Reviewerly/exHarmony huggingface dataset:
Optimizing Gradient Methods for IoT Applications

Solving linear programming (LP) and nonlinear programming (NLP) problems is momentous because of their wide applications in real-life problems. There is no unified way to find the global optimum for NLPs. But on the other hand, the simplex algorithm, as the dominating methodology for LPs for several decades moves only on the boundary (vertices) and ignores the vast majority of the feasible region in the process of searching. In this article, we study two gradient-based methodologies that explore the whole feasible region, which guarantee faster convergence rates for both LP and NLP optimization problems including IoT problems such as Software-Defined Internet of Vehicles (SDIoV) and vehicular ad hoc networks (VANETs). The gradient-simplex algorithm (GSA) for LPs, which moves inside the feasible region in the gradient direction at first to reduce the search space and then explores the reduced boundary to find an optimal solution. The evolutionary-gradient algorithm (EGA), on the other hand, is for NLPs and uses an evolutionary population to estimate gradients by evolving to find better solutions in steps. Based on extensive simulations, the obtained numerical results show that both approaches provide efficient solutions and outperform the state-of-the-art methods on optimization problems with large feasible spaces. Comparative results of applying the GSA on SDIoV and VANETs with different sizes are included.
Analysis of Computer Science Textbooks by Topic Modeling and Dynamic Time Warping

The design of appropriate curricula in the rapidly expanding field of computer science (CS) is one of the most important issues in higher education, and there are many features to be considered. To provide useful information for developing appropriate curricula, we investigated the actual textbooks traditionally used in CS by applying the latent Dirichlet allocation to actual textbooks. In this paper, we focused on the topic progression of the textbooks and showed the results of clustering analysis with sequence clustering. We discovered that the structure of CS textbooks can be explained by five or six topics, and that dynamic time warping (DTW) can classify knowledge areas for CS more appropriately than the LDA-based clustering without DTW.
A Step Towards On-Path Security Function Outsourcing

Security function outsourcing has witnessed both research and deployment in the recent years. While most existing services take a straight-forward approach of cloud hosting, on-path transit networks (such as ISPs) are increasingly more interested in offering outsourced security services to end users. Recent proposals (e.g., SafeBricks [40] and mbTLS [34]) have made it possible to outsource sensitive security applications to untrusted, arbitrary networks, rendering on-path security function outsourcing more promising than ever. However, to provide on-path security function outsourcing, there is one crucial component that is still missing — a practical end-to-end network protocol. Thus, the discovery and orchestration of multiple capable and willing transit networks for user-requested security functions have only been assumed in many studies without any practical solutions. In this work, we propose Opsec, an end-to-end security-outsourcing protocol that fills this gap and brings us closer to the vision of on-path security function outsourcing. Opsec automatically discovers one or more transit ISPs between a client and a server, and requests user-specified security functions efficiently. When designing Opsec, we prioritize the practicality and applicability of this new end-to-end protocol in the current Internet. Our proof-of-concept implementation of Opsec for web sessions shows that an end user can easily start a new web session with a few clicks of a browser plug-in, to specify a series of security functions of her choice. We show that it is possible to implement such a new end-to-end service model in the current Internet for the majority of the web services without any major changes to the standard protocols (e.g., TCP, TLS, HTTP) and the existing network infrastructure (e.g., ISP’s routing primitives).
Entanglement-Based Single-Photon Amplification With High Fidelity of Polarization Feature

The amplification protocol of single-photon is an effective method to overcome the photon loss in the practical quantum communication. Here, we propose an entanglement-based single-photon amplification to nearly eliminate the vacuum state resulting from the photon loss. By the quantum nondemolition detection with cross-Kerr nonlinearity, our proposal has more higher fidelity of the polarization feature. And the total success probability of the amplification can be improved with bigger nonlinearity. This absolute amplification of single-photon is of great value to lengthen the quantum communication distance.
Hiding Technology

These patterns address the fact that technical devices and cables are too present in rooms and what we can do about it. Devices blink, have cables everywhere and make a significant contribution to the design of a room. This is a state that we have learned to accept but do not have to. One goal of these patterns is that the representation of technology in various aspects is no longer just accepted but actively questioned. You will find tips on how to hide technology, hints on how to choose or build new technology and ask questions that help save technology. The main pattern “Hiding Technology” gives an overview of the topic and connects the sub patterns. The pattern “Room Evaluation” is used to get an overview about the state of a room and assist in changing habits. “Hiding Cables” is all about techniques to root cables and make them visually disappear. The pattern “Hiding LEDs” is about the annoying aspects of status and feedback mechanisms of devices and how to make them less annoying. “Hiding Hardware-Controls” discusses the need to order functions in a hierarchy and advice on how to hide less used functions in software controls. The final pattern “Hiding Whole Devices” is all about making technical devices disappear and the risks affiliated with different hiding spots.
Crypto-commodity Protocol for Addressing Blockchain Problems and Bottlenecks

Most public blockchains are riddled with problems such as centralization trends and increasing processing fees, along with scalability and throughput bottlenecks. In an attempt to mitigate these problems, we propose a new blockchain design: the crypto-commodity protocol. Unlike crypto-currencies whose valuation fluctuates due to market volatility, crypto-commodity has an intrinsic value that is not influenced by any external factor: digital commodity owned by users is the number of bytes that can be consumed in the form of block records. It is inherently spam resistant by virtue of its design, and does not rely upon processing fees to achieve this goal. We suggest to improve throughput scalability through a dynamic block size adaptive to the network’s hashing power. To combat centralization trends, we have augmented proof of work with two refinements: public key based mining history and proof of consumption. Our design also guarantees monotonous timestamps and incorporates a reward scheme that incentivizes processing of records without fee.
A Three-Dimensional Target Mapping Scheme with Pupil Corneal Reflection Visual Attention Measurement

Visual assistive control is a promise way for using robot enhance the ability of disabled people in daily life. The key technical issues include two dimensional (2D) visual attention tracking and accurate target mapping between 2D plane and three dimensional (3D) space. Here, we proposed a 3D target mapping scheme with pupil corneal reflection visual attention measurement. The hardware includes a binocular depth camera, an eye tracker, a computer and a display. The acquired left image from the camera is shown in the display for operator attention tracking. The eye tracker was used to capture the visual attention information and locate the operator gazing target. Then, the computer was used for inverse calculation, which combined with the pre-calibrated camera parameters and depth information from the binocular camera to realize the 2D to 3D coordinate mapping. To test the effectiveness of the proposed method, we carried out several validation experiments, and test our scheme in an arm robot gapping system. The skilled operator can quickly lock, locate and grape a spatial target. The average mission accomplished time is less than 1s. The uncertain blind area is less than 2cm. The measurement errors of 3D coordinate are less than 3mm. The proposed method has a good application prospect in assistive robot for disabled.
Template Update Based on 3D-Convolutional Siamese Network for Object Tracking

Object tracking is an important research area in the field of computer vision. In the past two years, the object tracking algorithms based on Siamese network have yielded brilliant results in CVPR. However, previous algorithms only extract the object feature of the first frame as a tracking template. In the process of tracking the object, the object template remains unchanged, leading to poor tracking accuracy. In view of this, the present paper proposes a new, end-to-end trained, fully convolutional 3D Siamese network-based tracking algorithm to extract multiple features. Logistic loss function and SGD are used to train the network. The trained network realizes the use of multi-frame features to update the object template in the process of tracking the video's object. The tracker in this paper can run at real-time frame-rates in OTB, VOT, and GOT-10k. The algorithm is applied to SiamFC, its accuracy is improved by 4% and 3% on the OTB and VOT-2016 data sets, respectively.
Clustering

So far we focused on ML methods that use the ERM principle and lean a hypothesis by minimizing the discrepancy between its predictions and the true labels on a training set.
Curvature-Oriented Splitting for Multivariate Model Trees

Model trees typically partition the input space by axis-orthogonal splits into local regions for local regression. Due to these splits, properties of the function to be approximated can only be taken into account to a limited extent, which increases the bias of model trees. To minimize this bias, multivariate model trees with axis-oblique splits can be used. However, existing methods to build axis-oblique splits are either not applicable, ineffective, or limited to certain data distributions and properties of the approximated function. In this work, we present a novel method for axis-oblique splitting that overcomes these drawbacks by using the average direction of non-linearity of the function. This direction is estimated by extending the dimensionality reduction method refined Outer Product of Gradients. Moreover, we present a tree construction algorithm in which our method is integrated, and evaluate the resulting multivariate model tree COMT on synthetic data in an experimental study. In this study, we compare COMT and our split method in terms of prediction accuracy with common regression models and a proven method to identify axis-oblique split directions called Principal Hessian Directions. We also analyze the improvements in bias and variance by our axis-oblique splitting over conventional (univariate) model trees and multivariate regression trees. On the used data, COMT outperforms the competitors in terms of prediction accuracy, and our split method significantly reduces bias and variance.
Proposed Model for Using Block Chain to Secure the University Management System in Online Education

In light of the Corona pandemic that is sweeping the entire world, and one of its negative effects was forcing the world to social distance between its members in an attempt to limit the spread of this epidemic, and one of the sectors that were directly affected by this epidemic and what led to it of social distancing is the education sector with all its institutions, Where educational institutions of all kinds were forced to resort to the education system through the Internet, it was necessary to think of a way to secure important data and information circulating over the Internet in this new system that educational institutions were forced to implement, and block chain technology is a qualitative revolution in information technology. It is a technology that aims to store and verify the validity of digital transactions on the Internet with a high degree of security. This research paper explains how to take advantage of the block chain technology in the educational process and secure its data so that it is not exposed to hacking, theft or modification, by developing a visualization How to use block chain technology and benefit from it in securing and protecting data in online education, which achieves security and confidentiality and not to modify it, such as securing academic degrees for students and to ensure the attendance of students and to ensure that they are not tampered with.
Golden games

We consider extensive form 2-player win-lose games, with alternating moves, of perfect and complete information. The games are played over a complete binary-tree of depth n, where 0/1 payoffs in the leaves are drawn according to an i.i.d. Bernoulli distribution with probability p. Whenever p differs from the golden ratio, asymptotically as n→∞, the winner of the game is determined. In the case where p equals the golden ratio, we call such a random game a golden game. In golden games the winner is the player that acts first with probability equal to the golden ratio. We suggest the notion of fragility as a measure for “fairness” of a game's rules. Fragility counts how many leaves' payoffs should be flipped in order to convert the identity of the winning player. Our main result provides a recursive formula for asymptotic fragility of golden games. Surprisingly, golden games are extremely fragile. For instance, with probability ≈0.77 a losing player could flip a single payoff (out of 2n) and become a winner. With probability ≈0.999 a losing player could flip 3 payoffs and become the winner.
Research and Application of Community Pension Service Data Analysis System Based on Data Mining Algorithm

In recent years, with the development of China's aging population, pension issue has become a major issue related to the national economy and people's livelihood continues to be concerned. This paper aims to study the design and implementation of data analysis system of community elderly care service based on data mining algorithm. Firstly, this paper introduces the theory of data mining, and designs three modules of community pension service. Secondly, the gender, age, education level, the number of people living with the elderly and the situation of chronic diseases are used as variables for model design, and finally a community service pension system is designed in accordance with the national conditions. The experimental results of this paper show that, on the basis of data mining algorithm, the construction of community pension service system satisfies the needs of the elderly, among which, the highest reaches 91.8%, which also shows that the pension service system brings great convenience to the elderly.
Postmodern Approach to Stephenson’s Snow Crash: Does Technology help to overcome Real-World illness?

Technology is inextricable from our daily lives. Since the Renaissance until the Post-modern Era technology is progressing rapidly. At present anything can be accessible with Networks and the internet. Gradually, Invention and Technology begin to occupy the human mind thereby giving birth to diseases, Physical, Psychological and Mental illness. The invention of Technology to its extremity is dreamt by the science fiction authors and also many such dreams came true today. In the present era, one such dream is the creation of a virtual world. Real-world illness is the main objective of this article which means common problems faced by everyone in their day to day life. One of the contemporary science fiction author Neal Stephenson in his work Snow crash picturizes the dystopic society where technology reaches its peak and create more problems in human life. As a result, he creates a parallel universe known as metaverse where people can forget worries and fulfil their desire. Do they live a very happy life in the substitute world is the big Question mark? The main objective of this paper is whether the advancements in technology really help people to overcome their real-world illnesses or creates many more problems.
Impact of Education Planning on Economic Development in Iraq: A Forward-Looking Approach

Education planning at different levels is a tool for human development and the rehabilitation of human forces to enter the labor market to improve their living and living conditions. However, it requires harmonizing the morgues of education and the labor market, as the labor market in Iraq suffers from an imbalance between the forces of supply and demand, which has led to its inability to absorb employment. The research is based on the hypothesis that the failure to adopt a correct mechanism for planning education in Iraq and the failure to harmonize the outputs of education to the labor market requirements has exacerbated the problem of unemployment, especially among graduates.
One run to attack them all: finding simultaneously multiple targeted adversarial perturbations

Deep neural networks are ubiquitous in computer vision applications such as autonomous vehicles, face recognition, and medical imaging. However, deep neural networks may be subject to adversarial perturbations (also called adversarial attacks) which, when performed in high-stakes environments, may lead to severe consequences. A particular class of adversarial attacks, called universal adversarial attacks, consists of attacks that do not fool a single image. Instead, they aim to fool as many images as possible. Universal adversarial attacks may be either untargeted or targeted. While the goal of an untargeted attack is to deceive the neural network under attack without caring about its output, a targeted attack aims to “steer” the predictions of the neural network towards a specific target. In this work, we propose a method, based on the clonal selection algorithm, to generate targeted universal adversarial attacks for multiple target classes in a single run of the algorithm. Our results show that our approach is able to find multiple targeted universal adversarial attacks for the network under attack, with good success rates. Moreover, by visualizing the attacked images, we observe that our algorithm is able to produce adversarial attacks that fool the network by attacking a relatively small number of pixels. Finally, by analyzing how the attacks generalize on different architectures, we observe intriguing properties of the discovered universal adversarial attacks.
Detection of Spliced Images in Social Media Application

Social media in the current technological era plays a major role in people&#x0027;s daily lives. Every day hundreds of thousands of images are circulated on social media applications such as WhatsApp, Instagram, Twitter, Facebook, and Snapchat. Photo is one of the most popular types of media that is shared among users on social media. It has become easy for small groups and even for individuals to edit and manipulate these images on a large scale in a very short time in such a way threatening the credibility of these images. In this paper, a detection system is implemented for verifying and classifying the content of social media images. The system adopted Deep Learning based on a convolutional neural network (CNN) to detect spliced images on WhatsApp. The images in dataset CASIA v2 (transformed to be appropriate for WhatsApp) are used for training and testing. The results point to an accuracy of 99.19&#x0025; of training and 87.438&#x0025; of testing.
It’s Fraud! Application of Machine Learning Techniques for Detection of Fraudulent Digital Advertising

Due to the on-going COVID-19 pandemic, industries are heavily reliant on online meeting platforms. The pandemic has forced most MNCs to rely on online platforms such as Zoom or Microsoft Teams to hold daily business meetings and even conferences and other corporate events. Schools and other educational institutions have also been forced to conduct classes and all other events through these platforms. This increased unavoidable dependence on online platforms has resulted in an exponential increase in advertising fraud. Advertising fraud is the application of any method or technology that hampers the proper delivery of advertisements to the proper audience or the proper place, or forcefully inserts advertisements at undesirable times or locations. This could take multiple forms and has become far more widespread with increased use of online platforms. Some common methods used for digital fraud can be the pay-per-click (PPC) model, domain spoofing or in the form of bots, but the main objective is to gain financial advantages from advertising transactions. The primary objective of this study is to identify and understand the factors lying behind the presence of fraudulent activities on any online medium and to analyse the probability of downloading an application after coming across the online advertisement, and watching it. The study also aims to highlight how marketing agencies tend to float fraud advertisement just to gain more revenue from their end.
Entity-Dependency Graph Enforced Quantity Relation Extraction for Solving Arithmetic Word Problems

An entity-dependency graph (EDG) is proposed to efficiently represent mathematical and non-mathematical relations of arithmetic word problems. The EDG of a problem contains nodes representing noun entities and links representing entity-dependencies. An algorithm was proposed to generate EDG which is used to identify entity associated with quantity relation in arithmetic word problems. The results are used to extract quantity relations. Through the experiments, we demonstrate that this method can improve the efficiency of quantity relation extraction by syntax-semantic method. The EDG can also be used to improve the interpretability of the problem solving.
Logical Foundations for Hybrid Type-Logical Grammars

This paper explores proof-theoretic aspects of hybrid type-logical grammars, a logic combining Lambek grammars with lambda grammars. We prove some basic properties of the calculus, such as normalisation and the subformula property and also present both a sequent and a proof net calculus for hybrid type-logical grammars. In addition to clarifying the logical foundations of hybrid type-logical grammars, the current study opens the way to variants and extensions of the original system, including but not limited to a non-associative version and a multimodal version incorporating structural rules and unary modes.
Building Modern Web Applications

Welcome! Congratulations on picking this book to learn how to build offline applications with Angular. This introductory chapter sets the expectations and framework for the book. It provides a brief look at traditional web application development and why creating just another traditional web application is not enough.