Reviewerly/exHarmony huggingface dataset:
Rider weed deep residual network-based incremental model for text classification using multidimensional features and MapReduce

Increasing demands for information and the rapid growth of big data have dramatically increased the amount of textual data. In order to obtain useful text information, the classification of texts is considered an imperative task. Accordingly, this article will describe the development of a hybrid optimization algorithm for classifying text. Here, pre-processing was done using the stemming process and stop word removal. Additionally, we performed the extraction of imperative features and the selection of optimal features using the Tanimoto similarity, which estimates the similarity between features and selects the relevant features with higher feature selection accuracy. Following that, a deep residual network trained by the Adam algorithm was utilized for dynamic text classification. Dynamic learning was performed using the proposed Rider invasive weed optimization (RIWO)-based deep residual network along with fuzzy theory. The proposed RIWO algorithm combines invasive weed optimization (IWO) and the Rider optimization algorithm (ROA). These processes are carried out under the MapReduce framework. Our analysis revealed that the proposed RIWO-based deep residual network outperformed other techniques with the highest true positive rate (TPR) of 85%, true negative rate (TNR) of 94%, and accuracy of 88.7%.
Counseling (ro)bot as a use case for 5G/6G

Abstract This paper presents a counseling (ro)bot called Visual Counseling Agent (VICA) which focuses on remote mental healthcare. It is an agent system leveraging artificial intelligence (AI) to aid mentally distressed persons through speech conversation. The system terminals are connected to servers by the Internet exploiting Cloud-nativeness, so that anyone who has any type of terminal can use it from anywhere. Despite a promising voice communication interface, VICA shows limitations in conversation continuity on conventional 4G networks. Concretely, the use of the current 4G networks produces word dropping, delayed response, and the occasional connection failure. The objective of this paper is to mitigate these issues by leveraging a 5G/6G slice inclusive of mobile/multiple edge computing (MEC). First, we propose and partly implement the enhanced and advanced version of VICA. Servers of enhanced versions collaborate to increase speech recognition reliability. Although it significantly increases generated data volume, the advanced version enables a recognition of the facial expressions to greatly enhance counseling quality. Then, we propose a quality assurance mechanism using multiple levels of catalog, as well as 5G/6G slice inclusive of MEC, and conduct experiments to uncover issues related to the 4G. Results indicate that the number of speech recognition errors in Internet Cloud is more than twofold compared to edge computing, implying that quality assurance using 5G/6G in conjunction with VICA Counseling (ro)bot has higher efficiency.
DUAL: Discrete Spoken Unit Adaptive Learning for Textless Spoken Question Answering

Spoken Question Answering (SQA) is to find the answer from a spoken document given a question, which is crucial for personal assistants when replying to the queries from the users. Existing SQA methods all rely on Automatic Speech Recognition (ASR) transcripts. Not only does ASR need to be trained with massive annotated data that are time and cost-prohibitive to collect for low-resourced languages, but more importantly, very often the answers to the questions include name entities or out-of-vocabulary words that cannot be recognized correctly. Also, ASR aims to minimize recognition errors equally over all words, including many function words irrelevant to the SQA task. Therefore, SQA without ASR transcripts (textless) is always highly desired, although known to be very difficult. This work proposes Discrete Spoken Unit Adaptive Learning (DUAL), leveraging unlabeled data for pre-training and fine-tuned by the SQA downstream task. The time intervals of spoken answers can be directly predicted from spoken documents. We also release a new SQA benchmark corpus, NMSQA, for data with more realistic scenarios. We empirically showed that DUAL yields results comparable to those obtained by cascading ASR and text QA model and robust to real-world data. Our code and model will be open-sourced.
Spoofing-Aware Speaker Verification by Multi-Level Fusion

Recently, many novel techniques have been introduced to deal with spoofing attacks, and achieve promising countermeasure (CM) performances. However, these works only take the stand-alone CM models into account. Nowadays, a spoofing aware speaker verification (SASV) challenge which aims to facilitate the research of integrated CM and ASV models, arguing that jointly optimizing CM and ASV models will lead to better performance, is taking place. In this paper, we propose a novel multi-model and multi-level fusion strategy to tackle the SASV task. Compared with purely scoring fusion and embedding fusion methods, this framework first utilizes embeddings from CM models, propagating CM embeddings into a CM block to obtain a CM score. In the second-level fusion, the CM score and ASV scores directly from ASV systems will be concatenated into a prediction block for the final decision. As a result, the best single fusion system has achieved the SASV-EER of 0.97% on the evaluation set. Then by ensembling the top-5 fusion systems, the final SASV-EER reached 0.89%.
[Sources Identification, Ecological Risk Assessment, and Controlling Factors of Potentially Toxic Elements in Typical Lead-Zinc Mine Area, Guizhou Province, Southwest China].

Carbonatite and basalt are widely distributed in southwest China, and potentially toxic elements (PTEs) are associated with the naturally high background properties. It is important to carry out ecological risk assessments and identify potential sources of PTEs. A total of 3180 soil samples (0-20 cm) were collected in Hezhang county, a typical high background area of PTEs with the parent lithology of carbonatite and basalt. Samples were obtained from 18 large lead-zinc mines, which belong to a multi-ecological risk superimposed area with high ecological risk. The concentration of PTEs (Cd, Cr, As, Hg, Pb, Cu, Zn, and Ni) in the topsoil were analyzed, and statistical analysis (SA), geographic information system (GIS), enrichment factor (EF), potential ecological risk index (RI), and positive matrix factorization (PMF) methods were used to assess the ecological risk and quantify sources of PTEs. The mean values of PTEs concentrations in topsoil were 24.55, 2.25, 176.40, 89.60, 0.19, 64.20, 102.00, and 257.00 mg·kg-1 for As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn, respectively, which were remarkably higher than the average background value (ABV) of soils in Guizhou Province. The average concentrations of Cd, Cr, Cu, Ni, Pb, and Zn exceeded the screening values specified for the soil contamination risk in agricultural land (GB 15618-2018) by 7.50, 1.18, 1.79, 1.07, 1.40, and 1.29 times, respectively. The EF showed that Cd had a large area of moderate pollution; Pb, Zn, and Cu had a small area of slight pollution; the EFs values of Hg, Cr, Ni, and As were near the baseline value (EF≈1), and contaminations were slight or nonexistent. The PMF indicated that there were five sources, namely pyrite mines, lead-zinc mines, natural sources of basalt and carbonatite, and agricultural activities; the risk contribution ratios were 5.25%, 27.37%, 28.94%, 17.91%, and 20.53%, respectively. The most toxic coefficients of Hg and Cd were mainly natural sources, with contribution ratios of 86.3% and 72.7%, respectively. The soil samples in the mining areas expectedly contained high ratios of Zn/Cd and Pb/Cd, which confirmed that PTEs in the soil were mainly derived from the smelting wastes. The contents of metal oxides such as Fe, Mn, and Si were the influencing factors of PTEs enrichment. On the contrary, soil samples exhibited much lower Zn/Cd and Pb/Cd ratios in non-mining areas, indicating that the main origin of these metals in soil was the smelting flue gas dusts and geological background.
Road to FAIR genomes: a gap analysis of NGS data generation and sharing in the Netherlands

<h3>Objective</h3> This study investigates current standards and operational gaps in the management and sharing of next generation sequencing (NGS) data within the healthcare and research setting and according to Findable, Accessible, Interoperable and Reusable (FAIR) principles. <h3>Methods</h3> The analysis was performed as the basis from which to bridge identified gaps and develop widely accepted working standards that ensure optimal reusability of genomic data in healthcare and research settings in the Netherlands. This work is part of the ‘Rational Pharmacotherapy Program’ led by ZonMw, The Netherlands Organisation for Health Research and Development, which aims to promote the efficient implementation of NGS and personalised medicine within Dutch healthcare, with an initial focus on oncology and rare diseases. <h3>Results</h3> Based on this analysis and as part of this programme, a consortium was formed to develop an instruction manual for FAIR genomic data in clinical care and research based on an inventory of commonly used workflows and standards in the (inter)national field of genome analysis. <h3>Conclusions</h3> The gap analysis presented and discussed in this paper represents the starting point for this inventory and is a possible contribution from the Netherlands to the European 1+ Million Genomes Initiative. This paper addresses the topics of data generation, data quality, (meta)data standards, data storage and archiving and data integration and exchange.
PROTOCOL: Mapping the scientific knowledge and approaches to defining and measuring hate crime, hate speech, and hate incidents

Abstract The overall aim of the review is to map the definitions and measurement tools used to capture the whole spectrum of hate motivated behaviors, including hate crime, hate speech and hate incidents. This will benefit the field of hate studies by providing a baseline that can inform the building of cumulative knowledge and comparative research. The first review objective is to map definitions of hate crime, hate incidents, hate speech, and surrogate terms. Specific research questions underpinning this objective are: (a) How are hate crimes, hate speech and hate incidents defined in the academic, legal, policy, and programming literature?; (b) What are the concepts, parameters and criteria that qualify a behavior as being hate crime, hate incident or hate speech?; and (c) What are the most common concepts, parameters and criteria found across definitions? What are the differences between definitions and the elements they contain? The second review objective is to map the tools used to measure the prevalence of hate crime, hate incidents, hate speech, and surrogate terms. Specific research questions underpinning this objective are: (a) How are definitions operationalised to measure hate crimes, hate speech, and hate incidents?; and (b) How valid and reliable are these measures?
Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition

One-shot fine-grained visual recognition often suffers from the problem of having few training examples for new fine-grained classes. To alleviate this problem, off-the-shelf image generation techniques based on Generative Adversarial Networks (GANs) can potentially create additional training images. However, these GAN-generated images are often not helpful for actually improving the accuracy of one-shot fine-grained recognition. In this paper, we propose a meta-learning framework to combine generated images with original images, so that the resulting "hybrid" training images improve one-shot learning. Specifically, the generic image generator is updated by a few training instances of novel classes, and a Meta Image Reinforcing Network (MetaIRNet) is proposed to conduct one-shot fine-grained recognition as well as image reinforcement. Our experiments demonstrate consistent improvement over baselines on one-shot fine-grained image classification benchmarks. Furthermore, our analysis shows that the reinforced images have more diversity compared to the original and GAN-generated images.
Decision Support for Oropharyngeal Cancer Patients Based on Data-Driven Similarity Metrics for Medical Case Comparison

Making complex medical decisions is becoming an increasingly challenging task due to the growing amount of available evidence to consider and the higher demand for personalized treatment and patient care. IT systems for the provision of clinical decision support (CDS) can provide sustainable relief if decisions are automatically evaluated and processed. In this paper, we propose an approach for quantifying similarity between new and previously recorded medical cases to enable significant knowledge transfer for reasoning tasks on a patient-level. Methodologically, 102 medical cases with oropharyngeal carcinoma were analyzed retrospectively. Based on independent disease characteristics, patient-specific data vectors including relevant information entities for primary and adjuvant treatment decisions were created. Utilizing the ϕK correlation coefficient as the methodological foundation of our approach, we were able to determine the predictive impact of each characteristic, thus enabling significant reduction of the feature space to allow for further analysis of the intra-variable distances between the respective feature states. The results revealed a significant feature-space reduction from initially 19 down to only 6 diagnostic variables (ϕK correlation coefficient ≥ 0.3, ϕK significance test ≥ 2.5) for the primary and 7 variables (from initially 14) for the adjuvant treatment setting. Further investigation on the resulting characteristics showed a non-linear behavior in relation to the corresponding distances on intra-variable level. Through the implementation of a 10-fold cross-validation procedure, we were further able to identify 8 (primary treatment) matching cases with an evaluation score of 1.0 and 9 (adjuvant treatment) matching cases with an evaluation score of 0.957 based on their shared treatment procedure as the endpoint for similarity definition. Based on those promising results, we conclude that our proposed method for using data-driven similarity measures for application in medical decision-making is able to offer valuable assistance for physicians. Furthermore, we consider our approach as universal in regard to other clinical use-cases, which would allow for an easy-to-implement adaptation for a range of further medical decision-making scenarios.
Feature Selection in High Dimensional Biomedical Data Based on BF-SFLA

High-dimensional biomedical data contained many irrelevant or weakly correlated features, which affected the efficiency of disease diagnosis. This manuscript presented a feature selection method for high-dimensional biomedical data based on the chemotaxis foraging-shuffled frog leaping algorithm (BF-SFLA). The performance of the BF-SFLA based feature selection method was further improved by introducing chemokine operation and balanced grouping strategies into the shuffled frog leaping algorithm, which maintained the balance between global optimization and local optimization and reduced the possibility of the algorithm falling into local optimization. To evaluate the proposed method’s effectiveness, we employed the K-NN (k-nearest Neighbor) and C4.5 decision tree classification algorithm with a comparative analysis. We compared our proposed approach with improved genetic algorithms, particle swarm optimization, and the basic shuffled frog leaping algorithm. Experimental results showed that the feature selection method based on BF-SFLA obtained a better feature subset, improved classification accuracy, and shortened classification time.
Dynamical repulsive fractional potential fields in 3D environment

Unmanned Aerial Vehicle applications have highly increased in the last years, from surveillance, exploration, rescue to transport applications. UAVs are more and more autonomous, therefore real-time trajectory planning is necessary and can be achieved thanks to artificial potential fields. The classic Ge & Cui repulsive force solely allows taking into account the velocity of the obstacles without any distinction. The Weyl potential force associates a degree of danger with an obstacle and has enabled to distinguish between the obstacles but the obstacle velocity is no more considered. Therefore, a new dynamical fractional repulsive force has been proposed by combining both advantages. The new dynamical fractional repulsive potential field is usable in a 3D environment and takes into account both the obstacle dynamics (position and speed) and their degree of dangerousness. Obstacle avoidance robustness is guaranteed, both from a safety point of view and from a trajectory optimization point of view. The proposed repulsive potential fields are first of all dynamical as they are based on the relative position and speed of the UAV in relation to the obstacle positions and speeds. Moreover, the dangerousness of the obstacles is also considered by introducing a fractional degree in their definition. Simulations results are provided to compare different repulsive potential field method (Ge & Cui, Weyl methods) to the proposed dynamical fractional potential field both in static and dynamical environments.
Encrypted Web traffic dataset: Event logs and packet traces

We present a dataset that captures seven days of monitoring data from eight servers hosting more than 800 sites across a large campus network. The dataset contains data from network monitoring and host-based monitoring. The first set of data are packet traces collected by a probe situated on the network link in front of the web servers. The traces contain encrypted HTTP over TLS 1.2 communication between clients and web servers. The second set of data is an event log captured directly on the web servers. The events are generated by the Internet Information Services (IIS) logging and include both the IIS default features and custom features, such as client port and transferred data volume. Anonymization of all features in the dataset has been carefully carried out to prevent private information leakage while preserving the information value of the dataset. The dataset is suitable mainly for training machine learning techniques for anomaly detection and the identification of relationships between network traffic and events on web servers. We also add tools, settings, and a guide to convert the packet traces to IP flows that are often preferred for network traffic analysis.
Detection of targets in filtered noise: whitening in space and spatial frequency

Most studies of detection in complex backgrounds have measured and modeled human performance for statistically uniform (stationary) backgrounds. However, natural and medical images have statistical properties that vary over space. We measured detection of various target shapes presented in Gaussian 1/f noise backgrounds that were statistically uniform over space, and in ones that modulated in contrast over space. We find that the pattern of human thresholds is not consistent with the ideal observer but is consistent with a suboptimal observer that performs partial whitening in spatial frequency and whitening (reliability-weighting) in space, and has a small level of intrinsic position uncertainty.
Query2Particles: Knowledge Graph Reasoning with Particle Embeddings

Answering complex logical queries on incomplete knowledge graphs (KGs) with missing edges is a fundamental and important task for knowledge graph reasoning.The query embedding method is proposed to answer these queries by jointly encoding queries and entities to the same embedding space.Then the answer entities are selected according to the similarities between the entity embeddings and the query embedding.As the answers to a complex query are obtained from a combination of logical operations over sub-queries, the embeddings of the answer entities may not always follow a uni-modal distribution in the embedding space.Thus, it is challenging to simultaneously retrieve a set of diverse answers from the embedding space using a single and concentrated query representation such as a vector or a hyperrectangle.To better cope with queries with diversified answers, we propose Query2Particles (Q2P), a complex KG query answering method.Q2P encodes each query into multiple vectors, named particle embeddings.By doing so, the candidate answers can be retrieved from different areas over the embedding space using the maximal similarities between the entity embeddings and any of the particle embeddings.Meanwhile, the corresponding neural logic operations are defined to support its reasoning over arbitrary first-order logic queries.The experiments show that Query2Particles achieves state-of-the-art performance on the complex query answering tasks on FB15k, FB15K-237, and NELL knowledge graphs.
Enhanced design and hardware implementation of a chaos-based block cipher for image protection

Nowadays chaos-based cryptography systems are widely used to protect sensitive data. A variety of chaos-based cryptosystems with software implementation has been published in the literature. However, due to the rapid growth of connected devices, the development of hardware secure and low-resource cryptographic systems is necessary. In this paper, we enhance the design of one of our software cryptosystems and then we address its FPGA-based implementation. The improvement in the design consists essentially to introduce an Initial Vector (IV) entry to the Chaotic Pseudo Random Number Generator (CPRNG) thus allowing to generate a new dynamic keys for the confusion layer (using a modified 2-D cat map) and diffusion layer (using a logistic map) with each new execution of the system. The CPRNG is formed by XORred a discrete Skew Tent Map (STM) with a discrete Piecewise Linear Chaotic Map (PWLCM). The proposed cryptosystem, which operates in Cipher Block Chaining (CBC) mode, is controlled by a Finite State Machine (FSM) to ensure that both the encryption and decryption are performed by the same reconfigurable architecture. The system is implemented on a Xilinx XC7Z020 ZYNQ FPGA platform. The results obtained in terms of hardware implementation costs (logic resources, throughput, and efficiency) and in terms of security against cryptographic attacks demonstrate the effectiveness of the proposed chaos-based cryptosystem.
Accelerated AI Inference at CNN-Based Machine Vision in ASICs: A Design Approach

Machine Vision (MV) has become an increasingly popular technology in Industry 4.0 for advanced manufacturing processes. Specially, assembly lines require fast MV of the machine objects, assembly-functions, and operations to improve accuracy and quality. In order to achieve this, MV deployed customized Application Specific Integrated Circuits (ASICs). These ASICs designed to run Convolution Neural Network (CNNs) and perform AI inference. The processing needs high performance computing (HPC) of a large number of tensors with least energy spending in the ASICs. In the paper, we propose a design approach for efficient tensor processing in tensor cores for accelerated AI inference at CNN based MV in ASICs. The design approach deploys the small DNN (Deep Learning Neural Network) model SqueezeNext along with application of quantization, fast arithmetic reduction, tensor cores aware tuning, pruning, and fusion for the efficient AI inference in ASICs. Successful implementation of proposed design can provide a competitive advantage to industries in terms of quality and cost of product along with time saving in manufacturing process.