Reviewerly/exHarmony huggingface dataset:
Implementation Of Movement Detection And Tracking Objects From Video Frames Using Image Processing

The moment detection and tracking in a video surveillance system video is a challenging framework. This paper aimed to detect the motion from video frames of compressor JPEG format. The approaches for motion detection using the sum of absolute difference. Once after performing motion detection, tracking the objects is processed for behavioral analysis. A Kalman filter method is applied to predict the position of an object. The video from the stationary camera and static background trajectory movement, and the direction of an object are relatively synchronized. The foreground detection and Kalman filters are used for object detection and motion tracking, respectively.
Segmentation Based Backdoor Attack Detection

Backdoor attacks have become a serious security concern because of the rising popularity of unverified third party machine learning resources such as datasets, pretrained models, and processors. Pre-trained models and shared datasets have become popular due to the high training requirement of deep learning. This raises a serious security concern since the shared models and datasets may be modified intentionally in order to reduce system efficacy. A backdoor attack is difficult to detect since the embedded adversarial decision rule will only be triggered by a pre-chosen pattern, and the contaminated model behaves normally on benign samples. This paper devises a backdoor attack detection method to identify whether a sample is attacked for image-related applications. The information consistence provided by an image without each segment is considered. The absence of the segment containing a trigger strongly affects the consistence since the trigger dominates the decision. Our proposed method is evaluated empirically to confirm the effectiveness in various settings. As there is no restrictive assumption on the trigger of backdoor attacks, we expect our proposed model is generalizable and can defend against a wider range of modern attacks.
Facial pain intensity estimation for ICU patient with partial occlusion coming from treatment

Pain is a vital warning of threats to health, and facial expressions are often credible indicators of pain intensity. Clinically, intensive care unit (ICU) patients are those most in need of an automatic facial expression pain intensity estimation system. There are numerous ICU patients with nasogastric tubes, which are fixed on the faces with tape and padded with gauze to prevent constant scratching against the skin. Those stuff prevents the effective use of existing automatic facial pain intensity estimation systems, which are designed for unobstructed faces. To estimate the facial pain intensity for such patients, we proposed a method that combines AAM (Active Appearance Model), CNN (Convolutional Neural Network) and ELM (Extreme Learning Machine). AAM was used to generate masks to remove the obstructed facial areas, and followed by inpainting to reduce the interference caused by abrupt brightness change at the mask border. CNN was employed for feature extraction and the pain intensity was regressed via ELM. Experimental results reveal that the proposed method outperforms the previous method in terms of mean squared error and Pearson correlation coefficient.
La educación, servicio esencial

espanolLa crisis derivada de la COVID‑19 modifico el normal funcionamiento del Consejo Escolar del Estado. Asumiendo con decision su mision de asesoramiento, se trabajo a fondo para analizar serenamente la extrana situacion vivida en el ambito escolar y formular propuestas que pudiesen ser de utilidad para la sociedad y, especialmente, para las administraciones publicas y para la propia comunidad educativa. En este articulo se presenta muy sucintamente el desarrollo de la actividad de este organo estatal de participacion respecto a la incidencia de la pandemia en los procesos de ensenanza y aprendizaje, ademas de las reflexiones planteadas reiteradamente en sus multiples reuniones telematicas, con la vista puesta en un deseado futuro normalizado en el que la educacion continue siendo un servicio esencial altamente valorado por la sociedad. EnglishThe COVID‑19 crisis has significantly changed the functioning of the State School Council. A firm decision was taken: counselling and assessment were still the main tasks and goals. But, at the same time, the Council has been working hard to analyse the strange situation arising from the current pandemic and has drawn up concrete and useful proposals addressed to the Public Administrations and the whole educational community. In this article we briefly present the Council’s activity, that has mainly focused on paying special attention to the impact of the pandemic in the teaching-learning process. We also want to highlight the development of innovative telematic solutions for online meetings that facilitated the Council's work and the discussions and deliberations. The aim was to be able to help the educational community to create a better future where education continues to be a highly appreciated essential service.
O PAPEL DA SÍNDROME METABÓLICA NA DOENÇA DE ALZHEIMER

INTRODUCTION disease (AD) is characterized by progressive cognitive loss coupled with age-related functional impairment. Its two major brands are β-amyloid plaques and neurofibrillary tangles. There is strong evidence for a relationship between (MS) and AD. Both pathologies are quite prevalent and dependent on aging. OBJECTIVE The present study seeks to understand the role of the metabolic syndrome in the pathophysiology of disease and to describe preventive and therapeutic interventions. METHODOLOGY The review was made based on the search for scientific articles in the electronic databases PUBMED and Web of Science, using the descriptors Alzheimer's Disease, Metabolic Syndrome DISCUSSION MS is a metabolic breakdown with the potential to damage insulin signaling in the brain, causing insulin resistance, inhibiting β-amyloid clearance and its accumulation, which generates neuroinflammation. In addition, it induces a prothrombotic state with ischemic effects, resulting in oxidative stress and neuroinflammation and progressive local brain atrophies. The components of the metabolic syndrome are related to AD, exacerbating neuroinflammation and insulin resistance. Preventive and therapeutic measures aiming at the MS are promising. CONCLUSION From the analyzes developed in this study, different relationships between the components of MS and AD are perceived, the first being possible causes and / or effects of the second. Since insulin resistance plays a major role in the initiation and perpetuation of cognitive impairment in AD. Furthermore, the components of MS associated with AD, when treated with preventive and therapeutic measures, break this association by promoting rebalancing of the metabolism.
A Study on the Influence of Information Welfare Policies on Information Satisfaction, Focusing On Those Alienated From Information Welfare

Information welfare has emerged as an important issue in our society since it can improve the quality of life and productivity of members of society. Just as economic polarization is due to the capital gap in capitalist society, the digital divide causes inequality, and the concept of information welfare is very important to minimize it and ensure equal opportunities for everyone to have easy access to information. To identify the relationship between information welfare policy activation and information satisfaction, this study selected increased information education for the underprivileged, the establishment of new information policies for the underprivileged, and the realization of information policies based on preceding studies as elements of information welfare policies and conducted empirical research. The main results of the empirical study are shown below. First, the information welfare policies for the underprivilegedhad a significant impact on the activation of information welfare for the underprivileged. Second, the activation of information welfare for the underprivilegedhad a significant influence on information satisfaction. This study is meaningful in that it suggests the direction of new information welfare policies to address the information divide.
Sensor Failure Identification in Industrial Big Data

With the rise of information and sensor technologies, sensors play an increasingly significant role in modern production systems. The reliability, safety, and productivity of a production system may largely depend on sensor performance. However, there has been a lack of unsupervised methods for sensor anomaly identification under the environment of industrial big data. This paper proposed an approach to detect sensor failure for industrial big data in an unsupervised manner with the help of random forest and long short-term memory neural networks. The data used in this research are time-series data collected from a gas turbine with 107 sensors. The dataset includes sensor data with 700,000 timestamps in recent years. In this research, random forest regression was first applied to identify the relationship among those sensor values. Afterward, a long short-term memory network is established to predict the values of the target sensor at the current time step. Then, sensor failures can be identified according to the difference between the predicted and actual sensor values. The conducted experiments show promising results that the approach successfully identifies the sensor failure in a completely unsupervised manner.
Hubungan Ketepatan Terminologi Medis Dengan Keakuratan Kode Diagnosis Rawat Jalan di Rumah Sakit “X” Pekanbaru Tahun 2020

Determining the diagnosis of a patient is an obligation, the rights and responsibilities of the doctor (medical personnel) concerned should not be changed, therefore the diagnosis in the medical record is filled in completely and clearly in accordance with the directions in the ICD-10 book. In medical terminology, which is a special language between the medical / health professions, both in written and oral form, the means between those who are continuous in the field of health care / services require high accuracy and precision for morbidity and mortality statistics. This type of research used in this research is quantitative analytic research using cross sectional design. The results of this study obtained the accuracy of medical terminology in medical records as many as 59 (59.9%) medical records, and 40 (40.4%) incorrect. And the code accuracy is 53 (53.5%), and the inaccurate is 46 (46.4%). So the inaccuracy of medical terminology will have an impact on the accuracy in carrying out the coding.Keywords: Relation of accuracy of medical terminology with accuracy of       outpatient diagnosis.
Understanding XR Learning Experiences in Undergraduate Logistics Programs

With emerging technologies and extended reality (XR) technology on the rise in logistics education programs, we evaluated the perceptions of Thai undergraduate students and faculty members on XR learning experiences using an XR Literacy Framework. The XR Literacy Framework was adapted from NMC Horizon Project’s digital literacy framework and informed by the research work of Pehlivanis, Papagianni, and Styliadi (2004), and DHL’s Key Developments and Implications (2019). Questions focused on perspectives on XR usage in logistics management undergraduate programs in Ladkrabang, Bangkok, Thailand in the key areas of training levels and confidence in application of XR technology. All research constructs were measured using multiple-item 5-point Likert scales and target respondents were evaluated in terms of XR for logistics management familiarity. The data was collected from a judgment sample of respondents on a voluntary basis and analyzed with SPSS software. A single t-test survey was performed to assess if there was a statistically significant difference between the sample qualified and the overall population. In both cases of training and confidence in application of XR technology, levels were deemed to be low.Following responses on identified critical points, we recommend further study on increasing XR training in undergraduate logistics education programs and building confidence in practical application of XR technology.
Advantages of Individual Teaching

We can face many major challenges in the education system today. But a special place among them is the teaching of children, which allows an individual approach to education. An individual approach to the process of educating a child is necessary to change the internal conditions for any pedagogical impact to break through the individual's personal abilities. This factor, in addition to the process of education and training, loses its effectiveness
Storage Optimization with High Security Measure for Data in Cloud

MapReduce assumes a basic part as the main structure for large data examination. In this paper, we consider a geo-distributed cloud engineering that gives MapReduce administrations dependent on the huge data gathered from end clients everywhere. Existing work handles MapReduce occupations by a customary calculation-centric methodology that all info data distributed in numerous clouds are amassed to a virtual cluster that lives in a solitary cloud. Its helpless proficiency and significant expense for huge data support inspire us to propose a novel data-centric design with three key strategies, specifically, cross-cloud virtual cluster, data-centric occupation situation, and organization coding-based traffic steering. Our plan prompts an enhancement system to limit both calculation and transmission cost for running a bunch of MapReduce occupations in geo-distributed clouds. We further plan a parallel algorithm by disintegrating the first huge scope issue into a few distributively resolvable subproblems that are composed of an undeniable level expert issue. At long last, we direct true examinations and broad reproductions to show that our proposition fundamentally beats the current works.
Coalgebra Encoding for Efficient Minimization

Recently, we have developed an efficient generic partition refinement algorithm, which computes behavioural equivalence on a state-based system given as an encoded coalgebra, and implemented it in the tool CoPaR. Here we extend this to a fully fledged minimization algorithm and tool by integrating two new aspects: (1) the computation of the transition structure on the minimized state set, and (2) the computation of the reachable part of the given system. In our generic coalgebraic setting these two aspects turn out to be surprisingly non-trivial requiring us to extend the previous theory. In particular, we identify a sufficient condition on encodings of coalgebras, and we show how to augment the existing interface, which encapsulates computations that are specific for the coalgebraic type functor, to make the above extensions possible. Both extensions have linear run time.
Competition from novel features drives scalar inferences in reference games

Author(s): Hu, Jennifer; Zaslavsky, Noga; Levy, Roger | Abstract: Scalar implicatures, one of the signatures of pragmatic reasoning, are believed to arise from competing alternative utterances, which the listener knows that the speaker could have used to express a strengthened meaning. But do scalar implicatures also arise in the presence of nonce objects, for which no alternative name is known? We conduct a series of experiments assessing the degree of scalar strengthening driven by familiar and nonce objects. We find that nonce objects can derive scalar implicatures as strongly as familiar objects in simple reference games. Our experiments also reveal an asymmetry in the relative strengths of familiar- and nonce-driven inferences: relative to the prior, participants preferentially interpret the name of a shared feature as referring to an object with an additional nonce feature over an object with an additional familiar feature, suggesting that familiar alternatives exert greater scalar pressure than nonce alternatives. We also present exploratory model simulations suggesting that our results may be explained by rationally reasoning about a high-cost description of the novel object. Our findings support the idea that novel lexical entries may be generated from one-shot encounters and spontaneously used in pragmatic inference.
The Effect of Collaborative Teaching Technique and Literacy Interest on the Literacy Skills of Eighth Grade Students on Recount Text at SMP Negeri 1 Sembawa

The aim of this study is to look at how cooperative learning strategies and literacy interest affect 8th students' reading comprehension at SMP Negeri 1 Sembawa.68 eighth-grade students from SMP Negeri 1 Sembawa are chosen as respondents for this analysis using a two-stage random sampling method.In this analysis, the factorial research design was used.A questionnaire and a test were used to collect the data.These interviews were transcribed using the Combined Sample t-test, Independent T, and Two Ways ANOVA.The results showed that when students were taught using the collaborative student learning, there was a significant difference in their literacy skills.Second, when students were taught using the conventional approach, there was a significant improvement in their reading comprehension.Third, when the cooperative learning approach and the traditional method were used, there was a significant difference in reading comprehension between students with lower and higher reading interest.Finally, there was a significant association impact of cooperative learning method and literacy interest on eighth grader students' reading comprehension at SMP Negeri 1 Sembawa.In conclusion, the cooperative learning approach worked well for pupils whom were enthusiastic about literacy.
A Deontic Stit Logic Based on Beliefs and Expected Utility.

The formalization of action and obligation using logic languages is a topic of increasing relevance in the field of ethics for AI. Having an expressive syntactic and semantic framework to reason about agents' decisions in moral situations allows for unequivocal representations of components of behavior that are relevant when assigning blame (or praise) of outcomes to said agents. Two very important components of behavior in this respect are belief and belief-based action. In this work we present a logic of doxastic oughts by extending epistemic deontic stit theory with beliefs. On one hand, the semantics for formulas involving belief operators is based on probability measures. On the other, the semantics for doxastic oughts relies on a notion of optimality, and the underlying choice rule is maximization of expected utility. We introduce an axiom system for the resulting logic, and we address its soundness, completeness, and decidability results. These results are significant in the line of research that intends to use proof systems of epistemic, doxastic, and deontic logics to help in the testing of ethical behavior of AI through theorem-proving and model-checking.
The Attitude of the Teaching Staff to the Profession and Colleagues as a Context of Pedagogical Activity

The purpose of this study is to develop and test a methodology for assessing the attitude of the school staff to the profession and colleagues as a context for pedagogical activity. The research methodology is presented with a questionnaire survey conducted according to a simplified scheme and offering respondents to assess the attractiveness of working at school (general mood), the relationship with the teacher's team, the relationship with the school management. The statistical processing of the results was carried out on the basis of the method of correlation analysis using the StatPlus LE program. As our research has shown, teachers' positive attitudes to work are often poorly mediated by their professional activities.
A Brief Study on More Accurate Appreciation of the Statistical Information

As usually, there are very many situations when a man is clashing with the necessity to treat/process any statistical data (may be, about reliability of technical system, results of investigations or experiments about weather, temperature, pressure, productivity of labour and so on). In addition to that all academics try to determine the right law of distribution with their help to definite mathematical equations/formulas or with the special ways to find the possibility to solve the problem. Such task isn’t mighty easy in many cases. It is needed to know perfectly well the theory of probability, laws of distribution (they are more 30) and criteria (more than 10) to confirm correctly the common conclusion of calculations [1-8,9,10,11,12]. In this article two ways how really we can get the right finish result without any mistake the researcher can receive and more accurate appreciation of the statistical information is given as well.
Research on Optimization Technology of Multi-data Center for Multi-site Integration

This paper designs a “end-cloud” resource coordination mechanism for efficient scheduling of edge computing and cloud computing, designs a “end-cloud” resource negotiation and allocation mechanism, mainly defines a “end-cloud” collaborative computing model, and designs terminal resource access to the cloud. The “end-cloud” collaborative computing model can be abstracted into three layers, namely the computing layer, the middle layer, and the user layer. This paper studies the efficient scheduling technology of edge computing and cloud computing and designs the “end-cloud” collaborative task allocation algorithm to achieve resource optimization scheduling. In this paper, we design a multi data center data consistency optimization and resource utilization improvement implementation scheme, and design a data model application method that satisfies various associated data linkage states when a normal model event occurs. Through the research of this paper, a complete set of multi-data center optimization technical solutions for multi-site integration has been formed, multi-site collaborative optimization effects can be exerted, hidden dangers can be discovered intelligently, optimization strategy development can be assisted, and the actual needs of front-line operation and maintenance personnel of power grid can be met.
The role of clustering in the efficient solution of small Traveling Salesperson Problems

Author(s): Marupudi, Vijay; Harsch, Rina; Rao, V. N. Vimal; Bye, Jeffrey K.; Park, Jimin; Varma, Sashank | Abstract: Human solutions to the Traveling Salesperson Problem (TSP) are surprisingly close to optimal and unexpectedly efficient. We posit that humans solve instances of the TSP by first clustering the points into smaller regions and then solving each cluster as a simpler TSP. Prior research has shown that participants cluster visual stimuli reliably. That is, their clustering and re-clustering of the same stimulus are similar, especially when the stimulus is relatively more clustered. In this study, participants solved the same TSP instances twice. On the second presentation, half of the instances were flipped about the horizontal and vertical axes. Participants solved the TSP reliably, with their two tours of the same instance sharing 77 percent of the same edges on average. In addition, within-participant reliability was higher for more clustered versus more dispersed instances. Our findings are consistent with the proposal that people use clustering strategies to solve the TSP.