Reviewerly/exHarmony huggingface dataset:
Hierarchical Skeleton Meta-Prototype Contrastive Learning with Hard Skeleton Mining for Unsupervised Person Re-Identification

With rapid advancements in depth sensors and deep learning, skeleton-based person re-identification (re-ID) models have recently achieved remarkable progress with many advantages. Most existing solutions learn single-level skeleton features from body joints with the assumption of equal skeleton importance, while they typically lack the ability to exploit more informative skeleton features from various levels such as limb level with more global body patterns. The label dependency of these methods also limits their flexibility in learning more general skeleton representations. This paper proposes a generic unsupervised Hierarchical skeleton Meta-Prototype Contrastive learning (Hi-MPC) approach with Hard Skeleton Mining (HSM) for person re-ID with unlabeled 3D skeletons. Firstly, we construct hierarchical representations of skeletons to model coarse-to-fine body and motion features from the levels of body joints, components, and limbs. Then a hierarchical meta-prototype contrastive learning model is proposed to cluster and contrast the most typical skeleton features ("prototypes") from different-level skeletons. By converting original prototypes into meta-prototypes with multiple homogeneous transformations, we induce the model to learn the inherent consistency of prototypes to capture more effective skeleton features for person re-ID. Furthermore, we devise a hard skeleton mining mechanism to adaptively infer the informative importance of each skeleton, so as to focus on harder skeletons to learn more discriminative skeleton representations. Extensive evaluations on five datasets demonstrate that our approach outperforms a wide variety of state-of-the-art skeleton-based methods. We further show the general applicability of our method to cross-view person re-ID and RGB-based scenarios with estimated skeletons.
Abstractive Meeting Summarization: A Survey

Abstract A system that could reliably identify and sum up the most important points of a conversation would be valuable in a wide variety of real-world contexts, from business meetings to medical consultations to customer service calls. Recent advances in deep learning, and especially the invention of encoder-decoder architectures, has significantly improved language generation systems, opening the door to improved forms of abstractive summarization—a form of summarization particularly well-suited for multi-party conversation. In this paper, we provide an overview of the challenges raised by the task of abstractive meeting summarization and of the data sets, models, and evaluation metrics that have been used to tackle the problems.
Retracted: Methods to Improve the Efficiency of Rural Physical Education Teaching Resources Allocation and Utilization in the Context of Artificial Intelligence

[This retracts the article DOI: 10.1155/2022/3226902.].
MCBeth: A Measurement-based Quantum Programming Language

This work introduces MCBeth, a quantum programming language that bridges the gap between near-term and non-near-term languages. MCBeth allows users to directly program and simulate measurement-based computation by building upon the measurement calculus. While MCBeth programs are meant to be executed directly on hardware, to take advantage of current machines we also provide a compiler to gate-based instruction sets. We argue that MCBeth is more natural to use than common low-level languages, which are based upon the quantum circuit model, but still easily runnable in practice.
Autonomous aerial robotics for package delivery: A technical review

Abstract Small unmanned aerial vehicles (UAVs) have gained significant interest in the last decade. More specifically these vehicles have the capacity to impact package delivery logistics in a disruptive way. This paper reviews research problems and state‐of‐the‐art solutions that facilitate package delivery. Different aerial manipulators and grippers are listed along with control techniques to address stability issues. Landing on a platform is next discussed which encompasses static and dynamic platforms. Landing on a dynamic platform presents further challenges. This includes delayed control responses and poor precision of the relative motion between the platform and the aerial vehicle. Subsequently, risks such as weather conditions, state estimation, and collision avoidance to ensure safe transit is considered. Finally, delivery UAV routing is investigated which categorizes the topic into two areas: drone operations and drone–truck collaborative operations. Additionally, we compare the solutions against design, environmental, and legal constraints.
Classification of Congestion in Jakarta Using KNN, Naïve Bayes and Decision Tree Method

Congestion has now become a problem that occurs in almost all big cities in Indonesia. The problem of traffic jams generally occurs in areas with high intensity of activity and land use. Given the increasing level of congestion that is happening, the capital city of DKI Jakarta is one of the most densely populated cities with high population activity. Population activities are also offset by the use of transportation. Both by public and private vehicles. Traffic jams are one of the problems that are still unsolved. West Palmerah Street is one of the roads with quite a lot of traffic jams. To prove it, he did some simple research. The method used is descriptive method, where the research begins with collecting the data needed at this time through several surveys. And the calculation is done by looking for the degree of saturation (DS) and vehicle speed at three checkpoints, the DS at the pre-market checkpoint is 0.89, the DS at the market checkpoint is 1.05, and the DS at the market checkpoint is 0.89. Then the movement speed was also obtained at the pre-market observation point of 32.05 km/hour, at the market review point of 27.5975 km/hour, and at the post-market observation point of 33.35 km/hour. The results prove that there is indeed a traffic delay in front of the market. This figure is due to the large number of angkots that stop and the narrowing of the traffic lane in front of the market due to the presence of street vendors and motorbikes stopping on the sidewalks with buying and selling activities on the sidewalks. Therefore, it is necessary to apply the best operational solutions to improve traffic flow on these roads.
A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Models

Abstract Generative modeling has seen a rising interest in both classical and quantum machine learning, and it represents a promising candidate to obtain a practical quantum advantage in the near term. In this study, we build over a proposed framework for evaluating the generalization performance of generative models, and we establish the first quantitative comparative race towards practical quantum advantage (PQA) between classical and quantum generative models, namely Quantum Circuit Born Machines (QCBMs), Transformers (TFs), Recurrent Neural Networks (RNNs), Variational Autoencoders (VAEs), and Wasserstein Generative Adversarial Networks (WGANs). After defining four types of PQAs scenarios, we focus on what we refer to as potential PQA, aiming to compare quantum models with the best-known classical algorithms for the task at hand. We let the models race on a well-defined and application-relevant competition setting, where we illustrate and demonstrate our framework on 20 variables (qubits) generative modeling task. Our results suggest that QCBMs are more efficient in the data-limited regime than the other state-of-the-art classical generative models. Such a feature is highly desirable in a wide range of real-world applications where the available data is scarce.
Investigating Impacts of Using Mobile Video Lectures on Student Satisfaction and Academic Achievement in Blended Learning

In this study, a mobile Blackboard blended learning (BL) environment was implemented to teach the introductory computer course by integrating a learning management system with supplementary video lectures into BL provided through mobile technologies. The purpose of the study was to explore the impact of the mobile Blackboard BL environment on student satisfaction and learning achievement. The quantitative collection method was employed through a quasi-experimental design with a pretest and posttest control group. For seven consecutive weeks, the experimental group of students was taught in the mobile Blackboard BL environment and the control group of students was taught in the traditional learning environment. The students were asked to complete the instructional materials motivation survey to gauge their satisfaction level and given an achievement test at the end of the learning process. The study's findings revealed that the satisfaction level was significantly higher in the experimental group of students than in the control group of students. However, there was no statistical difference between students in the experimental and control groups in terms of learning achievement although students in the experimental group obtained slightly better results from the achievement test.
SelfSeg: A Self-supervised Sub-word Segmentation Method for Neural Machine Translation

Sub-word segmentation is an essential pre-processing step for Neural Machine Translation (NMT). Existing work has shown that neural sub-word segmenters are better than Byte-Pair Encoding (BPE), however, they are inefficient as they require parallel corpora, days to train and hours to decode. This paper introduces SelfSeg, a self-supervised neural sub-word segmentation method that is much faster to train/decode and requires only monolingual dictionaries instead of parallel corpora. SelfSeg takes as input a word in the form of a partially masked character sequence, optimizes the word generation probability and generates the segmentation with the maximum posterior probability, which is calculated using a dynamic programming algorithm. The training time of SelfSeg depends on word frequencies, and we explore several word frequency normalization strategies to accelerate the training phase. Additionally, we propose a regularization mechanism that allows the segmenter to generate various segmentations for one word. To show the effectiveness of our approach, we conduct MT experiments in low-, middle- and high-resource scenarios, where we compare the performance of using different segmentation methods. The experimental results demonstrate that on the low-resource ALT dataset, our method achieves more than 1.2 BLEU score improvement compared with BPE and SentencePiece, and a 1.1 score improvement over Dynamic Programming Encoding (DPE) and Vocabulary Learning via Optimal Transport (VOLT) on average. The regularization method achieves approximately a 4.3 BLEU score improvement over BPE and a 1.2 BLEU score improvement over BPE-dropout, the regularized version of BPE. We also observed significant improvements on IWSLT15 Vi->En, WMT16 Ro->En and WMT15 Fi->En datasets, and competitive results on the WMT14 De->En and WMT14 Fr->En datasets.
BrePartition: Optimized High-Dimensional kNN Search with Bregman Distances (Extended Abstract)

Bregman distances (also known as Bregman divergences) are widely used in machine learning, speech recognition and signal processing, and kNN searches with Bregman distances have become increasingly important with the rapid advances of multimedia applications. Data in multimedia applications such as images and videos are commonly transformed into space of hundreds of dimensions. Such high-dimensional space has posed significant challenges for existing kNN search algorithms with Bregman distances, which could only handle data of medium dimensionality (typically less than 100). This paper addresses the urgent problem of high-dimensional kNN search with Bregman distances. We propose a novel partition-filter-refinement framework. Specifically, we propose an optimized dimensionality partitioning scheme to solve several non-trivial issues. First, an effective bound from each partitioned subspace to obtain exact kNN results is derived. Second, we conduct an in-depth analysis of the optimized number of partitions and devise an effective strategy for partitioning. Third, we design an efficient integrated index structure for all the subspaces together to accelerate the search processing. Moreover, we extend our exact solution to an approximate version by a trade-off between the accuracy and efficiency. Experimental results on four real-world datasets and two synthetic datasets show the clear advantage of our method in comparison to state-of-the-art algorithms.
Image Segmentation Keras : Implementation of Segnet, FCN, UNet, PSPNet and other models in Keras

Semantic segmentation plays a vital role in computer vision tasks, enabling precise pixel-level understanding of images. In this paper, we present a comprehensive library for semantic segmentation, which contains implementations of popular segmentation models like SegNet, FCN, UNet, and PSPNet. We also evaluate and compare these models on several datasets, offering researchers and practitioners a powerful toolset for tackling diverse segmentation challenges.
Towards Bridging the Digital Language Divide

It is a well-known fact that current AI-based language technology -- language models, machine translation systems, multilingual dictionaries and corpora -- focuses on the world's 2-3% most widely spoken languages. Recent research efforts have attempted to expand the coverage of AI technology to `under-resourced languages.' The goal of our paper is to bring attention to a phenomenon that we call linguistic bias: multilingual language processing systems often exhibit a hardwired, yet usually involuntary and hidden representational preference towards certain languages. Linguistic bias is manifested in uneven per-language performance even in the case of similar test conditions. We show that biased technology is often the result of research and development methodologies that do not do justice to the complexity of the languages being represented, and that can even become ethically problematic as they disregard valuable aspects of diversity as well as the needs of the language communities themselves. As our attempt at building diversity-aware language resources, we present a new initiative that aims at reducing linguistic bias through both technological design and methodology, based on an eye-level collaboration with local communities.
Globalization of the education space in EU countries and Great Britain through radical information reform

The article reveals the main principles of globalization of the educational space in the EU countries and Great Britain through radical information reformation. The action plan, which includes the six dimensions disclosed in the article, is of great importance from 2020 to 2025 for the formation of the European Education Area (European Education Area), in the conditions of the COVID-19 pandemic and for the development of innovativeness. Measures developed by the European Commission to achieve the specified dimensions are proposed (increasing and monitoring the quality of education; paying attention to gender sensitivity and inclusiveness in education; modernizing digital transformations in education, increasing the competence and motivation of specialists involved in the educational field). The importance and necessity for the education of distance learning programs based on educational models that are known all over the world (in the example of Great Britain) is shown. In the field of information, constant changes and improvements are taking place, leading to the emergence of a qualitatively new type of social system. The information society is the main and necessary in the educational space, therefore the article shows the basic prerequisites of technological, spatial, economic, cultural, and political approaches to the improvement of the information society.
VSES-MB: Verifiable Searchable Encryption Scheme in Metaverse by Blockchain

In the collaborative design metaverse application, the design team as data owner(DO) has demanded to outsource the digital model to a cloud service provider(CSP) due to the limit of local storage. They also need to frequently search and update the data for improving the design. But the virtual world of metaverse is mutual distrusted, which brings 4 major security concerns: 1) CSP may partially execute the DOs’ queries and is curious about the search keywords and the outsource data, how to protect the privacy of data and queries? 2) CSP may dishonestly execute queries to save cost or under other considerations, how to protect the correctness and integrity of results? 3) How to ensure the integrity of outsourced data? 4) When there occurs disputation between DO and CSP, how to provide trusted proofs and make a fair judgment? So, we achieved a verifiable searchable encryption scheme in metaverse by blockchain(VSES-MB). Our scheme protect the data and query privacy by combining verifiable searchable encryption and blockchain. Then, we introduced trapdoor commitment and dynamic interval accumulator(DIA) tree to accelerate the proof generate speed and decrease the proof updating workload. Furthermore, data integrity was protected with the help of blockchain. Next, on the one hand, the metadata of workflow is embedded in blockchain transactions to provide trusted proof. On the other hand, smart contract of blockchain can fairly automatically judge the dispute. Finally, security analysis shows VSES-MB achieves confidentiality and soundness, performance analysis shows our scheme is efficient and easy to deploy.
Optimal Multisecret Image Sharing Using Lightweight Visual Sign-Cryptography Scheme With Optimal Key Generation for Gray/Color Images

Problem: Digital devices are becoming increasingly powerful and smart, which is improving quality of life, but presents new challenges to privacy protection. Visual cryptographic schemes provide data sharing privacy, but have drawbacks such as extra storage space, lossy secret images, and the need to store permutation keys. Aim: This paper proposes a light-weight visual sign-cryptography scheme based on optimal key generation to address the disadvantages of existing visual cryptographic schemes and improve the security, sharing quality, and time consumption of multisecret images. Methods: The proposed light-weight visual sign-cryptography (LW-VSC) scheme consists of three processes: band separation, shares generation, and signcryption/un-signcryption. The process of separation and shares generation is done by an existing method. The multiple shares of the secret images are then encrypted/decrypted using light-weight sign-cryptography. The proposed scheme uses a novel harpy eagle search optimization (HESO) algorithm to generate optimal keys for both the encrypt/decrypt processes. Results: Simulation results and comparative analysis showed the proposed scheme is more secure and requires less storage space, with faster encryption/decryption and improved key generation quality. Conclusion: The proposed light-weight visual sign-cryptography scheme based on optimal key generation is a promising approach to enhance security and improve data sharing quality. The HESO algorithm shows promise in improving the quality of key generation, providing better privacy protection in the face of increasingly powerful digital devices.
An Improved Model for Medical Forum Question Classification Based on CNN and BiLSTM

Question Classification (QC) is the fundamental task for Question Answering Systems (QASs) implementation, and is a vital task, as it helps in identifying the question category. It plays a big role in predicting the answer to a question while building a QAS. However, classifying medical questions is still a challenging task due to the complexity of medical terms. Many researchers have proposed different techniques to solve these problems, but some of these problems remain partially solved or unsolved. With the help of deep learning technology, various text-processing problems have become much easier to solve. In this paper, an improved deep learning-based model for Medical Forum Question Classification (MFQC) is proposed to classify medical questions. In the proposed model, feature representation is performed using Word2Vec, which is a word embedding model. Additionally, the features are extracted from the word embedding layer based on Convolutional Neural Networks (CNNs). Finally, a Bidirectional Long Short Term Memory (BiLSTM) network is used to classify the extracted features. The BiLSTM model analyzes the target information of the representation and then outputs the question category via a SoftMax layer. Our model achieves state-of-the-art performance by effectively capturing semantic and syntactic features from the input questions. We evaluate the proposed CNN-BiLSTM model on two benchmark datasets and compare its performance with existing methods, demonstrating its superiority in accurately categorizing medical forum questions.
Determinants of Private Investment Performance in Ethiopia

This study was conducted with the main objective of investigating and analyzing factors that determine private investment performance in Ethiopia.Study may give valuable information to the stakeholders in the Investment arena including the government, investors and general public.The study may clearly show the prospects for the investment activity.Moreover, the study explains determinants of private Investment performance in Ethiopia.27-Years secondary data (i.e. from 1992 to 2018) was collected from various national and international institutions for example EIC NBE, MOFED, and WB.Then, ARDL regressions model was applied after the data sets were transformed to natural log form and, to account for inherent problems of time series data, different tests such as correlation, autocorrelation tests, Hetroscedasticity, multicolinarity and stationarity test ware made for.The regression results show that Growth GDP, and access to Domestic credit have significant positive effect on private investment, while Trade openness external debt, and real effective exchange have significant negative effect on performance of private investment under the study period.Hence, to promote the performance of private sector in the country, it is essential to take measures that can improve real income of people, and make trade liberalization and institutions that are crucial to attract private investment.Besides, strengthen financial institutions to provide sufficient financial resource to private investors.
REDESIGN OF IPUSNAS APPLICATION USING USER CENTERED DESIGN METHOD

The National Library of Indonesia (Perpusnas) is one of the libraries that already uses a Digital Library named Ipusnas. Ipusna provides access to literature available at National Library via smartphone. However, the Ipusnas application only gets a rating of 3.5 on the Google Playstore. This rating was obtained because the Ipusnas android application still has deficiencies, which is the User Interface and User Experience are still less modern and confusing in finding information. In this study, a redesign was developed using the User Centered Design method and Tested using A/B Testing. Results of the System Usability Scale Questionnaire From the calculation results using the System Usability Scale formula, the average score for evaluating the Ipusnas android application is 61.2. From the results of this SUS score, the research can be continued so that the Redesign of Ipusnas android application better than old one. Based on the evaluation results using the A/B Testing method, it can be concluded that users prefer the new design with a percentage value of 64% compared to the old design of 36%.