Reviewerly/exHarmony huggingface dataset:
Deviant truth-values algebras and deviant classes of general non-deterministic predicates

In this paper we study new classes of program-oriented logical formalisms – logics of general non-deter­ministic (GND) predicates. These logics reflect such properties of programs as nondeterminism, partiality, and non-fixed arity. GND-predicates can be modeled as 7-valued total deterministic (TD7) predicates. The main attention is paid to algebras of truth values (TV-algebras) of TD7-predicates. The set of truth values TV7 = {T, F, T#, F#, #, TF, TF#} defines TV-algebrа ATV7 = (TV7, {Ø*, Ú*}). There 20 subalgebras of ATV7 exist, and each of them induces a corresponding algebra of GND-predicates. At the same time there is a very large number of 7-valued logics so a lot of TV7 subsets are not closed under Ø* or Ú* and do not form subalgebras of ATV7. We call such subsets with the corresponding classes of GND-predicates deviant, they are not closed under logical connectives of GND-predicates. In order for deviant TV Í TV7 to form an algebra we need to modify Ø* or Ú*. Modifications can be made in a large number of ways. Modification of Ø* leads to specific non-classical logics and lies outside the scope of this paper. Modifications of Ú* sa­tisfying the TFC condition of predicate algebras logical connectives correctness are the most important, otherwise we obtain deviant TV-algebra which does not induce an algebra of GND-predicates. For all TV7 subsets we study the possibility of Ú* modification with TFC condition. Such modifica­tion induces corresponding classes of GND-predicates. We describe “natural” modifications of Ú* without TFC condition obtaining a number of deviant TV-algebras. There are no modifications with TFC condition for de­viant sets {#, TF, TF#}, {TF, #}, {TF#, #}, so for them we specify “relatively natural” deviant TV-algebras. Problems in programming 2019; 1: 14-26
Behavior Recognition Algorithm Based on Key Points of Human Bones

Human behavior recognition method based on video image easily influenced by background factors with lead to recognition accuracy is not high, and in view of the traditional Shuangliu network needs computing optical flow chart in advance and consume large amounts of time and need a lot of space to store an image of a light flow, an improved fusion skeleton information and image information of human behavior recognition algorithm. The experimental results show that the algorithm can effectively improve the accuracy of behavior recognition in video, and improve the identification ability of time-dependent behaviors and approximate behaviors.
PinocchioVR

In the Pinocchio fairy tale, the nose of a boy extends when he lies. Inspired by this tale, we created a Pinocchio VR system that presents the feeling of the nose extending through body ownership illusion. This illusion is created by pulling the nose while presenting the visuals of the nose growing in the head mounted display. Our research that explored different combinations of haptics and visuals indicated that the the minimum requirement for this body ownership illusion is the visuals and the 'nose-pulling' haptic sensation. Thus, the PinocchioVR system consists of a head mounted display and an integrated haptic nose-pulling mechanism. Furthermore, in this demonstration, we explore olfactory and vibrotactile sensations as multimodal effects to present new experiences such as interacting with far away objects with your nose, extending the nose to smell foods at a distance, and even, experience what it would feel like to hang clothes on your elongated nose.
Effectively Communicating With Group Decision Support Systems Using Information Theory

This chapter describes the results from a case study using information theory to examine the effectiveness of communicating using group decision support system (GDSS) technology. At its most basic level, information theory provides the means to measure the efficiency of communication systems. Using information theory as the theoretical foundation, this chapter examines how the use of GDSS facilitated computer-mediated communication (CMC) for one particular business with respect to entropy, redundancy, and noise, which are key components in information theory.
Kothe delivers ACM-wide webinar on exascale computing project

In late April 2019, Doug Kothe, Director of the Exascale Computing Project at Oak Ridge National Laboratory, delivered the ACM-wide webinar entitled, "The Exascale Computing Project (ECP) and the Future of HPC." John West (Texas Advanced Computing Center) moderated the event.
Dormitory allocation and constrained clustering algorithm

Clustering is a common model in machine learning and data mining. For different scenarios, it may be necessary to add corresponding constraints to clustering such as limiting the size of clusters. Students need dormitory allocation when they enter the University. Reasonable dormitory allocation can reduce roommate contradictions, promote roommate communication and benefit physical and mental development of college students. However, universities often use random allocation based on major and classes nowadays. This paper improves the traditional clustering algorithm to make the final clustering result completely uniform, that is, the number of samples in each cluster is equal. The improved algorithms are successfully applied to the allocation of college students dormitory in author’s campus. Moreover, This paper modifies K-means using bipartite matching to address the problem of specific size clustering, which can be seen as an extension of completely uniform clustering.
Investigations

The purpose of this chapter is to point to where digital evidence might be located and to take into consideration the role that digital evidence might play in an investigation, if any at all. The purpose is not to advise as to how an investigation should be conducted. After all, this is not a book about investigations, and there are many people far more qualified than I who can advise as to how an investigation should be conducted. Moreover, every investigating and intelligence organisation has its own doctrine for investigation and/or gathering and analysing intelligence.
Kinetic modeling and parameter optimization of n-butane isomerization reaction

The optimization of operating conditions for n-butane isomerization reaction is a typical nonlinear optimization problem. In order to improve the yield of the main product isobutane under the action of the same catalyst, in view of the difficulty in selecting the reaction process parameters, this paper proposes to apply the AWOA (Adaptive elite strategy whale optimization algorithm, AWOA) to the optimization of the process parameters of n-butane isomerization reaction. Firstly, according to the network structure and experimental data of n-butane isomerization reaction under laboratory conditions, the AWOA was used to identify the parameters of the reaction kinetics model, and the model was verified. Then, for the established n-butane isomerization reaction kinetics model, with the maximum yield of isobutane as the optimization objective, the process parameters at the maximum yield were obtained. Simulation results show that the proposed method can obtain a set of optimal process parameters and improve the yield of effective substances.
Pose-Guided Attention and Alignment (PGAA) for Dislocated Target Person Re-Identification Based on Multi-feature Fusion

Person re-identification is a critical method in video surveillance, which has a wide range of applications. However, because of the shift in shooting posture, the same person's physical attributes can become badly misplaced, and feature matching is challenging. A pose-guided attention and alignment (PGAA) technique is presented as a solution to this. The core of this technique is a multi-branch deep network. The first branch is based on keypoint multi attention technology, filtering the image's actual information and then executing horizontal segmentation to extract local characteristics. The second branch is a method based on local alignment. The pose estimation is realized using a pre-trained pose estimation model and local feature matching at keypoints. We may employ part features while also alleviating the offset of the part feature map caused by the occlusion of the part detection frame and the erroneous position, and improving the discrimination and robustness of the part features with this technique. The method can produce good outcomes in the situation of posture change and human image misalignment, according to testing results on some popular datasets.
Cloud Computing Applications in the Public Sector

Cloud computing brings key advantages to the governments facing conflicting IT challenges. However, the cloud paradigm is still fragmented and concerns over data privacy and regulatory issues presents significant barriers to its adoption. Cloud computing is expected to provide new ways to run IT in public sector. At the same time, it presents significant challenges for governments, and to make the most of cloud, public sector organizations need to make some important decisions. Governments planning to migrate to the cloud are actively moving to harness digital services but with different focus, reasons, and strategy. However, the degree of cloud adoption by the public sector around the globe varies significantly. Most governments are piloting cloud computing but there are huge differences between each country. This chapter explores the state of the art of cloud computing applications in the public sector; various implications and specific recommendation are also provided.
Principles of Genetic Algorithms

This chapter aims to review the fundamentals of genetic algorithms. Consequently, the chapter correspondingly lectures on the dissimilarities between the alteration genetic and evolutionary algorithms. The decision mathematical rules based on the Pareto algorithm are similarly deliberated. The Pareto optimization rule can have a significant role in the examination of the precise position of MH370 vanishing. In this circumstance, the majority of multi-objective optimization algorithms exercise this principle to acquire the non-dominated set of solutions, as a result of the Pareto-front to investigate how MH370 ended up in the Indian Ocean.
Temporal-Aware Self-Supervised Learning for Unsupervised Video Anomaly Detection

Video anomaly detection (VAD) is commonly formulated as the discrimination of events that do not confirm to the regular patterns in videos. Recently, deep neural network-based VAD approaches have gained remarkable progresses. Existing unsupervised approaches usually achieve VAD by frame reconstruction or prediction, and then identifying anomalies according to the reconstruction or prediction errors. However, these approaches suffer from two limitations: (1) They cannot obtain the semantic features of normal training samples. (2) It is suboptimal because of the non-alignment between the proxy and actual tasks. To address the above issues, we present a novel temporal-aware self-supervised learning framework to obtain the high-level semantic features and to perform VAD by solving multiple pretext tasks. In particular, we utilize temporal transformations to form multiple pretext tasks (transformations prediction) for VAD. A 3D encoder is trained to obtain semantic features by jointly solving these pretext tasks. Then, multi task heads utilize these features to solve different pretext tasks. In the inference phase, multiple task losses are used for calculating the final anomaly score. Extensive experiments are conducted on two benchmarks, which shows that the proposed method outperforms state-of-the-arts.
Strategic Values of Cloud Computing Transformation

This article aims to study the strategic transformative value dimensions of cloud computing. Organizational requirements, managerial strategic objectives, and their attendant challenges are very specific indicators that enable characterization of these dimensions. An exploratory study of 173 cases of companies, covering 17 distinct economic activity sectors, spread over all continents, was conducted, taking into account contingency factors such as culture, size, and structure. The elements of industry, strategy, and technology were also considered, as well as managerial cognition. The analyses of needs, objectives, challenges, implemented solutions, and results of the transformation to cloud computing, enabled us to identify these dimensions. Strategies to maximize the transformative value of cloud computing are then presented. The results of this study can be used by managers to facilitate and optimize this cloud computing transformation.
DeepTrace: A Secure Fingerprinting Framework for Intellectual Property Protection of Deep Neural Networks

Deep Neural Networks (DNN) has gained great success in solving several challenging problems in recent years. It is well known that training a DNN model from scratch requires a lot of data and computational resources. However, using a pre-trained model directly or using it to initialize weights cost less time and often gets better results. Therefore, well pre-trained DNN models are valuable intellectual property that we should protect. In this work, we propose DeepTrace, a framework for model owners to secretly fingerprinting the target DNN model using a special trigger set and verifying from outputs. An embedded fingerprint can be extracted to uniquely identify the information of model owner and authorized users. Our framework benefits from both white-box and black-box verification, which makes it useful whether we know the model details or not. We evaluate the performance of DeepTrace on two different datasets, with different DNN architectures. Our experiment shows that, with the advantages of combining white-box and black-box verification, our framework has very little effect on model accuracy, and is robust against different model modifications. It also consumes very little computing resources when extracting fingerprint.
The ScavengAR Hunt

This chapter explains the design and execution of a pre-service teacher training case study using Augmented Reality (AR), Quick Response (QR) codes, and social media mobile applications installed on iPads. The ScavengAR Hunt activity centered on a story narrative inspired by works of fine art using trigger images from the AR mobile application ARART® and incorporated elements of the board game Clue®. Pre-service teachers in the study were divided into groups of 4-6 and assigned specific, individual roles related to a mobile application used in the ScavengAR Hunt, and completed specific tasks while exploring the campus of a midwestern university. The research monitored the groups in real-time through reports submitted on Twitter and responses from QR code scans. The ScavengAR Hunt served as a model for designing a mobile learning activity incorporating multiple mobile applications.
The physical properties of densified Terentang wood (Campnosperma auriculatum (blume) hook. f) on various boiling and pressing time

Abstract The fulfillment of the need for wood at this time can only use wood from industrial park forests and community forests, in which the type of wood is fast-growing wood. One of these fast-growing woods is Terentang wood where this wood belongs to the strong class III to IV. To increase the strength of Terentang wood, in this case, the physical nature of the innovation needs to be done using one of the technologies that havethe potential to improve the quality of wood, which is compaction or wood compression. The results of compaction of Terentang wood process with boiling treatment which has a better value of control are the properties of moisture content and specific gravity, the development and shrinkage of dimensions which arestill low compared to controls. The moisture is influenced by boiling time, pressing time, and interaction both of that. The specific gravity is affected by boiling time, the development of wood is influenced by boiling time and interaction of boiling and pressing time, shrinkage is affected by boiling time.
Hyper-heuristics tutorial

tutorial Share on Hyper-heuristics tutorial Authors: Daniel R. Tauritz Missouri University of Science and Technology Missouri University of Science and TechnologyView Profile , John Woodward Operations Research Group, Queen Mary Operations Research Group, Queen MaryView Profile Authors Info & Claims GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2019 Pages 770–805https://doi.org/10.1145/3319619.3323382Published:13 July 2019Publication History 0citation49DownloadsMetricsTotal Citations0Total Downloads49Last 12 Months1Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Study and Comparison of Different Algorithms using GitHub

Now a days it is necessary to protect the information from unauthorized user. The solution is cryptograph. Security must be desired against desirable attacks through encryption algorithm. But in real scenario, cost of performance and implementation also matters. I compared security, Block Size, Key Size, Number of Rounds and Cipher Type. Through the basis of their performance and ease of implementation, the main part will be primary focused on comparing the encryption algorithms.
Exploration of "Tace to Point" Practical Teaching Mode Based on Online and Offline Blending Teaching — Taking the Course of Fundamentals of Web Design As an Example

In view of the problems existing in the practical teaching of the course "Fundamentals of web design" in local colleges and universities, such as the separation of theory and skills, backward teaching design, compressed class hours and few practical links, this paper constructs a practical teaching system of "technology supported, face-to-point" based on online and offline blending teaching, focusing on the cultivation of technical ability to improve students’ application and practical ability, so that the quality of talent training can meet the requirements of applied talents.
On More Paradigms of Steganalysis

Up to now, most researches on steganalysis concentrate on one extreme case. Typically, the a priori knowledge of the embedding way and cover-media is assumed known in the classifier training and even feature design stage. However, the steganalysis in the real world is done with different levels of such knowledge so that there can be various paradigms for doing it. Although some researchers have addressed the situations, there is still a lack of a systematic approach to defining the various paradigms. In this paper, the authors give such an approach by first defining four extreme paradigms, and then defining the rest among them. Each paradigm is related with two sets of assumed known a priori knowledge respectively about the steganographic algorithm and cover-media, and each paradigm corresponds to a particular case of steganalysis. Also we will see that different paradigms can have very different aims so that the designs may be various.
The Educational Affordances of Mobile Instant Messaging (MIM)

The popularity of Mobile Instant Messaging (MIM) has prompted educators to integrate it in teaching and learning in higher education. WhatsApp® is a multi-platform instant messaging application widely used worldwide, however, there is still little applied research on its use as a platform for educational activities in management higher education. In this article, the authors present a quantitative and qualitative assessment of a concrete experience of WhatsApp® use that involved 140 undergraduate management students. Data were collected through questionnaires answered by the participants after the end of the experience of use, and also via content analysis of their conversations inside their WhatsApp® groups. The results indicate five main educational affordances of MIM that can be considered in management education: interactivity, knowledge sharing, sense of presence, collaboration and ubiquity. The article also explores the limitations of this tool and provides suggestions of good practices of MIM use for teaching and learning.
Chapter 6. Virtual sociolinguistics

The limitations of apparent-time approaches and the difficulties of conducting studies in real-time for observing language change have traditionally been one of the main methodological concerns in sociolinguistics since the 1960s. However, archived radio recordings and historical corpora of written correspondence have recently been demonstrated to be excellent sources for both cross-sectional and longitudinal approaches. The aim of this paper is to show the results from two studies where radio and historical archival sources afford comparative evidence for tracing language change developments. The conclusions suggest that these resources offer a privileged perspective for the historical reconstruction of linguistic change in present-day or remote languages, functioning as virtual approaches where the problem of real-time in the longitudinal research procedure is conveniently neutralised.
Keypoint Detection Based on Deep Learning to Improve Illumination Robustness

Towards the problem of extracting suitable feature points in computer vision, this paper presents a learning- based method to detect keypoints with repeatability and reliability when the light intensity changes drastically of weather or climate conditions. In contrast to patch-based neural networks, a fully convolutional neural network structure is designed that can operate on a full-size image after a forward pass to generate detection points and corresponding descriptors. We introduce feature pyramids, FCN network structures, cell boundary drift, and metric learning for boosting the repeatability and light robustness of interest point detection. Our model, when training on the AMOS image dataset, is able to repeatedly detect a richer set of interest points than other conventional corner point detectors and filter out reliable feature points by non-maximum suppression. Through intense experiments and in-depth analysis, the experimental results show that the proposed method greatly improves the light robustness of keypoints detection compared to ORB, FAST, SURF, and SIFT.
Justification awareness

Abstract We offer a new semantic approach to formal epistemology that incorporates two principal ideas: (i) justifications are prime objects of the model: knowledge and belief are defined evidence-based concepts; (ii) awareness restrictions are applied to justifications rather than to propositions, which allows for the maintaining of desirable closure properties. The resulting structures, Justification Awareness Models, JAMs, naturally include major justification models, Kripke models and, in addition, represent situations with multiple possibly fallible justifications which, in full generality, were previously off the scope of rigorous epistemic modeling.
Security of Identity-Based Encryption Algorithms

The concept of identity-based cryptography introduced the idea of using arbitrary strings such as e-mail addresses and IP addresses to form public keys with the corresponding private keys being created by the trusted authority (TA) who is in possession of a systemwide master secret. Then a party, Alice, who wants to send encrypted communication to Bob need only Bob's identifier and the systemwide public parameters. Thus, the receiver is able to choose and manipulate the public key of the intended recipient which has a number of advantages. While IBC removes the problem of trust in the public key, it introduces trust in the TA. As the TA uses the systemwide master secret to compute private keys for users in the system, it can effectively recompute a private key for any arbitrary string without having to archive private keys. This greatly simplifies key management as the TA simply needs to protect its master secret.