Reviewerly/exHarmony huggingface dataset:
Multi-scale and Self-mutual Feature Distillation

In the past few years, there have been major strides in the study of knowledge distillation, of which feature distillation is an important branch. A growing amount of scholars have conducted extensive research on this topic and have produced a variety of techniques for feature distillation. However, there is still much space for growth in this area. To handle this problem, this paper designs an innovative feature distillation strategy, which consists of self-mutual feature distillation and multi-scale feature distillation. Self-mutual feature distillation improves the performance of student networks by effectively combining the self-supervised properties of student networks with the mutually supervised properties of student-teacher networks. Furthermore, alternate feature distillation training technique is designed to maximise the advantages of both properties. Multi-scale feature distillation can deal with the limitations of single-scale feature distillation. It can encourage the student network to provide greater consideration to the multi-scale hidden information of the teacher network. Through a large number of comparative experiments, it can be found that the approach suggested in this paper generates the best outcomes for all application tasks. For example, in the CIFAR100 and ImageNet classification tasks, our approach obtains the lowest error rate. In the semantic segmentation task, our method obtains the highest mIoU value. Moreover, the ablation study further demonstrates the potential of each of the suggested components.
CC-DBNet: A Scene Text Detector Combining Collaborative Learning and Cascaded Feature Fusion

In recent years, scene text detection technologies have received more and more attention and have made rapid progress. However, they also face some challenges, such as fracture detection in text instances and the problem of poor robustness of detection models. To address these issues, we propose a scene text detector called CC-DBNet. This detector combines Intra-Instance Collaborative Learning (IICL) and the Cascaded Feature Fusion Module (CFFM) to detect arbitrary-shaped text instances. Specifically, we introduce dilated convolution blocks in IICL, which expand the receptive fields and improve the text feature representation ability. We replace the FPN in DBNet ++ with a CFFM incorporating efficient channel attention (ECA) to utilize features of various scales better, thereby improving the detector's performance and robustness. The results of the experiment demonstrate the superiority of the proposed detector. CC-DBNet achieves 88.1%, 86%, and 88.6% F-measure on three publicly available datasets, ICDAR2015, CTW1500, and MSRA-TD500, respectively, with 0.8%, 0.7%, and 1.4% improvement compared with the baseline DBNet ++, respectively.
Robot Path Planning Using Swarm Intelligence Algorithms

Nowadays, robotic applications exist in various fields, including medical, industrial, and educational. The critical aspect of most of these applications is robot movement, where an efficient path-planning algorithm is required in order to guarantee a safe and cost-effective movement. The main goal of the path planning technique is to find the shortest possible path to the destination while avoiding the obstacles on the route. This study proposes a framework employing swarm intelligence optimization techniques based on an improved genetic algorithm and particle swarm optimization to obtain the optimum trajectory. The simulations are conducted using MATLAB R2022b. It is observed that the proposed particle swarm optimization achieves better accuracy of up to 99.5% and faster convergence time when compared with the genetic algorithm that attains 74.6% accuracy. The proposed optimized path planning algorithm is considerably advantageous, especially in realistic applications such as rescue robots and item delivery.
Decoding hidden darknet networks: What we learned about the illicit fentanyl trade on <scp>AlphaBay</scp>

Abstract The opioid epidemic, impacted from the proliferation of fentanyl, has added impetus to the need to detect fentanyl, sources of fentanyl, and places where fentanyl and drugs adulterated with fentanyl are available. Many darknet marketplaces (DNMs) have rules that ban fentanyl. However, it is unclear how these affect the fentanyl market. Using the AlphaBay DNM as a case study, we conducted mixed methods qualitative research. We scraped and analyzed data from the AlphaBay I2P website using, among other methods, content and social network analysis, to uncover hidden fentanyl networks. Our research highlights the next evolution of darknet marketplaces – the migration of DNMs from Tor to I2P and the methods that can be used identify fentanyl networks, irrespective of where sites are: I2P, Tor, or multihomed on I2P and Tor. Despite its ban in the Global AlphaBay Rules, our research revealed the sale of fentanyl on the AlphaBay DNM. Unlike previous studies, our findings predominantly revealed the covert sale of fentanyl on AlphaBay and predatory vendors selling illicit drugs, which unbeknownst to buyers, contained fentanyl. To a lesser extent, our findings identified the overt sale of fentanyl patches on AlphaBay. Although we examined only one DNM, the prevalence of the covert sale of fentanyl and the presence of predatory vendors underscores the importance of research that decodes the language of vendors who surreptitiously sell fentanyl or drugs adulterated with fentanyl or other illicit substances. The results of our research can inform strategies aimed at disrupting and dismantling DNM fentanyl networks.
Improving Early Breast Cancer Detection with Artificial Intelligence: A Promising Approach

N/A
Performance Evaluation of MobileNetV2 CNN Architecture in Localized Datasets

Facial features of humans are part of physiological characteristics that are the basis for identity verification. Face Recognition technology has expanded into a more thorough process to increase the accuracy rate in recognizing individuals. This paper assessed the performance of face recognition in Deep Convolutional Neural Networks with the application of localized datasets. Five (5) people were used as a class, with thirty (30) images per person. A total of 150 images were gathered from photo albums and collections. The images were preprocessed with some of the basic image processing techniques, including histogram equalization cropping, and resizing, before training using MobileNetv2 Pre-trained CNN architecture. Earlier layers of this architecture were used as feature extractors. The final 2-3 layers were fine-tuned following the number of classes. During the training, images were divided into 80% for training datasets, and 20% for testing and validation datasets. The graphical illustration showed an accuracy rate of 90% as well as the generation of a confusion matrix. The results indicate that MobileNetV2 is a promising CNN architecture that can be used in Face Recognition Technology with localized datasets
Seamless Data Capture: A QR Scanner with Google Sheets Integration

This study presents the development and evaluation of a user-friendly QR Scanner mobile app with seamless Google Sheets integration. The motivation behind the research was to address the limitations of conventional QR scanner apps, which often lack direct integration with data management platforms like Google Sheets. The app development process employed a hybrid methodology, combining prototyping and agile approaches, resulting in an intuitive and well-designed solution. The app allows users to effortlessly scan QR codes and automatically sync the captured data to Google Sheets in real-time, providing immediate access and analysis. Evaluation using the System Usability Scale (SUS) yielded a satisfactory level of usability, with users finding the app easy to use
EMPIRICAL ANALYSIS OF THE EFFICIENCY OF THE BANKING SECTOR IN WESTERN BALKAN COUNTRIES

There is no doubt that financial intermediation plays a crucial role in the economy of both developed and developing countries. At the same time, the banking sector represents the leading financial intermediary through which developing countries can enhance or boost economic growth in their country. In addition, importance is given to the profitability and efficiency of the banking sector for fulfilling such macroeconomic objectives of the country. &#x0D; In this regard, the main objective of this study is to analyze the efficiency of the banking sector of Western Balkans countries by utilizing secondary data collected from the official reports of the National Banks of the respective countries for the period 2004 – 2020. Thus, Data Envelopment Analysis was implemented to analyze the efficiency of the banks in Western Balkan for the period 2004 – 2020. Moreover, DEA results conclude that banks in Western Balkans operate at a good efficiency level due to an average score above 85% from 2004 – 2020. Montenegro had a continuous high-efficiency score during the last five years, followed by Serbia, Bosnia and Herzegovina, Kosovo, Albania, and North Macedonia.&#x0D; Due to the results of the efficiency of the banking sector of the Western Balkans and the specific analysis of the Republic of North Macedonia case, specific policy recommendations are given in this regard to enhance the higher efficiency of the banks in the Republic of North Macedonia.
Visualizing Google Sheets Data in Web Applications with Laravel

The study presents the design implementation of visualizing Google Sheets data in web applications using the Rapid Application Development (RAD) approach. It focuses on integrating Google Sheets data seamlessly into a web application using the Laravel PHP framework and employs effective data visualization techniques. Real-time data access is achieved through the integration of Google Sheets API, while the Google Maps API and High charts Library enabled interactive and informative data visualizations. The result is a user-friendly web application, empowering users to explore data insights and make well-informed decisions with ease
Normal calculation of artillery cradle before the robot arm assisted ultrasonic phased array automatic detection

This study is interested in the normal calculation of artillery cradle. When the beam axis of ultrasonic phased array probe cannot detect the workpiece surface vertically, the detection result is usually inaccurate. Thus, in order to determine the normal of the surface to be detected, a method based on visual point cloud to calculate the normal of artillery cradle is proposed in this paper. The proposed method solves two key problems. One is to sparse the point cloud while preserving the tiny information on the surface. A mesh division method along the uv direction is proposed. The disordered point cloud data in 3D space are connected into regular polygon meshes. While establishing the local relationship of point cloud data, it also maintains the overall integrity of point cloud data. The other is to calculate the normal vector of the point cloud. The local plane fitting method based on the least square method calculates the normal at a point of the point cloud. At this time, the calculated normal are nondirective. When there is no abrupt curvature change near the calculated point cloud, the direction of the normal is determined based on the same slope at two adjacent points. The calculated point cloud normal vector is compared with the actual normal vector of the mathematical model. The maximum deviation between the theoretical value and the actual value of the normal vector is 0.465°, and the average deviation is 0.302°. The experimental results show that the proposed method can be used to detect the actual normal of artillery cradle.
SSTNet: Saliency sparse transformers network with tokenized dilation for salient object detection

Abstract The vision Transformer structure performs better in salient object detection than the convolutional neural network (CNN)‐based approach. Vision Transformer predicts saliency by modelling long‐range dependencies from sequence to sequence with convolution‐free. It is challenging to distinguish the salient objects' location and obtain structural details for the influence of extracting irrelevant contextual information. A novel saliency sparse Transformer network is proposed to exploit sparse attention to guide saliency prediction. The convolution‐like with dilation in the token to token (T2T) module is replaced to achieve relationships in larger regions and to improve contextual information fusion. An adaptive position bias module is designed for the Vision Transformer to make position bias suitable for variable‐sized RGB images. A saliency sparse Transformer module is designed to improve the concentration of attention on the global context by selecting the Top‐k of the most relevant segments to improve the detection results further. Besides, cross‐modality to exploit the complementary RGB and depth modality fusion module (CMF) is used to take advantage of the complementary RGB image features and spatial depth information to enhance the feature fusion performance. Extensive experiments on multiple benchmark datasets demonstrate this method's effectiveness and superiority that it is suitable for saliency prediction comparable to state‐of‐the‐art RGB and RGB‐D saliency methods.
Benchmarking a large Twitter dataset for Arabic emotion analysis

Abstract The scarcity of available annotated Arabic language emotion datasets limits the effectiveness of emotion detection applications. Techniques such as semi-supervised self-learning annotation and transfer learning from models trained on large annotated datasets have been increasingly considered as alternative economic options for researchers working on Arabic sentiment and emotion detection tasks. Examining the quality of the data annotated using these techniques is particularly important in applications that require detecting emotions with high granularity such as mental health applications. This paper contributes an approach to benchmarking a semi-supervised self-learning annotated Arabic emotion large dataset. By extracting the lexical correlation of each emotion, and conducting content analysis, the quality of the annotation approach is demonstrated. Further, using a comprehensive set of experiments, we evidence the effectiveness of the transfer learning approach from the large dataset to smaller datasets in emotion and sentiment classification tasks.
Minimalist Sofa Redesign Using the Kano Method

UKM Mebel Berkah Raya is a company engaged in the field of furniture, one of the products produced is Sofa products. The large number of competitors who are the company's competitors, makes it difficult for the products produced to compete with similar products on the market, therefore, the company conducts a preliminary survey involving loyal customers owned by UKM Mebel Berkah Raya, in order to find out the desires of customers for sofa products, especially sofa products that are produced by the UKM furniture, Berkah Raya. The data obtained shows that the problem that is owned by the product sofa produced by UKM Mebel Berkah Raya lies in the product design attributes, so that with the problems that are owned, further research is carried out which aims to find out in detail the problems that are owned, to then carry out re-design of the sofa product, so that it will produce a sofa product that is in accordance with the customer's wishes. This study uses the Kano method to determine the level of customer satisfaction with a product by paying attention to the attributes attached to the product. so that the level of urgency of each Voice of Customers, so that action can be taken according to the priority level of the results obtained. The results of this study are that the attributes that will be developed with the provisions of Attractive that produce customer satisfaction. attributes Indiffrent that can generate positive feedback with customers.attribute One-Dimantion can increase customer satisfaction. So that the resulting design in the form of an existing sofa design is representative of the measurements that have been carried out with the method used.
QUARE: towards a question-answering model for requirements elicitation

Abstract Requirements elicitation is a stakeholder-centered approach; therefore, natural language remains an effective way of documenting and validating requirements. As the scope of the software domain grows, software analysts process a higher number of requirements documents, generating delays and errors while characterizing the software domain. Natural language processing is key in such a process, allowing software analysts for speeding up the requirements elicitation process and mitigating the impact of the ambiguity and misinterpretations coming from natural-language-based requirements documents. However, natural-language-processing-based proposals for requirements elicitation are mainly focused on specific domains and still fail for understanding several requirements writing styles. In this paper, we present QUARE, a question-answering model for requirements elicitation. The QUARE model comprises a meta-ontology for requirements elicitation, easing the generation of requirements-elicitation-related questions and the initial structuration of any software domain. In addition, the QUARE model includes a named entity recognition and relation extraction system focused on requirements elicitation, allowing software analysts for processing several requirements writing styles. Although software analysts address a software domain at a time, they use the same kind of questions for identifying and characterizing requirements abstractions such as actors, concepts, and actions from a software domain. Such a process may be framed into the QUARE model workflow. We validate our proposal by using an experimental process including real-world requirements documents coming from several software domains and requirements writing styles. The QUARE model is a novel proposal aimed at supporting software analysts in the requirements elicitation process.
Virtual Aquascape for Users Utilizing Augmented Reality

Aquascape is the art of arranging plants in water. The large number of complaints from aquascape buyers were not up to expectations, causing sellers to have difficulty in setting an example of an orderable aquascape, so researches created an aquascape application system to increase their promotional activities with augmented reality technology accessible through smartphone. One uses the methode MDLC (Multimedia Development Life Cycle). As a result, the application can implement a markless augmented reality to display 3D objects in real time with the help of a camera on a smartphone or a trilib feature by importing 3D object files found on file managers on a smartphone. User can easily provide the desired aquascape example of the aquascape application.
Course map learning with graph convolutional network based on AuCM

Abstract Concept map provides a concise structured representation of knowledge in the educational scenario. It consists of various concepts connected by prerequisite dependencies. With the abundance of educational resources available through MOOCs, encyclopedias, and electronic textbooks, extracting prerequisite dependencies and building concept maps becomes feasible. However, publicly accessible taxonomies or learning object information that can help identify prerequisites are rare. To address this, we have constructed a comprehensive dataset called the Australian Course Map data (AuCM), specifically tailored for training concept maps in the IT/CS field. The dataset comprises course descriptions from 14 different Australian universities. To identify prerequisite relationships between course concepts, we have employed an embedding-based approach that combines the Graph Convolutional Network (GCN) with pairwise features of concepts. We have evaluated the performance of our model with non-neural classifiers and neural networks for extracting these prerequisite relations.
Egocentric Hand Gesture Recognition on Untrimmed Videos Using State Activation Gate LSTMs

Deep Neural Networks have been used for recognising ego-hand gestures in trimmed videos extensively. However, recognising ego-hand gestures from untrimmed videos has been largely unexplored. In this work, we propose the concept of State Activation Gate (StAG) to extend the current LSTM framework and successfully apply it to recognising ego-hand gestures from untrimmed videos. We explore the usage of StAG LSTM combined with 3D convolutional neural networks to compare their performance to the state-of-the-art for two publicly available datasets. In addition, we present an intra-gesture (IG) loss function and a metric that favours continuity of gesture labels called Continuity Favouring Jaccard Index (CFJI). StAG LSTM reduces the need to use heuristics currently employed in ego-hand gesture recognition on untrimmed videos. Using the proposed IG loss function for training, achieves better performance on metrics like Jaccard Index (JI) and AUC scores compared to the state of the art.
Analyzing the Prosodic and Lingual Features of Popular Speakers

If the mission of Technology, Entertainment and Design (TED) is to spread ”great ideas” via talks, then high viewership of such talks is imperative to their success. But what are some stylistic differences between talks with higher viewership than others? To better understand this, we collect a large number of TED talk audio (N=2,065) from YouTube and create two categories: the lower (N1=402) and higher (N2=398) viewership classes. Using an explainable classifier (random forest), we find that prosody is not as effective a modality in predicting viewership classes, but augmenting it with linguistic features results in significantly better predictions. The prediction task is not the main objective of this work, rather it is implemented to help understand what prosodic and linguistic cues are important when differentiating between the delivery styles of low versus high viewership speakers. Although the main prosodic cues between the two classes are statistically significantly different, we found the most influential cue to be the “fraction of all words in the talk captured by LIWC”, a linguistic feature that has been shown to strongly correlate with the use of informal, nontechnical language.
BCTM: A Topic Modeling Method Based on External Information

Topic models are often used as intermediate algorithms for text mining and semantic analysis in natural language processing, and have a wide range of functions. However, most of the existing improvements to the topic model use word embedding to improve the accuracy of text modeling, but ignore the external information in the text. This paper proposes a topic model BCTM (Bi-Concept Topic Model) using the word feature information and concept information. Based on the BTM topic model, BCTM introduces word feature information through word vector technology and concept information based on ConceptNet to optimize topic modeling. The construction method of Bi-Concept pair is proposed. Based on ConceptNet semantic network, and the content of text is enriched with concept information. A more accurate topic distribution is obtained through the improved topic model, at the same time, due to the rich feature information, the model is also superior to the baseline model in short text modeling. The experiments prove that the bilingual topic model proposed in this paper has a good performance in modeling accuracy.