Reviewerly/exHarmony huggingface dataset:
Efficient Perfectly Secure Computation with Optimal Resilience

Secure computation enables n mutually distrustful parties to compute a function over their private inputs jointly. In 1988 Ben-Or, Goldwasser, and Wigderson (BGW) demonstrated that any function can be computed with perfect security in the presence of a malicious adversary corrupting at most \(t< n/3\) parties. After more than 30 years, protocols with perfect malicious security, with round complexity proportional to the circuit’s depth, still require sharing a total of \(O(n^2)\) values per multiplication. In contrast, only O(n) values need to be shared per multiplication to achieve semi-honest security. Indeed sharing \(\varOmega (n)\) values for a single multiplication seems to be the natural barrier for polynomial secret sharing-based multiplication.
Profile and Rating Similarity Analysis for Recommendation Systems Using Deep Learning

Recommendation systems are going to be an integral part of any E-Business in near future. As in any other E-business, recommendation systems also play a key role in the travel business where the user has to be recommended with a restaurant that best suits him. In general, the recommendations to a user are made based on similarity that exists between the intended user and the other users. This similarity can be calculated either based on the similarity between the user profiles or the similarity between the ratings made by the users. First phase of this work concentrates on experimentally analyzing both these models and get a deep insight of these models. With the lessons learned from the insights, second phase of the work concentrates on developing a deep learning model. The model does not depend on the other user's profile or rating made by them. The model is tested with a small restaurant dataset and the model can predict whether a user likes the restaurant or not. The model is trained with different users and their rating. The system learns from it and in order to predict whether a new user likes or not a restaurant that he/she has not visited earlier, all the data the trained model needed is the rating made by the same user for different restaurants. The model is deployed in a cloud environment in order to extend it to be more realistic product in future. Result evaluated with dataset, it achieves 74.6% is accurate prediction of results, where as existing techniques achieves only 64%.
Sensor Data Based Anomaly Detection in Autonomous Vehicles using Modified Convolutional Neural Network

Automated Vehicles (AVs) reform the automotive industry by enabling real-time and efficient data exchange between the vehicles. While connectivity and automation of the vehicles deliver a slew of benefits, they may also introduce new safety, security, and privacy risks. Further, AVs rely entirely on the sensor data and the data from other vehicles too. On the other hand, the sensor data is susceptible to anomalies caused by cyber-attacks, errors, and faults, resulting in accidents and fatalities. Hence, it is essential to create techniques for detecting anomalies and identifying their sources before the wide adoption of AVs. This paper proposes an anomaly detection model using a Modified-Convolutional Neural Network (M-CNN) with Safety Pilot Model Deployment (SPMD) dataset. The M-CNN model comprises specifically trained layers involving the ReLU activation function for feature extraction and detection of AV anomalies. Furthermore, the Adam is used as the optimization algorithm to train the model. The detection accuracy of the proposed model is compared with Isolation Forest (IF) and Support Vector Machine (SVM). The experimental result reveals that the proposed model outperforms the other models with an accuracy of 99.40% in AV anomaly detection.
Quantum Bell nonlocality as a form of entanglement

Bell nonlocality describes a manifestation of quantum mechanics that cannot be explained by any local hidden variable model. Its origin lies in the nature of quantum entanglement, although understanding the precise relationship between nonlocality and entanglement has been a notorious open problem. In this paper, we resolve this problem by developing a dynamical framework in which quantum Bell nonlocality emerges as special form of entanglement, and both are unified as resources under local operations and classical communication (LOCC). Our framework is built on the notion of quantum processes, which are abstract quantum channels mapping elements between fixed intervals in space and time. Entanglement is then identified as a quantum process that cannot be generated by LOCC while Bell nonlocality is the subset of these processes that have an instantaneous input-output delay time. LOCC pre-processing is a natural set of free operations in this theory, thereby enabling all entangled states to activate some form of Bell nonlocality. In addition, we generalize the CHSH witnesses from the state domain to the domain of entangled quantum measurements, and provide a systematic method to quantify the Bell nonlocality of a bipartite quantum channel.
Hierarchical Motion Planning for Autonomous Driving in Large-Scale Complex Scenarios

Motion planning algorithms, an essential part of the autonomous driving system, have been extensively studied. However, in large-scale complex scenarios, how to develop an optimal path to comply with the requirements of smoothness and safety remains a vital issue. In this study, a hierarchical search spacial scales-based hybrid A* (termed as HHA*) motion planning method is proposed, capable of efficiently generating smooth and safe paths. The proposed HHA* method covers two stages. First, the search space is divided on a coarse scale to generate local goals. Subsequently, the novel heuristic function and exploration strategies are adopted in the fine-scale search space to generate paths like that with a human driver guided by the local goals. Moreover, with the usage of the clothoid, the smoothness of the generated path is improved to be G <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> –continuous (i.e., curvature continuous), which fits the vehicle’s kinematic constraints without the need for later smoothing. Numerous experimental results from the simulation and on-road tests indicate that the proposed method can effectively perform motion planning that meets smoothness and safety in large-scale complex scenarios.
PoW and PoS and Related Applications

Consensus protocols can help guarantee security and sustainability to maintain the decentralized Blockchain system used in different scenarios and applications due to their different features. There are two main prevalent protocols, Proof of Work (PoW) used in Bitcoin and Proof of Stake (PoS) used in Ethereum. This paper reviews these two consensus protocols, PoW and PoS, and their related improvements and applications. We first introduce the basic concepts, implementations, advantages, and disadvantages of PoW and PoW and make a detailed comparison between these two protocols. We find that though providing security when honest participants are the majority, PoW may lead to excessive power consumption. While PoS is relatively insecure and leads to monopoly (centralization), though energy efficient by discarding complex hash operations. Therefore, some newly proposed consensus protocols have attempted to find a pleasant compromise between these two classic protocols. After a detailed analysis, we investigate the researches related to the defect improvements of PoS and PoW. Finally, we describe various applications of PoS and PoW, including cryptocurrencies and information storage.
Support Vector Machine based Remote Sensing using Satellite Data Image

Shadow makes the Image and Video critical and hence it is very mandatory step to remove the shadows especially in terms of Satellite Images. The method detects and subtract shadows from a Satellite Image. Our method’s objective is to detect and remove Shadow from Satellite Images by learning the features of the super pixels (pixels with same characteristics) and the edges of the image using Supervised Machine Learning model namely Support Vector Machines(SVM).Using a statistical model like CRF model to create the mask of the shadow. The features of shadow are extracted from the Images followed by a SVM classifier to precisely collect the shadow matte followed by the collection of shadows. This Shadow removal method is performed using Bayesian formulation and evaluated by Gray Level Co-Occurrence Matrix.
xAFCL: Run Scalable Function Choreographies Across Multiple FaaS Systems

Most well-known cloud providers offer advanced support for serverless applications that goes beyond single function invocation by enabling developers to build entire workflows, which are known as serverless function choreographies (FCs). Current support for FCs by many FaaS systems uncovered important problems including maximum number of parallel function executions, unexpected considerable delays, and provider lock-in. These limitations can result in longer execution times or even failure to execute individual functions or entire FCs. To overcome some of these limitations, we introduce a scalable middleware service xAFCL that can schedule and execute different functions of the same FC across multiple FaaS systems (currently supporting all top five providers). In order to support scheduling under xAFCL, we introduce a novel FaaS model which estimates the completion time of functions by considering FaaS system limitations, submission delays, and overheads for executing functions. Experimental results demonstrate that xAFCLs FaaS model shows very low inaccuracy of up to 2.9% for AWS and 20% for IBM for real-life BWA data-bound FC that uses S3. Moreover, xAFCL outperforms an earliest start time (EST) scheduler by up to 43% for makespan and 2.7x for throughput.
Impacts of the Application of Virtual and Augmented Reality on Teaching-Learning Processes in Engineering Courses

Cognitive approaches to teaching generate learning through the interaction between the subject and object of study. One of the strategies to create this interaction is related to the application of virtual and augmented reality in the teaching-learning processes. Through a systematic literature review, this work aims to describe the approaches used to measure the impacts on student learning who used virtual reality (VR) and augmented reality (AR) in the teaching-learning processes of engineering courses, the impacts on learning, and student satisfaction. The surveys showed that 70% of research analyzed, students who used virtual reality or augmented reality learned more, and 90% of the research described that students who used virtual or augmented reality were more satisfied with the new approach than the traditional teaching approach. The conclusion is that there are positive impacts, in the vast majority of cases, on learning and the satisfaction of students who use virtual or augmented reality in the teaching-learning processes applied in engineering courses.
SCA Strikes Back: Reverse-Engineering Neural Network Architectures Using Side Channels

This article proposes an attack that steals a neural network using side-channel attacks. — <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Jeyavijayan “JV” Rajendran, Texas A&amp;M University</i>
Accounting for “the social” in contact tracing applications: The paradox between public health governance and mistrust of government's data use

This essay adopts three accounts (sociological, neoliberal, and cybernetic) of “the social” to get a clearer picture of why there is a barrier faced by the government when implementing contact tracing mobile applications. In Hong Kong's context, the paradox involves declining trust of the government's protection of data privacy and growing concern about data surveillance since the 2019 social unrest I argue that exploring the idea of sociality is valuable in that it re-reconfigures the datafication of pandemic control by revealing different sets of social relations, particularly the asymmetrical power relation between the government and its people. The refusal to download or use the mobile app also shows that the public has a faith in human agency and human resistance in data-saturated cities.
Telecentres’ contribution to women's empowerment in rural areas of South Africa

The telecentre model was established to provide information and communication technology (ICT) skills to empower people, reduce poverty and unemployment in poor areas. Our study explored the relevance of telecentres as an ideal mechanism to empower women. Our study investigated how telecentres can contribute to women’s empowerment by analysing five telecentres in rural settings of the Western Cape, South Africa. A qualitative approach was adopted using semi-structured in-depth interviews, focus group discussions, and participant observation to collect data from 39 participants. The Dimensions of Empowerment Theory was used to describe the various empowerment outcomes. These include economic, social, informational, political, and cultural empowerment. The findings showed barriers hindering women’s utilization of telecentres included a lack of computer skills, education, language barriers, gender usage patterns, unemployment, and a lack of awareness. Our study contributes to the ICT4D/HCI gender field and suggests that ICT policymakers consider using telecentres for women empowerment.
Time-Distributed Feature Learning in Network Traffic Classification for Internet of Things

The plethora of Internet of Things (IoT) devices leads to explosive network traffic. The network traffic classification (NTC) is an essential tool to explore behaviours of network flows, and NTC is required for Internet service providers (ISPs) to manage the performance of the IoT network. We propose a novel network data representation, treating the traffic data as a series of images. Thus, the network data is realized as a video stream to employ time-distributed (TD) feature learning. The intra-temporal information within the network statistical data is learned using convolutional neural networks (CNN) and long short-term memory (LSTM), and the inter pseudo-temporal feature among the flows is learned by TD multi-layer perceptron (MLP). We conduct experiments using a large data-set with more number of classes. The experimental result shows that the TD feature learning elevates the network classification performance by 10%.
HIN-RNN: A Graph Representation Learning Neural Network for Fraudster Group Detection With No Handcrafted Features

Social reviews are indispensable resources for modern consumers' decision making. For financial gain, companies pay fraudsters preferably in groups to demote or promote products and services since consumers are more likely to be misled by a large number of similar reviews from groups. Recent approaches on fraudster group detection employed handcrafted features of group behaviors without considering the semantic relation between reviews from the reviewers in a group. In this paper, we propose the first neural approach, HIN-RNN, a Heterogeneous Information Network (HIN) Compatible RNN for fraudster group detection that requires no handcrafted features. HIN-RNN provides a unifying architecture for representation learning of each reviewer, with the initial vector as the sum of word embeddings of all review text written by the same reviewer, concatenated by the ratio of negative reviews. Given a co-review network representing reviewers who have reviewed the same items with the same ratings and the reviewers' vector representation, a collaboration matrix is acquired through HIN-RNN training. The proposed approach is confirmed to be effective with marked improvement over state-of-the-art approaches on both the Yelp (22% and 12% in terms of recall and F1-value, respectively) and Amazon (4% and 2% in terms of recall and F1-value, respectively) datasets.
Scheduling Bag-of-Tasks in Clouds Using Spot and Burstable Virtual Machines

Cloud providers offer several types of Virtual Machines (VMs) in diverse markets, with different guarantees in terms of availability and reliability. Among them, the most popular market models are the on-demand and the spot. On-demand VMs are allocated for a fixed cost per time, and their availability is ensured during the whole execution. On the other hand, in the spot market, VMs are offered with a huge discount, but their availability fluctuates according to cloud’s current demand that can terminate or hibernate a spot VM at any time. Furthermore, to cope with workload variations, cloud providers have also introduced the concept of burstable VMs, which can burst up their CPU performance during a limited period of time. In this work, we present the Burst Hibernation-Aware Dynamic Scheduler (Burst-HADS), a framework that executes Bag-of-Tasks applications with deadline constraints by exploiting both spot and on-demand burstable VMs, aiming at minimizing both the monetary cost and the execution time. Performance results on Amazon EC2 show that Burst-HADS reduces the monetary cost and meets the application deadline even in spot hibernation scenarios, when compared to other approaches from the related literature which uses only spot and non-burstable on-demand instances.
Analysis of Security-Based Access Control Models for Cloud Computing

Access control has become the most necessary requirement to limit unauthorized and privileged access to information systems in cloud computing. Access control models counter the additional security challenges like rules, domain names, job allocation, multi hosting and separation of tasks. This paper classifies the conventional and modern access control models which has been utilized to restrain these access flaws by employing a variety of practices and methodologies. It examine the frequent security threats to information confidentiality, integrity, data accessibility and their approach used for cloud solutions. This paper proposed a priority based task scheduling access control (PbTAC) model to secure and scheduled access of resources &amp; services rendered to cloud user. PbTAC model will ensure the job allocation, tasks scheduling and security of information through its rule policies during transmission between parties. It also help in reducing system overhead by minimize the computation and less storage cost.
Faster Repetition-Aware Compressed Suffix Trees Based on Block Trees

Suffix trees are a fundamental data structure in stringology, but their space usage, though linear, is an important problem in applications. We design and implement a new compressed suffix tree targeted to highly repetitive texts, such as large genomic collections of the same species. Our suffix tree builds on Block Trees, a recent Lempel-Ziv-bounded data structure that captures the repetitiveness of its input. We use Block Trees to compress the topology of the suffix tree, and augment the Block Tree nodes with data that speeds up suffix tree navigation. Our compressed suffix tree is slightly larger than previous repetition-aware suffix trees based on grammars, but outperforms them in time, often by orders of magnitude. The component that represents the tree topology achieves a speed comparable to that of general-purpose compressed trees, while using 2–10 times less space, and might be of independent interest.
Comparative Analysis of Steering Angle Prediction for Automated Object using Deep Neural Network

Deep learning's rapid gains in automation are making it more popular in a variety of complex jobs. The self-driving object is an emerging technology that has the potential to transform the entire planet. The steering control of an automated item is critical to ensuring a safe and secure voyage. Consequently, in this study, we developed a methodology for predicting the steering angle only by looking at the front images of a vehicle. In addition, we used an Internet of Things-based system for collecting front images and steering angles. A Raspberry Pi (RP) camera is used in conjunction with a Raspberry Pi (RP) processing unit to capture images from vehicles, and the RP processing unit is used to collect the angles associated with each image. Apart from that, we've made use of deep learning-based algorithms such as VGG16, ResNet-152, DenseNet-201, and Nvidia's models, all of which were trained using labeled training data. Our models are End-to-End CNN models, which do not require extracting elements from data such as roads, lanes, or other objects before predicting steering angle. As a result of our comparative investigation, we can conclude that the Nvidia model's performance was satisfactory, with a Mean Squared Error (MSE) value of 0.3521. But the Nvidia model outperforms the other pre-trained models, even though other models work well.
Natural language processing of head CT reports to identify intracranial mass effect: CTIME algorithm

The Mortality Probability Model (MPM) is used in research and quality improvement to adjust for severity of illness and can also inform triage decisions. However, a limitation for its automated use or application is that it includes the variable "intracranial mass effect" (IME), which requires human engagement with the electronic health record (EHR). We developed and tested a natural language processing (NLP) algorithm to identify IME from CT head reports.We obtained initial CT head reports from adult patients who were admitted to the ICU from our ED between 10/2013 and 9/2016. Each head CT head report was labeled yes/no IME by at least two of five independent labelers. The reports were then randomly divided 80/20 into training and test sets. All reports were preprocessed to remove linguistic and style variability, and a dictionary was created to map similar common terms. We tested three vectorization strategies: Term Frequency-Inverse Document frequency (TF-IDF), Word2Vec, and Universal Sentence Encoder to convert the report text to a numerical vector. This vector served as the input to a classification-tree-based ensemble machine learning algorithm (XGBoost). After training, model performance was assessed in the test set using the area under the receiver operating characteristic curve (AUROC). We also divided the continuous range of scores into positive/inconclusive/negative categories for IME.Of the 1202 CT reports in the training set, 308 (25.6%) reports were manually labeled as "yes" for IME. Of the 355 reports in the test set, 108 (30.4%) were labeled as "yes" for IME. The TF-IDF vectorization strategy as an input for the XGBoost model had the best AUROC:-- 0.9625 (95% CI 0.9443-0.9807). TF-IDF score categories were defined and had the following likelihood ratios: "positive" (TF-IDF score > 0.5) LR = 24.59; "inconclusive" (TF-IDF 0.05-0.5) LR = 0.99; and "negative" (TF-IDF < 0.05) LR = 0.05. 82% of reports were classified as either "positive" or "negative". In the test set, only 4 of 199 (2.0%) reports with a "negative" classification were false negatives and only 8 of 93 (8.6%) reports classified as "positive" were false positives.NLP can accurately identify IME from free-text reports of head CTs in approximately 80% of records, adequate to allow automatic calculation of MPM based on EHR data for many applications.