Reviewerly/exHarmony huggingface dataset:
The factor of complex interaction in responding to telephone fraud

The subject of the study is to identify effective methods of legislative work to counteract the use by telephone fraudsters of such technical means as illegal substitution of SIM cards and Internet services for substitution of incoming call numbers. The general scientific methodology of dialectical (meaningful) logic and comparative analysis of practical problems and legislative activity of federal authorities are used. Fraud causes huge damage to society and incurs huge costs to the state. The global spread of the Internet has allowed scammers to export their activities to a fast-growing market and attract previously untapped consumers. The evolution of technologies and the spread of fraudulent approaches on the Internet have exacerbated the problems faced by victims. The results serve as evidence that when detecting and timely stopping attempts at telephone fraud (suspending suspicious transactions), legislative support and the formation of a subordinate regulatory framework are necessary for the interaction of financial organizations, telecommunications operators and law enforcement agencies. The development of smartphones and cellular networks increases the need for mobile advertising and targeted marketing. However, it also causes invisible security threats. We have found that phone fraud with fake phone numbers with a very short service life is becoming more and more popular and is being used to deceive users. The article is devoted to the consideration of the problem of legal regulation to ensure information security. As phone fraud becomes more common, it is extremely important to understand how to increase the effectiveness of prevention. Conclusions are drawn about the need to strengthen the centralization of countering intruders in order to increase the effectiveness of preventing telephone fraud, following the example of creating an interbank digital platform "Know your Customer".
Breaking Panther

Panther is a sponge-based lightweight authenticated encryption scheme published at Indocrypt 2021. Its round function is based on four Nonlinear Feedback Shift Registers (NFSRs). We show here that it is possible to fully recover the secret key of the construction by using a single known plaintext-ciphertext pair and with minimal computational resources. Furthermore, we show that in a known ciphertext setting an attacker is able with the knowledge of a single ciphertext to decrypt all plaintext blocks expect for the very first ones and can forge the tag with only one call and probability one. As we demonstrate, the problem of the design comes mainly from the low number of iterations of the round function during the absorption phase. All of our attacks have been implemented and validated.
Modal shape reconstruction method for deflectometry based on deep learning

Deflectometry is a slope-based technique to measure specular surfaces. Modal reconstruction methods fit the surface shape with a certain mathematical model based on expansion polynomials and their coefficients. The coefficients are obtained by linear equations, which are consisted of the gradient of the polynomials and the measured slope data. Nevertheless, computing the large linear equations is time-consuming work and the noises and outliers will decrease the reconstruction accuracy. This paper uses the Chebyshev polynomials as the basis set and proposes a modal reconstruction method based on the deep convolutional neural network to directly output the corresponding Chebyshev coefficients. Compared with the conventional modal reconstruction method, the results demonstrate that the reconstruction accuracy and the computational efficiency are improved effectively using the proposed method.
Tracking and shooting system based on trajectory prediction

Moving target trajectory prediction is a typical multidisciplinary research issue involving intelligent science and technology and transportation engineering. Automatic tracking and shooting is a field with significant theoretical research and practical application values in the problem of moving target trajectory prediction. With the increasing demand for tracking shooting, the requirements for target tracking are also getting higher and higher, which has attracted widespread attention in recent years. In the last few years, automatic shooting has gained attention. It can automatically follow athletes and event training to provide more competition videos and also serve as a guiding tool for competition monitoring. Due to the complexity of the target trajectory, tracking shooting has gradually become one of the difficulties in the researches of the area. Most of the traditional methods of trajectory prediction use mathematical models to predict the states and behaviors of targets, however, the computational complexity of traditional methods is high and they show poor performance in real scenarios. The tracking shooting system proposed in this study converts the detected three-dimensional moving objects into two-dimensional images by joining the turntable, employing encoder-decoder model and using an end-to-end computing method based on long-term memory network, and makes full use of the position and action posture information of acquired moving objects, so that it can deeply mine the historical position information and behavioral habit semantics of moving targets and realize effective mapping with future trajectories, predict the position information at the future moment, and then control the turntable to realize the tracking and shooting of 3D moving targets.
A method for multi-target human behavior recognition in small and medium scenes

Aiming at the low accuracy of behavior recognition technology for multi-target human behavior recognition in small and medium scenes, a method for multi-target human behavior recognition in small and medium scenes is proposed. In this paper, YOLOv5 and DeepSort are used to detect, track and locate human targets in the video stream. According to the detection frame, the appropriate size of the human target is cropped as the input image of the behavior recognition module to reduce the interference of human behavior background, and finally realize the multi-target human body behavior recognition. The behavior recognition module is composed of an improved C3D network, and the features extracted by YOLOv5 are shared with the behavior recognition module to reduce the amount of computation. Experiments show that this method achieves end-to-end recognition,and can recognize the behavior of different target human bodies in small and medium scenes, and achieves comparable results.
3D human pose estimation based on multi view information fusion

3D human pose estimation refers to recovering the pose of the human body in the three-dimensional space from the image, which is estimating the coordinates of the key points of the human body in the three-dimensional coordinate system. The human skeleton model composed of these keypoints describes the human pose in the image.The traditional pose estimation method uses motion capture equipment, which requires athletes to wear auxiliary equipment and affects the normal training of athletes. With the development of deep learning, convolutional neural network has shown strong representational capabilities in the process of image feature extraction, and is an effective method to achieve human pose estimation. However, in the actual complex and changeable three-dimensional space, the environment is noisy and the limbs are occluded. The monocular capture of human posture has the problems of low accuracy and instability, and cannot provide sufficient semantic information. To solve this poroblem, this paper designs a multi-view information fusion algorithm. First, a two-dimensional attitude feature extraction module is designed. By connecting and interacting heat maps of different resolving power in parallel, the heat map always maintains a higher resolving power for higher precision. It extracts the two-dimensional human pose from a single perspective, and then uses the multi-view information fusion technology proposed in this paper to complete and correct the pose information from different perspectives to obtain a more accurate two-dimensional pose. Then, through the camera pose transformation, the three-dimensional human pose estimation is realized, and the experimental verification is carried out on the multi-eye machine vision pan-tilt zoom tracking shooting system.
A stereo camera self-calibration method using DIC-based planar feature matching

In stereo vision, depth information is obtained by establishing a spatial correspondence between the two cameras based on the triangulation, so it is important to maintain high-accuracy calibration parameters of the stereo cameras. However, under some extreme conditions, such as high temperature, high pressure, and strong vibration, there are irreversible changes in the parameters of the camera lens and the spacing between two cameras, which leads to the invalidation of known calibration parameters. In this paper, a stereo camera self-calibration method is proposed to get high-accuracy feature point pairs from speckle planar image pairs using the DIC-based grid point matching technique. The calibration result of cameras is corrected after SVD and RANSC iteration, which can enhance the quality of stereo rectification and the 3D measurement accuracy of stereo vision.
Interferometric measurement of thickness variation of double-sided polished wafer

Wafer, the primary material used to make semiconductor chips, are found in almost every type of electronic device used in everyday life. As the quality of wafer used in large-scale integrated circuits has improved considerably, the diameter of wafer has continued to increase, and the thickness of silicon wafer has become increasingly thin. Wafer manufacturers and device manufacturers are increasingly focusing on wafer thickness variation. In the past few years, the usual capacitive tools for wafer inspection have been replaced by interferometric tools for higher sensitivity and resolution. We, therefore, describe a method that uses two Fizeau-type phase-shift interferometers to simultaneously measure the front and back surfaces of a vertically placed wafer and calculate the thickness variation of the wafer based on the resulting morphologies. The reliability of the method was verified by comparing the wafer thickness variation obtained from experimental measurements with that obtained from optical glass bonding. Over three days, five consecutive measurements were performed daily on 50mm wafer using this method, and the experimental results showed that the average values of RMS (Root Mean Square) of the thickness variation calculated for each day were 41.843nm, 40.751nm, and 40.490nm, and the average values of PV (Peak to Veally) were and 206.761nm, 205.252nm, and 209.800nm, and the measurements proved to be highly reproducible. The method has good stability and reliability to meet the measurement of wafer thickness variation.
End-to-end active object tracking football game via reinforcement learning

Object detection and tracking in football video is a very challenging task, and it has good practical and commercial value. The traditional method of extracting the target movement trajectory of football matches is often carried out by players carrying recording chips, which is expensive and difficult to popularize in amateur stadiums. There are also some studies that only use the camera to process the targets in the football video, but due to the similar appearance and frequent occlusion of the targets in the football video, these methods can only segment the players and the ball in the image, but cannot. Track it or only for a short period of time. We study active object tracking method for football game, where a tracker takes visual observations (i.e., frame sequences) as input and produces the corresponding camera control signals as output (e.g., turn up, turn left, etc.). Conventional methods tackle tracking and camera control tasks separately, and the resulting system is difficult to tune jointly. These methods also require significant human efforts for image labeling and expensive trial-and-error system tuning in the real world. To address these issues, we propose, in this paper, an end-to-end solution via deep reinforcement learning. By building a football game simulation scene in the simulator (Unreal Engine), the entire field can be covered by turning the camera in the simulation scene.
Simulation research of illumination system with coherent noise suppression

An illumination system for coherent noise suppression is envisioned. The principle of coherent noise suppression is introduced, and this illumination system is designed this way. The illumination system is simulated, and the hybrid sequence model of Zemax analyzes the irradiance. To verify the noise suppression effect of this illumination system, it is used as the illumination part of the Fizeau interferometer. The interference process is simulated under the nonsequential model of Zemax to obtain an off-axis light source with a ring radius of 0.32 mm and a numerical aperture of 0.14. The Fizeau interferometer with a conventional light source is also simulated. A comparison experiment is set up to generate the same noise point in the two interferometers using different illumination modes to trace and produce the same four interference fringes with the same interferometric cavity length of 20 mm and the same tilt angle of the measured surface of 0.057°. Compared with the interferograms in the conventional illumination mode, the interference fringes formed by the illumination of this study are almost undamaged, and the near-complete interference information can be retained. The interferometer system with this light source was built and the test results were verified, and it was found that it could achieve the measurement accuracy of 1/20 wavelength and the measurement stability of 1.219 nm, and it also had a good contrast of interference fringe.
Research on subaperture stitching optimization algorithm for large flat mirror testing

To address the problem of high-precision detection of large flat aperture mirror shapes, the current subaperture stitching methods are analyzed. The results show that the full aperture surface shape stitching errors due to subaperture adjustment errors and localization errors are the main reasons affecting the high precision detection. To improve the detection accuracy of subaperture stitching, we propose an immune optimization algorithm for subaperture stitching. The algorithm reduces the influence of subaperture adjustment errors and positioning errors, so that the PV value of reconstructed shape reaches λ/100. It is shown that the proposed method can effectively control the stitching errors and improve the stitching accuracy.
An efficient speckle matching network based on deep learning

Speckle projection profilometry (SPP), as an efficient 3D measurement method based on structured light projection, projects the speckle pattern based on spatial encoding onto the measured scene to enhance its texture, thereby improving the accuracy of single-shot 3D measurement. However, the traditional stereo matching method in SPP compromises the measurement accuracy in order to ensure the robustness of 3D measurement. At present, some speckle matching methods based on deep learning have been proposed to obtain high-precision and dense disparity maps, but at the cost of expensive computational overhead, which occupies a lot of memory resources and reduces the running speed. Different from existing networks, this paper proposes a lightweight end-to-end stereo matching network by combining attention mechanism, spatial pyramid pooling module (SPPM), and multi-scale feature fusion, which achieves single-shot 3D measurement with competitive accuracy while running at 170 ms.
AN ANALYSIS OF READING HABITS OF STUDENTS

The current study presents an analysis of the reading habits of students at the elementary school level in Tehsil Ahmadpur East District Bahawalpur, Punjab. The main objectives of the study were to examine the reading habits (styles) of students and to evaluate factors affecting their reading habits. This study was descriptive in nature. The population of the study consisted of all elementary school students in Tehsil Ahmadpur East. The sample size of the study was 540 students and 90 teachers. A questionnaire was the tool of this study on five point likert scale. The data of the study were analyzed by applying frequency, percentage, mean score, standard deviation, t-test and ANOVA. The findings revealed statistically significant differences in student reading habits based on class, gender, parental qualification, and school type. There was insignificant impact of locality on the reading habits of students. In teachers’ opinions, use of the internet, mobile devices and social media were the main problems for students. It is recommended for teachers to pay proper attention to developing positive reading attitudes to enhance effective reading habits among students. Moreover, students need to develop the habit of reading for pleasure and acquiring knowledge and not only to pass their exams and tests. Institutional heads should concentrate on providing the best reading sources in school libraries, including computers and internet facilities. Library staff should be given training to monitor students while working in libraries and train students to search for and utilize reading sources. Keywords: Students, Teachers, Schools, Libraries and Computer Labs.
Disease Segmentation Using a Robust Method for Unclear Boundaries in Training with Annotations Including Boundary Errors

The predictive performance of segmentation highly depends on the quality of annotations. However, in case of medical images, it is difficult to annotate precise region of disease since they have variety of shapes and ambiguous boundaries. Improving segmentation performance of models trained with rough annotation will make it easier to create training data. This paper proposes a novel method to improve auto tumor segmentation from computed tomography (CT) images using noisy annotations. The proposed method uses pre-training to detect rough tumor regions and fine-tuning to refine the roughly detected regions. In order to refine roughly detected regions, Active Contours Dice (ACD) Loss is introduced in fine-tuning. In our experiments using simulated rough annotations, the proposed method improves segmentation performance.
Sparkle: Toward Accessible Meta-Algorithmics for Improving the State of the Art in Solving Challenging Problems

Many fields of computational science advance through improvements in the algorithms used for solving key problems. These advancements are often facilitated by benchmarks and competitions that enable performance comparisons and rankings of solvers. Simultaneously, meta-algorithmic techniques, such as automated algorithm selection and configuration, enable performance improvements by utilizing the complementary strengths of different algorithms or configurable algorithm components. In fact, meta-algorithms have become major drivers in advancing the state of the art in solving many prominent computational problems. However, meta-algorithmic techniques are complex and difficult to use correctly, while their incorrect use may reduce their efficiency, or in extreme cases, even lead to performance losses. Here, we introduce the Sparkle platform, which aims to make meta-algorithmic techniques more accessible to nonexpert users, and to make these techniques more broadly available in the context of competitions, to further enable the assessment and advancement of the true state of the art in solving challenging computational problems. To achieve this, Sparkle implements standard protocols for algorithm selection and configuration that support easy and correct use of these techniques. Following an experiment, Sparkle generates a report containing results, problem instances, algorithms, and other relevant information, for convenient use in scientific publications.
Calibration of Ring Multicamera System With Transparent Target for Panoramic Measurement

The calibration of a multicamera system is a prerequisite for high-accuracy measurement. In this study, a method for calibrating a ring multicamera system with a transparent target combined with digital image correlation (DIC) technology is proposed to achieve panoramic dynamic and static measurement of the object. The transparent target is placed in the middle of the ring camera array, and the target can be driven by the turntable to calibrate multiple cameras simultaneously. A complete refraction correction model is built on the transparent side of the target, and the corrected calibration data are integrated into the multicamera DIC system. The global optimization further improves the overall calibration accuracy of the system. Special markers are attached to the target surface to automatically identify the refractive and nonrefractive surfaces of the target. This type of camera can establish a coordinate relationship with the cameras on the other side of the target although no overlapping field of view exists between adjacent cameras on the same side of the target. The calibration data are analyzed, and dynamic and static experiments are implemented to verify the measurement accuracy of the system. All experimental results show that the calibration and measurement accuracies of the proposed method are high, and the system can be fully used for high-precision panoramic dynamic and static measurements.
LEVEL-BASED ASSIGNMENTS OF THE PROBLEM-PROGRAMMED TYPE IN THE SYSTEM OF PROFESSIONAL PHYSICS TEACHER TRAINING

On the basis of the analysis of method guidelines for laboratory works in physics and methods of teaching physics the authors suggest one of the ways to overcome the deficiencies in laboratory physics practical works organization in pedagogical universities. The article proposes to include the assignments of the problem-programmed kind into the structure of practical work. The description of these assignments from a position of three types of indicative basis of action is given. Requirements to works of the problem-programmed kind are formulated and prognoses of their use in the system of professional education of a physics teacher are made.
Big data analysis using a parallel ensemble clustering architecture and an unsupervised feature selection approach

Ensemble clustering is known as a challenging research direction in data mining. The results of several individual clustering methods are combined to produce higher quality final clusters. This study introduces a parallel hierarchical clustering approach based on the divide-and-conquer strategy, which is an attempt to realize faster and more efficient ensemble clustering. Here, we propose a cluster consensus selection approach that selects a subset of meriting primary clusters to participate in the final consensus. Considering the sample-cluster and cluster–cluster similarity on the selected primary clusters, we form the final clusters based on the clusters clustering technique as a consensus function. In addition, the proposed scheme is equipped with an unsupervised feature selection approach to remove features that do not contribute significantly to clustering. Extensive evaluations have been performed on datasets of different dimensions from the University of California Irvine (UCI) machine learning repository. The simulation results guarantee the efficiency of the proposed scheme and improves the average performance between 6% and 24% compared to the state-of-the-art clustering methods.