Reviewerly/exHarmony huggingface dataset:
Reliability and Validity of Low Temporal Resolution Eye Tracking Systems in Cognitive Performance Tasks

Eye tracking experiments are an important contribution to human computer interaction (HCI) research. Eye movements indicate attention, information processing, and cognitive state. Oculomotor activity is usually captured with high temporal resolution eye tracking systems, which are expensive and not affordable for everyone. Moreover, these systems require specific hard- and software. However, affordable and practical systems are needed especially for applied research concerning mobile HCI in everyday life. This study examined the reliability/validity of low temporal resolution devices by comparing data of a table-mounted system with an electrooculogram. Gaze patterns of twenty participants were recorded while performing a visual reaction and a surveillance task. Statistical analyses showed high consistency between both measurement systems for recorded gaze parameters. These results indicate that data from low temporal resolution eye trackers are sufficient to derive performance related oculomotor parameters and that such solutions present a viable alternative for applied HCI research.
Efficiency Persistence in Mutual Fund Performance

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Encrypted network traffic classification with convolutional auto-encoders

Network traffic classification has been used for more than two decades for various applications, including QoS provisioning, anomaly detection, billing systems, etc. With the wide-spread adaptation of deep learning models in various fields, researchers also started adopting deep models for traffic classification task. However, the biggest challenge with deep models is that they need considerably larger training data in comparison with classical machine learning algorithms. In this paper, we propose a two-step training process that significantly reduces the number of labelled training samples. In the first step, we train a convolutional auto-encoder with an unlabelled and large public dataset. Then, in the second step, we transfer the models weights to a CNN model and we train the CNN model with only a few labelled samples. We compared our approach with two state-of-the-art methods and we showed that our approach outperforms.
Image segmentation

Image segmentation is one of the key tasks in medical imaging upon which depend many downstream analyses. With the increase in quantity and quality of images to process, automation for robustness and reproducibility of results has become essential. This chapter presents different typical situations where segmentation is essential and their associated challenges and describes some of the automated solutions that have been proposed over time. The chapter finishes with the question of the evaluation of such methods in a context where ground truth may be particularly difficult to acquire.
Using the Internet to Plan for Terrorist Attack

In this chapter, we discuss how terrorists can use the Internet as a source of information to plan for terrorist attacks. Online anonymity services such as virtual private network (VPN) are discussed, along with advantages and disadvantages of using these services. We also discuss online bomb-making instructions and highlight ways in which these can be used to the advantage of law enforcement. Finally, the use of the Internet as a reconnaissance tool for target selection is discussed, with descriptions of current and past research in this field to identify key information that is available to terrorists, and how this information can be manipulated to reduce the likelihood or severity of a terrorist attack.
Investigating suspicious vessel behaviour in light of context

Hybrid threat events are rare and cannot be modelled solely based on data. Instead they require a focus on discovery of emergent knowledge through information sharing across agencies and systems. However, multi-intelligence can bring about reasoning challenges with multiple sources such as confirmation biases. In this paper, we present how context can be used to combat these reasoning biases. Firstly, we show how it can reduce the impact of the overly confident sources and secondly, how it can be used to provide counter-evidence. It is shown that when context is used in such a manner the reasoning results display less false confidence while still supporting the original hypothesis. We apply the reasoning scheme to the post-analysis of a real case event. The story of Andromeda was widely reported upon when the vessel loaded with 410 tonnes of explosives supposedly sailing to Libya was arrested near Crete in early 2018. Using media headlines, AIS signals and analyst reports, we show how realistic, uncertain, heterogeneous reports and contextual information can be put together to reason about its intent. We propose a reasoning model framed within the theory of evidence to combine the information from these sources. The modularity of our method allows us to easily compare different approaches to context-aware reasoning. We finally conclude on future steps for this work.
Investigation into the Status of Skills Training of Normal School Students in the Context of Artificial Intelligence

Under the background of artificial intelligence, the cognition and attitude of artificial intelligence education application is an important manifestation of teachers’ intellectual education literacy. Most normal school students have strong professional qualities and have mastered a wealth of professional knowledge and traditional teaching methods, which is the particularity of the teacher major. The main purpose of this research is to understand the status quo of the training of normal school students' educational skills, discover the existing problems, analyze the causes of these problems, and propose strategies to optimize the training of normal school students' teacher skills.
License Plate Character Segmentation Algorithm in Intelligent IoT Visual Label

One of the most important core technologies in intelligent visual Internet of Things is visual labeling technology. It identifies, understands, and classifies the content in a video or image, “tags” it, and displays the contents of the label information corresponding to the identified object. In the intelligent Visual IoT vehicle visual label production process, including access to vehicle images, license plate positioning, license plate correction, character segmentation, character recognition and other processing, and finally identify the license plate number to provide the most important vehicle information for the vehicle visual label. This paper mainly focuses on the research of character segmentation algorithm for intelligent Visual IoT vehicle visual label making and puts forward some better-improved algorithms through the experiment, comparison, and analysis of three character segmentation methods of horizontal projection, template matching, and c-mean clustering analysis. For example, a given type of class center method is used in clustering analysis, and the steps of upper and lower boundary detection are added to template matching and clustering analysis.
Particle Swarm Optimization

In the present article, the fundamentals of particle swarm optimization (PSO) are reviewed and illustrated by means of examples both taken from standard mathematical optimization and more chemically-oriented. Swarm-based algorithms take their inspiration from collective behavior of social animals and translate these concepts to solve optimization problem. Among this family, particle swarm optimization (PSO) describes the set of candidate solutions to the optimization problem as a swarm of particles moving across the search space along trajectories, governed by their own and neighbors’ best performances.
Query Languages for Graph Databases

In the past few years, many NoSQL databases have emerged, including graph databases. NoSQL databases have certain advantages and they can be used in certain domains as an alternative to relational databases. In order to use graph databases, one needs to be familiar with specific languages like Cypher Query Language (CQL) or Gremlin. However, some statements in CQL can be considered too complex for end users as it is shown later on. Because of that, the main idea of this chapter is to explore two other languages for graph databases. One of them is new and it is used to pose queries visually. Since CQL does not support recursion, views, etc., the other language is used to show how to use recursion and views on a graph database.
Visual Analytics

We are in a data-driven era. Making sense of data is becoming increasingly important, and this is driving the need for systems that enable people to analyze and understand data. Visual analytics presents users with a visual representation of their data so that they make sense of it: reason about it, ask important questions, and interpret the answers to those questions. This paper shows how effective visual analytics systems combine information visualisation (to visually represent data) with machine learning (to project data to a low-dimensional space). The methods are illustrated with two real-world case studies in drug discovery and condition monitoring of helicopter airframes. This paper is not the final word on the subject, but it also points the way to future research directions.
Fast Chaotic Encryption Using Circuits for Mobile and Cloud Computing

In last few decades, a lot of work has been done in the field of cryptography; it is being considered one of the safe methods to protect data. It was first used to protect communication by individuals, armies, and organizational companies. With the help encryption method, anyone can protect their data from a third-party attack. Images are used in various areas like biometric authentication, medical science, military, etc., where they are being stored or transferred over the network and the safety of such images are very important. The newest movement in encryption is chaos-based, which is a better encryption technique than AES, DES, RSA, etc. It consists of different property such as sensitive independence on original situation, non-periodicity, non-convergence, etc. In recent times, many chaos-based image encryption algorithms have been proposed, but most of them are not sufficient to provide full protection to data. In this chapter, a survey of different chaos-based image encryption techniques is discussed.
Related Work

In this chapter, we first review attention-based neural machine translation (NMT). Attentional mechanism make NMT outperform the non-attentional models. The related works confirm the importance of attentional mechanisms. Then we investigate how the bidirectional information is captured in SMT and NMT. Next we summarize a number of work which incorporate additional data resources, such as monolingual corpora and pivot language corpora, into machine translation systems. Finally, we make a simple review of the studies about contrastive learning, which is a key technique in our fourth work.
The Digital Politics of Pain

After 9/11, the upsurge of the Internet and intensification of mass media has provided Afghans with access to a global information highway of new perspectives, narratives, ideas, and images. Global connectivity has likewise brought with it cultural challenges over meaning. Within these digital spaces, the politics of ideological warfare ensue for the battle of representation and signification, which are inevitably interlinked to questions of power and powerlessness. Within this digital space of ideological contestation, I explore the power of the Afghan Women's Writing Project and its ability to empower women to bear witness and share their geographies of pain. Moreover, I demonstrate how AWWP operates as a social media democratizing campaign meticulously employing Western feminist rhetoric to shape Afghan cultural and social systems and subvert opposing Islamic forces that attempt to undermine protections against women and principles of free market democracy.
Capital and Corporal Punishment: The Situation in Tanzania

Capital and Corporal punishment have been a talk of many years worldwide. In some countries both corporal and capital punishment have been abolished, some have only abolished one of them, and other neither of them. Despite of having a number of criticism and legal battle to avoid capital punishment in Tanzania, nothing has changed, but for corporal punishment some developments have been made in a number of legislations. This article entails about the situation of the two above punishments, their implementations, short-coming and their success in achieving the purpose of preventing and controlling of crimes.
Application of artificial intelligence in toxicopathology

Traditionally, pathologists examine tissue slides under a microscope to find pathological lesions, and have the burden of finding the lesions among so many histopathology slides.Furthermore, inconsistency of diagnoses results differ corresponding to training among researchers.Therefore, accumulated research experience has led to the use of novel tools for increasing accuracy and consistency of diagnoses.With rapid transition from analog to digital methods and new developments in digital pathology, it is possible to use whole slide imaging (WSI) by scanning glass slides.Artificial intelligence (AI), including machine learning and deep learning using WSI, is starting to be applied to automatically classify and count microscope images, and this method has been expanded to include the field of medical image analysis.This review aims to define current trends toward AI application in the biomedical area, especially in the field of toxicopathology, outline current future business trends, and discuss multiple issues of diagnosis, quantification, three-dimensional reconstruction, molecular pathological research, and the future direction of AI in toxicopathology.Big data systems including a large amount of welldefined toxicopathological information will be highly useful for accuracy and corrections of diagnoses.In addition, the need for critical peer review is profound in the continuing educational process.Taken together, it is highly promising that AI model based on big data in the toxicopathological field could classify, detect, and segment pathological lesions in numerous organs of experimental animals and could help explain various biological mechanisms.This promising approach will provide an accurate and fast analysis of tissue structure and biological pathways using AI algorithms and big data.
Real-Time Streaming Data Analysis Using a Three-Way Classification Method for Sentimental Analysis

This article describes how recent advances in computing have led to an increase in the generation of data in fields such as social media, medical, power and others. With the rapid increase in internet users, social media has given power for sentiment analysis or opinion mining. It is a highly challenging task for storing, querying and analyzing such types of data. This article aims at providing a solution to store, query and analyze streaming data using Apache Kafka as the platform and twitter data as an example for analysis. A three-way classification method is proposed for sentimental analysis of twitter data that combines both the approaches for knowledge-based and machine-learning using three stages namely emotion classification, word classification and sentiment classification. The hybrid three-way classification approach was evaluated using a sample of five query strings on twitter and compared with existing emotion classifier, polarity classifier and Naïve Bayes classifier for sentimental analysis. The accuracy of the results of the proposed approach is superior when compared to existing approaches.
Cryptanalysis

Cryptanalytic attacks against cryptosystems can be divided into two different classes: direct and indirect attacks.
Server-side Segment Selection For Low-latency Streaming - S4S

The OTT streaming growth is driven by the increasing number of wireless connected devices such as tablet and phones. An enabler for using these devices is ABR streaming (Adaptive Bitrate) that allows them to select at each moment the stream quality that best fits the available bandwidth. The overall experience is highly dependent on the bandwidth estimation. The current approach to bandwidth estimation in HTTP-based ABR players is challenged by the evolution toward low latency protocols (DASH CTE) or (HLS LL) which result in micro-burst of traffic. Indeed, estimation at the client-side, at the HTTP level, relies on the assumption that a relatively large segment of data is available and can be sent at the link-speed which is not the case anymore with low latency protocols. — To address this issue, we propose a novel scheme S4S, that allows players to interact with an S4S-enabled server in order to improve the overall experience. First, bandwidth estimation is done at the server-side, using transport's congestion control sender-side stats, leading to more precise estimates even in the presence of small burst, especially when using modern congestion control algorithms such as BBR or PCC. Second, we define a protocol for allowing the Network to control the bandwidth versus quality tradeoff.
Government's Dynamic Approach to Addressing Challenges of Cybersecurity in South Africa

Cybersecurity is the practice of making the networks that constitute cyber space secure against intrusions. The aim is to maintain the confidentiality, the availability and integrity of information, by detecting interferences. Traditionally, cybersecurity has focused on preventing intrusions and monitoring ports. The evolving threat landscape, however, calls for a more dynamic approach. It is increasingly clear that total cybersecurity is impossible, unless government develops a cyber-security strategy. The aim of this chapter is to discuss government's dynamic approach to addressing challenges of cybersecurity. The chapter looks at the co-ordination of cyber-security activities so as to have a coordinated approach to cyber-crime. This chapter also highlights the idea of protecting sensitive data for the public good. It is generally accepted that technology has become indispensable in modern society. Government's cybersecurity presents a unique challenge simply because of the volume of threats that agencies working for government face on a daily basis.
A Survey of Authentication Schemes in the Internet of Things

In the recent years, the Internet of Things (IoT) has been widely deployed in different daily life aspects such as home automation, electronic health, the electric grid, etc. Nevertheless, the IoT paradigm raises major security and privacy issues. To secure the IoT devices, many research works have been conducted to counter those issues and discover a better way to remove those risks, or at least reduce their effects on the user's privacy and security requirements. This article mainly focuses on a critical review of the recent authentication techniques for IoT devices. First, this research presents a taxonomy of the current cryptography-based authentication schemes for IoT. In addition, this is followed by a discussion of the limitations, advantages, objectives, and attacks supported of current cryptography-based authentication schemes. Finally, the authors make in-depth study on the most relevant authentication schemes for IoT in the context of users, devices, and architecture that are needed to secure IoT environments and that are needed for improving IoT security and items to be addressed in the future.
A Proactive Approach to Intrusion Detection in Cloud Software as a Service

Cloud computing environment is very much malicious intrusion prone hence cloud security is very vital. Existing network security mechanisms face new challenges in the cloud such as DDOS attacks, virtual machine intrusion attacks and malicious user activities. This chapter includes brief introduction about cloud computing, concept of virtualization, cloud security, various DDOS attacks, tools to run these attacks &amp; various techniques to detect these attacks, review of threshold methods used for detection of DDOS attacks &amp; abnormal network behavior and proposed dynamic threshold based algorithmic approach. Although various cloud security measures are prevailing to avoid virtual machine attacks and malicious user activities but these are not foolproof. Hence, new security methods are required to increase users' level of trust in clouds. By scrubbing traffic at major Internet points and backbone connection, a defense line is created for mitigation of DDOS attacks. Dynamic threshold algorithm based approach is proposed as a proactive approach to detect DDOS attacks for achieving secure cloud environment.
A Blockchain-based Online Transaction System for Physical Products Trading with Fairness, Privacy Preservation, and Auditability

Fair trading, privacy preservation, and auditability are core requirements for any online transaction system. Existing systems achieving these requirements mainly focus on digital products and rely on a centralized design. However, these systems are unsatisfactory for physical products and suffer from the single-point-of-failure problem in reality. In this paper, we analyze the inherent characteristics of actual online transaction systems for physical products, where we point out that express could not be fully trusted in reality. Specifically, the express is a rational entity and could launch various attacks to break the security or violate user’s privacy for profits. We propose PBOT, which is a blockchain-based online transaction system for physical product trading with fairness, privacy preservation, and auditability. Our security analysis and performance evaluation of its core modules demonstrate that PBOT is secure, feasible, and efficient.
Information Security Compliance Behaviour of Supply Chain Stakeholders

Supply chain security is an emerging topic in the supply chain management literature. Information security is a key component of supply chain security, and this study aims to identify the factors that influence the compliance behaviour with respect to information security. A related objective is to understand the extent to which compliance was substantive or symbolic. Adopting a qualitative approach, the authors conducted semi-structured interviews with stakeholders based in New Zealand who are involved in international supply chains. The interviews find that compliance behaviour is affected by the influence of other organizations, organizational perceptions of compliance, and the rules and norms of exchange in different contexts. The results also indicate that compliance behaviour is more symbolic than substantive in the supply chain environment.
On Solving the Multi-Objective Software Package Upgradability Problem

Free and open source software (FOSS) distributions are increasingly based on the abstraction of packages to manage and accommodate new features before and after the deployment stage. However, due to inter-package dependencies, package upgrade entails challenging shortcomings of deployment and management of complex software systems, inhibiting their ability to cope with frequent upgrade failures. Moreover, the upgrade process may be achieved according to some criteria (maximize the stability, minimize outdated packages, etc.). This problem is actually a multi-objective optimization problem. Throughout the article, the authors propose a Leximax approach based on mixed integer linear programming (MILP) to tackle the upgradability problem, while ensuring efficiency and fairness requirements between the objective functions. Experiments performed on real-world instances, from the MANCOOSI project, show that the authors' approach efficiently finds solutions of consistently high quality.