Reviewerly/exHarmony huggingface dataset:
GHE Mechanism for Preserving Mean Brightness of the Original Image

Histogram equalization (HE) is the one of the simplest and most effective methods for contrast enhancement. However, the HE does not use a spatial feature included in the input image. The differential gray-level histogram (DH) which is contained edge information of the input image, were defined and the differential gray-level histogram equalization (DHE) has been proposed. Furthermore, the generalized histogram equalization (GHE) including HE and DHE was proposed. the GHE can control the mean brightness of the enhancement image and the mean brightness of the input image is preserved. In this paper, we clarify the mechanism for preserving mean brightness of GHE
NFC Payments Security in Light of COVID-19 Pandemic: Review of Recent Security Threats and Protection Methods

The COVID-19 pandemic has changed many aspects of behavior, often leading to more cautious lifestyles. Financial transactions have not been an exception; as people have reduced cash transactions due to fears of virus transmission. Naturally, this was accompanied by an acceleration of growth of online payment services. Online transactions, Mobile Money, and Near-Field Communication (NFC) contactless payments have all experienced noticeable growth during the pandemic. Consequently, related cyber-risks have simultaneously increased. This paper reviews the impact of the COVID-19 pandemic on NFC contactless payments internationally and in the Kingdom of Bahrain. Then the paper briefly explains the technological components of NFC payments, comparing NFC card payments and NFC mobile payments. It then reviews three recently identified NFC payments security threats and their respective protection methods. Finally, the paper analyzes the common factors between these threats and provides recommendations related to physical security and user awareness.
Triggering Specialised Knowledge in the Software Development Process

Many factors determine the success of software development projects. The exchange and harnessing of specialized knowledge amongst and between the project team members is one of these. To explore this situation, an ethnographic case study of the product-testing phase of a new human resources management system was undertaken. Extempore verbal exchanges occur through the interplay of project team members in weekly meetings, as the software was tested, analyzed, and altered in accordance with the customer's needs. Utilizing tacit knowledge from the project members as well as the group, new tacit knowledge surfaces and spirals, which allows it to build over time. Five extempore triggers surfaced during the research generated through explicit stimuli, allowing project members to share and create new knowledge. The theoretical development places these learning triggers in an interpretive framework, which could add value to other software development and project management contexts.
Image Transmission Using Base64 Encoding and Advanced Encryption Standard Algorithm Based on Socket Programming

Socket programming has many advantages to transmit data. Socket programming is widely used to create a communication system to connect one host to another host. In addition to transmitting data in the form of text, socket programming can also be used to transmit images. In a communication system, data integrity is a crucial thing so a security system is needed to ensure the confidentiality of the data is transmitted. Especially to build the communication system that is considered confidential. This also applies to send images over a network. The solution is to use cryptography which can encode information by using a key so that the data transmitted cannot be read by an unauthorized party. AES encryption is estimated to be the safest algorithm compared to other algorithms. In this research, a communication system will be built to transmit images using the Base64 encoding and AES 256-bit encryption algorithm based on socket programming.
Image-Based Visual Servoing with Backstepping for the Drone

This paper proposes an image-based visual servoing controller with a backstepping scheme for drones. For the purpose of controller design, the drone dynamics are simplified by yaw angle decoupling and dynamic expansion. In addition, a virtual image plane is defined to apply image-based visual servoing to the drone system, and a feature position on this plane is used as the output variables of the system. Through simulation, it is shown that the proposed controller is suitable for the task of tracking an object or moving to a point in the camera image.
Using Augmented Reality Technology in Learning Archeological Places

The purpose of this research is to use augmented reality (AR) technology in learning archeological places of 9 places including Wat Thewarat Kunchorn, Wat Sukhantharam, Wat Benchamabophit Dusitwanaram, Wat Mai Thong Sen, Wat Ratchaphatikaram, Morphon House Palace, Suan Sunandha Palace, Parusakawan Palace, and Wat Bot Samsen. Each place can see the history of the content through VDO and 3-D models that can be viewed 360 degrees. They presented it through the learn archeological places guidebook. The sample groups of the research were 30 students of bachelor's degree in industrial education program, Rajamangala University of Technology Phra Nakhon. The research results showed that the learn archeological places guidebook was at a high level (the mean equaled to 4.36) and the students' satisfaction with the learn archeological places guidebook was at a high level (the mean equaled to 4.13).
Verification methodology for Smart Awakening Systems

A mental and physical recovery after an awakening moment depends not only on the overall sleep duration and quality but mostly on the sleep stage in the waking moment. The most comfortable awakening moment is during the Light or Wake sleep stages. But the fix-time alarm clock doesn't take into account the sleep stage in the awakening moment, which often results in awakening during the Deep or Rapid Eyes Movement stages. To reduce the negative recovery effect, big companies and research groups develop various awakening systems. Such systems recognize sleep stages based on wearable sensors' data (mostly from accelerometer sensors) and thus can find the easiest awakening moment time with minimal recovery effects.However, it is quite hard to measure and verify the efficiency of such systems without using polysomnography (which can be performed only in clinical conditions by medical experts). To solve this problem we developed a methodology based on questionnaires and psychological tests. Such an approach has big scalability, does not require special medical equipment, and can be evaluated in a home environment with minimal support effort. The proposed verification approach has been tested on smartwatches with the sleep stages forecast model. The proposed model accuracy was 78%. Results of our experiment show that the majority of users demonstrated a correlation between awakening quality and the verification tests performance.
A Game Model of River Eco-hydrological Evolution under Big Data Water Resources Allocation

Water is one of the most active factors in the overall environment. The amount of water resources maintains the survival of human beings and the healthy development of the biosphere. In arid areas, water resources are the "bottleneck" restricting regional socio-economic development and ecological construction. Competitive use of water resources in arid regions has induced various conflicts, which have seriously affected the stability, security and development of the region. This article first gives a brief overview of water resources allocation and river eco-hydrological system. In order to promote the sustainable use of water resources in the region, the game model of river eco-hydrological evolution under big data water resources allocation is studied.
A Model Driven Approach to Building Domain Specific Search Engines

In many application domains, general purpose search engines are not very effective as they are not designed for deep processing of semantic relations present in these domains. They need a search engine that understands domain concepts and relations. However, building a domain specific search engine is a non-trivial task. To build an effective search engine, it is not only the text processing technologies that one has to master, one also has to understand the nuances of domain entities and their relationships. Domain knowledge such as generalization hierarchies, relation types, cardinalities, property value types, units, ranges, etc., play a key role in understanding the text. To achieve right accuracy levels, these domain specific nuances have to be encoded into text processing algorithms. Developing a search engine that takes all this into account requires substantial time and effort. To address this, we present a model driven approach to realize domain specific search engines. We propose a metamodel to specify a domain in terms of concepts, relations and their extraction models. An information extraction component interprets this metamodel to access the domain model and the information contained in it. It uses this information to determine what entities, relations and properties need to be extracted, what extraction models to use for the same, and how to resolve the ambiguities that arise from the text processing stage. We also show how a domain specific query language interface can be generated from the domain model. We discuss the results of applying the proposed approach on two domains: materials science and urban mining. The model driven approach results in substantial savings in development efforts.
Multiple Stream Oriented Siamese Network for RGB-T Tracking

RGB-T tracker owns the capability of fusing two different yet complementary target observations, thus it will become a promising technology to fulfill all-weather tracking. Existing convolutional neural network based tracking methods often consider the multi-source oriented deep feature fusion from global viewpoint, leading to inevitable negative effect when feature maps of the target pair only contain little useful information. To solve this problem, we propose a four-stream oriented Siamese network, named as FS-Siamese, for RGB-T tracking. In particular, we introduce co-attention mechanism in bilinear pooling to explore the partial feature interaction between the RGB and thermal targets. This can effectively avoid uninformed image blocks disturbing feature embedding fusion. To enhance the efficiency of our Siamese network, we also propose an inner product based logistical loss for training the feature embedding and the graph convolutional neural network based bilinear pooling in an end-to-end manner. Extensive experiments on GTOT datasets demonstrate that the proposed method achieves state-of-the-art performance in the task of RGB-T tracking.
Synchronous microdeformation and first derivative dynamic measurement based on laser shearing speckle interferometry

This paper presents a multi-carrier frequency spatial phase shift digital shearing speckle interferometry method which is capable of the first derivative and microdeformation synchronously. The dynamic deformation of the composite defects is carried out by thermal loading.The shear speckle interferometry is used to locate the defect position and holography was used to measure microdeformation of the defect. In Michelson shearing device, an optical fiber is used to introduce a reference beam,two shear images form a shear fringe pattern, and the reference beam introduced is combined with one of the shear patterns to generate a hologram. The spatial phase shifting technique is used to obtain the frequency domain after Fourier transform, holograms and shearograms are obtained at the same time. In this paper, the direct synchronous measurement of microdeformation and first derivative of deformation is realized without the need of numerical integration process. Finally, the experimental results show that the proposed method can quickly obtain the shear fringe image, which represents the first derivative of the deformation to position the defect in the composite material, and the hologram is filtered, unwrapped and 3D displayed to obtain accurate micron scale deformation values.
Precise Estimation of Local Probabilities for Bayesian Attack Graph Analysis

A Bayesian Attack Graph (BAG) is an essential model for red teams in cyber security to detect the most vulnerable components of a system. It is a probabilistic graphical model in which each node is initially assigned a probability value called local probability. For realistic and better analysis of BAGs, it is essential to evaluate local probabilities precisely. For that purpose, in this paper, we use the Common Vulnerability Scoring System (CVSS) to estimate temporal and environmental scores. We further consider various factors reflecting attackers' characteristics in BAG analysis. In this respect, we inaugurated a new environmental variable named “host type” that influences an attacker's motivation and abolishes the need for earlier network architecture knowledge to determine the factor values.
Evaluation of color transfer algorithm in IR images nocturnal scenes

Color transfer is one of the main concerns on automatic colorization of gray-scale images [1]. Modern approaches were created based on Welsh's colorization algorithm, improving the core method but relying on specialized hardware. In this paper, we explored a series of algorithms, previously tested in gray-scale image scenarios, to colorize night-vision images on common security systems using affordable hardware. In addition, we demonstrated the efficiency of borrowing color from standard to IR images applying a similarity function integrating luminance information and chromatic values of previously colorized pixels [2].
Reviews

Elizabeth Parang reviews Open Praxis, Open Access: Digital Scholarship in Action; Roxanne Backowski reviews Beyond Reality: Augmented, Virtual, and Mixed Reality in the Library.
Comparative Study on The Results of Student Assessment Data Developed by SURE Model

The Mongolian University of Science and Technology collects information (data) related to teaching methods and learning environment using a brief questionnaire from each student each semester. The data collected via questionnaires should be used for analysis of education activities, as well as development of policy level measures directed to improve education quality and teaching methods. However, as was observed by teachers, there is still an insufficiency in development of collected data and actions taken based on the results. In order to conduct comparative study within the framework of this issue, one module was taught by the same teacher (Theoretical mechanics 2) and other three different subjects (Mathematics 1, Information Technology 1, Theoretical Mechanics 1) were chosen, which are selected by most of the students, and ultimately processed the results with the use of the SURE model of structure-based evaluation. In this article, a total of 66681 evaluation data were provided by students who completed general education courses, professional foundation courses and other specialized courses at three schools (School of Industrial Technology, School of Mechanical Engineering and Transportation, School of Information and Telecommunication Technology) under the MUST, and these data were processed with the use of evaluation model mentioned above and the results were compared with the outcomes of previous study.
Ethical problems in the use of algorithms in data management and in a free market economy

Abstract The problem that I present in this paper concerns the issue of ethical evaluation of algorithms, especially those used in social media and which create profiles of users of these media and new technologies that have recently emerged and are intended to change the functioning of technologies used in data management. Systems such as Overton, SambaNova or Snorkel were created to help engineers create data management models, but they are based on different assumptions than the previous approach in machine learning and deep learning. There is a need to analyze both deep learning algorithms and new technologies in database management in terms of their actions towards a person who leaves their digital footprints, on which these technologies work. Then, the possibilities of applying the existing deep learning technology and new Big Data systems in the economy will be shown. The opportunities offered by the systems mentioned above seem to be promising for many companies and—if implemented on a larger scale—they will affect the functioning of the free market.
Modal Type of Weak Intuitionistic Fuzzy Implications Generated by the Operation Δ

Abstract In 2020 L. Atanassova has been introduced the new operation Δ over intuitionistic fuzzy sets and over intuitionistic fuzzy pairs. Some of its properties have been studied in 2021 from L. Atanassova and P. Dworniczak. In 2021 L. Atanassova and P. Dworniczak generated an intuitionistic fuzzy implication by the operation Δ, and it has been introduced and some of its basic properties have been described. Here, eight modal type of intuitionistic fuzzy implications are generated by the first one and some of their properties are discussed.
On Scalability of Processing in Computational Clouds

The paper presents selected issues concerning effectiveness of processing and its evaluation in computational clouds. Especially the scalability of processing is discussed and investigated. The scalability of processing in computational clouds can be explored from different points of view and under different conditions. The performed and presented in the work computational experiments took into consideration the evaluation of processing in multiservice virtual environment. It was tested execution of different services (or tasks) simultaneously, in different configurations (i.e. virtual environments of given parameters) of different processing power, and under several loads. The presented results show how scalability of computational clouds works differently for different tasks (or services) in spite of running the same set of tasks in tested processing environments.
Neural view synthesis with appearance editing from unstructured images

We present a neural rendering framework for simultaneous view synthesis and appearance editing of a scene with known environmental illumination captured using a mobile camera. Existing approaches either achieve view synthesis alone or view synthesis along with relighting, without control over the scene's appearance. Our approach explicitly disentangles the appearance and learns a lighting representation that is independent of it. Specifically, we jointly learn the scene appearance and a lighting-only representation of the scene. Such disentanglement allows our approach to generalize to arbitrary changes in appearance while performing view synthesis. We show results of editing the appearance of real scenes in interesting and non-trivial ways. The performance of our view synthesis approach is on par with state-of-the-art approaches on both real and synthetic data.
VarDefense: Variance-Based Defense against Poison Attack

The emergence of poison attack brings a serious risk to deep neural networks (DNNs). Specifically, an adversary can poison the training dataset to train a backdoor model, which behaves fine on clean data but induces targeted misclassification on arbitrary data with the crafted trigger. However, previous defense methods have to purify the backdoor model with the compromising degradation of performance. In this paper, to relieve the problem, a novel defense method VarDefense is proposed, which leverages an effective metric, i.e., variance, and purifying strategy. In detail, variance is adopted to distinguish the bad neurons that play a core role in poison attack and then purifying the bad neurons. Moreover, we find that the bad neurons are generally located in the later layers of the backdoor model because the earlier layers only extract general features. Based on it, we design a proper purifying strategy where only later layers of the backdoor model are purified and in this way, the degradation of performance is greatly reduced, compared to previous defense methods. Extensive experiments show that the performance of VarDefense significantly surpasses state-of-the-art defense methods.
Personalized book recommendation of university library coupled with collaborative filtering and FP-Growth algorithm

research-article Share on Personalized book recommendation of university library coupled with collaborative filtering and FP-Growth algorithm Author: Xiaohan Zhou library, Southwest university, China library, Southwest university, ChinaView Profile Authors Info & Claims MLMI'21: 2021 The 4th International Conference on Machine Learning and Machine IntelligenceSeptember 2021 Pages 172–176https://doi.org/10.1145/3490725.3490751Online:29 December 2021Publication History 0citation22DownloadsMetricsTotal Citations0Total Downloads22Last 12 Months22Last 6 weeks4 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
Research and Application on Text Classification Model Based on Keywords

Text classification is the process of assigning predefined labels to text by its content. It is a common task of Natural Language Processing (NLP). The traditional way for text classification is machine learning and its effect is greatly depended on the amount and accuracy of the training data set which is difficult to obtain in most cases. The job of building the training data set is inefficient and expensive [1]. This has motivated a research word to break this barrier, with a method using keywords instead to complete text classification. In this work, we explore the usage of label-keywords pairs (each label has a set of keywords) for assigning text documents to one or more categories automatically even without the training data set, obtaining results comparable to those systems that classify the text manually.
Classification of the Degree of Dehydration in Diarrhea Using the Naïve Bayes Algorithm

Diarrhea is a global health issue with significant morbidity and fatality rates. Diarrhea is the second leading cause of mortality in children under the age of five, behind pneumonia. Many reasons can obstruct the diagnosis of diarrhea, including a lack of parental information about diarrhea, a continued underestimation of diarrhea, a lack of understanding about the kind of diarrhea, and misdiagnosis. The goal of this study is to identify and define the kind of diarrhea disease in toddlers, as well as to offer basic treatment options that can be conducted by the parents. The public health center of Mojopanggung provided the dataset for this study, which included 166 cases and 21 attributes. However, only eight attributes were shown have relation with the class following the Bivariate Pearson correlation test, namely defecate frequency, feces consistency, fever, nausea/vomiting, eye condition, thirst, and skin turgor. The categorization yielded a degree of dehydration, namely none, mild/moderate dehydration, and severe dehydration. Because the percentage of the number of classes in this dataset is still unbalanced, an oversampling approach is used to correct the problem, resulting in a final data count of 318 instances. The Naive Bayes Classifier (NBC) algorithm, which is a statistical classification approach for predicting the likelihood of membership in a class, is used in this work. Cross Validation was used in this study with a 10 folds test. For the categorization of kinds of diarrhea illnesses based on the degree of dehydration in toddlers, the findings of this study utilizing Confusion Matrix show an average value of 97.48 percent accuracy, precision of 96.55 percent, recall of 96.27 percent, and an error rate of 2.52 percent.