Reviewerly/exHarmony huggingface dataset:
Distributed Graph Embedding with Information-Oriented Random Walks

Graph embedding maps graph nodes to low-dimensional vectors, and is widely adopted in machine learning tasks. The increasing availability of billion-edge graphs underscores the importance of learning efficient and effective embeddings on large graphs, such as link prediction on Twitter with over one billion edges. Most existing graph embedding methods fall short of reaching high data scalability. In this paper, we present a general-purpose, distributed, information-centric random walk-based graph embedding framework, DistGER, which can scale to embed billion-edge graphs. DistGER incrementally computes information-centric random walks. It further leverages a multi-proximity-aware, streaming, parallel graph partitioning strategy, simultaneously achieving high local partition quality and excellent workload balancing across machines. DistGER also improves the distributed Skip-Gram learning model to generate node embeddings by optimizing the access locality, CPU throughput, and synchronization efficiency. Experiments on real-world graphs demonstrate that compared to state-of-the-art distributed graph embedding frameworks, including KnightKing, DistDGL, and Pytorch-BigGraph, DistGER exhibits 2.33x-129x acceleration, 45% reduction in cross-machines communication, and > 10% effectiveness improvement in downstream tasks.
Obstacle Avoidance in Dynamic Environments via Tunnel-following MPC with Adaptive Guiding Vector Fields

This paper proposes a motion control scheme for robots operating in a dynamic environment with concave obstacles. A Model Predictive Controller (MPC) is constructed to drive the robot towards a goal position while ensuring collision avoidance without direct use of obstacle information in the optimization problem. This is achieved by guaranteeing tracking performance of an appropriately designed receding horizon path. The path is computed using a guiding vector field defined in a subspace of the free workspace where each point in the subspace satisfies a criteria for minimum distance to all obstacles. The effectiveness of the control scheme is illustrated by means of simulation.
SimpleNet: A Simple Network for Image Anomaly Detection and Localization

We propose a simple and application-friendly network (called SimpleNet) for detecting and localizing anomalies. SimpleNet consists of four components: (1) a pre-trained Feature Extractor that generates local features, (2) a shallow Feature Adapter that transfers local features towards target domain, (3) a simple Anomaly Feature Generator that counterfeits anomaly features by adding Gaussian noise to normal features, and (4) a binary Anomaly Discriminator that distinguishes anomaly features from normal features. During inference, the Anomaly Feature Generator would be discarded. Our approach is based on three intuitions. First, transforming pre-trained features to target-oriented features helps avoid domain bias. Second, generating synthetic anomalies in feature space is more effective, as defects may not have much commonality in the image space. Third, a simple discriminator is much efficient and practical. In spite of simplicity, SimpleNet outperforms previous methods quantitatively and qualitatively. On the MVTec AD benchmark, SimpleNet achieves an anomaly detection AUROC of 99.6%, reducing the error by 55.5% compared to the next best performing model. Furthermore, SimpleNet is faster than existing methods, with a high frame rate of 77 FPS on a 3080ti GPU. Additionally, SimpleNet demonstrates significant improvements in performance on the One-Class Novelty Detection task. Code: https://github.com/DonaldRR/SimpleNet.
ACO-tagger: A Novel Method for Part-of-Speech Tagging using Ant Colony Optimization

Swarm Intelligence algorithms have gained significant attention in recent years as a means of solving complex and non-deterministic problems. These algorithms are inspired by the collective behavior of natural creatures, and they simulate this behavior to develop intelligent agents for computational tasks. One such algorithm is Ant Colony Optimization (ACO), which is inspired by the foraging behavior of ants and their pheromone laying mechanism. ACO is used for solving difficult problems that are discrete and combinatorial in nature. Part-of-Speech (POS) tagging is a fundamental task in natural language processing that aims to assign a part-of-speech role to each word in a sentence. In this research paper, proposed a high-performance POS-tagging method based on ACO called ACO-tagger. This method achieved a high accuracy rate of 96.867%, outperforming several state-of-the-art methods. The proposed method is fast and efficient, making it a viable option for practical applications.
Regularity of center-outward distribution functions in non-convex domains

For a probability P in $R^d$ its center outward distribution function $F_{\pm}$, introduced in Chernozhukov et al. (2017) and Hallin et al. (2021), is a new and successful concept of multivariate distribution function based on mass transportation theory. This work proves, for a probability P with density locally bounded away from zero and infinity in its support, the continuity of the center-outward map on the interior of the support of P and the continuity of its inverse, the quantile, $Q_{\pm}$. This relaxes the convexity assumption in del Barrio et al. (2020). Some important consequences of this continuity are Glivenko-Cantelli type theorems and characterisation of weak convergence by the stability of the center-outward map.
Weighted Multi-view Clustering Based on Internal Evaluation

As real-world data are often represented by multiple sets of features in different views, it is desirable to improve clustering results with respect to ordinary single-view clustering by making use of the consensus and complementarity among different views. For this purpose, weighted multi-view clustering is proposed to combine multiple individual views into one single combined view, which is used to generate the final clustering result. In this paper we present a simple yet effective weighted multi-view clustering algorithm based on internal evaluation of clustering results. Observing that an internal evaluation criterion can be used to estimate the quality of clustering results, we propose to weight different views to maximize the clustering quality in the combined view. We firstly introduce an implementation of the Dunn index and a heuristic method to determine the scale parameter in spectral clustering. Then an adaptive weight initialization and updating method is proposed to improve the clustering results iteratively. Finally we do spectral clustering in the combined view to generate the clustering result. In experiments with several publicly available image and text datasets, our algorithm compares favorably or comparably with some other algorithms.
Overview of Transfer Learning Algorithms

This chapter gives an overview of transfer learning algorithms so that readers can learn and understand detailed algorithms in other chapters with a thorough view. To facilitate such an understanding, we establish a unified representation framework through which most of existing methods can be derived. Then, other chapters will introduce more details on each kind of algorithms. We want to emphasize that you are encouraged to do such overview when learning new materials.
Iterative Prompt Learning for Unsupervised Backlit Image Enhancement

We propose a novel unsupervised backlit image enhancement method, abbreviated as CLIP-LIT, by exploring the potential of Contrastive Language-Image Pre-Training (CLIP) for pixel-level image enhancement. We show that the open-world CLIP prior not only aids in distinguishing between backlit and well-lit images, but also in perceiving heterogeneous regions with different luminance, facilitating the optimization of the enhancement network. Unlike high-level and image manipulation tasks, directly applying CLIP to enhancement tasks is non-trivial, owing to the difficulty in finding accurate prompts. To solve this issue, we devise a prompt learning framework that first learns an initial prompt pair by constraining the text-image similarity between the prompt (negative/positive sample) and the corresponding image (backlit image/well-lit image) in the CLIP latent space. Then, we train the enhancement network based on the text-image similarity between the enhanced result and the initial prompt pair. To further improve the accuracy of the initial prompt pair, we iteratively fine-tune the prompt learning framework to reduce the distribution gaps between the backlit images, enhanced results, and well-lit images via rank learning, boosting the enhancement performance. Our method alternates between updating the prompt learning framework and enhancement network until visually pleasing results are achieved. Extensive experiments demonstrate that our method outperforms state-of-the-art methods in terms of visual quality and generalization ability, without requiring any paired data.
Hate speech detection on twitter using machine learning techniques

People can now create, post, and share content to connect with each other as social media platforms have grown in popularity. On the other hand, it has also become a forum for hatred and war. The rampant spread of hatred on social media has had a significant impact on society, dividing people into pros and cons on topics that govern the status of a person, place, community, and country. Hate speech on social media is difficult to recognize because messages contain paralinguistic signs, jumbled language, and poorly written content. Due to the lack of consensus on what constitutes hate speech and the lack of background information, it becomes more difficult to detect. Creating huge markup corpora with lots of relevant contexts is a difficult task. Even though scientists have found that hate is a problem on all social media platforms, there is no perfect method to detect accurately. The current state and complexity of the field, as well as the main algorithms, methodologies, and key characteristics used, are described in this paper. It has focused on the important areas that have been explored for hate speech detection and also applied machine learning algorithms to detect it.
CBDCs and the More Proactive Roles of International Organizations

With the rise of cryptocurrency, along with the downward trend of physical currency usage, which was especiallyaccelerated during the COVID-19 pandemic, many national governments are exploring the idea of implementingcentral bank digital currencies (CBDCs). While CBDCs are like cryptocurrencies in that they are digital, theiroverarching concept is different. They are issued by a country’s central bank or monetary authority undergovernment control, so they are centralized and equal in value to the nation’s fiat currency. This article aims toprovide a succinct summary of what CBDC implementation could mean for nations and their citizens, with a focuson the possible routes in and impacts on developing countries in light of the potential that CBDCs could providethem. Section 1 explores the rise of CBDCs, namely, the recent surge in developing nations’ interest in themworldwide. Section 2 discusses the benefits of CBDCs, partitioning the potential upsides into four majorpoints—greater accessibility, easier domestic and cross-border transactions, cost-efficient management, and moretransparent foreign aid—with a focus on developing countries. Section 3 provides a list of the challengesaccompanying CBDC implementation, such as an exhaustive complex framework, questionable financial stability,and technological infrastructure. Section 4 presents the roles that international organizations, such as the IMF andthe BIS, could play in the entire process writ large, ranging from setting international standards, intervening duringfinancial crises, and providing infrastructural support to incentivizing CBDC adoption. Section 5 concludes thearticle.
Implementation of Data Collection and Payment Control Systems Automatically Using Android -Based QR Code

The wider spread of the use of smartphones is also very helpful for UMKM not only in terms of marketing or sales, but daily operations. On the Senyum Manis cafe the owner of the business owner conducts data on stocks of goods, materials, and prices for drinks manually. When doing the process of collecting the goods manually there is an error in stock calculation so that it takes a long time to check the stock of the item. With this innovation using the application of QR Code which can be used as a label in every stock of goods and beverage materials. In this case the implementation of the RFID tag is also used to get the customer queue number. From this researcher the system is considered successful because the data input can appear in the application , QOS (Quality of Service) test results when uploading data get a 33K throughput value that belongs to the medium category, Packet Loss produces a value of 0.097% which is classified in a very good category, average delay produces a value of 87.4 s which is classified in a very good category . This aims to anticipate changes that will occur, the review is useful to see the condition of whether the system is in accordance with the needs of a sweet smile cafe and whether it is necessary to add or change the existing system in order to keep up with development technology.
BENTİK FORAMİNİFER GÖRÜNTÜ SINIFLAMASI VE TANIMLAMALARINDA EVRİŞİMLİ SİNİR AĞI (CNN) TABANLI YENİ BİR MODEL

Fossil studies are of great importance in order to observe the change of living species over the years, to make inferences by using the information provided by the observed species, and to understand the developing and changing structure of the world we live in over the years. However, the examination and interpretation of fossil specimens is a complex and long process. Artificial intelligence studies have begun to be applied to this field in order to facilitate the working methods of paleontologists. The detection and classification of fossil specimens with the aid of computers simplifies this process as much as possible compared to manual classification processes and reduces foreign dependency for fossil assemblages for which paleontologists are not experts. To achieve this, 9 benthic foraminiferal species and non-foraminiferal sample photographs from a selected dataset were used. In this study, a new method developed for the classification of benthic foraminifera using deep convolutional neural networks, reaching higher accuracy than the results in the literature, is presented. With this method, at least 70% accuracy rates were achieved in the test results of the trained system. This study, which reached high accuracy rates with a new method, has created a successful development for the branch of paleontology in the use of artificial intelligence in microfossil identification.
Comparison of Various Machine Learning Algorithms on Color Quantization Techniques

Color image quantization is used to lower the range of colors of an image in an effort to decrease the size of the image and trying to keep the quantized image visibly similar to the input image. Color quantization is performed to reduce the color information while trying to maintain the quality. The technique is used in various fields like computer graphics and image processing. In this paper, comparison of various algorithms is carried out to calculate the mean absolute error and mean squared error and determine the accuracy for each algorithm. The algorithms used in this paper are K-means, median cut, Lloyd max and fast octree.
Research and Application of Block Chain Technology in Electricity Market Transactions

With the entry of large-scale commercial power users into the market, the electricity trading retail market faces a surge in the number of users, agency relationships, and retail-side settlement. All these pose huge challenges to the support capability of the retail side business of power transactions, the system’s ability to process concurrently with big data, and the compliance and electronization of business processes. block chain technology has technical characteristics such as anti-tampering and node sharing, which can well solve the difficulties and pain points in the process of electricity retail transactions. The block chain technology is integrated into the research and application of electricity market transactions, and the technology can not be tampered with., high reliability and advantages, and then properly solve the current problems of privacy protection, transaction security and low transaction efficiency, while reducing transmission costs.
Feature Representation in Deep Metric Embeddings

In deep metric learning (DML), high-level input data are represented in a lower-level representation (embedding) space, such that samples from the same class are mapped close together, while samples from disparate classes are mapped further apart. In this lower-level representation, only a single inference sample from each known class is required to discriminate between classes accurately. The features a DML model uses to discriminate between classes and the importance of each feature in the training process are unknown. To investigate this, this study takes embeddings trained to discriminate faces (identities) and uses unsupervised clustering to identify the features involved in facial identity discrimination by examining their representation within the embedded space. This study is split into two cases; intra class sub-discrimination, where attributes that differ between a single identity are considered; such as beards and emotions; and extra class sub-discrimination, where attributes which differ between different identities/people, are considered; such as gender, skin tone and age. In the intra class scenario, the inference process distinguishes common attributes between single identities, achieving 90.0\% and 76.0\% accuracy for beards and glasses, respectively. The system can also perform extra class sub-discrimination with a high accuracy rate, notably 99.3\%, 99.3\% and 94.1\% for gender, skin tone, and age, respectively.
Examining the attitudes of students’ towards using computer assisted language learning in foreign language classrooms

Problem statement . The use of technology determines the level of development of a country. The issue of technology adaptation in the field of education is important to secure technological integration. Due to the success in providing equality of opportunity to the entire population, the use of communication technology in the field of education is becoming more and more widespread in this context. In recent years, the significance of technological integration in the sphere of education has become apparent. A great number of research conducted on the views of students towards integrating computer assisted language learning (CALL) into the language learning classrooms, however, few studies addressing the attitudes of students towards using CALL in the Kazakhstani contexts exist in the field of language teaching. The aim of this study was to examine university level students’ attitudes towards the use of computer assisted language learning in Kazakhstan. Methodology . The quantitative method of research by analyzing the data collected through survey with the help of SPSS Statistics 23.0, as well as questionnaire developed by M. Vandewaetere and P. Desmet to measure attitudes of students towards the use of computer assisted language learning were applied. The participants of the study were 126 EFL pre-service teachers enrolled at Foreign Languages Teaching Department of Khoja Akhmet Yassawi International Kazakh-Turkish University. Results . The participants have overall positive attitudes towards using CALL in the language classroom. The difference between participants’ attitudes towards using CALL according to their age of study at the university was revealed. It was found statistically significant differences between first year and second year students and first year and third year students’ views towards using CALL in language learning. Conclusion . Further research studies might be conducted in the form of mixed design in order to get more detailed information to support or understand the general results found with the help of questionnaire.
Context-based Bengali Next Word Prediction: A Comparative Study of Different Embedding Methods

Next word prediction is a helpful feature for various typing subsystems. It is also convenient to have suggestions while typing to speed up the writing of digital documents. Therefore, researchers over time have been trying to enhance the capability of such a prediction system. Knowledge regarding the inner meaning of the words along with the contextual understanding of the sequence can be helpful in enhancing the next word prediction capability. Theoretically, these reasonings seem to be very promising. With the advancement of Natural Language Processing (NLP), these reasonings are found to be applicable in real scenarios. NLP techniques like Word embedding and sequential contextual modeling can help us to gain insight into these points. Word embedding can capture various relations among the words and explain their inner knowledge. On the other hand, sequence modeling can capture contextual information. In this paper, we figure out which embedding method works better for Bengali next word prediction. The embeddings we have compared are word2vec skip-gram, word2vec CBOW, fastText skip-gram and fastText CBOW. We have applied them in a deep learning sequential model based on LSTM which was trained on a large corpus of Bengali texts. The results reveal some useful insights about the contextual and sequential information gathering that will help to implement a context-based Bengali next word prediction system. DUJASE Vol. 7 (2) 8-15, 2022 (July)
VOID - An Assistant (Chatbot) to Education Queries System

Chatbots are the future of technology and it has become a basic requirement of various industries. Chatbots are designed to interact with human-like humans with the help of AI-based technology. Every industry cannot afford man-power to interact with clients and customers 24×7 so that chatbot has come into play which does the same work with no extra cost. It is a new and emerging technology that is in high demand across a wide range of businesses. It is used to simulate a human like conversation and a machine to relieve the workload. It can be easily deployed in any field where products must be developed and evaluated before being released. During the admission time in college, reception is packed, and people have to wait for their turn, and this entire process takes too much time. Although every college has their own official website, but everyone cannot find the appropriate answer of their query. The objective of this paper is to reduce the workload by developing an interactive environment between a human and a machine that gives a reply to the query of the user on behalf of a human for the education system.
Designing and Evaluating Speech Emotion Recognition Systems: A reality check case study with IEMOCAP

There is an imminent need for guidelines and standard test sets to allow direct and fair comparisons of speech emotion recognition (SER). While resources, such as the Interactive Emotional Dyadic Motion Capture (IEMOCAP) database, have emerged as widely-adopted reference corpora for researchers to develop and test models for SER, published work reveals a wide range of assumptions and variety in its use that challenge reproducibility and generalization. Based on a critical review of the latest advances in SER using IEMOCAP as the use case, our work aims at two contributions: First, using an analysis of the recent literature, including assumptions made and metrics used therein, we provide a set of SER evaluation guidelines. Second, using recent publications with open-sourced implementations, we focus on reproducibility assessment in SER.