Reviewerly/exHarmony huggingface dataset:
Rule-Based Classifiers for Identifying Fake Reviews in E-commerce: A Deep Learning System

The way in which people purchase online products or services has greatly improved because of the development of Internet technologies. Customer purchase behavior has also modified as more people write reviews about items procured. As previously mentioned, when buyers intend to acquire products from online e-commerce websites, they often go through the details of the products such as ratings and initial posted reviews, instead of selecting a product based purely on price. Furthermore, they also give preference to the products that have higher score ratings and a high positive feedback. An online product with a higher percentage of positive reviews has a better chance than one which has a higher percentage of negative reviews. Unfortunately, some persons take advantage of the importance and value of posting online reviews by attempting to generate fake reviews, either to increase the promotion of specific products or to deceive customers into purchasing low-quality products. Skillful paid persons called “fraudsters” or “fake reviewers” aim to write deceptive positive and/or negative reviews. However, the problem of fake reviews can financially influence online business companies and primarily might be used to mislead customers into making wrong decisions, purchasing undesired products. Recently, fake reviews detection has captivated the attention of e-commerce, media, and governments. In the present paper, the researchers focus on detecting and classifying the online products reviews into fake or truthful via rules-based classifiers. For that purpose, the proposed methodology contains various phases, namely collection of dataset acquired from the Amazon platform, preprocessing, feature extraction, labeling process performed on extracted features, and finally machine learning as well as deep learning-based models adopted for the task of classification. When comparing the results obtained from the experiments carried out in this research work, it has been observed that the Convolutional Neural Networks incorporated in Bidirectional Long Short-Term Memory (CNN-BiLSTM) technique provides better performance than Random Forest (RF) technique in terms of accuracy metric.
A Simple Method for Robust and Accurate Intrinsic Subtyping of Breast Cancer

The PAM50 signature/method is widely used for intrinsic subtyping of breast cancer samples. However, depending on the number and composition of the samples included in a cohort, the method may assign different subtypes to the same sample. This lack of robustness is mainly due to the fact that PAM50 subtracts a reference profile, which is computed using all samples in the cohort, from each sample before classification. In this paper we propose modifications to PAM50 to develop a simple and robust single-sample classifier, called MPAM50, for intrinsic subtyping of breast cancer. Like PAM50, the modified method uses a nearest centroid approach for classification, but the centroids are computed differently, and the distances to the centroids are determined using an alternative method. Additionally, MPAM50 uses unnormalized expression values for classification and does not subtract a reference profile from the samples. In other words, MPAM50 classifies each sample independently, and so avoids the previously mentioned robustness issue.A training set was employed to find the new MPAM50 centroids. MPAM50 was then tested on 19 independent datasets (obtained using various expression profiling technologies) containing 9637 samples. Overall good agreement was observed between the PAM50- and MPAM50-assigned subtypes with a median accuracy of 0.792, which (we show) is comparable with the median concordance between various implementations of PAM50. Additionally, MPAM50- and PAM50-assigned intrinsic subtypes were found to agree comparably with the reported clinical subtypes. Also, survival analyses indicated that MPAM50 preserves the prognostic value of the intrinsic subtypes. These observations demonstrate that MPAM50 can replace PAM50 without loss of performance. On the other hand, MPAM50 was compared with 2 previously published single-sample classifiers, and with 3 alternative modified PAM50 approaches. The results indicated a superior performance by MPAM50.MPAM50 is a robust, simple, and accurate single-sample classifier of intrinsic subtypes of breast cancer.
Efficient Re-parameterization Operations Search for Easy-to-Deploy Network Based on Directional Evolutionary Strategy

Traditional NAS methods improve performance by sacrificing the landing ability of the architecture and the re-parameterization technology is expected to solve this problem. However, most current Rep methods rely on prior knowledge to select the re-parameterization operations, which limits the architecture performance to the type of operations and prior knowledge. At same time, some re-parameterization operations hinder the optimization of the network. To break these restrictions, in this work, an improved re-parameterization search space is designed, including more type of re-parameterization operations. Concretely, the performance of convolutional networks can be further enhanced by the search space. An automatic re-parameterization enhancement strategy is designed to effectively explore this search space based on neural architecture search (NAS), which can search an excellent re-parameterization architecture. Then, we solved the optimization problem caused by using some re-parameterization operations to enhance ResNet-style network. Besides, we visualize the output features of the architecture to analyze the reasons for the formation of the re-parameterization architecture. On public datasets, we achieve better results. Under the same training conditions as ResNet, we improve the accuracy of ResNet-50 by 1.82% on ImageNet-1k.
Comparison in virtual reality based on efficiency for product assembly

Virtual Reality technology (VR) can be used for manufacturing training within a reduced space to allow trainees to manipulate objects in a simulated environment. Based on the information obtained from this recreation exercise, the production planning team can implement the proper adjustments prior to the beginning of an assembly. During a Project Evaluation and Management course, five students from Tecnologico de Monterrey created a VR space with pieces for assembly in a laboratory, designed with the previous user experience from the students. The main objective of this study was to test the effectiveness of a drill pump assembly process to analyze the assembly time. The study included 44 third-year undergraduate engineering students, 22 females. After the VR study, a statistical test was performed to analyze the results, which indicated that the group of students reduced the cycle time by 67% after two times compared to the first tryout. In addition, no significant differences were observed in the assembly time between the male and female students.
A MULTI-STAGE APPROACH FOR INTRODUCING IOT PROTOTYPING INTO STEM-DRIVEN COMPUTER SCIENCE EDUCATION

Appears in: INTED2023 Proceedings Publication year: 2023Pages: 7224-7233ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.1983Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain
Quantized Fields for Optimal Control in the Strong Coupling Regime

We tailor the quantum statistics of a bosonic field to deterministically drive a quantum system into a target state. Experimentally accessible states of the field achieve good control of multilevel or multiqubit systems, notably also at coupling strengths beyond the rotating-wave approximation. This extends optimal control theory to the realm of fully quantized, strongly coupled control and target degrees of freedom.
Assemble it like this! – Is AR- or VR-based training an effective alternative to video-based training in manual assembly?

AR- and VR-based training is increasingly being used in the industry to train workers safely and effectively for new tasks. In this study, we investigated and compared the effects of AR-, VR- and video-based training on short- and long-term objective performance measures and subjective evaluations in a manual assembly task. Our results showed that there was no difference between AR-, VR- and video-based training concerning the objective performance measures task completion time and error count. However, in the subjective evaluations VR-based training showed a significantly higher perceived task load and a lower usability rating than the AR- and video-based training regimes. An exploratory analysis additionally revealed partially better results for AR than for VR after adjusting the data for the age of the participants. Future research should further investigate the advantage of AR- and video-based methods over VR when the age and technology experience of participants are taken into account.
Career management in digital age: a study of leadership’s competencies and practices applied for IT professionals

Purpose: this research seeks to identify the main competencies and career management practices applied by leaders of large IT services organizations, highlighting them in degrees of relevance for career guidance for technical teams between Generations X and Y coexisting in the Digital Age. Additionally, the research factors a major transformation in the work relationships environment, turning to the full remote mode, besides the digital acceleration caused by the coronavirus pandemic in Brazil during 2020. Design/Methodology/approach: the research was carried out with a qualitative methodology, with in-depth interviews with a semi-structured script, portraying the experiences of top leaders. Findings: highlights as relevant competencies: the importance of the manager's role as to career guidance, management skills of generational expectations, leadership style oriented to the knowledge worker, besides the affiliative aspects in the relationship with the employees. As for management practices, highlights: training diagnostic, frequent communication, promotion of talents with high potential, adaptation of career plans, adoption of a differentiated coaching process for each generation and, above all, encouragement of the employee's protagonism throughout the career planning process. Research limitations: the study has limitations regarding companies’ profiles with restricted instruments related to human resources’ methodologies, proper people’s development tools, or internal support developing talent and minimum maturity processes for managers leading career development counting with Information Technology business orientation in Brazil. Originality/value: significant contribution regarding career management in a remote work environment with 2 generations co-existing in an accelerated digitalization season.
Toward Designing a Secure Authentication Protocol for IoT Environments

Authentication protocol is a critical part of any application to manage the access control in many applications. A former research recently proposed a lightweight authentication scheme to transmit data in an IoT subsystem securely. Although the designers presented the first security analysis of the proposed protocol, that protocol has not been independently analyzed by third-party researchers, to the best of our knowledge. On the other hand, it is generally agreed that no cryptosystem should be used in a practical application unless its security has been verified through security analysis by third parties extensively, which is addressed in this paper. Although it is an efficient protocol by design compared to other related schemes, our security analysis identifies the non-ideal properties of this protocol. More specifically, we show that this protocol does not provide perfect forward secrecy. In addition, we show that it is vulnerable to an insider attacker, and an active insider adversary can successfully recover the shared keys between the protocol’s entities. In addition, such an adversary can impersonate the remote server to the user and vice versa. Next, the adversary can trace the target user using the extracted information. Finally, we redesign the protocol such that the enhanced protocol can withstand all the aforementioned attacks. The overhead of the proposed protocol compared to its predecessor is only 15.5% in terms of computational cost.
Comparison analysis of Bangla news articles classification using support vector machine and logistic regression

In the information age, Bangla news articles on the internet are fast-growing. For organizing, every news site has a particular structure and categorization. News article classification is a method to determine a document’s classification based on various predefined categories. This research discusses the classification of Bangla news articles on the online platform and tries to make constructive comparison using several classification algorithms. For Bangla news articles classification, term frequencyinverse document frequency (TF-IDF) weighting and count vectorizer have been used as a feature extraction process, and two common classifiers named support vector machine (SVM) and logistic regression (LR) employed for classifying the documents. It is clear that the accuracy of the experimental results by applying SVM is 84.0% and LR is 81.0% for twelve categories of news articles. In this research work, when we have made comparison two renowned classification algorithms applied on the Bangla news articles, LR was outperformed by SVM.
Role of data normalization in k-means algorithm results

The quality of data is an important issue in data analysis process. While raw data comes from real word may have different problems in its quality; missing data, outliers. Also numeric data may be come in different scales because it is measured using different units according to features that maintains. The less data quality can give down the analysis results. This problem is more obvious in data clustering, however, data clustering is unsupervised task that based on the data. One of probabilistic solutions to overcome the problem of using different scales in measuring data, normalization technique has been used to scale the data in a unified ones. So the produced data have same domain. Different normalization techniques can be applied. This paper shed light on the performance of normalization with one of the most famous clustering algorithms that is k-means clustering algorithm. We chose this algorithm because it is very sensitive to data quality, so the results can be distinguished according to this problem. The practical work used four specific data sets. The work covers the application of data without normalization. Then the different normalization techniques would applied on these data separately. To perform the comparison, accuracy metric would be done to evaluate the performance. The results were obtained revel the important of the normalization in increasing the accuracy of data sets; but in diverse directions according to data used. However, data sets respond very well to specific normalization technique, in opposite side, there is shut down in accuracy with other. So this results must be taken more attention to work in this field in selection the suitable technique to improve the results. Not always give the same tool for applying with different data set. At last, the paper was organized in: introduction, methods and materials, practical work and results, discussion and conclusion.
Modeling verbal short-term memory: A walk around the neighborhood.

When remembering over the short-term, long-term knowledge has a large effect on the number of correctly recalled items and little impact on memory for order. This is true, for example, when the effects of semantic category are examined. Contrary to what these findings suggest, Poirier et al. in 2015 proposed that memory for order relies on the level of activation within long-term networks. Importantly, although their view has been criticized, they showed that manipulating semantic associations led to item migrations that were atypical. In this article, we show that similar migrations can be obtained with another knowledge-based factor: orthographic neighborhood. In three experiments, we manipulated the orthographic neighborhood of to-be-recalled items. The latter is a sublexical factor; as such, it is much less likely than semantic relatedness to involve demand characteristics or grouping strategies. The first experiment established that the neighborhood manipulation produced the pattern of item migrations previously observed with semantic relatedness, confirming that the migration effect can generalize to other variables. The last two experiments suggested that migrations were due to the features shared across list items rather than to item co-activation (as in Poirier et al.). The results were successfully modeled by calling upon the Revised Feature Model, where recall depends on selecting a retrieval candidate based on the features of the cueing information. Overall, our findings underline the usefulness of a model where retrieval is determined by relative distinctiveness and underline that multiple mechanisms can lead to order errors in recall. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Multimodal Reconstruct and Align Net for Missing Modality Problem in Sentiment Analysis

Multimodal Sentiment Analysis (MSA) aims at recognizing emotion categories by textual, visual, and acoustic cues. However, in real-life scenarios, one or two modalities may be missing due to various reasons. And when text modality is missing, obvious deterioration will be observed since text modality contains much more semantic information compared to vision and audio modality. To this end, we propose the Multimodal Reconstruct and Align Net (MRAN) to tackle the missing modality problem, especially to relieve the decline caused by the text modality’s absence. We first propose the Multimodal Embedding and Missing Index Embedding to guide the reconstruction of missing modalities features. Then, visual and acoustic features are projected to the textual feature space, and all three modalities’ features are learned to be close to the word embedding of their corresponding emotion category, making visual and acoustic features aligned with textual features. In this text-centered way, vision and audio modality benefit from the more informative text modality. Thus it improves the robustness of the network for different modality missing conditions, especially when text modality is missing. Experimental results conducted on two multimodal benchmarks IEMOCAP and CMU-MOSEI show that our method outperforms baseline methods, gaining superior results on different kinds of modality missing conditions.
Multi-Receptive Field Soft Attention Part Learning for Vehicle Re-Identification

Vehicle re-identification across multiple cameras is one of the main problems of intelligent transportation systems (ITSs). Since the differences in the appearance between different vehicles of the same model are small and the appearance of the same vehicle changes drastically from different viewpoints, vehicle re-identification is a challenging task. In this paper, we propose a model called multi-receptive field soft attention part learning (MRF-SAPL). The MRF-SAPL model learns semantically diverse vehicle part-level features under different receptive fields through multiple local branches, alleviating the problem of small differences in vehicle appearance. To align vehicle parts from different images, this study uses soft attention to adaptively locate the positions of the parts on the final feature map generated by a local branch and maintain the continuity of the internal semantics of the parts. In addition, to obtain parts with different semantic patterns, we propose a new loss function that punishes overlapping regions, forcing the positions of different parts on the same feature map to not overlap each other as much as possible. Extensive ablation experiments demonstrate the effectiveness of our part-level feature learning method MRF-SAPL, and our model achieves state-of-the-art performance on two benchmark datasets.
Dental image enhancement network for early diagnosis of oral dental disease

Abstract Intelligent robotics and expert system applications in dentistry suffer from identification and detection problems due to the non-uniform brightness and low contrast in the captured images. Moreover, during the diagnostic process, exposure of sensitive facial parts to ionizing radiations (e.g., X-Rays) has several disadvantages and provides a limited angle for the view of vision. Capturing high-quality medical images with advanced digital devices is challenging, and processing these images distorts the contrast and visual quality. It curtails the performance of potential intelligent and expert systems and disincentives the early diagnosis of oral and dental diseases. The traditional enhancement methods are designed for specific conditions, and network-based methods rely on large-scale datasets with limited adaptability towards varying conditions. This paper proposed a novel and adaptive dental image enhancement strategy based on a small dataset and proposed a paired branch Denticle-Edification network (Ded-Net). The input dental images are decomposed into reflection and illumination in a multilayer Denticle network (De-Net). The subsequent enhancement operations are performed to remove the hidden degradation of reflection and illumination. The adaptive illumination consistency is maintained through the Edification network (Ed-Net). The network is regularized following the decomposition congruity of the input data and provides user-specific freedom of adaptability towards desired contrast levels. The experimental results demonstrate that the proposed method improves visibility and contrast and preserves the edges and boundaries of the low-contrast input images. It proves that the proposed method is suitable for intelligent and expert system applications for future dental imaging.