Reviewerly/exHarmony huggingface dataset:
Escuela secundaria en tiempos de pandemia: La fragmentación del sistema educativo al descubierto

With the outbreak of the COVID-19 pandemic, schools were identified as potential focal points of the virus massive transmission. That is why they closed their doors and education moved to the students’ homes. Schools did not stop working, but as they form part of a fragmented educational system, the activities they managed to do would be different in each social sector. Educational fragmentation shows a diversification in social structures, setting social groups apart from each other. In this work, we want to analyze and reflect on how the high schools of the capital city of the province of Catamarca have continued their activities in the various social sectors, as well as what each institution expects from their graduates. The empirical material in our study comes from semi-structured interviews we had with principals of the high schools from Catamarca and from field data collected from the teaching staff. The pandemic has deepened inequalities: while the most privileged sectors could quickly adapt to the new reality and managed to support the students, in some cases, the more disadvantaged ones lost contact with their teachers. For the latter, traditional, in-person class is still irreplaceable, no matter the efforts schools make to continue providing for their guardian role from virtual environments.
WITHDRAWN: An effective text mining approach for product safety surveillance using Latent semantic Dirichlet Allocation

The main motivation of this work for suggest as well as perform a useful text mining technique to product safety surveillance applying effective ranked smoke word list and positive, negative pattern detections on huge customer reviews. The suggested method is to be matched with already presented methods by applying different performance patterns, along with this proposed system provide and analytically estimate a computerized approach by using the benefits of best matching content mining approaches and effective disambiguation feature it can analyze reviews through web source ecommerce sites. The proposed system improves the text classification and opinion mining by adopting a new “Latent semantic Dirichlet Allocation” (LSDA) technique with the active learning process. This will group the reviews according to the topics and analyses the reviews to detect the safety and security of the products.
The effect of Kalimantan Institute of Technology on land use change in Karang Joang

Abstract Placement of a university in an area can be one of the attractions for people in the city and also for people outside the area. This activity will certainly trigger the development of other activities as a form of support for educational activities and it will be affected the numerous changes in land use. This study aims to determine the effect of the presence of the university to land-use changes in surrounding Kalimantan Institute of Technology. This research is a quantitative approach with a descriptive analysis, that used to analysis activity system that supports educational activities and land use change. From 2015 until 2018 there are 512 new buildings that change from cultivated land to residential, industrial, transportation and also trade and service, only 19% of them that appear to meet the needs of students, such as a boarding house, rent houses, stationery stores, restaurants, and grocery stores. In order to anticipated the effect of uncontrolled development for the community, it needs to increase the public awareness to build with the proper conditions and based on the regulation, and for the government is the existence of government regulations especially on building permits.
TrustyShare: A Sharing Scheme using ARM TrustZone

As the applications on smartphones and tablets are getting richer and more powerful, people are more willing to do their daily works and entertainments on mobile devices. Sharing and collaborating on these devices in a convenient and safe way has become a growing demand in modern life. However, due to the lack of a flexible access control strategy of the official online services, typical applications such as email, video and music do not support this kind of sharing scheme. In this paper, we propose TrustyShare to provide a general sharing solution based on the ARM TrustZone technology which has a extremely wide range of applications in mobile terminal devices and Internet of Things devices. Our solution allows users to share services they enjoy without the official support from these services, while avoiding the disclosure of their private credentials. In addition, credential owners have full control on how and when others can use their services through flexible access control policies. We implement our solution on OP-TEE platform, and our experimental results demonstrate that TrustyShare works well with good performance.
Application of Artificial Intelligence Based on Simulation in Image Processing

Abstract Nowadays, image processing and computer vision detection technology have very obvious advantage, which are applied in all walks of life, it also has the characteristics of intelligent and fast speed. Now the development of science and technology rapidly, computer vision detection technology is widely used, and at the same time, People are paying more and more close attention, and now the computer has been popular, every household has a computer, people’s dependence on computer are higher and higher, Many aspects of capturing and processing visual information are also inseparable from computers. this article expounds the development of computer vision information detection technology, And the principle of the technology, and the field of application and so on.
Protecting Access Privacy in Ethereum Using Differentially Private Information Retrieval

The last decade has witnessed fast development of blockchain techniques. However, the high cost of storage space and network bandwidth caused by data synchronization prevents many nodes from joining the network, and becomes a bottleneck impeding the development of blockchain. Traditional schemes typically attempt to transfer most of the storage and computation tasks from a light client to a full node. Nevertheless, they remain susceptible to privacy attacks because light clients need to query and retrieve blockchain data. In this paper, we first describe the privacy issues and challenges for Ethereum data retrieval and then propose a privacy-preserving scheme based on private information retrieval (PIR) to secure retrieval of blockchain data. The main idea is to achieve pointer based PIR search by keywords and introduce differential privacy to mitigate PIR's performance barrier. Hence we achieve a tradeoff between privacy and performance. The evaluations on the Ethereum dataset and analysis show that our scheme is both effective and practical in protecting blockchain access privacy.
The WeChat public platform: strengthening HSS academics’ global competitiveness in non-English speaking countries

espanolLa plataforma publica WeChat es un medio de interaccion social que se ha convertido en un canal de comunicacion sincronica eficaz y de bajo coste entre la poblacion china y el mundo occidental. El proyecto que se describe en este articulo pretende mejorar la competitividad global en las areas de humanidades y ciencias sociales tanto en China como en Europa en el ambito de las becas internacionales utilizando la plataforma WeChat. El estudio se llevo a cabo en tres fases: una encuesta previa, una intervencion utilizando la plataforma WeChat y una encuesta posterior. En total, 224 participantes utilizaron la plataforma WeChat para llevar a cabo el estudio cuantitativo. Tras la una encuesta previa, se realizaron una serie de seminarios disenados para presentar la plataforma WeChat y en los que se explico a los docentes de Humanidades y Ciencias sociales como utilizar la plataforma durante la primera fase. Tras la intervencion, se administro otro cuestionario adaptado para evaluar los efectos del programa. Los resultados muestran que los docentes de Humanidades y Ciencias Sociales en China estan interesados en el uso de la plataforma WeChat para establecer colaboraciones con sus colegas occidentales para facilitar la publicacion de sus trabajos de investigacion, preparar presentaciones en conferencias, programas de intercambio academico y programas de movilidad de estudiantes. EnglishThe WeChat public platform has become a low-cost, synchronistic and effective social media channel for daily communications between the Chinese and the West. This project aims to enhance Chinese and European humanities and social sciences (HSS) academics’ global competitiveness in international scholarships via WeChat public platforms. The study was conducted in three parts, the pre-survey, the intervention using the WeChat public programme and the post-survey. There were 224 participants, who used the social media tool WeChat in the quantitative study. After the pre-survey, workshops were designed to introduce the WeChat platform and to explain to the HSS staff at the selected universities how the platform was constructed at the first stage. This was followed by a post-survey questionnaire which was adapted to evaluate the effects of the programme. The results show that Chinese HSS academics are interested in using WeChat public platforms in order to have potential collaborations with the West in research publications, conference presentations, visiting scholar programme applications and students’ mobility programmes.
Multiscale Feature Filtering Network for Image Recognition System in Unmanned Aerial Vehicle

For unmanned aerial vehicle (UAV), object detection at different scales is an important component for the visual recognition. Recent advances in convolutional neural networks (CNNs) have demonstrated that attention mechanism remarkably enhances multiscale representation of CNNs. However, most existing multiscale feature representation methods simply employ several attention blocks in the attention mechanism to adaptively recalibrate the feature response, which overlooks the context information at a multiscale level. To solve this problem, a multiscale feature filtering network (MFFNet) is proposed in this paper for image recognition system in the UAV. A novel building block, namely, multiscale feature filtering (MFF) module, is proposed for ResNet-like backbones and it allows feature-selective learning for multiscale context information across multiparallel branches. These branches employ multiple atrous convolutions at different scales, respectively, and further adaptively generate channel-wise feature responses by emphasizing channel-wise dependencies. Experimental results on CIFAR100 and Tiny ImageNet datasets reflect that the MFFNet achieves very competitive results in comparison with previous baseline models. Further ablation experiments verify that the MFFNet can achieve consistent performance gains in image classification and object detection tasks.
An Adaptive Fuzzy Modeling of Visual Attention in Real-world Interaction with Health Information System

Bottom-up and top-down processes are the two mechanisms of visual attention allocation, which allow people to efficiently spot task-relevant stimuli from cluttered and noisy environments, while staying alert to abnormalities within the visual field of view. This paper presents a preliminary study of the physicians’ real-life interaction with Information Communication Technology (ICT) in their own offices, along with extensively analyzing one case of an hour-long interaction of a physician, in which one performs a daily routine of reviewing patient electronic health records (EHRs) and writing diagnostic notes to the system interface. The physician interactions were captured in a time series data by recording display screen, keystrokes and mouse movements, also by simultaneously tracking eye movements. Then, a fuzzy-based model that can distinguish bottom-up and top-down processes were defined by using statistical random variables in terms of eye-movement patterns. The shift between those two attentional processes was detected by tracking the parametric changes of gaze behaviors as input: significant shift of fixation, sustained gazing, and fixation trajectory over time. Based on those gaze metrics, a random variable was assigned to the discrete probability of low (0), medium (0.5), or high (1.0), for a quantified fuzzy output, which was further machine-learned into an Adaptive Neuro-Fuzzy Inference System (ANFIS) model in order to judge how a physician is likely to be dominated by a bottom-up or top-down processes in performing a task at that instance in time. On training the ANFIS model with three different types of fuzzy membership functions (Gaussian, triangular and trapezoidal), the model performed best with the Gaussian function (after 100 iterations, the predicted root mean-square error (RMSE) converged at 0.07%, yielding a smooth linear curve). For a proof-of concept, the model was implemented by using one physician’s gaze behaviors, of which the average, machine-learned fuzzy output probability indicated that the physician was veering toward bottom-up visual attention. This individualized, task-specific pattern of visual attention has implications for the designs of intelligent interface in ICT. Our ANFIS model can scale up to different physicians and tasks to predict the likelihood of bottom-up or top-down information processing based on real-world gaze behaviors.
Identification of main control parameters for train unmanned driving systems

This chapter introduces the theory of system identification such as the identification model and the basic steps of identification as well as the complexity, convergence, and computational efficiency of identification methods. Some common identification methods for train driving control are introduced. The traction and braking characteristics of train running are analyzed and the force analysis of trains is carried out by calculating the tractive force, braking force, and resistance. The resistance includes basic resistance generated inside a vehicle and between the vehicle and track and additional resistance due to environmental factors such as slopes, curves, tunnels, and other line conditions. According to the force analysis of trains, the single-particle dynamic model and multiparticle dynamic model of train driving control are established. Finally, this chapter introduces identification methods based on artificial intelligence algorithms such as artificial neural networks, the swarm intelligence algorithm, etc.
Why effective testing is essential to keep pace with advancing threats

It is well understood that there is no single best way to keep an organisation secure against cyberthreats. Every enterprise has its own unique configuration of network infrastructure, hardware and software applications, as well as its own risk profile and security priorities. With so many variables, the combinations are close to infinite – and so is the potential for security vulnerabilities. Outdated or misconfigured systems and applications can create easy attack paths for any threat actors who discover them – and this is increasingly easy for them to do thanks to the use of automated tools. Every enterprise has its unique network infrastructure, hardware and software applications. With so many variables, the potential for security vulnerabilities is high. Security testing is the key to discovering and closing these vulnerabilities before they are exploited. However, businesses often fail to utilise testing effectively. Using automated tools to identify the bulk of vulnerabilities and deeper human-led testing to find more advanced threats, organisations can keep pace with the advancing capabilities of adversaries and close off vulnerabilities before they can be exploited, says Ed Williams, SpiderLabs at Trustwave.
Research on Image Motion Measurement of the Space Camera Based on Optical Correlator Method

During the imaging process of the space camera, the image motion is caused by the relative motion between the motion of the captured image and the transfer of the photogenerated charge packet. The image motion causes image degradation and distortion. Therefore, the specific analysis of image motion and the study of measuring the image motion of remote sensing camera have practical significance for improving the image quality of high-resolution remote sensing camera. This paper proposes a new method based on automatic threshold and subpixel correlation power spectrum correlation peak processing using spatial light modulator to measure image motion, correlation peaks of images obtained by optical joint transform optical modulator correlation, adaptive global threshold segmentation method and subpixel correlation method to accurately identify the position of the correlation peak. Finally, the simulation and actual measurement experiments are used, respectively. The simulation results show that the method is insensitive to the key factors affecting image motion measurement such as input image and image SNR (signal-to-noise ratio). It has strong robustness and uses spatial light modulator to build image motion. In the measurement test platform, the measured results show that the image motion measurement error is less than 0.25 pixels.
Counterfeit drugs prevention using block chain techniques

Abstract The production and distribution of counterfeit drugs is an urgent and increasingly critical worldwide issue, especially in developing countries. The pharmaceutical counterfeiting’s market price has hit over billions of dollars yearly. The flawed supply chain system in the pharmaceutical industry is one of the main reasons for drug imitations. Before meeting the clients, the ownership of the medicaments shifts from suppliers to wholesalers. Knowledge is not shared between processes in modern supply chain systems; manufacturers don’t even have any input at what occurs to their goods, just that the drug administration has no summary of the system; reminder programs are complex and inefficient and patients can not follow up. This project describes how in the pharmaceutical supply chains we use block chain technologies to provide the monitoring and protection of the drug supplier. The presented method will also be used by the pharmaceutical industry to track the medication from the particular time it is manufactured to the patient. The result of each drug used is recorded for future use in the database. An accepted block chain is used for storing transactions and only trusted parties can enter the network and move data in the block chain.
An Improved Quantum Algorithm for Spectral Regression

The following topics are dealt with: antenna radiation patterns; multifrequency antennas; microwave antennas; 5G mobile communication; UHF antennas; frequency selective surfaces; wireless LAN; microstrip antennas; security of data; and data privacy.
Blockchain Covid 19 Tracker for Educational Institutions

The current COVID-19 pandemic has changed our lives in unimaginable ways. It has brought forth the need to know our location, the people we’ve been in contact with, and other details like our body temperature and more to contain the spread of this virus. This information is even more vital when it comes to students, as their return to school increases their exposure level. There are quite a few existing systems that must be integrated to solve this problem. Currently, we have in use thermal sensors to record people’s temperatures by scanning, we have card readers in buses to confirm the student’s presence, and we have online portals where teachers can log a student’s attendance for each class. Using blockchain, we can incorporate these systems to create an effective, transparent COVID-19 tracker. In this paper, we discuss a new blockchain-based tracking system that ensures transparency between the student, school and government to prevent the spread of the virus and help in contact tracing. We facilitate the integration of the above-mentioned systems using blockchain and school national ID cards.
Increasing the Reliability of Full Text Documents Based on the Use of Mechanisms for Extraction of Statistical and Semantic Links of Elements

Approaches, principles and methods for increasing the reliability of information in electronic document management systems based on the use of statistical and semantic information redundancy have been developed. Methods are proposed, including mechanisms of statistical coding, extraction of statistical and semantic links, binary modular summations, and parity control. The probabilities of occurrence of single, double and t -multiple errors in texts, the required amount of information redundancy, the size of additional characters included in the information and control bits of the coding method are determined. A technique is proposed for optimizing the reliability of information based on mechanisms for coding, summing, checking, extracting statistical and semantic links of elements, as well as regulating the length of control bits. A software package for detecting and correcting one, two and t - multiple errors in the Sphinx-4 framework environment has been implemented.
IoTPerimeter: A Passive Fingerprinting of Smart Environment using Graph-based Features

Integration of orthogonal services in a single IoT device makes it difficult to apply traditional mechanism of device identification vis-a-vis reconnaissance of a smart home environment. Rule based white listing of IoT devices becomes difficult when IP camera is integrated with a smart bulb for example. In this paper, we aim to build a graph of (private or public) IP addresses used by IoT devices within a specific time window and check if the graph is consistent across other time windows. We plan to design and implement a system, IoTPerimeter, that can construct such graphs and extracts features in order to finger a smart environment. We evaluate the system performance using publicly available three datasets. Our system extracts 121 features and utilize six supervised machine learning algorithms. Our preliminary analysis shows that a same environment can have up to 20-30% of outliers. Only about four features have more than 10% of importance score and others have a low score.
Developing Expert System Platform: Architecture and Methodology

There is an imbalance in the distribution of experts in the health sector between urban and regional areas in Indonesia, so that some research results in the field of expert systems are separated from one type of expertise to another. The public also difficult to access this research openly. Based on this background, the researcher built an expert system platform that gathered many expert systems, or it can be said that a platform that contains a lot of knowledge, especially in the health sector. This platform applies an inference method and an integrated interface. The system architecture used is n-tier, consisting of four layers. Namely data collection, knowledge base, inference engine and presentation. Data collection performed knowledge acquisition using natural language processing methods which are then stored in the knowledge base. The inference engine used classification and reasoning techniques. The n-tier architecture was chosen in order to provide a fast computation process for running several inference methods. The proposed research is realistically realized based on the prototype platform that has been made.
WITHDRAWN: An efficient model of schedule accuracy using PAS agile simulation model

Completion of project in scheduled time is big challenge in current competitive market where customer requirements usually changes frequently. In this scenario, meeting customer expectation, market demands and deliver the project within budget, time and defined quality is not easy tasks. This research paper focuses more on comparative study of different available simulation models and proposed the appropriate one based on current operating model to expedite the schedule time faster. Author has ensured to consider all the aspect of execution model (SDLC, Agile) in his proposed system using simulation techniques.