Reviewerly/exHarmony huggingface dataset:
Effective Methods and Techniques for the Formation of Value Orientations of Adolescent Children

This article discusses in detail effective methods and techniques for the formation of value orientations of adolescent children. Value orientations of a person are an important characteristic of a child's life, since value orientations fix the connection of children with the environment, differences in their own relationships and psyche. In addition, children, realizing their value orientations, look for their place in the classroom, reflect on the meaning and purpose of each action in society. Expressing a conscious opinion about actions in society, he formulates his attitude towards other people, the environment and himself, largely determining the direction and content of personal development. The formation of value orientations that occur intensively in children of early adolescence is an important stage for the formation of a worldview, the search for the meaning of life and the definition of their role in society. In the conditions of modern education, a set of effective methods of organizing the educational process is necessary for the formation of value orientations of adolescent children. Therefore, the authors of the article note that at the present stage of the development of pedagogical and psychological science, special attention should be paid to the peculiarities of the formation of adolescent children, including value orientations, emphasizing that classes using training exercises are an effective method of working with adolescents. Training exercises in a group under the guidance of experienced teachers-psychologists allow children to form the necessary knowledge, skills, skills, values, as well as to better understand themselves and others.
An Intelligent Real-Time Object Detection System on Drones

Drones have been widely used in everyday life and they can help deal with various tasks, including photography, searching, and surveillance. Nonetheless, it is difficult for drones to perform customized online real-time object detection. In this study, we propose an intelligent real-time object detection system for drones. It is composed of an FPGA and a drone. A neural-network (NN) engine is designed on the FPGA for NN model acceleration. The FPGA receives activation data from an NN model, which are assembled into the data stream. Multiple fetch and jump pointers catch required activation values from the data stream, which are then filtered and sent to each thread independently. To accelerate processing speed, multiple processing elements (PEs) deal with tasks in parallel by using multiple weights and threads. The image data are transferred from the drone host to the FPGA, which are tackled with high speed by the NN engine. The NN engine results are returned to the host, which is used to adjust the flying route accordingly. Experimental results reveal that our proposed FPGA design well utilizes FPGA computing resources with 81.56% DSP and 72.80% LUT utilization rates, respectively. By using the Yolov3-tiny model for fast object detection, our system can detect objects at the speed of 8 frames per second and achieves a much lower power consumption compared to state-of-the-art methods. More importantly, the intelligent object detection techniques provide more pixels for the target of interest and they can increase the detection confidence score from 0.74 to 0.90 and from 0.70 to 0.84 for persons and cars, respectively.
Movement Evaluation Algorithm-Based Form Tracking Technology and Optimal Control of Limbs for Dancers

This study designs an optimal control model of dance personnel from tracking technology and limb control based on an action evaluation algorithm by constructing a human action evaluation algorithm model and conducting an in-depth study of dance personnel from tracking technology and limb control. This study proposes an OpenPose method based on pose flow optimization to address the false detection of vital human points and misconnection between critical issues in traditional OpenPose-based human pose estimation. The human pose estimation results of OpenPose are optimized by using the human pose flow information in the image sequence. This makes up for the shortcomings of traditional OpenPose that ignores the interframe image information. In this study, we analyze the experimental data of action evaluation, define a set of formulas to evaluate the action after summarizing the distribution pattern of DTW difference sample points from 8 angles, and design an action evaluation system to demonstrate the rationality of this action evaluation method. Since the bases and factors in the evaluation formula are constantly recalculated as the action changes, which increases the complexity of the evaluation method, the following work is to improve the parameters of the activity evaluation formula, so that the evaluation method has better efficiency and adaptability. To enhance the effect of action recognition, this study uses the Kinect sensor to obtain the 3D coordinates of 20 skeletal joints of the human body. It uses the relative distance and angle sequence of the joints as the feature parameters according to the characteristics of human posture. In static pose recognition, the feature vector’s sample set is trained, and the KNN algorithm is used as a classifier to recognize the pose.
Variability Management in Dynamic Software Product Lines for Self-Adaptive Systems—A Systematic Mapping

Context: Dynamic software product lines (DSPLs) have considerably increased their adoption for variability management for self-adaptive systems. The most widely used models for managing the variability of DSPLs are the MAPE-K control loop and context-aware feature models (CFMs). Aim: In this paper, we review and synthesize evidence of using variability constraint approaches, methodologies, and challenges for DSPL. Method: We conducted a systematic mapping, including three research questions. This study included 84 papers published from 2010 to 2021. Results: The main results show that open-dynamic variability shows a presence in 57.1% of the selected papers, and on the other hand, closed-dynamic variability appears in 38.1%. The most commonly used methodology for managing a DSPL environment is based on proprietary architectures (60.7%), where the use of CFMs predominates. For open-dynamic variability approaches, the MAPE-K control loop is mainly used. The main challenges in DSPL management are based on techniques (28.6%) and open variation (21.4%). Conclusions: Open-dynamic variability has prevailed over the years as the primary approach to managing variability in DSPL, where its primary methodology is the MAPE-K control loop. Response RQ3 requires further review.
Scalable implementation of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>(</mml:mo><mml:mi>d</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn><mml:mo>)</mml:mo></mml:mrow></mml:math> mutually unbiased bases for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>d</mml:mi></mml:math> -dimensional quantum key distribution

A high-dimensional quantum key distribution (QKD) can improve error rate tolerance and the secret key rate. Many $d$-dimensional QKDs have used two mutually unbiased bases (MUBs), while $(d+1)$ MUBs enable a more robust QKD, especially against correlated errors. However, a scalable implementation has not been achieved because the setups have required $d$ devices even for two MUBs or a flexible convertor for a specific optical mode. Here, we propose a scalable and general implementation of $(d+1)$ MUBs using $\log_p d$ interferometers in prime power dimensions $d=p^N$. We implemented the setup for time-bin states and observed an average error rate of 3.8% for phase bases, which is lower than the 23.17% required for a secure QKD against coherent attack in $d=4$.
DeepVenn -- a web application for the creation of area-proportional Venn diagrams using the deep learning framework Tensorflow.js

Motivation: The Venn diagram is one of the most popular methods to visualize the overlap and differences between data sets. It is especially useful when it is are 'area-proportional'; i.e. the sizes of the circles and the overlaps are proportional to the sizes of the data sets. There are some tools available that can generate area-proportional Venn Diagrams, but most of them are limited to two or three circles, and others are not available as a web application or accept only numbers and not lists of IDs as input. Some existing solutions also have limited accuracy because of outdated algorithms to calculate the optimal placement of the circles. The latest machine learning and deep learning frameworks can offer a solution to this problem. Results: The DeepVenn web application can create area-proportional Venn diagrams of up to ten sets. Because of an algorithm implemented with the deep learning framework Tensorflow.js, DeepVenn automatically finds the optimal solution in which the overlap between the circles corresponds to the sizes of the overlap as much as possible. The only required input is two to ten lists of IDs. Optional parameters include the main title, the subtitle, the set titles and colours of the circles and the background. The user can choose to display absolute numbers or percentages in the final diagram. The image can be saved as a PNG file by right-clicking on it and choosing "Save image as". The right side of the interface also shows the numbers and contents of all intersections. Availability: DeepVenn is available at https://www.deepvenn.com. Contact: tim.hulsen@philips.com
A Comparative Study of Metaheuristics Based Task Scheduling in Cloud Computing

Cloud computing is a standard way of hosting software applications and services that is booming day by day thanks to the different utilities offered to users according to their needs and contracts. Most of these services are in the form of tasks and their execution in such environments requires efficient scheduling strategies that take into account both algorithmic and architectural features. The objective is to orchestrate the suitable assignment of the submitted tasks to the available resources on the basis of various functional requirements of end users. To overcome the scheduling issue, which is an NP-hard problem, various metaheuristic algorithms are used in literature to achieve near optimal solution. For this purpose, this paper aims to perform a comparative investigation of three common metaheuristic algorithms in the optimization process such as Shuffled Frog Leaping Algorithm (SFLA), Flower Pollination Algorithm (FPA) and Gray Wolf Optimization (GWO). Both standard and synthetic workloads are employed to analyze the performance of these algorithms by evaluating its objective function in term of two metrics which are makespan and resource utilization rate. The simulation results obtained using the CloudSim framework are very satisfactory and clearly show the value of our study.
Original research article digital literacy in EFL learning: University students’ perspectives

The 21st century has widely opened the opportunity for society, specifically in the educational party, to excel their performance in digital literacy. This relates to the competence required in accessing information using media and ICT tools in the process of learning. Technology integration in EFL learning has been a common issue, yet studies in digital literacy in language learning are still scarcely being conducted. This study explores university students’ digital literacy skills and their perspectives on digital literacy in EFL learning. Two instruments were utilized through a qualitative method, including a questionnaire and interview. The findings showed some aspects of digital literacy influencing students’ EFL learning. This involves students’ information literacy, ICT and media literacy, and their perspectives on digital literacy. The result indicated that EFL university students are encouraged to apply digital literacy since almost all learning components are integrated with digital technologies. As a consequence, they showed high awareness to keep up with the latest issue on digital literacy in their EFL learning.
Deep transfer network of heterogeneous domain feature in machine translation

In order to address the shortcoming of feature representation limitation in machine translation(MT) system, this paper presents a feature transfer method in MT. Meta feature transfer of the decoding process considered not only their own translation system, but also transferred knowledge of another translation system. The domain meta feature and the objective function of domain adaptation are used to better model the domain transfer task. In this paper, extensive experiments and comparisons are made. The experiment results show that the proposed model has a significant improvement in domain transfer task. The first model has better performance than baseline system, which improves 3.06 BLEU score on the news test set, improves 3.27 BLEU score on the education test set, and improves 3.93 BLEU score on the law test set; The second model improves 3.16 BLEU score on the news test set, improves 3.54 BLEU score on the education test set, and improves 4.2 BLEU score on the law test set.
A Novel Nonlinear Pseudorandom Sequence Generator for the Fractal Function

A pseudorandom sequence is a repeatable sequence with random statistical properties that is widely used in communication encryption, authentication and channel coding. The pseudorandom sequence generator based on the linear feedback shift register has the problem of a fixed sequence, which is easily tracked. Existing methods use the secret linear feedback shift register (LFSR) and built-in multiple LFSRs and is difficult to prevent cracking based on the hardware analysis. Since the plaintext depends on a specific language to be generated, using pseudo-random sequence encryption, it faces the problem that the encryptor cannot hide the characteristics of the plaintext data. Fractal functions have the following properties: chaotic, unpredictable and random. We propose a novel pseudorandom sequence generator based on the nonlinear chaotic systems, which is constructed by the fractal function. Furthermore, we design a data processing matrix to hide the data characteristics of the sequence and enhance the randomness. In the experiment, the pseudo-random sequences generator passed 16 rigorous test items from the National Institute of Standards and Technology (NIST), which means that the nonlinear pseudorandom sequence generator for the fractal function is effective and efficient.
Rate-Compatible LDPC Codes for Continuous-Variable Quantum Key Distribution in Wide Range of SNRs Regime

Long block length rate-compatible low-density parity-compatible (LDPC) codes are designed to solve the problems of great variation of quantum channel noise and extremely low signal-to-noise ratio in continuous-variable quantum key distribution (CV-QKD). The existing rate-compatible methods for CV-QKD inevitably cost abundant hardware resources and waste secret key resources. In this paper, we propose a design rule of rate-compatible LDPC codes that can cover all potential SNRs with single check matrix. Based on this long block length LDPC code, we achieve high efficiency continuous-variable quantum key distribution information reconciliation with a reconciliation efficiency of 91.80% and we have higher hardware processing efficiency and lower frame error rate than other schemes. Our proposed LDPC code can obtain a high practical secret key rate and a long transmission distance in an extremely unstable channel.
How can the principles of learning be used to select the best ICT tools for computer-based chemistry instruction in high school?

The role of information and communication technology as well as teaching aids in specialized chemistry instruction was examined in the article. One of the key outcomes of specialized instruction in chemistry is the development of research skills in high school students. To ensure conformity with the principles of specialized chemistry teaching, ICT tools have been analyzed and systematized. The decision to use ICT tools to implement specialized chemistry training and strengthen the research skills of high school students in specialized chemistry training had supported. The findings of a study to assess the feasibility of implementing certain ICT tools to strengthen the research skills of high school students in specialized chemistry training are provided.
A Natural Human-Drone Embodied Interface: Empirical Comparison With a Traditional Interface

Despite the importance of usability in human-machine interaction (HMI), most commonly used devices are not usable by all potential users. In particular, users with low or null technological experience, or with special needs, require carefully designed systems and easy-to-use interfaces supporting recognition over recall. To this purpose, Natural User Interfaces (NUIs) represent an effective strategy as the user's learning is facilitated by features of the interface that mimic the human "natural" sensorimotor embodied interactions with the environment. This paper compares the usability of a new NUI (based on an eye-tracker and hand gesture recognition) with a traditional interface (keyboard) for the distal control of a simulated drone flying in a virtual environment. The whole interface relies on "dAIsy", a new software allowing the flexible use of different input devices and the control of different robotic platforms. The 59 users involved in the study were required to complete two tasks with each interface, while their performance was recorded: (a) exploration: detecting trees embedded in an urban environment; (b) accuracy: guiding the drone as accurately and fast as possible along a predefined track. Then they were administered questionnaires regarding the user's background, the perceived embodiment of the device, and the perceived quality of the virtual experience while either using the NUI or the traditional interface. The results appear controversial and call for further investigation: (a) contrary to our hypothesis, the specific NUI used led to lower performance than the traditional interface; (b) however, the NUI was evaluated as more natural and embodied. The final part of the paper discusses the possible causes underlying these results that suggest possible future improvements of the NUI.
MANAGING URBAN DIGITAL TWINS WITH AN EXTENDED CATALOG SERVICE

Abstract. A city is a complex system and involves many stakeholders with different interests and information about the physical city objects. Information integration from distributed sources is therefore a common challenge in many Smart City projects, as well as in projects for setting up Urban Digital Twins (UDT). In the context of distributed data infrastructures, catalog systems provide a significant contribution to solving the data integration challenge. However, neither existing catalogs for managing urban data nor catalogs used in Spatial Data Infrastructures (SDI) are tailored for managing UDTs. In response to this gap, our research focuses on the development of an extended catalog service including a metadata model specifically designed to manage distributed UDTs. The requirements for the metadata model and the functionality of the catalog were obtained from several smart city projects. As a proof of concept, the paper shows how the catalog implemented on the basis of the CKAN software package is used for managing the UDT of an urban redevelopment project in the city of Munich, Germany.
Method of micro hole tilt adjustment based on a vision-guided touch probe

In order to meet the increasing miniaturization and high precision requirements of high-performance devices in aerospace and other fields for space array micro holes with a high aspect ratio, a method of measuring geometric parameters by penetrating the micro holes with a contact probe guided by vision is proposed, which can achieve rapid and efficient measurements. This method adopts the principle of vision measurement, preliminarily determines the geometric parameters of measurement through the processing of micropore images, and then needs to establish a collaborative measurement model of vision and probe using the principle of vision to guide the probe to go deep into the hole to measure and adjust the inclined micropores. According to this principle, a five-axis measuring system is set up, and a hole with a diameter of 3 mm is tested at different angles. The experimental results preliminarily verify the effectiveness and feasibility of the proposed method.
The Impact of Culture on Employees’ Performance: The Case of Lebanese Companies

Organizational culture is well-documented in the literature for its impact on company performance and its role in creating competitive advantages for companies. Organizational culture has long been acknowledged as a significant aspect in the analysis of organizations in many situations. An organization's culture serves as an integrated way of controlling members' behavior and as a particular catalyst for the organization's growth and development culture is an essential part of every facet of organizational life. As a result, a company's culture shapes how people think, work, and feel about their job and the business as a whole. Sampled respondents were surveyed with the use of a survey tool. A questionnaire served as the tool. Using the instrument ensured that data was collected in a consistent manner. The six sections of the survey were designed to answer the research questions. The study's target demographic was the Premium Shopping Center's 500 workers. A study's sampling frame is a list of all the research population's components that will be taken into account throughout the investigation. There are 11 divisions within the corporation, with four of their divisions situated in Beirut. The list of 500 workers of Premium Shopping Center served as the study's sample frame. However, only 250 respondents answered the questionnaires. Likert scale questions were used for the closed-ended questions. To elicit even more information from the participants, open-ended questions were used. The research demonstrated that a cooperative work environment had a positive impact on performance because of the existence of a supportive work environment. The research found that psychological safety is fostered by helpful and trustworthy interpersonal interactions as well as supportive management.
Towards a Better Understanding of Impersonation Risks

In many situations, it is of interest for authentication systems to adapt to context (e.g., when the user's behavior differs from the previous behavior). Hence, during authentication events, it is common to use contextually available features to calculate an impersonation risk score. This paper proposes an explainability model that can be used for authentication decisions and, in particular, to explain the impersonation risks that arise during suspicious authentication events (e.g., at unusual times or locations). The model applies Shapley values to understand the context behind the risks. Through a case study on 30,000 real world authentication events, we show that risky and non-risky authentication events can be grouped according to similar contextual features, which can explain the risk of impersonation differently and specifically for each authentication event. Hence, explainability models can effectively improve our understanding of impersonation risks. The risky authentication events can be classified according to attack types. The contextual explanations of the impersonation risk can help authentication policymakers and regulators who attempt to provide the right authentication mechanisms, to understand the suspiciousness of an authentication event and the attack type, and hence to choose the suitable authentication mechanism.