Reviewerly/exHarmony huggingface dataset:
Detecting Trademark Image Infringement Using Convolutional Neural Networks

The rapid development of consumer products with short life spans, along with fast, global e-commerce and e-marketing distribution of products and services requires greater corporate diligence to protect intangible assets such as brands which can easily be coped or placed in grey markets. Trademarks are the government registered legal intellectual property rights (IPRs) used to protect companies’ brands and build brand equity. Given the rapid growth in the number of global trademark registrations, the number of trademark infringement cases is also increasing, a great challenge for the original trademark owner to detect the infringement and takes action to protect the brand image and related commercial interests. This research develops a trademark similarity assessment methodology based on the US trademark law related to the high likelihood of confusion and associated regulations. The research focuses on identifying trade mark image similarity using a deep learning approach. The convolutional neural network (CNN) and Siamese neural network (SNN) algorithms are modeled and trained using Cifar-10 and TopLogo-10 corpuses. These corpuses consist of more than 100,000 positive image pairs and more than 150,000 negative image pairs as training data. After training the model, an image input to the model extracts and recommends similar trade mark images found in the corpus. The solution assists users registering new trademarks to identifying similar marks that may lead to disputes. The solution also automatically screens images to identify marks that potentially infringe upon registered trademarks.
Sentimen Analisis Komentar Toxic pada Grup Facebook Game Online Menggunakan Klasifikasi Naïve Bayes

Toxic comments are comments made by social media users that contain expressions of hatred, condescension, threatening, and insulting. Social media users who are on average still teenagers with a nature that still cannot be controlled completely becomes a matter of great concern when they comment, their comments can be studied as text processing. Sentiment analysis can be used as a solution to identifying toxic comments by dividing them into two classifications. Where the data used amounted to 1,500 taken from social media Facebook in the private group Arena of Valor community. The dataset is divided into 2 classes: toxic and non-toxic. This research uses Naive Bayes with TF-IDF transformation and Information Gain feature selection and use distribution ratio 80:20. It will be compared the results of the evaluation where Naive Bayes without transformation, using TF-IDF transformation, and TF-IDF using Information Gain feature selection. The results of the comparison of evaluations from confusion matrix that have been carried out obtained the best classification model is to use the ratio of training and testing data 80:20 with TF-IDF transformation resulting in an accuracy of 75%, precision of 63%, recall of 67%, and F-measure of 64%.
Forming Meta-Subject Competencies In The System Of Secondary Vocational Education

The article deals with the problem of forming meta-subject competencies in terms of professional training. The “meta-subject competence” phenomenon is presented in the psychological and pedagogical literature abstractly; meanwhile the implementation of the requirements of the Federal State Educational Standard for meta-subject results is difficult for the professional pedagogical community. There are different ways of understanding meta-competencies in technical and humanitarian spheres. The most appropriate way to form them, as we see it, is to use the meta-subject, which is considered as the principle of integration of the educational content, and as a way of forming theoretical thinking and universal activity methods. The meta-subject competencies within the framework of secondary vocational education (SVE) phenomenologically represent the unity of the fundamental knowledge, skills, practical experience that is manifested and used by a person in integrative, inter-subject activity. From the perspective of psychological science, these are new forms that are developed on the basis of general professional and personal qualities. These may include regulatory, communicative and cognitive abilities. For example, to assess the students’ regulatory meta-subject competence, we may use indicators that formalize student’s ability to set goals, plan, organize, control, evaluate, and correct. The communicative component of meta-subject competence is determined by the ability to cooperate, to perform certain role functions, to present the results of their activities. Cognitive competence is characterized by attention, memory, perception and thinking. Here we also investigate the effectiveness of pedagogical methods of forming meta-subject competencies in the system of secondary vocational education.
Further comments on ‘SFVCC: Chaotic map‐based security framework for vehicular cloud computing’

By simulating a clogging attack [1-5], an adversary forces a communicating entity to process a forged/replayed authentication request by the adversary impersonating on behalf of a legitimate entity. The clogging attack represents a significant class of denial of services (DoS) attack and quality of services (QoS) degradation attack [3-6]. The clogging attack can be launched through the replay of old messages and get these accepted as legitimate and fresh ones from the receiving entity or adversary can try to construct a forged message, which can pass authentication requests from the receiving entity [6]. In both cases, the legitimate entity on the other side processes the whole request and constructs a session key, whereas, by launching a clogging attack, the adversary cannot construct the legitimate session key. However, the adversary forces the receiving entity to process all fabricated requests on behalf of a legal entity and believing that the correct session key has been shared with the legal entity. Therefore, execution of a clogging attack burdens the system and can cause DoS and/or QoS degradation through sending forged/replayed requests [5, 6] As argued in the original comment, using replay of previous messages, an active adversary can easily launch clogging attack on the scheme of Mishra et al. [7]. In case of a clogging attack, the database server (DBS) and radio-frequency identification reader (RFIDR) process all requests generated by a legitimate device and replayed by an adversary causing the burdens on both RFIDR and DBS. A large number of the replayed requests may cause both DoS and QoS degradation.
Multi-modal Feature Attention for Cervical Lymph Node Segmentation in Ultrasound and Doppler Images

Cervical lymph node disease is a kind of cervical disease with a high incidence. Accurate detection of lymph nodes can greatly improve the performance of the computer-aided diagnosis systems. Presently, most studies have focused on classifying lymph nodes in a given ultrasound image. However, ultrasound has a poor discrimination of different tissues such as blood vessel and lymph node. When solving confused tasks like detecting cervical lymph nodes, ultrasound imaging becomes inappropriate. In this study, we combined two common modalities to detect cervical lymph nodes: ultrasound and Doppler. Then a multimodal fusion method is proposed, which made full use of the complementary information between the two modalities to distinguish the lymph and other tissues. 1054 pairs of ultrasound and Doppler images are used in the experiment. As a result, the proposed multimodal fusion method is 3% higher (DICE value) than the baseline methods in segmentation results.
Approach of Analysis of Data Mining Prediction In Earthquake Case Using Non Parametric Adaptive Regression Method

Data Mining is the process of finding certain patterns and knowledge from big data. In general, the data mining process can be grouped into two categories, namely descriptive data mining and data mining prediction. There are several Math functions that can be used in the data mining process, one of which is the Classification and Regression function. Regression Analysis is also called Prediction analysis, which is a statistical method that is widely used to investigate and model relationships between variables. Regression analysis to estimate the regression curve can be done by analyzing Nonparametric Regression. One well-known method in non-parametric regression is MARS (Multivariate Adaptive Regression Spline). The MARS method is used to overcome the weaknesses of the Linear Regression method. The use of a stepwise backward algorithm with the CQP quadratic programming framework (CQP) from MARS resulted in a new method called CMARS (Conic Multivariate Adaptive Regression Splines). The CMARS method is able to model high dimensional data with nonlinear structures. The flexible nature of the CMARS model can be used in the process of analyzing earthquake predictions, especially in Lombok, West Nusa Tenggara. Test results Obtained a mathematical model of four independent variables gives significant results to the dependent variable, namely Peak Ground Acceleration (PGA). Contributions of independent variables are the distance of the epicenter 100%, magnitude 31.1%, the temperature of the incident location 5.5% and a depth of 3.5%.
PRIMENE SOFTVERSKIH ALATA U BIG DATA ANALIZI SENZORSKIH PODATAKA U SAOBRAĆAJU

The Thirty-Eighth Symposium on New Technologies in Postal and Telecommunications Traffic – PosTel 2020, is scheduled for December 1 and 2, 2020. The Symposium is organized by the Department of Postal and Telecommunication Traffic of the Faculty of Transport and Traffic Engineering – University of Belgrade. The co-organizers are: A state-owned enterprise "Post of Serbia", "Telekom Srbija", and Regulatory Agency for Electronic Communications and Postal Services (RATEL). The Symposium is supported by the Ministry of Education, Science and Technological Development and the Ministry of Trade, Tourism and Telecommunications of the Republic of Serbia. The main aim of this event and the accompanying Symposium Proceedings is to present state-of-the-art scientific and technological achievements in the field of postal and telecommunications traffic. All presented and published papers are invited and their number is limited. The topics of the Symposium and the most competent authors are determined by the Program Committee.
Make One-Shot Video Object Segmentation Efficient Again.

Video object segmentation (VOS) describes the task of segmenting a set of objects in each frame of a video. In the semi-supervised setting, the first mask of each object is provided at test time. Following the one-shot principle, fine-tuning VOS methods train a segmentation model separately on each given object mask. However, recently the VOS community has deemed such a test time optimization and its impact on the test runtime as unfeasible. To mitigate the inefficiencies of previous fine-tuning approaches, we present efficient One-Shot Video Object Segmentation (e-OSVOS). In contrast to most VOS approaches, e-OSVOS decouples the object detection task and predicts only local segmentation masks by applying a modified version of Mask R-CNN. The one-shot test runtime and performance are optimized without a laborious and handcrafted hyperparameter search. To this end, we meta learn the model initialization and learning rates for the test time optimization. To achieve optimal learning behavior, we predict individual learning rates at a neuron level. Furthermore, we apply an online adaptation to address the common performance degradation throughout a sequence by continuously fine-tuning the model on previous mask predictions supported by a frame-to-frame bounding box propagation. e-OSVOS provides state-of-the-art results on DAVIS 2016, DAVIS 2017, and YouTube-VOS for one-shot fine-tuning methods while reducing the test runtime substantially. Code is available at this https URL.
STUDENT’S FOLK MUSIC BAND: TECHNOLOGY OF GROUP INTERPRETATION OF MUSICAL COMPOSITION

The article deals with the problem of collaboration and group interaction during the production of a musical composition. The terms “music band”, “pedagogic technology” and “interpretation” are revealed. Based on author’s experience, the technological dimensions in the activity of student’s folk music band are shown. These dimensions are aimed at performance of musical compositions during collective interactions. According to this problem the four main stages are market out, such as familiarization with a musical composition, technical mastering, realization of artistic content and music performance. It is stressed, that performance interpretation covers all possibilities of musician. Thus for the sake of realization owns ideas it is necessary to have professional skills. Among them we point out creative thinking, performance skills, including ensemble mastering, possibilities, specifically good ear for music, and psychological stability, i.e. the high level of self-regulation during public performances. The main part of the article shows theoretical grounds, methodological recommendation and practical advice as for formation of collective thinking and common masterly performance. It is stressed, that necessary condition of good activity of student group is implementation of methods and tasks directed at development of creative individuality of every folk music band member, improvement of masterly performance and increasing of psychological stability during the performances. It is pointed out that team work during permanent cooperation creates band performance style and makes the conditions for generalization of masterly performance and development of creative potential, intuition and artistic feeling
Development of a sampling protocol for the resource definition of sulphidic Cu-Zn-Pb tailings in an industrial tailings storage facility

&amp;lt;p&amp;gt;Fine-grained residues of ore processing, known as tailings, are an inevitable product of metal production. Such tailings are typically stored in dedicated Tailings Storage Facilities (TSF). The sedimentary-style deposition of tailings within the TSF results in a structure of sub-horizontal, internally graded layers which heterogeneously concentrate the minerals comprising the residues. Primary depositional structures may be overprinted by subsequent chemical redistribution of minerals and elements during chemical reactions and metal mobilisation. Sulphidic tailings are problematic in terms of the potential for generation of Acid and Metalliferous Drainage, while providing interesting prospects for extraction of recoverable metals. However, efforts to build accurate and reproducible geospatial models of TSFs are hampered by a lack of understanding of how to sample heterogeneous tailings materials in a way that allows the effective characterisation of both the horizontal and vertical variability. This study introduces a sampling protocol for the resource characterisation of TSFs, following the Theory of Sampling. The Davidschacht TSF in Freiberg, Germany, was used as a case study. The Davidschacht TSF was deposited between 1944 and 1969; it contains around 760,000 m&amp;lt;sup&amp;gt;3 &amp;lt;/sup&amp;gt;of Cu-Zn-Pb sulphidic flotation residues originating from the processing of polymetallic hydrothermal vein ores of the Freiberg mining district. A historical drilling campaign of 10 drill holes through the whole depth of the tailings provided a basis for the study. A second drilling campaign of 68 drill holes to a depth of 3 m was carried out on a 30 m grid, and nested grids of 15 m and 7.5 m in the centre of the TSF. The drill cores were logged and a bulk sample was collected for each 1 m section. Representative samples, with 10% randomly selected for duplication, will be analysed with X-Ray Fluorescence for chemical composition and sieving and laser diffraction for particle size distribution. The modal mineralogy, mineral associations and mineral liberation of selected samples will be assessed with the Scanning Electron Microscope-based Mineral Liberation Analyser. A detailed geospatial model of the surface zone of the tailings will be constructed to assess the intrinsic horizontal variability of the TSF. Comparison with the 3D model produced by the previous deep drilling campaign will determine if the sampling and modelling was sufficient to account for the variability of the tailings.&amp;lt;/p&amp;gt;
Comment on ‘SFVCC: Chaotic map‐based security framework for vehicular cloud computing’

This comment is presented to identify the drawbacks in a recently published scheme SFVCC by Mishra et al. doi:10.1049/iet-its.2019.0250. In this scheme, a malicious adversary may initiate a replay attack and denial of service attack after eavesdropping the communication. These attacks render the scheme inapplicable for practical deployment.
Chaotic Evolution Algorithm with Multiple Chaotic Systems

We introduce and discuss the methodology of the chaotic evolution (CE) algorithms, which is supported by a chaotic system. Because properties of the chaotic systems are the essential factors to influence the performance of the CE algorithms, we design and analyse several CE algorithms using different chaotic systems: logistic map, tent map, Gauss map and Henon map in a well-designed chaotic evolution framework. We propose a new CE algorithm using the combination of these chaotic systems to accelerate the convergence speed and improve convergence performance and analyse its effectiveness by comparing these new algorithms with our previous proposed CE algorithm with the logistic map. From the evaluation, our proposed CE helps the search to avoid the local optimum.
IMPROVING WRITING SKILL OF RECOUNT TEXT THROUGH GIVING CHAIN AND QUESTIONS TECHNIQUES IN SMAN 3 TEBING TINGGI

This classroom action research aims to improve students' skills in writing recounts using chain drawing techniques and several questions.The subjects of this study were students X IIS.1 SMA 3 TEBING TINGGI 2017/2018 lessons, amounting to 22 people consisting of 11 men and 11 women.In collecting data, recount writing and observation or observation tests are conducted.All data are collected and analyzed. In analyzing the results of the tests using a system scale the rating criteria are referent values. The results that the researchers obtained from this study were qualitative data and quantitative data.Qualitative data shows that students are very active and fluent in writing recounts, this can be seen by the increase in student activity in doing their own writing recount activities in the first cycle and second cycle, and students smoothly answer questions based on the images set out in recount essays.Whereas from quantitative data, it can be seen that there is an increase in the value of students from the level or level of information written on their recount.The number of students who get A in the first cycle is 1 meeting (1 person), 2nd meeting (2 people), and 3rd meeting (4 people). On the second cycle of the 1st meeting (9 people), 2nd meeting (17 people) and 3rd meeting (22 people). Students who get B score in cycle 1, meeting 1 (4 people), meeting 2 (4 people) and meeting 3 (12 people). On cycle 2, meeting 1st (6 people), 2nd meeting (5 people) and no B value at 3rd meeting. This means giving serial images and several questions, so that the writing skills of students X IIS.1 of SMA Negeri 3 Tebing Tinggi can be improved.
Recent Trends and its Effect in Incomplete Spinal Cord Injury Loco-Motor Rehabilitation andndash; A Review

Objective: The purpose of this study is to find out the current trends and its effects in locomotor rehabilitation of individuals with incomplete spinal cord.Methods: Articles were searched through PubMed and Google scholar (year 2016-2020) written in English literature was performed regarding recent advances in the rehabilitation technologies of incomplete SCI patients.Results: Results of this research were according to the based on the clinical findings: Exoskeleton-assisted Gait Training (EGT), Virtual Reality (VR), Robot-Assisted Locomotor Training (RALT), Home based Virtual Reality, Dynamic weight shifting into treadmill, are currently being used.Conclusion: As per the study, there has been advancement in rehabilitation technologies and a significant improvement was noticed in incomplete spinal cord injury patients. More clinical trials and further study is needed for better improvement.
HYPA: Efficient Detection of Path Anomalies in Time Series Data on Networks.

The unsupervised detection of anomalies in time series data has important applications in user behavioral modeling, fraud detection, and cybersecurity. Anomaly detection has, in fact, been extensively studied in categorical sequences. However, we often have access to time series data that represent paths through networks. Examples include transaction sequences in financial networks, click streams of users in networks of cross-referenced documents, or travel itineraries in transportation networks. To reliably detect anomalies, we must account for the fact that such data contain a large number of independent observations of paths constrained by a graph topology. Moreover, the heterogeneity of real systems rules out frequency-based anomaly detection techniques, which do not account for highly skewed edge and degree statistics. To address this problem, we introduce HYPA, a novel framework for the unsupervised detection of anomalies in large corpora of variable-length temporal paths in a graph. HYPA provides an efficient analytical method to detect paths with anomalous frequencies that result from nodes being traversed in unexpected chronological order.
Aplikasi Penerimaan Siswa Baru (PSB) Berbasis Web Pada SDIT Yasir Cipondoh

SDIT Yasir is an Islamic elementary school located in Cipondoh, Tangerang. The Web-Based New Student Admission was created to make prospective students easier to register the school and to manage data student easily for school. Users of this application are prospective students and the registration part at SDIT Yasir. There are 5 menus available on this application, namely registration, check email, payment, payment verification and change payment data.
Watch and Learn: Mapping Language and Noisy Real-world Videos with Self-supervision

In this paper, we teach machines to understand visuals and natural language by learning the mapping between sentences and noisy video snippets without explicit annotations. Firstly, we define a self-supervised learning framework that captures the cross-modal information. A novel adversarial learning module is then introduced to explicitly handle the noises in the natural videos, where the subtitle sentences are not guaranteed to be strongly corresponded to the video snippets. For training and evaluation, we contribute a new dataset `ApartmenTour' that contains a large number of online videos and subtitles. We carry out experiments on the bidirectional retrieval tasks between sentences and videos, and the results demonstrate that our proposed model achieves the state-of-the-art performance on both retrieval tasks and exceeds several strong baselines. The dataset can be downloaded at https://github.com/zyj-13/WAL.