Reviewerly/exHarmony huggingface dataset:
A Comparison of Metric Learning Loss Functions for End-To-End Speaker Verification

Despite the growing popularity of metric learningapproaches, very little work has attempted to perform a fair comparison of these techniques for speaker verification. We try to fill this gap and compare several metric learning loss functions in a systematic manner on the VoxCeleb dataset. The first family of loss functions is derived from the cross entropy loss (usually used for supervised classification) and includes the congenerous cosine loss, the additive angular margin loss, and the center loss. The second family of loss functions focuses on the similarity between training samples and includes the contrastive loss and the triplet loss. We show that the additive angular margin loss function outperforms all other loss functions in the study, while learning more robust representations. Based on a combination of SincNet trainable features and the x-vector architecture, the network used in this paper brings us a step closer to a truly end-to-end speaker verification system, when combined with the additive angular margin loss, while still being competitive with the x-vector baseline. In the spirit of reproducible research, we also release open source Python code for reproducing our results, and share pretrained PyTorch models on torch.hub that can be used either directly or after fine-tuning.
VerbNet based Citation Sentiment Class Assignment using Machine Learning

Citations are used to establish a link between articles.This intent has changed over the years, and citations are now being used as a criterion for evaluating the research work or the author and has become one of the most important criteria for granting rewards or incentives.As a result, many unethical activities related to the use of citations have emerged.That is why content-based citation sentiment analysis techniques are developed on the hypothesis that all citations are not equal.There are several pieces of research to find the sentiment of a citation, however, only a handful of techniques that have used citation sentences for this purpose.In this research, we have proposed a verb-oriented citation sentiment classification for researchers by semantically analyzing verbs within a citation text using VerbNet Ontology, natural language processing & four different machine learning algorithms.Our proposed methodology emphasizes the verb as a fundamental element of opinion.By developing and assessing the proposed methodology and according to benchmark results, the methodology can perform well while dealing with a variety of datasets.The technique has shown promising results using Support Vector Classifier.
IMPLEMENTATION OF MOBILE-BASED INTERACTIVE LEARNING MEDIA CASE STUDY : CLASS XI HYDROCARBONS MATERIAL

The Covid-19 pandemic has had a huge impact on all countries in the world. Indonesia has not been spared the effects of this pandemic. The educational aspect was also affected. The teaching and learning process that was originally face to face has now changed and must be done online (online). The internet is the spearhead of the teaching and learning process. However, not all regions in Indonesia can access the internet, so we need an interactive learning media that can be run without using the internet (offline). One of them is by making mobile-based interactive learning media offline. In digitizing subject matter into software, it must be done gradually. This study focuses on the implementation of interactive learning media with class XI chemistry materials, namely hydrocarbons. testing is carried out based on functional testing obtained based on the results of interviews with chemists. the results of functional testing (Blackbox Testing) get 100% results based on the function of the interactive learning media that has been made.
Kemampuan Berpikir Kreatif Matematis dengan Menggunakan Education Game Berbantuan Android pada Barisan dan Deret

AbstrakPermasalahan yang melatarbelakangi penelitian ini yaitu karena masih rendahnya kemampuan berpikir kreatif matematis siswa. Tujuan penelitian ini untuk mengetahui apakah bantuan media pembelajaran education game yang juga sebagai alternatif solusi permasalahan dapat meningkatkan kemampuan berpikir kreatif matematis siswa. Kuasi eksperimen dengan desain Nonequivalent group pretest-postest design menjadi metode penelitian ini. Instrumen tes berbentuk essai tentang literasi matematis, berupa pretest dan posttest. Seluruh siswa kelas XI MAN 1 Cianjur pada tahun ajaran 2019/2020 menjadi populasinya, sedangkan sampelnya dipilih melalui teknik purposive sampling yaitu kelas XI MIPA 4 sebanyak 25 siswa sebagai kelas eksperimen yang diberikan perlakuan dengan menggunakan education game berbantuan android dan XI MIPA 3 sebanyak 25 siswa sebagai kelas kontrol dengan pembelajaran biasa. Hasilnya, peningkatan kemampuan berpikir kreatif matematis siswa dengan education game berbantuan android lebih baik dibandingkan dengan peningkatan pada siswa dengan pembelajaran biasa. Mathematical Creative Thinking Ability Using Education Game Android Assisted on Sequence and SeriesAbstractThis study aims to determine whether the help of education game learning media which is also an alternative solution to problems can improve students' mathematical creative thinking abilities. The research method used was a quasi-experimental design with a Nonequivalent group pretest-posttest design. The instruments are in the form of a mathematical literacy test in the form of a pre-test and a post-test. The population of this research was all students of class XI MAN 1 Cianjur in the academic year 2019/2020 which were spread out in eleven classes, while the sample was selected by using two purposive sampling techniques, namely class XI MIPA 4 as many as 25 students as an experimental class who were treated using media assistance learning educational games and Mathematics and Natural Sciences 3 as many as 25 students as a control class with ordinary learning. The result is the increase in students' mathematical creative thinking abilities with educational game android assisted is better than the increase in students with ordinary learning.
NONLINEAR PROOF-OF-WORK: IMPROVING THE ENERGY EFFICIENCY OF BITCOIN MINING

Bitcoin is probably the most well-known blockchain system in existence. It employs the proof-of-work (PoW) consensus algorithm to add transactions to the blockchain. This process is better known as Bitcoin mining. PoW requires miners to compete in solving a cryptographic puzzle before being allowed to add a block of transactions to the blockchain. This mining process is energy-intensive and results in high energy wastage. The underlying cause of this energy inefficiency is the result of the current implementation of the PoW algorithm. PoW assigns the same cryptographic puzzle to all miners, creating a linear probability of success between the miner’s computational power as a proportion of the total computational power of the network. To address this energy inefficiency of the PoW mining process, the researchers investigated whether a nonlinear probability of success, between the miner’s computation power and its probability of success, will result in better energy usage. A nonlinear proof-of-work (nlPoW) algorithm was constructed by using a design science approach to derive the requirements for and structure of the algorithm. The Bitcoin mining process was tested through statistical simulation, comparing the performance of nlPoW with PoW. Preliminary results, simulating a network of 1000 miners with identical computational power, indicate that nlPoW reduce the number of hash computations, and therefore the energy consumption, required by Bitcoin mining. The findings are significant because nlPoW does not reduce the degree of decentralised consensus, or trade energy usage for some other resource as is the case with many other attempts to address the energy consumption problem in PoW.
ANALISIS SENTIMEN PEMINDAHAN IBU KOTA NEGARA DENGAN KLASIFIKASI NAÏVE BAYES UNTUK MODEL BERNOULLI DAN MULTINOMIAL

Text mining is a variation on a field called data mining that tries to find interesting patterns from large databases. Indonesian President affirmed that the capital would be moved to East Kalimantan on August 26, 2019. That planning would receive pros and cons from public. Sentiment analysis is part of text mining that typically involves taking data from opinion, comment, or response. Sentiment analysis is the choice to do on this topic to get results about the public’s opinion. As the most used social media in Indonesia, Youtube is able to be data source by crawling the comments on a video uploaded by Kompas TV channel. Those comments were crawled on October 15, 2019, and selected 1500 latest comments (August 26 – October 12, 2019). The selected comments get transformed by using data pre-processing technique that involves case folding, removing mention, unescaping HTML, removing numbers, removing punctuation, text normalization, stripping whitespace, stopwords removal, tokenizing, and stemming. Labeling of sentiment class uses the sentiment scoring technique. The number of negative comments is 849, while the number of positive comments is 651. The ratio between training data and testing data is 80%: 20%. The classification method used to do sentiment analysis is the Naive Bayes Classifier for Bernoulli and Multinomial model. Bernoulli model only uses occurrence information, whereas the multinomial model keeps track of multiple occurrences. The results show that Bernoulli Naïve Bayes has a 93,45% level of sensitivity (recall) and Multinomial Naïve Bayes has a 90,19% level of sensitivity (recall). It means that both Bernoulli and Multinomial have a good result for this research. Keywords: Text Mining, Relocation of Indonesia’s Capital, Youtube, Bernoulli Naïve Bayes, Multinomial Naïve Bayes, Sensitivity (Recall).
Going Beyond T-SNE: Exposing whatlies in Text Embeddings

We introduce whatlies, an open source toolkit for visually inspecting word and sentence embeddings.The project offers a unified and extensible API with current support for a range of popular embedding backends including spaCy, tfhub, huggingface transformers, gensim, fastText and BytePair embeddings.The package combines a domain specific language for vector arithmetic with visualisation tools that make exploring word embeddings more intuitive and concise.It offers support for many popular dimensionality reduction techniques as well as many interactive visualisations that can either be statically exported or shared via Jupyter notebooks.
Technologies of influencing the audience in the modern media space

The article is devoted to the analysis of the peculiarities of mass media’s impact on an audience in a situation of intensive development of social, informational, cognitive technologies and their mutual influence on each other. In this process, special techniques are used for the production, processing, and dissemination of the information in the media space, which are described through such theoretical constructs as imprinting, priming, storytelling, framing and design. They include a specific set of manipulative techniques as a means of controlling communication processes in the modern information society in the context of social engineering development. At the same time, along with multimedia systems, preattentive ones, which are ergonomic information systems created in the style of communication eco-design, are increasingly being used. They are functionally oriented to the decision-making process by the user almost at a subconscious level by focusing his attention on the necessary information in the most appropriate place and time. The purpose of the article is to identify the cognitive andperceptual characteristics of media influence on a mass audience. The research tasks include the formulation of the basic principles of theoretical, scientific, and practical developments of social engineering as a special branch of knowledge, as well as identifying the features of pre-attentive technologies used in modern media in addition to explicating the contents of the concepts of imprinting, priming, storytelling, framing, and design as the main techniques ofmedia exposure. Based on the conceptual and comparative analysis of scientific publications devoted to this topic, it is concluded that the preattentive aspects of the cognitive processes of perception and understanding of information by the mass audience today become the main tool for creating effective media work. In this situation, the media actively use the capabilities of multimedia technologies to apply the basic principles of communication eco-design in theprocess of generating an information product.
Answering Binary Causal Questions: A Transfer Learning Based Approach

Causal question answering is a task of answering causality related questions. The questions are referred to as binary causal questions when the questions e.g., "Could X cause Y?" can be answered by yes/no answers. Answer to the previous question is yes if X is a cause of Y, and otherwise no. The binary causal question answering systems can be used to validate causal relationships, which can be particularly useful for decision making. For example, it could be useful for the tourism authorities to know the answer to the question "Could growing social tension cause reduction in tourism?". We aim to automatically answer such binary causal questions by developing a machine learning model. However, training a machine learning model to detect causal relationships is challenging due to the lack of large and high quality labeled datasets. In this paper, we propose a transfer learning-based approach which fine-tunes pretrained transformer based language models on a small dataset of cause-effect pairs to detect causality and answer binary causal questions. The proposed approach achieves performance comparable to a number of benchmark approaches on five benchmark test datasets extracted by human experts conditioned on the same small training dataset.
Environmental Adaptation and Differential Replication in Machine Learning

When deployed in the wild, machine learning models are usually confronted with an environment that imposes severe constraints. As this environment evolves, so do these constraints. As a result, the feasible set of solutions for the considered need is prone to change in time. We refer to this problem as that of environmental adaptation. In this paper, we formalize environmental adaptation and discuss how it differs from other problems in the literature. We propose solutions based on differential replication, a technique where the knowledge acquired by the deployed models is reused in specific ways to train more suitable future generations. We discuss different mechanisms to implement differential replications in practice, depending on the considered level of knowledge. Finally, we present seven examples where the problem of environmental adaptation can be solved through differential replication in real-life applications.
Secure Data Uploading and Accessing Sensitive Data Using Time Level Locked Encryption to Provide an Efficient Cloud Framework

Executing cloud computing participate from multiple points of view for Web-based overseeing commitments to mark various issues.Here the assurance and data security has considered into a significant issue with downside that limits numerous applications that identified with cloud.These days cloud computing has been generally perceived as one of the most compelling data advances in view of its phenomenal focal points.Regardless of its generally perceived social and monetary advantages, in cloud computing clients lose the immediate control of their information and totally depend on the cloud to deal with their information and calculation, which raises huge security and protection concerns and is one of the significant obstructions to the appropriation of open cloud by numerous associations and people.In the proposed work a Time Level Locked Encryption (TLLE) framework is designed for deduplication of huge documents to accomplish space-proficient capacity in cloud and also to monitor data uploading and data access.The proposed model is compared with the traditional AES encryption model and the results show that the proposed model is exhibiting better performance.
Using Wearable Sensors for Human Activity Recognition in Logistics: A Comparison of Different Feature Sets and Machine Learning Algorithms

The topic of human activity recognition has gained a lot of attention due to its usage for exercise monitoring, smart health and assisted living.Even though the aforementioned domains have received significant interest by researchers, activity recognition for industrial settings has received little attention in comparison.Industry 4.0 involves the assimilation of industrial workers with robots and other equipment used in the industry and necessitates the development of recognition methodologies for activities being performed in industries.In this regard, this paper presents a comparison in performance of various time/frequency domain features and popular machine learning algorithms for use in activity recognition in a logistics scenario.Experiments were conducted on inertial measurement sensor data from the recently released LARa dataset which involved three feature sets being used with four machine learning algorithms; Support Vector Machines, Decision Trees, Random Forests and Extreme Gradient Boost (XGBoost).The best result achieved in the experiments was an average accuracy of 78.61% using the XGBoost classifier while using both time and frequency domain features.This work serves as a baseline for activity recognition in logistics using IMU sensors and enables the development of solutions to support fulfillment of Industry 4.0 goals.
Multi-Label Learning with Deep Forest

In multi-label learning, each instance is associated with multiple labels and the crucial task is how to leverage label correlations in building models. Deep neural network methods usually jointly embed the feature and label information into a latent space to exploit label correlations. However, the success of these methods highly depends on the precise choice of model depth. Deep forest is a recent deep learning framework based on tree model ensembles, which does not rely on backpropagation. We consider the advantages of deep forest models are very appropriate for solving multi-label problems. Therefore we design the Multi-Label Deep Forest (MLDF) method with two mechanisms: measure-aware feature reuse and measure-aware layer growth. The measure-aware feature reuse mechanism reuses the good representation in the previous layer guided by confidence. The measure-aware layer growth mechanism ensures MLDF gradually increase the model complexity by performance measure. MLDF handles two challenging problems at the same time: one is restricting the model complexity to ease the overfitting issue; another is optimizing the performance measure on user's demand since there are many different measures in the multi-label evaluation. Experiments show that our proposal not only beats the compared methods over six measures on benchmark datasets but also enjoys label correlation discovery and other desired properties in multi-label learning.
Modified pqsigRM: RM Code-Based Signature Scheme

We present a novel code-based signature scheme called modified pqsigRM. This scheme is based on a modified Reed-Muller (RM) code, which reduces the signing complexity and key size compared with existing code-based signature schemes. In fact, it strengthens pqsigRM submitted to NIST for post-quantum cryptography standardization. The proposed scheme has the advantage of the pqsigRM decoder and uses public codes that are more difficult to distinguish from random codes. We use (U, U + V)-codes with the high-dimensional hull to overcome the disadvantages of code-based schemes. The proposed decoder samples from coset elements with small Hamming weight for any given syndrome and efficiently finds such an element. Using a modified RM code, the proposed signature scheme resists various known attacks on RM-code-based cryptography. For 128 bits of classical security, the signature size is 4096 bits, and the public key size is less than 1 MB.
PERFORMANCE COMPARISON OF MACHINE LEARNING ALGORITHMS FOR PREDICTIVE MAINTENANCE

The consequences of failures and unscheduled maintenance are the reasons why engineers have been trying to increase the reliability of industrial equipment for years. In modern solutions, predictive maintenance is a frequently used method. It allows to forecast failures and alert about their possibility. This paper presents a summary of the machine learning algorithms that can be used in predictive maintenance and comparison of their performance. The analysis was made on the basis of data set from Microsoft Azure AI Gallery. The paper presents a comprehensive approach to the issue including feature engineering, preprocessing, dimensionality reduction techniques, as well as tuning of model parameters in order to obtain the highest possible performance. The conducted research allowed to conclude that in the analysed case , the best algorithm achieved 99.92% accuracy out of over 122 thousand test data records. In conclusion, predictive maintenance based on machine learning represents the future of machine reliability in industry.
Chinese fingerspelling recognition via gray-level co-occurrence matrix and fuzzy support vector machine

INTRODUCTION: Chinese deaf-mutes communicate in their native language, Chinese sign language which contains gesture language and finger language.Chinese finger language conveys information through various movements of fingers, and its expression is accurate and convenient for classification and recognition.OBJECTIVES: In this paper, we proposed a new model using gray-level co-occurrence matrix (GLCM) and fuzzy support vector machine (FSVM) to improve sign language recognition accuracy.METHODS: Firstly, we acquired the sign language images directly by a digital camera or selected key frames from the video as the data set, meanwhile, we segmented the hand shapes from the background.Secondly, we adjusted the size of each images to N×N and then switched them into gray-level images.Thirdly, we reduced the dimension of the intensity values by using the Principal Component Analysis (PCA) and acquired the data features by creating the gray-level co-occurrence matrix.Finally, we sent the extracted and reduced dimensionality features to Fuzzy Support Vector Machine (FSVM) to conduct the classification tests.RESULTS: Moreover, we compared it with similar algorithms, and the result shows that our method performs the highest classification accuracy which is up to 86.7%. CONCLUSION:The experiment result displays that our model performs well in Chinese finger language recognition and has potential for further research.