Reviewerly/exHarmony huggingface dataset:
Egomap: Hierarchical First-Person Semantic Mapping

We consider unsupervised learning of semantic, user-specific maps from first-person video. The task we address can be thought of as a semantic, non-geometric form of simultaneous localisation and mapping, differing in significant ways from formulations typical in robotics. Locations, termed stations, typically correspond to rooms or areas in which a user spends time, places to which they might refer in spoken conversation. Our maps are modeled as a hierarchy of probabilistic station graphs and view graphs. View graphs capture an aspect of user behaviour within stations. Visits are temporally segmented based on qualitative visual motion and used to update the map, either by updating an existing map station or adding a new map station. We contribute a labelled dataset suitable for evaluation of this novel SLAM task. Experiments compare mapping performance with and without the use of view graphs and demonstrate better online mapping than when using offline clustering.
An Optimization Framework for Privacy-preserving Access Control in Cloud-Fog Computing Systems

The cloud-based Internet-of-Things (IoT) has been applied to support ubiquitous data collection and centralized data processing among various applications. Equipped with powerful resources, a semi-trusted cloud is able to deduce private information by launching inference attack. Homomorphic Encryption (HE) has been proposed as an effective way to preserve privacy from inference attack while allowing certain computation over ciphertext. However, HE leads to longer latency due to additional communication and computation overheads. In this paper, we propose an optimization framework in privacy-preserving access control under cloud-fog computing systems. The optimization goal is to maximize the average user satisfaction in the system, where cost and latency serve as key metrics measuring user satisfaction. Due to the NP-hardness of the formulated problem, we propose a low-complexity suboptimal algorithm to solve it, where the access offloading decision making, user cooperation, and resource allocation are considered. Simulation results are presented to show the advantages of our proposed algorithm in terms of the average USI (User Satisfaction Index) and the number of users with zero USI.
Automated android-based book order system

Abstract An ordering system is a collection of elaborate procedures used to handle and manage the ordering process of resources. An online book ordering system is a system that allows customers to purchase books online from a seller’s website. The objective of this study was to develop an android-based application for ordering books. The system consists of two main sub-systems: the administrator system and the user system. The plan, which was built using a private university in the North Central of Nigeria as a case study, is user-friendly, efficient, and robust. The study overcame the shortcomings in the existing book order systems. It provided an effective and efficient book order system for customers and the system as well as provided an environment where a customer was provided with a secure mode of operation and searching platform to access books online. The study developed an infrastructure accessible through the internet that can be used to order books and make payments for books bought and lastly the system created a more user-friendly interface.
Modern types of communication: peculiarities of the word-formation system of SMS language

&amp;nbsp; It is hard to imagine interpersonal communication without cell phones: this method of communication not only simplifies the process of data transmission, but also significantly saves time, which is of prime important in the conditions of constant acceleration of the pace of life. This refers not only to oral form of communication using cell phones, but also high use of SMS &amp;ndash; a short text messages with a standard of 160 characters or less. The scholars have different opinion on the SMS language: some believe that it negatively affects grammar and spelling of the national language, while others see it as a platform for language games and experiments, which originates new forms of interpersonal communication. Philologists note that examination of this layer of &amp;nbsp;lexicon allows to not only comprehensively assessment this phenomenon, but due to popularization of different chats and messengers and increasing role of Internet communication, make projections on the development of cyber language, which is an evolutionary continuation of the language of short text messages. This research examines the lexicon of French SMS communication from a formal perspective, clarifies the existing theoretical positions, and gives a detailed description to the mechanisms of formation of the vocabulary. The acquired results can become the foundation for further development of the question on the material of other languages. The presented pattern for describing the SMS language can be used as a basic algorithm for studying this linguistic phenomenon in other language communities , and allow carrying out a comparative analysis of SMS language in other countries. &amp;nbsp;
Exploring the FPGA Implementations of the LBlock, Piccolo, Twine, and Klein Ciphers

In this work, the implementations of the LBlock, Piccolo, Twine, and Klein lightweight ciphers in FPGA technology are studied in terms of area, frequency, throughput, and throughput/area. To accomplish this, loop unrolling and pipelining were employed in two phases. In the first phase, different loop unrolling factors were used to implement the round function of each cipher, while in the second phase, 2-stage pipelining with loop unrolling per stage was applied. The produced designs were implemented in Xilinx (Kintex-7) FPGA technology. Based on the implementation results, a detailed study on the above-mentioned design metrics was performed and important outcomes were derived.
On the Elicitation of an Optimal Outer Approximation of a Coherent Lower Probability

The process of outer approximating a coherent lower probability by a more tractable model with additional properties, such as 2- or completely monotone capacities, may not have a unique solution. In this paper, we investigate whether a number of approaches may help in eliciting a unique outer approximation: minimising a number of distances with respect to the initial model, or maximising the specificity of the outer approximation. We apply these to 2- and completely monotone approximating lower probabilities, and also to possibility measures.
Implementation of K-Means Algorithm using Clustering Rules on Medical Data Sets

During the process of mining frequent item sets, when minimum support is little, the production of candidate sets is a kind of time-consuming and frequent operation in the mining algorithm. The K-Means algorithm does not need to produce the candidate sets, the database which provides the frequent item set is compressed to a frequent pattern tree (or FP tree), and frequent item set is mining by using of FP tree. These algorithms considered as efficient because of their compact structure and also for less generation of candidates itemsets compare to Apriori and Apriori like algorithms. Therefore this paper aims to presents a basic Concepts of some of the algorithms (K-Means Algorithmn, COFI-Tree, CT-PRO) based upon the FP- Tree like structure for mining the frequent item sets along with their capabilities and comparisons. Data mining implementation on spatial data to generate rules and patterns using Frequent Pattern (FP)-Growth algorithm is the major concern of this research study. We presented in this paper how data mining can apply on spatial data.
Applying Parallel and Distributed Computing Curriculum to Cyber Security Courses

Parallel technologies evolve at a fast rate, with new hardware and programming frameworks being introduced every few years. Keeping a Parallel and Distributed Computing (PDC) lecture up to date is a challenge in itself, let alone when one has to consider the synergies between other courses and the shifts in direction that are industry-driven and echo inside the student body. This paper details the process of aligning parallel and distributed curriculum at the Military Technical Academy of Bucharest (MTA) over the last five years, with government and industry demands as well as faculty and student expectations. The result has been an adaptation and an update of the previous lectures and assignments on PDC, and the creation of a new course that relies heavily on parallel technologies to provide a modern outlook on software security and the tools used to combat cyber threats. Concepts and assignments originally designed for a PDC course have molded perfectly into a new supporting paradigm focused on malicious code (malware) analysis.
Attributed Heterogeneous Network Embedding for Link Prediction

Network embedding aims to embed the network into a low-dimensional vector space wherein the structural characteristic of the network and the attribute information of nodes are preserved as much as possible. Many existing network embedding works focused on the homogeneous or heterogeneous plain networks. However, networks in the real world are usually not plain since the nodes in the networks have rich attributes, and these attributes play important roles for encoding nodes’ vector representations. Although some works took into account the attribute information, they could not handle the homogeneous and heterogeneous structure information of the network simultaneously. In order to solve this problem, a new network embedding method that considers both the network’s homogeneous and heterogeneous structure information and nodes attribute information simultaneously is proposed in this paper. The proposed method first obtains nodes attribute information, homogeneous and heterogeneous structure information as three views of the network and learns network embeddings of the three views through different technologies respectively. Then, an attention mechanism is utilized to fuse the embedding results learned from the three views to obtain the final vector representations of nodes. We verify the performance of the proposed model through link prediction tasks on four real-world datasets, and extensive experimental results show that the proposed model outperforms the advanced baselines.
Corpus and Models for Lemmatisation and POS-tagging of Classical French Theatre

This paper describes the process of building an annotated corpus and training models for classical French literature, with a focus on theatre, and particularly comedies in verse. It was originally developed as a preliminary step to the stylometric analyses presented in Cafiero and Camps [2019]. The use of a recent lemmatiser based on neural networks and a CRF tagger allows to achieve accuracies beyond the current state-of-the art on the in-domain test, and proves to be robust during out-of-domain tests, i.e.up to 20th c.novels.
Poisson Graphical Granger Causality by Minimum Message Length

Graphical Granger models are popular models for causal inference among time series. In this paper we focus on the Poisson graphical Granger model where the time series follow Poisson distribution. We use minimum message length principle for determination of causal connections in the model. Based on the dispersion coefficient of each time series and on the initial maximum likelihood estimates of the regression coefficients, we propose a minimum message length criterion to select the subset of causally connected time series with each target time series. We propose a genetic-type algorithm to find this set. To our best knowledge, this is the first work on applying the minimum message length principle to the Poisson graphical Granger model. Common graphical Granger models are usually applied in scenarios when the number of time observations is much greater than the number of time series, normally by several orders of magnitude. In the opposite case of "short" time series, these methods often suffer from overestimation. We demonstrate in the experiments with synthetic Poisson and point process time series that our method is for short time series superior in precision to the compared causal inference methods, i.e. the heterogeneous Granger causality method, the Bayesian causal inference method using structural equation models LINGAM and the point process Granger causality.
Courting Disaster: The Underappreciated Risk of a Cyber-Insurance Catastrophe

Cyberattacks have the potential to cause simultaneous, very large losses to numerous firms across the globe, thus resulting in a “catastrophe.” Moreover, there are plausible reasons for believing that a future cyberattack could produce world-wide losses that are larger by an order of magnitude than any past attack. This Article argues that such a cyber-catastrophe could cripple the industry, for two primary reasons. First, many traditional property/casualty policies might well provide “silent-cyber” coverage of a much larger portion of these costs than is now anticipated, because a catastrophe could well result in “physical damage or loss” to tangible property resulting from cyberattacks. This is especially true for auto and homeowners coverage, which generally do not expressly exclude cyber-risk due to the historic absence of claims in these domains. But even general commercial property and CGL policies might cover significant elements of this loss, depending on their terms. Second, a catastrophe could cripple the growing number of insurers providing cyber insurance coverage, which expressly cover various losses associated with cyberattacks and the compromise of electronic data. These cyber-insurers face unique difficulties in using two of the most important tools for limiting their exposure to catastrophe risk: coverage exclusions and underwriting. Although cyber-insurers have historically offset these difficulties by insisting on artificially low coverage limits, competition is increasingly rendering this strategy unworkable. In short, both traditional forms of and new forms of are courting disaster. The Article therefore closes by identifying and analyzing several alternative approaches to protecting traditional from catastrophic loss and encouraging new forms of that provide increased coverage without exposing insurers to excessive financial risk.
A Novel Approach to News Archiving from Newswires

A news archive is the core operational tool a media relations team depends on in order to effectively feed a data-hungry organization. An ingrained approach to news archiving in existence is the use of a relational database. As a consequence, integrating search engines that support full-text search is practically impossible due to the strict data schema that is defined in relational database systems. Therefore, there is a need for news archives that support full-text search with relevance ranking of news. In this paper, an approach that supports full-text search is proposed. The process is started by crawling newswire websites for news that are relevant with respect to some predefined keywords and extracting them. Then, they are stored in a data structure known as an inverted-index which supports full-text search, aggregation, and relevance ranking of search results. Search results are ranked and returned to a user in the order of decreasing relevance to the search term. We were able to provide a software solution written in java, the jsoup library for HTML parsing, and an elasticsearch implementation of a search engine. We tested our solution on nine newswires using ten keywords and were able to retrieve a total of 42 relevant news matching seven keywords. The approach proposed in this paper when compared to the manual approach performed better in terms of retrieval speed and accuracy. We conclude that three main components are important in a good digital archive: relevance, extraction, and search. This work is an integration of a good relevance marking technique, an extraction method, and a search engine.
Distributed Data Trading Model Based on Blockchain

The current flow and trading of data has been a trend, but the current enthusiasm for data trading is not high. Due to the particularity of data and the current trading pattern of data, the ability of data trading platform to retain trading data has become a potential threat to both sides of the transaction and an obstacle to data trading. Due to the sensitivity of data, how to protect the privacy security of both sides of the transaction is also an important factor hindering the current data transaction. In response to the existing problems, this paper proposes a feasibility plan for data security transactions based on blockchain and in an untrusted environment, and designs a new decentralized transaction platform that will help transform data through online transactions. At the same time, it is technically guaranteed that the platform does not have the possibility of viewing, copying and detaining data, thereby ensuring the secure transaction of data and the privacy of both parties to the transaction.
Going virtual in the early years: 30-month-old toddlers recognize commonly used emojis

People nowadays do not only display facial expressions in face to face communication but also in on-line communication using graphical symbols named emojis. The present study explored 30-month-old toddlers' ability to recognize emojis that represented six basic human emotions. In the study, 38 toddlers first saw scenarios that elicited different emotions in an actor and were asked to visually identify the matching emoji in the presence of a distractor. Eye-tracking results showed that toddlers could correctly identify the emoji that represented the emotion in each scenario. Toddlers then heard different emotion words and were again found to be able to identify the matching emoji. These findings provide preliminary evidence that the ability to recognize and understand the emotional value of emotion symbols in the virtual world emerges early in development.
Access Control Mechanism in Emergency Departments: A Quality Improvement Project

目的：提升並分析急診門禁管理。方法：南部某醫學中心急診部於2020年初因應冠狀病毒疾病（COVID-19）疫情發展，執行門禁管控品質提升計畫。該計畫包含開發急診病人陪伴證，管制進出自動刷卡門，資訊化管理駐警手機陪伴證稽查等。2020年3月16日至2020年3月26日進行網路問卷調查，以評量品質提升成效。結果：總共回收161份問卷，其中32（19 9%）是急診醫師，64（39 7%）護理師，34（21 1%）警衛，31（19 3%）是病人及家屬。總體滿意度為93 5%，在環境安全，隱私，及感染控制的滿意度最高。門禁管控對於減少臨床及護理作業干擾及職場暴力也受到肯定。然而，門禁可能造成探病不便，病人及家屬的情緒壓力增高。結論：急診門禁管制不僅對環境與感染控制極為重要，也助於醫療與護理臨床作業。 Purpose: To evaluate and improve the access control of emergency departments Materials and methods: In early 2020, we conducted a project to establish an access control mechanism for inhibiting the spread of coronavirus disease (COVID-19) in the emergency department of a tertiary teaching hospital in Tainan, Taiwan The project included the use of a pass for accompanying individuals, the use of an auto sensor for door control, and the information management of security guards by using mobile applications A web-based questionnaire survey was conducted between March 16, 2020, and March 26, 2020, to evaluate the outcome of the project Results: We collected 161 survey responses, of which 32 (19 9%), 64 (39 7%), 34 (21 1%), and 31 (19 3%) were from emergency physicians, nurses, security guards, and patients and their families, respectively The overall satisfaction rate was 93 5%, and most participants agreed that access control is necessary for environmental security, privacy, and infection control Access control is also beneficial for minimizing care interruption and workplace violence However, access control may increase emotional distress and inconvenience for patients' families while visiting patients Conclusion: Access control of emergency departments is crucial for environmental and infection control as well as for effective medical and nursing practices
Research on Image Texture Feature Recognition Method based on Rotation Invariant Histogram

The traditional image texture feature recognition method is deeply affected by the complexity of the image background and the amount of noise, resulting in a low recognition rate. To solve the above problems, an image texture feature recognition method based on rotation invariant histogram is proposed. After denoising the image to be recognized, the image texture feature is extracted and the image feature texture set is established. After the rotation invariant region of the image histogram is selected, the classifier is used to recognize the image texture features. Design a comparative experiment with the current recognition method. The experimental results show that the research method can improve the recognition accuracy by about 12.9%, and the recognition time is short and reliable.
The Effect of Various Broker Policy Algorithms in Geo-distributed Datacenters on Response Time and Load Balance Review and analysis

Cloud computing represents a new way to deploy computing technology to give users the ability to access, work on, share, and store information using the Internet. Developments in Cloud computing gain rapid attention of the researcher to maximize the utilization of computing resources such as storage, CPUs, and network bandwidth as service-by-service providers at less cost. The optimization models aims to optimize both resource centric such as utilization, availability, reliability and user centric like response time, budget spent fairness, and since the broker is responsible of route the user request to the most appropriate datacenter in the cloud system, choosing the right broker policy affect directly the optimization of the resources, in this paper we explore various number of existing broker policy and compare them in different scenarios in order to show the advantages and limitations of each one of them due there impact on the response time and the load distribution over the system.
Design and Evaluation of Speed Forms for Design of an Amphibious Vehicle

Amphibious vehicles (AV) are vehicles that can operate on land as well as on water. Historical evidences of different models of amphibious vehicles show that it has been lacking aesthetic and styling aspects in its design. An effort has been made in this paper to derive bio-inspired speed forms considering aesthetic and dynamism characteristics to design the AV for amusement purpose. Aesthetics based on visual characteristics or expressions that are created in 2D and 3D by taking inspirations from biological organisms are called speed forms. Speed Forms for AV are evaluated to know the user choices. User survey was conducted for understanding aesthetic preferences, perception of dynamism and their selection criteria. A total of 22 users participated (age range: 18–35 years; 70% males, 30% females) in this study and they like to explore AV for amusement purpose. The data was analyzed by using SPSS software using descriptive and inferential statistics (Friedman ANOVA and Wilcoxon signed test). It was observed that speed form 1 (inspired from frog) was most preferable among three speed forms explored in this study. The speed form 1 was aesthetically better than other speed forms; however, all the selected speed forms had dynamic characteristics. Hence, speed form 1 has the potential to be considered for designing amphibious vehicles used for amusement purpose.