Reviewerly/exHarmony huggingface dataset:
Air Leakage Detection of Pneumatic Train Door Subsystems Using Open Set Recognition

For pneumatically operated train doors, continuous monitoring and earlier detection of air leakages would greatly reduce train downtime and ensure both safety and high availability of the rail system. Deep learning, a powerful tool to extract useful information from a large amount of sensor data is widely used in fault detection. A large collection of known fault samples is necessary to generate reliable deep learning models. However, in real-world applications, it is very difficult to precollect all possible types of fault or anomaly conditions to train the model. When an unknown sample is fed into a traditional deep learning model, the model may make misclassification and recognize this sample as one of the known classes. To address this issue, the concept of OSR was proposed, whose goal is not only to classify known classes but also to detect unknown samples. Existing OSR methods are commonly based on complex generative models, and the testing phase is divided into multiple stages, which makes it difficult to apply these OSR methods to real-time industrial tasks. In this article, we proposed an end-to-end OSR method with less computational cost based on a streamlined and lightweight convolutional neural network. The proposed method is applied to detect different levels of air leakage on the pneumatic train door subsystem. Compared with other existing OSR methods, the proposed method dramatically shortens the running time and reduces the use of parameters in the model with negligible loss of OSR accuracy.
Analyzing the Effects of AI Education Program based on AI Tools

Purpose: In recent years, AI technology has emerged as a core technology for the fourth industrial revolution. Along with the existing SW education, it is time to prepare for AI education, since the interest in AI education is also growing. The fact that learning about the theory and principles of AI is important is also stressed in the college education. However, non-major students tend to perceive AI technology to be difficult, and hence, a more efficient AI education is required. The purpose of this research is to guide the learners direction for AI education, helping them to break away from their fears of AI through the AI education appropriate for the learners’ level. Method: In this study, to analyze the effect of the developed educational program on the learners’ AI literacy, the dependent variables changed before and after the application of the program were analyzed, and the single group’s ex ante and ex post testings were applied for the designing method for inferring the causal relationship. Results: The AI literacy of undergraduate students was analyzed for the educational program via the ex ante and ex posts testings of a single group by operating this program. As a result of the analysis, significant results were obtained where all three areas of the AI literacy were improved through the educational program Conclusion: This study is meaningful in that the AI educational program was proposed for the liberal arts class at college for the non-majors, and that the AI educational program which is generally applicable for the liberal arts class was developed and directly applied to validate its effectiveness. Discussions are expected to continue to develop the various details of the AI basic education to ensure that the non-majors can enter the AI education without fearing the AI based on the results of this study.
Machine Moderators in Content Management System Details Essentials for Iot Entrepreneurs

The need for Content moderation is indispensable as publishers can't allow inappropriate or illegal content to be published through or on their platforms. Also, every publisher has the responsibility of checking works to ensure that the content of the results is ethical for readers, and viewers can get clean and explicit content. Machine moderators may be the all-important response to the growing need for moderators. The push for machine moderators is often justified as a necessary response to the scale of work supposed to be edited. The use of machines, Artificial Intelligence (AI) will make content editing faster and possibly more accurate than human moderators. This work looks into the meaning of content moderation, how machines are used for content moderation and the advantages of using machines in content moderation. It also highlights the significant innovations in the use of machine moderators in content management. The simulation of Human Intelligence in machines programmed to think like humans and mimic their actions is referred to as artificial intelligence (AI). The term may also be used for any machine or computer that exhibits characteristics resembling the human mind, such as problem-solving and learning. The optimal feature of artificial intelligence is defined as the ability to rationalize, learn and take actions that have the highest potential to be most beneficial for the intended goal and objectives.
Unsupervised multi-source domain adaptation with no observable source data

Given trained models from multiple source domains, how can we predict the labels of unlabeled data in a target domain? Unsupervised multi-source domain adaptation (UMDA) aims for predicting the labels of unlabeled target data by transferring the knowledge of multiple source domains. UMDA is a crucial problem in many real-world scenarios where no labeled target data are available. Previous approaches in UMDA assume that data are observable over all domains. However, source data are not easily accessible due to privacy or confidentiality issues in a lot of practical scenarios, although classifiers learned in source domains are readily available. In this work, we target data-free UMDA where source data are not observable at all, a novel problem that has not been studied before despite being very realistic and crucial. To solve data-free UMDA, we propose DEMS (Data-free Exploitation of Multiple Sources), a novel architecture that adapts target data to source domains without exploiting any source data, and estimates the target labels by exploiting pre-trained source classifiers. Extensive experiments for data-free UMDA on real-world datasets show that DEMS provides the state-of-the-art accuracy which is up to 27.5% point higher than that of the best baseline.
A Brief Survey of Future Computing Technologies in Cloud Environment

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A New Modeling Approach for the Video Pre-Analysis of Video Surveillance Systems

The work presented in this paper aims to develop a new architecture for video surveillance systems. Among the problems encountered when tracking and classifying objects are groups of occluded objects. Simplifying the representation of objects leads to other reliable object tracking with smaller amounts of information used but protection of the necessary characteristics. Therefore, modeling moving objects into a simpler form can be considered a pre-analysis technique. Objects can be represented in different ways, and the choice of the representation of an object strongly depends on the field of application. An example of a video surveillance system respecting this architecture and using the pre-analysis method is proposed.
Dental implants success prediction by classifier ensemble on imbalanced data

In recent years, increasing advances in data storage have led to an increase in the volume of medical data. Data mining has a significant role in different aspects of medical sciences, including extracting knowledge from the volume of data, diagnosing diseases, determining the effectiveness of drugs, identifying treatment methods, and predicting the success of treatment. The dental implant is one of the most common methods for tooth root replacement used in prosthetic dentistry. Accurate prediction of implant success or failure before the surgery is an interesting and challenging topic for dentists. However, imbalanced data is one of the main problems in any medical data mining application. This problem occurs when samples of one or more classes of data are scarce or difficult to collect. This challenge poses severe problems in extracting knowledge from data. In this paper, we present a new approach for predicting the success or failure of dental implants in the presence of imbalanced data. To this aim, first, the imbalanced data is clustered, and then each cluster is balanced by SMOTE algorithm. In the next step, the balanced data are classified by an ensemble of four well-known classifiers, including the decision tree, the support vector machine, the k-nearest neighbor, and the Naïve Bayes. To improve the classification accuracy, an optimal weight is determined for each classifier using the genetic algorithm. Thus, each classifier will have a different effect on determining the final decision. : The proposed method was applied to a dataset consists of 224 patients who underwent implant and bone graft treatments at the Faculty of Dentistry of the University of Tehran, Iran. Different criteria were used to evaluate the proposed method, including accuracy, sensitivity, specificity, AUC, and G-mean. The evaluation results show that the proposed ensemble algorithm provides a more accurate implant success prediction on imbalanced data compared to using the single-use of each classifier and also state-of-the-art researches. This paper presents a novel ensemble method algorithm to predict the success or failure of dental implants for imbalanced data. The obtained results reported that the use of the hybrid algorithm increases the accuracy parameter by about 5.5%, the sensitivity parameter by about 0.3%, and the specificity parameter with a significant amount of about 25%, in comparison to the best results achieved by single classifiers.
Legal framework for the use of artificial intelligence in India’s military sphere in the context of patent rights protection

This article aims to examine India’s approaches to handling the items of ethics and legal regulation (framework) of the development and application and the use of artificial intelligence in the military sphere in the context of national acts, capabilities and needs of India. It was revealed that the country’s lag behind its neighbors (China, Pakistan) and recognized leaders in this area (USA, Israel) is a motive for formulating the concept of ensuring India’s military superiority based on AI as a force multiplier. It was revealed that the identified problems require a prompt solution based on the concerted joint efforts of the relevant interested parties with the leading role of the government. The theoretical and practical significance of the results obtained is determined by the fact that the readers will be provided with current scientific information about India’s approaches to the designated areas from the standpoint of law and ethics.
Actuator Fault Diagnosis and Counterplan Using Extended Kalman Filter Considering Symmetry of Hexacopter UAV

In this study, we used the extended Kalman filter to perform actuator fault diagnosis and develop a corresponding counterplan based on the symmetry of a hexacopter unmanned aerial vehicle. A dynamic model of a hexacopter, containing six degrees of freedom and incorporating the actuator faults, was developed. Next, an extended Kalman filter was designed to estimate the efficiency coefficient of each actuator in real-time. These estimated coefficients were used to perform fault diagnosis and propose an antifault counterplan, along with a residual, based on the symmetry of the thrust generation. Finally, the applicability of the proposed approach was validated through numerical simulations of the hexacopter dynamics.
A prototype of a handheld augmented reality application to get familiar with ultrasound equipment knobology

Aims: A prototype application has been developed that is adapting modern augmented reality technology to support learning in ultrasound skills-labs or at home. The application enables students to get in real-time an orientation and information about the knobs on an equipment´s control panel that is available in the lab.Material and methods: The Augmented reality (AR) application for smartphones or tablets was created in Unity (San Francisco) with the software development kit Vuforia (Vuforia PTC Inc., Zurich) to support students with their own devices to learn the knobology of an ultrasound equipment. An interface was modelled, which is superimposed on the live camera image and provides information about the displayed knobs and sliders on the control panel.Results: The smartphone or tablet app is able to detect the specific ultrasound equipment in real in the skills-lab, as well as from a hardcopy photo or displayed by a monitor and to provide further detailed information. The application can be used therefore as preparation of a practical course or for orientation within the lab at the equipment.Conclusions: The flexibility of the app is optimal for students to become familiar with the equipment before they are having their skills-lab lecture and might help to reduce time for introduction to orientation at the equipment´s console.
Monazite as an Exploration Tool for Iron Oxide-Copper-Gold Mineralisation in the Gawler Craton, South Australia

The chemistry of hydrothermal monazite from the Carrapateena and Prominent Hill iron oxide-copper-gold (IOCG) deposits in the IOCG-rich Gawler Craton, South Australia, is used here to define geochemical criteria for IOCG exploration in the Gawler Craton as follows: Monazite associated with IOCG mineralisation: La + Ce &gt; 63 wt% (where La &gt; 22.5 wt% and Ce &gt; 37 wt%), Y and/or Th &lt; 1 wt% and Nd &lt; 12.5 wt%; Intermediate composition monazite (between background and ore-related compositions): 45 wt% &lt; La + Ce &lt; 63 wt%, Y and/or Th &lt; 1 wt%. Intermediate monazite compositions preserving Nd &gt; 12.5 wt% are considered indicative of Carrapateena-style mineralisation; Background compositions: La + Ce &lt; 45 wt% or Y or Th &gt; 1 wt%. Mineralisation-related monazite compositions are recognised within monazite hosted within cover sequence materials that directly overly IOCG mineralisation at Carrapateena. Similar observations have been made at Prominent Hill. Recognition of these signatures within cover sequence materials demonstrates that the geochemical signatures can survive processes of weathering, erosion, transport and redeposition into younger cover sequence materials that overlie older, mineralised basement rocks. The monazite geochemical signatures therefore have the potential to be dispersed within the cover sequence, effectively increasing the geochemical footprint of mineralisation.
An exploration of chess personalities in grandmasters and class-A players using virtual humans

The topic of virtual humans is increasingly vital in entertainment. They offer an influential medium for amusement and learning. In this article, the researcher investigates virtual chess players of different personalities to explore the psychology of competition between two groups of chess players: grandmasters and class-A players. More specifically, the researcher evaluates the game lengths and different errors made by the two groups of virtual players while competing against each other. The two virtual grandmasters are represented by Anderssen and Leko, who vary in their attack and defence styles. While Anderssen is an aggressive grandmaster, who starts attacking his opponent at an early stage of the game, Leko is known for being a solid defensive player. The class-A players in this study vary in their preferences to knights and bishops. The study reveals many interesting findings of the errors made by different virtual chess players. These findings have their grounds in social sciences and can be beneficial to social scientists and computing researchers.
Digital Literacy Competencies of Mechanical Engineering Vocational Education Teacher Candidates

The era of the Industrial revolution 4.0 collaborates on cyber technology and automation technology. Rapid changes due to the 4.0 industrial revolution, require anticipatory steps by the government through increasing the quality of education. Preparing a great generation who has competencies as capital to anticipate the 4.0 industrial revolution, the 21st-century learning education. One of the competencies expected for 21st-century learning is digital literacy skills. Therefore, this study aims to determine the ability of prospective teachers prepared by the Mechanical Engineering Vocational Study Program, Sarjanawiyata University. The population of prospective teachers is students who have implemented teaching practices in schools in the 2019/2020 school year. Quantitative research with descriptive method approach. Data analysis with the percentage of students who use and integrate Information and Communication Technology (ICT) in learning during practical field experience (PPL). The results obtained conclude that digital literacy skills need to be improved, because students in their learning rarely (50%) use and integrate ICT in learning. Because with digital literacy skills students can design learning that facilitates students to think critically, creatively, and innovatively.
On p-group isomorphism: search-to-decision, counting-to-decision, and nilpotency class reductions via tensors

In this paper we study some classical complexity-theoretic questions regarding GROUP ISOMORPHISM (GPI). We focus on p-groups (groups of prime power order) with odd p, which are believed to be a bottleneck case for GPI, and work in the model of matrix groups over finite fields. Our main results are as follows. • Although search-to-decision and counting-to-decision reductions have been known for over four decades for GRAPH ISOMORPHISM (GI), they had remained open for GPI, explicitly asked by Arvind & Toran (Bull. EATCS, 2005). Extending methods from TENSOR ISOMORPHISM (Grochow & Qiao, ITCS 2021), we show moderately exponential-time such reductions within p-groups of class 2 and exponent p. • Despite the widely held belief that p-groups of class 2 and exponent p are the hardest cases of GPI, there was no reduction to these groups from any larger class of groups. Again using methods from TENSOR ISOMORPHISM (ibid.), we show the first such reduction, namely from isomorphism testing of p-groups of small class and exponent p to those of class two and exponent p. For the first results, our main innovation is to develop linear-algebraic analogues of classical graph coloring gadgets, a key technique in studying the structural complexity of GI. Unlike the graph coloring gadgets, which support restricting to various subgroups of the symmetric group, the problems we study require restricting to various subgroups of the general linear group, which entails significantly different and more complicated gadgets. The analysis of one of our gadgets relies on a classical result from group theory regarding random generation of classical groups (Kantor & Lubotzky, Geom. Dedicata, 1990). For the nilpotency class reduction, we combine a runtime analysis of the Lazard Correspondence with TENSOR ISOMORPHISM-completeness results (Grochow & Qiao, ibid.).
Single image dehazing method based on current strategies

Recently the most role and wide application fields have seen in computer vision and graphics, Computer vision applications are largely impaired when weather is unfavorable, which typically causes reduced visibility in capturing clear images. In image processing context, haze is a surrounding condition that affects scene objects clarity attributed to the influence of moisture in the atmosphere. Haze worsens clarity of scene objects, which consequently impact computer vision and graphic applications negatively. Here the role of the dehazing method still it is very important in computer vision applications, it can make images more clear by taking off haze from them, thus the scene vision will increment. From earlier up to now, many methods have been proposed for improving images. The single image dehazing method is one of them and recently the researchers are more interested in this method. The primary contributions of this review study focus on explaining the cause of haze formation for images, general historical overview of single image dehazing methods, explain the most equation used in the dehazing process those help to contribute the new methods to remove haze and clarify the hazy image, and clarify the pros and cons of haze removal methods.
Continuous Safety Verification of Neural Networks

Deploying deep neural networks (DNNs) as core functions in autonomous driving creates unique verification and validation challenges. In particular, the continuous engineering paradigm of gradually perfecting a DNN-based perception can make the previously established result of safety verification no longer valid. This can occur either due to the newly encountered examples (i.e., input domain enlargement) inside the Operational Design Domain or due to the subsequent parameter fine-tuning activities of a DNN. This paper considers approaches to transfer results established in the previous DNN safety verification problem to the modified problem setting. By considering the reuse of state abstractions, network abstractions, and Lipschitz constants, we develop several sufficient conditions that only require formally analyzing a small part of the DNN in the new problem. The overall concept is evaluated in a 1/10-scaled vehicle that equips a DNN controller to determine the visual waypoint from the perceived image.
Unifying the data center caching layer

Data centers today host large numbers of workloads and many of these workloads consume significant storage resources. Given the long history of successes in storage caching, it is only natural such successes bear fruit in modern data centers, at scale. This paper presents CaaS, a generalized caching service for cloud data centers. Departing from existing application, storage, or data-type specific caches, CaaS unifies and abstracts data center caching resources making these available to any workload and for any data type. Also departing from past caching practices, CaaS is fault-tolerant allowing it to cache writes without risk of data loss. We expect that systems such as CaaS will help bridge the gap between heterogeneous and distributed cache resources and data-intensive applications in a data center.
Text and Data Mining Exception in South America: A Way to Foster AI Development in the Region

Abstract In 2015, authors wondered if Europe was falling behind in the artificial intelligence (AI) race because of the lack of a text and data mining (TDM) exception. What can then be said for South America? Copyright regimes and their interaction with the development of digital technologies on this continent have been overlooked by authors. This paper intends to start filling this gap by mapping the current state of copyright exceptions that serve computational analysis in South America. After reviewing the copyright regimes of the five largest economies of the region (i.e. Argentina, Brazil, Chile, Colombia and Peru), I concluded that they are not prepared for digital research techniques such as text and data mining. Researchers in these countries are at a competitive disadvantage, as rigid and outdated copyright regimes act as a constraint against keeping pace with the latest developments in subsequent years. If policymakers want to develop their nations’ AI capabilities, as many governments and international organizations claim they do, they will need to look for a more flexible and enabling approach to copyright.