Reviewerly/exHarmony huggingface dataset:
Securing image-based document transmission in logistics and supply chain management through cheating-resistant visual cryptographic protocols

In today's digital landscape, securing multimedia visual information-specifically color images-is of critical importance across a range of sectors, including the burgeoning fields of logistics and supply chain management. Traditional Visual Cryptography (VC) schemes lay the groundwork for encrypting visual data by fragmenting a secret image into multiple shares, thereby ensuring no single share divulges the secret. Nevertheless, VC faces challenges in ascertaining the integrity of reconstructed images, especially when shares are manipulated maliciously. Existing solutions often necessitate additional shares or a trusted third party for integrity verification, thereby adding complexity and potential security risks. This paper introduces a novel Cheating-Resistant Visual Cryptographic Protocol (CRVC) for Color Images that aims to address these limitations. Utilizing self-computational models, this enhanced protocol simplifies the integrated integrity verification process, eliminating the need for extra shares. A standout feature is its capability to securely transmit meaningful shares for color images without compromising the quality of the reconstructed image as the PSNR maintains to be ∞. Experimental findings substantiate the protocol's resilience against quality degradation and its effectiveness in verifying the authenticity of the reconstructed image. This innovative approach holds promise for a wide array of applications, notably in sectors requiring secure document transmission, such as Logistics and Supply Chain Management, E-Governance, Medical and Military Applications.
JPEG image encryption with grouping coefficients based on entropy coding

In the existing JPEG image encryption schemes, the block feature values that can be used to reduce key search space are either difficult to be changed or need to be changed through overflow processing which leads to low generality and high encryption runtime. To change block feature values without overflow processing, a novel JPEG image encryption scheme is proposed. For AC encryption, the complete and end AC groups based on undivided RSV (run/size, value) (ACG-URSV) are permuted separately to change different features. Complete ACG-URSV containing different number of RSVs is used to change the non-zero coefficients count (NCC) and energy of AC coefficients (EAC). End ACG-URSV containing the zero coefficients after position of last non-zero AC coefficient (PLZ) is mainly used to change PLZ. Besides, the intra-block RSV permutation and block permutation are used to further destroy correlation. For DC encryption, the positive DC prediction error (PDC-PE) groups modulo encryption is proposed to avoid overflow processing. The experimental results show that this paper reduces encryption runtime by more than half, improves generality by at least 20%. When quality factor of 90, the average change rates of NCC, EAC and PLZ values are increased by 96.28%, 11.68% and 29.15%, respectively.
Place-and-Route Analysis of FPGA Implementation of Nested Hardware Self-Organizing Map Architecture

Self-organizing map (SOM) is a type of artificial neural network that provides a nonlinear mapping from a given high-dimensional input space to a low-dimensional map of neurons for clustering. The clustering of high-dimensional vectors is too slow for SOM when implemented in software. In such cases, an application-specific hardware SOM accelerator is highly desirable. Field-programmable gate array (FPGA) implementation is a popular platform to implement hardware SOM. In our previous work, a nested hardware SOM architecture, which has a homogeneous modular structure to enhance expandability, was proposed. This paper investigates the impact of the nested hardware SOM on FPGA implementation tools that perform logic synthesis, place, and route (PAR). Experiments revealed that the nested architecture provided better results in resource usage and performance. FPGA resource usage of the nested architecture was 97.2% of that of the flat design on average. Importantly, the nested architecture operated at 10% higher clock frequencies compared to flat SOM designs. In addition, the pipeline computation was improved by increasing the pipeline stages so that it operates with a higher clock frequency. The operable clock frequency was 81 MHz, which was 21 MHz higher than its predecessor.
What's The Point: AutoEncoding Building Point Names

Deciphering the point names of various equipment within commercial buildings remains a vexing challenge. Both the lack of standards, and the usual idiosyncrasies in naming conventions, add to the complexity faced by engineers as they attempt to analyse and optimise a building's systems. For example, T, Tmp, Temp, C, and Celsius could all be used to denote a temperature sensor. Likewise, the delimiters (e.g., ., -, or _) that apportion parts of a point name varies considerably. This results in the possibility of both f1-AHU_tmp and Floor02.AHU.Temperature appearing in the same dataset (and often even within the same building). The initial work here explores foundational deep-learning techniques to help a human expert understand previously unseen building point names. We've applied a simplified yet effective neural-network architecture called AutoEncoders (AE) to the task, with tests done against an expansive dataset comprising of point names gathered from publicly available Brick and Mortar datasets, as well as a private corpus. This novel implementation is augmented by a unique sequential tokenising method that applies tri-grams in a sliding window over the point names, which preserves both the linearity and delimiters within point names. This is diametrically opposed to prior approaches that have either ignored the character sequences and/or removed the presence of delimiting characters. Early results show promising 91% accuracy levels achieved against previously unseen point names.
Investigating the Support Provided by Chatbots to Educational Institutions and Their Students: A Systematic Literature Review

A chatbot, or else a conversational agent (CA), is a technology that is used in order to imitate the process of a conversation between a human being and a software application for supporting specific services. The utilization of this technology has been increasing considerably over the past five years, particularly in education where CAs are mostly utilized as teaching assistants that provide educational content. This paper aims to contribute to the existing body of knowledge by systematically reviewing the support provided by chatbots both to educational institutions and their students, investigating their capabilities in further detail, and highlighting the various ways that this technology could and should be used in order to maximize its benefits. Emphasis is given to analyzing and synthesizing the emerging roles of CAs, usage recommendations and suggestions, student’s desires, and challenges recorded in the literature. For this reason, a systematic literature review (SLR) was carried out using the PRISMA framework in order to minimize the common biases and limitations of SLRs. However, we must note that the SLR presented has specific limitations, namely using only Scopus as a search engine, utilizing a general search query, and selecting only journal articles published in English in the last five years.
Cross-modal Semantically Augmented Network for Image-text Matching

Image-text matching plays an important role in solving the problem of cross-modal information processing. Since there are nonnegligible semantic differences between heterogenous pairwise data, a crucial challenge is how to learn a unified representation. Existing methods mainly rely on the alignment between regional image features and corresponding entity words. However, the regional features in the image are often more concerned with the foreground entity information, and the attribute information of the entities and the relational information are ignored. How to effectively integrate entity-attribute alignment and relationship alignment has not been fully studied. Therefore, we propose a Cross-Modal Semantically Augmented Network for Image-Text Matching (CMSAN), which combines the relationships between entities in the image with the semantics of relational words in the text. CMSAN (1) proposes an adaptive word-type prediction model that classifies the words into four types, i.e., entity word, attribute word, relation word, and unnecessary word. It can align different image features at multiple levels. CMSAN (2) designs a sophisticated relationship alignment module and an entity-attribute alignment module that maximizes the exploitation of the semantic information, which enables the model to have more discriminative power and further improves the matching accuracy.
Exploring Spatial-Based Position Encoding for Image Captioning

Image captioning has become a hot topic in artificial intelligence research and sits at the intersection of computer vision and natural language processing. Most recent imaging captioning models have adopted an “encoder + decoder” architecture, in which the encoder is employed generally to extract the visual feature, while the decoder generates the descriptive sentence word by word. However, the visual features need to be flattened into sequence form before being forwarded to the decoder, and this results in the loss of the 2D spatial position information of the image. This limitation is particularly pronounced in the Transformer architecture since it is inherently not position-aware. Therefore, in this paper, we propose a simple coordinate-based spatial position encoding method (CSPE) to remedy this deficiency. CSPE firstly creates the 2D position coordinates for each feature pixel, and then encodes them by row and by column separately via trainable or hard encoding, effectively strengthening the position representation of visual features and enriching the generated description sentences. In addition, in order to reduce the time cost, we also explore a diagonal-based spatial position encoding (DSPE) approach. Compared with CSPE, DSPE is slightly inferior in performance but has a faster calculation speed. Extensive experiments on the MS COCO 2014 dataset demonstrate that CSPE and DSPE can significantly enhance the spatial position representation of visual features. CSPE, in particular, demonstrates BLEU-4 and CIDEr metrics improved by 1.6% and 5.7%, respectively, compared with a baseline model without sequence-based position encoding, and also outperforms current sequence-based position encoding approaches by a significant margin. In addition, the robustness and plug-and-play ability of the proposed method are validated based on a medical captioning generation model.
On Underdispersed Count Kernels for Smoothing Probability Mass Functions

Only a few count smoothers are available for the widespread use of discrete associated kernel estimators, and their constructions lack systematic approaches. This paper proposes the mean dispersion technique for building count kernels. It is only applicable to count distributions that exhibit the underdispersion property, which ensures the convergence of the corresponding estimators. In addition to the well-known binomial and recent CoM-Poisson kernels, we introduce two new ones such the double Poisson and gamma-count kernels. Despite the challenging problem of obtaining explicit expressions, these kernels effectively smooth densities. Their good performances are pointed out from both numerical and comparative analyses, particularly for small and moderate sample sizes. The optimal tuning parameter is here investigated by integrated squared errors. Also, the added advantage of faster computation times is really very interesting. Thus, the overall accuracy of two newly suggested kernels appears to be between the two old ones. Finally, an application including a tail probability estimation on a real count data and some concluding remarks are given.
Research on Financial Fraud Detection Models Integrating Multiple Relational Graphs

The current fraud risk in digital finance is increasing year by year, and the mainstream solutions rely on the inherent characteristics of users, which makes it difficult to explain fraud behaviors and fraud behavior patterns are less researched. To address these problems, we propose an integrated multiple relational graphs fraud detection model Tri-RGCN-XGBoost, which analyzes the impact of user association patterns on fraud detection by mining the behavioral associations of users. The model builds a heterogeneous information network based on real transaction data, abstracts three types of bipartite graphs (user–device, user–merchant, and user–address), aggregates the information of the user’s neighbor nodes under the three types of behavioral patterns, and integrates the graph convolution classification results under the three behavioral patterns with the XGBoost model to achieve fraudulent user detection with integrated multiple relational graphs. The results show that the performance of this model in fraud identification is significantly improved, especially in reducing the fraudulent user underreporting rate. Further, the behavioral associations that play a key role in fraud user identification are analyzed in conjunction with shape value to provide a reference for fraud pattern mining.
A Secret Key Classification Framework of Symmetric Encryption Algorithm Based on Deep Transfer Learning

The leakage signals, including electromagnetic, energy, time, and temperature, generated during the operation of password devices contain highly correlated key information, which leads to security vulnerabilities. In traditional encryption algorithms, the length of the key greatly affects the upper limit of its security against cracking. Regarding side-channel attacks on long-key algorithms, traditional template attack methods characterize the energy traces using multivariate Gaussian distribution during the template construction phase. The exhaustive key-guessing process is expected to consume a significant amount of time and computational resources. Therefore, to analyze the effectiveness of obtaining key values from the side information of password devices, we propose an innovative attack method based on a divide-and-conquer logical structure, targeting semi-bytes. We construct a collection of key classification submodules with symmetric correlations. By integrating a differential network model for byte-block sets and an end-to-end direct attack method, we form a holistic symmetric decision framework and propose a key classification structure based on deep transfer learning. This structure consists of three main parts: side information data acquisition, analysis of key-value effectiveness, and determination of attack positions. It employs multiple parallel symmetric subnetworks, effectively improving attack efficiency and reducing the key enumeration range. Experimental results show that the optimal attack accuracy of the network model can reach 91%, with an average attack accuracy of 78%. It overcomes overfitting issues under small sample dataset conditions.
Q-YOLO: Efficient Inference for Real-Time Object Detection

Real-time object detection plays a vital role in various computer vision applications. However, deploying real-time object detectors on resource-constrained platforms poses challenges due to high computational and memory requirements. This paper describes a low-bit quantization method to build a highly efficient one-stage detector, dubbed as Q-YOLO, which can effectively address the performance degradation problem caused by activation distribution imbalance in traditional quantized YOLO models. Q-YOLO introduces a fully end-to-end Post-Training Quantization (PTQ) pipeline with a well-designed Unilateral Histogram-based (UH) activation quantization scheme, which determines the maximum truncation values through histogram analysis by minimizing the Mean Squared Error (MSE) quantization errors. Extensive experiments on the COCO dataset demonstrate the effectiveness of Q-YOLO, outperforming other PTQ methods while achieving a more favorable balance between accuracy and computational cost. This research contributes to advancing the efficient deployment of object detection models on resource-limited edge devices, enabling real-time detection with reduced computational and memory overhead.
CTC-Net: A Novel Coupled Feature-Enhanced Transformer and Inverted Convolution Network for Medical Image Segmentation

In recent years, the Vision Transformer has gradually replaced the CNN as the mainstream method in the field of medical image segmentation due to its powerful long-range dependencies modeling ability. However, the segmentation network leveraging pure transformer performs poor in feature expression because of the lack of convolutional locality. Besides, the channel dimension information are lost in the network. In this paper, we propose a novel segmentation network termed CTC-Net to address these problems. Specifically, we design a feature-enhanced transformer module with spatial-reduction attention to extract the region details in the image patches by the depth-wise convolution. Then, the point-wise convolution is leveraged to capture non-linear relationship in the channel dimension. Furthermore, a parallel convolutional encoder branch and an inverted residual coordinate attention block are designed to mine the clear dependencies of local context, channel dimension features and location information. Extensive experiments on Synapse Multi-organ CT and ACDC (Automatic Cardiac Diagnosis Challenge) datasets show that our method outperforms the methods based on CNN and pure transformers, obtaining up to 1.72 $$\%$$ and 0.68 $$\%$$ improvement in DSC scores respectively.
A comparative assessment of the power generation via S-shape and M-shape PV system and its impact on a residential consumer

This paper compares the differences in power generation and resident experience between S-shape and M-shape photovoltaic (PV) systems at the same location with the same number of modules. First, the sky anisotropy model (Hay-Davies-Klucher-Reindl, HDKR) is used to simulate solar irradiance (direct, diffuse, and reflection radiation) received by solar modules based on the laws of movement between the Earth and the Sun in the solar system. Next, the annual output power of two different PV system arrangements is calculated based on the solar irradiation received. Furthermore, because the solar module receives three types of solar radiation, interference is added to each of the three parts one by one to simulate shadows cast by moving objects such as birds, leaves, or clouds. Subsequently, off-grid and grid-connected power consumption models are established, and two different photovoltaic power generation systems are used at the power generation end. Lastly, the photovoltaic systems were tested based on residents’ electricity consumption habits, considering both off-grid and grid-connected households. Among them, the grid-connected model adopts the Particle Swarm Optimization (PSO) algorithm to generate an intelligent and flexible power consumption strategy. The results, based on data from Naha, Japan, show that with the same number of modules, the M-shape arrangement generates less annual power than the S-shape. In summer, though, the M-shape can generate more power, and the two structures have similar immunity to disturbances. The S-shape and M-shape have little difference in the power consumption experience. However, the general applicability of these results to other regions remains uncertain, and further testing is needed to account for environmental variations.
Competencies Development Strategy Using Augmented Reality for Self-Management of Learning in Manufacturing Laboratories (AR-ManufacturingLab)

Technological tools in education open new learning possibilities. This proposal describes integrating augmented reality and different educational elements to develop engineering competencies, specifically using specialized machinery in the manufacturing laboratory. It is necessary to encourage self-management of learning, where users interact with the devices employing an application that contains significant information about procedures to perform and essential elements to consider when manipulating the machinery. The machines involved include industrial robots, CNC (Computer Numerical Control) lathes and milling machines, and PLCs (Programmable Logic Controls). In a traditional training model, an instructor guides the users in learning how to manipulate the equipment. This proposal intends that the participants use their mobile devices to receive each machine's instruction and the necessary documentation. The participants' learning pace varies. So, the research aimed to reduce the deficit in the disciplinary competencies since, in the traditional methodology, there is no heterogeneity in understanding the contents. We designed a competency development strategy with six laboratory practices, integrating multimedia elements to address the significant learning content of each manufacturing cell device. Each lesson contained information about each device and a practice activity and self-evaluation to ensure learning the content.
Building Domain Ontologies for Tunisian Dialect: Towards Aspect Sentiment Analysis from Social Media

Sentiment analysis from social media has received increasing attention during the last few years. Indeed, getting the opinion of customers, users, or people about a product or a service is paramount for many firms in different sectors, ranging from food to politics. Recently, Aspect Sentiment Analysis (ASA) constitutes a rapidly evolving research area. The idea is to determine for each comment, not only an overall polarity (i.e., positive, negative, or neutral) but a detailed polarity per aspect. This paper proposes an original work for building domain ontologies towards aspect sentiment analysis on Tunisian social media. These ontologies will be exploited to improve classification performance for both aspect detection and aspect sentiment analysis. To our best of knowledge, there is no Tunisian ontology extracted from social media content dedicated to sentiment analysis or any other close research domain. Our proposed method is based on an NLP pipeline that we apply on raw Tunisian dialect datasets scraped from social media. We focused on four domains which are Mobile phones, Food, Tunisian election and Radio/TV Programs and applied our proposed method to build Tunisian dialect domain ontologies.
New engines of economic growth: How digital currencies lead the way to growth in the era of digital economy

Drawing from city-level data spanning 2013 to 2020 in China, this study examines the economic outcomes of a digital currency pilot test policy. Through the application of a Difference-in-Differences (DID) model, the findings indicate areas promoting digital currencies are more inclined to draw businesses to establish and allocate investments into fixed assets, thereby fostering economic growth. Our empirical evidence also suggests that the robust adaptability of digital currencies across varied cultural contexts and geographical locations.