Reviewerly/exHarmony huggingface dataset:
DiffForward: On Balancing Forwarding Traffic for Modern Cloud Block Services via Differentiated Forwarding

Modern cloud block service provides cloud users with virtual block disks (VDisks), and it usually relies on a forwarding layer consisting of multiple proxy servers to forward the block-level writes from applications to the underlying distributed storage. However, we discover that severe traffic imbalance exists among the proxy servers at the forwarding layer, thus creating a performance bottleneck which severely prolongs the latency of accessing VDisks. Worse yet, due to the diverse access patterns of VDisk s, stable traffic and burst traffic coexist at the forwarding layer, and thus making existing load balancing designs inefficient for balancing the traffic at the forwarding layer of VDisk s, as they are unaware of and also lacks the ability to differentiate the decomposable burst and stable traffic. To this end, we propose a novel traffic forwarding scheme DiffForward for cloud block services. DiffForward differentiates the burst traffic from stable traffic in an accurate and efficient way at the client side, then it forwards the burst traffic to a decentralized distributed log store to realize real-time load balance by writing the data in a round-robin manner and balances the stable traffic by segmentation. DiffForward also judiciously coordinates the stable and burst traffic and preserves strong consistency under differentiated forwarding. Extensive experiments with reallife workloads on our prototype show that DiffForward effectively balances the traffic at the forwarding layer at a fine-grained subsecond level, thus significantly reducing the write latency of VDisks.
Physical security risk management and enhancements

In the current business environment, organizations of all sizes, regardless of their area of expertise, are vulnerable to a substandard level of understanding the implications of delayed or missing preventive actions, corroborated with the prevalence of security incidents. The article aims to provide an in-depth analysis, visually facilitated by the Fault Tree Analysis of physical access vulnerabilities, to propose solutions based on the authors’ multidisciplinary expertise in order to tighten security protocols, and to assist organizations to be in compliance with international regulations and standards. Consequently, organizations will improve their reputation and customer assurance, avoid security violation-related fines, strengthen their internal confidence, boost the marketing and sales prospects, and ensure business continuity, by implementing security controls based on procedures and policies as part of an ISMS, providing the necessary training for all personnel, and enforcing multi-layer physical security systems, with a focus on restricted areas, facilitated by the usage of a redundant structure comprised of thermal cameras, movement sensors and improved Velostat keypads.
A protocol to establish trust on biometric authentication devices

Abstract One of the most extensively utilized mechanisms for person authentication is a system built using biometric‐based authentication. However, many applications use biometric authentication devices that do not support any device authentication mechanisms. As a result, a counterfeit scanning device may be substituted for the genuine one. Non‐authentic biometric authentication devices may perform some additive / subtractive or malicious functions. This paper proposes a technique for establishing trust in biometric authentication devices. The device authentication procedure is essential to build trust in biometric authentication devices such that non‐genuine biometric authentication devices are not used, which may compromise the loss of authentication factor and its replay when the genuine user is not getting authenticated. The protocol uses strong cryptographic mechanisms to authenticate the biometric authentication device with the application server and includes mechanisms for protection against the tampering of biometric templates and to prevent replay attacks. We also perform a formal verification using BAN logic to demonstrate that the proposed protocol meets the defined objectives. The proposed protocol can be used with any biometric authentication device to achieve the same objectives.
Novel heuristic-based hybrid ResNeXt with recurrent neural network to handle multi class classification of sentiment analysis

Abstract Present-day, interdisciplinary research is increasing in social network-related applications, and it is a daily routine activity in every human life. So, sentiment analysis (SA) based on opinion mining is the most sophisticated concept in the well-known social network environment. Different machine learning methods were implemented to extract different text label features in SA, and all of those methods can detect whether a given text is positive or negative based on the text features. Analysis of sentiment has been suffering from inaccuracies while using machine learning and sentiment-based lexical methods dependent on domain-specific problems. Multi-class SA is an expensive task where memory, label samples, and other parameters are insufficient. So, we propose and implement a novel hybrid model which is a combination of ResNeXt and recurrent neural framework (NH-ResNeXt-RNF) to explore multi-class sentiment from textual features. This framework investigates the polarity of words connected to a specific domain across the entire dataset and eliminates noisy data in an unsupervised manner using pre-processing. Optimization is required to perform efficient multi-class classification to reduce the effort associated with annotation for multi-class SA via unsupervised learning. The proposed model performance is evaluated on two data sets namely: Amazon and Twitter. We increase the accuracy of the sentiment of polarity on each sentence present in the data set. Experimental results of the proposed approach give better and more efficient multi-class ( positive, negative, very positive, neutral and highly negative) domain-specific sentiment than traditional approaches related to supervised, semi-supervised, and unsupervised domains. The proposed hybrid model accuracy is 96.5% and 95.37% for Amazon and Twitter datasets respectively.
Visual language integration: A survey and open challenges

With the recent development of deep learning technology comes the wide use of artificial intelligence (AI) models in various domains. AI shows good performance for definite-purpose tasks, such as image recognition and text classification. The recognition performance for every single task has become more accurate than feature engineering, enabling more work that could not be done before. In addition, with the development of generation technology (e.g., GPT-3), AI models are showing stable performances in each recognition and generation task. However, not many studies have focused on how to integrate these models efficiently to achieve comprehensive human interaction. Each model grows in size with improved performance, thereby consequently requiring more computing power and more complicated designs to train than before. This requirement increases the complexity of each model and requires more paired data, making model integration difficult. This study provides a survey on visual language integration with a hierarchical approach for reviewing the recent trends that have already been performed on AI models among research communities as the interaction component. We also compare herein the strengths of existing AI models and integration approaches and the limitations they face. Furthermore, we discuss the current related issues and which research is needed for visual language integration. More specifically, we identify four aspects of visual language integration models: multimodal learning, multi-task learning, end-to-end learning, and embodiment for embodied visual language interaction. Finally, we discuss some current open issues and challenges and conclude our survey by giving possible future directions.
Multi-layer encryption flexible integrating algorithm

Advanced concepts are applicable in practical activities when a certain level of technological development is reached. Accordingly, the reusability of some of the data encryption algorithms is feasible, because of the new concepts in cryptography and of the fast nowadays computers. Starting from a study dedicated to several ciphers, the paper presents a framework meant to integrate encryption methods, considering a multilayer encryption structure. The encryption methods were implemented as classes in order to identify their main features, to check their flexibility and to assess the opportunity to consider them in upper level approaches. The resulting software applications are parameterized and they consider several variants of a given cipher, for comparison reasons. Regarding the rotating grille encryption method, important aspects are presented regarding the creation of the grills. Several tests were run and an application which integrates the aforementioned classes is under development.
Randomness testing original software instrument

Semi-numerical analysis, cryptography, operations research, gaming, gambling and simulation models where is desirable an unpredictable result rely on random numbers. In order to produce long sequences of random numbers, several ideas may be used: physical sources of randomness, arithmetic operations, chaos theory, machine learning which uses physical sources of randomness, functions included in the libraries of the programming languages, hash functions. Regardless the method to produce pseudo-random numbers, the results of the random numbers generators (RNGs) must be tested in order to provide a high degree of randomness. The paper presents several methods to produce pseudo-random numbers and proposes a framework to test the degree of randomness using criteria originating from Monte Carlo methods and from NIST requirements. A software application was developed in C++ for testing files containing pseudo-random numbers. Its flexible structure allows the addition of new modules that implement new testing methods. The files containing sequences of pseudo-random numbers were generated using original applications developed in C++ GNU Multiple Precision, GNU Octave, Java, Python, PHP, JavaScript.
Effectiveness of web-based mindfulness program on college students with social network addiction

Aims: In this study a web-based mindfulness cultivation program was implemented to investigate its effectiveness on the addiction symptoms and negative emotions of college students with social network addiction. Methods: A total of 66 students were recruited and randomly divided into the intervention group or the control group. Participants in the intervention group received a web-based mindfulness cultivation program including the group training and self-cultivating. The primary outcome was addiction level, and the secondary outcome was anxiety, depression and perceived stress. Repeated measures analysis of variance were utilized to analyze the differences between the control and intervention group over the course of the intervention and the follow-up. Results: There were significant interaction effects on addiction level (F = 39.39, P &lt; .00), anxiety (F = 31.17, P &lt; .00), depression (F = 37.93, P &lt; .00) and perceived stress (F = 22.04, P &lt; .00). Conclusion: A web-based mindfulness cultivation program could improve the addiction level and negative emotions of college students with social network addiction.
Compositional Prompting Video-language Models to Understand Procedure in Instructional Videos

Instructional videos are very useful for completing complex daily tasks, which naturally contain abundant clip-narration pairs. Existing works for procedure understanding are keen on pretraining various video-language models with these pairs and then fine-tuning downstream classifiers and localizers in predetermined category space. These video-language models are proficient at representing short-term actions, basic objects, and their combinations, but they are still far from understanding long-term procedures. In addition, the predetermined procedure category faces the problem of combination disaster and is inherently inapt to unseen procedures. Therefore, we propose a novel compositional prompt learning (CPL) framework to understand long-term procedures by prompting short-term video-language models and reformulating several classical procedure understanding tasks into general video-text matching problems. Specifically, the proposed CPL consists of one visual prompt and three compositional textual prompts (including the action prompt, object prompt, and procedure prompt), which could compositionally distill knowledge from short-term video-language models to facilitate long-term procedure understanding. Besides, the task reformulation enables our CPL to perform well in all zero-shot, few-shot, and fully-supervised settings. Extensive experiments on two widely-used datasets for procedure understanding demonstrate the effectiveness of the proposed approach.
Fast speech encryption algorithm based on Arnold 3D chaotic system

This paper presents a fast encryption algorithm for speech signals using the Arnold 3D and the Tent Map chaotic transformations. Arnold 3D chaotic map is known for the property of simultaneously combining pixel scrambling with changing their value in the process of image encryption. The aim of the architecture was to obtain a simple, robust algorithm with short encryption and decryption times, as well as resistant to attacks. All these features make it suitable for real-time communication. The algorithm consists in segmenting the speech signal, then transforming this frame into a cubic three-dimensional structure of samples. The samples are inserted sequentially in vertical slices. Arnold 3D transformation is applied to this structure on horizontal slices, each slice having its own encryption key generated by a Tent Map transformation.
Expanding on the concept of quality

This paper expands on the concept of quality and links it with the concept of fractal by means of Theory of secrecy, taking into account that the additive model of quality has virtually the same mathematical expression as one of the models presented by C. E. Shannon in his work regarding Theory of secrecy. Based on the theory of Shannon, a possible enciphering model is constructed. This model generated to implement the theory of Shannon is explained for the general case and then based on this, a particular model is presented. This particular model is then approached using a set of C++ subroutines which implement the subject model. This paper shows that, if quality evolution is properly analyzed, quality could be to a certain extent, also predicted. Furthermore, this prediction of quality can be used in a more general way, namely to predict evolution in a structure exhibiting more features or dimensions, such as, for example, a fractal structure such as presented in [3], [4] and [5]. Taking into account that the very same weighted addition method or model is used for both, to generate the encryption in a secrecy system, but also to calculate the quality value of a product or service exhibiting a certain number of quality features, one can link these two in an appropriate way, such as will be presented in this paper. Thus, quality evolution properly analyzed, could lead also to a prediction to a certain extent, of the respective quality. This paper extends the work of C. E. Shannon and is based on his papers: Communication Theory of Secrecy Systems and A Mathematical Theory of Communication The theory presented and developed by Shannon has been successfully used and has been proven during the last seventy years since its discovery. This theory is practically used in this paper by means of particular encryption/ decryption C++ algorithms, which show how to put this theory into practice in order to make predictions about quality or other abstract or material natural systems. These C++ subroutines may very well be extended for a more general, complicated case of encryption, similar to the enciphering case presented in this paper. The algorithms presented in this paper show how an encryption model should be approached using a certain software. This paper also expands previous results obtained by author in [3], [4] and [5].
Control room design for subjective audio critical listening

In the context of music production “Listening” is the main tool for judging the aesthetic quality of audio recordings. However, for our ears to correctly judge artistic and technical quality, they must “work” in controlled conditions. Overall, this paper puts emphasis on the main methods of simulating acoustical parameters for a given room, acquiring real-world data by measuring the room in question, underlining issues, possible solutions, and establishing the accuracy of the methods used. This research aims to design and develop a listening room for proper subjective assessment of release ready program materials, with the principal focus on further development of intelligent music production tools.
Security in remote access, based on zero trust model concepts and SSH authentication with signed certificates

This paper aims to resume the information on the various ways in which security can be achieved when it comes to accessing external resources from the outside through company devices or personal devices. When planning security policies, it is essential to start with the idea that anything outside is exposed to the risk of infection. We will analyze the Zero Trust model and other security concepts in order to use them for remote access. The model has been designed to adapt to the modern needs of the workforce and employers, including remote work in terms of maximum cyber security, minimizing the risk of resource exploitation (people, devices, applications or data). In order to make the transition to such a model, this paper will implement and test a solution for remote management of resources (applications, servers), which implements some of the principles of Zero Trust architectures, starting from the classic version SSH (Secure Shell) access. The solution involves accessing, securing and authorizing a secure virtual machine via SSH, and then securing access to production resources. The technical mechanism by which these operations are performed presupposes that after authentication on the buffer machine a user certificate is automatically requested which will be signed by a certification authority. This certificate contains the group to which the operator belongs so that, by presenting this certificate, it is allowed to authenticate, authorize and audit access to the resources that will be managed by the user in that session.
A Meta-analysis on the Effects of Coding Education Programs

The purpose of this study is to encyclopedically analyze and generalize the effects of coding education programs. The study subjects are 36 academic journals and dissertations published until December 2022. The research method used is meta-analysis to determine the effect size. The results are as follows: first, the overall effect size of coding education programs is 0.570, indicating a moderate effect size. Second, there are significant differences in the results of coding education programs, with creativity showing the largest effect size, followed by achievement and problem-solving, computational thinking, and motivation and attitude. The use of robots or devices has a larger effect size compared to not using them. Third, there are significant differences in the types of coding used in coding education programs, with unplugged coding having the largest effect size at 0.865, followed by text coding and block coding. This study is significant as it provides comprehensive research trends and useful information for designing coding education programs and offers ideas for future studies.
A Study on the Perception of Basic Vocational Competencies of High School Students in Fisheries and Maritime through Importance-Performance Analysis(IPA)

This study aims to find out the basic vocational skills developed as part of the NCS (National Competency Standards) construction project, which is the core of the current specialized high school curriculum. To this end, the Importance-Performance Analysis (IPA) method, which was first developed in the field of corporate marketing and is used in research across social sciences, was used. The results of the Importance-Performance Analysis (IPA) matrix for each area of basic vocational ability of students at specialized high schools in fisheries and maritime are as follows. Due to its high importance and low implementation, "concentration", which is an area to focus on in the future, was found to be self-development ability (a) and resource management ability (f). In addition, "low ranking", an area that students should continue to strive to recognize importance and improve their performance, was found to be communication ability (b), mathematical utilization ability (c), and organizational understanding ability (j).
Research and Optimization of Neural Network Accelerator Based on NVDLA

Convolutional neural network (CNN) has been widely used in image recognition and natural language processing. Its computing and storage overhead are also increased with the advent of massive amounts of data and complex models, Neural processing accelerators are in urgent need. NVDLA is a convolutional neural network accelerator developed by NVIDIA company for deep learning reasoning. It has very good acceleration performance, and its code is completely open source, which is very helpful for our in-depth research. However, due to the lack of multi-functional tools, DLA is also faced with great limitations. Firstly, the number of operators it supports is insufficient, and the network model it can support is very limited, so it can only realize simple classification function; Secondly, the official compiler can only support Caffe framework, but can not support some current mainstream frameworks, such as pytorch, tensorflow, etc; Finally, before the hardware reasoning of the network, we need to quantify the designed network to meet the data format requirements of the hardware. This paper explores the research and optimization of NVDLA-based neural network accelerators, we design a heterogeneous acceleration system of FPGA and CPU, and Let the CPU handle the parts that the NVDLA accelerator cannot handle, which expands the function of NVDLA.The task division of heterogeneous operation is implemented by segmenting network to minimize the processing part of CPU, thusmprove the utilization of NVDLA and minimize the data communication overhead. We also design a conversion method between the network model and data under the common framework and the Caffe. Finally, the quantization interface and the quantization calibration table are designed to meets the data bit requirements of hardware reasoning. The system is verified by LeNet-5, ResNet-50 and YOLOv3.
A Northern Goshawk Optimization Algorithm With Improved Elite Opposition-based Learning

This research presents a better version Northern Goshawk Optimization (NGO) algorithm. The algorithm adopts the elite opposition-based learning(EOBL) strategy, which overcomes the disadvantage of the original algorithm that the population distribution is not random in the initial stage of exploration. In IERNGO, the currently found global optimal solution is set as the elite solution. After calculating the inverse solution based on the elite solution, combining with the position updating equations in the original NGO algorithm, the better solution of the individual in the group is produced. An S-operator is proposed to give the individual a chance to select a new position in the searching space. These two mechanisms have improved the variety of groups and effectively and directly alleviated the main problems. Experiments on 10 complex functions in the IEEE CEC_2020 competition and 23 classical benchmark functions show that the presented algorithm outperforms the selected algorithms and solves optimization problems more effectively.