Reviewerly/exHarmony huggingface dataset:
Inferring the Meaning of Non-personal, Anonymized, and Anonymous Data

On the awareness of the dynamism pertaining to data and its processing, this paper investigates the problem of having two mutually exclusive definitions of personal and non-personal data in the legal framework in force. The taxonomic analysis of key terms and their context of application highlights the risk to crystalize the whole system upon which the digital single market is built, suffocating its future development. With this premise, the paper discusses the extent of the two main data processing tools provided by the GDPR, questioning the ex-ante categorization of data and its outcome, supporting stakeholders in overcoming this issue.
Anomaly Detection for Hydraulic Systems under Test

This work focuses on computationally efficient difference metrics of time series and compares two different unsupervised methods for anomaly classification. It takes place in the domain of hardware systems testing for reliability, where several structurally identical devices are tested at the same time with a load expected in their lifetime use. The devices perform different maneuvers in predefined testing cycles. It is possible that rare, unexpected system defects appear. They often show up in the measured data signals of the system, for example as a decrease in the output pressure of a pump. Due to the intended aging of the parts under load, the measured data also exhibits a concept drift, i.e. a shift in the data distribution. It is of interest to detect anomalous behavior as early as possible to reduce cost, save time and enable accurate root-cause-analysis. We formulate this problem as an anomaly detection task on periodic multivariate time series data. Experiments are evaluated using an open access hydraulic test bench data set by Helwig et al. [1]. The method's performance under concept drift is tested by simulating an aging system using the same data set. We find that Mean Squared Error towards the median in combination with the Modified z-Score is the most robust method for this use case. The solution can be applied from the beginning of a hardware testing cycle. The computations are intuitive to understand, and the classification results can be visualized for better interpretability and plausibility analysis.
Segmentation of Tumour Region on Breast Histopathology Images for Assessment of Glandular Formation in Breast Cancer Grading

Abstract Breast cancer is the most silent killer among cancers nowadays. NHG system is widely accepted worldwide as a gold standard in providing the overall grade to breast cancer. One of the breast cancer features used in the NHG system is tubule formation. Assessment of tubule formation requires pathologist to identify tumour regions. However, colour variation on breast histopathology could influence tumour regions detection on breast histopathology images. Manual identification of tumour regions using microscope may also vary between pathologists. Thus, automatic segmentation is crucial to segment tumour regions. In this study, a simple approach of segmentation was proposed to segment tumour region on breast histopathology images. The proposed segmentation involved three stages: pre-processing, segmentation and post-processing. The proposed approach using GHE and median filter in the pre-processing stage; Otsu thresholding in the segmentation stage and; morphological operation and pixel removal in the post-processing stage was found able to segment the tumour region with average segmentation accuracy of 90.4 %.
Advanced Immersive Capabilities in Virtual Reality Applications Using IMU Sensors

Abstract The rapid development of virtual and augmented reality technologies today takes place in almost all areas of activity. The relevance of the study is due to the modern demand for developers to use the latest technologies in the field of game development. Currently, technology is becoming more popular, ensuring that the player is immersed in virtual reality. One of these technologies is a suit with wearable sensors that track the position of a person in space in real time. However, this technology is quite young, but has already gained popularity due to its extensive use capabilities and combinations with other game gadgets. Therefore, at the moment there is a problem of no examples of application development using this technology. This article demonstrates the possibilities of a suit with wearable sensors when creating an entertainment application.
An IMM-PKF Method for Helicopter Tracking using Acoustic Arrays

This paper presents a tracking algorithm based on the interacting multiple model (IMM) algorithm and pseudo linear Kalman filter (PKF) in order to improve the performance of traditional methods in maneuvering target tracking. The IMM-PKF algorithm uses the constant velocity (CV) model and coordinated turn (CT) model to form a model set, and the filtering algorithm uses PKF. In target tracking, IMM is used to improve the matching between state prediction and actual motion, therefore improving the accuracy of maneuvering tracking. The performance of extended Kalman filter (EKF), PKF, and IMM-PKF is compared through simulation and experiment. The results show that the IMM-PKF method has the best tracking accuracy among the three methods in helicopter tracking.
Cloud based data center energy efficacy system using end-to-end &amp; deniable encryption methodologies

As more and more people have started using instant messaging apps on a daily basis to not just text, but send and receive files and other types of data, the need for a good security protocol that offers an encryption standard and method of key-exchange has radically increased. With the advent of end-to-end encryption (E2EE), apps such as WhatsApp and Signal have implemented their own version of E2EE. However, none of these protocols are 100% safe and don’t guarantee 100% security against man-in-the-middle attacks. These protocols merely make it time-consuming and resource intensive to find a solution to crack these keys using a brute-force approach. Algorithms like RSA and SHA512 can be broken by a Quantum Computer. We present a solution to the public-key exchange challenges faced by cryptographers through our proprietary cryptographic system - ‘The BlackBox’, which uses QR codes to initially exchange keys and implements subsequent key exchanges using the already established shared secret keys and thereby help us to achieve energy efficient holistic cloud data centers. This proposed model is capable of achieving energy efficient model of 80% in comparison with the existing cloud models.
Research on the Evaluation of “Special Delivery Classroom” to Promote the Children Development in Weak Rural Schools

Around the “high-quality and balanced development of compulsory education,” the experiments and practical explorations have been carried out all over the country, including special delivery class, famous teacher class, and famous school classroom. However, there have been a lack of professional researches on the impact of “three classrooms” on rural children. Based on this, this study carried out an evaluation study on the theme of “special delivery classroom and rural children development.” This study constructs an index system for rural children development, then tests 1068 students in rural schools in Chongyang County, Hubei Province. Finally, based on the evaluation results, some suggestions are put forward.
On structures of regular standard contradictions in propositional logic

The contradiction separation (CS) based automated deduction is an extension of the binary resolution principle for deductive inference rules, whose CSE prover and combined versions have unique performance among others in CADE ATP system competitions since their first appearances in 2018. The contradictions constructing method is one of the most important factors to determine the efficiency of the CS based deduction, which is more complex compared to the complementary pair finding in the binary resolution. To get structures and concrete forms of contradictions, this paper examines two types of standard contradictions, i.e., the regular standard contradictions and the standard contradictions for non-unit clauses sets. The concepts of the regular standard contradiction and its extended form are presented. Some properties of them are discussed. In order to get simpler contradiction separation clauses in every deductive step, four types of structures for standard contradictions obtained by adding related literals in the regular standard contradiction and its extended form are gotten. Two structures for the standard contradictions for non-unit clauses set are obtained. Two unified algorithms for constructing standard contradictions are also contrived.
Implementation of English Digital Evaluation Model for Higher Vocational Colleges Based on Chaoxing Teaching Platform

With the development of "Internet + education", the informationalized teaching mode, teaching resources and teaching design have been integrated into daily teaching activities. So the digital teaching evaluation model should also match the trend of teaching development, especially after the outbreak of the new crown disease in 2020, in order to ensure the normal progress of education, our country implemented online course teaching. At the same time, the teaching evaluation model was still using the traditional model which didn't conform to the teaching development trend. The establishment of a digital teaching evaluation model is necessary to match the information-based teaching development trend. The paper, taking higher vocational English as an example, studies and designs the English digital evaluation model for higher vocational colleges based on Chaoxing teaching platform.
Design of Image Copy-Paste Forensics System Based on Moment Invariants

Copy-paste tampering is a common type of digital image tampering, which refers to copying a part of the image area in the same image, and then pasting it into another area of the image to generate a forged image, so as to carry out malicious operations such as fraud and framing. This kind of malicious forgery leads to the security problem of digital image. The research of digital image copy paste forensics has important theoretical significance and practical value. For digital image copy-paste tampering, this paper is based on moment invariant image copy paste tampering detection algorithm, and use Matlab software to design the corresponding tampering forensics system.
Construction and Technology Research of Automated Test Platform Based on Cloud Computing

With the rapid development of information technology, the era of cloud computing has come, and great changes will take place in the way people apply information, which will also bring great changes to IT-related industries and technologies such as software service mode, R&D mode and testing mode. Based on PaaS cloud platform, an open architecture and mature advanced automatic test platform are established, and the design principles of modularization, componentization and standardization are followed, which realizes the portability and testability of test programs, effectively enhances the flexibility of automatic test system, and can be used to support the application of open universal test software in functional performance testing and maintenance support for complex systems.
Real-time Motion Control of Robot Using Kinect

Abstract The goal of Industry 4.0 is to maximize efficiency and reduce human efforts. Artificial Intelligence, Cloud Computing, Robotics, Mobile Technologies and many other technological advances generate Industry 4.0. Humanoid robots resemble humans and their primary goal is to perform any task programmed by the user. Our goal is to create a portable and safer system that can be used in any dynamic environment to control the robot and avoid any sort of complexity. With the rapid technological advances, there is no need to worry about unemployment since it requires a technical expert in the field to control the robot. This paper discusses how this control system processes gesture data along with a brief discussion of certain useful applications.
Automating Live Cricket Commentary Using Supervised Learning

Cricket is one of the most popular sports in the world. It is a dynamic game involving complex rules and strategies. There are many websites like Cricbuzz and Cricinfo that provide live text commentary using journalists who report from the ground or use live telecast of the match. This process of using journalists for typing out live text commentary is both labor and cost intensive. This research presents an approach that uses dynamic web scraping to scrape live scores and associated parameters like run rate, outcome of each ball, etc. which are then fed to a supervised learning algorithm that uses all these parameters and generates a commentary for the current ball event by selecting an appropriate commentary template from a pool of predefined commentary templates. We also compare results between the different supervised learning algorithms, i.e., Neural Network and Random Forest, in terms of accuracy metrics and prove that Random Forest performs better by having an accuracy of 92.7% in generating the appropriate commentary.
Lo sviluppo della competenza lessicale: incursioni tra le ipotesi teoriche e le applicazioni glottodidattiche

The paper analyses the issue of the development of lexical competence in Italian as L2 learners. The notion of lexicon is considered from three different points of view: the point of view of the speaker/learner, the point of view of the researcher interested in language issues and the point of view of the foreign language teacher. The question of what to intend and how to manage the lexicon within a second language courses is an apparently trivial one that might be solved with a simple: "teaching Italian words"; while, it is one of the most rich of theoretical implications and methodological choices. This contribution aims at investigating the complex relationship between teaching language and lexicon, providing also a small list of instructions on how to teach the words of a new language for teachers of Italian language as L2.
The Worth of Cardinal Information in MCDM – a Guide to Selecting Weight-Generating Functions

There exist a variety of methods for extracting weights and values in multi-criteria decisions based on different rankings. However, it is difficult to determine which the useful ones are and how they correspond to the decision makers' perceptions, if at all. How do we know that what we are using is really significant, especially in situations when the decision bases are vague? One category of methods that has proved relatively successful is to use so-called generated surrogate weights that are, in some sense, meant to represent rankings and there are various suggestions as to how best to distil them from input information. In this paper, using a number of simulations, we review some leading algorithms for automatic weight generation without external parameters besides cardinal and ordinal rankings and provide some guidelines for selecting a surrogate weight-generating function for MCDM applications, in ordinal as well as cardinal information settings. We also propose an alternative with some attractive properties compared with the existing ones.
Paradigm of Handling Data Linked to Cloud Database Impacting Cloud Computing: A Case Study Based on Simulation

It has been observed the significant improvement of implementation of database applications related to information technology. Somehow these applications have now been replaced with specified remote applications linked with committed and appropriate servers along with storage allocation. However, the cloud databases are more appropriate as well as having more commonalities that are provisioned with scalabilities and flexibilities. Actually, the databases in all respect should support the specified functionalities, i.e., atomicity, consistency, isolation as well as consistency. In such situation, it is also necessary to focus on minimization of resources and obtain optimality. The cloud computing concept in real sense helps in enhancement of multiple accessibilities without the prior knowledge of any requisite software or operating system. It is probably an enhanced technology based on internet distributed computing. Its main intention is to develop efficient and powerful computing capabilities with minimal expenditure. In fact, as it is originated from the conventional large-scale distributed computing technology, its focus is towards minimization of processing issues and enhances the services through the service providers. Now considering applications linked to cloud databases, it should be noted that in these cases also the accumulation of relevant data structures as well as query language in structured pattern can be prioritized along with all the components associated with database management system. In true sense, the overall efficiency of handling data can be enhanced. In this paper, it is primarily intended on the evaluation mechanisms of cloud databases and its impact on cloud computing based on simulation approach.
Automatic Incremental Training of Object Detection by Using GAN for River Level Monitoring

Terrain problems have caused floods to occur frequently, which will cause flooding, landslides, destroying facilities and casualties in Taiwan. With the growth of machine performance and the gradual completion of training data, object recognition approaches based on convolutional neural network (CNN) have performed well in various disaster monitoring systems. However, in the actual domain, as time goes by, changes in the background, viewpoint, and environment caused by weather and time conditions will cause the model to be inaccurate for the outdoor CCTVs. In this paper, we proposed an automatic incremental training approach combined with deep convolutional generative adversarial network (DCGAN) to greatly reduce the time and labor cost of model retraining. The experimental results show that the proposed approach can effectively reduce labeling effort and achieve the recall closing to the model with manually labeled and training by human.
A Kinect-based finger pointing detection algorithm

This paper proposes an algorithm for real-time detection of the user's finger pointing. The innovations of this algorithm are: (a) In order to enhance the robustness of the detection results, the Kinect data is comprehensively used to segment the hand, and the depth value is divided into three situations to better avoid interference ; (b) In order to improve the real-time performance of the detection results, the position of the fingertip is obtained by using the contour information of the hand, thereby narrowing the scope of the solution ; (c) In order to improve the accuracy of detection, the Hough line detection algorithm is used to detect possible straight lines in the contour, and the angle is compared with the straight line from the fingertip to the palm of the hand. The algorithm in this paper can realize real-time pointing detection and has achieved good results.
An Improved Artificial Potential Field Escape Method with Weight Adjustment

Abstract Aiming at the problems of obstacle avoidance and bullet avoidance during the patrol swarm, this paper analyzed the defects of the classical artificial potential field, proposed an adjustable escape method, which establish the relationship between the adjustment coefficient and the distance. This method avoid too large or too small escape force that get the bullet into new local shock problem near the target. Then given the weight calculation and parameter selection method, restricted the escape motion by kinematics according to the constraints in the actual motion. This improved method can effecting solve the problem of avoidance in dynamic and complex environment.
Modelling of Facial Images for Analysis of Recognition System

Abstract Face recognition is categorized as a biometric technology that employs the use of computer ability in image processing to detect and recognize human faces. Face recognition system has numerous applications for many purposes such as for access control, law enforcement and surveillance thus this system is dominant in present technology. Generally, face recognition system become more advance in term of the accuracy and implementation. However, there are a few parameters that effects the accuracy of recognition system for examples, the pose invariant, illumination effect, size of image and noise tolerance. Even though there are a number of systems were already available in the literature, the complete understanding of their performances are relatively limited. This is due to many systems focused on a narrow application band – therefore, a comprehensive analysis are needed in order to understand their performances leading to establishing the conditions for successful face recognition system. In this paper we developed a synthetic model to represent facial images to be used as a platform for performance analysis of facial recognition systems. The model includes 5 face types with the ability to vary all parameters that are affecting recognition performance – measurement noise, face size and face-background intensity differences. The model is important as it provide an avenue for performance analysis of facial recognition systems.
Improving Students Speaking Ability Trough Hidden Object Game at The Second Grade of SMPN 8 Bandar Lampung

The research was conducted to find out whether i). Is there any significant improvement of students speaking ability before and after being taught trough hidden object game ii) the aspects improve better of students speaking ability after being taught trough hidden object game iii) The students perception about the use of hidden object game. This research is quantitative research and used one group pretest-posttest design as the method. The subject of this research are 30 students at second grade of SMPN 8 Bandar Lampung in class VIII B. Instruments that used in this research were speaking test and questioner. The data were collects trough paired sample t-test. The results of the research are first there is significant improvement of students speaking ability after being taught trough hidden object game. Second the aspect that improve is fluency and In conclusion, hidden object game has a significant effect in increasing students’ speaking achievement then the aspects that improve better was fluency and the students give positive perception about the implementation of hidden object game. Keywords : speaking, hidden object game, descriptive speaking.
Long-distance gesture detection based on deep learning for 3D spatial interaction

With the technological development of stereoscopic display, an immersive 3D space with large size can be reconstructed more and more easily, and a 3D spatial interaction method with high-efficiency become more and more urgent. Gesture interaction, as the most natural and efficient way of human-computer interaction, can convey information very quickly and efficiently. However, the effective interaction distance of most existing gesture interaction methods is less than one meter, and can not meet the demand of the long distance 3D spatial interaction. In this paper, an efficient network named Gesture YOLO for long-distance gesture detection is proposed to achieve the small gesture object detection with improved accuracy. There are two modules in our Gesture YOLO, one is the Dual CSPDarknet53-tiny Backbone module for fusing person features and gesture features, and the other is the Progressive Multi-Scale Feature Fusion module for enhancing output features. The experimental results on our test set show that our Gesture YOLO can achieve higher gesture detection accuracy than the YOLOv4-tiny at distances ranging from 2m to 5m, and can mitigate the significant drop in gesture detection accuracy when the distance increases.