Reviewerly/exHarmony huggingface dataset:
Utilising Artificial Intelligence to Turn Reviews into Business Enhancements through Sentiment Analysis

The use of sentiment analysis methodology has become crucial for e-commerce enterprises in order to optimise their marketing tactics. In the present setting, the authors strive to demonstrate the ethical and efficient use of artificial intelligence in the realm of business. The researchers used qualitative research methodologies to analyse a total of 1687 evaluations obtained from 85 online retailers associated with electronic commerce Europe Trustmark. These stores were linked with 18 different nations and operated over 14 distinct domains. The investigation used the combined power of natural language processing and machine learning, implemented via a Software-as-a-Service (SaaS) platform. The results of the study indicate that consumers often exhibit a neutral emotional tone while leaving one-star ratings. Although the influence of unfavourable evaluations is generally limited, it highlights the need for more attentiveness in their management. The extent to which users interact with goods and services has a substantial impact on the probability of publishing reviews, regardless of whether the encountered experience is unpleasant or favourable. The authors urge for the acquisition of tools and skills in order to boost the efficiency of managers and experts in parallel with expanding technological landscapes, with a particular emphasis on the utilisation of artificial intelligence for sentiment analysis.
PauseSpeech: Natural Speech Synthesis via Pre-trained Language Model and Pause-Based Prosody Modeling

Although text-to-speech (TTS) systems have significantly improved, most TTS systems still have limitations in synthesizing speech with appropriate phrasing. For natural speech synthesis, it is important to synthesize the speech with a phrasing structure that groups words into phrases based on semantic information. In this paper, we propose PuaseSpeech, a speech synthesis system with a pre-trained language model and pause-based prosody modeling. First, we introduce a phrasing structure encoder that utilizes a context representation from the pre-trained language model. In the phrasing structure encoder, we extract a speaker-dependent syntactic representation from the context representation and then predict a pause sequence that separates the input text into phrases. Furthermore, we introduce a pause-based word encoder to model word-level prosody based on pause sequence. Experimental results show PauseSpeech outperforms previous models in terms of naturalness. Furthermore, in terms of objective evaluations, we can observe that our proposed methods help the model decrease the distance between ground-truth and synthesized speech. Audio samples are available at https://jisang93.github.io/pausespeech-demo/ .
MobileViT Based Lightweight Model for Prohibited Item Detection in X-Ray Images

The application of computer vision technology to detect prohibited items in X-ray security inspection images holds significant practical research value. We have observed that object detection models built upon the Visual Transformer (ViT) architecture outperform those relying on Convolutional Neural Networks (CNNs) when assessed on publicly available datasets. However, the ViT’s attention mechanism, while offering a global response, lacks the CNN model’s inductive bias, which can hinder its performance, demanding more samples and learning parameters. This drawback is particularly problematic for time-sensitive processes like security inspections. This research paper aims to develop a lightweight prohibited item detection model grounded in the ViT framework, utilizing MobileViT as the underlying network for feature extraction. To enhance the model’s sensitivity to small object features, we have established dense connections among various network layers. This design ensures effective integration of both high- and low-level visual features without increasing computational complexity. Additionally, learnable group convolutions are employed to replace traditional convolutions, further reducing model parameters and computational demands. Simulation experiments conducted on the publicly available SIXray dataset validate the effectiveness of the proposed model in this study. The code is publicly accessible at https://github.com/zhg-SZPT/MVray.
Exploring Model Depth Adaptation in Image Super-Resolution for Efficient Inference

In the field of single-image super-resolution, different images have varying levels of difficulty in restoration. The restoration for images with a significant proportion of smooth regions is easier than images with complex textures or edge areas. This implies that the restoration for easy images requires fewer computational resources, whereas the restoration for difficult images requires more. Based on this, we have designed Model Depth Adaptation SR (MDASR) network, which can adaptively adjust the capacity of the model based on the difficulty level of the input image restoration. Specifically, we have employed a dynamic structural neural network that incorporates a gating mechanism to adaptively decide whether to execute or skip the current layer based on the current input, thereby achieving adaptive modeling in deep dimension. The experimental results demonstrate that our network significantly reduces FLOPS and achieves the best LPIPS performance on all the test datasets.
Towards Time-Variant-Aware Link Prediction in Dynamic Graph Through Self-supervised Learning

Dynamic graph link prediction is a challenging problem because the graph topology and node attributes vary at different times. A purely supervised learning scheme for the dynamic graph data usually leads to poor generalization due to insufficient supervision. As a promising solution, self-supervised learning can be introduced to dynamic graph analysis tasks. However, the self-supervised learning paradigm for dynamic graph learning has not been sufficiently investigated due to the complicated properties of the evolving graphs. We assume that the dynamic graphs consist of three independent types of key factors, i.e., graph time-variant information, time-invariant information and noise. Based on this assumption, we propose the Self-supervised Decoupling for Dynamic Graph (SDDG) framework for explicitly decoupling the representation which characterizes these three factors, thus enhancing the interpretability of the learned representation and link prediction performance. More specifically, we design an encoder-decoder architecture to sufficiently exploit the dynamic graph itself with multiple regularizations, so that the time-variant embedding of dynamic graph data can be effectively decoupled from the perspectives of node and structure for time-variant-aware link prediction. Experiments on five benchmark link prediction tasks demonstrate the significant improvement of our SDDG over the state-of-the-art methods. For example, SDDG achieves 98.2% and 97.3% Top-1 AUC on Reddit-B and Reddit-T, outperforming the DDGCL model, by 1.9% and 1.8%, respectively.
Broad Learning System Based on Fusion Features

Multimodal fusion is the integration of data from different modalities according to the principle of optimality to produce more reliable perception of objects. Deep learning has been widely used in multimodal fusion domain, but how to reduce model parameters and training time while maintaining good performance is still an academic problem. To address these issues, we design a system capable of learning and fusing features from both modalities, the Deep Residual network Broad Learning System (DR-BLS). First we use a pre-trained deep residual network (ResNet18) to extract features of different modalities separately. And then we use canonical correlation analysis to project the two obtained features in the same feature space for correlation learning and fusion of features. Finally the broad learning system is used as classifier for target recognition and classification. Experiments on target classification recognition on the University of Washington RGB-D dataset show that the method pro-posed in this paper is more stable and faster than other algorithms.
TabMentor: Detect Errors on Tabular Data with Noisy Labels

Existing supervised methods for error detection require access to clean labels in order to train the classification models. This is difficult to achieve in practical scenarios. While the majority of the error detection algorithms ignore the effect of noisy labels, in this paper, we design effective techniques for error detection when both data and labels contain noise. Nevertheless, we present TabMentor, a novel deep-learning model for error detection on tabular data with noisy training labels. TabMentor introduces a deep model for the prediction, i.e., Tabclassifier that suggests the most salient features for the decision step, enabling efficient learning. For feature extraction, it uses existing error detection algorithms, along with some raw features from the datasets. To reduce the negative effect of noisy training labels on the model, TabMentor uses another deep model, i.e., Teachernet, to supervise the training of Tabclassifier. During the training process, both Teachernet and Tabclassifier dynamically learn curriculum from data, allowing Tabclassifier to focus more on clean labeled samples. Performance evaluation using five different data sets shows that the TabMentor excels over the best baseline error detection system by 0.05 to 0.11 in terms of F1 scores.
Multi-modal Multi-emotion Emotional Support Conversation

This paper proposes a new task of Multi-modal Multi-emotion Emotional Support Conversation (MMESC), which has great value in various applications, such as counseling, daily chatting, and elderly company. This task aims to fully perceive the users’ emotional states from multiple modalities and generate appropriate responses to provide comfort for improving their feelings. Traditional works mainly focus on textual conversation, while a single-modal cannot accurately reflect the users’ emotions, such as saying fine with an inconsistent disgusting feeling. To address this problem, we propose a new task on multi-modalities and exploit a new method called FEAT for this new task. FEAT can integrate fine-grained emotional knowledge from multiple modalities. It first recognizes the users’ mental states based on an emotion-aware transformer. It then generates supportive responses using a hybrid method with multiple comfort strategies. To evaluate our method, we construct a large-scale dataset named MMESConv. It is almost two times larger than existing single-modal datasets. There are three modalities in this dataset (text, audio, and video) with fine-grained emotion annotations and strategy labels. Extensive experiments on this dataset demonstrate the advantages of our proposed framework.
Navigating English Writing Proficiency Tests in the Era of Artificial Intelligence

The establishment and growth of artificial intelligence (AI) technology has sparked tremendous interest in the field of writing skills, as AI writing tools have the potential to assist humans in creating and refining text. The availability of these tools may lead to a reliance on AI in writing, which raises concerns about the potential degradation of writing skills. To that extent, will the writing skill session in English proficiency tests (e.g: TOEFL, IELTS, PTE Academic, TOEIC) be displaced? Utilizing a phenomenological case study, the researcher interviews four experienced test administrators in North Sumatera, Indonesia, to explore their perspective regarding to the impact of AI writing tools on the applications of writing tests in English proficiency tests. Various insightful perspectives are elaborated by the participants, implicating the future of English writing proficiency tests in the age of AI. However, in case this study is conducted in Indonesia and only involves four test administrators, a wider scale of research is needed to further investigate the potential impact of AI writing tools on writing skills across various contexts and languages.
Reconstruction of the Tea-Serving Automaton in the Ancient Japanese Book “Karakuri Zui”

This research aims to trace back the historical origin of the ancient Japanese automaton tea-serving doll and clarify its operation method. By analyzing the original records in the book “Karakuri Zui”, the mechanical structure can be separated into six subsystems namely start-stop, speed-controlling, transmission, head-bowing, steering, and stepping mechanisms. The reconstruction is made through 3D modeling and simulation, and an unreasonable component illustrated in the original book is also pointed out. A process to select the main spring as the power source is also provided. Finally, a prototype of the reconstructed automaton is made to verify the movement characteristics and the feasibility of the presented reconstructed designs.
Classification of Credit Card Frauds Detection using machine learning techniques

Credit card fraud refers to the illegal activities carried out by criminals. In this research paper, we delve into the topic by exploring four different approaches to analyze fraud, namely decision trees, logistic regression, support vector machines, and Random Forests. Our proposed technique encompasses four stages: inputting the dataset, balancing the data through sampling, training classifier models, and detecting fraud. To analyze the data, we utilized two methods: forward stepwise logistic regression analysis (LR) and decision tree analysis (DT), in addition to Random Forest and support vector machine. Based on the outcomes of our analysis, the decision tree algorithm produced the highest AUC and accuracy value, achieving a perfect score of 1. On the other hand, logistic regression yielded the lowest values of 0.33 and 0.2933 for AUC and accuracy, respectively. Moreover, the implementation of forest algorithms resulted in an impressive accuracy rate of 99.5%, which signifies a significant advancement in automating the detection of credit card fraud.
Generating Enlightened Suggestions Based on Mental State Evolution for Emotional Support Conversation

Emotional support conversation aims to provide comfort and suggestions to users and gradually reduce their negative emotions such as anxiety. It is a valuable topic for many applications, including mental health support and customer service chats. However, due to the lack of enough expert knowledge, existing methods fail to provide enlightened suggestions to reverse users’ worries. Additionally, these methods neglect to grasp the mental state evolution of users. To address these problems, we propose a novel method that considers Mental State Evolution to provide Knowledge-grounded Suggestions (MEKS). In detail, we first create a suggestion corpus called MentalQA to grasp the psychological knowledge by resorting to the mental health forum. The relevant passages are selected based on both the context and the original response. Then we leverage graph structure to enrich the context with the inferred user’s mental state evolution. Furthermore, we introduce a gate to combine textual expert knowledge with the mental state evolution graph, so as to facilitate the generation of supportive responses. Experimental results show that this method can provide reasonable solutions to help the users.
Label Correlation Guided Feature Selection for Multi-label Learning

Multi-label learning has received much attention due to its wide range of application domains. Multi-label data often has high-dimensional features, which brings more challenges to classification algorithms. Feature selection based on sparse learning is one of the most important approaches, which can select discriminative features to alleviate the curse of dimensionality. However, most of these methods do not consider feature redundancy and label correlation simultaneously. In this work, we propose a multi-label feature selection method that takes feature redundancy and label correlation into account. Specifically, the proposed method first divides features into groups according to feature correlation and then encourages competition within each group but relaxes competition between groups to eliminate redundant and irrelevant features. Moreover, we propose an approach to capture label correlation and exploit it to improve the feature selection. Finally, an iterative optimization algorithm is designed to obtain the feature weights for multi-label feature selection. Extensive experiments on various multi-label datasets demonstrate the superiority of the proposed method compared with some state-of-the-art multi-label feature selection methods.
Role of Psychological Contract Violation between Employee Expediency and Exploitative Leadership

Previous studies rarely examined the antecedents of employee expediency(EE) with interactive effect of exploitative leadership(EL) with mediating role of psychological contract violation(PCV) in banking sector of developing country like Pakistan. Based on social cognitive theory, this study tried to fill the research gap by investigating the direct relationship between EE and EL and also checked the mediating role of PCV between the relationship of EL and EE. To validate this relationship, 400 employees who were working in different banks of Punjab, Pakistan were investigated through survey questionnaire. The smart PLS 4.0 was used to measure the relationship and test the hypothesis in which structured equation modeling played a vital role in checking the relationship between variables. The results showed that EL directly effect on EE. This study also found that PCV mediates the relationship between EL and EE. This study enhances the literature and body of knowledge in HRM and OB. Finally, this study helps managers to understand the employees, co-operate and support them in order to get good performance from them and to improve the overall performance of organization.
Traffic Sign Classification Using ASIC

Abstract: The main aim of this project is to focus on traffic signs classification using Tensor flow and deploying the model onto an ASIC for engineering applications. The classification is done using Convolutional Neural Networks (CNNs) in TensorFlow, where the model is trained using a large dataset of traffic sign images. The training images are pre-processed and labelled, and the model is fine-tuned using transfer learning techniques. After the model is trained, it is tested with a separate dataset to ensure its accuracy. Once the model is finalized, it is optimized for deployment onto an ASIC using Micro Python. The ASIC is a custom-designed hardware that is optimized for the specific task of traffic sign classification. The performance of the ASIC is then compared with a standard computer to evaluate its speed and efficiency. This project demonstrates the ability to implement machine learning algorithms onto custom-designed hardware for specific engineering applications, specifically for the purpose of traffic sign classification.
Hubungan Kompetensi Sosial Guru Terhadap Motivasi Belajar Siswa Pada Mata Pelajaran Pendidikan Agama Kristen Kelas IX SMP Satu Atap Negeri 5 Pangururan

The aim of this research is to determine the relationship between teacher social competence and the learning motivation of class VIII Christian Religious Education students at SMP Satu Roof Negeri 5 Pangururan. The method used in this research is a descriptive quantitative research method. The population is all 102 students of VIII SMP Satu Atap Negri 5 Pangururan for the 2022/2023 academic year and a sample of 35 people was determined using a random sampling technique. Data was collected using a positive closed questionnaire with 31 items, namely 20 items for variable data as follows: 1) test the positive relationship obtained by the value rxy = 0.574 &gt; rtable(a=0.05,n=35) = 0.334, thus it is known that there is a positive relationship between variable X and variable Y. 2) Test the relationship The significant value obtained is tcount= 4.026 &gt; ttable(a=0.05, dk=n-2=33)=2.042, thus there is a significant relationship between variable X and variable Y. So Ha says there is a relationship between teacher social competence and learning motivation Class VIII Christian Religious Education students at One Roof Middle School 5 Pangururan were accepted and H0 which said there was no relationship was rejected.
Estimation of Efficiency and the Effect of Access to Finance on Efficiency of Small and Medium Enterprises (SMES) in the Western area of Sierra Leone

Abstract To estimate the efficiency of SMEs and the effect of access to finance on the efficiency of SMEs the study adopted the stochastic frontier estimation method of determining efficiency of firms. A model of maximum performance (capacity) was estimated using 450 SMEs randomly selected from the population of registered SMEs in the Western Area of Sierra Leone from 2018 to 2020. The model of net business earnings was estimated using the Maximum Likelihood procedure and the firm efficiencies were consequently estimated. The mean inefficiencies are estimated by various categories, including SMEs access to bank credit to determine firm characteristics that are associated with higher mean efficiencies. The empirical results reveal that the potential of firms is determined positively by capital productivity and labour productivity and negatively by experience of firms, the latter results suggesting that more experience (in terms of age) does not push their production outwards but inwards. However, other factors are found significant in efficiency differences among firms. These are: gender of the head of the SME, educational level, professional training of the firm heads, sector of operation and the area of operation. JEL classification numbers: F14, F18, G14, G21. Keywords: Efficiency, Finance, Small and Medium Enterprises.
Word Length in Chinese: The Menzerath-Altmann Law is Valid After All

According to the Menzerath-Altmann law, longer language constructs consist, on average, of shorter constituents. It is most often studied at the level of words and syllables (the mean syllable length gets shorter with the increasing word length). Its validity at this level was corroborated in several languages. However, it was claimed that Chinese is an exception with respect to the validity of the Menzerath-Altmann law. We show that the law is valid if word types are considered, while the behaviour of word tokens is different. This difference can be explained by the fact that the Zipf law of abbreviation is valid not only for words but also for syllables (shorter syllables are used more frequently).
A Query-based Black-box Attack to Evaluate the Robustness of One-Stage Object Detectors

Recent research has shown that one-stage object detectors are vulnerable to adversarial attacks in the query-based black-box setting. Parallel Rectangle Flip Attack (PRFA) is the first work using this approach. However, the performance and success rate of PRFA is not good enough. Therefore, this paper proposes a method named QA4OD to address these limitations. The main idea is to use an alternative loss function and prioritize high objectness areas. To prove the effectiveness of the proposed method, QA4OD is compared with PRFA in terms of success rate, quality, and performance. The target model is YOLOv5 trained on COCO dataset. In terms of performance, QA4OD outperforms PRFA by being 13 times faster. While QA4OD achieves a success rate of 72%, the success rate of PRFA is lower significantly, at about 58%. In terms of quality, there is no major difference between QA4OD and PRFA. This result demonstrates the potential ability of QA4OD in practice, which would help to reduce the effort of testing one-stage object detectors.