Reviewerly/exHarmony huggingface dataset:
Classification of network traffic using supervised machine learning algorithms within NFV environment

Deep Packet Inspection (DPI) of the network traffic is used on a regular basis within the traditional and virtualized environments. But changes in the network architecture with the introduction of containers, microservices, application functions, network functions, and the penetration of 5G access technology are adding more traffic complexity, especially in the so-called east-west flow direction. Network Functions Virtualization (NFV) has become an unavoidable step for further IP network development. In this context, DPI is becoming a challenge. Furthermore, the penetration of 5G allows access of various kinds of devices to the network with cloudification logic which drives them. This paper provides a performance analysis of a selected set of supervised machine learning (ML) algorithms for classification of network traffic within an NFV environment. The goal is to find a suitable algorithm that will classify the traffic from a point of both precision and speed, especially because in the 5G networks any packet delay may compromise the quality of service requirements. The research shows that out of the 6 algorithms tested, Decision Tree algorithm has the best overall performance, from both classification precision and time consumption point of view. It has proved as a reliable classifier that is performing evenly across different classes. Due to the specifics of the virtualized environments and encryption methods, payload data, source, destination, and port information of the network traffic packets are excluded from any statistical operation used for classification by the ML algorithms.
Effect of Upward Social Comparison in SNS on Depression among Middle School Students: The Mediating Effect of Self-Deprecation and the Moderated Mediating Effect of Cognitive Flexibility

The purpose of this study was to examine the mediating effect of middle school students’self-deprecation in the relationship between upward social comparison in social network service (SNS) and depression and the moderated mediating effect of cognitive flexibility. The participants were 288 middle school students, in the first to third grades from four middle schools located in Seoul, Gyeong-gi, and Jeonnam. The date were analyzed with descriptive statistics, Pearson’s correlation coefficients and the Process Macro Model 4, 1, and 14.The results of this study are as follows. First, an upward comparison in SNS has a significant positive influence on students’depression, and students' self-deprecation of students mediated the relation between two. Second, the level of control, which is a sub-factor of cognitive flexibility, moderated the mediating effect of self-deprecation. That is, if students are more likely to perceive difficult situations as controllable, upward social comparison in SNS mediated by self-deprecation has smaller effect on depression.Based on these results, we suggest practical interventions to reduce depression among middle school students by decreasing upward social comparison in SNS and self-deprecation. In addition, helping students perceive difficult situations as controllable could be another effective way of reducing depression among those students who have a high level of self-deprecation in upward social comparison in SNS.
Signal Detection in Degree Corrected ERGMs

In this paper, we study sparse signal detection problems in Degree Corrected Exponential Random Graph Models (ERGMs). We study the performance of two tests based on the conditionally centered sum of degrees and conditionally centered maximum of degrees, for a wide class of such ERGMs. The performance of these tests match the performance of the corresponding uncentered tests in the $β$ model. Focusing on the degree corrected two star ERGM, we show that improved detection is possible at criticality using a test based on (unconditional) sum of degrees. In this setting we provide matching lower bounds in all parameter regimes, which is based on correlations estimates between degrees under the alternative, and of possible independent interest.
Intelligent VR-AR for Natural Disasters Management

Considering the significance of improving natural disasters emergency management and recognizing that catastrophe scenes are almost impossible to reconstruct in real life, forcing persons to experience real hazards violates both law and morality, in this research is presented an engine for Virtual Reality/Augmented Reality (VR/AR) that works enhancing human capacities for prevention, response and recovery of natural phenomena effects. The selected novel techniques have very advantageous qualities to overcome the inconveniences detected in the most recent seismic devastating experience in Mexico City, the Sept 19th, 2017, earthquake M7.2: total collapse of more than 230 buildings, partial fall of 7 000 houses, 370 people were killed, and over 6,000 were injured. VR and AR provide researchers, government authorities and rescue teams with tools for recreating the emergencies entirely through computer-generated signals of sight, sound, and touch, when using VR, and overlays of sensory signals for experiences a rich juxtaposition of virtual and real worlds simultaneously, when AR is applied. The gap between knowledge and action is filled with visual, aural, and kinesthetic immersive experiences that poses a possibility to attend to the population in danger in a deeply efficient way, never experimented before.
Education as a Way of Human Existence in a Postmodern Society

Without exaggeration, learning problems are among the most complex and most controversial not only in pedagogical and philosophical science but also in other scientific areas. We are talking about the philosophy of education as a general paradigm of the organization and content of all that knowledge - scientific and non-scientific - that we pass on to pupils and students through education, about worldview values that are brought up, merged in the process of educational and educational activities, the certainty of the spiritual world of the individual which is formed at school and university and determines her personal position, role and ways of behavior in postmodern society. Education is an important stage in the formation of the human soul, his spiritual world. Consciousness, like other qualities of the human psyche, results from education and acquisition of social experience. An important means of forming a person's consciousness is the educational system, which contributes to the spiritual development of a person. Since it is education that is that social institution, it performs the functions of preparing and including the individual in various spheres of the life of postmodern society and attracting him to culture. Human consciousness is the ideological foundation of aim and reliable knowledge. In this context, consciousness is the possibility and condition of the world, the individual can perceive and develop his spirituality. Education in this case is the most effective way to form a person's spirituality. Since, besides a certain profession, education gives a person the opportunity to spiritually improve himself.
An Enquiry on Similarities between Renormalization Group and Auto-Encoders Using Transfer Learning

Physicists have had a keen interest in the areas of Artificial Intelligence (AI) and Machine Learning (ML) for some time now. With a special inclination on unraveling the mechanism at the core of the process of learning. In particular, exploring the underlying mathematical structure of a neural net (NN) is expected to not only help us in understanding the epistemological meaning of `Learning' but also has the potential to unravel the secrets behind the working of the brain. Here, it is worthwhile to establish correspondences and draw parallels between methods developed in core areas of Physics and the techniques developed at the forefront of AI and ML. Although recent explorations indicating a mapping between the Renormalisation Group(RG) and Deep Learning(DL) have shown valuable insights, we intend to investigate the relationship between RG and Autoencoders (AE) in particular. We will use Transfer Learning(TL) to embed the procedure of coarse-graining in a NN and compare it with the underlying mechanism of encoding-decoding through a series of tests.
Arsitektur Convolutional Neural Network (CNN) Alexnet Untuk Klasifikasi Hama Pada Citra Daun Tanaman Kopi

Indonesia is the fourth largest coffee producing country in the world. However, when compared to 3 other countries, Indonesia's coffee production is still relatively small. Many factors cause this to happen, including the number of farmers' coffee trees that are attacked by diseases. If the handling of this disease is slow, then the disease in one tree can be transmitted to other trees. This causes a decrease in Indonesian coffee productivity. In this study, the author implemented the Alexnet Convolutional Neural Network (CNN) architecture using the MATLAB programming platform for the identification of diseases in coffee plants through images. The total number of datasets used is 300 data which is divided into 3 classes, namely health, rust and red spider mite. The training process involving 260 training data resulted in an accuracy of 69.44-80.56%. The network testing process using 40 test data resulted in an accuracy of 81.6%. Based on the results of the study, it can be said that the Alexnet architecture is accurate for the classification of leaf pests on coffee plants
The Role of Virtual Reality to Enhance Cultural Communication

This paper aimed to recognize the role of virtual reality to enhance cultural communication by allowing to the participators who divided into three groups to watch the same substance in three different displays—virtual reality, normal video, images and text—to measure the process of retrieving information or events with a specific cue. The results of the study indicate that the virtual world experience enhances people's awareness of the culture. This new medium, VR, allows for participants to be immersed in the culture in ways that are not possible through the use of other media. Also, it can have an effective impact in cultural communication by allowing the participators to be virtually present at civilized events.
TOOD: Task-aligned One-stage Object Detection

One-stage object detection is commonly implemented by optimizing two sub-tasks: object classification and localization, using heads with two parallel branches, which might lead to a certain level of spatial misalignment in predictions between the two tasks. In this work, we propose a Task-aligned One-stage Object Detection (TOOD) that explicitly aligns the two tasks in a learning-based manner. First, we design a novel Task-aligned Head (T-Head) which offers a better balance between learning task-interactive and task-specific features, as well as a greater flexibility to learn the alignment via a task-aligned predictor. Second, we propose Task Alignment Learning (TAL) to explicitly pull closer (or even unify) the optimal anchors for the two tasks during training via a designed sample assignment scheme and a task-aligned loss. Extensive experiments are conducted on MS-COCO, where TOOD achieves a 51.1 AP at single-model single-scale testing. This surpasses the recent one-stage detectors by a large margin, such as ATSS (47.7 AP), GFL (48.2 AP), and PAA (49.0 AP), with fewer parameters and FLOPs. Qualitative results also demonstrate the effectiveness of TOOD for better aligning the tasks of object classification and localization. Code is available at https://github.com/fcjian/TOOD.
RoBIC: A Benchmark Suite For Assessing Classifiers Robustness

Many defenses have emerged with the development of adversarial attacks. Models must be objectively evaluated accordingly. This paper systematically tackles this concern by proposing a new parameter-free benchmark we coin ROBIC. ROBIC fairly evaluates the robustness of image classifiers using a new half-distortion measure. It gauges the robustness of the network against white and black box attacks, independently of its accuracy. ROBIC is faster than the other available benchmarks. We present the significant differences in the robustness of 16 recent models as assessed by ROBIC.We make this benchmark publicly available for use and contribution at https://gitlab.inria.fr/t;maho/robustness_benchmark.
Learning the Travelling Salesperson Problem Requires Rethinking Generalization

End-to-end training of neural network solvers for graph combinatorial optimization problems such as the Travelling Salesperson Problem (TSP) have seen a surge of interest recently, but remain intractable and inefficient beyond graphs with few hundreds of nodes. While state-of-the-art learning-driven approaches for TSP perform closely to classical solvers when trained on trivially small sizes, they are unable to generalize the learnt policy to larger instances at practical scales. This work presents an end-to-end neural combinatorial optimization pipeline that unifies several recent papers in order to identify the inductive biases, model architectures and learning algorithms that promote generalization to instances larger than those seen in training. Our controlled experiments provide the first principled investigation into such zero-shot generalization, revealing that extrapolating beyond training data requires rethinking the neural combinatorial optimization pipeline, from network layers and learning paradigms to evaluation protocols. Additionally, we analyze recent advances in deep learning for routing problems through the lens of our pipeline and provide new directions to stimulate future research.
Investigating Augmented Reality Educational Games for Head-Mounted Devices

In this paper we present My-CHIC AR Farm, an educational Augmented Reality (AR) game for the Microsoft HoloLens. The game has been designed and implemented with the aim of investigating the potential outcomes and benefits of educational AR games for Head Mounted Displays (HMDs). This type of devices allows the user to interact with the virtual objects with her own hands. Also, they support a wide range of view of the augmented world and, in general, a more immersive experience that the one obtained when using AR based on mobile devices. Although in theory they provide an ideal mean to deliver AR educational games, the evidence on the benefits obtained from these experiences when compared with standard mobile-based AR technology has not been sufficiently investigated. In this work we present two versions of the game designed for the two versions of the HoloLens 1 and 2, respectively. Each version of the game has been designed with the aim to exploit the interaction capabilities afforded by each HMD. We also present the results of an informal evaluation of the game version for the HoloLens 1, which allowed to disclose some limitations and problems that users experienced when using the technology.
The Usability Measurement of E–Faculty of Education UNESA Service Using USE Questionnaire

In this 21st-century era, the development of a management information system is one of the efforts that can be made to improve the quality of public services, both in general and in certain sectors, for example, education, where because of the effect of industrial revolution 4.0 and society 5.0, these days the community often to use the assistance of technology for their social activities. One of many ways that can be used to measure the success rate of a system is using usability measurement. The usability level of an information system shows whether the system is accepted by the user or not. This study aims to measure the usability level of the educational service in the Faculty of Education, State University of Surabaya (UNESA), using the USE Questionnaire. The data from this research are processed using a descriptive statistics method and then analyzed both for each aspect and the correlation of all aspects. The results of this study included; (1) the usefulness aspect got an eligibility percentage of 83,6%; (2) the ease of use aspect got an eligibility percentage of 81,7%; (3) the satisfaction aspect got eligibility percentage of 79,8%; (4) the result of calculation of all usability aspects are 81,9%, meaning that the E-Service Faculty of Education, Unisa very useful and worthy to be used.
Developing A Parser Framework based on OpenPose Skeleton Detection

Artificial Intelligence (AI) as a field of research and interest has been around for quite a long time. With the current state of technology, AI has been utilized in various fields, such as vehicle automation in the automotive, disease preliminary detection in medical, and cybersecurity in information technology. Spatial recognition has become popular owing to the rapid hike in computing power and as a result, real-world applications are starting to appear. In developed countries around the world, particularly those upgraded from developing nations, all faced the problem of the aging population. Based on the lack of frameworks available when using a state-of-the-art pose estimation of OpenPose, the research looks into building a framework to lower the bar of entering the utilization of OpenPose, thus creating opportunities for people without knowledge on data preparation. The framework used by a group of high schoolers proved the ease of access useful for the relative tech-illiterate group and bridging the gap between them and the higher education.
Implementasi Metode UCD (User Centered Design) Pada Rancang Bangun Sistem Informasi Perpustakaan: Studi Kasus : SMK Negeri 1 Gelumbang

Libraries play an important role in advancing the institute of education because the library is a place to improve knowledge for the students. In the library of SMK Negeri 1 Gelumbang running processes such as member registration, input data on prospective member, borrowing and returning books, book data input and book catalog still written in the ledger. Avoid delays in the system library service SMK Negeri 1 Gelumbang, it is necessary to design an information system library that can help and easier the activities of the circulation of the books. In the information system library SMK Negeri 1 Gelumbang uses (User Centered Design) dan UML (Unified Modeling Language) as well as for language programmer PHP with a MySQL database.Hopefully with this library information system it can solve the problems which exist in the library and can help in the development of science students.
Multi-person vision tracking approach based on human body localization features

This paper presents a multi-person vision tracking approach based on human body localization features to address the problem of interactive object localization and tracking in a home monitoring scenario. Firstly, the human body localization model is used to obtain the 3D position of the human body, which is then used to construct the human body motion model based on the Kalman filter method. At the same time, the human appearance model is constructed by fusing human color features and features of the histogram of oriented gradient to better characterize the human body. Secondly, the human body observation model is constructed based on the human body motion model and appearance model to measure the similarities between the human body state sequence in the historical frame and the human body observation result in the current frame, and the cost matrix is then obtained. Thirdly, the Hungarian maximum matching algorithm is employed to match each human body in the current and historical frames, and the exception detection mechanism is simultaneously constructed to further reduce the probability of human tracking and matching failure. Finally, a multi-person vision tracking verification platform was constructed, and the achieved average accuracy was 96.6% in the case of human body overlapping, occlusion, disappearance, and appearance; this verifies the feasibility and effectiveness of the proposed method.
Collecting Evidence in Forensic Events and Comparison of the Digital Evidence Practices of Kosovo and Turkey

Evidence is an important concept in order to reveal whether a crime really exists or not and integrate it with all its elements. There are numerous methods of crime scene investigation in forensic cases. During the judgment phase, the most important factor that will help understand and decide the manner in which the incident took place is the evidence that will provide proof with regard to the incident. Therefore, evidence helps prosecutors and judges correctly understand and establish the relationship between the crime and the criminals and prove the committed crime and ensure correct, fair and rapid execution of the trial with the aim of reaching the material truth. Evidence obtained in the crime scene provides information with regard to the manner in which the crime was committed, the time of the crime, thebehaviour of the perpetrator, the suspect, the victim and the crime scene ensuring the establishment of the relationship between them. On the other hand, with the digital systems gaining more placein the life of the society, the crime scene has shifted from a physical environment to a digital one. Consequently, new types of crimes are committed digitally and as a result, the concept of digital evidence has arisen. There are no specific laws on the collection of digital evidence under Kosovo's legislation. On the other hand, there are legal regulations regarding digital evidence in exemplary countries such as Turkey. This study will comprise of the methods of gathering evidence in forensic cases and the comparison of the legal legislation on digital evidence in Kosovo and Turkey. Received: 5 June 2021 / Accepted: 15 July 2021 / Published: 5 September 2021
Pockets: User-Assigned Menus Based on Physical Buttons for Virtual Environments

We present Pockets, a simple means of organizing and carrying 3D tools and other objects in virtual environments. Previous examples exist of using 3D tools with visually obvious affordances in virtual immersive environments instead of more traditional menus, however, in these applications a 2D menu is still necessary to select 3D tools from. Pockets make use of a belt with physical buttons, that objects can be assigned to. The Pockets design not only enables users to use their muscle memory to store and retrieve objects, thereby making tool use more efficient, but also solves the occlusion problem associated with state of the art approaches such as 2D menus tied to the body or to world space.
Three-Module Modeling For End-to-End Spoken Language Understanding Using Pre-Trained DNN-HMM-Based Acoustic-Phonetic Model

In spoken language understanding (SLU), what the user says is converted to his/her intent. Recent work on end-to-end SLU has shown that accuracy can be improved via pre-training approaches. We revisit ideas presented by Lugosch et al. using speech pre-training and three-module modeling; however, to ease construction of the end-to-end SLU model, we use as our phoneme module an open-source acoustic-phonetic model from a DNN-HMM hybrid automatic speech recognition (ASR) system instead of training one from scratch. Hence we fine-tune on speech only for the word module, and we apply multi-target learning (MTL) on the word and intent modules to jointly optimize SLU performance. MTL yields a relative reduction of 40% in intent-classification error rates (from 1.0% to 0.6%). Note that our three-module model is a streaming method. The final outcome of the proposed three-module modeling approach yields an intent accuracy of 99.4% on FluentSpeech, an intent error rate reduction of 50% compared to that of Lugosch et al. Although we focus on real-time streaming methods, we also list non-streaming methods for comparison.