Reviewerly/exHarmony huggingface dataset:
Refined Answer Selection Method with Attentive Bidirectional Long Short-Term Memory Network and Self-Attention Mechanism for Intelligent Medical Service Robot

Answer selection, as a crucial method for intelligent medical service robots, has become more and more important in natural language processing (NLP). However, there are still some critical issues in the answer selection model. On the one hand, the model lacks semantic understanding of long questions because of noise information in a question–answer (QA) pair. On the other hand, some researchers combine two or more neural network models to improve the quality of answer selection. However, these models focus on the similarity between questions and answers without considering background information. To this end, this paper proposes a novel refined answer selection method, which uses an attentive bidirectional long short-term memory (Bi-LSTM) network and a self-attention mechanism to solve these issues. First of all, this paper constructs the required knowledge-based text as background information and converts the questions and answers from words to vectors, respectively. Furthermore, the self-attention mechanism is adopted to extract the global features from the vectors. Finally, an attentive Bi-LSTM network is designed to address long-distance dependent learning problems and calculate the similarity between the question and answer with consideration of the background knowledge information. To verify the effectiveness of the proposed method, this paper constructs a knowledge-based QA dataset including multiple medical QA pairs and conducts a series of experiments on it. The experimental results reveal that the proposed approach could achieve impressive performance on the answer selection task and reach an accuracy of 71.4%, MAP of 68.8%, and decrease the BLUE indicator to 3.10.
Exploring Maintainability Index Variants for Software Maintainability Measurement in Object-Oriented Systems

During maintenance, software systems undergo continuous correction and enhancement activities due to emerging faults, changing environments, and evolving requirements, making this phase expensive and time-consuming, often exceeding the initial development costs. To understand and manage software under development and maintenance better, several maintainability measures have been proposed. The Maintainability Index is commonly used as a quantitative measure of the relative ease of software maintenance. There are several Index variants that differ in the factors affecting maintainability (e.g., code complexity, software size, documentation) and their given importance. To explore the variants and understand how they compare when evaluating software maintainability, an experiment was conducted with 45 Java-based object-oriented software systems. The results showed that the choice of the variant could influence the perception of maintainability. Although different variants presented different values when subjected to the same software, their values were strongly positively correlated and generally indicated similarly how maintainability evolved between releases and over the long term. Though, when focusing on fine-grained results posed by the Index, the variant selection had a larger impact. Based on their characteristics, behavior, and interrelationships, the variants were divided into two distinct clusters, i.e., variants that do not consider code comments in their calculation and those that do.
WCC-JC 2.0: A Web-Crawled and Manually Aligned Parallel Corpus for Japanese-Chinese Neural Machine Translation

Movie and TV subtitles are frequently employed in natural language processing (NLP) applications, but there are limited Japanese-Chinese bilingual corpora accessible as a dataset to train neural machine translation (NMT) models. In our previous study, we effectively constructed a corpus of a considerable size containing bilingual text data in both Japanese and Chinese by collecting subtitle text data from websites that host movies and television series. The unsatisfactory translation performance of the initial corpus, Web-Crawled Corpus of Japanese and Chinese (WCC-JC 1.0), was predominantly caused by the limited number of sentence pairs. To address this shortcoming, we thoroughly analyzed the issues associated with the construction of WCC-JC 1.0 and constructed the WCC-JC 2.0 corpus by first collecting subtitle data from movie and TV series websites. Then, we manually aligned a large number of high-quality sentence pairs. Our efforts resulted in a new corpus that includes about 1.4 million sentence pairs, an 87% increase compared with WCC-JC 1.0. As a result, WCC-JC 2.0 is now among the largest publicly available Japanese-Chinese bilingual corpora in the world. To assess the performance of WCC-JC 2.0, we calculated the BLEU scores relative to other comparative corpora and performed manual evaluations of the translation results generated by translation models trained on WCC-JC 2.0. We provide WCC-JC 2.0 as a free download for research purposes only.
A systematic literature review about the consumers’ side of fake review detection – Which cues do consumers use to determine the veracity of online user reviews?

Consumers rely heavily on online user reviews when shopping online and cybercriminals produce fake reviews to manipulate consumer opinion. Much prior research focuses on the automated detection of these fake reviews, which are far from perfect. Therefore, consumers must be able to detect fake reviews on their own. In this study we survey the research examining how consumers detect fake reviews online. We conducted a systematic literature review over the research on fake review detection from the consumer-perspective. We included academic literature giving new empirical data. We provide a narrative synthesis comparing the theories, methods and outcomes used across studies to identify how consumers detect fake reviews online. We found only 15 articles that met our inclusion criteria. We classify the most often used cues identified into five categories which were (1) review characteristics (2) textual characteristics (3) reviewer characteristics (4) seller characteristics and (5) characteristics of the platform where the review is displayed. We find that theory is applied inconsistently across studies and that cues to deception are often identified in isolation without any unifying theoretical framework. Consequently, we discuss how such a theoretical framework could be developed.
Genetic hyperparameter optimization with Modified Scalable-Neighbourhood Component Analysis for breast cancer prognostication

Breast cancer is common among women resulting in mortality when left untreated. Early detection is vital so that suitable treatment could assist cancer from spreading further and save people's life. The traditional way of detection is a time-consuming process. With the evolvement of DM (Data Mining), the healthcare industry could be benefitted in predicting the disease as it permits the physicians to determine the significant attributes for diagnosis. Though, conventional techniques have used DM-based methods to identify breast cancer, they lacked in terms of prediction rate. Moreover, parametric-Softmax classifiers have been a general option by conventional works with fixed classes, particularly when huge labelled data are present during training. Nevertheless, this turns into an issue for open set cases where new classes are encountered along with few instances to learn a generalized parametric classifier. Thus, the present study aims to implement a non-parametric strategy by optimizing the embedding of a feature rather than parametric classifiers. This research utilizes Deep CNN (Deep Convolutional Neural Network) and Inception V3 for learning visual features which preserve neighbourhood outline in semantic space relying on NCA (Neighbourhood Component Analysis) criteria. Delimited by its bottleneck, the study proposes MS-NCA (Modified Scalable-Neighbourhood Component Analysis) that relies on a non-linear objective function to perform feature fusion by optimizing the distance-learning objective due to which it gains the capability of computing inner feature products without performing mapping which increases the scalability of MS-NCA. Finally, G-HPO (Genetic-Hyper-parameter Optimization) is proposed. In this case, the new stage in the algorithm simply denotes the enhancement in the length of chromosome bringing several hyperparameters into subsequent XGBoost, NB and RF models having numerous layers for identifying the normal and affected cases of breast cancer for which optimized hyper-parameter values of RF (Random Forest), NB (Naïve Bayes), and XGBoost (eXtreme Gradient Boosting) are determined. This process helps in improvising the classification rate which is confirmed through analytical results.
Detection of Bastnäsite-Rich Veins in Rare Earth Element Ores Through Hyperspectral Imaging

Spectral indices are a well-established remote sensing tool showcased in many studies pertaining to fields such as agriculture, forest ecology, geology, soil sciences, vegetation, water resources, and urban development. In the case of geology, several indices already exist for the purpose of highlighting regions with interesting lithologies, such as iron oxides, aluminous clays, carbonates, and mafic to ultramafic minerals, based on signature absorption features in their reflectance spectra. However, in the literature, most indices often depend on the few, wide spectral bands used by spaceborne multispectral imagers such as Landsat 8 OLI, Terra ASTER, and Sentinel-2 MSI. Hyperspectral imagers are able to glean more detailed compositional information about a rock surface by operating with a myriad of narrow spectral bands, though the usage of these bands in spectral indices for mining and exploration purposes remains largely unexplored. We propose a new bastnäsite index (BI) capable of detecting the presence of rare earth elements (REEs) and mapping their relative abundances across rock surfaces. Using four reference bands and four absorption bands in the visible to near-infrared wavelength range, we apply this novel technique in a laboratory setting to ore samples extracted from the carbonatite body localized in the Sulfide Queen mine in Mountain Pass, CA, USA. Visual inspection is used to compare an index map to corresponding reflectance spectra for areas of known high and low REE abundances, demonstrating its potential as a fast and straightforward means to assess REE ore grade remotely.
Learning Spatial-Frequency Transformer for Visual Object Tracking

Recently, some researchers have begun to adopt the Transformer to combine or replace the widely used ResNet as their new backbone network. As the Transformer captures the long-range relations between pixels well using the self-attention scheme, which complements the issues caused by the limited receptive field of CNN. Although their trackers work well in regular scenarios, they simply flatten the 2D features into a sequence to better match the Transformer. We believe these operations ignore the spatial prior of the target object, which may lead to sub-optimal results only. In addition, many works demonstrate that self-attention is actually a low-pass filter, which is independent of input features or keys/queries. That is to say, it may suppress the high-frequency component of the input features and preserve or even amplify the low-frequency information. To handle these issues, in this paper, we propose a unified Spatial-Frequency Transformer that models the Gaussian spatial Prior and High-frequency emphasis Attention (GPHA) simultaneously. To be specific, Gaussian spatial prior is generated using dual Multi-Layer Perceptrons (MLPs) and injected into the similarity matrix produced by multiplying Query and Key features in self-attention. The output will be fed into a softmax layer and then decomposed into two components, i.e., the direct and high-frequency signal. The low- and high-pass branches are rescaled and combined to achieve all-pass, therefore, the high-frequency features will be protected well in stacked self-attention layers. We further integrate the Spatial-Frequency Transformer into the Siamese tracking framework and propose a novel tracking algorithm termed SFTransT. The cross-scale fusion based SwinTransformer is adopted as the backbone, and also a multi-head cross-attention module is used to boost the interaction between search and template features. The output will be fed into the tracking head for target localization. Extensive experiments on short-term and long-term tracking benchmarks all demonstrate the effectiveness of our proposed framework. Source code will be released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Tchuanm/SFTransT.git</uri> .
Animal Intrusion Detection and Ranging system using YOLOv4 and LoRa

The Man animal conflict is on an exponential rise in the past few years. This is mainly due to the rise in human population, and rapid urbanization and globalization. This puts pressure on the ecological balance of the world. In this article, we propose a novel system to deal with the maintenance of crop fields from animal intrusion and mainly we propose the system to provide a system to prevent man-animal conflict. We propose an integrated system with YOLOv4 and LoRa which can detect the presence of wild animals and can alert the wildlife authority.
IoT and Cloud Server based Industry Power Monitoring and Management System

Most organizations seeking to boost their energy efficiency discover that there are insufficient Business Intelligence solutions to support their dynamic cycle and execute appropriate energy-efficiency project frameworks. Business intelligence developments include information hoarding, querying, information management, rule-based reenactment, and improved perception skills, as well as processing data from Internet of Things (IoT) testing stages. Since a result, the majority of businesses have distinct obstacles in analyzing energy use within buildings, as each organization must cope with a unique set of circumstances that must be thoroughly examined. When the firm employs energy creation arrangements, the project's dependability is impacted by the company's energy interest, fluctuations in consumption patterns, local atmospheric conditions, and unanticipated expenses due to the equipment's double-dealing. This study provides an example of a Cloud-based Business Intelligence energy checking and recreation stage that attempts to aid organizations in monitoring and calculating energy creation and consumption inside a structure, as well as reenacting financial productivity over long periods of time.
Impact of Artificial Intelligence on Supply Chain Management Performance

Artificial Intelligence (AI) has the potential to revolutionize various aspects of business operations.AI can be used to analyze data and make predictions about demand, optimize logistics and transportation routes, and identify inefficiencies in the supply chain.This can lead to improved responsiveness to changes in demand, reduced lead times, and lower costs.This paper reviews and analyzes the applications of AI in supply chain management (SCM) using the Scopus database.The objective is to address the current research gap of AI's impact on the performance of SCM, determining the AI techniques that can enhance the performance of SCM, the SCM subfields that have high potential to be enhanced by AI, the impact of AI application on the performance of SCM, and how the performance can be described in agile-lean perspective.Scopus database was utilized to outline and identify the active countries/regions in the field of AI impact on SCM performance, subject area, and type of documents.
Trimap-guided feature mining and fusion network for natural image matting

Mining interesting objects with trimap guidance and fusing multi-level features are two important issues for trimap-based matting. For feature mining, existing methods simply feed trimaps to an encoder network and mine features evenly in different regions with a decoding module, which lacks an efficient and effective way to mine interesting objects pointed out by trimaps. For emerging content-based feature fusion, most existing matting methods only focus on local features which lack the guidance of a global feature with strong semantic information related to the interesting object. In this paper, we propose a trimap-guided feature mining and fusion network consisting of our trimap-guided non-background multi-scale pooling (TMP) module and global-local context-aware fusion (GLF) modules. Considering that trimap provides strong semantic guidance, our TMP module focuses effective feature mining on interesting objects under the guidance of trimap without extra parameters. Furthermore, our GLF modules use global semantic information of interesting objects mined by our TMP module to guide an effective global-local context-aware multi-level feature fusion. In addition, we build a common interesting object matting (CIOM) dataset to advance high-quality image matting. Experimental results on the Composition-1k test set, Alphamatting benchmark, and our CIOM test set demonstrate that our method outperforms state-of-the-art approaches. Codes and models will be publicly available at https://github.com/Serge-weihao/TMF-Matting.
On the performance and interpretability of Mamdani and Takagi-Sugeno-Kang based neuro-fuzzy systems for medical diagnosis

Neuro-fuzzy systems aim to combine the benefits of artificial neural networks and fuzzy inference systems: a neural network can learn patterns from data and achieves high performance, whereas a fuzzy system matches inputs and outputs using linguistic and interpretable rules. The combination of these two techniques yields models that can both perform well and provide interpretability in a fuzzy linguistic manner. In this paper, the performance and interpretability of five neuro-fuzzy classifiers were evaluated (three Takagi-Sugeno-Kang (TSK) classifiers: adaptive neuro-fuzzy inference system (ANFIS), dynamic evolving neuro-fuzzy system (DENFIS), self-organizing fuzzy neural network (SOFNN), and two Mamdani classifiers: hybrid fuzzy inference system (HyFIS) and neuro-fuzzy classifier (NEFCLASS)). All the empirical evaluations were over four benchmark medical datasets (Wisconsin breast cancer dataset, SPECTF heart dataset, Parkinsons dataset, and diabetic retinopathy Debrecen dataset), and used five performance criteria (accuracy, precision, recall, f score, and training time) and two interpretability criteria (number of rules and number of membership functions). Results showed that the TSK-based self-organizing fuzzy neural network classifier, in general, outperformed the others. In terms of interpretability, DENFIS and NEFCLASS were the best Takagi-Sugeno-Kang and Mamdani classifiers respectively. The findings also suggested that three classifiers: DENFIS, SOFNN, and NEFCLASS achieved a good performance-interpretability tradeoff. To the best of our knowledge, no study has compared the neuro-fuzzy techniques presented in this paper in terms of performance and interpretability in the medical domain.
Medical matting: Medical image segmentation with uncertainty from the matting perspective

High-quality manual labeling of ambiguous and complex-shaped targets with binary masks can be challenging. The weakness of insufficient expression of binary masks is prominent in segmentation, especially in medical scenarios where blurring is prevalent. Thus, reaching a consensus among clinicians through binary masks is more difficult in multi-person labeling cases. These inconsistent or uncertain areas are related to the lesions' structure and may contain anatomical information conducive to providing an accurate diagnosis. However, recent research focuses on uncertainties of model training and data labeling. None of them has investigated the influence of the ambiguous nature of the lesion itself. Inspired by image matting, this paper introduces a soft mask called alpha matte to medical scenes. It can describe the lesions with more details better than a binary mask. Moreover, it can also be used as a new uncertainty quantification method to represent uncertain areas, filling the gap in research on the uncertainty of lesion structure. In this work, we introduce a multi-task framework to generate binary masks and alpha mattes, which outperforms all state-of-the-art matting algorithms compared. The uncertainty map is proposed to imitate the trimap in matting methods, which can highlight fuzzy areas and improve matting performance. We have created three medical datasets with alpha mattes to address the lack of available matting datasets in medical fields and evaluated the effectiveness of our proposed method on them comprehensively. Furthermore, experiments demonstrate that the alpha matte is a more effective labeling method than the binary mask from both qualitative and quantitative aspects.
A blockchain-enabled IoT auditing management system complying with ISO/IEC 15408-2

Recently, blockchain has attracted attention as one of the most promising key platform technologies for leveraging IoT (Internet of Things) security and auditing management with trusted and decentralized architecture. Therefore, the possibilities of using blockchain for providing scalable management, efficient access, and accountability for the massive numbers of extant and emerging IoT devices merits investigation. In this study, we implement a blockchain-enabled IoT auditing management system complying with ISO/IEC 15408-2 requirements. The implemented system is operated with a stable data retrieval time, i.e. 25.74 to 26.77 microseconds, and a stable compressing and uploading time, i.e. 32.51 and 39.9 microseconds when handling and accessing below 100 logs. With more logs, the processing time raises in a proportional manner. The introduced auditing system represents an evolutionary paradigm not only possessing the advantageous characteristics of blockchain technology, i.e. de-centralization, robustness and tamper-resistance, but also fulfilling the critical requirements from ISO/IEC 15408-2 with respect to auditing perspective. Moreover, the scalability is guaranteed, as our proposed IoT auditing management system is suitable for versatile IoT-oriented application services (even on heterogeneous platforms).
Deep debiased contrastive hashing

Hashing has achieved great success in multimedia retrieval due to its high computing efficiency and low storage cost. Recently, contrastive-learning-based hashing methods have achieved decent retrieval performance in label-free scenarios by learning distortion-invariant representations with Siamese networks. Their learning principle, i.e., instance discrimination, maximizes the correlation between self-augmented views and treats all others as negative samples. However, it may learn with false negative samples that are naturally similar, resulting in biased hash learning. To bridge this flaw, we reveal the between-instance similarity of naturally similar samples by exploring the latent structure of the training data. As a result, we propose the Deep Debiased Contrastive Hashing (DDCH) algorithm, using the neighborhood discovery module to explore the intrinsic similarity relationship that can help contrastive hashing reduce false negatives for superior discriminatory ability. Furthermore, we elucidate the rationale for incorporating the module into the contrastive hashing framework and explain our hashing process from an Expectation-Maximization (EM) perspective. Extensive experimental results on three benchmark image datasets demonstrate that DDCH significantly outperforms the state-of-the-art unsupervised hashing methods for image retrieval.