Reviewerly/exHarmony huggingface dataset:
Interpretations of Weak Positive Modal Logics

This paper investigates set-theoretical semantics for logics that contain unary connectives, which can be viewed as modalities. Indeed, some of the logics we consider are closely related to linear logic. We use insights from the relational semantics of relevance logics together with a new version of the squeeze lemma in our semantics for logics with disjunction (but no conjunction). The ideal-based semantics, which takes co-theories to be situations, dualizes the theory-based semantics for logics with conjunction (but no disjunction).
Using UX Personas to Improve Library GIS and Data Services

This study describes the process of persona-creation in a liberal-arts university well-integrated within a global network of Geographic Information System researchers. We introduce the concept of personas as an effective user experience research methodology with potential applications to academic library services. We detail the collection of data from high-level researchers as a way to inform the library’s strategic planning and programming. We present the results of the study in the form of three personas: Rachel the Novice, A.J. the Professor, and Jean-Paul the Big-Thinker. These results are specific to our institution, but the method itself can transfer to other contexts. We note the study’s limitations in the final analysis, and make suggestions for maintaining and refining the personas to accurately represent the community of users.
Multiple-Preys Pursuit based on Biquadratic Assignment Problem

The multiple-preys pursuit (MPP) is the adversarial game between predators and preys. If the capture of a prey is defined as that it cannot move anymore due to the surrounding of predators, there are two kinds of task allocations. One is about assigning which prey to which group of predators so that all preys can be captured. The other is about assigning which capturing position to which predator to encircle the prey simultaneously. In this paper, the MPP is modeled as a dynamic optimization problem and each its time step is solved in two stages. Firstly, the first kind of task allocation problem is modeled as the biquadratic assignment problem (BiQAP) and a MPP fitness function is proposed for the evaluation of such BiQAP task allocations. In this way, the MPP is transformed to several single-prey pursuit (SPP) problems. Secondly, for each SPP, we extend the coordinated SPP strategy CCPSO-R (cooperative coevolutionary particle swarm optimization for robots) to its parallel version as PCCPSO-R to enable the parallel implicit capturing position allocating by parallel observation, decision making, and moving of predators. Through experiments of the current BiQAP solvers on the task allocation, we improve the best one of them in statistic based on the domain knowledge. Moreover, the advantages of PCCPSO-R in the capturing efficiency over CCPSO-R is testified in the MPP experiments.
Reforms of the perspective course in practice-oriented talent training in the big data age

As information technology develops, big data has found its way into every corner of different industries, and utilization of big data has become a dominating trend in reforms of practice-oriented education at universities amid the opportunities and challenges in the contemporary world. As known to all, practice-oriented universities are student-oriented in training. The perspective course is a fundamental course for design majors. Thus, this study probes into the existing problems in this course, and explores the reforms of this course in terms of the teaching content, teaching method and training mode with the support of big data. Lots of data from diverse sources are obtained through the online learning platforms and questionnaire surveys, and the data are processed to help teachers analyze the teaching effect from diverse perspectives and thereby design connected teaching modules, all-around teaching methods and scientific training plans. It will not only help the students grasp the essence of perspective, extend the depth and breadth of their knowledge, but train them in a well-rounded manner through observation, analysis, experiment, innovation and expression. This study is expected to facilitate relevant studies.
Online Transfer Learning and Organic Computing for Deep Space Research and Astronomy

Deep space exploration is the pillars within the field of outer space analysis and physical science. The amount of knowledge from numerous space vehicle and satellites orbiting the world of study are increasing day by day. This information collected from numerous experiences of the advanced space missions is huge. These information helps us to enhance current space knowledge and the experiences can be converted and transformed into segregated knowledge which helps us to explore and understand the realms of the deep space. Online Transfer Learning (OTL) is a machine learning concept in which the knowledge gets transferred between the source domain and target domain in real time, in order to help train a classifier of the target domain. Online transfer learning can be an efficient method for transferring experiences and data gained from the space analysis data to a new learning task and can also routinely update the knowledge as the task evolves.
Traffic Sign Recognition and Retrieval Using Limited Dataset in the Wild

Most deep learning models require a large amount of annotated data for training, many individuals and institutions have provided a large number of high-quality open datasets, and quite a lot of academic works has been done based on such open datasets and solved quite a lot of classical problems in the field of computer vision. However, the actual scenarios of many engineering problems and the scenarios covered by open datasets are very different, and the data distribution of the data taken from different scenarios varies greatly, so it is difficult for models trained from open datasets to solve the problems encountered in actual scenarios. In addition, the cost of model training and data labeling is relatively high. For an already trained classification or model, adding new data often requires retraining, and the newly added data also needs to be labeled according to certain labeling standards, which greatly increases the monetary cost and time cost of using deep learning models in industry. In this paper, we propose a object detection process that can identify unlabeled categories of traffic signals: we use a object detection model to extract traffic labels based on an image content retrieval method that partially trains the labeled data to match the traffic sign categories that do not appear in the training data. The method is tested on the TSRD dataset and achieves functions that are not possible with traditional end-to-end detectors, while the proposed method also achieves good results on object detection tasks that are achievable with object detectors.
Risk Management System for Operational Services in Data Center : DC Papa Oscar Cikeas Case study

The presence of the Information Technology System (ITS) has become one of the components for basic needs that must be met in navigating through the ages. Organizational programs in responding to the industrial era 4.0 make the use of ITS is a must in order to facilitate all processes related to quality service in carrying out the main task of protecting and serving the community. The implementation of ITS is actually not easy forthe threat of challenges and disturbances in the form of risks haunts ITS’s operations. These conditions must be able to be identified and analyzed and then action can be executed to reduce the negative impact, so the risks are acceptable. This research will study about ITS risk management using the the guideline of Information Technology Infrastructure Library (ITIL) to formulate an operational strategy in order ensure that STI services at the Papa Oscar Cikeas Data Center (DC) can run well in the form of recommendations. Based on a survey on the implementing elements of IT function, 82.18% of respondents considered that the IT services provided by DC were very important, 86.49% of respondents knew the importance of having an emergency plan to ensure their products and services were always available, and 67.17% of respondents believes that DC is well managed. The results of the study concludes that it is necessary to immediately form a structural DC organization to prepare a good path for the establishment of a professional data center in supporting public service information technology systems.
On the Structure of Binary LCD Codes Having an Automorphism of Odd Prime Order

The aim of this work is to study the structure and properties of the binary LCD codes having an automorphism of odd prime order and to present a method for their construction.
Sign language Conversion For Hearing And Speech Impaired

Inability to talk is taken into account to be a real disability. People with this disability use different modes to speak with others, there are several methods available for his or her communication, one such common method of communication is dactylology. Sign language allows people to communicate with human body language; each alphabet has a set of human hand signs representing a particular expression. The purpose of the paper is to convert sign language to text and sentences with human gesture classification. Sign Language Recognition is one among the foremost growing fields of research. Many new techniques have been developed recently within this area. Sign Language is especially used for the communication of deaf-mute people. This paper shows the sign language recognizing 26 hand gestures in Sign language using CONVOLUTIONAL NEURAL NETWORK. The proposed system contains three modules such as Image capture module which contains image capturing using webcam and database formation, the feature extraction module having color detection and shape detection, the pattern generation module having trained model and sign recognition. The final output will be predicted text from the sign expressed by the user
Analytical Confidence Intervals for the Number of Different Objects in Data Streams

This paper develops a new mathematical-statistical approach to analyze a class of Flajolet-Martin algorithms (FMa), and provides analytical confidence intervals for the number F0 of distinct elements in a stream, based on Chernoff bounds. The class of FMa has reached a significant popularity in bigdata stream learning, and the attention of the literature has mainly been based on algorithmic aspects, basically complexity optimality, while the statistical analysis of these class of algorithms has been often faced heuristically. The analysis provided here shows deep connections with mathematical special functions and with extreme value theory. The latter connection may help in explaining heuristic considerations, while the first opens many numerical issues, faced at the end of the present paper. Finally, the algorithms are tested on an anonymized real data stream and MonteCarlo simulations are provided to support our analytical choice in this context.
Seeing fringes everywhere: impact of James C. Wyant's contributions to optical metrology

Few in history have had as great an impact on their field of study than James C. Wyant. This paper provides an historical overview of Wyant’s contributions to the field of optical metrology and how they have impacted today’s technology. Beyond his role as an innovator, inventor, leader, enterpreneur and philanthropist he will perhaps be best known for his teaching and the legacy of the multitudes of students he taught over his career.
A StackNet Based Model for Fraud Detection

With the rapid development of e-commerce and the increasing popularity of credit cards, online transactions have become increasingly smooth and convenient. However, many online transactions suffer from credit card fraud, resulting in huge losses every year. Many financial organizations and e-commerce companies are devoted to developing advanced fraud detection algorithms. This paper presents an approach to detect fraud transactions using the IEEE-CIS Fraud Detection dataset provided by Kaggle. Our stacked model is based on Gradient Boosting, LightGBM, CatBoost, and Random Forest. Besides, implementing StackNet improves the classification accuracy significantly and provides expandability to the network architecture. Our final model achieved an AUC of 0.9578 for the training set and 0.9325 for the validation set, demonstrating excellent performance in classifying different transaction types.
Efficient Face Detector Using Spatial Attention Module in Real-Time Application on an Edge Device

The practical application requires a vision-based face detector to work in real-time. The robot application uses the face detection method as the initial process for the face analysis system. During its development, it utilizes an edge device to be used to process sensor information. Jetson Nano is a mini portable computer that is easily synchronized with sensors and actuators. However, traditional detectors can work fast on this device but have low performance for occlusion cases, multiple poses, and small faces. On the other hand, CNN-based detectors that implement deep layers are slow to run on low memory GPU devices. In this work, an efficient real-time face detector using a simple spatial attention module was developed to localize faces rapidly. The proposed architecture consists of the backbone module to efficiently extract features, the light connection module to reduce the size of the detection layer, and multi-scale detection to perform prediction of faces on various scales. As a result, the proposed detector achieves competitive performance from state-of-the-art fast detectors on several benchmark datasets. In addition, this efficient detector can run at 55 frames per second in video graphics array resolution on a Jetson Nano.
Organization of Design and Research Activities of School Students Using Information and Communication Technologies

The paper deals with the analysis of the organization of design and research activities of school students using information and communication technologies. In our opinion information and communication technologies that can develop a student’s creative activity, expand the sector of their independent research work, information retrieval and analytical methods of information analysis. We focused on unique qualities of using information and communication technologies in education as interactive, multimedia, modeling, communication and productivity. We considered basic requirements for creating projects: the presence of a task that requires integrated knowledge, research search for its solution; practical, theoretical, cognitive significance of the intended results; independent activities; structuring the substantive part of the project.
Surveillance Personnel Behavior Analysis Technology based on Operation Log

A large number of operation logs will be generated when surveillance personnel open surveillance video and based on these operation logs, this paper studies the behavior visualization, automatic anomaly detection and abnormal behavior analysis of the surveillance personnel by using data mining, machine learning and other methods. Using the above methods and research, this paper can mine valuable information from the operation log, which can help the surveillance personnel to realize behavior visualization and find abnormal behavior problems during the work, thus effectively avoid the occurrence of security incidents caused by improper operation of the surveillance personnel, and reduce the potential risks in the surveillance process.
Bayesian Approaches to Finding the Needles in the Microscopy Haystack

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MetamorHockey: A Projection-based Virtual Air Hockey Platform Featuring Transformable Mallet Shapes

We propose a novel projection-based virtual air hockey system in which not only the puck but also the mallet is displayed as an image. Being a projected image, the mallet can freely “metamorphose” into different shapes, which expands the game design beyond the original air hockey. We discuss possible scenarios with a resizable mallet, with mallet shapes defined by drawing, and with a mallet whose collision conditions can be modified. A key challenge in implementation is to minimize the latency because the direct manipulation nature of the mallet positioning imposes a higher demand on latency than the puck positioning. By using a high-speed camera and a high-speed projector running at 420 fps, a satisfactorily quick tracking became possible such that we feel a projected mallet head to be an integral part of a mallet held by hand.
Integrating Information Visualization and Dimensionality Reduction: A pathway to Bridge the Gap between Natural and Artificial Intelligence

By importing some natural abilities from human thinking into the design of computerized decision support systems, a cross-cutting trend of intelligent systems has emerged, namely, the synergetic integration between natural and artificial intelligence. While natural intelligence provides creative, parallel, and holistic thinking, its artificial counterpart is logical, accurate, able to perform complex and extensive calculations, and tireless. In the light of such integration, two concepts are important: controllability and interpretability. The former is defined as the ability of computerized systems to receive feedback and follow users’ instructions, while the latter refers to human-machine communication. A suitable alternative to simultaneously involve these two concepts—and then bridging the gap between natural and artificial intelligence—is bringing together the fields of dimensionality reduction (DimRed) and information visualization (InfoVis).
Fast and faithful scale-aware image filters

Abstract This paper proposes a fast and accurate computational framework for scale-aware image filters. Our framework is based on accurately approximating $$L^{1}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>L</mml:mi> <mml:mn>1</mml:mn> </mml:msup> </mml:math> Gaussian convolution with respect to a transformed pixel domain representing geodesic distance on a guidance image manifold in order to recover salient edges in a manner faithful to scale-space theory while removing small image structures. Our framework possesses linear computational complexity with high approximation precision. We examined it numerically in terms of speed, accuracy, and quality compared with conventional methods.
Phase congruence implementation in ImageJ using Radix-2 FFT

Automatic edge detection in images is an area of great interest to industry and the scientific community. A problem usually experienced is that edge detectors are sensitive to the magnitude of changes in brightness. However, this disadvantage disappears when employing the technique known as phase congruency, which allows edge detection in an image regardless of its illumination level. This technique is based on phase alignment of frequency components. This principle states that the edges of an image occur when the phases of the Fourier components coincide. By using phase, the direct dependence on brightness intensity in edge detection is avoided. A difficulty of phase congruency implemented with monogenic filters is that it requires the computation of the complex Fourier transform. However, its computational cost is high. There are some approaches that seek to reduce its cost, such as the FHT, but they only allow to obtain the Fourier transform of real images. Due to this limitation, methods based on phase congruency were not available in several image processing tools, such as ImageJ, a program widely used by biologists and microscopists for the analysis of biological images, since these programs make use mainly of the Fast Hartley Transform transformation. Therefore, in this work the implementation of phase congruency for ImageJ with monogenic filters using the Fourier Radix-2 FFT transform is described. The results obtained with the proposed implementation were compared with those found with the Kovesi code in GNU Octave, showing that both implementations obtain equivalent results and even better with the proposed method when at least one side of the images is not a power of two, in which case tile-mirror is used to complete the image.
Runtime Randomized Relocation of Crypto Libraries for Mitigating Cache Attacks

Crypto libraries such as OpenSSL and Libgcrypt are essential building blocks for implementing secure cloud services. Unfortunately, these libraries are subject to cache side-channel attacks, which are more devastating in cloud environments where inevitable cache contention among different tenants occurs. Previous approaches for mitigating cache side-channel attacks have limitations in terms of the deployability and security; these hinder utilization in cloud services. In this paper, we propose an R2-relocator, a novel library protection technique based on moving target defence. When injected into a running process, the R2-relocator performs randomized relocation of the library during runtime. By doing this, it transforms a vulnerable crypto library into one that randomly changes its memory (cache) location, thereby preventing the delivery of cache side-channel attacks against the library. The proposed technique achieves robust protection against cache side-channel attacks for all crypto libraries, even those containing unpatched critical vulnerabilities, without the need for reconfiguration of the library. Extensive evaluations of security, performance, and deployability of the R2-relocator demonstrate its effectiveness for secure cloud services.
Coarse-to-Fine Siamese Network for Tooth Object Tracking

Abstract At present, object tracking algorithms based on deep learning have been widely used in the computer vision industry and has great application value. In this paper, we track the tooth, which has the problem of poor texture and high similarity, with the object tracking algorithm. In general, previously existing single object tracking algorithms are derived from the siamese network architecture. These algorithms have the problem of losing a tracked object with poor textures and high similarities. In order to address the issue of object loss, we put forward a novel coarse-to-fine siamese network (CFSN) for tooth object tracking. The coarse detection network is based on the features extracted by the Feature Pyramid Networks (FPN) and the Region Proposal Network (RPN). The fine detection network has a similar structure to the coarse detection network, and the main purpose is to fine-tune the results of the previous stage. Based on the coarse-to-fine network, a framework of multi-task is proposed, that is, the mask prediction branch is added to the fine detection network, which lead to significant improvements in tracking efficiency. Through the comprehensive experimental evaluation of the tooth dataset, we find that the CFSN model is more effective in tracking tooth objects than the existing methods.
Artificial ecosystem-based optimization algorithm

This chapter introduces the inspiration, rules, and framework of the artificial ecosystem-based optimization (AEO) algorithm. The optimization performance is investigated from different aspects using unimodal, multimodal, low-dimension, and composite benchmark functions. The analytical results prove the superiority of the AEO algorithm.