning technique and inspiring students to acquire practical skills. However, the current coronavirus pandemic (COVID-19) has increased educational uncertainty significantly. Laboratory experiments have been suspended indefinitely, compelling the education institutions to accept-wider use of virtual and remote learning. This research aims to understand the issues students confront in physical/remote laboratories and explores how physical labs could be digitalized through Augmented Reality (AR) smart glasses. The research paper presents a survey of undergraduate engineering students&#x2019; perceptions towards the use of AR smart glasses in laboratories. Fosters student to carry out experiments through AR smart glasses to view digital warning messages superimposed on the actual environment, allowing them to dive deeper into information and better comprehend the topic According to the findings, more than 85&#x0025; of students are interested in adopting AR smart glasses. The survey findings show that integrating emerging technologies such as Augmented Reality (AR) and Artificial Intelligence (Al)-will improve student engagement and learning outcomes by increasing students&#x2019; depth of connections between the content students are learning and the real-world outside the classroom. Additionally, it enables students to perform experiments remotely without affecting their self-efficacy by experimenting-with laboratory kits delivered off-campus.
DPrime+DAbort: A High-Precision and Timer-Free Directory-Based Side-Channel Attack in Non-Inclusive Cache Hierarchies using Intel TSX

Recent CPUs have begun to adopt non-inclusive cache hierarchies for more effective cache utilization. Non-inclusive cache hierarchies have an additional advantage in that they eliminate the vulnerability to cache-based side-channel attacks. In addition, precise timers are often disabled or added with noise to defeat timer-based side-channel attacks. With the combination of such countermeasures, existing cache- and directory-based side-channel attacks can robustly be defeated on commodity systems.In this work, we discover the vulnerability caused by the undocumented interactions between the coherence directories and Intel TSX transactions in latest Intel CPUs with non-inclusive cache hierarchies. Guided by the observation, we propose a high-precision and timer-free directory attack called DPrime+DAbort in non-inclusive cache hierarchies using Intel TSX, which nullifies the aforementioned countermeasures. Our quantitative evaluation conducted on real systems equipped with latest Intel CPUs in three different generations demonstrates the practicality of the DPrime+DAbort attack in that it can be used to attack cryptographic and genomesequencing applications. We also discuss potential countermeasures and evaluate the feasibility of an Intel TSX-based countermeasure against the DPrime+DAbort attack.
Geometric Metric Learning for Multi-Output Learning

Due to its wide applications, multi-output learning that predicts multiple output values for a single input at the same time is becoming more and more attractive. As one of the most popular frameworks for dealing with multi-output learning, the performance of the k-nearest neighbor (kNN) algorithm mainly depends on the metric used to compute the distance between different instances. In this paper, we propose a novel cost-weighted geometric mean metric learning method for multi-output learning. Specifically, this method learns a geometric mean metric which can make the distance between the input embedding and its correct output be smaller than the distance between the input embedding and the outputs of its nearest neighbors. The learned geometric mean metric can discover output dependencies and move the instances with different outputs far away in the embedding space. In addition, our objective function has a closed solution, and thus the calculation speed is very fast. Compared with state-of-the-art methods, it is easier to explain and also has a faster calculation speed. Experiments conducted on two multi-output learning tasks (i.e., multi-label classification and multi-objective regression) have confirmed that our method provides better results than state-of-the-art methods.
Quantifying means-end reasoning skills in simulation-based training: a logic-based approach

We develop a logic-based approach for designing simulation-based training scenarios. Our methodology embodies a concise definition of the scenario concept and integrates the notions of training goals, acceptable versus unacceptable actions and performance scoring. The approach applies classical artificial intelligence (AI) planning to extract coherent plays from a causal description of the training domain. The domain- and task-specific parts are defined in a high-level action description language [Formula: see text]. Generic causal and temporal logic is added when the causal theory is compiled into the underlying Answer Set Programming (ASP) language. The ASP representation is used to derive a scoring function that reflects the quality of a play or training session, based on a distinction of states and actions into green (acceptable) and red (unacceptable) ones. To that end, we add to the casual theory a set of norms that specify an initial assignment of colors. The ASP engine uses these norms as axioms and propagates colors by consulting the causal theory. We prove that any set of such norms constitutes a conservative extension of the underlying causal theory. With this work, we hope to lay the foundation for the development of design and analysis tools for exercise managers. We envision a software system that lets an exercise manager view all plays of a tentative scenario design, with expediency information and scores for each possible play. Our approach is applicable to any domain in which means-ends reasoning is pertinent. We illustrate the approach in the domain of crisis response and management.
A SPARQL benchmark for distributed databases in IoT environments

Data transmission is one of the biggest energy consumers in low power networks. So, any application running in such environment must reduce the volume of communication caused by its presence. We want to deploy our database on a wireless sensor network. Therefore, we want to gain insights into the interoperability between routing and application in order to reduce communication costs. To do this, we propose our new benchmark framework in this contribution. Using this benchmark framework, we compare state-of-the-art database indexes and how much traffic they cause in a fully distributed environment. We believe that computing speed shouldn't be the only metric to compare databases with each other. If the database uses fewer resources, the actual application has more resources available.
Sociohumanistic Knowledge and the Future of Science

The author's understanding of the role of the social sciences and humanities in preserving and developing science as a sociocultural phenomenon is substantiated. This justification is built as an explication and critical analysis of the program of philosophical research in science and technology, the main content and results of which are presented in the monograph by I.T. Kasavin Science As a Humanistic Project (2020). This article describes how the search for new strategies for the study of science is carried out within the framework of Russian philosophy. The historicity of science is analyzed, and the complex topology of its genesis is shown, which does not fit into the linear scheme of historical continuity, reflecting the synchronous coexistence of different variations of science as a cultural phenomenon. The historicity of science makes us turn to the question of its cultural and historical agency; it is substantiated that this agency acquires a political character in the current activity. How can the political agency of science be perceived and consistently combined with the ethos of the expert community? According to the author, political agency and the range of interests that scientists defend in public are determined by understanding the essence of science as a cultural phenomenon. The existential and cultural-creative functions of science are emphasized; on this basis the author's interpretation of the humanistic project suggested by Kasavin is proposed as a program for the humanization of technoscience.
MAGMA: inference and prediction using multi-task Gaussian processes with common mean

A novel multi-task Gaussian process (GP) framework is proposed, by using a common mean process for sharing information across tasks. In particular, we investigate the problem of time series forecasting, with the objective to improve multiple-step-ahead predictions. The common mean process is defined as a GP for which the hyper-posterior distribution is tractable. Therefore an EM algorithm is derived for handling both hyper-parameters optimisation and hyper-posterior computation. Unlike previous approaches in the literature, the model fully accounts for uncertainty and can handle irregular grids of observations while maintaining explicit formulations, by modelling the mean process in a unified GP framework. Predictive analytical equations are provided, integrating information shared across tasks through a relevant prior mean. This approach greatly improves the predictive performances, even far from observations, and may reduce significantly the computational complexity compared to traditional multi-task GP models. Our overall algorithm is called \textsc{Magma} (standing for Multi tAsk Gaussian processes with common MeAn). The quality of the mean process estimation, predictive performances, and comparisons to alternatives are assessed in various simulated scenarios and on real datasets.
Remarn: A Reconfigurable Multi-threaded Multi-core Accelerator for Recurrent Neural Networks

This work introduces Remarn, a reconfigurable multi-threaded multi-core accelerator supporting both spatial and temporal co-execution of Recurrent Neural Network (RNN) inferences. It increases processing capabilities and quality of service of cloud-based neural processing units (NPUs) by improving their hardware utilization and by reducing design latency, with two innovations. First, a custom coarse-grained multi-threaded RNN/Long Short-Term Memory (LSTM) hardware architecture, switching tasks among threads when RNN computational engines meet data hazards. Second, the partitioning of this hardware architecture into multiple full-fledged sub-accelerator cores, enabling spatially co-execution of multiple RNN/LSTM inferences. These innovations improve the exploitation of the available parallelism to increase runtime hardware utilization and boost design throughput. Evaluation results show that a dual-threaded quad-core Remarn NPU achieves 2.91 times higher performance while only occupying 5.0% more area than a single-threaded one on a Stratix 10 FPGA. When compared with a Tesla V100 GPU implementation, our design achieves 6.5 times better performance and 15.6 times higher power efficiency, showing that our approach contributes to high performance and energy-efficient FPGA-based multi-RNN inference designs for datacenters.
The Effect of Batch Normalization on Noise Resistant Property of Deep Learning Models

The fast execution speed and energy efficiency of analog hardware have made them a strong contender for deploying deep learning models at the edge.However, there are concerns about the presence of analog noise which causes changes to the weight of the models, leading to performance degradation of deep learning models, despite their inherent noise-resistant characteristics.The effect of the popular batch normalization layer(BatchNorm) on the noise-resistant ability of deep learning model is investigated in this work.This systematic study has been carried out by first training different models with and without BatchNorm layer on the CIFAR10 and the CIFAR100 datasets.The weights of the resulting models are then injected with analog noise, and the performance of the models on the test dataset is obtained and compared.The results show that the presence of the BatchNorm layer negatively impacts the noiseresistant property of deep learning models i.e ResNet44 and VGG16 models with BatchNorm layers trained with CIFAR10 dataset has an average normalized inference accuracy of 41.32% and 10.75% respectively compared to 91.95% and 93.80% obtained for same ResNet44 and VGG16 model without the BatchNorm layer respectively .Furthermore, the impact of the BatchNorm layer also grows with the increase of the number of BatchNorm layers.
Teaching Method of Japanese Professional Writing Course Based on Big Data Fusion

In recent years, the number of Japanese learners has increased year by year. When Japanese is learned as a language, knowledge of the language itself is indispensable. This article aims to explore the teaching methods of Japanese professional writing courses based on the integration of big data. This article first introduces big data fusion and introduces the definition and model of data fusion. Generally, there are two types of data fusion models: distributed and centralized. Then, I analyzed the artificial neural network algorithm, which is a computational model that imitates the animal brain, and then researched the context-based teaching of Japanese professional courses and analyzed the context-based teaching of Japanese professional writing. The results of the context-based Japanese course teaching research show that the average Japanese writing score of the experimental class is 72.7833, the control class score is 66.3333, and the composition fluency scores are 156.27 and 119.73. It can be concluded that the composition performance and composition fluency of students in the experimental class are higher than those in the control class, so educators should let students strengthen vocabulary spelling and memory skills in situational teaching.
State of the Art in Authorship Attribution With Impact Analysis of Stylometric Features on Style Breach Prediction

The most influential research was studied that spans over the domains from Authorship attribution and stylometry. The reference material contributes robust classifiers with reasonable array of feature extraction techniques, such as Dirichlet–multinomial change point regression to extract the progress of inscription elegance with time, comprising plodding variations in stylishness as the author ages and unexpected vicissitudes. This paper presents quantifiable evaluation of the research in terms of year-wise research output, diversity of applications, nature of collaboration, characteristics of highly productive techniques and the benchmark of performance criteria by eminent high impact researchers. The outcomes of this study can by deployed for dialectology analysis and corpus linguistics, stylistics, natural language processing, classification, and literary and historical analysis, forensic analysis etc.
Social Media Security Awareness in Saudi Arabia

With the daily use of social media, the cybersecurity and privacy are challenging for most communities. This research is aimed to explore and evaluate the awareness of cybersecurity in Saudi Arabia. It is also designed to discover how the ordinary users performances in using internet security while using social media. This research employed mixed methods. The researcher sent the online questionnaire to random people who used the social media to participate in this study. At the end of the online survey, one more question was attached to ask participants to involve in the second round of data collection (interview). This research found that social media users' awareness about internet security is different from users based on their gender. In addition, the ways of contact were affecting the reaction of users to share their information with others. The results also indicated that when the level of awareness is high the way of dealing with others is different. This study also confirmed the users' belief in internet security and most of them knew the strengths of internet security enabled them to protect their devices and personal information from outside intruders. Moreover, the findings of this study showed that the users of social media need to give more attention to all types of security threats. The findings of this paper can be used theoretically and practically by identifying the level of security awareness based on social media usage and the purpose of use.
Distributed Deep Learning: From Single-Node to Multi-Node Architecture

During the last years, deep learning (DL) models have been used in several applications with large datasets and complex models. These applications require methods to train models faster, such as distributed deep learning (DDL). This paper proposes an empirical approach aiming to measure the speedup of DDL achieved by using different parallelism strategies on the nodes. Local parallelism is considered quite important in the design of a time-performing multi-node architecture because DDL depends on the time required by all the nodes. The impact of computational resources (CPU and GPU) is also discussed since the GPU is known to speed up computations. Experimental results show that the local parallelism impacts the global speedup of the DDL depending on the neural model complexity and the size of the dataset. Moreover, our approach achieves a better speedup than Horovod.