Reviewerly/exHarmony huggingface dataset:
Influence of conditional holoentropy-based feature selection on automatic recommendation system in E-commerce sector

This paper presents a new contribution to the recommendation system following E-commerce reviews. Here, it implements a dictionary-based sentiment analysis based on four main steps (i) Pre-processing (ii) Semantic word extraction (iii) Feature Extraction (iv) Classification. Initially, the tweets or reviews extracted from the database are subjected to pre-processing, which involves three processes: stop word removal, stemming, and blank space removal. In the semantic word extraction process, the semantic words from the dictionary will be extracted by matching with the extracted keywords. The next process is the feature extraction, which extracts joint holoentropy and cross holoentropy of all keywords, and further proceeds with conditional holoentropy-based feature selection process. Finally, the selected features are subjected to a classification process using Deep Belief Network (DBN), which is a well-performing deep learning algorithm. Moreover, the main contribution of the paper relies on the improvement of DBN architecture, where the number of hidden neurons and activation function is optimally tuned by a hybrid optimization algorithm termed as Grey linked Chick Update-based Chicken Swarm Optimization (GCU-CSO). At last, the comparative analysis of proposed and conventional algorithms in terms of various performance metrics proves the efficacy of the proposed E-commerce recommendation system.
Annotating Topics, Stance, Argumentativeness and Claims in Dutch Social Media Comments: A Pilot Study

One of the major challenges currently facing the field of argumentation mining is the lack ofconsensus on how to analyse argumentative user-generated texts such as online comments. Thetheoretical motivations underlying the annotation guidelines used to generate labelled corporararely include motivation for the use of a particular theoretical basis. This pilot study reports onthe annotation of a corpus of 100 Dutch user comments made in response to politically-themednews articles on Facebook. The annotation covers topic and aspect labelling, stance labelling, argumentativeness detection and claim identification. Our IAA study reports substantial agreementscores for argumentativeness detection (0.76 Fleiss’ kappa) and moderate agreement for claimlabelling (0.45 Fleiss’ kappa). We provide a clear justification of the theories and definitionsunderlying the design of our guidelines. Our analysis of the annotations signal the importance ofadjusting our guidelines to include allowances for missing context information and defining theconcept of argumentativeness in connection with stance. Our annotated corpus and associatedguidelines are made publicly available.
Parsing Address Texts with Deep Learning Method

The variability of address writing forms makes it difficult to process free-text postal addresses. Since postal addresses have consecutive patterns, sequential probabilistic models such as CRF or rule-based methods are often used in the address parsing processes. On the other hand, since these methods are very sensitive to typographical errors, they fail to detect detailed patterns and require a serious expert knowledge of address writing to develop rules. It is possible to identify hidden address classes without requiring pre-processing and expert knowledge by using deep learning tools that provides successful results in solving many similar problems. In this study, a one-dimensional version of the CNN networks has been redesigned and applied for address parsing problem. According to the results obtained, 96% accuracy was obtained for the labeled data set consisting of close to 20.000 samples. The network architecture we have proposed in this study has a scalable nature that does not require any pre-post processing stages.
LTG-ST at NADI Shared Task 1: Arabic Dialect Identification using a Stacking Classifier

This paper presents our results for the Nuanced Arabic Dialect Identification (NADI) shared task of the Fifth Workshop for Arabic Natural Language Processing (WANLP 2020). We participated in the first sub-task for country-level Arabic dialect identification covering 21 Arab countries. Our contribution is based on a stacking classifier using Multinomial Naive Bayes, Linear SVC, and Logistic Regression classifiers as estimators; followed by a Logistic Regression as final estimator. Despite the fact that the results on the test set were low, with a macro F1 of 17.71, we were able to show that a simple approach can achieve comparable results to more sophisticated solutions. Moreover, the insights of our error analysis, and of the corpus content in general, can be used to develop and improve future systems.
Semi-infinite Programming for Trajectory Optimization with Nonconvex Obstacles

This paper presents a novel optimization method that handles collision constraints with complex, non-convex 3D geometries. The optimization problem is cast as a semi-infinite program in which each collision constraint is implicitly treated as an infinite number of numeric constraints. The approach progressively generates some of these constraints for inclusion in a finite nonlinear program. Constraint generation uses an oracle to detect points of deepest penetration, and this oracle is implemented efficiently via signed distance field (SDF) versus point cloud collision detection. This approach is applied to pose optimization and trajectory optimization for both free-flying rigid bodies and articulated robots. Experiments demonstrate performance improvements compared to optimizers that handle only convex polyhedra, and demonstrate efficient collision avoidance between nonconvex CAD models and point clouds.
HW-TSC’s Participation in the WMT 2023 Automatic Post Editing Shared Task

The paper presents the submission by HW-TSC in the WMT 2020 Automatic Post Editing Shared Task. We participate in the English-German and English-Chinese language pairs. Our system is built based on the Transformer pre-trained on WMT 2019 and WMT 2020 News Translation corpora, and fine-tuned on the APE corpus. Bottleneck Adapter Layers are integrated into the model to prevent over-fitting. We further collect external translations as the augmented MT candidates to improve the performance. The experiment demonstrates that pre-trained NMT models are effective when fine-tuning with the APE corpus of a limited size, and the performance can be further improved with external MT augmentation. Our system achieves competitive results on both directions in the final evaluation.
Concept Learners for Few-Shot Learning

Developing algorithms that are able to generalize to a novel task given only a few labeled examples represents a fundamental challenge in closing the gap between machine- and human-level performance. The core of human cognition lies in the structured, reusable concepts that help us to rapidly adapt to new tasks and provide reasoning behind our decisions. However, existing meta-learning methods learn complex representations across prior labeled tasks without imposing any structure on the learned representations. Here we propose COMET, a meta-learning method that improves generalization ability by learning to learn along human-interpretable concept dimensions. Instead of learning a joint unstructured metric space, COMET learns mappings of high-level concepts into semi-structured metric spaces, and effectively combines the outputs of independent concept learners. We evaluate our model on few-shot tasks from diverse domains, including fine-grained image classification, document categorization and cell type annotation on a novel dataset from a biological domain developed in our work. COMET significantly outperforms strong meta-learning baselines, achieving 6-15% relative improvement on the most challenging 1-shot learning tasks, while unlike existing methods providing interpretations behind the model's predictions.
Determining the Composition Functions of Persian Non-standard Sentences in Terminology using a Deep Learning Fuzzy Neural Network Model

Organizations can enhance the speed of well-informed decision-making by correctly understanding and using data. Since there is a tremendous gap between the speed of data processing and data generation in the world, exploring data mining in the digital world becomes inevitable. In the Persian language, similar to other languages, with the expansion of communications through social networks, the spelling of words has become abridged and the engagement of foreign loan words and emoticons has been increasing on a daily basis. Given the richness of Persian and its typographical-grammatical similarities to Arabic, research in Persian can be applied to other akin languages as well.  In this regard, the current study deals with data mining of Persian non-standard sentences in order to find the function of each word in the sentence. The volume of computation might be limited in traditional methods of natural language processing for each factor contributing to functions. That is because the minimum number of computations is (5 × number of words 9) + (5 × number of words 15). Therefore, this study adopted the Gated Recurrent Unit (GRU) method to process such computations. The newly proposed method reinforces the results of word function identification by using two categories of independent and dependent Persian language functions as well as five factors contributing to the functions of words in sentences as five output gates. Meanwhile, the values of the training tables in this method are fuzzy, where the center-of-gravity method is adopted to decide on the values as well as to reduce the complexity and ambiguity of such computations on the probability of each event occurring. Therefore, the new method is briefly called fuzzy GRU. The results show that the proposed algorithm achieves 80 % reduction in the amount of calculations per gate of updates and reinforcement is approximately 2 % up from 67 % in standard sentences to 69 % of the non-standard sentences.
MODALS: Modality-agnostic Automated Data Augmentation in the Latent Space

Data augmentation is an efficient way to expand a training dataset by creating additional artificial data. While data augmentation is found to be effective in improving the generalization capabilities of models for various machine learning tasks, the underlying augmentation methods are usually manually designed and carefully evaluated for each data modality separately, like image processing functions for image data and word-replacing rules for text data. In this work, we propose an automated data augmentation approach called MODALS (Modality-agnostic Automated Data Augmentation in the Latent Space) to augment data for any modality in a generic way. MODALS exploits automated data augmentation to fine-tune four universal data transformation operations in the latent space to adapt the transform to data of different modalities. Through comprehensive experiments, we demonstrate the effectiveness of MODALS on multiple datasets for text, tabular, time-series and image modalities.
Intelligent Voice Assistant

Now-a-days voice assistants are making people’s day-to-day routine simpler and it saves time as well. But, even after voice assistants are easy to operate, there are many people who cannot use voice assistant even if they wanted to. The main reason behind this issue is language barrier. People living in urban India are using voice assistants on daily basis. But, people living in rural areas are not fluent in English even to start a conversation with any voice assistant. Hence, paper proposes of the unique system based on the initiative to improve connectivity between the people living in rural India and the world. The core idea behind this initiative is to create an intelligent voice assistant system that can interact with people in their local language so that the system can help them to make their day to day life simpler. The system is designed while taking the rural life in consideration as well as their basic needs. This paper also tends to explore some new possibilities.
Cultural and Educational Practices in the Museum Environment: Transmission of Cultural Heritage

In the age of digitalisation and globalisation, one of the essential tasks addressed at the level of cultural policy, having relevance for all generations, consists in the preservation of cultural heritage. Cultural and educational practices, integrated in the preschool-, school- and higher education environments and aimed at the formation of the worldview and identity of the younger generation, are considered by the authors as effective and relevant mechanisms for transmitting the memory of values, meanings, places, cultural artefacts, etc. Therefore, it seems advisable for the organisers of multi-level projects to address the potential of the museum as a cultural institute. Today, museums are oriented towards a wide variety of visitors, including professionals and creative audiences of all ages, in the presented activities and services. The transmission of cultural memory in the museum environment is implemented not only in traditional ways, but also through contemporary information and media technologies. The introduction of the younger generation to cultural heritage provides them with an opportunity to experience significant values and meanings of the cultural space and time of the city, region, or country at the personal level.
Blockchain Technology: Introduction, Integration, and Security Issues with IoT

Blockchain (BC) was mainly introduced for secure transactions in connection with the mining of cryptocurrency bitcoin (BTC). This chapter discusses the fundamental concepts of BC technology and its components, such as block header, transaction, smart contracts (SCs), etc. BC uses the distributed databases, so this chapter also explains the advantages of distributed BC over a centrally located database. Depending on the application, BC is broadly categorized into two categories; permissionless and permissioned. This chapter elaborates on these two categories as well. Further, it covers the consensus mechanism, and it is working along with an overview of the Ethereum (ETH) platform. BC technology has been proved to be one of the remarkable techniques to provide security to IoT devices. An illustration of how BC will be useful for IoT devices has been given. A few applications are also illustrated to explain the working of BC with IoT.
Amas de casa como storytellers y anunciantes de Instagram

The aim of this study was to analyze how housewives with personal qualities similar to the microinfluencer Maya de la Borda (@mayasportsandfamily) build an advertising storytelling through Instagram. A qualitative case study was the approach selected to undertake the study. The instagrammer Maya de la Borda, a 46-year-old peruvian housewife, was chosen as a case study, because she exposes her life daily to an audience of 7809 followers through this social network. Semi-structured interviews were conducted to address the research questions. Through the snowball sampling technique, ten modern housewives, from 38 to 51 years old, who are all active users of Instagram, were contacted to participate. The interviews were analyzed using a thematic analysis to identify themes, categories, concepts and meaning. In view of the findings, for the housewives to create an effective advertising storytelling on Instagram is to communicate authenticity in the message content along with a format and experiences where the audience can learn and feel like they are part of the narrative.
Learning-based Support Estimation in Sublinear Time

We consider the problem of estimating the number of distinct elements in a large data set (or, equivalently, the support size of the distribution induced by the data set) from a random sample of its elements. The problem occurs in many applications, including biology, genomics, computer systems and linguistics. A line of research spanning the last decade resulted in algorithms that estimate the support up to ±en from a sample of size O(log2⁡(1/e)⋅n/log⁡n), where n is the data set size. Unfortunately, this bound is known to be tight, limiting further improvements to the complexity of this problem. In this paper we consider estimation algorithms augmented with a machine-learning-based predictor that, given any element, returns an estimation of its frequency. We show that if the predictor is correct up to a constant approximation factor, then the sample complexity can be reduced significantly, to  log⁡(1/e)⋅n1−Θ(1/log⁡(1/e)). We evaluate the proposed algorithms on a collection of data sets, using the neural-network based estimators from {Hsu et al, ICLR'19} as predictors. Our experiments demonstrate substantial (up to 3x) improvements in the estimation accuracy compared to the state of the art algorithm.
Collaborative Filtering Based on a Variational Gaussian Mixture Model

Collaborative filtering (CF) is a widely used method in recommendation systems. Linear models are still the mainstream of collaborative filtering research methods, but non-linear probabilistic models are beyond the limit of linear model capacity. For example, variational autoencoders (VAEs) have been extensively used in CF, and have achieved excellent results. Aiming at the problem of the prior distribution for the latent codes of VAEs in traditional CF is too simple, which makes the implicit variable representations of users and items too poor. This paper proposes a variational autoencoder that uses a Gaussian mixture model for latent factors distribution for CF, GVAE-CF. On this basis, an optimization function suitable for GVAE-CF is proposed. In our experimental evaluation, we show that the recommendation performance of GVAE-CF outperforms the previously proposed VAE-based models on several popular benchmark datasets in terms of recall and normalized discounted cumulative gain (NDCG), thus proving the effectiveness of the algorithm.
Efficient Implementation of SHA-3 Hash Function on 8-Bit AVR-Based Sensor Nodes

The Keccak algorithm was selected by NIST as the standard SHA-3 hash algorithm for replacing currently used SHA-2 algorithm in 2015. Despite SHA-3’s improved security compared to SHA-2, its low performance in software implementation limits its wide use. In this paper, we propose an optimized SHA-3 implementation on 8-bit AVR microcontrollers (MCU) which are dominantly used for sensor devices in WSNs. Until now, there are only a few researches on optimization of SHA-3 in spite of its security importance. Furthermore, it is very challenging to optimize hash function, especially, SHA-3, on 8-bit AVR MCUs. This is because the internal state of SHA-3 is 1,600-bit which is much larger than internal state of symmetric algorithms (typically, 128-bit) like AES, ARIA, and so on. In other words, it is difficult to accommodate the whole of SHA-3’s internal state on the registers of AVR MCUs, which incurs heavy memory accesses during computation. Thus, we analyzed the structure of SHA-3 algorithm and found that each lane of the internal state can be executed independently for each process in SHA-3. By using this fact, we propose an optimization method which can reduce efficiently the times of memory accesses to the internal state. With this proposed method minimizing the memory accesses, our implementation of SHA3-256 achieves around 25.0% of performance improvement when hashing 500 bytes message compared with the previous best work on 8-bit AVR MCU. To the best of our knowledge, our software is the fastest SHA-3 implementation on AVR platforms until now. In addition, the proposed optimization method can be easily extended to other embedded MCUs such as 16-bit MSP430, 32-bit RISC-V and ARM-based MCUs.
Recording of Student Attendance with Blockchain Technology to Avoid Fake Presence Data in Teaching Learning Process

University operational activities are a routine part of university operations and supervisory control and monitoring function.The low controlling and monitoring of operational activities can cause irrelevance in the teaching and learning process.A graduate may have a graduation document but has never attended the teaching and learning process.An official institution can issue this graduation document, but it is fake because no teachinglearning activity occurred.It happens because the data is easily being manipulated and changed in the current system.From the problem, this is what drives this research to be carried out.With the characteristics of distributed, secure, and traceable information, Blockchain will solve this problem.Based on a previous study, blockchain technology facilitates university operational activities so that it will solve the current problems.This research uses qualitative research methods.The research process starts with literature studies and forum group discussions conducted on nine universities in Indonesia (public and private universities).This study used the User-Centered Design Technique.This research focuses on the user, so the results possibly are applied.The research results prove that Blockchain technology can record student attendance as part of the graduation process's teaching and learning process.Blockchain's immutable, unchangeable, and distributed characteristics will ensure the student attendance record's validity in the teaching and learning process.
State-of-the-Art of Artificial Intelligence

Artificial Intelligence (AI) as a technology has existed for less than a century. In spite of this, it has managed to achieve great strides. The rapid progress made in this field has aroused the curiosity of many technologists around the globe and many companies across various domains are curious to explore its potential. For a field that has achieved so much in such a short duration, it is imperative that people who aim to work in Artificial Intelligence, study its origins, recent developments, and future possibilities of expansion to gain a better insight into the field. This paper encapsulates the notable progress made in Artificial Intelligence starting from its conceptualization to its current state and future possibilities, in various fields. It covers concepts like a Turing machine, Turing test, historical developments in Artificial Intelligence, expert systems, big data, robotics, current developments in Artificial Intelligence across various fields, and future possibilities of exploration.
Optimal Huffman Coding Performance of Ad-Hoc Networks Based on Cross-Layer Design.

Effective data compression and sustainable management of limited bandwidth are the fundamental challenges in the design of current ad-hoc wireless networks. Dynamic Huffman coding protocol with optimal prefix-free codewords assigned to network parameters through cross-layer methodology can considerably address these issues for performance enhancement in these networks. In this paper, the entropy based Huffman binary and ternary coding schemes are implemented with cross-layer design in mobile ad-hoc networks. This cross-layer approach for achieving higher adaptivity incorporated four layers of the traditional networking stack. The simulation results demonstrated the performance tradeoff between throughput and coding metrics for the proposed cross-layer architecture. Moreover, our proposed model exhibits substantially higher compression ratio and coding efficiency than the other existing methods through the integration of this lossless coding protocol in the developed cross-layer framework.