Reviewerly/exHarmony huggingface dataset:
Security and QoS issues in blockchain enabled next-generation smart logistic networks: A tutorial

The blockchain-enabled smart logistics market is expected to grow worth USD 1620 billion and at a compound annual growth rate of 62.4%. Smart logistics ensures intelligence infrastructure, logistics automation, real-time analysis of supply chain data synchronization of the logistics process, cost transparency, unbroken shipment tracking all the way down to the transportation route, etc. In the smart logistics domain, significant advancement and growth of the Internet of Things (IoT) sensors are evident. However, the connectivity of IoT systems, including Tactile Internet, without proper safeguards creates vulnerabilities that can still be deliberately or inadvertently cause disruption. In view of this, we primarily notice two key issues. Firstly, the logistics domain can be compromised by a variety of natural or man-made activities, which eventually affect the overall network security. Secondly, there are thousands of entities in the supply chain network that use extensive machine-learning algorithms in many scenarios, and they require high-power computational resources. From these two challenges, we note that the first concern can be addressed by adding blockchain to IoT logistic networks. The second issue can be addressed using 6G. This will support 1-μs latency communications, support seamless computing at the edges of networks, and autonomously predict the best optimal location for edge computing. Motivated by this, we have highlighted motivational examples to show the necessity to integrate 6G and blockchain in smart logistic networks. Then, we have proposed a 6G and blockchain-enabled smart logistic high-level framework. We have presented the key intrinsic issues of this framework mainly from the security and resource management context. In this paper, recent state-of-the-art advances in blockchain enabled next-generation smart logistic networks are analyzed. We have also examined why 6G and not 5G would be compatible with the smart network. We have introduced five different use cases of blockchain technology in smart logistics. Later, this paper discusses some important concerns that blockchain in smart logistics might face. We have also provided potential solutions to tackle these concerns.
Modelling sentiments based on objectivity and subjectivity with self-attention mechanisms

<ns5:p><ns5:bold>Background</ns5:bold><ns5:bold>:</ns5:bold><ns5:bold> </ns5:bold>The proliferation of digital commerce has allowed merchants to reach out to a wider customer base, prompting a study of customer reviews to gauge service and product quality through sentiment analysis. Sentiment analysis can be enhanced through subjectivity and objectivity classification with attention mechanisms.</ns5:p><ns5:p> <ns5:bold>Methods</ns5:bold>: This research includes input corpora of contrasting levels of subjectivity and objectivity from different databases to perform sentiment analysis on user reviews, incorporating attention mechanisms at the aspect level. Three large corpora are chosen as the subjectivity and objectivity datasets, the Shopee user review dataset (ShopeeRD) for subjectivity, together with the Wikipedia English dataset (Wiki-en) and Internet Movie Database (IMDb) for objectivity. Word embeddings are created using Word2Vec with Skip-Gram. Then, a bidirectional LSTM with an attention layer (LSTM-ATT) imposed on word vectors. The performance of the model is evaluated and benchmarked against classification models of Logistics Regression (LR) and Linear SVC (L-SVC). Three models are trained with subjectivity (70% of ShopeeRD) and the objectivity (Wiki-en) embeddings, with ten-fold cross-validation. Next, the three models are evaluated against two datasets (IMDb and 20% of ShopeeRD). The experiments are based on benchmark comparisons, embedding comparison and model comparison with 70-10-20 train-validation-test splits. Data augmentation using AUG-BERT is performed and selected models incorporating AUG-BERT, are compared.</ns5:p><ns5:p> <ns5:bold>Results:</ns5:bold> L-SVC scored the highest accuracy with 56.9% for objective embeddings (Wiki-en) while the LSTM-ATT scored 69.0% on subjective embeddings (ShopeeRD). Improved performances were observed with data augmentation using AUG-BERT, where the LSTM-ATT+AUG-BERT model scored the highest accuracy at 60.0% for objective embeddings and 70.0% for subjective embeddings, compared to 57% (objective) and 69% (subjective) for L-SVC+AUG-BERT, and 56% (objective) and 68% (subjective) for L-SVC.</ns5:p><ns5:p> <ns5:bold>Conclusions</ns5:bold>: Utilizing attention layers with subjectivity and objectivity notions has shown improvement to the accuracy of sentiment analysis models.</ns5:p>
A Review of Cybersecurity Risk Management Framework in Malaysia Higher Education Institutions

Cybersecurity risk management has been applied to many aspects of modern life, including banking, finance, health, life, business ventures, and project management.It is currently gaining much attention in universities for operation safety reasons.Higher education institutions have to face new challenges and increasing information technology threats more sophisticatedly.Therefore, this study will investigate the current cybersecurity risk management frameworks used in Malaysian public universities.The qualitative research method will be applied to collect data by interviewing experts in cybersecurity risk management.The literature review showed the primary constructs of the cybersecurity risk management framework for Malaysian universities.Moreover, the results show the factors that lead to risks and benefits obtained when the stakes are managed.The researchers will clarify the method or mechanisms for risk management in a university environment.This research shows a greater understanding and knowledge of risk management.The future direction of this study is to propose a cybersecurity risk management framework based on the reviews of the existing frameworks used in Malaysian public universities.
Consensus of Multi-agent Reinforcement Learning Systems: The Effect of Immediate Rewards

This paper studies the consensus problem of a leaderless, homogeneous, multi-agent reinforcement learning (MARL) system using actor-critic algorithms with and without malicious agents. The goal of each agent is to reach the consensus position with the maximum cumulative reward. Although the reward function converges in both scenarios, in the absence of the malicious agent, the cumulative reward is higher than with the malicious agent present. We consider here various immediate reward functions. First, we study the immediate reward function based on Manhattan distance. In addition to proposing three different immediate reward functions based on Euclidean, $n$-norm, and Chebyshev distances, we have rigorously shown which method has a better performance based on a cumulative reward for each agent and the entire team of agents. Finally, we present a combination of various immediate reward functions that yields a higher cumulative reward for each agent and the team of agents. By increasing the agents’ cumulative reward using the combined immediate reward function, we have demonstrated that the cumulative team reward in the presence of a malicious agent is comparable with the cumulative team reward in the absence of the malicious agent. The claims have been proven theoretically, and the simulation confirms theoretical findings.
Embedded Object Detection with Custom LittleNet, FINN and Vitis AI DCNN Accelerators

Object detection is an essential component of many systems used, for example, in advanced driver assistance systems (ADAS) or advanced video surveillance systems (AVSS). Currently, the highest detection accuracy is achieved by solutions using deep convolutional neural networks (DCNN). Unfortunately, these come at the cost of a high computational complexity; hence, the work on the widely understood acceleration of these algorithms is very important and timely. In this work, we compare three different DCNN hardware accelerator implementation methods: coarse-grained (a custom accelerator called LittleNet), fine-grained (FINN) and sequential (Vitis AI). We evaluate the approaches in terms of object detection accuracy, throughput and energy usage on the VOT and VTB datasets. We also present the limitations of each of the methods considered. We describe the whole process of DNNs implementation, including architecture design, training, quantisation and hardware implementation. We used two custom DNN architectures to obtain a higher accuracy, higher throughput and lower energy consumption. The first was implemented in SystemVerilog and the second with the FINN tool from AMD Xilinx. Next, both approaches were compared with the Vitis AI tool from AMD Xilinx. The final implementations were tested on the Avnet Ultra96-V2 development board with the Zynq UltraScale+ MPSoC ZCU3EG device. For two different DNNs architectures, we achieved a throughput of 196 fps for our custom accelerator and 111 fps for FINN. The same networks implemented with Vitis AI achieved 123.3 fps and 53.3 fps, respectively.
Tuning the Hyperparameters of the 1D CNN Model to Improve the Performance of Human Activity Recognition

The human activity recognition (HAR) field has recently become one of the trendiest research topics due to ready-made sensors such as accelerometers and gyroscopes equipped with smartphones and smartwatches as an embedded devices, decreasing the cost and power consumption. As a result, human activity is considered a time series classification problem. Now a day, deep learning approaches such as Convolutional Neural Network (CNN) have been successful when implemented with HAR to learn automatically higher-order features and, at the same time, work as a classifier. Recently, a one-dimensional Convolutional Neural Network (1D CNN) has been suggested and carried out at the best performance levels in numerous applications, such as the classification of personalized biomedical data and time series classification. This paper studies how to leverage a 1D single CNN model to produce an excellent performance on the human activity raw data. This is done by empirically tuning the values of hyperparameters, such as kernel size, filter maps, number of epochs, batch size, and promoting an advanced multi-headed 1D CNN by employing each convolutional layer with a different kernel size to gain an ensemble–like results. The selected hyper parameter's impact is evaluated on a publicly available dataset named UCI HAR collected from smartphone sensors to perform six activities. A significant determinant of better results depends on the hyperparameter that has been chosen. The results demonstrated that tuning the hyperparameter of 1D CNN increased activity recognition accuracy.
A Comparative Text Classification Study with Deep Learning-Based Algorithms

As a well-known Natural Language Processing (NLP) task, text classification can be defined as the process of categorizing documents depending on their content. In this process, selecting classification algorithms and tuning classification parameters are crucial for efficient classification. In recent years, many deep learning algorithms have been used successfully in text classification tasks. This paper performed a comparative study utilizing and optimizing several deep learning-based algorithms. We have implemented deep neural networks (DNN), convolutional neural networks (CNN), long shortest-term memory (LSTM), and gated recurrent units (GRU). In addition, we performed extensive experiments by tuning hyperparameters to improve classification accuracy. In addition, we implemented word embeddings techniques to acquire feature vectors of text data. Then we compared our word embeddings results with traditional TF-IDF vectorization results of DNN and CNN. In our experiments, we used an open-source Turkish News benchmarking dataset to compare our results with previous studies in the literature. Our experimental results revealed significant improvements in classification performance using word embeddings with deep learning-based algorithms and tuning hyperparameters. Furthermore, our work outperformed previous results on the selected dataset.
Facial Motion Analysis beyond Emotional Expressions

Facial motion analysis is a research field with many practical applications, and has been strongly developed in the last years. However, most effort has been focused on the recognition of basic facial expressions of emotion and neglects the analysis of facial motions related to non-verbal communication signals. This paper focuses on the classification of facial expressions that are of the utmost importance in sign languages (Grammatical Facial Expressions) but also present in expressive spoken language. We have collected a dataset of Spanish Sign Language sentences and extracted the intervals for three types of Grammatical Facial Expressions: negation, closed queries and open queries. A study of several deep learning models using different input features on the collected dataset (LSE_GFE) and an external dataset (BUHMAP) shows that GFEs can be learned reliably with Graph Convolutional Networks simply fed with face landmarks.
An image encryption scheme based on block compressed sensing and Chen system​

Abstract Image encryption is a common way of information protection. However, most of the existing image encryption schemes have been restricted to low transmission efficiency and large memory consumption due to ciphertext redundancy. A high-efficiency and security image algorithm with Block Compressed Sensing (BCS) is designed in this paper to protect image information and reduce redundant data. For a start, the original image is sparsely processed by Discrete Wavelet Transform (DWT), and then pixel positions in the global scope are scrambled by chaotic sequence. Secondly, several image blocks are obtained by dividing scrambled images and sequentially the computational complexity can be cut down. Next, the image blocks are measured by a key-controlled partial Hadamard matrix to reduce the dimension. Finally, image blocks are merged and a proposed Randomly Selected Pixel Diffusion (PSPD) is used to further enhance security performance. Specifically, SHA-512 value of the original image is used for key generation, and then plaintext sensitivity is improved. The pseudo-random sequences that are required for scrambling and diffusion are produced by Chen system and logistic map. Simulation results show that this mechanism has low complexity, higher performance of security compared with others.
Research on System Construction and Strategy of Intelligent Sports in the Implementation of National Fitness

This paper studies the construction and development strategy of intelligent sports system in the context of Chinese National Fitness Program with methods of literature review and model construction. The research shows that there are four dilemmas in the implementation of intelligent sports in national fitness: data security, market monopoly, legal supervision, and product iteration. However, there are also three promoting factors in this regard, including policy guarantee, market demand, and industrial upgrading. Following the principles of scientificity, effectiveness, public welfare, and collaboration, this paper designs a system for intelligent sports in national fitness. The construction of the national fitness intelligent sports system mainly consists of four modules, including basic framework construction, function design, content design, and operation analysis. With the systematic analysis of the status quo of intelligent sports application in national fitness, this paper puts forward intelligent sports development strategies in the implementation of national fitness from four aspects: optimizing the top-level design of government, speeding up industrial transformation and upgrading, constructing market supervision mechanism, and establishing a talent training system.
Optimally preserving quantum correlations and coherence with eternally non-Markovian dynamics

Abstract We demonstrate, both analytically and experimentally, the usefulness of non-Markovianity for preserving correlations and coherence in quantum systems. For this, we consider a broad class of qubit evolutions, having a decoherence matrix separated from zero for large times. While any such Markovian evolution leads to an exponential loss of correlations, non-Markovianity can help to preserve correlations even in the limit t → ∞. In fact, under general assumptions, eternally non-Markovian evolution naturally emerges as the one that allows for optimal preservation of quantum correlations. For covariant qubit evolutions, we also show that non-Markovianity can be used to preserve quantum coherence at all times, which is an important resource for quantum metrology. We explicitly demonstrate this effect experimentally with linear optics, by implementing the optimal non-Markovian quantum evolution.
Analysis of Music Teaching in Basic Education Integrating Scientific Computing Visualization and Computer Music Technology

In the current music education, the music teaching method is too simple, boring, relatively backward, unable to attract the attention of the students, and the students gradually lose their interest in music courses. In basic education, music, as a highly practical subject, must create a good classroom atmosphere, make the classroom active, and enable students to better experience music, enjoy music, and like music. Therefore, in order to change this situation, this paper, based on scientific computing visualization, studies the application of computer music technology in basic education music teaching, and proposes a related computing method based on scientific computing visualization-image segmentation method. This review compares the spectrum extraction accuracy between the improved algorithm and the two traditional algorithms and finds that the average accuracy of the improved algorithm is 90.88%. It can be seen that the method proposed in this review is more suitable for the study of computer music technology in basic education music teaching. Applied research and most students are more interested in music teaching combined with computer music technology, so learning music teaching combined with computer music technology is very necessary. The development of computer music technology is closely related to the development of modern information technology.
Feature Engineering for development of a Machine Learning Model for Clash Resolution

To automate clash resolution tasks, it is important to capture domain knowledge for the Machine Learning (ML). One way to add domain knowledge is by training data that divides tasks into input and output variables. The selection of input variables that are most relevant to a task is an important step towards automation. In this paper, the authors detail framework that uses literature review, industry interviews, and Modified Delphi to capture domain knowledge for clash resolution. The features identified through this paper can in future be processed through Feature Selection, that can provide empirical evidence of why the selected features or set of features are important to ML algorithm. Data collection processes discussed in this paper is not finalized and is discussed to help provide readers with framework of the proposed systematic method. Factors considered when resolving clashes were identified through literature review (22 factors) and industry interviews (16 factors). 14 factors identified from the interviews had a similar matching factor in the literature reviewed, the other 2 factors were not mentioned in any publications found during the initial literature review. After comparing results from literature review and interviews, 13 factors were considered critical for automating clash resolution.
Evaluation of Basic Principles of Information Security at University Using COBIT 5

Information security issues commonly arise in a company and institution, including those in University. Some of the threats and attacks are unauthorized access, system user accountability, and logical and physical issues. This study reveals the obedience rate of the information security principle in Universitas Amikom Purwokerto and provides a recovery strategy. The domains being used were APO13, DSS5, and MEA3. The researcher employed a descriptive quantitative method by having documentation, interview, and administering a questionnaire to the respondents. The respondents were 83 employees who got selected by using the purposive sampling technique. The result shows that the capability level is in level 3, known as the established process. It means that the employees have applied the current procedure, even though they have not applied information security management. The proposed refinement strategy emphasizes the security policy, classification and asset management, physics and environment security, and business continuity management. The gap can be fixed by implementing the proposed refinement strategy. Future researchers may evaluate obedience based on the identified variables by keeping the standard in mind.
A Novel Deep Learning Architecture with Multi-Scale Guided Learning for Image Splicing Localization

The goal of image splicing localization is to detect the tampered area in an input image. Deep learning models have shown good performance in such a task, but are generally unable to detect the boundaries of the tampered area well. In this paper, we propose a novel deep learning model for image splicing localization that not only considers local image features, but also extracts global information of images by using a multi-scale guided learning strategy. In addition, the model integrates spatial and channel self-attention mechanisms to focus on extracting important features instead of restraining unimportant or noisy features. The proposed model is trained on the CASIA v2.0 dataset, and its performance is tested on the CASIA v1.0, Columbia Uncompressed, and DSO-1 datasets. Experimental results show that, with the help of the multi-scale guided learning strategy and self-attention mechanisms, the proposed model can locate the tampered area more effectively than the state-of-the-art models.
PoseNet based Model for Estimation of Karate Poses

In the domain of computer vision, human pose estimation is becoming increasingly significant. It's one of the most compelling areas of research, and it's gaining a lot of interest due to its usefulness and flexibility in a variety of fields, including healthcare, gaming, augmented reality, virtual trainings and sports. Human pose estimation has opened a door of opportunities. This paper proposes a model for estimation and classification of karate poses which can be used in virtual karate posture correction and trainings. A pretrained model, PoseNet has been used for pose estimation using the results of which the angles between specific joints are calculated and fed into a K-Nearest Neighbors Classifier to classify the poses. The results obtained show that the model achieves an accuracy of 98.75%.