Reviewerly/exHarmony huggingface dataset:
Securing Smart Environments with Authentic Execution

Securing Smart Environment applications is a huge concern nowadays. The use of small,embedded devices to connect the physical to the digital world is a big challenge in terms of security, due to the limited hardware resources available and the real-time constraints the system must ensure. As an example, a paper released in 2018 by the US Department of Homeland Security highlights numerous, potential issues and threats regarding the use of new Internet of Things (IoT) technologies in Precision Agriculture and Smart Farming.To address this problem, the concept of Authentic Execution was proposed in 2017 by Noorman et al., which described a secure framework for a distributed, event-driven application. This solution relied on the use of Trusted Computing (TC) and Trusted Execution Environments (TEE) to achieve strong security properties such as confidentiality, integrity and authenticity of software and data. An implementation of this approach was provided for Sancus, an embedded TEE that extends the TI MSP430 CPU.However, this solution is not sufficient in a real scenario: in fact, most IoT systems need a cloud backend, to gather data and perform expensive computation. Hence, an heterogeneous system is needed in such applications, composed by both embedded devices and remote servers.Therefore, this Master’s Thesis describes an implementation of the Authentic Execution approach for Software Guard eXtensions (SGX), a TEE included in recent Intel processors.We developed a framework that allows a developer to write only the logic of his own application, as well as providing a high-level description of the system (e.g., to specify how the modules are connected to each other). The framework implicitly handles the execution of a module inside an isolated, trusted environment (called enclave) and the communication between different modules. Moreover, the framework is entirely written in Rust, a modern, fast programming language that provides by design numerous features to enhance security, such as protection against many memory-management vulnerabilities (e.g., buffer overflows), as well as a safe use of threads and concurrency. Along with the SGX implementation, we provide tools to easily deploy a heterogeneous, distributed application on a shared infrastructure.In addition to the Trusted Execution of their modules, Sancus and SGX can bring further advantages if used together: while the former is able to perform Secure I/O, the latter provides a feature called data sealing, to securely store data on disk. Particularly, Secure I/O is used to establish trusted paths between high-end computation nodes and I/O devices.Finally, we prove the effectiveness of Authentic Execution by implementing a prototype for a smart irrigation system. A security evaluation shows that this approach ensures strong confidentiality and integrity guarantees. From a performance point of view, instead, tests reveal that our solution is widely acceptable for an irrigation system, whereas it might not be feasible for applications with stricter real-time constraints.
Interesting Receptive Region and Feature Excitation for Partial Person Re-identification

Partial person ReID tasks have become a research focus recently for it is challenging but significant in practical applications. The major difficulty within partial person ReID is that only incomplete and even noisy person features are available for extraction and matching, which puts forward higher requirement to model robustness. To settle down this problem, our paper proposes a novel IRRFE-ReID model, which includes two major innovations, the interesting receptive region selection module and the feature excitation module. The former module can adaptively select the region of interest from original image while the latter one is applied to distinguish representative person features and weight them during matching. Proven by ablation analysis, these two modules are embeddable and considerably conducive for partial person ReID tasks. Additionally, our IRRFE-ReID model achieves the state-of-the-art performance in two mainstream partial person datasets, PartialReID and PartialiLids, with its Rank1 reaching 85.7% and 74.8% respectively.
Research on the Conditions of Non-Face-to-Face Writing Instruction of Korean Language Teachers

This study suggests effective ways to teach writing in the post COVID-19 era by analyzing the condition of non-face-to-face writing instruction in Korean class. For doing this, the study analyzed the results of a survey of 130 in-service Korean language teachers on the status, method, content of writing instruction and their perception on the writing instruction. The results are as follows. First, Korean language teachers mainly instructed writing by using face-to-face and non-face-to-face methods parallelly amid the COVID-19 pandemic. Second, in non-face-to-face situations, Korean language teachers mainly instructed writing by using videos in online classes. Third, Korean language teachers addressed “writing expressing friendship and emotions” the most in non-face-to-face writing instruction. Fourth, in non-face-to-face situations, Korean language teachers tended to perceive the reviewing process as the most difficult process and the content generating process as the easiest process to instruct in the writing process. Finally, Korean language teachers tended to perceive face-to-face writing instruction as the most effective method for writing instruction.
Securing the Internet of Things with Security-by-Contract

Smart homes, industry, healthcare, robotics; virtually every market has seen the uprising of Internet of Things (IoT) devices with different degrees and nuances. IoT devices embody different desira ...
The experiences of opioid sourcing cryptomarketusers.

Cryptomarkets, or drug marketplaces found on thedark web, began in 2011, with the creation of Silk Road (Bilton,2018; Ormsby, 2014). Since that time, cryptomarkets haveproliferated, and as of 2018, they have been publicly associatedwith furthering the ongoing opioid epidemic in the United States(Associated Press, 2018). However, despite this claim, a study thatfocused on the experiences of cryptomarket users who source theiropioids from cryptomarkets did not exist, and cryptomarketliterature that discussed opioid sourcing and opioid use fromcryptomarkets remained scacre. The purpose of this study was tobegin filling that literature gap, and to capture the experiencesof opioid sourcing cryptomarket users. A constructivist groundedtheory approach (Charmaz, 2014) was taken to explore theexperiences of 15 opioid sourcing cryptomarket users from variousparts of the United States through semi-structured interviews.Findings indicate that participants use cryptomarkets to avoidstreet opioids containing fentanyl, are dissatisfied withhealthcare options for treating substance use issues, struggle withphysical and emotional maladies that encourage opioid use, and areconstantly attempting to change stigma surrounding opioid use.Implications for healthcare workers in substance use settings, andfor those policymakers and law enforcement entities who areregulating illegal opioid use within the United States, arediscussed in the final chapter.
US, China and the race for digital currency

Paypal co-founder Max Levchin said the United States runs the risk of losing the digital currency battle if it doesn't fully digitize the dollar.
Development Of Framework To Recognize Akhara-Muni Character Using Ann

Pattern recognition problem always be a tough task because of the different type of shape of the characters and it became more challenging when ancient script is going to recognize. Various types of Ancient scripts are available in India and Akhara-muni is one of them scripts. To recognize the Akhara-muni character with good performance is the main task of the study. Optical character recognition technique is used to recognize the Akhara-muni characters where this study is used zoning and density method to extract the features and ANN to classify the Akhara-muni character. Achieved accuracy by the proposed system is 87.24%, which is better than the previous Akhara-muni character recognition system.
Research Guides: Streaming Videos: Video Licenses

Kathryn A. Martin Library provides access to thousands of streaming video titles that are licensed for course use by UMD faculty, staff, and students.
Semantic Data Extraction Using Video Analysis

Semantic data extraction using video analysis aims at making use of the tremendous amount of video data captured by CCTV cameras daily and performing analysis on it [1]. Apart from solving the storage constraints, it provides a substitute for manual surveillance, saving lots of time and human effort. In this chapter, we have considered the fundamental example of extracting the vehicle number of vehicles from a CCTV captured video. Here we take the video feed as input and proceed through the four stages of analysis to reach the goal. The four steps include conversion of video to a continuous image sequence, image segmentation, character segmentation and character recognition, with some preprocessing at every stage to improve the quality of input data available for the next step. In the era of the growing complexity of problems along with the increasing functionalities offered by video analytics, this can be the most basic and generic solution supporting lots of applications.
A Phase Compensation Method for Surface Reconstruction of Moving Object

Recently, the surface reconstruction of moving objects based on phase shifting profilometry (PSP) becomes increasingly important in the academic and industrial fields. Accurate phase of fringe patterns on captured images should be compiled to yield high quality reconstruction results. However, it is still hard to ensure the quality of phase map when the object is moving. Besides, moving object can introduce phase error to PSP, which will lead to reconstruction errors, for instance, waviness errors on the surface. The existing methods of reducing phase error in dynamic scene have certain disadvantages. Such as highspeed photography that requires enormous expenses and advanced hardware, phase calibration which is inexpensive but computationally intensive. This paper introduces a phase compensation method, which strikes a balance between computational complexity and reconstruction accuracy. Experiments demonstrate that, the proposed method can significantly reduce computational complexity comparing with current method, while achieving adequate accuracy.
The credibility of the news content on social media for Algerian journalists: a study within the framework of the structural model of credibility: DOI: 10.46988/IJMMC.03.01.2021.04

The reliability of social networks has sparked wide debate and debate among academics and professional specialists and at the level of the mass community, on the scale of different Arab societies, especially with the increasing public dependence on these networks to provide them with news media about the various facts, which are sometimes inaccurate, hence the importance of addressing Factors affecting the credibility of traditional media, including paper and audio-visual ones, especially in light of the intense competition between traditional media and new media in the extent of the credibility of their news content.Therefore, this study comes in order to know the reliability of news through social networks and how this affects the freedom of expression of a sample of media professionals within the framework of a field study.
Navigation of an omnidirectional robot based on Takagi-Sugeno Logic Model Type-I

In this article we present an algorithm for control and navigation of an omnidirectional mecanum wheels with based on a Takagi-Sugeno Fuzzy Model Type-I, aiming to avoid various obstacles in each environment defined. To achieve this, the robot receives the data from 3 proximity sensors and readjusts its direction and speeds to control its navigation. This article presents research work results obtained from the implementation and experimentation of the fuzzy algorithm. In the simulation, the duration was defined. The robot used was a KUKA youBot, to which 3 proximity sensors were installed to detect the various obstacles within the simulated scenario.
Real-time movement control based on eye gestures for people with neurological disorder / Afif Rafiqin Adnan

People with total paralysis as experienced by tetraplegia patients must have total assistance during movement. The use of electrical wheelchair able to reduce the dependency of patient to caretaker. However, current technique of electrical wheelchair control that use joystick is not efficient due to disability of the patient. Some facial features such as eyes gestures have the potential as control inputs to the electrical wheelchair. Therefore, this project aims to develop a system that can classify different eyes gestures of human subject and convert it into different state of control instructions. Methods for object detection that had been developed by researchers in recent years are suitable to be used to detect faces and eyes. This work proposed the combination use of Haar Cascade classifier and Dlib facial detector for detecting face and eye region, respectively. Next, several image enhancement techniques and morphological operations are performed to detect the iris. Image moments is used to calculate the centre coordinate of the iris. Afterward, the iris coordinate is used to determine the classification of eye gestures. The proposed method has been proven to be efficient in detecting eyes gestures. The ratio of detection accuracy is ranged between 73.5% and 99.83% depending on the ambient lighting.
Secure Searching Mechanism for Cloud Computing Based Cloud Storage System

Cloud computing has been considered an enterprise for IT infrastructure, which can organize huge resource of computing, storage and applications, and enable users to enjoy ubiquitous, convenient on-demand network access to a configurable computing resources with great efficiency and minimal economic overhead for shared pool. Attracted by these appealing features, both individuals and enterprises are motivated to contract out their data to the cloud, instead of purchasing software and hardware to manage the data themselves. So far, most of the works have been proposed under different threat models to achieve various search functions, such as single keyword search, similarity search, multi- keyword Boolean search, ranked search, multi-keyword ranked search, etc. Among them, multikeyword ranked search achieves more attention for its practical applicability. propose a secure and ranked multikeyword search protocol in a multi-owner cloud model over encrypted cloud data,
Explaining Decision-Tree Predictions by Addressing Potential Conflicts between Predictions and Plausible Expectations

We offer an approach to explain Decision Tree (DT) predictions by addressing potential conflicts between aspects of these predictions and plausible expectations licensed by background information. We define four types of conflicts, operationalize their identification, and specify explanatory schemas that address them. Our human evaluation focused on the effect of explanations on users’ understanding of a DT’s reasoning and their willingness to act on its predictions. The results show that (1) explanations that address potential conflicts are considered at least as good as baseline explanations that just follow a DT path; and (2) the conflict-based explanations are deemed especially valuable when users’ expectations disagree with the DT’s predictions.
Cursor-based Adaptive Quantization for Deep Convolutional Neural Network

Recent years have witnessed wide applications of deep convolutional neural network (DCNN) in different scenarios. However, its large computational cost and memory consumption seem to be barriers to computing restrained applications. Model quantization is a common method to reduce the storage and computation burden by decreasing the bit width. In this work, we propose a novel cursor based adaptive quantization method using differentiable architecture search (DAS). The multiple bits' quantization mechanism is formulated as a DAS process with a continuous cursor that represents the quantization bit width. The cursor-based DAS adaptively searches for the desired quantization bit width for each layer. The DAS process can be solved via an alternating approximate optimization process. We further devise a new loss function in the search process to collaboratively optimize accuracy and parameter size of the model. In the quantization step, based on a new strategy, the closest two integers to the cursor are adopted as the bits to quantize the DCNN together to reduce the quantization noise and avoid the local convergence problem. Comprehensive experiments on benchmark datasets show that our cursor based adaptive quantization approach can efficiently obtain lower size model with comparable or even better classification accuracy.
Design and Application of Public Opinion Analysis System Based on Python

The Internet has penetrated into every aspect of our lives, but freedom of speech does not mean that we can be outspoken on the Internet. Tibet is the border area of our country, so speech security is particularly important. This paper mainly takes the post bar of Tibet University as an example to complete the public opinion analysis system, and uses Python crawler to crawl all kinds of posts of the post bar of Tibet University in real time, set keywords, quickly and effectively screen out all kinds of sensitive information, so as to better maintain the speech security of the post bar of Tibet University.
OPEN-SCIENCE SPACE ISSUE: CALIBRATION OF MEASURING CHANNELS OF NON-DISMANTLING CYBER-PHYSICAL SYSTEMS

The analysis of the concept of Open-Science Space is carried out. The existence of ways to achieve reproducibility and traceability of research results performed by a group of worldwide situated Cyber-physical system operators/supervisors is shown. Ways to ensure the efficient operation of Cyber-physical systems as complex technological nondemountable objects with high requirements for metrological characteristics have been studied. To develop the scattered cyberphysical systems, the portable stable-in-time code-controlled measures of physical quantities have been studied. They have to be metrologically confirmed in the laboratory before the delivery to the site of the measuring subsystem for its calibration.
An Abstract Argumentation and Logic Programming Comparison Based on 5-Valued Labellings

Abstract argumentation and logic programming are two formalisms of non-monotonic reasoning that share many similarities. Previous studies contemplating connections between the two formalisms provided back and forth translations from one to the other and found they correspond in multiple different semantics, but not all. In this work, we propose a new set of five argument labels to revisit the semantic correspondences between abstract argumentation and logic programming. By doing so, we shed light on why the two formalisms are not absolutely equivalent. Our investigation lead to the specification of the novel least-stable semantics for abstract argumentation which corresponds to the L-stable semantics of logic programming.
Tsunami post disaster robot: Simulation and implementation of aerial path planning to explore unknown environment

Post tsunami, the authorities still highly dependent on human labor in most of the search and rescue processes, while at the same time, the lives of the rescuers are also at stake and extremely crucial to avoid the further accident. Based on these conditions, the assistance of disaster robots is highly needed, which capable of operating in the unknown disaster area. This research presents an implementation of 2D local aerial path planning with obstacle avoidance for quadcopter as an aerial disaster robot. The research is divided into 2 different approaches, simulation, and prototype implementation. The simulation of aerial path planning algorithm utilized RViz 3D visualization tool for ROS Melodic Morenia on Ubuntu 18.04. The prototype quadcopter robot implementation combines the utilization of X-type frame mechanics, Pixhawk flight controller, DroneKit-Python software API, and VL53L0X Lidars as laser distance sensors facing in 5 sides around the quadcopter. In both approaches, laser distance sensor readings are processed into a histogram as basis data for the local path planning algorithm. Vector Field Histogram + algorithm is utilized to determine the path taken and obstacle avoidance maneuver. The research result targets are simulation and prototype implementation of the proposed aerial path planning algorithm. It is proven with the curving flight trajectory of the quadcopter when flying to the given waypoint while avoiding obstacles along the way. Different cost function adjustments of the aerial path planning algorithm resulted in different motion responses of the quadcopter robot while avoiding obstacles along the way to the given waypoint. The higher the safety distance and turning radius variables, the more responsive the motion of the quadcopter robot while avoiding obstacles and vice versa.
Sentimen Analisis Pengguna Twitter pada Event Flash Sale Menggunakan Algoritma K-NN, Random Forest, dan Naive Bayes

There is a sales system called Flash Sale in e-commerce. Basically the concept of a Flash Sale is to offer a lower price and a predetermined time and number of products. The sales system is only held at certain moments, by making cheaper product sales but with a limited time and number of products it will make sales increase because buyer interest will be higher. But apart from all the advantages of course there will be pros and cons. Sentiment analysis on Twitter was chosen because Twitter itself is a social media that allows users to be free to comment or write opinions about anything, including opinions about events that exist in e-commerce today. Thus, this research exists to find out the opinions of existing Twitter users regarding the Flash Sale event held by e-commerce. By using the methodology of three classification algorithms, Naive Bayes, K-Nearest Neighbor and Random Forest in classifying the data to determine the accuracy of the sentiment value of Twitter users in the Flash Sale event. This research takes two data samples from the keywords flash sale and flash shopee, the results accuracy of the implementation of the three classification algorithms are 83.53% Naive Bayes, 82.94% K-NN, 80.59% Random Forest for the keyword flash sale” and 81.48% Naive Bayes, 77.78% K-NN, 74.07% Random Forest for the keyword “flash shopee”. With this, the Naive Bayes Algorithm becomes a recommendation for classifying Sentiment Analysis data with greater accuracy and more stability to be used for large and small data.
Automatic IP Configuration and Duplicate Node Detection Using Improved Extreme Learning Machine Based Hunger Games Search

One of the challenging issues in mobile ad hoc networks (MANET) is IP address auto reconfiguration thereby ensuring best routing. Generally, the stateful and stateless are two classes of IP address reconfiguration protocols. Addresses must be unique, and address conflicts must be avoided. In this study, we have proposed Hunger Games Search (HGS) algorithm based Improved Extreme Learning Machine (IELM) named as HGS-IELM framework for IP address auto reconfiguration in MANET. The fresh read based on each access is ensured using HGS-IELM voting enforces. The cooperation among both data consistency and message overhead is made. There is no involvement of the central server and the proposed HGS-IELM method is scalable. Based on the experimental studies, the proposed HGS-IELM framework accomplished less message overhead as well as lower latency. By keeping suitable redundancy, the proposed HGS-IELM method demonstrated improved availability of address.