Reviewerly/exHarmony huggingface dataset:
Entropy-Based Dynamic Rescoring with Language Model in E2E ASR Systems

Language models (LM) have played crucial roles in automatic speech recognition (ASR), whether as an essential part of a conventional ASR system composed of an acoustic model and LM, or as an integrated model to enhance the performance of novel end-to-end ASR systems. With the development of machine learning and deep learning, language modeling has made great progress in natural language processing applications. In recent years, efforts have been made to leverage the advantages of novel LM to ASR. The most common way to apply an integration is still shallow fusion because it can be easily implemented by zero-overhead while obtaining significant improvement. Our method can further enhance the applicability of shallow fusion without hyperparameter tuning while maintaining similar performance.
A Qualitative Case Study on the Legislative Movement Experience of Social Workers in Civil Society Organization

The purpose of this study is to suggest ways to improve social welfare practice by in-depth exploration of the experiences of social workers in civil society organizations in the welfare policy legislative movement. The participants of this study were 6 social workers who belonged to civil society organizations and have been active in the welfare policy legislative movement for more than 3 years, and data collection was conducted through individual in-depth interviews. The collected data were analyzed using the qualitative case study method. As a result of the study, 11 central themes and 4 categories were analyzed: 'Pursuit of structural change in society', 'Collective approach and response for social change', 'Multidimensional and strategic political action', and 'Building a foundation for lasting social change'. . Based on the research results, suggestions for improvement of social welfare practices were suggested.
CoSPARP: Collaborative and Social-Personality Aware Recommendation of Programmes

Globally, the selection of tertiary programmes for higher education in a university by prospective applicants is a daunting task. Different universities offer a wide range of programmes using different education delivery modes for teaching and learning. This creates information overload in the context of tertiary programmes. To tackle the information overload problem of tertiary programmes in the context of higher education institutions (HEIs), this paper, therefore, proposes a novel recommendation model called Collaborative and Social-Personality Aware Recommendation of Programmes (CoSPARP) for tertiary programme selection. CoSPARP utilizes a hybrid filtering system that incorporates the computation of similarities relating to the CF, personality traits, and the tie strength of users (prospective applicants) to generate effective programme recommendations for a tertiary programme applicant (TPA). The proposed CoSPARP recommendation method employs the above recommendation entities to create profiles of the TPAs as a basis of profile similarity for tertiary programme recommendations. Results of benchmarking experiments showed that CoSPARP overcomes cold-start due to the proposed (innovative) hybridization process. Additionally, using a relevant real-world dataset and suitable evaluation metrics such as precision, recall, and F-measure, CoSPARP produces more favourable outcomes in comparison to other state-of-the-art methods.
Multi-objective Dynamic Virtual Machine Consolidation Algorithm for Cloud Data Centers with Highly Energy Proportional Servers and Heterogeneous Workload

Present Dynamic VM Consolidation (DVMC) algorithms assume that optimal energy efficiency can be achieved via maximum load on Physical Machines (PMs). Such assumption has become invalid with the advent of the highly energy proportional PMs. Additionally, these algorithms consider only varying resource demand, ignoring dissimilarity of workload finishing time, aka the VM Release Time (VMRT), whereas both aspects are strongly associated with energy consumption. Consequently, traditional algorithms fail to proffer optimal performance under real Cloud scenarios. Although minimization of VM migration brings massive benefit for Cloud Data Center (CDC), it is complete opposite of what is needed to minimize energy consumption through DVMC. As such, our proposed multi-objective Stochastic Release Time aware DVMC (SRTDVMC) algorithm is unique in addressing concomitant minimization of energy consumption and VM migration in the presence of state-of-the-art PMs and heterogeneous workloads.
Steganalysis of Compressed Speech Based on Association Rule Mining

Currently, steganography based on compressed speech streams is gathering more and more attention. Meanwhile, it poses a huge threat to cyber security. As a counter technique, steganalysis can detect whether an illegal secret message is embedded in a compressed speech. To further improve the detection performance of current methods, a novel steganalysis method based on codeword association rule mining (CARM) is proposed in this paper. Firstly, we analyzed the spatiotemporal relationships between codewords in compressed speech. Secondly, the steganography-sensitivity codeword association rule base in training set was built based on the confidence change of codeword association rules before and after steganography. Thirdly, the steganography characteristic index and the corresponding dynamic partition threshold in validation set were computed to determine whether the compressed speech segment contains covert communication or not. Finally, comprehensive experiments were conducted to evaluate the performance of the proposed CARM steganalysis method under various conditions, including different association rule patterns, whether to use dynamic partition threshold, different embedding rates, different speech lengths, etc. The experimental results verify that CARM can achieve better performance than the comparison methods. In addition, the detection accuracy of CARM method can be improved significantly by using dynamic partition threshold at low embedding rates.
Human Behavior Recognition Based on Multiscale Convolutional Neural Network

The key problem in human behavior recognition is how to build a spatiotemporal feature extraction and classification network. Aiming at the problem that the existing channel attention mechanism directly pools the global average information of each channel and ignores its local spatial information, this paper proposes two improved channel attention modules, namely the space-time (ST) interaction module of matrix operation and the depth separable convolution module, combined with the research of human behavior recognition. Combined with the superior performance of convolutional neural network (CNN) in image and video processing, a multi-scale convolutional neural network method for human behavior recognition is proposed. Firstly, the behavior video is segmented, and low rank learning is performed on each video segment to extract the corresponding Low rank behavior information, and then these Low rank behavior information are connected on the time axis to obtain the Low rank behavior information of the whole video, so as to effectively capture the behavior information in the video, avoiding tedious extraction steps and various assumptions. The ability of neural network to model human behavior can be transferred and reused in networks with different structures. According to the different characteristics of data features at different network levels, two effective feature difference measurement functions are introduced to reduce the difference between features extracted from different network structures. Experiments on several public datasets show that the proposed method has a good classification effect. The experimental results show that the method has a good accuracy in human behavior recognition. It is proved that the proposed model not only improves the recognition accuracy, but also effectively reduces the computational complexity of output weights and improves the compactness of the model structure.
Emerging Cyber Security Threats During the COVID-19 Pandemic and Possible Countermeasures

The world suffering from COVID-19. In this situation, people are focusing on virtual or online modes of working, which can be done from home or anywhere. Cybersecurity has become the priority for all of us to protect data. This chapter mentions the most used cyber-attack techniques for stolen money and data from different sectors.
Privacy and Data Rights for Adult English as a Second Language (ESL) Students

In the USA, instructors need to ensure the user privacy and data rights of their adult English as a second language (ESL) students. The ways in which educational technology (EdTech) companies track user activity and sell user data to third parties raises ethical concerns for student privacy and data rights. ESL students are particularly vulnerable because of the vague language in privacy policies and user agreements, differences in terms of state surveillance, and insufficient user privacy and data protections. In addition to a discussion on the ethical concerns within EdTech and higher education, one method and two tools to help ESL instructors and educators are provided. These assist with ESL or international students in their classrooms as a means to evaluate EdTech tools and make decisions on whether to adopt or require a digital tool.
Construction of sharing mode based on VR technology under the background of big data

In the context of big data, the construction of sharing mode based on VR technology is sharing construction, which can make people feel comfortable and at ease. This mode will be able to provide users with various information through virtual reality, such as real-time monitoring, interactive experience and other functions. It can also provide users with a more realistic sense of scene than traditional media. The main function is to use big data technology and VR technology to realize sharing construction. The concept behind the project: it aims to combine big data technology (such as face recognition) with VR technology (such as head tracking). It is a system that allows users to experience the same things in real time, and also allows you to do things together in real time. This kind of thing has been done before, but this project aims to implement such a system from scratch. What are the advantages? What are its disadvantages? Advantages: the biggest advantage is that it will allow users to experience what they cannot see or hear alone, so that they can enjoy themselves.
Breast Cancer Diagnosis Using Histopathology and Convolution Neural Network CNN Method

High-performance computer tools have been more widely available, and deep learning systems that utilize deep neural networks have become increasingly common in many fields. Deep learning approaches based on convolution neural networks (CNN) have become more widespread as high-performance computer facilities have grown. An overview of the growth of deep learning models and a concise explanation of various learning approaches, such as supervised learning, trains the neural network using labeled data. Solid experiments are required in medical image analysis studies to prove the efficacy of proposed approaches. Many architectures, such as Pre-trained Networks and Convolution Neural Networks CNN, are employed to achieve breast cancer diagnosis. Various classification measures may be utilized, making comparison of the methodologies challenging. Medical screening methods have grown increasingly important in the detection and treatment of diseases. Early identification of breast cancer is regarded to be a crucial element in lowering women's mortality rates. Several different breast screening modalities are being investigated to improve breast cancer diagnosis. Histopathology is used in a current cancer detection and localization method that uses artificial intelligence to screen for breast cancer and identify the existence of tumors in the breast. This study focused on an experimental dataset that employed convolution neural network (CNN) techniques to detect and localize breast tumors (i.e., pre-trained CNN). CNNs are a powerful tool for solving real-world problems, and neural networks with learning algorithms are a promising new technology.
Applying Partition Method to Adopt Spark-Based Eclat Algorithm for Large Transactional Datasets

In discovering association rules and correlations from transactional datasets, frequent itemset mining (FIM) has emerged as a key data mining technique. Finding frequent itemsets becomes a tedious task for large-scale datasets because of the huge demand for computational resources. To meet the demands of the ever-growing data, recently, a variety of MapReduce-based algorithms on Hadoop and Spark clusters have been redesigned to achieve efficiency in terms of runtime and scalability. These are developed to overcome the limits imposed by data size, mining speed, and cluster synchronization. However, because of the iterative nature of the FIM algorithms, existing methods still lack scalability due to the large size of intermediate data produced, high disk I/O and network I/O, workload skewness, etc. In this paper, we propose a Spark-based algorithm named Parallel-Eclat (pEclat) that uses Eclat as the base method. The benefits of the vertical layout of the input dataset in combination with the partition method are utilized to improve the efficiency. It helps to limit the movement of key-value pairs across the cluster nodes (shuffle overheads) during iterations. It deals with the issues arising due to large transaction ID (TID) sets while processing large datasets in a vertical layout. The computational overhead imposed in computing the intersection of these large TID sets or diffsets is also managed. Extensive experiments with benchmark datasets have been conducted to explore the efficiency and scalability performance. Experimental results show that the proposed scheme outperforms other state-of-the-art methods. A significant speedup can be observed for some lower minimum support values.
Balanced odd-variable rotation symmetric Boolean functions with optimal algebraic immunity and higher nonlinearity

By modifying the support of the majority function, we propose a construction of balanced odd-variable rotation symmetric Boolean functions with optimal algebraic immunity. For n≥17, the nonlinearity of these constructed functions is the highest among all known rotation symmetric Boolean functions with optimal algebraic immunity. The algebraic degree can reach the maximum for some special numbers of variables. Moreover, simulations show that such functions have good fast algebraic immunity for some small numbers of variables.
Design of network security storage system based on under cloud computing technology

Computer networks are frequently utilised today. Along with the convenience, internet viruses and attackers are a concern point for users. Currently, it has been challenging to meet the demands of standard defence measures in the fight over network security threats. As a result, high-quality network storage systems can be progressively improved in order to satisfy the requirements of security of computer networks under the cloud computing paradigm. Its cloud administrations were open whenever and anyplace customers required them. As an outcome, shoppers are allowed to buy anything that IT administration they need, and they don't need to stress over how simple things can be managed. The far off server is utilized in another data stockpiling processing engineering that is viewed as an Internet age. Guaranteeing security, material at asset suppliers' destinations is a test that should be tended to in cloud innovation. Therefore, this research suggests developing development for security of information storing with a variety of tasks, wherein information is mixed and partitioned into various code outlines and dispersed across numerous supplier places, as opposed to dependence on a single supplier for information putting away. This assistance was used to enhance the new construction's anticipated security, adaptability, or unchanging quality.
Text Classification of Conversational Implicatures Based on Lexical Features

Following the guiding hypothesis in NLP, similar word frequency vectors may have similar implicatures, but some scholars are more inclined conversational implicatures cannot be obtained only through lexical features. To judge which view is more reasonable and explore the reasons for the divergence between them, whether conversational implicatures can be obtained only through lexical features is verified empirically. Main work of this paper includes: First, based on 600 corpora in the annotated dataset, the values of 20 lexical features of each corpus are obtained by automatic calculation. Second, meta-transformer of logistic regression for selecting features is adopted for feature selection and ranking. Third, after determining the features, the text is classified by the binomial logistic regression with the type of implicatures as labels. Fourth, results are tested for significance to identify relationships between variables. Experiments show that there is a statistical dependence between lexical features and conversational implicatures, and the text classification of implicatures can be performed only based on lexical features. In addition, the results of text classification will not be different due to the difference in context utterance or the type of implicature, and the text classification of implicatures only based on “response utterance” is more efficient.
High-quality facial-expression image generation for UAV pedestrian detection

For UAV pedestrian detection in the wild with perturbed parameters, such as lighting, distance, poor pixel and uneven distribution, traditional methods of image generation cannot accurately generate facial-expression images for UAV pedestrian detection. In this study, we propose an improved PR-SGAN (perceptual-remix-star generative adversarial network) method, which combines the improved interpolation method, perceptual loss function, and StarGAN to achieve high-quality facial-expression image generation. Experimental results show that the proposed method for discriminator-parameter update improves the generated facial-expression images in terms of image-generation evaluation indexes (5.80 dB in PSNR and 24% in SSIM); the generated images for generator-parameter update have high robustness against color. Compared to the traditional StarGAN method, the generated images are significantly improved in high frequency details and textures.
Ontology modeling method applied in simulation modeling of distribution network time series operation

Aiming at the difficulties in the realization of timing simulation in the digital twin environment, this article proposes an ontology modeling method applied to the simulation modeling of distribution network timing series operation. First, the two-layer ontology modeling architecture is introduced, and the power system domain structure ontology and the business-based application ontology are constructed. Second, based on the dynamic ontology modeling method, combined with the operational simulation business requirements, the sequential operation simulation ontology model is dynamically constructed. Finally, the effects of the modeling method in terms of simulation expression and modeling efficiency are shown through practical application example, and the modeling methods are compared and analyzed. The results provide a reference for the technical realization of power grid modeling in digital twin environments.
Collaborating eye to eye: Effects of workplace design on the perception of dominance of collaboration robots

The concept of Human-Robot Collaboration (HRC) describes innovative industrial work procedures, in which human staff works in close vicinity with robots on a shared task. Current HRC scenarios often deploy hand-guided robots or remote controls operated by the human collaboration partner. As HRC envisions active collaboration between both parties, ongoing research efforts aim to enhance the capabilities of industrial robots not only in the technical dimension but also in the robot's socio-interactive features. Apart from enabling the robot to autonomously complete the respective shared task in conjunction with a human partner, one essential aspect lifted from the group collaboration among humans is the communication between both entities. State-of-the-art research has identified communication as a significant contributor to successful collaboration between humans and industrial robots. Non-verbal gestures have been shown to be contributing aspect in conveying the respective state of the robot during the collaboration procedure. Research indicates that, depending on the viewing perspective, the usage of non-verbal gestures in humans can impact the interpersonal attribution of certain characteristics. Applied to collaborative robots such as the Yumi IRB 14000, which is equipped with two arms, specifically to mimic human actions, the perception of the robots' non-verbal behavior can affect the collaboration. Most important in this context are dominance emitting gestures by the robot that can reinforce negative attitudes towards robots, thus hampering the users' willingness and effectiveness to collaborate with the robot. By using a 3 × 3 within-subjects design online study, we investigated the effect of dominance gestures (Akimbo, crossing arms, and large arm spread) working in a standing position with an average male height, working in a standing position with an average female height, and working in a seated position on the perception of dominance of the robot. Overall 115 participants (58 female and 57 male) with an average age of 23 years evaluated nine videos of the robot. Results indicated that all presented gestures affect a person's perception of the robot in regards to its perceived characteristics and willingness to cooperate with the robot. The data also showed participants' increased attribution of dominance based on the presented viewing perspective.
Sensitivity analysis for a Bitcoin simulation model

Bitcoin is a popular and widely traded cryptocurrency. The Bitcoin blockchain technology makes it easy for users to conduct pseudo-anonymous financial transactions. However, it also facilitates criminals to secrete their actual identities from law enforcement agencies. Heuristic-based address clustering is the subject regarding Bitcoin de-anonymization. But no heuristic algorithm has a known or potential error rate due to the lack of ground truth. This paper uses sensitivity analysis to validate and verify a constructed Bitcoin simulation model. The evaluation and validation processes examine the model behavior and model outputs from multiple simulation runs to demonstrate fidelity and credibility. The analysis results show no model uncertainties, and the simulation model is stable and can effectively simulate Bitcoin transactions. With a reasonable number of nodes and transaction volumes in the simulated network, the simulation model can be used to verify the effectiveness of two widely used heuristic-based address clustering algorithms and measure the corresponding error rates.