Reviewerly/exHarmony huggingface dataset:
A Novel Probabilistic Projection Model for Multi-camera Object Tracking

Correlation Filter (CF)-based algorithms have achieved remarkable performance in the field of object tracking during past decades. They have great advantages in dense sampling and reduced computational cost due to the usage of circulant matrix. However, present monocular object tracking algorithms can hardly solve fast motion which usually causes tracking failure. In this paper, a novel probabilistic projection model for multi-camera object tracking using two Kinects is proposed. Once the object is found lost using multimodal target detection, the point projection using a probabilistic projection model is processed to get a better tracking position of the targeted object. The projection model works well in the related experiments. Furthermore, when compared with other popular methods, the proposed tracking method grounded on the projection model is demonstrated to be more effective to accommodate the fast motion and achieve better tracking performance to promote robotic autonomy.
Monitoring ‘Cyber Related’ Discussions in Online Social Platforms

As the use of social platforms continues to evolve, in areas such as cybersecurity and defence, it has become imperative to develop adaptive methods for tracking, identifying and investigating cyber-related activities on these platforms.This paper introduces a new approach for detecting "cyberrelated" discussions in online social platforms using a candidate set of terms that are representative of the cyber domain.The objective of this paper is to build and evaluate these candidate terms for detection and tracking of cyberrelated activities across various online platforms.The methodology presented in this paper applies natural language processing techniques to representative data from multiple social platforms to develop a representative cyber lexicon that can be applied to filter and text retrieval tasks in online social platforms.This paper also evaluates the terms' performance in classifying discussions as 'cyber-related' or 'non-cyberrelated'.The results presented are most applicable to cyber threat monitoring and detection of malicious cyber activities in online social platforms.
Extremely Fast Neural Computation Using Tally Numeral Arithmetic

The tally numeral is an ancient and simple counting system. Yet, it achieves extremely fast neural computation by combinatorial logic. This paper introduces the tally numeral arithmetic logic and shows that the tally numeral AND operation performs as a Rectified Linear Unit, suggesting its potential applications to deep neural networks. The first tally numeral arithmetic ADD and AND were implemented on a Field Programmable Gate Array (FPGA) in 2016 to build a perceptron. We prototyped a three-layer XOR network to exercise a fast and flexible tally numeral multiplier, which can perform non-linear transformation of input values as well.
The End of Marketing as You Know It. Or Not!

The paper aims to analyze and synthesize the effects of the General Data Protection Regulation (GDPR) on the marketing of organizations. Currently, information is essential in marketing, and as a result, marketing is increasingly seen as a data-driven activity. Marketers are aware of the changes that information technologies have brought in their activities, offering the opportunity to develop innovative and creative customer-oriented strategies. This type of strategy is based on a deep understanding of customer profile and customer behavior materialized in collecting, storing and analyzing customer data. Will GDPR change this? What will be the problems associated with this regulation? How will they adapt their marketing activities to the new regulations? In this article, we looked at various aspects of the answers to these questions. Based on the analysis of the experts’ opinions, we highlighted the changes companies will make to comply with the GDPR provisions.
Robust Perception for Aerial Inspection: Adaptive and On-Line Techniques

This chapter explains an adaptive on-line object detection and classification technique for robust perception due to varying scene conditions, for example partial cast shadows, change on the illumination conditions or changes in the angle of the object target view. This approach continuously updates the target model upon arrival of new data, being able to adapt to dynamic situations. The method uses an on-line learning technique that works on real-time and it is continuously updated in order to adapt to potential changes undergone by the target object. The method can run in real-time.
Software Solution Incorporating Activation Congnitive Memory Portion in Early Stages of Alzhaimer’s Disease

The publication is focused on the research and use of software in the field of nursing care. The software for households has been developed at the university and it assists in activation of the memory part of the cognitive function for people with initial phases of Alzheimer’s disease. The application is also applicable to those who are at the vulnerability group because the disease could appear.
Influence of Hyperparameter in Deep Convolution Neural Network Using High-Resolution Satellite Data

In recent years, Deep Convolution Neural Network (DCNN) has evolved in the various fields of computer vision and performed state of the art in image classification task including feature extraction in high-resolution satellite imagery. During the training process, DCNN is required to achieve the highest accuracy but framework should be optimized with the help of hyperparameters (knobs) to reach that accuracy. In this study, we investigated the effect of various hyperparameters like batch size, learning rate, and other additional factors like test ratio w.r.t different variants of gradient optimizer algorithms, which can help to give the direction for the searching of better hyperparameter setting so that DCNN could achieve better classification accuracy with less computation time. Herein, we used SAT4 and SAT6 datasets to train the ALEXNET framework.
Reflective Practice: The Hidden Superpower behind Faculty Peak Performance

The Department ChairVolume 30, Issue 1 p. 15-16 Articles Reflective Practice: The Hidden Superpower behind Faculty Peak Performance Mary Devine, Mary DevineSearch for more papers by this authorJennifer Woodill, Jennifer WoodillSearch for more papers by this author Mary Devine, Mary DevineSearch for more papers by this authorJennifer Woodill, Jennifer WoodillSearch for more papers by this author First published: 02 July 2019 https://doi.org/10.1002/dch.30268Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume30, Issue1Summer 2019Pages 15-16 RelatedInformation
Five Free Expression Safeguards from a Facebook User's Perspective

Pressure is mounting on Facebook to moderate content more aggressively, risking collateral damage to free expression. Activists have responded by demanding new policies to protect expression rights but, until now, there has been no empirical evidence about public opinion of these safeguards.Using an original survey of a non-representative sample of US adults who post regularly on Facebook (n=496), I explore concerns about moderation and reactions to five proposed safeguards: increased transparency reporting of moderation decisions, the right to an explanation of those decisions, the right to appeal them, an independent oversight board, and a government audit. (Some are already partially implemented.)The findings are statistically significant (p < 0.05) and have implications for evidence-based policymaking. The right to appeal a moderation decision would ease concerns about overmoderation about as effectively as the moderation oversight board which Facebook is already building. I recommend that Facebook prioritize implementing the right to appeal, but also note that most respondents want more power ceded to the board. A US government audit of Facebook’s moderation would intensify concerns, so I recommend that potential government intervention be limited to mandated transparency.Challenging a dominant narrative, I find respondents are more concerned about overmoderation than undermoderation, and add this to the list of reasons that content moderation AI should not be tuned aggressively.Further, I compare the data against the power expression protection theory (Andsager, et al. 2004. Free expression and five democratic publics.), which claims people of more powerful demographics tend to value freedom of expression more. Finding no support in this context, I conclude it would be a mistake to stereotype those concerned about overmoderation as limited to privileged groups.
Greek Culinary Tourism Is Lost in Translation

In the framework of the ongoing development of an application that both offers a multilingual, comprehensive (ingredients, nutritional properties, cultural context, location) presentation of the culinary wealth of Central and East Macedonia and Thrace and draws on OCR and Machine Translation techniques, we develop an ontology of national and local foods and wines supported by a standards compatible multilingual thesaurus. We talk about the problems encountered in the particular terminological domain and outline our methodology of populating the thesaurus.
A peek into the swarm

The Gravitational Search Algorithm is a swarm-based optimization metaheuristic that has been successfully applied to many problems. However, to date little analytical work has been done on this topic.
A Bayesian displacement field approach to accurate registration of SAR images

Precise registration of synthetic aperture radar (SAR) images is a nontrivial task since a change in radar-acquisition geometry generates image shifts. In existing system, either the transformation functions are oversimplified, or external measures such as digital elevation model and flight track are required to be precise. In this paper, we proposed a generative Bayesian approach to modelling the displacement vectors that map the position of each pixel in the image, thus avoiding degradation of the transformation function. Rather than providing a point estimate for the transformation function, the proposed method yields a full posterior density function of the transformation function. Especially, the Bayesian model learns all the parameters adaptively, and the procedure is fully automatic. The proposed model is comparable in accuracy to state-of-the-art optical flow methods on the challenging Sintel benchmarks, and outperforms currently published SAR image registration methods on some real SAR data with critical scenes.
A Human-Computer Interaction Framework for Interface Analysis and Design

Digital device ubiquity increased the need for HCI’s analysis. The establishment of a structured body of knowledge is essential for the development of more efficient interfaces and interaction experiences. The methodical analysis of interaction’s possibilities requires models fostered by robust classification systems. Its development is vital for the establishment of a common language between researchers and designers, towards praxis and innovation (clarifying the relationships between entities and interaction solutions). We propose an HCI Model, supported in agents positioning neutrality, as well as the required concepts and processes’ deconstruction. HCI is constructed on a succession of goal-related communicational events, here termed Interactive Processes. We propose an analytical classification system determined by the relation that Interactive Processes establish with each other within an HCI, as well as the ones established between the Interactive Processes of different HCIs, and also by the number of Interactive Processes’ Conjugated Pairs developed within an HCI.
EMOCS

Clinical scorecards of risk factors associated with disease severity or mortality outcome are used by clinicians to make treatment decisions and optimize resources. This study develops an automated tool or framework based on evolutionary algorithms for the derivation of scorecards from clinical data. The techniques employed are based on the NSGA-II Multi-objective Optimization Genetic Algorithm (GA) which optimizes the Pareto-front of two clinically-relevant scorecard objectives, size and accuracy. Three automated methods are presented which improve on previous manually derived scorecards. The first is a hybrid algorithm which uses the GA for feature selection and a decision tree for scorecard generation. In the second, the GA generates the full scorecard. The third is an extended full scoring system in which the GA also generates the scorecard scores. In this system combinations of features and thresholds for each scorecard point are selected by the algorithm and the evolutionary process is used to discover near-optimal Pareto-fronts of scorecards for exploration by expert decision makers. This is shown to produce scorecards that improve upon a human derived example for C.Difficile, an important infection found globally in communities and hospitals, although the methods described are applicable to any disease where the required data is available.
Substantive and Procedural Legislation in Australia to Combat Webcam-Related Child Sexual Abuse

Australia has a range of substantive and procedural laws that can be used to combat webcam-related child sexual abuse. This includes legislation at the national (Commonwealth) level as well as in the various States and Territories. In fact, Australia was the first jurisdiction to prosecute an offender identified through the initial Sweetie operation conducted by Terre des Hommes. Numerous others have been convicted and sentenced as a result of their online, including webcam-based, sexual exploitation of children in other countries such as the Philippines. Crucial to this success is the ability of law enforcement investigators to infiltrate online groups, or to interact with individual child predators, using covert techniques. These include posing as a child online, successfully used in child grooming investigations. The legislation used in various Australian jurisdictions is intentionally written so as to allow the use of fictitious identities, and numerous courts have accepted as lawfully obtained the evidence gathered in such investigations, allowing it to be admitted. These principles can be readily extended to the hypothetical use of a Sweetie-style chatbot. However, further regulations governing the use of controlled operations and assumed identity authorities for this kind of covert activity by law enforcement investigators would clarify the legitimacy and limitations of the technique.
Appendix B. Mark I and Mark I* Software Details

To modern eyes, the programming conventions of the early Ferranti computers seem both primitive and complex, the complexity arising from the fact that coding was based on the 5-bit teleprinter characters. Users were expected to learn the 5-digit binary equivalents by heart, with the added complication that ‘backwards binary’ was employed with the least-significant digit at the left-hand end so as to be compatible with the engineer’s serial waveforms where time flowed from left to right. All this, and much more, is explained in this chapter—to the point where the bold reader can write a small program. The bootstrapping, routine-calling and monitoring processes are also described, along with the scheme for memory management, the integration of the small but fast primary store with the slower but larger magnetic drum secondary store. We detail the differences between the instruction sets for the Mark I and for the Mark I*—the latter being much easier to use.
Correspondence Analysis of Breast Cancer Diagnosis Classification

Abstract There are five indicators used in the diagnosis of breast cancer classification. The indicators are a type of malignancy, location, topography, morphology, behavior, and grade. This study aimed to assess how the relationship between types of diagnostic classification was given to breast cancer patients. The research is a quantitative method and used the hospital medical records were collected in the form of anatomical pathology examination results for hospital patients in the year 2017. Data were obtained from 317 pathology examinations which included 282 breast cancer patients. Each patient is given a diagnosis according to the pathologist’s observations into various classifications according to location, topography, type, morphology, grade, and behavior. The result of the analysis showed a relationship between the location of cancer and the type of malignancy. Furthermore, there is a difference in the probability of a malignant or benign tumor-attacking the right or left breast. Correspondence analysis was carried out between the location of the tumor with topography, type of malignancy, morphology, grade, and behavior respectively. The results showed that there was a significant correspondence between topography with the type of malignancy, type of malignancy with morphology, morphology with grade, and grade with behavior. Each type of diagnosis of breast cancer diagnosis is entirely accurate and has a significant correspondence relationship with each other.
Ternary-Decimal Exclusion Algorithm for Multiattribute Utility Functions

We propose methods to eliminate redundant utility assessments in decision analysis applications. We abstract a set of utility assessments such that the set is represented as a matrix of ternary numbers. To achieve efficiency, the matrix is converted to a decimal vector for further processing. The resulting approach demonstrates excellent performance on random sets of utility assessments. The method eliminates the redundant questions for the decision maker and can serve for consistency check.
Advanced Searchable Encryption: An Adaptively Secure Keyword Search for Matrix-type Storages

IoT technologies and cloud storages have been developed remarkably, and many types of data including private information have been gradually outsourced. For such a situation, new convenient functionalities such as arithmetic and a keyword search over ciphertexts are required to allow users to obtain information without leaking queried results, contents of ciphertext, and so on. Especially, searchable encryptions have been paid much attention to realize a keyword search on an encrypted domain. In addition, an architecture of searchable symmetric encryption (SSE) is a suitable and efficient solution for data outsourcing. In this paper, we focus on an SSE scheme which employs a secure index for searching a keyword. In conventional studies, it has been widely considered that the scheme searches whether a queried keyword is contained in encrypted documents or not. On the other hand, we additionally take into account the location of the queried keyword in the documents by targeting a matrix-type data format. The method enables an administrator to search personal information listed per line or column in CSV-like format data.
Arquivo da Coordenadoria de Capacitação de Pessoas da Universidade Federal de Santa Catarina: relato de estágio supervisionado II

This is a supervised internship II report of the undergraduate course in Archivology, whose activities were developed in the file of the Personnel Coordination of the Department of People Development (DDP) of the Federal University of Santa Catarina (UFSC). This paper aims to describe the practices of the activities mentioned in the Internship Plan and to report on the experience gained through the internship at the Coordination of People Training (CCP) of the Federal University of Santa Catarina (UFSC). The specific objectives are: a) To carry out a diagnosis of the institution and the archive of the CCP; b) To present the activities developed in the said stage; c) Report suggestions of the activities of the archive. In this way, the activities were developed between March 20 and May 11, 2018, in a total of 108 hours/class.
The Use of the Project Method in Teaching Programming Languages

The article deals with one of the methods of overcoming the low quality level of training of former students, and now fi rst-year students, in the field of information technology through targeted training in programming languages with the help of practice-oriented projects. The implementation of the main didactic units in the form of a single project contributes to the development of pupils’ competences in searching, storing and processing information, forming and developing a creative approach to solving the set task. The article gives examples to illustrate the practical component of the work performed on the technology described in the article. The considered algorithm of the project method is successfully used by the author in the process of teaching disciplines in the fi eld of “Automation of technological processes and production”, “Management in technical systems” at the Moscow Technical University of Communications and Informatics, it can be also used in teaching high school students in the classroom of Informatics.
DPD with IPF and GD method

The localisation of a stationary emitter with several separated sensors is studied. At low signal-to-noise ratio, the direct position determination (DPD) approach is more precise than the two-step method which estimates the time difference of arrival (TDOA) first and locates the emitter by the TDOAs. However, the exhaustive search is usually employed to find the global maximum since the object function of DPD is non-convex. After analysing the characteristics of the object function, a DPD with improved particle filter and gradient descent method (GDIPF-DPD) is proposed. The initial and fine estimations are provided by the IPF and GD methods, successively, as the object function has single peak around the expected position and is differentiable. The IPF decreases the times of resampling through mapping the particle weights before normalisation into 0–1. The GD method improves the accuracy by seeking within the single peak. Furthermore, the position Cramér–Rao lower bound of DPD with attenuation coefficient is derived and proved to be consistent with the two-step method of TDOA. Simulation results indicate that the position accuracy of the proposed algorithm is equivalent with grad search method and its computation cost is less by two orders of magnitude.
How to Design Society from a Data-Centric Point of View

This chapter explains the goals of this book in relation to three major aspects: human behavior, data-centric science, and social design. These three issues have been independently developing in different disciplines; however, in reality, they are deeply interrelated with one another. Specifically, the understanding of human behavior should be foundational for investigating human society. Thus, insights based on both quantitative and qualitative understandings of human collective behavior are very useful when designing social systems. Concerning design, we see how to design our social systems based on a heterogeneous membership. This chapter also discusses the three aspects (human behavior, data-centric science, and social design) independently as well as the relationships among them from the perspective of participatory design.
SEMANTIC GROUPING METHOD WHILE SOLVING LOGICAL TASKS IN LESSONS ON INFORMATICS

The article focuses on a way of solving the modified Einstein’s riddle that is based on a semantic grouping while verifying the problem presented in a form of logically interrelated and not mutually exclusive statements. This method is propounded as an alternative to the tabular method well-known from the Internet and the tabular-graphical method proposed by teachers from Chelyabinsk and published in No 9, 2018 of “Informatics at School”.