Reviewerly/exHarmony huggingface dataset:
Studying the objectivity of educational assessment in schools based on the system of automated monitoring and evaluation

Introduction. The article focuses on increasing the objectivity of educational assessment of schoolchildren’s learning achievements. The purpose of the research is to substantiate the increase in the objectivity of educational assessment on the basis of an automated monitoring and evaluation system. Materials and Methods. The study followed an interdisciplinary approach, characterized by integrative properties of educational assessment theory and designing automated learning tools. The authors used the following theoretical research methods: analysis, synthesis and generalization on the problems of improving the objectivity of assessment and the development of automated learning tools. Empirical methods included questionnaires, testing, and educational action research. Results. The factors contributing to the decrease in the objectivity of educational assessment are identified: the subjective and ascertaining nature of assessment, inconsistent recording of students’ academic performance, and delayed correction of students’ learning activities. A system of automated monitoring and evaluation has been developed and implemented, which includes processing, analysis and interpretation of information and monitors the dynamics of students’ learning achievements in real time. During the application of the system, positive changes in the indicators of students’ involvement, motivation and emotional attitude to learning were revealed. The most significant changes were observed in the following indicators: cognitive efficiency, learning motivation, and anxiety (a decrease in the high level of anxiety, an increase in the levels of motivation and cognitive efficiency). The design and implementation of the system were based on organizational, educational and technical conditions. The organizational and educational conditions include various formats for incorporating the system in the structure of the assessment process, while the technical ones set the requirements for the system. Conclusions. The introduction of the automated monitoring and evaluation system contributes to formulating clear criteria for educational assessment, expanding the possibilities for building individual learning trajectories, and increasing the transparency of learning process. The objectivity of the assessment is realized due to the clarity of the requirements for the level of knowledge, skills and abilities of students; specification of the objects of assessment; compliance of the tasks with the goals of educational assessment; well-timed analysis of learning outcomes; and increasing primary schoolchildren’s motivation for learning.
Improved phase-shifting method for pinhole point diffraction interferometer based on a precise driving of the substrate

We report a phase-shifting method based on a pinhole point diffraction interferometer. Using the random two-frame phase-shifting algorithm, the piezo electric transducer (PZT) drives the pinhole moving a certain step length along the axis of the tested aspheric mirror. In each step, the CCD collects an interferogram. Then two interferograms are processed by the phase-shifting algorithm. After that, we can acquire the phase map of the interferograms. This technique has great potential for increasing the measuring aperture of the aspheric mirror in the pinhole point diffraction interferometer (PPDI) under the premise of keeping the advantages of PPDI of which the optic devices, as well as error sources, are few behind the substrate.
Classification of Breast Cancer Histopathological Images with Deep Transfer Learning Methods

It is very important to have a high accuracy rate in detecting cancerous cells in histopathological images. Thanks to high-accuracy images, cancerous cells will be detected more sensitively, and there will be a chance for more accurate and early diagnosis. Thus, a very important preliminary step will be taken in the treatment of cancerous cells. In this study, classification performances were comparatively analyzed by applying various methods to four different cancer cell types (benign, normal, carcinoma in situ and invasive carcinoma). By using BACH and Bioimaging as datasets, the desired parts are tried to be obtained primarily by several image processing methods (pyramid mean shifting, line detection, spreading). After obtaining images of different sizes, their performances are examined by using VGG16, DenseNet121, ResNet50, MobileNetV2, InceptionResNetV2, CNN deep transfer learning methods.
Real Time Cheating Detection Pipeline for Online Exams

Remote exams and job interviews have gained popularity due to pandemics and the advantage of remote working conditions. Most companies and institutions use online platforms for recruitment and examination. However, one of the critical problems of remote exam systems is that the exams cannot be held in a reliable environment. In this work, we present a cheating detection system for online exams. This system provides real-time detection of suspicious events such as another person, electronic device usage, candidate absent status by accessing the microphone and camera stream of the candidate&#x2019;s device through the browser. The cheating detection system can detect other people than candidates, electronic device usage, voice activity, and candidate absence status using state-of-the-art deep learning algorithms. When a suspicious event is detected, its moments are recorded as a video. The advantages of the real time cheating detection pipeline are as follows: 1) The system provides real time suspicious activity detection and produce instant alarm for proctors, 2) the system does not require an external program installation, 3) the number of user the system will serve at the same time has become scalable since it is run through the browser.
Traffic Signs Detection Based on Enhanced YOLOv5 Network Model

In this paper, in order to improve the accuracy of YOLOv5s6 in traffic sign detection, we propose two types of methods to improve the problem of missing detection and low accuracy in small traffic signs. Our type 1 method is based on the stacking of residual blocks to increase the depth of the network, and we add channel attention Squeeze-and-Excitation (SE) blocks to increase the learning ability of the network. In type 2 method, we halve the channel of type 1, reducing computation and parameters, and add an additional 3×3 convolution to improve the learning ability of the model. We choose the traffic signs dataset Tsinghua-Tencent 100K (TT100K) to train the model. TT100K contains 221 categories. The large categories make detection difficult. In addition, we collected Taiwan traffic signs as a customized dataset to validate our method. We also test the performance of the proposed methods on 3 public datasets. The experimental results show that in the TT100K dataset, the mAP of type 1 method is increased by 1.9% and the mAP of type 2 method is increased by 3.2%. In the customized Taiwan traffic signs dataset, and the mAP are increased by 4.9% and 5.7% for type 1 method and type 2 method, respectively.
A siamese network-based approach for vehicle pose estimation

We propose a deep learning-based vehicle pose estimation method based on a monocular camera called FPN PoseEstimateNet. The FPN PoseEstimateNet consists of a feature extractor and a pose calculate network. The feature extractor is based on Siamese network and a feature pyramid network (FPN) is adopted to deal with feature scales. Through the feature extractor, a correlation matrix between the input images is obtained for feature matching. With the time interval as the label, the feature extractor can be trained independently of the pose calculate network. On the basis of the correlation matrix and the standard matrix, the vehicle pose changes can be predicted by the pose calculate network. Results show that the network runs at a speed of 6 FPS, and the parameter size is 101.6 M. In different sequences, the angle error is within 8.26° and the maximum translation error is within 31.55 m.
A Secure Health Management Framework with Anti-fraud Healthcare Insurance Using Blockchain

Telemedicine and electronic health record management are playing an inevitable role in today’s era. The smart healthcare system suffers from security issues due to no control of patients over their personal data. On the other hand, insurance companies are suffering from falsifying information where the claimant produces false or tampered diagnosis report during their claiming. In e-health system, secure communication among patient, healthcare provider, and insurance companies are essential part. Blockchain can overcome the aforesaid challenges due to its numerous advantages such as distributed database, transparency, trusted networks, decentralization, and autonomous connectivity. Authentication and the privacy of health data are ensured through cryptographic key exchange using blockchain. In this paper, a healthcare management framework is proposed where patient, healthcare provider, and insurance companies are sharing their transactions in blockchain. In our proposed approach, e-health management system is protected from different types of security attacks such as replay and man-in-the-middle attacks.
Page Level Input for Handwritten Text Recognition in Document Images

Important information present all over the world in the form of libraries need to be digitized. An end-to-end handwritten text recognition system covers every aspect, from capturing an image as input to predict text written inside it. In the present study, we have presented a document image-based handwritten text recognition system with three parts as: page level image as input with multiple lines, recognition of text of input image and attention level of text. This model is trained and tested on various split ratios of labelled words of the IAM handwriting dataset. The recognition model evaluation is performed on two publicly available datasets that are IAM handwriting dataset and handwritten short answer scoring dataset. We have used popular CTC word beam search technique by Scheidl et al. [1] in recognition phase, and attention of text has been incorporated using established sentic data by Ref. [2]. Our findings at recognition level result in 8.99% validation character error rate on IAM handwriting dataset and 35.92 BLEU score on the handwritten short answering scoring dataset. The post recognition findings suggest text attention role in acceptance of recognized words useful for real life business documents.
Compression of hyperspectral images based on Tucker decomposition and CP decomposition

Hyperspectral imagers are developing towards high resolution, high detection sensitivity, broad spectra, and wide coverage, which means that hyperspectral data are getting more and more substantial. This brings a great challenge to data storage and real-time transmission of hyperspectral data. A compression method based on Tucker decomposition and CANDECOMP/PARAFAC decomposition (TD-CP) is proposed. The hyperspectral data are treated as a third-order tensor. First, TD is performed on the hyperspectral data to obtain a core tensor and three factor matrices, and then CP decomposition is performed on the core tensor. Compared with principal component analysis (PCA) <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mo>+</mml:mo> </mml:math> JPEG2000, TD, and CP, TD-CP can retain spatial information and spectral information better at the same time, and running time is shorter.
Differential cryptanalysis of full-round ANU-II ultra-lightweight block cipher

Lightweight ciphers are often used as the underlying encryption algorithm in resource-constrained devices. Their cryptographic security is a mandatory goal for ensuring the security of data transmission. Differential cryptanalysis is one of the most fundamental methods applicable primarily to block ciphers, and the resistance against this type of cryptanalysis is a necessary design criterion. ANU-II is an ultra-lightweight block cipher proposed in 2017, whose design offers many advantages such as the use of fewer hardware resources (logic gates), low power consumption and fast encryption for Internet of Things devices. The designers of ANU-II claimed its resistance against differential cryptanalysis and postulated that the design is safe enough for Internet of Things devices. However, as addressed in this article, the security claims made by designers appear not to be well grounded. Using mixed-integer linear programming–like techniques, we identify one-round differential characteristic that holds with probability 1, which is then efficiently employed in mounting the key recovery attack on full-round ANU-II with only 2 2 chosen plaintexts and 2 62.4 full-round encryptions. The result shows that the designers’ security evaluation of ANU-II against differential cryptanalysis is incorrect and the design rationale is flawed. To remedy this weakness, we provide an improved variant of ANU-II, which has much better resistance to differential cryptanalysis without affecting the hardware and/or software implementation cost.
Education in a VUCA-driven World: Salient Features of an Entrepreneurial Pedagogy

Rendering learners future-ready has always been one of the primary purposes of education. It makes a critical appreciation of the emerging societal scenarios a necessary prerequisite for defining essential features of the conduct of education at any age. VUCA is an acronym that describes some salient features of modern day, characterized by Volatility, Uncertainty, Complexity and Ambiguity. VUCA exerts its influence on every field of human activity, including education. VUCA may also be interpreted in terms of quantity and quality of information, which make turbulent transitions in a globalized and digitalized world. The current article explores different responses toward VUCA and identifies the best-fit response for a VUCA-driven world of information. It also investigates different aspects of intellectual formation of the new generation of learners, that shall equip them to conduct their lives and careers in a VUCA-driven world. The article concludes by sketching a blueprint for a pedagogy of the digital natives founded on their very intrinsic motivations for learning. An entrepreneurial pedagogy may help new generation learners develop an entrepreneurial mindset in them, making them future-ready.
Construction of a Safety Management System for University Laboratories Based on Artificial Intelligence and IoT Technology

Based on IoT technology, it can combine the actual needs of the university laboratory system to establish an equipment safety monitoring system, share data in real time, and monitor the operation equipment of university laboratories comprehensively. Through the collection of equipment-related data, we have a comprehensive analysis of data in order to have an accurate judgment of the state of the equipment, so that the chances of equipment failure can be gradually reduced. In this paper from the architectural point of view, IoT contains four layers: perception layer, network layer, platform layer, and application layer. The perception layer mainly solves the problem of data collection, the network layer mainly solves the problem of data transmission, the platform layer mainly solves the problem of data management, and the application layer mainly solves the problem of data value creation. From the technical point of view, the university laboratories’ Internet of Things widely used big data, cloud computing, Internet of Things, artificial intelligence, blockchain, edge computing, and other types of information technology and intelligent technology. From the role, the university laboratories’ IoT is to provide sufficient and effective information and technical support for planning and construction, production and operation, operation and management, integrated services, new business, new model development, enterprise ecosystem construction, and other aspects by bringing together resources from all aspects. The whole university laboratory system sustainability monitoring equipment maintains the continuity of monitoring and can be the first time to staff feedback fault information, to achieve the human-machine dialogue function; staff in the control of equipment safety monitoring system just enter the relevant instructions to fully achieve the purpose of control; staff can also print a certain period of time fault-related information, which will be properly stored in a reasonable location.
The Implementation Path of Labor Education in Applied Universities Driven by Artificial Intelligence Technology

Labor education is an endogenous demand for the construction and development of applied colleges and universities, and it is of great significance for the times to promote the construction of applied colleges and universities. The labor spirit shaping of college students in applied colleges and universities is conducive to helping college students grow up to be the new man of the times, promoting the core task of transformation and development of applied colleges and universities and meeting the construction needs of a modern educational power with the socialism of Chinese characteristics. In view of the outstanding problems of cognitive bias, inaccurate positioning, one-sided means, and disregard of results in the process of shaping labor spirit in applied colleges and universities, the university should grasp the three key words of local, applied, and open system construction in the stage of spirit generation, use specific tactics to promote the combination of educators, educated and educational influence in the stage of spirit strengthening, and form the overall style and action of interpreting labor spirit by the struggle in the stage of spirit sublimation. In the stage of spiritual sublimation, the overall style and action consciousness of interpreting the spirit of labor by struggle are formed. Cultivating students’ technical application ability, nurturing quality spirit, and cultivating students’ sense of professional identity and responsibility are the value demands of labor education in applied colleges and universities. In view of the problems of the implementation path of labor education in applied colleges and universities, a labor education status assessment model based on artificial intelligence technology is proposed for establishing the value goal of talent cultivation in applied colleges and universities as the guide and promoting the effective labor education in applied colleges and universities in the new era by continuously solving various problems in labor education and cultivating qualified socialist builders and successors.
An Edge Filter Based Approach of Neural Style Transfer to the Image Stylization

Transferring artistic styles onto any image or photograph has become popular in industry and academia in recent years.The use of neural style transfer (NST) for image style transfer is getting more popular.Convolution Neural Networks (CNN) based style transfer provides a new edge and life to the images, videos, and games.The re-rendering procedure of the content of one image with the style of another using various models and approaches is widely used for image style transfer.However, there are many drawbacks, including image quality, enormous loss, unrealistic artefacts, and the style of localized regions being less compared to the desired artistic style.For the reason that transfer technique fails to capture detailed, miniature textures and keep the true artwork's texture scales.We propose a multimodal CNN that stylizes hierarchically with several losses of increasing sizes while considering faithful representations of both colour and luminance channels.We may transfer not only large-scale, evident style cues but also subtle, exquisite ones by effectively handling style and texture cues at different sizes using various modalities.Our approach providing aesthetically pleasing results and is more comparable to multiple desirable creative styles using colour and texture cues at different scales.
An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification

Malaria is a serious and lethal disease that has been reported by the World Health Organization (WHO), with an estimated 219 million new cases and 435,000 deaths globally. The most frequent malaria detection method relies mainly on the specialists who examine the samples under a microscope. Therefore, a computerized malaria diagnosis system is required. In this article, malaria cell segmentation and classification methods are proposed. The malaria cells are segmented using a color-based k-mean clustering approach on the selected number of clusters. After segmentation, deep features are extracted using pre-trained models such as efficient-net-b0 and shuffle-net, and the best features are selected using the Manta-Ray Foraging Optimization (MRFO) method. Two experiments are performed for classification using 10-fold cross-validation, the first experiment is based on the best features selected from the pre-trained models individually, while the second experiment is performed based on the selection of best features from the fusion of extracted features using both pre-trained models. The proposed method provided an accuracy of 99.2% for classification using the linear kernel of the SVM classifier. An empirical study demonstrates that the fused features vector results are better as compared to the individual best-selected features vector and the existing latest methods published so far.
How uncanny is the «Uncanny valley»? Experience of deconstructing a discourse

After the Japanese scientist Masahiro Mori first described the phenomenon that he called the «Uncanny Valley», numerous attempts were made to explain it. However, the uncanny valley still remains a problem not only for people who encounter it in technical production (robotics engineers, programmers, and designers of anthropomorphic robots) but also for philosophy, in particular, for philosophical anthropology and ontology. This article attempts not so much to explain this phenomenon — this work primarily remains with psychologists, sociologists, and philosophers who study the human mentality — but demonstrate a program to increase this problem into the schemas of some contemporary ontologies. We will follow Timothy Morton who explores the uncanny valley in his books Hyperobjects: Philosophy and Ecology after the End of the World, Being Ecological, and in a number of articles. We will attempt to demonstrate the insufficiency of both the «classical» (Modern) approaches to the analysis of the technical and the tool analysis proposed by M. Heidegger in Being and Time. Then we will try to show that the problem of the uncanny valley is only an example of the ontological gap the foundation for which was laid by the Modern, and, therefore, its solution is only possible by diffusion it in broader problems of the philosophy of technology. The implementation of such «diffusion» both in the theoretical aspect of ontology and in the aspect of transformation of our practices — everyday, mental, and industrial — can become one of the cornerstones in the foundation of the future ecological coexistence of the human and non-human entities.