Reviewerly/exHarmony huggingface dataset:
Dynamical Wasserstein Barycenters for Time-series Modeling

Many time series can be modeled as a sequence of segments representing high-level discrete states, such as running and walking in a human activity application. Flexible models should describe the system state and observations in stationary pure-state periods as well as transition periods between adjacent segments, such as a gradual slowdown between running and walking. However, most prior work assumes instantaneous transitions between pure discrete states. We propose a dynamical Wasserstein barycentric (DWB) model that estimates the system state over time as well as the data-generating distributions of pure states in an unsupervised manner. Our model assumes each pure state generates data from a multivariate normal distribution, and characterizes transitions between states via displacement-interpolation specified by the Wasserstein barycenter. The system state is represented by a barycentric weight vector which evolves over time via a random walk on the simplex. Parameter learning leverages the natural Riemannian geometry of Gaussian distributions under the Wasserstein distance, which leads to improved convergence speeds. Experiments on several human activity datasets show that our proposed DWB model accurately learns the generating distribution of pure states while improving state estimation for transition periods compared to the commonly used linear interpolation mixture models.
Associative Memories via Predictive Coding

Associative memories in the brain receive and store patterns of activity registered by the sensory neurons, and are able to retrieve them when necessary. Due to their importance in human intelligence, computational models of associative memories have been developed for several decades now. In this paper, we present a novel neural model for realizing associative memories, which is based on a hierarchical generative network that receives external stimuli via sensory neurons. It is trained using predictive coding, an error-based learning algorithm inspired by informationprocessing in the cortex. To test the model’s capabilities, we perform multiple retrieval experiments from both corrupted and incomplete data points. In an extensive comparison, we show that this new model outperforms in retrieval accuracy and robustness popular associative memory models, such as autoencoders trained via backpropagation, and modern Hopfield networks. In particular, in completing partial data points, our model achieves remarkable results on natural image datasets, such as ImageNet, with a surprisingly high accuracy, even when only a tiny fractionof pixels of the original images is presented. Our model provides a plausible framework to study learning and retrieval of memories in the brain, as it closely mimics the behavior of the hippocampus as a memory index and generative model.
Building a New Semantic Social Network Using Semantic Web-Based Techniques

Most people are more or less related to the web by participating in a kind of social networking site. Semantic Web technology plays a crucial role in these sites as they contain an enormous amount of data about ‎persons, pages, events, places, corporations, etc. This research is a Semantic Web application designed to create a new ‎semantic social community called Socialpedia. It links the already existing social public information to the newly ‎public ones. This information is linked with different information on the web to construct a new immense ‎data container. The resulting data container can be processed using a variety of Semantic Web techniques to produce ‎machine-understandable content. This content shows the promise of using integrated data to improve Web search and ‎Web-scale data analysis, unlike conventional search engines or social ones. This community involves obtaining data ‎from traditional users known as contributors or participants, linking data from existing social networks, extracting ‎structured data in triples using predefined ontologies, and finally querying and inferring such data to obtain ‎meaningful pieces of information. Socailpedia supports all popular functionalities of social networking websites ‎besides the enhanced features of the Semantic Web, providing advanced semantic search that acts as a semantic ‎search engine.
Experience in Teaching Mathematical Disciplines Using E-learning and Distance Learning Technologies during the COVID-19 Pandemic

The experience of e-learning (EL) with distance learning technologies (DLT) implementing for teaching of Numerical in the Mathematics and Computer Science department of V.I. Vernadsky Crimean Federal University during COVID-19 pandemic period from March to May 2020 is studied in the article. 107 students from third course that learn in the direction of 01.03.01 Mathematics, 01.03.02 Applied Mathematics and Computer science, 01.03.04 Applied Mathematics are participated in the experiment. As the main platform, the social network VKontakte (VK) with Discord, Youtube and HP Reveal platforms are used. The author's video lecture is the main type of educational content in the developed e-learning model. The students feedback is realized through discussion in the text and voice chats, video presentations of programming code for numerical methods realization, the tasks results demonstration in hand-written form (photo of problems decision) or in print format, in screenshots or video explanations including elements of the augmented reality technologies. The effectiveness of the developed methodological approach is confirmed by the results of students questionnaires (81,7% of the students that participate in the anonymous questionnaire estimate the quality of the numerical methods teaching as excellent) and the academic performance growth based on the tests results using the peer review method (the growth of the academic performance on average is 32,2%). It is confirmed that for e-learning organizing for mathematical disciplines teaching in the educational process planning, training contents and tests preparing the teachers need to use the interactive learning and multimodality principles, as well as web accessibility requirements. The best method of new learning content presentation is the author's screencast video lecture (the choice of 83,7% students) accompanied with subtitles and detailed explanations of the lecturer (audio descriptions) and text analogue (the lecture notes or verbatim report). The new e-learning model can be transferred to the MOOC format without significant logical changes or time costs.
ПРИМЕНЕНИЕ АНАЛИЗА РЫНОЧНОЙ КОРЗИНЫ ДЛЯ ОПРЕДЕЛЕНИЯ МОДЕЛИ ПОКУПОК КЛИЕНТОВ

Market Basket Analysis (MBA) is an approach that finds the strength of association between pairs of products that customers buy and can determine patterns of co-occurrence. The main aim of MBA is to determine customer buying behavior and predict next purchase. It can help companies to increase cross-selling. To generate association rules, the Apriori algorithm employs frequently purchased item-sets. It is based on the idea that a frequently purchased item’s subset is also a frequently purchased item. If the support value of a frequently purchased item-set exceeds a minimum threshold, the item-set is chosen. This paper observes the advantages of implementing MBA, algorithms that applies in this technique and ways to identify customer buying patterns.
Training Neural Networks is ER-complete

Given a neural network, training data, and a threshold, it was known that it is NP-hard to find weights for the neural network such that the total error is below the threshold. We determine the algorithmic complexity of this fundamental problem precisely, by showing that it is ER-complete. This means that the problem is equivalent, up to polynomial-time reductions, to deciding whether a system of polynomial equations and inequalities with integer coefficients and real unknowns has a solution. If, as widely expected, ER is strictly larger than NP, our work implies that the problem of training neural networks is not even in NP.
Causal Navigation by Continuous-time Neural Networks

Imitation learning enables high-fidelity, vision-based learning of policies within rich, photorealistic environments. However, such techniques often rely on traditional discrete-time neural models and face difficulties in generalizing to domain shifts by failing to account for the causal relationships between the agent and the environment. In this paper, we propose a theoretical and experimental framework for learning causal representations using continuous-time neural networks, specifically over their discrete-time counterparts. We evaluate our method in the context of visual-control learning of drones over a series of complex tasks, ranging from short- and long-term navigation, to chasing static and dynamic objects through photorealistic environments. Our results demonstrate that causal continuous-time deep models can perform robust navigation tasks, where advanced recurrent models fail. These models learn complex causal control representations directly from raw visual inputs and scale to solve a variety of tasks using imitation learning.
TEACHING LISTENING BY USING JIGSAW TECHNIQUE FOR SECOND GRADE STUDENTS AT SMK PGRI 2 KEDIRI

The aim of study is to describe the teaching listening by using jigsaw technique for second grade students at SMK PGRI 2 Kediri. Those are including of: the teacherâ€™s preparation in teaching listening by using jigsaw, the teacherâ€™s steps of teaching listening process, the teacherâ€™s media, the teacherâ€™s evaluation, the studentsâ€™ response, the problem faced by the teacher, the result of studentsâ€™ listening score, the teacherâ€™s educational background and the teacherâ€™s experience in teaching listening by using jigsaw technique.This research used descriptive qualitative approach as a research design. The subject was 46 students of the second level of Office Administration 2 at SMK PGRI 2 Kediri. The methods used in collecting data were observation, interview, and documentation. The data analysis was based on three concurrent flows of activities, they were: data reduction, data display, and verification. The result of this study showed that the teacher had good preparation before starting teaching listening. The steps of teaching listening were appropriate with the principles of implementing jigsaw. The teacherâ€™s media were audio box, flashdisk, Win the Day book, picture and card. The teacher assessed the students not only in written test but also in performance test. There were two kinds of score, team score and individual score. The studentsâ€™ average of listening score was 8.12,it was in good category. he teacher is a multi-talented teacher, he is a teacher at SMK PGRI 2 and SMK N 2 Kediri, he is a lecture at Wakhidiyah University, he is also a coach of English Debate. While teaching listening he got difficulty while monitoring, controlling the students in the class and he got problem in managing time while the process of teaching listening.
Integrated Building Management System

This Paper present an Integrated Building Management System and details about Intrusion Detection System, Surveillance and Access Control System. When one genuinely considers about alarm systems, one usually talks about high-tech piece of equipment created during the silicon era. The reality is that even the alarm system principle was created considerably earlier than we thought. Humanity always depended on animals, especially dogs, for countless generations to defend and alert people against anybody intending to meddle with or steal his property. During the Middle Ages, enormous gongs or gigantic bonfires are being used to alert a community of impending tragedy or incoming invasion. Despite the importance of these discoveries, it wasn't until 1852 that the very first electromechanical warning system was developed. Edwin Holmes was a Boston, Massachusetts-based inventor., who created a simple yet efficient alarm system. When a trip wire was disrupted, a solenoid struck a gong. Despite the fact that his alarm system pales in comparison to today's options, it was well accepted at the time.
Global Unified Security Certification System and Standard Evolution for 5G Security

为了介绍5G安全的全球统一认证体系和标准演进,首先论述5G网络安全的全球统一框架(即GSMA NESAS/3GPPSCAS 5G安全认证框架),接着论述5G安全的标准演进,包括5G基础网络安全、5G云化安全、5G MEC安全以及几个5G典型的垂直行业安全,然后论述5G安全相对于公有云、Wi-Fi的不同和专业性要求,最后对于5G安全的未来的技术发展趋势给出了研究展望,包括5G内生安全、零信任、后量子安全、MEC安全、隐私保护以及安全责任共建模型.
Desmitificando al gamer: una aproximación ontológica desde el pensamiento crítico ante un cambio de época

Objective: Crystallize a critical and reflective body with respect to the myth that for more than three decades has deteriorated the image of the gamer before the human, social and administrative sciences. Method: It is considered of the documentary type with bibliographic design, where the technique of digital ethnographic observation is applied for the analysis and interpretation of contents. Results and discussion: It was possible to reflect on the labor reality for which the gamers, analyzing the way in which they respond to different dialectical scenarios and the impact that Information and Communication Technologies have had on human nature, where opportunities for behavioral transformation are generated organizational organization towards gamification. Conclusions: From an ontological approach, the gamer phenomenon is proposed as an element of strategic interest for contemporary organizations, since in its professional practice, it exposes qualities that could favor the processes taking place in that context.
UPAYA PENINGKATAN KETERAMPILAN MENULIS KARANGAN SEDERHANA MELALUI GAMBAR SERI PADA SISWA KELAS III SD NEGERI TUMBANG BARINGEI 1 DENGAN MEMANFAATKAN ALAT PERAGA PEMBELAJARAN TAHUN PELAJARAN 2018 / 2019

Teaching Indonesian in elementary schools focuses on language skills. As we all know, language skills are divided into 4 aspects, namely listening, reading, writing, and speaking. Although each has its own characteristics and peculiarities, the four aspects of language skills have an integral relationship and cannot be separated.Writing, which is one aspect of language, seems to be an obstacle for most elementary school children. Whereas writing skills for elementary school children should be given from an early age. Theory and writing techniques should have been introduced and applied since the lower grades. To anticipate the difficulties experi-enced by students in developing writing skills, on this occasion, I will conduct a class action research, as a fol-low-up in improving learning. Based on the results of data collection and study of pedagogic theories contained in research activities, it turns out that the use of media or teaching aids in learning becomes the most influential element in helping optimize students' thinking. It can be seen from the results of the study which showed that the ability of students to express their ideas in the form of simple essays increased further after the improvement of learning activities by diversifying learning media.
Multilingual Code-Switching for Zero-Shot Cross-Lingual Intent Prediction and Slot Filling

Predicting user intent and detecting the corresponding slots from text are two key problems in Natural Language Understanding (NLU). Since annotated datasets are only available for a handful of languages, our work focuses particularly on a zero-shot scenario where the target language is unseen during training. In the context of zero-shot learning, this task is typically approached using representations from pre-trained multilingual language models such as mBERT or by fine-tuning on data automatically translated into the target language. We propose a novel method which augments monolingual source data using multilingual code-switching via random translations, to enhance generalizability of large multilingual language models when fine-tuning them for downstream tasks. Experiments on the MultiATIS++ benchmark show that our method leads to an average improvement of +4.2% in accuracy for the intent task and +1.8% in F1 for the slot-filling task over the state-of-the-art across 8 typologically diverse languages. We also study the impact of code-switching into different families of languages on downstream performance. Furthermore, we present an application of our method for crisis informatics using a new human-annotated tweet dataset of slot filling in English and Haitian Creole, collected during the Haiti earthquake.
Pelatihan Public Speaking untuk Meningkatkan Kepercayaan Diri Siswa SMAN 1 Bebandem

Public speaking skills is the skills that persue people to design words and beautiful sentences which is able to attract people's attention. Therefore, the purpose of this activity is to increase the knowledge of students at SMAN 1 Bebandem in  public speaking. This activity focuses on providing socialization about the importance of understanding and implementing public speaking to increase self-confidence, improve self- quality, and be able to think critically. The methods used in this activity are lecture, simulation, and practice methods. The participants were actively involved in this activity, seen from the enthusiasm they had during the practice. Significant changes in students' confidence in public speaking could be seen in the pre-test results and the post-test data. In addition, the significant changes  could be seen from the enthusiasm of the participants in the activity by asking and discussing.
Open Aspect Target Sentiment Classification with Natural Language Prompts.

For many business applications, we often seek to analyze sentiments associated with any arbitrary aspects of commercial products, despite having a very limited amount of labels or even without any labels at all. However, existing aspect target sentiment classification (ATSC) models are not trainable if annotated datasets are not available. Even with labeled data, they fall short of reaching satisfactory performance. To address this, we propose simple approaches that better solve ATSC with natural language prompts, enabling the task under zero-shot cases and enhancing supervised settings, especially for few-shot cases. Under the few-shot setting for SemEval 2014 Task 4 laptop domain, our method of reformulating ATSC as an NLI task outperforms supervised SOTA approaches by up to 24.13 accuracy points and 33.14 macro F1 points. Moreover, we demonstrate that our prompts could handle implicitly stated aspects as well: our models reach about 77% accuracy on detecting sentiments for aspect categories (e.g., food), which do not necessarily appear within the text, even though we trained the models only with explicitly mentioned aspect terms (e.g., fajitas) from just 16 reviews - while the accuracy of the no-prompt baseline is only around 65%.
When is Wall a Pared and when a Muro?: Extracting Rules Governing Lexical Selection.

Learning fine-grained distinctions between vocabulary items is a key challenge in learning a new language. For example, the noun “wall” has different lexical manifestations in Spanish – “pared” refers to an indoor wall while “muro” refers to an outside wall. However, this variety of lexical distinction may not be obvious to non-native learners unless the distinction is explained in such a way. In this work, we present a method for automatically identifying fine-grained lexical distinctions, and extracting rules explaining these distinctions in a human- and machine-readable format. We confirm the quality of these extracted rules in a language learning setup for two languages, Spanish and Greek, where we use the rules to teach non-native speakers when to translate a given ambiguous word into its different possible translations.
The Effect of Organizational Culture Dimensions on Organizational Performance Variables: A Study of Selected IT Companies at Hyderabad

This study examined the impact of organizational culture dimensions on organizational performance metrics such as Sales growth, Client Satisfaction and Market Share. Organizational cultureplays an indispensable role to be considered by various dynamic organizations to sustain competitive advantage for exaggerating the organizational performance. The main aim of the present study is to investigate the relationship among the organizational culture dimensions and organizational performance metrics in the Software (IT) companies which are operating in state capital region of Telangana.
The Role of Informal Learning in Employee Innovative Work Behaviour: Mediating Role of Transformational Leadership

Long lasting learning has turned into a need for individuals’ employability and a wellspring of feasible competitive gain for organizations. Learning does not just happen through formal education but informal learning can be getting via informally. This crosssectional research demonstrated the connectionamong informal learning and employee innovative work behaviour mediated by transformational leadership. Previous studies had been done over the effect of informal learning on various outcome variables, but this study investigated to reduce the gap that employee innovative work behaviour could be confirmed by taking transformational leadership as mediating variable. A quantitative research method in shape of questionnaire survey was used to collect data from all the administrative staff (below bps-17) in four public universities of khyber pakhtunkhwa. A standardized questionnaire (adopted) was used for the collection of the required data. The gathered data was analysed through spss software to examine the mediating effect between informal learning and employee innovative work behaviour. The study found that transformational leadership has partially correlated with information learning and employee innovative work behaviour. The area for future is given at the end of the study.
Understanding and Overcoming the Challenges of Efficient Transformer Quantization.

Transformer-based architectures have become the de-facto standard models for a wide range of Natural Language Processing tasks. However, their memory footprint and high latency are prohibitive for efficient deployment and inference on resource-limited devices. In this work, we explore quantization for transformers. We show that transformers have unique quantization challenges – namely, high dynamic activation ranges that are difficult to represent with a low bit fixed-point format. We establish that these activations contain structured outliers in the residual connections that encourage specific attention patterns, such as attending to the special separator token. To combat these challenges, we present three solutions based on post-training quantization and quantization-aware training, each with a different set of compromises for accuracy, model size, and ease of use. In particular, we introduce a novel quantization scheme – per-embedding-group quantization. We demonstrate the effectiveness of our methods on the GLUE benchmark using BERT, establishing state-of-the-art results for post-training quantization. Finally, we show that transformer weights and embeddings can be quantized to ultra-low bit-widths, leading to significant memory savings with a minimum accuracy loss. Our source code is available at https://github.com/qualcomm-ai-research/transformer-quantization.
The relationship among job enthusiasm, resilience and job performance of life sport instructor

This study was to investigate the structural relationship model among job enthusiasm, resilience and job performance of life sport instructor. To prevent the spread of COVID-19, a total 215 date collected on-line of life sport instructor in Seoul, Kyuggi and Dae-jeon prvince. The frequency analysis, confirmatory factor analysis, reliability analysis, correlation analysis and structural equating modeling were analyzed using SPSS 18.0 and AMOS 18.0 programs for Windows. The results of this study are follows: 1) The job enthusiasm of life sport instructor had influence on resilience. 2) The resilience of life sport instructor had influence on job performance. 3) The job enthusiasm of life sport instructor had no influence on job performance. 4) The resilience mediated the relationship between job enthusiasm and job performance in life sport instructor.