Reviewerly/exHarmony huggingface dataset:
BLB-gcForest: A High-Performance Distributed Deep Forest with Adaptive sub-Forest Splitting

As an emulous alternative to deep neural networks, Deep Forest emerges with features like low complexity, fewer hyper-parameters, and good robustness, which are predominantly desired in distributed computing applications and ecosystems. Recently, an efficient distributed Deep Forest system, named ForestLayer, was proposed, designing a fine-grained sub-Forest-based task-parallel algorithm to improve the parallel computing efficiency of Deep Forest. However, the sub-Forest splitting of ForestLayer is static and one-off without adaptability to the computing environment, nevertheless, the size of splitting granularity has a significant impact on the system performance. To further improve the computing efficiency and scalability of the distributed Deep Forest, in this paper, we propose a novel distributed Deep Forest algorithm, named BLB-gcForest (Bag of Little Bootstraps-gcForest), which augments the gcForest (multi-Grained Cascade Forest) approach for constructing Deep Forest. BLB-gcForest carries out parallel computation for each tree in sub-Forests at a finer parallel granularity and integrates with the Bag of Little Bootstraps (BLB) mechanism to reduce massive transmitted feature instances for Cascade Forest Layers, utterly improving both computation efficiency and communication efficiency. Moreover, to solve the problem of the forest splitting granularity, we further design an adaptive sub-Forest splitting algorithm to ensure the maximum resource utilization for parallel computation of each sub-Forest. Experimental results on four well-known large-scale datasets, namely YEAST, LETTER, MNIST, CIFAR10, show that the training efficiency of BLB-gcForest achieves up to 20.3x and 1.64x speedups compared with the state-of-the-art gcForest and ForestLayer, respectively while guaranteeing higher accuracy and better robustness
Quantum information effects

We study the two dual quantum information effects to manipulate the amount of information in quantum computation: hiding and allocation. The resulting type-and-effect system is fully expressive for irreversible quantum computing, including measurement. We provide universal categorical constructions that semantically interpret this arrow metalanguage with choice, starting with any rig groupoid interpreting the reversible base language. Several properties of quantum measurement follow in general, and we translate (noniterative) quantum flow charts into our language. The semantic constructions turn the category of unitaries between Hilbert spaces into the category of completely positive trace-preserving maps, and they turn the category of bijections between finite sets into the category of functions with chosen garbage. Thus they capture the fundamental theorems of classical and quantum reversible computing of Toffoli and Stinespring.
Learning to compress videos without computing motion

With the development of higher resolution contents and displays, its significant volume poses significant challenges to the goals of acquiring, transmitting, compressing, and displaying high-quality video content. In this paper, we propose a new deep learning video compression architecture that does not require motion estimation, which is the most expensive element of modern hybrid video compression codecs like H.264 and HEVC. Our framework exploits the regularities inherent to video motion, which we capture by using displaced frame differences as video representations to train the neural network. In addition, we propose a new space-time reconstruction network based on both an LSTM model and a UNet model, which we call LSTM-UNet. The new video compression framework has three components: a Displacement Calculation Unit (DCU), a Displacement Compression Network (DCN), and a Frame Reconstruction Network (FRN). The DCU removes the need for motion estimation found in hybrid codecs and is less expensive. In the DCN, an RNN-based network is utilized to compress displaced frame differences as well as retain temporal information between frames. The LSTM-UNet is used in the FRN to learn space-time differential representations of videos. Our experimental results show that our compression model, which we call the MOtionless VIdeo Codec (MOVI-Codec), learns how to efficiently compress videos without computing motion. Our experiments show that MOVI-Codec outperforms the Low-Delay P veryfast setting of the video coding standard H.264 and exceeds the performance of the modern global standard HEVC codec, using the same setting, as measured by MS-SSIM, especially on higher resolution videos. In addition, our network outperforms the latest H.266 (VVC) codec at higher bitrates, when assessed using MS-SSIM, on high-resolution videos.
Hidden Markov and Semi-Markov Models When and Why are These Models Useful for Classifying States in Time Series Data?

Hidden Markov models (HMMs) and their extensions have proven to be powerful tools for classification of observations that stem from systems with temporal dependence as they take into account that observations close in time are likely generated from the same state (i.e., class). When information on the classes of the observations is available in advanced, supervised methods can be applied. In this paper, we provide details for the implementation of four models for classification in a supervised learning context: HMMs, hidden semi-Markov models (HSMMs), autoregressive-HMMs, and autoregressive-HSMMs. Using simulations, we study the classification performance under various degrees of model misspecification to characterize when it would be important to extend a basic HMM to an HSMM. As an application of these techniques we use the models to classify accelerometer data from Merino sheep to distinguish between four different behaviors of interest. In particular in the field of movement ecology, collection of fine-scale animal movement data over time to identify behavioral states has become ubiquitous, necessitating models that can account for the dependence structure in the data. We demonstrate that when the aim is to conduct classification, various degrees of model misspecification of the proposed model may not impede good classification performance unless there is high overlap between the state-dependent distributions, that is, unless the observation distributions of the different states are difficult to differentiate. Supplementary materials accompanying this paper appear on-line.
Fast Multi-Robot Motion Planning via Imitation Learning of Mixed-Integer Programs

We propose a centralized multi-robot motion planning approach that leverages machine learning and mixed-integer programming (MIP). We train a neural network to imitate optimal MIP solutions and, during execution, the trajectories predicted by the network are used to fix most of the integer variables, resulting in a significantly reduced MIP or even a convex program. If the obtained trajectories are feasible, i.e., collision-free and reaching the goal, they can be used as they are or further refined towards optimality. Since maximizing the likelihood of feasibility is not the standard goal of imitation learning, we propose several techniques aimed at increasing such likelihood. Simulation results show the reduced computational burden associated with the proposed framework and the similarity with the optimal MIP solutions.
Khmer Speech Translation Corpus of the Extraordinary Chambers in the Courts of Cambodia (ECCC)

Speech translation (ST) is a subject of rapidly increasing interest in the area of speech processing research. This interest is apparent from the increasing tools and corpora for this task. However, the lack of sufficient datasets is still the biggest challenge for under-resourced languages. Specifically, ST requires a large corpus of parallel speech, transcription, and translation text. In this work, we construct a large corpus of the Extraordinary Chambers in the Courts of Cambodia (ECCC), including simultaneous translation from Khmer into English and French. We also address the problem of sentence segmentation of Khmer by conducting a bilingual sentence alignment from English to Khmer with a monotonic assumption. This corpus has approximately 155 hours of speech in length and 1.7M words of text. We also report the baseline results of automatic speech recognition (ASR), machine translation, and ST systems, which show reasonable performance.
Evaluation of DBSCAN algorithm on different programming languages: An exploratory study

DBSCAN is a well-known clustering algorithm that is often used to find associations and structures in large spatial data. Due to its popularity, built-in functions for DBSCAN have been implemented on top of many different programming languages. Researchers and practitioners (i.e., data scientists) have been using these built-in functions to cluster and analyze a prolific area of research in data science. Due to the many implementations of DBSCAN and its utilization in many different languages, the output of each built-in DBSCAN function is assumed to be identical. In this paper, we present a systematic approach to evaluate the built-in functions of DBSCAN algorithms and to identify discrepancies in their output. The evidence from the study shows that there are some discrepancies and recommends caution in dealing with built-in functions.
cSmartML: A Meta Learning-Based Framework for Automated Selection and Hyperparameter Tuning for Clustering

Novel technologies in automated machine learning ease the complexity of algorithm selection and hyper-parameter optimization. However, these are usually restricted to supervised learning tasks such as classification and regression, while unsupervised learning remains a largely unexplored problem. In this paper, we offer a solution for automating machine learning specifically for the case of unsupervised learning with clustering, in a domain-agnostic manner. This is achieved through a combination of state-of-the-art processes based on meta-learning for algorithm and evaluation criteria selection, and evolutionary algorithm for hyper-parameter tuning. We introduce a robust and scalable interactive tool, named cSmartML, built on scikit-learn with 8 clustering algorithms. In order to capture more than a single measure of goodness of the output clustering solution, cSmartML optimizes multiple objective functions. A pareto-approach evaluates each objective simultaneously for each clustering solution. On each of the 27 real and synthetic benchmark datasets, we show that the performance of cSmartML is often much better than using standard selection and hyper-parameter optimization methods. In addition, experimentation reveals that cSmartML takes advantage of the defined objective functions on multi-objective functions framework.
Co-Occurrence of Online and Offline Victimization: A Latent Class Analysis in University Students

Most people are exposed to risks both in the online and offline world. Several studies have provided definitions and measures of cybervictimization based on different theoretical approaches and most of them have focused on specific forms of cybercrime, depicting a limited portrayal of victimization. The current study explored victimization configurations in a sample of 749 university undergraduates from Spain (61.6% women; M age = 26.9), utilizing latent class analyses to account for the nature and frequency of various types of online and offline victimization along their life span. Among them, 35.9% were victims of a cyberattack, 24.4% reported being victims of cyberfraud and 49% of property crime. The analysis uncovered two classes of cybervictims—consisting of economic cybervictimization (victims of economic cybercrimes only) and cyber-polyvictimization (victims of various types of cybercrimes)—and allowed us to compare them with a group of non-victims. Younger respondents (15 to 25 years old), conventional university students, women, people with lower incomes and LGBTQI+ individuals have a higher representation in the cyber-polyvictimization class. In addition, members of this class have suffered more offline victimization in all the areas analyzed. The present study has found co-occurrence between online and offline victimization, thus reinforcing the relevance of simultaneously studying both areas and the interaction between them. From this empirical ground, prevention strategies should not be focused merely on opportunity factors related to the online interactions and behavior of potential victims, without facing the deep human and social roots of victimization.
FedVF: Personalized Federated Learning Based on Layer-wise Parameter Updates with Variable Frequency

Federated learning is a new and increasingly popular distributed machine learning paradigm, which can employ multiple clients such as mobile phones to collaboratively train a deep neural network model under the coordination of the central server. The raw data is always kept locally on the clients, and only the model parameters are communicated between clients and the server, so that data privacy can be largely preserved. To cope with the effect of not independent and identically distributed (Non-IID) data, personalized federated learning that can provide personalized and customized models for clients participating in federated training has been proposed and widely studied. In this paper, we propose a personalized federated learning algorithm that can provide a personalized local model for each client while storing the latest global model on the central server through only one federated training. The deep neural network model is divided into two parts of global layers and personalized layers, and the whole training process is partitioned into earlier and later stages. Based on the division of layer-wise parameters, the cumulative learning strategy of updating parameters on different layers with variable frequency is adopted to better personalize local models in the later stage based on the global features learned in the earlier stage. Compared with existing personalized federated learning algorithms, our proposed algorithm can achieve a good balance between the personalized model and the global model in federated learning, and performs better in communication efficiency and model accuracy.
Dense Image Captioning in Hindi

Much work has been done on dense image captioning in the English language. In this paper, we propose a Encoder-Decoder architecture for the first time, to the best of our knowledge, to generate Hindi dense image captions. We leverage the proven encoder-decoder architecture and provide a Hindi data-set along with results of dense captioning on the data-set. We also propose a novel post-processing technique to further improve the quality of captions by using multiple discriminators (language discriminator, length penalty and dissimilarity score). Hindi is the fourth most spoken language in the world. It is the official language of India and to the best of our knowledge, this is the first attempt at dense image captioning in the Hindi language. The data-set is manually created by translating Visual Genome captions from English to Hindi under manual supervision. Both qualitative (in terms of adequacy and fluency) and quantitative (in terms of BLEU scores) analysis of the generated captions reveal the effectiveness of our proposed approach compared to several strong baselines.
Wasserstein-Distance-Based Multi-Source Adversarial Domain Adaptation for Emotion Recognition and Vigilance Estimation

To build a subject-independent affective model based on electroencephalography (EEG) is a challenging task due to the domain shift problem caused by individual differences in EEG data. In this paper, we prove a new generalization bound based on Wasserstein distance for multi-source classification and regression problems. Based on our bound, we propose two novel Wasserstein-distance-based multi-source adversarial domain adaptation methods (wMADA) for learning domain invariant and task discriminative domain mappings by dynamically aligning different domain mappings. We evaluate our methods on two typical EEG datasets. The experimental results demonstrate that our wMADA methods successfully handle the multi-source domain shift problem in creating subject-independent affective models and outperform the state-of-the-art domain adaptation methods.
Natural Language Processing with Improved Deep Learning Neural Networks

As one of the core tasks in the field of natural language processing, syntactic analysis has always been a hot topic for researchers, including tasks such as Questions and Answer (Q&amp;A), Search String Comprehension, Semantic Analysis, and Knowledge Base Construction. This paper aims to study the application of deep learning and neural network in natural language syntax analysis, which has significant research and application value. This paper first studies a transfer-based dependent syntax analyzer using a feed-forward neural network as a classifier. By analyzing the model, we have made meticulous parameters of the model to improve its performance. This paper proposes a dependent syntactic analysis model based on a long-term memory neural network. This model is based on the feed-forward neural network model described above and will be used as a feature extractor. After the feature extractor is pretrained, we use a long short-term memory neural network as a classifier of the transfer action, and the characteristics extracted by the syntactic analyzer as its input to train a recursive neural network classifier optimized by sentences. The classifier can not only classify the current pattern feature but also multirich information such as analysis of state history. Therefore, the model is modeled in the analysis process of the entire sentence in syntactic analysis, replacing the method of modeling independent analysis. The experimental results show that the model has achieved greater performance improvement than baseline methods.
HDM-Chain: A Secure Blockchain-based Healthcare Data Management framework to ensure Privacy and Security in the Health Unit

In conventional medical data monitoring systems are suffering key challenges in phases of information immutability, traceability, transparency, observation, data validation, access permission, reliability, privacy, and safety. Personal Health Records(PHR) have various advantages globally, but at PHRs data is ruled to essential safety and privacy concerns. This paper suggests a method to implement a reliable clarification to these points. Traditionally sophisticated methods trading with the security of health records ordinarily makes information inaccessible system to patients. Certain methods struggle to adjust information reliability, patient desire, and regular communication with supplier information. Blockchain(BC) resolves the preceding difficulties from it shares data in a decentralized and transactional way. The utilize of BC could support the healthcare division to adjust the accessibility, privacy, and security of PHRs. This document suggests a BC framework to efficiently and securely collect and keep health records. It represents a reliable and skilled means of achieving healthcare data for patients, physicians, and security insurance agencies while defending the patients of information. The goal of this activity is to show how the suggested system fits the safety requirements of participants (patients), physicians, and third performances and discusses privacy and safety attention in the medical division.
SBOOSP for Massive Devices in 5G WSNs Using Conformable Chaotic Maps

The commercialization of the fifth-generation (5G) wireless network has begun. Massive devices are being integrated into 5G-enabled wireless sensor networks (5G WSNs) to deliver a variety of valuable services to network users. However, there are rising fears that 5G WSNs will expose sensitive user data to new security vulnerabilities. For secure end-to-end communication, key agreement and user authentication have been proposed. However, when billions of massive devices are networked to collect and analyze complex user data, more stringent security approaches are required. Data integrity, non-repudiation, and authentication necessitate special-purpose subtree-based signature mechanisms that are pretty difficult to create in practice. To address this issue, this work provides an efficient, provably secure, lightweight subtree-based online/offline signature procedure (SBOOSP) and its aggregation (Agg-SBOOSP) for massive devices in 5G WSNs using conformable chaotic maps. The SBOOSP enables multi-time offline storage access while reducing processing time. As a result, the signer can utilize the pre-stored offline information in polynomial time. This feature distinguishes our presented SBOOSP from previous online/offline-signing procedures that only allow for one signature. Furthermore, the new procedure supports a secret key during the pre-registration process, but no secret key is necessary during the offline stage. The suggested SBOOSP is secure in the logic of unforgeability on the chosen message attack in the random oracle. Additionally, SBOOSP and Agg-SBOOSP had the lowest computing costs compared to other contending schemes. Overall, the suggested SBOOSP outperforms several preliminary security schemes in terms of performance and computational overhead.
Model-driven approach running route two-level SVD with context information and feature entities in recommender system

• Singular value decomposition : Machine learning algorithms on dimensionality space. • Recommender system : Application of contextual information by following footsteps. • Sparse data : Deal with an issue occurring to decreasing user experience. • Cold start : Prediction historic rating for determination of neighborhood. • Context information that affects user preferences and situations. In digital businesses, the offers of goods and services to users in recommendation systems are generally based on the features of the items and the demands of the users. Recommendation systems use contextual information, such as location, time, activity, individual, and relationship to generate relevant and even personalized recommendations. A large proportion of users do not always rate items that they have used which creates the challenge of sparse data in recommendation systems. On the other hand, when a new user or item enters the system, it does not have much information about it. This leads to the challenge a cold start. In this paper, a new hybrid method is presented to reduce the challenge of cold start and sparse data based on the items desired by users according to the similarity measure of user-contextual information, item-contextual information, and two-level singular value decomposition. Two data sets IMDB and STS due to the exerting of user's feature, items feature, and contextual information to review the proposed method. In order to the accuracy of the prediction of the criterion MAE and RMSE with an accuracy of 95%. However, since the user's rating of the item is of particular importance in the recommender systems. The method CSSVD compared to the methods TF, CACF, CTLSVD, and MF-LOD is used evaluation measure Precision, Recall, F1-score. The results contribute to improving the accuracy of the suggestions given to users in recommender systems.
Arabic Word Sense Disambiguation for Information Retrieval

In the context of using semantic resources for information retrieval, the relationship and distance between concepts are considered important for word sense disambiguation. In this article, we experiment with Conceptual Density and Random Walk with graph methods to enhance the performance of the Arabic Information Retrieval System. To do this, a medium-sized corpus was used. The results proved that Random Walk can enhance the performance of the information retrieval system by achieving a mean improvement of 13%, 16%, and 12% in terms of recall, precision, and F-score, respectively.