Reviewerly/exHarmony huggingface dataset:
Addressing Security and Privacy in Cloud Computing: Blockchain as a Service

In recent times, the IT industry has been marked as a new era of software advancement with the introduction of cloud computing technology. This technology has become very essential for delivering infrastructure and has been adopted widely due to its performance and availability. Cloud computing allows us to access many applications and services over the Internet. A majority of the activities performed by this technology revolves around different forms of data, making it an invaluable resource. The importance and usage of data have reached new heights and like all precious resources, even data requires security and privacy. Unfortunately, this is an issue that concerns many cloud computing users as the data proprietors cannot have complete control over certain aspects like the location of the data storage and access permissions. Blockchain Technology, one of the emerging technologies can be used to complement the issues concerning cloud users. This technology provides features like decentralization, distribution, authenticity, immutability, trust, etc. that can be used to face the threats of data integrity. In this chapter, we will address the issues concerning the security and privacy of the data in the cloud model and how the features of the blockchain technology can enhance the integrity of this model. Although, there are certain limitations like high latency and low throughput, decreasing the practical utility of blockchain technology.
Discretionary Workplace Behaviors: The Effect of Communication Satisfaction on Workplace Deviance and OCB with the Mediation Role of Justice Sensitivity

Both workplace deviance (WD) and organizational citizenship behaviors (OCBs) are noted as being discretionary behaviors that are rooted in social exchange and responses to the environment. This study aims to investigate the mediation role of justice sensitivity between communication satisfaction and discretionary workplace behaviors. The statistical population consisted of 650 employees of Guilan Technical & Vocational Training Organization, and the optimal sample was determined by Cochran's formula to be 271. Questionnaires were distributed among the employees using the stratified random sampling method and statistical data were processed using SPSS 19 and Smart PLS 2. The results revealed that communication satisfaction has a positive effect on justice sensitivity, and justice sensitivity has a negative effect on workplace deviance. As well as, communication satisfaction has a direct effect on workplace deviance. So, justice sensitivity has a partial mediator role in significant impression of communication satisfaction to workplace deviance. The findings also indicate the justice sensitivity has a positive effect on OCB. But, communication satisfaction has no a direct effect on OCB. The results of this study yield that justice sensitivity has a full mediator role between communication satisfaction and OCB.
Securing Deep Learning Models with Autoencoder based Anomaly Detection

Deep learning models are on the rise in many scientific fields. Their ability to solve complex and nonlinear tasks has made them very popular. However, in comparison to physical models, they struggle with extrapolation. Hence, it is important that the input data in the production stage is similar to the data seen in training. Deviations to the training often occur in real world applications due to sensor delays and drifts, aging of the system, and communication errors such as noise. Especially in safety relevant applications, securing those models against these influences, which can be seen as anomalies, is essential.&#x0D; &#x0D; In the past, Autoencoders, especially Variational Autoencoders (VAEs), have been proven useful for anomaly detection. Many researches focus on improving the Autoencoder’s separation ability for an optimal set anomaly threshold. However, the setting of the threshold is not trivial and is crucial for a good anomaly detection. Setting the threshold optimal becomes especially challenging if the anomaly is unknown in the training process, which is often the case in a real world application.&#x0D; &#x0D; The proposed method combines a deep learning model with an Autoencoder. The input data is handed to the trained Autoencoder which reconstructs the input. If the data is similar to the training data, the Autoencoder should be able to reconstruct the input data accurately. Otherwise, an anomaly is suspected. The reconstruction and the original input data are both passed through the deep learning model, generating two predictions, which are then compared.&#x0D; &#x0D; For classification and reinforcement tasks with discrete result space the prediction of non-anomalous data should lead to the same class for both samples. For those tasks this allows us to sort out samples as anomalies for which the two results are not the same, hence the threshold becomes obsolete.&#x0D; &#x0D; For regression and continuous reinforcement tasks, the difference between the two predictions can be interpreted as a safety measure and is easier to grasp than the Autoencoder’s reconstruction error, which is typically used.&#x0D; &#x0D; The advantage of this method is the distinction between samples that can or cannot be handled by the subsequent application model instead of just deciding if the input is anomalous. This leads to a higher robustness of the joint model and a better usage of resources of the deep leaning model. Moreover, the Autoencoder and the deep learning model are trained separately which makes the training a lot more stable than using coupled training methods.&#x0D; &#x0D; The proposed method is proven on both, a classification and a regression task. For the classification, the publicly available UEA multivariate time series classification dataset was used. For regression, a dataset simulating a SCR catalyst as part of an automotive exhaust gas aftertreatment system was evaluated. For both tasks common anomalies such as delay and noise were applied to the data.
Solving the Story Cloze Test Using Graph Feature Extraction

The Story Cloze Test (SCT) is a dataset for evaluating story comprehension and common-sense knowledge of natural language systems. We explore combining the existing knowledge of word embeddings, parts-of-speech (POS) tagging, dependency parsing, and sentiment analysis to build graphs of sequential sentences. Then we extract features to be fed into a MultiLayer Perceptron for classification. Our results show organizing information within a graph and extracting features does add to the model’s ability to classify, increasing accuracy from 57.67% without graphs and 58.78% with graphs .
NeuSaver: Neural Adaptive Power Consumption Optimization for Mobile Video Streaming

Video streaming services strive to support high-quality videos at higher resolutions and frame rates to improve the quality of experience (QoE). However, high-quality videos consume considerable amounts of energy on mobile devices. This paper proposes NeuSaver, which reduces the power consumption of mobile devices when streaming videos by applying an adaptive frame rate to each video chunk without compromising the user experience. NeuSaver generates a policy that can determine the appropriate frame rate for each video chunk using reinforcement learning (RL). The RL model automatically learns the policy that optimizes the QoE goals based on previous observations. NeuSaver also uses an asynchronous advantage actor-critic algorithm to reinforce the RL model quickly and robustly. Streaming servers that support NeuSaver preprocess videos into segments with various frame rates, which is similar to the process of creating videos with multiple bit rates in dynamic adaptive streaming over HTTP. NeuSaver utilizes the commonly used H.264 video codec. We evaluated NeuSaver in various experiments and a user study through four video categories along with the previously proposed model. Our experiments showed that NeuSaver effectively reduces the power consumption of mobile devices when streaming video by an average of 16.14&#x0025; and up to 23.12&#x0025; while maintaining high QoE.
Cloud Security Concepts, Threats and Solutions: Artificial Intelligence Based Approach

Artificial intelligence is an advanced technology that helps companies to manage complex operations efficiently and improve productivity. In this generation, many business communities are using AI-based networks to maximize organizational performance but also to evade risks and threats. Security and privacy are both major issues with AI technology that contributes to data privacy and leads to hacking problems. The proposed study focused on security issues of AI technology and explored effective counter measures to improve data privacy. Privacypreserving cooperative filtering recommendation systems are aiming to supply users with correct recommendations to maintain bound assurances regarding the privacy of their information. This survey examines the latest literature on collaboratively secured collections, provides an overview of the sector and distinguishes significant contributions. The study also focuses and comforts to identify the kind of vulnerability they address and also the form of approach they use to handle it.
Classification of arrester defects based on Naive Bayesian inference

Abstract Under the special environment of high temperature, high humidity and high salt, the development of the deterioration or latent defects of the arrester is accelerated. It is difficult to identify the abnormal state of arrester in special environment, only relying on the monitoring index of arrester. Therefore, this paper proposes a kind of lightning arrester defect recognition technology based on Naive Bayesian, which extracts the key features that affect the operation state of lightning arrester in special environment, and calculates the prior probability of training samples and the posterior probability of test samples through naive Bayesian algorithm, so as to identify the type of lightning arrester defects. The feasibility and correctness of the proposed method are analyzed and verified by the actual monitoring and detection data.
A Study on the Public’s Perception on Police Officer’s Corruption

과거 공직부패는 생계형비리가 대부분이었고 하위직공직자들이 부패혐의에 많이 연루됐었다. 특히 경찰부패는 공직부패중에서 가장 많이 발생되고 있고 지금도 국민들의 불신과 냉소로 이어지고 있다. 경찰부패의 이론 중에 ‘썩은 사과 가설’이론이 있는데 이 이론은 경찰관 개개인의 윤리와 인성에 초점을 맞추고 있는데 부패 가능성이 있는 경찰관을 채용단계에서 도덕성을 걸러내지 못하고 조직에 흡수되어 마치 사과상자의 흠있는 사과가 썩어 전체사과를 썩게 만들듯이 경찰전체가 부패할 가능성이 심 하다는 것이다. 즉 부패발생 원인을 개인적 결함에 두고 있다고 본다. 그런데 경찰부패중 부패의 원인을 개인적 결함보다는 조직의 체제적인 측면을 비리원인으로 보는 것이 ‘구조원인 가설’이다 이것은 경찰관들 사이에서 끼리끼리 알아서 도와주고 눈감아주는 침묵의 규범으로 받아들여지고 있고 조직 내부에서 묵시적 관행으로 내려온 것이 사실이다. 이와 같이 개인적인 측면,구조적인 측면, 전체적인 측면에서 경찰부패가 발생하고 있다고 본다. 본 연구는 시민들의 경찰부패인식을 분석하였는데 과거의 경찰부패 당사자인 경찰관 의 부정부패 연구에서 벗어나 시민관점에서 연구하고자 노력하였다. 본 연구는 다음과 같은 방법으로 고찰하였다. 첫째, 시민들의 경찰부패 인식원인이 무엇인지 고찰하였다. 둘째, 경찰부패의 개인적 제도적 조직 문화적 관점에서 고찰하였다. 셋째, 원인에 대한 개선방안은 무엇인지 살펴보았다.In the past, corruption in public offices was mostly a livelihood-related corruption scandal, with many lower-level officials implicated in corruption charges. In particular, police corruption is the most frequent occurrence of official corruption and continues to lead to public distrust and cynicism. Among the theories of police corruption is the theory of rotten apples theory . The rotten apples theory is that the entire police force is highly likely to become corrupt, as the apple box s flawed apple rotters away from the recruitment process of police officers who are potentially corrupt in focusing on individual personality of police officers. In other words, the cause of corruption lies in a personal defect especially personality character, moral, ethical factors. And Other theory is systematic approach. It is a theory that the cause of corruption among police officers is not a personal defect but rather a systematic cause of organization. This is considered a standard of self-help and blind silence among police officers, and has been downgraded to a tacit practice within the organization. This study analyzed the public s perception of police corruption and tried to study it from a citizen s perspective, instead of from the corruption study of police officers who were involved in police corruption. This study was considered in the following ways: First, we discussed what causes citizens to recognize police corruption. Second, it was considered from a personal, institutional, organizational and cultural standpoint of police corruption. Third, we looked at what was the way to improve the cause.
Box filtering for real-time curvature scale-space computation

Abstract Curvature scale-space (CSS) analysis is an important technique for contour-based object recognition in digital images. To compute the CSS for a given contour, it is systematically convolved (smoothed) with Gaussians with increasing standard deviation. The convolutions are computationally expensive, especially for large and high resolution contours, but can be approximated using box filtering (also known as mean and average filtering). Together with running sums, the convolutions can be accelerated by 2–3 magnitudes without significant loss of precision. Nonetheless, box filtering has not been systematically investigated in connection with CSS computation. In this work, we present a theoretical and experimental analysis of different box-filtering techniques in this context and conclude which is the most efficient implementation. Based on this, the CSS of a contour can be computed in real time with high precision.
Number of research papers about Agricultural production, Meat, and Egg During COVID-19 Pandemic: Does it Changed than Before?

COVID-19 pandemic limits many physical activities of the society in public areas.The agricultural production sector is one of the important sectors in supporting the country's nutrition demand.Research is a way to maintain the productivity of the agricultural sector.This study has a purpose to analyze if pandemic COVID-19 has an impact on the number of papers about agriculture recorded by Scopus.This study uses a quantitative with secondary data analysis approach in collecting data.The samples are paper on the Scopus website contains keywords "agriculture& meat", and "agriculture&egg".the period of samples are during 2019-2020.The data are analyzed using Vosviewer software to show the institution with the most related topic paper.The result shows that some papers are decreasing while others are increase based on the document type.For paper related to "agriculture" meat", article as full type research paper increases along with review type paper.For a paper about "agriculture & egg", conference type paper and review increase, while other decreases.It can be concluded that the type of paper along with the topic affects the institutions' productivity in publishing papers.
Perun: Confidential Multi-stakeholder Machine Learning Framework with Hardware Acceleration Support

Confidential multi-stakeholder machine learning (ML) allows multiple parties to perform collaborative data analytics while not revealing their intellectual property, such as ML source code, model, or datasets. State-of-the-art solutions based on homomorphic encryption incur a large performance overhead. Hardware-based solutions, such as trusted execution environments (TEEs), significantly improve the performance in inference computations but still suffer from low performance in training computations, e.g., deep neural networks model training, because of limited availability of protected memory and lack of GPU support. To address this problem, we designed and implemented Perun, a framework for confidential multi-stakeholder machine learning that allows users to make a trade-off between security and performance. Perun executes ML training on hardware accelerators (e.g., GPU) while providing security guarantees using trusted computing technologies, such as trusted platform module and integrity measurement architecture. Less compute-intensive workloads, such as inference, execute only inside TEE, thus at a lower trusted computing base. The evaluation shows that during the ML training on CIFAR-10 and real-world medical datasets, Perun achieved a $$161\times $$ to $$1560\times $$ speedup compared to a pure TEE-based approach.
Channel parameters-independent multi-hop nondestructive teleportation

A multi-hop nondestructive teleportation scheme independent of channel parameters based on Bell pairs is presented, where the coefficients of the quantum channel are unknown to all the communication nodes. With Bell measurement and channel matching technology the unknown channel parameters can be eliminated probabilistically with the help of the intermediate nodes. Then the source node Alice can teleport an unknown state to the remote destination node Bob. In our scheme the teleportation is generalized first to the scenario independent of channel parameters, which makes the requirement of quantum channel reduced. Our scheme still preserves the initial unknown state even if this teleportation fails. Moreover, performance analysis shows that our scheme has a higher communication efficiency.
The Combination of AI, Blockchain, and the Internet of Things for Patient Relationship Management

The use of the Internet of Things (IoT) in the healthcare sector has shown to be a promising solution to reduce the workload of doctors and provide better service to patients. However, shared data may be subject to theft or misuse due to the security issues on various devices. Moreover, transparency among stakeholders, confidentiality, and micropayments need to be addressed. The objective of this work is to use federated learning over blockchain data generated from IoT devices with the usage of zero-knowledge proof or confidential transactions. The proposed architecture ensures the user a level of privacy set by them while making sure of sharing relevant insights with the concerned parties.
Detecting Faulty Elements in Antenna Array Using Extreme Learning Machine

This paper proposes a method for diagnosing the position and number of failed elements in antenna array using Extreme Learning Machine. Elements failure will directly change the aperture distribution and seriously affect the normal use of the antenna array. Therefore, it is urgent and necessary to detect the faulty elements when antenna array is damaged. A large number of experiments have shown that Extreme Learning Machine algorithm can be used to locate the faulty elements of antenna arrays from the distortion pattern. Most of the failed elements could be successfully diagnosed by adopting the algorithm.
Which hyperparameters to optimise?

Most GNNs for molecular property prediction are proposed based on the idea of learning the representations for the nodes by aggregating the information of their neighbour nodes in graph layers. Then, the representations can be passed to subsequent task-specific layers to deal with individual downstream tasks. Facing real-world molecular problems, the hyperparameter optimisation for those layers are vital. In this research, we focus on the impact of selecting two types of GNN hyperparameters, those belonging to graph layers and those of task-specific layers, on the performance of GNN for molecular property prediction. In our experiments, we employed a state-of-the-art evolutionary algorithm (i.e., CMA-ES) for HPO. The results reveal that optimising the two types of hyperparameters separately can improve GNNs' performance, but optimising both types of hyperparameters simultaneously will lead to predominant improvements.
Heritage Augmented Reality Applications for Enhanced User Experience

This paper presents the continuous research in enhanced learning in heritage with user experience evaluation. The objective is to evaluate learning methodologies with students of architectural degree with visualization technologies applied in projects for the understanding of heritage. The projects are developed with the aim of enriching how we experience our patrimony using Augmented Reality techniques. The main contribution of this research is the design of experiences that embrace new technologies and tourism, to offer innovative projects for the enhancement of cultural and natural heritage. The present research shows the results of the last workshop. The purpose was to develop an AR video game applied to Architectural Heritage for children. The analysis of user experience is based in quantitative and qualitative surveys. Results extracted from the evaluation reveals that ‘motivation’ category was the best valued by the students. The main conclusions are that AR technology applied in applications for visitors of heritage sites improves the user experience, PBL methodology enhances learning with the use of new technologies and evaluation methods demonstrates the adoption of AR is acceptable by students in development of innovative projects.
IoT Processing Layer Security

This chapter introduces security threats, security requirements and common techniques of security protection in IoT processing layer. A typical IoT processing layer is cloud computing, hence the security of cloud computing can represent the IoT processing layer security. Cloud computing security includes cloud computing platform security, security of cloud computing services, and data security in cloud computing environment. As in other information systems, access control techniques are described as part of IoT processing layer security. Cloud computing platform also uses the technique of virtual computing, and the security mechanism of virtual computing is introduced.
Electronic Speckle Pattern Interference technique for measuring thickness of metallic nano thin films

Abstract Electronic Speckle Pattern Interference (ESPI) is a non-contact optical technique generally used for the measurement of surface deformations of various kinds. With the help of phase retrieval algorithms, phase of the deformed object is calculated in this method which can be then converted into displacement information. This paper demonstrates the use of ESPI method to measure the thickness of some nano-sized metallic thin films. Calculated thickness values are in good agreement with the values measured via Profilometer. The least thickness (z-axis) measured by this method is ~ 52 ± 3.1 nm. The z-axis resolution of the used method is at par with other expensive microscopy techniques like atomic force microscopy. Therefore, a low-cost alternative to expensive microscopy techniques is presented in this paper as far as z-axis is concern. Surface roughness of thin films can also be calculated using retrieved surface profiles and they are also found to be in the nanometer range.
Surface roughness estimation using structured light projection

How we visually perceive non-emissive objects in our surrounding depends on the interaction of light with the optical characteristics of the materials that comprise them. The macroscopic surface roughness can also influence the appearance through shadowing and interreflections. In this work, we use a structured light scanner to estimate the surface structure of near-planar surfaces, namely of printing textiles. We compare our scans, both qualitatively and quantitatively, to those from a commercial highgrade profilometer based on the confocal principle. We achieve comparable results to the profilometer on samples with moderately complex surfaces. We discuss the possible reasons for errors in the scans of complex surfaces, thus providing guidelines for robust depth estimation. This comparison can help other researchers build more robust acquisition setups by understanding and minimizing the errors inherent to the reconstruction methods.