Reviewerly/exHarmony huggingface dataset:
Improved <b>multi‐party</b> verification protocol with reduced computational overhead in cloud storage system

Abstract Although in industry and academia cloud computing provides several services, cloud storage is the most unavoidable one. Cloud storage allows remote data validation of the deployed user's data without keeping a local copy. A third‐party auditor is delegated by the users in order to examine the remote data integrity thereby reducing the burden of the users. Data integrity checking protects the user's data from tinkering and illicit access especially when they store data in public clouds such as AWS. Various schemes were proposed by several scholars which possess computational overhead and communication overhead during the integrity checking. Most of the schemes proposed were possessing computational overhead in the cloud server side which increases the financial overwhelming of the clients. In this article an improved multiparty computation architecture is proposed to minimize the computational overhead in the cloud server side while generating the proof of the data blocks in public clouds. The proposed scheme can be implemented on semi‐trusted cloud environments and allows a set of server nodes called ingestion nodes to compute on private inputs thus preserving the privacy and soundness of the computing data. The improved protocol also uses an indistinguishability obfuscation program with a message authentication code tag to minimize the verification overhead of the third‐party auditor as well as preserves the privacy and security of the stored data.
The Breakthrough and Development of Broadcasting and Hosting Talents under the Environment of Big Data Media Innovation

In the era of big data, new changes, the shift from “the vertical one-way structure” mode to “the complex two-way structure” mode, have taken place in the way of social communication, forming an interactive communication mode, the mainstream communication mode of the new media. Taking 151 junior college students majoring in broadcasting and art in Henan Province as samples, the survey was conducted from three dimensions, students’ career selection behavior of new media, their cognitive attitude towards new media and their professional learning expectation at college. Guided by the employment standards of new media organizations, colleges and universities should reintegrate the curriculum content and form the teams of double teachers. In addition, this paper re-examines the issues of the curriculum, training programs, talent output, which are optimized and constructed on the original basis, and explores how broadcasting and hosting art professionals can better adapt to the new employment environment of big data media so as to achieve the perfect connection between the demand for professionals and enterprises.
Two Stream Convolution Fusion Network based on Attention Mechanism

Abstract Due to the intuitiveness and low cost of image data, action recognition based on image data has always been the focus of action recognition research. The most of studies show that spatio-temporal two-stream network has very superior performance in the field of action recognition. However, traditional two-stream convolutional model needs multiple training, and the method of fusion two-stream models is too simple. In this research, a new two stream convolution fusion model is proposed, which can train our model end-to-end. At the same time, the basic model of spatio-temporal flow model is replaced by CBAM_Resnet. The model is improved on the basis of resnet_101, which gives weight to channel and spatial features, so as to improve the performance of the model. In this paper, the RGB image and the optical flow in X and Y directions are taken as the input features of the fusion network, and the final classification result of the model is obtained. Our method is validated on HMDB51 datasets, and the experimental results show that the accuracy of our model is improved by 4%.
Informativity, topicality, and speech cost: comparing models of speakers’ choices of referring expressions

This study formalizes and compares two major hypotheses in speakers’ choices of referring expressions: the topicality model that chooses a form based on the topicality of the referent, and the rational model that chooses a form based on the informativity of the form and its speech cost. Simulations suggest that both the topicality of the referent and the informativity of the word are important to consider in speakers’ choices of reference forms, while a speech cost metric that prefers shorter forms may not be.
From the Athenian silver to the bitcoin standard: Private money in a state-enforced free banking model

Currently numerous market-driven cryptocurrencies challenge to dethrone the state-controlled supply of fiat money. The outcome hinges on the old question of whether privately produced money with or without government is possible. This paper revisits the issue by looking for insights in the successful precedent of classical Athens. It is found that in this case the government defined a unit of account (Attic drachma), linked it to a commodity (silver), and used it in its domestic and foreign transactions. Βy drawing on its share of silver from the Laurion mines and other sources, the government acted as a major supplier of the said currency; and lastly, it enacted and enforced rules and regulations that aimed at safeguarding the integrity of the currency while leaving private markets free to co-determine the supply of the currency and credit, and the drachma to circulate in competition with all other foreign currencies within its borders. On this account and the evidence that the Attic drachma dominated for three centuries in the wider Mediterranean region, it is concluded that the application of the silver standard in classical Athens provides a most suitable framework of free banking after which to pattern the emerging regime of a privately produced bitcoin-based digital standard with amicable state sponsorship and enforcement.
Digital Pitfalls: The Politics of Digitalization in Bangladesh

Abstract The state-led investment in digital infrastructure under the ruling party’s political agenda of “Digital Bangladesh” has given rise to scholarly and policy debates, especially around issues of digital surveillance and media censorship. Such concerns have intensified during the COVID-19 pandemic. This article analyzes contemporary Bangladesh in the context of emerging trends related to the digitalization of society. In particular, I employ the concept of “digital pitfalls” to explore the state’s use of surveillance and the politics of fear to limit freedom of expression and silence critical voices in the digital age.
Adaptive time-bound access control for internet of things in fog computing architecture

This paper proposes an adaptive Time-Bound Attribute-Based Encryption Scheme (TB-ABE). TB-ABE is a fine-grained data access control scheme that maintains a secure data exchange environment between IoT devices. The lifetime of the system is divided into equal periods of time slots with unique encryption keys. TB-ABE combines Cyphertext-Policy Attribute Based Encryption with Time-bound keys. It only permits data users with attributes that satisfy the access policy set by the data owner to access the data for a specific period. It also proposes an efficient indirect revocation handling mechanism with minimal computations. The proposed scheme is implemented in Fog-Cloud computing architecture, where fog devices are used for partial outsourcing of encryption and decryption operations. This paper provides security and performance analysis for the proposed scheme in terms of computation, storage, communication and energy overheads. Analysis of proposed scheme proves its feasibility for limited-resource devices, while maintaining a secure communication between IoT devices.
Requirements Elicitation for Collaborative Systems: A Systematic Review

Requirements elicitation is one of the most essential activities in requirements engineering. A growing trend in collaborative applications has imposed a challenge for requirements engineers to properly elicit requirements. The aim of this work is to identify and present the elicitation techniques that have been applied in the collaborative systems development. We conducted a systematic literature review by surveying 2576 initial and 21 primary studies. The findings of this review revealed that interviews, observation and audio/screen recording were the most frequently used elicitation techniques. The elicitation techniques were also grouped considering two classification perspectives. The results obtained from this research may assist requirements engineers to identify the suitable techniques that can be adopted in future collaborative applications.
An Efficient Image Privacy-Preserving Scheme Based On Mixed Chaotic Map and Compression

In the digital modern era, multimedia security has turned into a major concern by the rapid growth of network technologies and digital communications. Accordingly, from the last few decades, the application of nonlinear dynamics and chaotic phenomena for multimedia data security earn significant attention. In this paper, an efficient image-encryption technique based on a two-dimensional (2D) chaotic system combine with the finite field of the specific order is introduced. The proposed scheme consists of four modules which are the separation of bits, compression, 2D chaotic map, and small S-boxes. Initially, the suggested scheme separates the pixels of the image into the least significant bits (LSB) and the most significant bits (MSB). Subsequently, the compression algorithm on these separated bits is applied and instantly transformed the MSB of the image into LSB. The key objective of the first module is to minimize the range of the pixel value up to eight times less than the original image, which consequently reduces the time complexity of the scheme. In the end, a 2D chaotic map is used to reshuffle the bytes to interrupt the internal correlation amongst the pixels of the image. At the tail end, the small S-boxes have been used to substitute the permuted image. The significance of small S-boxes plays a vital role to maintain the optimum security level, prevent computational effort, and reduced time complexity. The result of the suggested encryption system is tailor-made for instantaneous communication.
Method for Self-Healing Course of Action Revision (SCOAR)

This paper describes a method for dynamically generating and modifying a mission plan, sometimes referred to as a Course of Action (COA), in real-time, during mission execution. This process is called the Method for Self-Healing Course of Action (COA) Revision (SCOAR). A key contribution of SCOAR is that of revising a COA without having to generate the COA from scratch.
Collaboration Awareness Exploration through Visual Analysis of Group Traces

The increasingly powerful browsers and smart devices provide more and more convenience for people&#x0027;s lives. At the same time, remote collaborative work can effectively reduce the cost of teamwork. Team members use various tools on the browser to quickly share information and coordinate tasks. As collaborative activities among members become more frequent, the traces produced by their interaction are rapidly growing. Although they are easily overlooked, they will subtly affect every member&#x0027;s understanding of group collaboration over time. Therefore, it is of great significance to explore the group collaboration awareness though visual analysis of group traces. In this paper, three important classes of properties of trace that are necessary to describe the process of group collaboration are proposed and explained by detail. Besides, we present a group traces based visualization framework that can facilitate extracting insight from collaborative activities. For practical applications, the framework is applied and validated in the web-based collaborative working environment MCWE2.0.
Behavioral Phenotyping for Predictive Model Equity and Interpretability in STEM Education

Predictive models are increasingly being deployed in social and behavioral applications in support of decision making that directly affects people’s lives. Given such high stakes, it is important to develop models with interpretable and defensible features, with decisions that are unbiased toward historically marginalized groups. In this work we investigate the use of nonnegative matrix factorization (NMF) for generating interpretable features in an educational setting, combined with a standard bias mitigation algorithm for training predictive models. Our application in this work is predicting enrollment in STEM degrees, and improving fairness of our models through bias mitigation. We perform our experiments on the High School Longitudinal Study of 2009, and evaluate our results using both objective metrics and subjective interpretation of the NMF factors, or behavioral phenotypes. Our empirical results from these experiments suggest that NMF combined with bias mitigation can potentially be used to improve fairness measures while simultaneously aiding in interpretability.
Hybrid Tracking of Low SNR Targets

In general, target tracking follows one of two basic approaches: either the individual scan/stares of an area are thresholded and the detections are tracked, or all the energy received in the scan/stare can be utilized by a track-before-detect (TBD) algorithm. TBD is the preferred approach for tracking targets with low signal-to-noise-ratios (SNR), as thresholding above a set SNR may not detect the target and thresholding below a set SNR may produce far too many detections. There are several different types of TBD algorithms, one of which is the multitarget histogram-probabilistic multiple hypothesis tracker (H-PMHT). However, the issue with methods such as these is that they are primarily track maintenance algorithms and do not have a natural track initiation phase. A hybrid approach seeks to initialize the tracks using low thresholds and then maintain the track with TBD algorithms. It has been found that track initiation (as opposed to track termination) is responsible for discarding tracks unless the algorithm is sure they are valid. This implies that a great deal of false tracks are formed in initiation, and may use extensive resources to monitor and promote to confirmed tracks. Additionally, there are also TBD algorithms such as the Hough Transform, that have the initiation and tracking combined, which are also very computationally expensive. Therefore, to maintain computational efficiency and more accurately initiate tracks, we will introduce smart detections. In this paper we will utilize convolutional neural networks (CNN`s) that examine a chip of pixels surrounding low threshold detections to determine if the energy in the chip came from a target or background noise/phenomenology. The tracks will be initiated with these smart detections and maintained either with future smart detections or the H-PMHT TBD algorithm when the CNN cannot determine that the target is present. The CNN will err on the side of discarding ambiguous detections (meaning real detections will be missed) so that few false tracks are formed. The hybrid approach of using smart detections and PMHT for track maintenance will ensure the target is kept in track even though the CNN may miss detections.
Camouflage Learning

Federated learning has been proposed as a concept for distributed machine learning which enforces privacy by avoiding sharing private data with a coordinator or distributed nodes. However, information on local data might be leaked through the model updates. We propose Camouflage learning, a machine learning scheme that distributes both the data and the model. Neither the distributed devices nor the coordinator is at any point in time in possession of the complete model. Furthermore, data and model are obfuscated during distributed model inference and distributed model training. Camouflage learning can be implemented with various Machine learning schemes.
Extension of ITU-T P.1204.3 Model to Tile-Based VR Streaming Services

Tile-based virtual reality (VR) streaming services using head-mounted displays (HMDs) distribute high-quality images in the user viewing directions and distribute low-quality images in other areas. However, when users change their viewing directions, the low-quality images are displayed on the HMD and users perceive the quality degradations. In addition, quality degradations are perceived due to the display and its time (i.e., delay) of low-quality tiles as the viewing direction changes. Since these quality degradations severely impact quality, tile-based VR video quality needs to be evaluated. To do that, a model needs to be established to estimate quality by using the low-quality tiles and the display time of low-quality tiles in addition to the image quality degradations due to encoding. Since the ITU-T P.1204.3 model was developed for the quality estimation of 2D video, this paper proposes an extension of the ITU-T P.1204.3 model to the quality estimation of tile-based VR services by using the high- and low-quality tiles' quantization parameters and the delay. Subjective test results revealed that the proposed extended model estimates quality with high accuracy.
Credit Evaluation and Blockchain Consensus for Supply Chain

The crisis of trust in the supply chain platform is a serious issue. Blockchain provides the technology to solve the multi-party trust problem. So the integration of supply chain and blockchain makes it possible to build a trust economic system. Consensus mechanism is an important content in the blockchain. But traditional consensus protocols cannot effectively meet the requirements of supply chain management. Based on the practical Byzantine fault tolerance protocol in alliance blockchain, this paper introduces the credit evaluation mechanism and designs an efficient consensus protocol. We construct a multi-class SVM, and evaluate the credits of supply chain enterprises from four aspects of financial risk, operating and profitability capabilities, supply chain cooperation level and information sharing degree. Then, the enterprises with high credits are chosen to participate in the consensus process. Due to the reduction of communication costs, the method has better performance in the case of a large number of participating nodes. It is suitable for the networks with supply chain and blockchain. In addition, it has the characteristics of openness, sharing and tamper resistance, and thus it avoids unfair and fraudulent transactions in the supply chain platform.
Exploiting Extensive External Information for Event Detection Through Semantic Networks Word Representation and Attention Map

Event detection is one of the key tasks to construct knowledge graph and reason graph, also a hot and difficult problem in information extraction. Automatic event detection from unstructured natural language text has far-reaching significance for human cognition and intelligent analysis. However, limited by the source and genre, corpora for event detection can not provide enough information to solve the problems of polysemy, synonym association and lack of information. To solve these problems, this paper proposes a brand new Event Detection model based on Extensive External Information (EDEEI). The model employs external corpus, semantic network, part of speech and attention map to extract complete and accurate triggers. Experiments on ACE 2005 benchmark dataset show that the model effectively uses the external knowledge to detect events, and is significantly superior to the state-of-the-art event detection methods.
OCR with the Deep CNN Model for Ligature Script-Based Languages like Manchu

Manchu is a low-resource language that is rarely involved in text recognition technology. Because of the combination of typefaces, ordinary text recognition practice requires segmentation before recognition, which affects the recognition accuracy. In this paper, we propose a Manchu text recognition system divided into two parts: text recognition and text retrieval. First, a deep CNN model is used for text recognition, using a sliding window instead of manual segmentation. Second, text retrieval finds similarities within the image and locates the position of the recognized text in the database; this process is described in detail. We conducted comparative experiments on the FAST-NU dataset using different quantities of sample data, as well as comparisons with the latest model. The experiments revealed that the optimal results of the proposed deep CNN model reached 98.84%.
TF-IDF and Data Visualization For Syafie Madhhab Hadith Scriptures Authenticity

This paper describes a study on the adaptation of information retrieval (IR) using Term Frequency-Inverse Document Frequency and data visualization and to ease the searching for hadith process. The distribution of fabricated hadith that happened nowadays could negatively affect religion, Muslims, and Muslims' faith. The information technology's advent provides the platform for any applications and systems by indexing and IR to access the hadith's contents. However, the hadith's nature of scattered and disordered causes unpleasant usage of the indexing. The present IR method uses only the straightforward keyword search that does not highlight the details, significance, and relevance of the results retrieved. Therefore, this research expected to provide the progressive web-based application as the platform for users to locate hadith by entering any keyword as an input. It is also visualizing the most relevant source of hadith to enable users to make a better decision for them to read. The proposed solution's functionality and usability were tested, and as a result, it is proven that all the system features were functioning well and managed to receive 78.4% acceptance of system usability.
Fine-Grained Network Traffic Classification Using Machine Learning: Evaluation and Comparison

The network traffic classification problem is classified into coarse-grained traffic classification and fine-grained traffic classification. Previous researchers have successfully applied machine learning techniques to solve the coarse-grained traffic classification problem with very high accuracy. However, there are few studies associated with the fine-grained traffic classification problem because of an appropriate lack of labeled data of the application flows. This paper proposes a data collection method and investigates various unsupervised and supervised learning techniques in our collected data to solve the fine-grained traffic classification problem. Experimental results showed that the decision tree and random forest got the highest accuracy at 96%. The decision tree also had the lowest prediction time, which is well-suited to be implemented in real-time fine-grained traffic classification applications.
Multiple Hub-Driven Attention Graph Network for Scene Graph Generation

Existing works of scene graph generation usually learn global context information leading to excessive redundant information being considered. As a matter of fact, most relationships only exist around several hub objects. To exploit this fact, we propose a novel Multiple Hub-driven Attention Graph Network (MHAGN) for scene graph generation. Specifically, we first classify objects into multiple subgraphs according to the degree of correlations with the hub objects. Then, we design Multiple Hub-driven Attention (MHA) to drive context information to be passed within multiple subgraphs separately, and force objects to attend more to associated objects in the sub-graph. Finally, MHAGN captures precise and diverse context information by combining MHAs from multiple subgraphs and generates compact relation-aware representations for objects. Experimental results on popular benchmarks show that the proposed MHAGN achieves better performance over baselines on several datasets, especially in terms of alleviating the imbalance of predicted relationship categories.