Reviewerly/exHarmony huggingface dataset:
A Perspective-Embedded Scale-Selection Network for Crowd Counting in Public Transportation

Crowd counting in congested urban transport systems is a highly challenging task for computer vision and deep learning due to several factors such as mutual occlusion, perspective change, and large-scale variations. In this paper, a novel perspective-embedded scale-selection multi-column network named PESSNet is proposed for crowd counting and high-quality density maps generation. The proposed method aligns the branches to various scales by leveraging different receptive fields, and utilizes perspective parameters to adjust the sensitivity of each branch to different perspective areas in the scene. Specifically, the PESSNet consists of four key components: 1) feature pyramid network (FPN) fuses multi-stage features extracted from the backbone network; 2) scale-selection dilated layer (SSDL) extracts features by using different dilated convolution kernels for each stage; 3) perspective-embedded fusion layer (PEFL) encodes the spatial perspective relationships across all feature levels into the network and provides a more effective fine-grained weight map; and 4) density maps generator (DMG) employs deconvolution layer as a decoder to generate high-quality density maps. The above strategies maximizes the ability of multi-column network to extract the features of instances with various scales. Extensive experiments on seven crowd counting benchmark datasets, JHU-CROWD, ShanghaiTech, UCF-QNRF, ShanghaiTechRGBD, WorldEXPO’10, TRANCOS, and NWPU-Crowd indicate that PESSNet achieves reliable recognition performance and high robustness in difference crowd counting.
Sparse Membership Affinity Lasso for Fuzzy Clustering

The membership matrix is a key element in fuzzy clustering, enabling novel data representation in multiple clusters. The row vectors of the membership matrix represent each sample's degree of membership to different clusters. Notably, researchers have confirmed the presence of the local affinity among these row vectors, effectively preserving the local structure of the original data distribution. However, in this work, we consider that most sample points have insignificant fuzziness, with fuzziness found primarily in a few clusters, resulting in most membership vectors being sparse. To tackle this issue, we present the SMalFCM model, which leverages the sparsity of the row vectors and its affinity to establish a more appropriate representation, along with an optimization algorithm. Our experimental results on both simulated and real datasets demonstrate that the combination of sparsity and affinity can significantly enhance fuzzy clustering performance over other models.
A Holistic Energy-aware and Probabilistic Determined VMP Strategy for Heterogeneous Data Centers

The expansion of data centers, driven by the continuous development of network services, has led to a significant issue of high energy consumption. Due to the real-time interaction between IT and non-IT equipments, it is difficult to consider the holistic energy consumption of heterogeneous data centers. Therefore, this paper proposes a holistic-energy-aware-virtual machine placement (VMP) strategy for data centers that use heterogeneous resources to provide services. Firstly, we propose the energy-aware VMP strategy by using the probabilistically determining mechanism to effectively minimize the number of activated servers and improve server resource utilization. Secondly, within this strategy, we leverage dynamic voltage and frequency scaling (DVFS) technology, enabling nodes to operate at lower frequencies and voltages while meeting performance requirements, thus further reducing computing energy consumption. Thirdly, in addition to the previous two points, the probabilistic determined genetic algorithm (PDGA) is proposed to reasonably distribute the workloads based on the heat-recirculation effect and reduces the cooling energy consumption. The above mechanisms collectively optimize the global energy consumption of heterogeneous data centers. Experimental results demonstrate that the PDGA can significantly reduce the energy consumption of IT and non-IT equipment. The total energy consumption of the data center is significantly reduced (the PDGA is 20.83% lower than the simulated annealing based algorithm and 20.76% lower than the big data task scheduling algorithm based on thermal-aware and DVFS-enabled techniques).
SAMCL: Subgraph-Aligned Multiview Contrastive Learning for Graph Anomaly Detection

Graph anomaly detection (GAD) has gained increasing attention in various attribute graph applications, i.e., social communication and financial fraud transaction networks. Recently, graph contrastive learning (GCL)-based methods have been widely adopted as the mainstream for GAD with remarkable success. However, existing GCL strategies in GAD mainly focus on node-node and node-subgraph contrast and fail to explore subgraph-subgraph level comparison. Furthermore, the different sizes or component node indices of the sampled subgraph pairs may cause the "nonaligned" issue, making it difficult to accurately measure the similarity of subgraph pairs. In this article, we propose a novel subgraph-aligned multiview contrastive approach for graph anomaly detection, named SAMCL, which fills the subgraph-subgraph contrastive-level blank for GAD tasks. Specifically, we first generate the multiview augmented subgraphs by capturing different neighbors of target nodes forming contrasting subgraph pairs. Then, to fulfill the nonaligned subgraph pair contrast, we propose a subgraph-aligned strategy that estimates similarities with the Earth mover's distance (EMD) of both considering the node embedding distributions and typology awareness. With the newly established similarity measure for subgraphs, we conduct the interview subgraph-aligned contrastive learning module to better detect changes for nodes with different local subgraphs. Moreover, we conduct intraview node-subgraph contrastive learning to supplement richer information on abnormalities. Finally, we also employ the node reconstruction task for the masked subgraph to measure the local change of the target node. Finally, the anomaly score for each node is jointly calculated by these three modules. Extensive experiments conducted on benchmark datasets verify the effectiveness of our approach compared to existing state-of-the-art (SOTA) methods with significant performance gains (up to 6.36% improvement on ACM). Our code can be verified at https://github.com/hujingtao/SAMCL.
An intelligent extension of the training set for the Persian n-gram language model: an enrichment algorithm

In this article, we are going to introduce an automatic mechanism to intelligently extend the training set to improve the n-gram language model of Persian. Given the free word-order property in Persian, our enrichment algorithm diversifies n-gram combinations in baseline training data through dependency reordering, adding permissible sentences and filtering ungrammatical sentences using a hybrid empirical (heuristic) and linguistic approach. Experiments performed on baseline training set (taken from a standard Persian corpus) and the resulting enriched training set indicate a declining trend in average relative perplexity (between 34% to 73%) for informal/spoken vs. formal/written Persian test data.
Doubly Interpretable Fuzzy Apriori Classifier by Successive Stacking and One-step Wide Calculation

Except for linguistic interpretability and uncertainty-handling ability, fuzzy Apriori method (FAM) is being hurdled by both very expensive computational burdens and low generalization capability caused by serious correlation between short to long fuzzy rules generated. The novel doubly interpretable classifier DI-FAM with FAM-based hybrid structure is proposed to circumvent the above shortcomings of FAM. DI-FAM successively stacks the short rule bundles of each FAM sub-classifier on both a sampled feature subset and the outputs of the previous stacking layer. DI-FAM then finds out the output weights of short rule bundles at each stacking layer, followed by a linear sub-classifier (as a compensator) on all the original input features with one-step wide calculation. DI-FAM has four distinct merits: (1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">low computational complexity</i> stemmed from both its fast generation way of short rule bundles by FAM sub-classifiers respectively on their own features, and its one-step calculation for the output weights. (2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">theoretical guarantee</i> about no violation of the importance ranking orders of the short rules by each FAM sub-classifier on its own features at each stacking layer with regard to all the fuzzy rules by FAM on all the input features. (3) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">enhanced generalization capability</i> by successively stacking short rule bundles at each stacking layer according to the stacked generalization principle. (4) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">double interpretability</i> that DI-FAM shares both linguistic interpretability of all FAM sub-classifiers and feature-importance-based interpretability of a linear sub-classifier. Extensive experimental results indicate the effectiveness of DI-FAM in the sense of classification performance, training speed, incremental learning and double interpretability.
XSS Attack Detection by Attention Mechanism Based on Script Tags in URLs

XSS (Cross-Site Scripting) attacks exploit vulnerabilities in web applications, and many victims have been reported. As a countermeasure for this, existing studies propose methods to detect XSS attacks by combining natural language processing techniques and machine learning models. Few studies reveal the features that contribute to the classification and the validity of the dataset. In this study, we analyzed the weights of words in the attention mechanism to identify the features that contribute to classification. Our models are combinations of LSTM (Long Short Term Memory) and attention mechanism. Validation experiments were conducted on two different datasets. The experimental result shows that typical features such as script tags and text-encoded hex strings contribute to the classification. In addition, when the benign sample of the test data was increased by a factor of 5, the f1 score was found to decrease to a maximum of 0.55. Since the focused features depend on the dataset, the generality and practicality of the classification model are still to be evaluated. In other words, the utility of the classification model based on the dataset is not high, and it is necessary to verify the model to improve its utility.
Isogeny-Based Multi-signature Scheme

Multi-signatures are protocols that allow multiple signers to produce a joint signature on the same message. They are used in areas such as blockchains for cryptocurrencies. In recent years, multi-signature schemes have been proposed in lattice-based cryptography as well as pairing-based cryptography but there are currently no multi-signature schemes in isogeny-based cryptography. In this paper, we propose a multi-signature scheme by extending the Commutative Supersingular Isogeny based Fiat-Shamir signature (CSI-FiSh), as well as its variant CSI-FiSh with Sharing-friendly Keys (CSI-SharK) to the multiple signers setting. To adapt our scheme to the isogeny setting, we use a round-robin during both the key aggregation and the signature aggregation. We also prove the security of our scheme in the Random Oracle Model (ROM) by using the Double Forking Lemma Technique.
Quantum Key Distribution as a Service and Its Injection into TLS

Quantum key distribution (QKD) is a key agreement method that relies on the laws of physics and ensures that the keys have not been eavesdropped on or modified by a third party. While commercial QKD devices are available, they are expensive, require specific infrastructure, and have high operational expenses. In this paper, we propose an architecture and a set of protocols that allow us to implement QKD as a service (QaaS). End users communicate with QaaS via classical TLS channels secured with post-quantum cryptography (PQC). We show how to further strengthen the security of these classical links to make them sustainable to active attacks (classical and quantum) on any single segment of QaaS. We also show how to integrate QaaS into the state-of-the-art TLS 1.3 protocol. As a result, QKD becomes available for a larger community of end-users. Furthermore, we show how QaaS can reduce the number of digital signatures within a TLS 1.3 handshake, which is essential since post-quantum signatures are much longer than the conventional RSA/ECC-based ones.
SIOCEN: Secure Integrity Verification of Outsourced Data in Cloud Storage using Blockchain

Storing large volumes of data is a bottleneck on edge devices. Thus, Cloud Storage Technologies have emerged as a viable alternative for large-scale data storage. However, the data owner loses control of the file (once uploaded), which may lead to data integrity issues. Even though there are existing schemes to check the integrity of the files using a third-party auditor, they rely on the unrealistic assumption that the auditors are completely trustworthy. It has been reported in many works of literature that auditors sometimes collude with the cloud server to cheat the system. In this paper, we propose a scheme named secure integrity verification of outsourced data in cloud storage using blockchain (SIOCEN) which protects the system from entity collusion. Using smart contract, we enforce the incentive mechanism in the scheme, which is the most appealing feature of the SIOCEN that increases fairness. The security of the proposed scheme is analysed and proved to be secure. We implement the SIOCEN system on Ethereum network and do a performance analysis, showing the effectiveness of the proposed system.
Romanesque Prague Monuments – Dimension Stone and Its Cutting

The study of building stone cutting is still a new discipline, which is currently based on evolving methods of mechanoscopy and analytical trasology. The presented article is an extract of a study that systematically maps the stone-cutting work in the territory of Prague from the oldest buildings to the present day. The most used dimension stone of Romanesque Prague is “opoka”. Although the opoka also occurs in other countries, its use as a predominant building material of monuments of this age is most common, especially in Prague. The systematic research of historic building stone cutting in the historic centre of Prague reveals the world of stonework from the earliest time. Individual traces of tools may be considered a historical source. Each work has its own unique pattern and bears traces of individual stonemason workshops, traditions, and training of the craftsman’s predecessors. Stonework in 9th century Prague was gradually evolving from simple stone block modelling to sophisticated cutting of blocks in the 12th century. For the distinctive work of Prague, stonemasons rock face cutting was typical, which means they gradually obliquely cut the areas from the corners to the centre of the block. However, they did not use various decorative patterns so typical for European work. In Western Europe, the aim was to use innovative tooth tools that were highly effective in working with stone; nevertheless, in Prague, these tools did not become popular, although they were sporadically used as European craftsmen arrived in Prague. When studying the stone surface, the latest 3D modelling technology was used, and consequently, the models were used to create mechanoscopic analyses using Global Mapper software. Based on the identified traces and their shapes in connection with the dynamics of the hits, the tools used could be reconstructed.
Pemeriksaan HCG (Human Chorionic Gonadotropin) Dengan Metode Latex Dan Metode Strip Test Untuk Deteksi Kehamilan

HCG (Human Chorionic Gonadotropin) is a hormone produced by young placental tissue and secreted by urine in the event of abnormal proliferation of chorionic epithelial tissue. The presence of urinary HCG can be used for pregnancy detection. Pregnancy check can be done by latex method and strip test method. This study aims to determine the description of HCG (Human Chorionic Gonadotropin) examination with latex method and strip test method for pregnancy detection. The research used Purposive sampling technique with 25 samples of pregnant women in the first trimester. The determination of gestational age in a simple way is by counting the first day of the last menstrual period (HPHT), 3 weeks, 2 people (8%), 4 week HPHT, 4 (16%), 5 week HPHT, 3 (12%) , 6-week. HPHT was 2 people (8%), HPHT 7-8 weeks there were 6 people (24%), HPHT 9- 12 weeks there were 8 people (32%). Of the 25 samples examined using two methods namely the method of latex and the method of Strip Test, all samples tested positive pregnant (100%).
The Improvement Path of Teacher Digital Literacy under the Background of Industry Standards for Teacher Digital Literacy

In the context of the development of digital education, teachers' digital literacy has become a key factor affecting the effectiveness of education. The release of the industry standard "Digital Literacy for Teachers" conforms to the requirements of the digital era for educational development, and proposes new industry standards for teachers to adapt to digital change and the future of education. In this context, conducting a systematic study on the value connotation, challenges, and improvement paths of teachers' digital literacy is undoubtedly an era proposition with strong theoretical and practical significance.
"5G+Education"—Reflection and Practice on Smart Education in Technical Colleges

5G is a new type of mobile communication technology that has emerged with the continuous progress of society, economy, technology, and other aspects. In such an environment, many industries have faced new development opportunities and challenges. Therefore, in the context of "5G+education", this article explored the curriculum teaching reform of technical colleges in the 5G network environment, which was a very meaningful work. Therefore, this article first started with the changes caused by 5G technology in the curriculum teaching of technical colleges. On this basis, the curriculum teaching in universities under the conditions of "5G+education" was explored, hoping to provide some reference for the reform and development of curriculum teaching in technical colleges under the conditions of "5G+education". According to the survey questionnaire, the average score of students' evaluation of the effectiveness of autonomous preview courses and their evaluation of smart classrooms exceeded 3, indicating that smart classrooms are widely praised by students and can improve their learning efficiency.
Research on the Information Reform of Experimental Teaching by the Construction of Resource Library

Aiming at the problems of resource shortage, insufficient investment and insufficient equipment in experimental teaching in colleges and universities in China, it is proposed to use information technology to establish an experimental resource library on the basis of existing experimental conditions, so as to reform the existing experimental teaching mode and improve the level of practical teaching. This paper analyzes and gives the content, structure, management mode and use method of the experimental resource library, which provides ideas and references for different schools to implement practical teaching reform. Through the establishment of experimental resource library, the sharing of experimental resources can save the cost of laboratory construction in colleges and universities[1], and improve the efficiency and management level of experimental equipment.
Exploration on the Reform of Accounting Practice Curriculum in Higher Vocational Education under the Background of Financial Shared Service Center

As Financial Shared Service Center become increasingly important in the modern economy, accounting practice courses in higher vocational education are also facing new challenges and opportunities. However, there is currently relatively limited research on how to effectively implement such courses to improve student employment and satisfaction. In order to solve this problem, this study uses the SPSS regression model analysis method to explore the key factors affecting student employment and satisfaction, and propose improvement measures. The research results show that learning attitudes, learning strategies, personal abilities, professional knowledge, teaching methods, educational concepts, curriculum, practical opportunities and cooperation opportunities have a significant impact on students' employment and satisfaction. Based on the above conclusions, this study proposes a series of improvement measures, including optimizing curriculum, strengthening professional knowledge cultivation, and diversifying teaching methods. These measures aim to improve students' practical abilities, employment competitiveness and satisfaction. This study provides a useful reference for the reform of accounting practice courses in higher vocational education in the context of the Financial Shared Service Center. At the same time, this study also provides guidance for educational institutions and teachers, promoting the development of higher vocational education and the optimization of practical courses.
The Correlation Between Students’ Reading Attitude and Their Reading Comprehension

The act of comprehending a text is known as reading comprehension. The aim is to fully comprehend what is being described in the text. L2 reading may be influenced by a variety of factors, one of them is reading attitude. A learner's reading attitude, which affects whether they enjoy or hate reading, may have an impact on their reading skills through how involved they are with their reading. The aim of this study is to determine whether or not reading comprehension and student attitudes are correlated. A total of 48 junior high school students in the eighth grade participated in this study. The data was collected by distributing a reading attitude questionnaire and reading comprehension TOEFL Junior Test. The result found that the students’ reading attitude was fairly good (M = 3.40) and their reading comprehension was mostly at B1 level (M = 288.35). However, the result of Pearson Correlation showed that there is a weak correlation between students’ reading attitude and their reading comprehension (r = .279, p = 0.055). The students believe that reading can give them a positive impact, yet they do not really like the activity of reading.