Reviewerly/exHarmony huggingface dataset:
Two-way unclonable encryption with a vulnerable sender

Unclonable Encryption, introduced by Gottesman in 2003 [Quantum Inform. Comput. 3 (2003) 581], is a quantum protocol that guarantees the secrecy of a successfully transferred classical message even when all keys leak at a later time. We propose an Unclonable Encryption protocol with the additional property that the sender’s key material is allowed to leak even in the case of an unsuccessful run. This extra feature makes it possible to achieve secure quantum encryption even when one of the parties is unable to protect its keys against after-protocol theft. Such an asymmetry occurs e.g. in case of server–client scenarios, where the client device is resource constrained and/or located in a hostile environment. Our protocol makes use of a bidirectional quantum channel in a manner similar to the two-way protocol LM05 [Phys. Rev. Lett. 94 (2005) 140501]. Bob sends random qubit states to Alice. Alice flips the states in a way that depends on the message and a shared key, and sends the resulting states back to Bob. Bob recovers Alice’s message by measuring the flips. We prove that our protocol satisfies the definition of unclonable encryption and additionally that the message remains secure even if all of Alice’s keys leak after the protocol. Furthermore, we show that some of the key materials can be safely re-used. Our security proof is formulated in terms of diamond norms, which makes it composable, and allows for noisy quantum channels. We work out the details only for the asymptotics in the limit of long messages. As a side result, we construct a two-way QKD scheme with a high key rate. We show that its key rate is higher than the rate of the two-way QKD scheme LM05 proven by Beaudry et al. [Phys. Rev. A 88 (2013) 062302] for the case of independent channel noise.
Multi-Layer Collaborative Graph with BPR Similarity Embedding for Recommender System

Learning high-level representations for users and items is the main objective of modern recommender systems (RS). Since data in RS inherently has graph structure the graph neural networks (GNN) have been widely used for feature extraction of RS. To take advantage of GNN in representation learning we present a new efficient method, named as Multi-Layer Collaborative Graph with BPR Similarity Embedding (MLCGBPRSE) by applying multi-layer graph convolutional network for obtaining high-order neighborhood information in propagation process. Moreover, for better information exploiting we present an auxiliary loss function to capture user-user and item-item similarities as well. The experimental results indicate the superiority of our proposed model in both recall and NDCG compared to the state-of-the-art approaches.
Computer-Aided Polyp Removal Detection in Endoscopic Images

One of the most common gastrointestinal (GI) diseases is polyps. It is capable of causing the most lethal colon and rectal cancers. As a result, it must be removed during the primitive stage. In this paper, we investigated seven different classifiers with seven distinct features and proposed a simple polyp removal detection approach. It has also compared the performance of all classifiers for the features. The researchers noted that the Support Vector Machine (SVM) classifier provided the highest result for all features except Auto Color Correlogram. Besides, Bag of Features has achieved the highest result accuracy of 70.35%, the precision of 69.93%, F1-score 70.66%, and recall 71.40% for SVM classifier among all the features. It is optimistic that the research will open a path for future research in Polyp removal classification.
Research on nameplate image recognition algorithm based on R-CNN and SSD deep learning detection methods

In order to prevent sudden equipment failures and accidents, it is necessary to conduct regular and irregular regular inspections on important substations and lines. Nameplate is an important power facility, which is usually installed in a prominent position of tower. Due to its long-term exposure to wind and rain, it is prone to damage, scratches and other defects. In this paper, Faster-RCNN based on region suggestion is adopted as the detection algorithm, and the nameplate in the image is identified and located by this algorithm. By selecting the improved LeNet-5 proposed in this paper, the classical Convolutional Neural Network (CNN) and ResNetl01 as the basis to extract the network, and comparing the models, the detection results under different framework models are discussed. optimize SSD algorithm to improve the speed and accuracy of image recognition, deeply study the parameter adjustment strategy of deep learning (DL), and build a rich nameplate database for training. The experimental results show that the improved fast SSD algorithm has better performance.
Cyber security and the Leviathan

Dedicated cyber-security functions are common in commercial businesses, who are confronted by evolving and pervasive threats of data breaches and other perilous security events. Such businesses are enmeshed with the wider societies in which they operate. Using data gathered from in-depth, semi-structured interviews with 15 Chief Information Security Officers, as well as six senior organisational leaders, we show that the work of political philosopher Thomas Hobbes, particularly Leviathan, offers a useful lens through which to understand the context of these functions and of cyber security in Western society. Our findings indicate that cyber security within these businesses demonstrates a number of Hobbesian features that are further implicated in, and provide significant benefits to, the wider Leviathan-esque state. These include the normalisation of intrusive controls, such as surveillance, and the stimulation of consumption. We conclude by suggesting implications for cyber-security practitioners, in particular, the reflexivity that these perspectives offer, as well as for businesses and other researchers.
Lung Cancer Image Segmentation Algorithm Based on TA-FCMSPCNN

The correct and fast segmentation of the diseased tissue is of vital significance for computer-aided diagnosis of lung cancer. Thus, this work provided a novel segmentation algorithm based on the improved pulse coupled neural network model (TA-FCMSPCNN). In this study, we applied the network to the field of lung cancer image segmentation based on adjustable threshold FC-MSPCNN that without changed the structure of the model. It combined with the adaptive parameter setting method and redefined the link strength β, attenuation factor α and amplitude parameters V. Through analyzing correlation between neurons, fully improving the matrix W <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ijkl</inf> , and other related parameters β. It reduced the computational complexity of the model. We adjusted the threshold value via introduced adjusting parameters G, meanwhile, its effectively ensured the stability of the model and rationality of the parameter setting. Finally, the segmentation results were refined by morphological operation to obtain the refined segmentation results of lung cancer images. The experimental results show that the TA-FCMSPCNN is excellent to the traditional classic algorithm.
Fine-Grained Visual Classification Based on Wisely Feature Map Filtering Mechanism

Fine-Grained Visual Classification (FGVC), also named as sub-category recognition, is a research hotspot in computer vision and pattern recognition recently. The objective of FGVC is to classify objects that are in the same basic class. There are two main challenges in FGVC tasks: small interclass differences and large intraclass differences. Before the development of deep learning, there had been little significant achievement in the research of these challenges. However, most methods focus on the step of generating the feature maps but pay little attention to the further processing of feature maps. All of them are used directly by most methods after they are extracted from the original image, which lacks further processing of feature maps and may lead to irrelevant features to negatively affect network performance. In order to refine the processing of feature maps and improve the classification ability of FGVC tasks, we propose a Wisely Feature Map Filtering Network (WAMFN), which is proposed to extract the feature maps for subcategories and filter out the most distinguishable and representative feature maps to generate attention maps. Results of multi experiments show that the WAMFN outperforms the state-of-the-art methods on several fine-grained classification datasets.
Two-level iteration method for multi-task learning with task-isolated labels

Face attribute recognition plays a vital role in face-related tasks. Common face attributes include person age, person gender, mask-wearing, glasses-wearing, etc. Using one network to predict all attributes can save many computation costs. However, these attributes can hardly be fully labelled on every image in the same dataset since the labelling costs and the requirement on the sample balance. In many cases, each of the datasets are labelled with a single attribute. With several such datasets, how to use one network to generate the multi-task predictions for all attributes is a problem. In this paper, we propose a two-level iteration training method for multi-task face attribute learning with task-isolated labels. The two-level iteration method includes a task-level inner iteration and the regular outer iteration. With this scheme, the network receives the gradients from all tasks after each inner iteration. After training, the network is able to predict all attributes. Experiments show the effectiveness of the method and the advantages of multi-task learning over single-task learning on network accuracy and efficiency, which demonstrate the broad applicability and effectiveness of the proposed approach.
Weakly Supervised Fine-Grained Visual Classification Through Spatial Information Mining and Attention-guided Regularization

Over-fitting is a severe problem when we adopt deep neural networks with a large number parameters in fine-grained visual classification. Many data augmentation methods are proposed through weakly supervised learning to alleviate over-fitting issue. Different from those methods, we propose a weakly supervised attention-guided regularization by object parts&#x2019; attention maps to fine-tune the Fully Connected (FC) layer and relieve over-fitting issue during training in this paper. On the other hand, the neural units in the last convolutional layer contain the same receptive fields that limit recognition performance due to involving lots of background noises. To alleviate this issue, we devise a spatial information mining module with an auxiliary penalty loss to aggregate multi-scale receptive fields feature maps with the selected precedent layer. Comprehensive experiments are conducted to show our method achieves or surpasses state-of-the-art results on common fine-grained classification datasets.
Overview of Financial Fraud Digital Forensic Investigation Framework

Financial fraud cases are dangerously escalating. Industries and consumers are struggling to prevent being victims of fraud cases. Fraudsters typically aim to gain financial benefits from pulling fake documents or information to perceive as a legitimate beneficiary claimant. However, issue of tracking down fraudsters committed fraud through the Internet is increasingly challenging due to the advancement of computer technology nowadays. The lack of evidence traces and enormous data to be processed to filter out relevant evidence exhaust both time and manpower. Hence, based on the summary of other research work, criminal profiling and machine learning are proposed to be integrated into digital forensic to improve both speed and accuracy in investigation financial fraud cases.
Multi-Label Text Classification Based on Multidimensional Information Extraction

At present, there is a common problem of project labels missing in open source software (OSS) communities. However, proper project labels help OSS users find the expected OSS projects more efficiently and improve the quality of project recommendations. In response to the above challenges, this paper proposes a multi-label text classification based on syntactic and semantic (MLSS), which combines semantic, syntactic, implicit topic, and text distribution, can effectively solve the problems of sparse text features and poor topic focus of open source projects, and improve the classification effect of OSS project text labels. Compared with the classical ML-kNN and BERT model, the experiment results show that the proposed MLSS algorithm has advantages in nearly all five evaluation criteria, especially the average precision is increased 16.46% higher than that of the BERT model and 103.80% higher than the ML-kNN model.
CIAFill: Lightweight and Fast Image Inpainting with Channel Independent Attention

Image inpainting is a classic technique in computer vision research. The quality of image inpainting has improved significantly since the advent of convolutional neural networks. However, this approach generally results in blurry and semantically inconsistent reconstruction because of operating valid and invalid pixels with equal weight. The gated convolution computing feature attention is proposed to resolve this issue but this attention mechanism was less efficient and computationally expensive. This study proposed CIAFill that alleviates this problem using channel independent attention. This mechanism applied channel attention to each channel for activating valid channels and reduced the computational cost to the dimension of the channel. The proposed architecture included a channel attention generator and a channel attention projection PatchGAN that utilize the channel independent attention mechanism. This study proved that CIAFill could successfully train the inpainting model with 1/1600 smaller gating parameters than the earlier gated convolution-based study. CIAFill achieved comparable performance to other feature attention-based approaches in the experiments on CelebA-HQ and Places2 datasets.
The effectiveness of self-supervised representation learning in zero-resource subword modeling

For a language with no transcribed speech available (the zero-resource scenario), conventional acoustic modeling algorithms are not applicable. Recently, zero-resource acoustic modeling has gained much interest. One research problem is unsupervised subword modeling (USM), i.e., learning a feature representation that can distinguish subword units and is robust to speaker variation. Previous studies showed that self-supervised learning (SSL) has the potential to separate speaker and phonetic information in speech in an unsupervised manner, which is highly desired in USM. This paper compares two representative SSL algorithms, namely, contrastive predictive coding (CPC) and autoregressive predictive coding (APC), as a front-end method of a recently proposed, state-of-the art two-stage approach, to learn a representation as input to a back-end cross-lingual DNN. Experiments show that the bottleneck features extracted by the back-end achieved state of the art in a subword ABX task on the Libri-light and ZeroSpeech databases. In general, CPC is more effective than APC as the front-end in our approach, which is independent of the choice of the out-domain language identity in the back-end cross-lingual DNN and the training data amount. With very limited training data, APC is found similar or more effective than CPC when test data consists of long utterances.
PCNetOP: Partial Completion Network with Order Prediction

Self-supervised methods can solve scene de-occlusion without a modal and order annotation, while supervised methods can only parse visible parts, leading to incomplete and unstructured scenario interpretation. The existing supervised methods that try to solve scene de-occlusion also need many manual annotations of invisible masks, which are costly and inaccurate. PCNets that use self-supervised method are introduced to solve this problem through new and unified frameworks that restore the hidden scene structure without the need for ordering and a modal annotations. This is achieved by Partial Completion Network (PCNet) -mask (M) and -content (C), which restore the components of the object mask and content respectively in a self-supervised way. We find that the accuracy of its order recovery in complex scenes is still low. In response to this problem, we propose a new occlusion order recovery method called PCNetOP (Partial Completion Network with order prediction), which combines the attention mechanism to redesign the network structure and training strategy to improve the occlusion complement effect in complex scenarios. Experiments show that our method solves the problem of self-supervised occlusion completion better than PCNet-M.
The Creation of Contemporary Character of Lord Wessuwan in Buddhist Art

The purpose of this study is to analyze the traditional ancient Buddhism art of Lord Wessuwan, to create contemporary character of Lord Wessuwan in different figure, and to design a figure of Lord Wessuwan for producing amulet product based on the principles of Contemporary Comic Art in the styles of Southeast Asia and Western World that are blended together to create new style. In addition, the principles of the designing of Thai motifs, which are the combination between traditional Thai graphics and contemporary Lanna graphics, which have been analyzed and synthesized. As for the test, the finished works are sold and publicized in the system of Blockchain NFT (a non-fungible token). Apart from the designing in the sense of art, there is the creation of amulets of Lord Wessuwan which has been consecrated in accordance with the belief and process of the consecration of amulets in Buddhism. However, the amulets in this project are made with digital technology, making them art works in the jewelry level. The findings from this study point out that the new style of Lord Wessuwan will lead to more popularity and faith, which have been reflected from the request to buy the amulets. From such feedback, it means this new style is accepted by the society and has become a significant unique character that paves way for arts in the future with which Thai culture and arts are still conserved.
Document Filter for Writer Identification

The writing can be used as an important biometric modality which allows to unequivocally identify an individual. It happens because the writing of two different persons present differences that can be explored both in terms of graphometric properties or even by addressing the manuscript as a digital image, taking into account the use of image processing techniques that can properly capture different visual attributes of the image (e.g. texture). In this work, we perform a detailed study in which we dissect whether or not the use of a dataset with only a single sample taken from some writers may skew the results obtained in the experimental protocol. In this sense, we propose here what we call “Document Filter”. The Document Filter protocol is supposed to be used as a preprocessing technique, in such a way that all the data taken from fragments of the same document must be placed either into the training or into the test set. The rationale behind it, is that the classifier must capture the features from the writer itself, and not features regarding other particularities which could affect the writing in a specific document (e.g. emotional state of the writer, pen used, paper type, and etc.). By analyzing the literature, one can find several works dealing with the writer identification problem. However, the performance of the writer identification systems must be evaluated also taking into account the occurrence of writer volunteers who contributed with a single sample during the creation of the manuscript databases. To address the open issue investigated here, a comprehensive set of experiments was performed on the IAM, CVL and BFL databases. They have shown that, in the most extreme case, the recognition rate obtained using the DF protocol drops 30.94% points.
Graph Optimization Algorithm Using Symmetry and Host Bias for Low-Latency Indirect Network

It is known that an indirect network with a small host-to-host Average Shortest Path Length (h-ASPL) improves overall system performance in a parallel computer system. As a means to discuss such indirect networks in graph theory, the Order/Radix Problem (ORP) has been proposed. ORP involves finding a graph with a minimum h-ASPL that satisfies a given number of hosts and radix. A graph in ORP represents an indirect network and has two types of vertices: host and switch. We propose an optimization algorithm to generate graphs with a sufficiently small h-ASPL. The primary features of the proposed algorithm are the symmetry of the graph and the bias of the hosts adjacent to each switch. These features reduce the computational time to calculate the h-ASPL and improve the search performance of the algorithm. The performance of the proposed algorithm is evaluated using problems presented by Graph Golf, an international ORP competition. Our results show that the proposed algorithm can generate graphs with a smaller h-ASPL than the existing algorithm. To evaluate the performance of the graphs generated by the proposed algorithm, we use the parallel simulation framework SimGrid and the parallel benchmark collection NAS Parallel Benchmarks. Our results also show that the graphs generated by the proposed algorithm have higher performance than those generated by the existing algorithm.
Effect of Environment, Safety, and Health on Employee Performance

This study aims to determine whether there is an influence on the Work Environment, Safety, Health, and Employee Performance. Safety, and Health on the Performance of Employees of the Perusahaan Daerah Air Minum Cabang Kota Bekasi (PDAM). The method used in this research is the quantitative approach. The population in this study were all employees as many as 55 respondents with sampling using random sampling technique by calculating the Slovin formula in a sample of 55 respondents. Collecting data using an online questionnaire distributed using Google Forms. Technical data analysis used SPSS (Statistical Product and Service Solutions) application software. The results showed that there was a significant effect between the independent variables, namely the Work Environment, Safety, and Health on the dependent variable, namely Employee Performance.
LBP index for evaluation of disk degaussing achievement based on AFM image

In the field of information security, it is very important to judge whether the information on a magnetic storage medium is completely destroyed. But so far, domestic research on the degaussing effect of magnetic storage media is still lacking. Previous studies have shown that the magnetic images before and after degaussing can reflect the amount of meaningful information left on the disk, which is closely related to the degaussing effect. Therefore, this paper proposes a new method to study the magnetic images before and after degaussing. This paper introduces the LBP texture feature extraction algorithm to process the magnetic images before and after degaussing, and evaluates the degaussing effect of the magnetic storage medium through the extracted texture feature values. A new LBP degaussing evaluation index is proposed, and the parameters of the index are optimized to achieve the best evaluation performance.