Reviewerly/exHarmony huggingface dataset:
Successive Refinement of Privacy

This work examines a novel question: how much randomness is needed to achieve local differential privacy (LDP)? A motivating scenario is providing multiple levels of privacy to multiple analysts, either for distribution or for heavy hitter estimation, using the same (randomized) output. We call this setting successive refinement of privacy, as it provides hierarchical access to the raw data with different privacy levels. For example, the same randomized output could enable one analyst to reconstruct the input, while another can only estimate the distribution subject to LDP requirements. This extends the classical Shannon (wiretap) security setting to local differential privacy. We provide (order-wise) tight characterizations of privacy-utility-randomness trade-offs in several cases for distribution estimation, including the standard LDP setting under a randomness constraint. We also provide a non-trivial privacy mechanism for multi-level privacy. Furthermore, we show that we cannot reuse random keys over time while preserving privacy of each user.
Research on Camera Calibration Technology Based on Deep Neural Network in Mine Environment

achieve the calibration accuracy of the Kinect camera in a complex mine environment, the calibration process was simplified. In this paper, a camera calibration method based on a deep neural network is proposed, which can achieve flexible and high-precision calibration in-plane regions under a complex environment. Without data feature extraction or classification, the deep neural network can be trained quickly and effectively by optimizing network structure, super parameters, and training algorithm. Experimental results show that compared with the Zhang Zhengyou calibration method and shallow neural network calibration method, this method can achieve higher calibration accuracy in a wide range and multi angle shooting.
Privacy-preserving contact tracing using homomorphic encryption

Digital contact tracing is an essential countermeasure for an epidemic as a society, and balancing the surveillance resolution and user privacy for contact tracing remains an open challenge. Existing contact tracing schemes are primarily based on proximity tracing, which uses Bluetooth to detect coexistence. Proximity tracing has a strong advantage in anonymizing the users, but shows low epidemiological resolution and lacks the flexibility to be integrated with other data sources. To address this problem, we propose an alternative scheme we phrase as context tracing. Our scheme achieves strong performance in both surveillance resolution and user privacy protection by integrating multi-modal sensor fusion and homomorphic encryption. While this advantage comes at the cost of high computational overhead, we discuss possible optimization strategies for reducing energy consumption on mobile devices.
Cryptocurrency, Stablecoins and Blockchain: Exploring Digital Money Solutions for Remittances and Inclusive Economies

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Teaching Quality Evaluation Model for Human Resource Development and Management Major Under the Background of Big Data

The evaluation of teaching quality in colleges relies on reliable and comprehensive evaluation data, which is also an important basis for educational decision-making. The application of big data technology can realize scientific analysis and in-depth mining of a large number of data, analyze the value and connection implied in the data on a multidimensional level, and enable the evaluation of teaching quality to transform from the previous analysis of segment information and small sample data to the whole process of all-round data decision-making. This paper proposes a learning quality evaluation model based on big data in cloud computing environment, using the structure method, AHP, fuzzy comprehensive evaluation and multi-objective optimization method. It is also combined with a variety of theories such as user perception theory and user experience theory, and the development of this model is studied. Finally, we develop and design the learning quality evaluation system under cloud computing, to test the system and prove the credibility of our scheme.
The decision trees and the optimization of resources in Big Data solutions

every day, we see that a quantitative explosion of digital data has forced researchers to find new strategies to collect, store, analyze and visualize data. In the context of storage and processing of a large massive amount of data we find a lack of powerful tools to master and control them. Also, during the process of executing tasks in real time in clustered IT platforms, we encounter the problem of optimizing parallel tasks. So we will propose in this article a method based on the algorithm of decision trees as an interpretable machine learning algorithm which can allow us to evaluate the impact of certain characteristics on the variable of the task execution time. This decision tree algorithm is useful and it helps us understand how we can optimize the different parameters that affect workloads in clustered applications. We can thus optimize the number of tasks in Big Data clustered applications without failure and performance degradation.
SBGen: A Framework to Efficiently Supply Runtime Information for a Learning-Based HIDS for Multiple Virtual Machines

Much compelling evidence urges that the isolation provided by the hypervisor in a virtualized system is not complete at all, and in practice can be neutralized by elaborated adversaries, which consequently emphasizes the need of techniques to detect attacks on the guest VM kernels. In this regard, learning-based HIDSs have received much attention, which inspect the internals of each VM through monitoring models built by machine learning techniques. The inspection capability of learning-based HIDSs depends on the quality of the monitoring models, which in turn can be improved by using rich runtime information reflecting the exact behavior of VMs. However, as extracting such runtime behavior information is onerous on account of its vast quantity, many learning-based HIDSs have resorted to using only fragmentary runtime behavior information. To address this problem, in this paper, we present SBGen, a framework for efficient extraction of rich runtime behavior information of VMs, namely the system call traces and the execution paths of the kernel taken to serve system calls. To trace execution of the kernel efficiently, SBGen leverages a salient hardware feature, Intel Processor Trace (PT). Once receiving the execution of the kernel traces from PT, SBGen elaborately decodes and purifies them to extract execution paths of the kernel associated with system calls. The extracted runtime behavior information of VMs is fed into learning-based HIDSs to improve their detection accuracy. Our experiments show that SBGen can extract and supply runtime behavior information efficiently enough for learning-based HIDSs to detect in a timely fashion real-world attacks on the guest VM kernels running in a virtualized system, while incurring a reasonable amount of performance overhead.
Fine-grained emoji sentiment analysis based on attributes of Twitter users

Emoji is a visual character that is popular on social media today, with rich semantic and emotional information. At present, each kind of emoji is usually treated as the same in sentiment analysis, but we think that different people using the same emoji will express different emotions. We collected tweets containing different emojis to calculate the emotion intensity scores of different emojis under different emotions, and combined the scores with different attributes of Twitter users to prove our point. Furthermore, we also provide analysis of experimental results and suggestions for future improvements.
Recontextualising partisan outrage online: analysing the public negotiation of Trump support among American conservatives in 2016

Abstract This article conceptualises the role of audience agency in the performance of American conservative identities within a hybridised outrage media ecology. Audience agency has been under-theorised in the study of outrage media through an emphasis on outrage as a rhetorical strategy of commercial media institutions. Relatively little has been said about the outrage discourse of audiences. This coincides with a tendency to consider online political talk as transparent and "earnest," thereby failing to recognise the multi-vocality, dynamism, and ambivalence—i.e., performativity—of online user-generated discourse. I argue the concept of recontextualisation offers a means of addressing these shortcomings. I demonstrate this by analysing how the users of the American right-wing partisan media website TheBlaze.com publicly negotiated support for Donald Trump in a below-the-line comment field during the 2016 US presidential election. These processes are situated with respect to the contested, dynamic, and creative construction of partisan identities in the contemporary United States.
Two-stage single image reflection removal with reflection-aware guidance

Removing undesired reflection from an image captured through a glass surface is a very challenging problem with many practical applications. For improving reflection removal, cascaded deep models have been usually adopted to estimate the transmission in a progressive manner. However, most existing methods are still limited in exploiting the result in prior stage for guiding transmission estimation. In this paper, we present a novel two-stage network with reflection-aware guidance (RAGNet) for single image reflection removal (SIRR). To be specific, the reflection layer is firstly estimated due to that it generally is much simpler and is relatively easier to estimate. Reflection-aware guidance (RAG) module is then elaborated for better exploiting the estimated reflection in predicting transmission layer. By incorporating feature maps from the estimated reflection and observation, RAG can be used (i) to mitigate the effect of reflection from the observation, and (ii) to generate mask in soft partial convolution for mitigating the effect of deviating from linear combination hypothesis. A dedicated mask loss is further presented for reconciling the contributions of encoder and decoder features. Experiments on five commonly used datasets demonstrate the quantitative and qualitative superiority of our RAGNet in comparison to the state-of-the-art SIRR methods. The source code and pre-trained model are available at https://github.com/liyucs/RAGNet .
Database Security: An Overview and Analysis of Current Trend

Information is the core and most vital asset these days. The subject which deals with Information is called Information Science. Information Science is responsible for different information related affairs from collection, selection, organization, processing, management and dissemination of information and contents. And for this information related purpose Information Technology plays a leading role. Information Technology has different components viz. Database Technology, Web Technology, Networking Technology, Multimedia Technology and traditional Software Technology. All these technologies are responsible for creating and advancing society. Database Technology is concerned with the Database. It is worthy to note that, Database is concerned with the repository of related data in a container or base. The data, in Database normally stored in different forms and Database Technology play a lead role for dealing with the affairs related to database. The Database is very important in the recent past due to wider applications in different organizations and institutions; not only profit making but also nonprofit making. Today most organizations and sectors which deal with sensitive and important data keep them into the database and thus its security becomes an important concern. Large scale database and its security truly depend on different defensive methods. This paper talks about the basics of database including its meaning, characteristics, role etc. with special focus on different security challenges in the database. Moreover, this paper highlights the basics of security management, tools in this regard. Hence different areas of database security have mentioned in this paper in a simple sense.
Activity-selection Behavior and Optimal User-distribution in Q&amp;A Websites

Based on various factors such as experience, interest, and motivation, users in any system choose to perform certain activities more than the others. In this study, we investigate a similar behavior and its dynamics in the websites of StackExchange. We find that most of the users in these websites tend to contribute predominantly to one of the activities available on these websites. Such a behavior yields a high-level distribution of users across the activities, referred to as User-distribution. We find that this distribution varies for different websites of StackExchange. We also observe that the users contributing to different activities trigger each other to provide more contribution. Using these insights, we build a model that explains the effect of change in user-distribution on the amount of knowledge produced on these websites. The model shows that one can formulate an optimal ecosystem by motivating the right kind of users, which leads to the maximum fostering of knowledge on these websites.
Implementation of YOLO and Smoke Sensor for Automating Public Service Announcement of Cigarette's Hazard in Public Facilities

Smoking in public facilities is one of the most unwanted behaviours that shall be given serious and continuous approaches. Using automation-based approaches, nowadays, a common technique used is using detectors which trigger an alarm upon detecting smoke. While this approach is better than none, it is more detective than preventive and is still lacking awareness-stimulation. This research aims to propose a more comprehensive solution. A real-time alert is not only triggered when the system detects a certain level of smoke, but also when it recognizes cigarette objects, to add the preventive purpose. Not only a buzzer is triggered, upon detection of either smoke or cigarette or both, a public service announcement (PSA) is also automatically displayed to provide education about smoking's hazard to conduct responsible behavior in public facilities. The system combines the concepts of embedded systems and deep learning. The process consists of a continuous monitoring of smoke level from several sensors reading and also a continuous cigarette object detection using YOLO. The outputs are two binary digits that will role as the inputs for an OR operation which results in the bit required to trigger an anti-smoking PSA and a buzzer. The system consists of a Raspberry Pi 4B 4GB, smoke sensors, a camera module as the visual input device, a monitor, and also a buzzer. The results show that the system is able to perform in a semi indoor environment for detecting both smoke and cigarette then automating an alert and a PSA in a soft real-time manner.
On the reduction of authoritative DNS cache timeouts: Detection and implications for user privacy

The domain name system (DNS) is an Internet network service that is used by hosts to resolve IP addresses from symbolic names. This basic service has been attacked and abused many times, as it is one of the oldest and most vulnerable services on the Internet. Some DNS resolvers conduct DNS manipulation, in which authoritative DNS responses are modified. This DNS manipulation is sometimes used for legitimate reasons (e.g., parental control) and other times is used to support malicious activities, such as DNS poisoning or data collection. Between these DNS manipulation activities, some Internet service providers (ISPs) are changing the DNS cache timeout of the DNS responses with which their DNS resolvers responded to obtain additional data about their subscribers. These data can be a detailed web browsing profile of the user. This approach does not require a large investment and can yield huge benefits if the information is used or sold. Therefore, user privacy is disputed. We conducted a study in which we analyse how ISPs use this DNS manipulation, propose a method for identifying this DNS manipulation by the end-user and determine the amount of information an ISP can collect by using it. We also developed a public web tool, for which the source code is available, that can help Internet users determine whether their privacy is being compromised by their ISP via the exploitation of DNS cache timeouts. This service can facilitate the collection of data on how many people are victims of this abuse and which ISPs around the world are utilizing this technique.
Method of Detecting Bots on Social Media. A Literature Review

The introduction of the online social media system has unquestionably facilitated communication as well as being a prime and cheap source of information. However, despite these numerous advantages, the social media system remains a double-edged sword. Recently, the online social media ecosystem although fast becoming the primary source of information has become the medium for misinformation and other malicious attacks. These malicious attacks are further exacerbated by the use of social bots that have implacable consequences to victims. In this study, we examine the various methods employed by experts and academia to detect and curb Sybils attack. We define and explain three types of social bots such as the good, the bad and the ugly. We surmised that although the various social media giants have peddled in orthogonal techniques to uncloak and perturb Sybils activities, the adversaries are also working on a robust method to evade detection, hence, a heuristic approach including hybrid crowdsourced-machine learning technique is required to avert future attacks.
Effective Video Frame Acquisition for Image Stitching

We present an effective video frame (including reference frame and key frames) acquisition method for image stitching. The method simultaneously analyzes different types of factors, namely, the video-level stability, image-level stability, and content scale stability, to take advantage of their complementary strengths. We model the three factors with three modules that are learned from an analysis of the shooting process. The video stabilization module (VSM) selects a stable segment, while the shooting distance module (SDM) obtains a similar content scale. They collaborate during the reference video sequence so that they can benefit from each other. Then, the image quality module (IQM) obtains a reference frame from the above sequence by choosing high-quality images. Finally, to obtain the key frame set, the SDM and IQM are again used to continuously filter the overlapping video sequences formed by the reference frame or the latest key frame. In particular, a comprehensive dataset containing a variety of challenges and scenarios is introduced. We have conducted an extensive set of experiments on this dataset. The results confirm the effectiveness of each module and their collaboration; our method outperforms current state-of-the-art methods.
A Genetic Programming-Based Low-Level Instructions Robot for Realtimebattle

RealTimeBattle is an environment in which robots controlled by programs fight each other. Programs control the simulated robots using low-level messages (e.g., turn radar, accelerate). Unlike other tools like Robocode, each of these robots can be developed using different programming languages. Our purpose is to generate, without human programming or other intervention, a robot that is highly competitive in RealTimeBattle. To that end, we implemented an Evolutionary Computation technique: Genetic Programming. The robot controllers created in the course of the experiments exhibit several different and effective combat strategies such as avoidance, sniping, encircling and shooting. To further improve their performance, we propose a function-set that includes short-term memory mechanisms, which allowed us to evolve a robot that is superior to all of the rivals used for its training. The robot was also tested in a bout with the winner of the previous “RealTimeBattle Championship”, which it won. Finally, our robot was tested in a multi-robot battle arena, with five simultaneous opponents, and obtained the best results among the contenders.
How to evaluate and license an e-resource during a pandemic (without scheduling a meeting)

The COVID-19 virus pandemic of 2020 thrust libraries into managing situations many never imagined they would have to face. The author details one academic library’s experience in successfully evaluating, trialing and licensing a faculty-requested electronic resource subscription during the health pandemic in the face of significant fiscal challenges and describes the ways in which following established procedures as well as a set of principles that encourages open dialogue with faculty, administrators and vendors contributed to the positive outcome.
Sentiment Analysis by Using Supervised Machine Learning and Deep Learning Approaches

With the growth of online review sites, we can use the opportunity to find out what other people think. Sentiment analysis is a popular text classification task in the data mining domain. In this research, we develop a text classification machine learning solution that can classify customer reviews into positive or negative class by predicting the overall sentiment of the review text on a document level. For this purpose, we carry out the experiment with two approaches, i.e. traditional machine learning approach and deep learning. In the first approach, we utilize four traditional machine learning algorithms with TF-IDF model using n-gram approach. These classifiers are multinomial naive Bayes, logistic regression, k-nearest neighbour and random forest. Out of these four classifiers, logistic regression achieves the highest accuracy. The second approach is to utilize deep learning methodologies with the word2vec approach, for which we develop a sequential deep learning neural network. The accuracy we achieve with deep learning is much lower than our traditional machine learning approach. After finding out the best performing approach and the classifier, the next step of the work is to build our final model with logistic regression using some advanced machine learning methodologies, i.e. Synthetic Minority Over-sampling for data balancing issues, Shuffle Split cross-validation. The accuracy of the final logistic regression model is approximately 87% which is 3% higher from the initial experimentation. Our finding in this research work is that, in smaller dataset scenarios, traditional machine learning would outperform deep learning models in terms of accuracy and other evaluation metrics. Another finding in this work is, by addressing data balancing issues in the dataset the accuracy of the model can be improved.
Pose Invariant 3D Facial Landmark Detection Via Pose Normalization and Deep Regression

Despite the rapid development of detecting facial landmarks on 2D images in the past decade, detecting the landmarks on 3D faces under varying poses is still challenging. Existing methods rely either on pose-invariant feature descriptors, which are vulnerable to missing data (e.g., due to self-occlusion at large poses), or on multi-view landmark models, which are complicated to apply. In this paper, instead, we propose to first normalize the 3D face to frontal pose and then use a single deep model to regress the facial landmarks. We adapt a pose estimation network to estimate the yaw and pitch angles of the input 3D face based on its depth image, and then rotate the face to frontal pose and encode it as a position map. We employ another deep network to predict the location of landmarks on the position map, which is projected back onto the input 3D face to get the final detection results. Evaluation experiments using three public datasets (FRGCv2, UND and Bosphorus) prove the superiority of the proposed method for pose-invariant 3D facial landmark detection.