Reviewerly/exHarmony huggingface dataset:
An Investigation of Placement of Textual and Graphical Information Using Human Performance and Eye Tracking Data

This paper reports the findings of a human-machine system (HMS) experiment, which was conducted to explore how to combine textual and graphical information in an interface. Specifically, this paper explored how the location of textual and graphical information would influence response time, accuracy and eye movement. We also explored the effectiveness of three different configurations (9, 16, and 25 points). Our findings suggest that if the accuracy was the highest priority, the textual information should be placed at the left side of the screen and the graphical information should not be placed at the center of the screen. If a quicker response time was the highest priority, the graphical information should not be placed at the corners and bottom right margins. Finally, if an interface includes both textual and graphical elements, graphical information could be placed at corner and margin areas and textual information could be placed at corner areas to facilitate the efficiency of information processing. From the perspective of response accuracy and response time, the 9-point configuration was most appropriate for the calibration process.
Control Sets of Linear Control Systems on Matrix Groups and Applications

We review recent results on control sets of linear control systems on matrix groups. We also mention some applications of systems with algebraic structures.
Ship Detection Using Machine Learning and Optical Imagery in the Maritime Environment

Machine Learning (ML) is increasingly being used to enable machines to aid our understanding of digital imagery. Convolution Neural Network (CNN) models have become a common method of implementing ML. In this paper, five award winning CNN models have been assessed for their ability to identify ship types in images from remote sensors. The CNNs are trained with a collection of known ship images using supervised learning techniques, and, due to the low number of images available for training, Transfer Learning (TL) is adopted to take advantage of pre-trained low-level features.
Modelling of the Advanced Level National Examination Average Pass Rate in Zimbabwe using Bayesian Hierarchical Log-logistic and Normal Mixture Approach

Abstract The national examination as one of the standard evaluation systems of education in Zimbabwe is used for the educational developments that seek to improve the quality of education in the educational sectors. This research aims to find the best model and its factors affecting the average pass rate of the Advanced Level (A-Level) national examination in Zimbabwe. Modelling was conducted using a two-level hierarchical model with factors influencing the national examination at district in the first level and those influencing the national examination provincial level in the second level. The Bayesian approaches namely hierarchical log-logistic and normal mixture were used in the modelling. The estimation of these Bayesian approaches posterior parameters was done using Markov Chain Monte Carlo (MCMC) and the Deviance Information Criterion (DIC) value was used to select the best model. The hierarchical normal mixture was found to be the best model to explain the variability of the average pass rate percentage of the A–Level national examination and all the micro and macro variables in this study significantly influenced the A-Level national examination in Zimbabwe.
Functional and Structural Features of Recurrent Neural Networks with Controlled Elements

The features of recurrent neural networks with controlled elements are considered. The functions of these networks for controlled associative processing of signals are refined. A number of the space-time structures of such networks are analyzed. Among them, the neural networks with one-, two-, and three-level structures of layers are investigated. The results of studies on the stability of the associative processing of distorted signals by these networks are reflected. Based on the simulation results, recommendations are formulated to expand the possibilities of associative signal processing in recurrent neural networks with controlled elements.
Convolutional Neural Network Single-Point Control Model

The artificial intelligence as-a-service represented by the convolutional neural network faces many security problems, such as the adversary bypassing the cloud access control to obtain the control of the convolutional neural network model. In view of the above security threats, this paper designs and proposes a singlepoint control model aiming at realizing the control of the neural network by the owner of the convolutional neural network. Control model will make the adversary can not use convolutional neural network normally. On the basis of ensuring the security and control of the convolutional neural network, only the bias in the single convolutional layer is mutated, which can reduce the computational complexity of the convolutional neural network in the operation process and reduces the running time overhead. Experiments show that the single-point control model of convolutional neural network can realize the security control of the neural network model and has less overhead than the previous model.
Fight to Be Forgotten: Exploring the Efficacy of Data Erasure in Popular Operating Systems

A long history of longitudinal and intercultural research has identified decommissioned storage devices (e.g., USB memory sticks) as a serious privacy and security threat. Sensitive data deleted by previous owners have repeatedly been found on second-hand USB sticks through forensic analysis. Such data breaches are unlikely to occur when data is securely erased, rather than being deleted. Yet, research shows people confusing these two terms. In this paper, we report on an investigation of possible causes for this confusion. We analysed the user interface of two popular operating systems and found: (1) inconsistencies in the language used around delete and erase functions, (2) insecure default options, and (3) unclear or incomprehensible information around delete and erase functions. We discuss how this could result in data controllers becoming non-compliant with a legal obligation for erasure, putting data subjects at risk of accidental data breaches from the decommissioning of storage devices. Finally, we propose improvements to the design of relevant user interface elements and the development of official guidelines for best practice on GDPR compatible data erasure procedures.
Apprenticeship: precision optics manufacturing technician

As U.S. manufacturing works to overcome a technical skills gap, apprenticeship is gaining favor as a post-secondary educational path. Registered apprenticeship is a structured program that requires on the job training of an employee, accompanied by related instruction, and a progressive wage scale as the apprentice progresses through the program. This presentation describes how the Occupation Title for a Precision Optics Manufacturing Technician was defined for the industry, and approved by the US Department of Labor (USDOL). The occupation was later deemed apprenticeable by the DOL, and the apprenticeship was designed and registered with NY State. The program entails a three year (6,000 hour) rotation through every department of an optics manufacturing firm, with defined outcomes regarding hours of experience and competencies gained. In this program, structured, hands on technical training is combined with theoretical class room learning to develop highly skilled manufacturing technicians. This presentation will explain many of the details surrounding registered apprenticeship, including adjustments and results encountered during the first three years of the Optimax program.
Large-Scale Image Search using Region Division

In this paper, we focus on the problem of image feature extraction and similarity measure using region division search. Specifically, we proposed a novel image region division to roughly mimic the location distribution of image color and deal with the color histogram failing to describe spatial information. Furthermore, an image descriptor combining local color histogram and Gabor texture features with reduced feature dimensions are developed for optimizing our region division search method. Moreover, an extended Canberra distance is proposed for images similarity measure to increase the faulttolerant ability of the whole large-scale image search. Extensive experiments on several benchmark image retrieval databases validate the superiority of the proposed approaches.
A Novel Improved System Based on CVSS

CVSS is one of the most representative quantitative assessment algorithms for security vulnerabilities. The calculated values of vulnerability harm according to the given index and formula. However, there is still insufficient objectivity of CVSS. Expert System is a kind of expert system for vulnerabilities; the severity of vulnerability can be assessed by experts based on the Expert System. The objectivity of Expert System and CVSS is analyzed, and CVSS is revised based on Expert System. The relationship between the above systems and the CWE, and between above systems and Product is analyzed, respectively, in the process. A new way of using Expert System is put forward based on CWE, namely, CWE Cycle Sorting Algorithm, in order to sort the average of vulnerability severity. Meanwhile, CWE Sort Factor is put forward based on CWE Cycle Sorting Algorithm to modify CVSS. The result is closer to the Expert System in terms of objectivity.
Optimization of Echo Parameter in Intelligent Instrument Under the Condition of Numerical Stability

The design of acquisition parameters is of great significance for intelligent instrument. In order to achieve better application of this method to formation fluid identification and reservoir evaluation, the observed echo amplitude and data kernel matrix are not affected by the diffusion coefficient and relaxation time. The FDTD method uses a set of finite difference equations to replace the Maxwell’s rotation equation, that is, the solution of the differential equations is replaced by the solution of the difference equations. This substitution is meaningful only when the convergence and stability of the discrete differential equations are explained. Compared with the transmission of some broadband information, the use of a specific feature structure can increase the degree of freedom intelligent instrument, and give more detailed description of the echo parameter that can be used. By analyzing the basic principle of intelligent instrument, this paper uses the FDTD method to explain the signal of the instrument, and the simulation results are good.
Convert Probability Network to Artificial Neural Network based on Position, Time and Speed of Events

Abstract Building a probability function to get the best answer between events in many review problems is a very complex one. In this study, several events were given as a case study to find a solution, after which an analysis will be carried out when and where the event contained position and time features. So that the distance factor, travel time factor and speed factor for each event can be calculated. Then a concept comparison between Probability Network from the Bayes Theorem versus Artificial Neural Network (ANN) was made, accompanied by a review of the Propositional Logic (PL) or First Order Logic (FOL) concept compared to using the Uncertainty concept in calculating the probability of an eventin a pseudo event that has often been ignored because it is considered to have no influence and the probability is small whereas it can be the opposite. In fact, sometimes, the pseudo event is the main factor determining the real event and the probability is much greater than expected. In addition, we also give an Engineering Events Theorem that can intentionally set event-by-event syntheses to increase the probability of an event which is initially smaller than the probability of another event. After being given an injection/dummy/synthesis event, however, then the initially small probability of the event becomes very high and the time of occurrence can be accelerated or decelerated, or even minimized so that it does not occur.
Multiscale Autoregressive (MAR) Models with MODWT Decomposition on Non-Stationary Data

Time series forecasting often shows behavior that is non-stationary, so it is necessary to do a forecasting method that can predict non-stationary data in order to obtain good forecast results, including Autoregressive Integrated Moving Average (ARIMA) and Multiscale autoregressive (MAR). The characteristics of this model do not include predictor variables in the model. The MAR model is a model that performs the transformation process using wavelet. The MAR Model adopts an autoregressive (AR) time series model with predictors used are wavelet and scale coefficients. The wavelet and scale coefficients are obtained by decomposing using Maximal Overlap Discrete Wavelet Transformation (MODWT). MODWT functions to decipher data based on the level of each wavelet filter. This research aims to determine the best forecasting model using the ARIMA and MAR models. Testing performed on non-stationary data, so the ARIMA and MAR models can be used in this research. This research is expected to be able to obtain the best time series model and the most suitable to be used in predicting on non-stationary data.
Breaking Hardware Implementation of SM4 Algorithm using Principal Component Analysis and Machine Learning Classifiers

In this paper, we executed a power analysis of hardware implementation of SM4 algorithm using machine learning classifiers (support vector machine (SVMs), Decision Trees (DT), k-Nearest Neighbor, (KNN), Ensemble learning (EB), etc.). We first download hardware design to the FPGA in power acquisition platform based on SASEBO-G board to obtain the power traces of SM4 during its encryption process. Then we introduced a machine learning method to the Hamming-weight attack model of SM4 algorithm. In the data preprocessing phase, principal component analysis (PCA) method was applied, and we explored how many points of interested (PoIs) should we take to achieve the best test accuracy. We have tried different kinds of classifiers and their performance against Gaussian noise. The results show that for different classifiers, the number of PoIs selected when achieving maximum precision is different, PCA can reduce the dimensions of power trace effectively but probably decrease the test accuracy. Furthermore, most of these classifiers have excellent resolution (up to 100%) for Gaussian noise superimposed on the power traces, which means that machine learning classifiers such like SVM and Decision Trees can compete with template attacks based on Gaussian distribution.
Substantive and Procedural Legislation in Israel to Combat Webcam-Related Child Sexual Abuse

The Sweetie project is designed to detect and collect evidence against online attempts made by adults to sexually interact with children who are victimized by sex abusers. This chapter analyses the compatibility of Israeli law with the utilization of the Sweetie software. The Israeli substantive law has undergone very few changes to accommodate the age of cyberspace. Most of the Israeli sex offenses have kept their physical-world version, including rape, forbidden intercourse by consent, sodomy and indecent act. By judicial broad interpretation, many of these offenses have been applied to online interaction between adults and minors online, including statutory rape and rape by fraud. Some of these offenses have been applied to interaction between adults and fake profiles of children. Offenses of obscenity, also not formally defined by Israeli law as sex offenses, have changed a little bit to accommodate for the computerized age. Today they are applied in Israel regarding any form of child nudity and child pornography. The Israeli law and jurisprudence do not define obscenity. Like the substantive law, Israeli procedural law is largely unimpressed by cyberspace. Some investigatory powers have not been set, such as expedited preservation of stored computer data. Other powers are very easy to use, such as seizure and search of computers in any offense suspected. There are no laws regarding entrapment. Undercover operations are not mandated by any law, but still they are far from rare. Israeli law of evidence is extremely thin in content and almost in all instances the court does not have to exclude evidence that was illegally obtained. The Israeli constitutional law has had very little effect on Israeli criminal law. In light of the above, and due to the easiness of operating agents online, the chapter suggests that possible consequences and implications must be contemplated before using a tool like Sweetie, including the effects on social media and of other fields of law, and after carefully analysing the destination of the initiative. Regarding online agent operation, especially paedophile operations, taking suspect human rights into account must be done in advance in a well-balanced and accountable procedure, since no remedy will be given later to anyone assumed to be a paedophile.
Novel Concept of Spelling Correction for Semantic Tourism Search Interface

In this technical era, searchingLaddha, Shilpa the Web has accelerated, butJawandhiya, Pradip M. the user satisfaction is still not up to the mark. However, retrieval of required data is yet a challenging job. Users still struggle to get the precise facts on the Internet. To fetch the most correct end result is a hard mission due to the giant unstructured repository on the Web, huge list of results returned by search engines, etc. Spelling errors in query are the main attribute that degrades the performances of any search interface. In this paper, the effectiveness of the search interface is improved and the performance is accelerated by implementing novel spelling correction module which is implemented and tested on Indian tourism semantic search interface to handle misspelled words in query.
The Chain Alignment Problem

This paper introduces two new combinatorial optimization problems involving strings, namely, the , and a multiple version of it, the . For the first problem, a polynomial-time algorithm using dynamic programming is presented, and for the second one, a proof of its $$\mathcal {NP}$$ -hardness is provided and some heuristics are proposed for it. The applicability of both problems here introduced is attested by their good results when modeling the .
Proposed Method for the Type-2 Fuzzy Sugeno Integral

In this chapter, the proposed method for extending the Sugeno integral with the operators of generalized type-2 fuzzy logic is explained and illustrated.
HCI in the Wild Mêlée of Office Life—Explorations in Breaching the PC Data Store

‘HCI in the wild’ was meant to be a call to get HCI investigations out of the lab into the mêlée of real life. This is of course a commendable suggestion, though begs questions about what kinds of methods and topics are suited for exploring in this mêlée as against in the lab. Claims by some experimentalists that they seek ecological validity in lab studies are largely missing the point since the thing that studies in the wild seek are essentially only those things that occur outside the lab—and hence are not things that can be replicated, modelled, or emulated. But in any case, some of those who have taken up the call for studies in the wild have taken this rather too literally—they have sought wild places, places where HCI researchers have not gone before. Needless to say this being HCI, the places in question are not often that wild, woods near Brighton, for example, street life in south Cambridge. What they ignore as they venture into these settings is the mêlée of office life, the place where the bulk of computer systems are located and the place in which, oddly enough, increasingly little HCI research gets done.
Blind Face Retrieval for Mobile Users

Recently, cloud storage and processing have been widely adopted. Mobile users in one family or one team may automatically backup their photos to the same shared cloud storage space. The powerful face detector trained and provided by a 3rd party may be used to retrieve the photo collection which contains a specific group of persons from the cloud storage server. However, the privacy of the mobile users may be leaked to the cloud server providers. In the meanwhile, the copyright of the face detector should be protected. Thus, in this paper, we propose a protocol of privacy preserving face retrieval in the cloud for mobile users, which protects the user photos and the face detector simultaneously. The cloud server only provides the resources of storage and computing and cannot learn anything from the user photos and the face detector. We test our protocol inside several families and classes. The experimental results reveal that our protocol can successfully retrieve the proper photos from the cloud server and protect the user photos and the face detector.
A Comparative Study on Two Ground Truth Inference Algorithms based on Manually Labeled Social Media Data

In the booming information era, smart devices such as smart phones accompany peoples’ lives all the time. Social media platforms provide users with uninterrupted communication and information acquisition including posting users’ feelings and sharing ideas. This study focuses on short texts posted by users. Their true meaning is defined as ground truth. However, acquiring it from the users directly is extremely difficult and time-consuming. In other words, in many cases, short texts do not have their ground truth. Thus, we deal with a no ground truth problem. In this work, we ask for labelers to label short texts completely based on their own judgment of these texts. Two ground truth inference approaches, majority voting (MV) and positive label frequency threshold (PLAT), integrate the labels from different labelers and deduce the ground truth. We then analyze which one better suits for labeling unlabeled short texts. The work is of great significance in helping us obtain useful knowledge from massive social media data.
Substantive and Procedural Legislation in the United States of America to Combat Webcam-Related Child Sexual Abuse

This chapter studies the legality of Sweetie 2.0 under the Federal Law of the United States of America. We review both substantive and procedural criminal law, and formulate recommendations for the instigators of the project. We conclude that the design of Sweetie 2.0 that was used as a basis for this analysis comprises several flaws, both from a legal and policy standpoint. In order to address these flaws, we suggest a re-design of Sweetie as a semi-supervised bot, instead of a fully automated one.
Factors Affecting Cloud Infra-Service Development Lead Times: A Case Study at ING

The development of Cloud Infra-Services has shifted over the past decade in the direction of a software code development process, also known as infrastructure as code (IaC). Contemporary continuous delivery settings in industry require fast feedback. As a consequence, companies need insight in time spent, especially in the development of such services. We examine a series of 28 Cloud Infra-Services within ING, and explore which factors affect their overall time to market and development time. An initial perception among several stakeholders in the Cloud Infra-Service development process, that Cloud Infra-Services within ING take longer than those in peer companies, is not confirmed by our benchmark. Development team members identified the time to internal market of services to be affected negatively by the portal where consumers can order a service and the Orchestration Workflows and by team dynamics. This perception is supported by additional metrics. We propose that promising ways to reduce lead time include reducing the complexity of the ING environment, by treating Cloud Infra-Services like regular software deliveries and by reducing the dependencies between teams in terms of tooling and collaboration.