Reviewerly/exHarmony huggingface dataset:
Emergence of a scale-free network topology in a blockchain-based Shared Manufacturing

The development of smart manufacturing modes, following the guidelines of the Industry 4.0 paradigm, suggests the use of new technologies to ensure greater agility, individualization and personalization. Blockchain technology, as one of the new technologies, enables decentralization and trustless environment, however, on the other hand, introduces scalability limitations into the system. This paper presents an exploratory network analysis of money flow in blockchain-based Shared Manufacturing. We conduct an experiment in the form of an online game with people in order to obtain real data on network of prosumers in the said concept. We describe a relation between the money flow network, the supply of services in the market and the state of occupancy of the blockchain network in blockchain-based Shared Manufacturing. A greater supply of services in the market and a more occupied blockchain network causes a tendency for money flow network to organize like scale-free networks, however, this claim will be further verified in future work.
VPER: Visual Product Entity Recognition for Fashion-wear

In this work, we address the challenging task of detecting and recognizing fashion products, in addition to simultaneously computing several coarse and fine grained attributes for e-commerce applications. Specifically, while cataloging, when a seller uploads a fashion product, the proposed system automatically detects the region of interest (RoI) of the products, which is further passed to a deep learning framework for attribute extraction. The first contribution is a semi-supervised coarse-grained object detector, which utilizes bounding box annotations of a small number of classes, along with easily available unlabeled data. The second contribution is the attribute extraction module, which takes in the RoI generated by the object detector and uses an attention mechanism to accurately extract multiple visual attributes of varying granularity. The effectiveness of the complete framework is demonstrated on real seller uploaded images for a popular e-commerce platform. Our visual recognition system is built for 25 fashion categories and a total of 49 attributes.
Visual Tracking by Adaptive Continual Meta-Learning

We formulate the visual tracking problem as a semi-supervised continual learning problem, where only an initial frame is labeled. In contrast to conventional meta-learning based approaches that regard visual tracking as an instance detection problem with a focus on finding good weights for model initialization, we consider both initialization and online update processes simultaneously under our adaptive continual meta-learning framework. The proposed adaptive meta-learning strategy dynamically generates the hyperparameters needed for fast initialization and online update to achieve more robustness via adaptively regulating the learning process. In addition, our continual meta-learning approach based on knowledge distillation scheme helps the tracker adapt to new examples while retaining its knowledge on previously seen examples. We apply our proposed framework to deep learning-based tracking algorithm to obtain noticeable performance gains and competitive results against recent state-of-the-art tracking algorithms while performing at real-time speeds.
A fine-grained computational interpretation of Girard’s intuitionistic proof-nets

This paper introduces a functional term calculus, called pn, that captures the essence of the operational semantics of Intuitionistic Linear Logic Proof-Nets with a faithful degree of granularity, both statically and dynamically. On the static side, we identify an equivalence relation on pn-terms which is sound and complete with respect to the classical notion of structural equivalence for proof-nets. On the dynamic side, we show that every single (exponential) step in the term calculus translates to a different single (exponential) step in the graphical formalism, thus capturing the original Girard’s granularity of proof-nets but on the level of terms. We also show some fundamental properties of the calculus such as confluence, strong normalization, preservation of β-strong normalization and the existence of a strong bisimulation that captures pairs of pn-terms having the same graph reduction.
Malaria Detection Using Convolutional Neural Network

Malaria detection and classification is still time and money costly. Identification of malaria cells can be done through some costly techniques. Those techniques are good, but they require time and high cost. Hence, there is a need of discovering alternative techniques to identify blood cells, that saves both time and reduce cost. In addition to time and cost, those new techniques should also be accurate and effective. Thus, in this work, we propose a transfer learning based GoogleNet approach for the classification of Malaria cells. The depth and inception of GoogleNet made it a very robust deep network that can classify accurately if trained and fine-tuned on enough amount of data. Thus, in this study, 27558 of the 2 types of cells are used for fine-tuning and testing the pre-trained network GoogleNet. Experimentally, the employed GoogleNet fine-tuned to classify Malaria, showed a great capability in generalizing accurate and correct diagnosis of images that were not seen during training, in which it achieved a testing accuracy of 95% with a relatively short time and small number of epochs.
2D Multi-Person Pose Estimation Combined with Face Detection

Pose estimation is the basis and key of human motion recognition. In the two-dimensional human pose estimation based on image, in order to reduce the adverse effects of mutual occlusion among multiple people and improve the accuracy of motion recognition, a structurally symmetrical two-dimensional multi-person pose estimation model combined with face detection is proposed in this paper. First, transfer learning is used to initialize each sub-branch network model. Then, MTCNN is used for face detection to predict the number of people in the image. According to the number of people, the image is input into the improved two-branch OpenPose network. What is more, the double judgment algorithm is proposed to correct the false detection of MTCNN. The experimental results show that compared with TensorPose, which is the latest improved method based on OpenPose, the Average Precision (AP) (Intersection over Union [Formula: see text]) on the validation set is 8.8 higher. Furthermore, compared with OpenPose, the mean AP ([Formula: see text]) is 1.7 higher on the validation set and is 1.3 higher on the Test-dev test set.
Generating Topic-Preserving Synthetic News

The text generation methods have witnessed great success in text summarization, machine translation, and synthetic news generation. However, these techniques may be abused to generate disinformation and fake news. To better understand the potential threats of synthetic news, we develop a novel generation method RLTG to generate topic-preserving news content. The majority of existing text generation methods are either controlled by specific attributes or lack topic consistency between the input claims and output news, making synthetic news less coherent and realistic. In this paper, we study the problem of topic-preserving synthetic news generation by proposing a novel deep reinforcement learning-based method to control the output of large pre-trained language models. Experiment results on real-world datasets demonstrate that the news contents generated by RLTG are topic-consistent and realistic.
Legal Entity Extraction using a Pointer Generator Network

Named Entity Recognition (NER) is the task of identifying and classifying named entities in unstructured text. In the legal domain, named entities of interest may include the case parties, judges, names of courts, case numbers, references to laws, etc. We study the problem of legal named entity extraction from noisy text extracted from PDF files of filed court cases from US courts. The "gold standard" training data for classical NER systems provide annotation for each token of the text with the corresponding entity or non-entity label. We work with only partially complete training data, which differ from the gold standard NER data in that the exact location of the entities in the text is unknown and the entities may contain typos and/or OCR mistakes. To overcome the challenges of our noisy training data, e.g., text extraction errors and/or typos and unknown label indices, we frame the NER task as a sequence generation task (seq2seq) and train a pointer generator network to generate the entities in the document rather than label them. We attempt to create a NER gold standard dataset via sequence matching and use this dataset to train classical NER baselines and compare them with our seq2seq approach for Named Entity (NE) extraction. We show that the seq2seq approach can effectively extract legal named entities, in the absence of gold standard data, and outperform the common neural network architectures for NER in long legal documents.
wSPIRE: A Parallel Multi-Device Corpus in Neutral and Whispered Speech

Most of the speech technologies for whispered speech are lagging behind due to the scarcity of data. Hence, in this paper, we present and open source a multi-device parallel speech corpus in neutral and whispered mode, called wSPIRE. The wSPIRE consists of 88 (54 males and 34 females) speakers recorded using five recording devices in neutral and whispered modes. So this dataset contains almost 44000 audio recordings resulting in a total of ~36 hours speech corpus. Apart from this, wSPIRE corpus also contains word-level annotations for each recording. Different types of analyses on the speech recordings and word level annotations are presented. We also throw light on the possible research areas and problems which can be addressed using the wSPIRE.
Fall Detection System Using Novel Median Deviated Ternary Patterns and SVM

In recent years, we have noticed exponential growth in the elderly population of the world due to the advancement in the medical field that necessitates proper care and more attention of elderly people. Accidental falls can be life threatening and can cause severe head trauma, bone fractures, and internal bleedings. Moreover, the most devasting problem of accidental fall incident is that the person remains on the floor for a long time without getting any immediate assistance and response. Research community proposed various fall detection systems but still there exist certain limitations of the existing methods i.e., computational complexity, expensive sensors, unable to wear wearable sensors, and associated privacy issues. To address these issues, we proposed a novel feature descriptor median deviated ternary patterns (MDTP) for audio representation to effectively capture the discriminatory traits of fall and non-fall events. We used the proposed MDTP features to train the support vector machine (SVM) to classify the fall and non-fall incidents. Our proposed method is evaluated against two datasets i.e. A3 fall 2.0 dataset and the MSP-UET fall detection dataset. Our proposed method achieved remarkable accuracy of 98% and 97%, precision of 100% and 96%, recall of 97% and 96%, and F1-score of 98% and 96% on the A3 fall 2.0 and MSP-UET fall detection datasets respectively. Experimental results signify the effectiveness of the proposed system for reliable monitoring of elderly people for fall detection.
Know-GNN: An Explainable Knowledge-Guided Graph Neural Network for Fraud Detection

Fraud is on the rise under modern e-commence scenarios, which will critically damage the market system. Thus, it is essential to detect fraudsters to prevent unpredictable risks. There are two challenges toward this problem. First, real world fraud detection usually lack of labeled samples. Second, recent ML-based detection method lack of interpretation. Knowledge may help with these problems. Hence, we propose a Knowledge-Guided Graph Neural Network, namely Know-GNN, which utilizes the expertise to roughly mark unlabeled data and uses an explainable semi-supervised method to train a fraud detector. We adopt Graph Functional Dependency (GFD) as a uniform expression of knowledge to mark unlabeled data and give explanations of the detection results. Experiments on banking transaction funds supervision data (BTFSD) demonstrate the effectiveness of our model. By utilizing only 13 GFD rules conducted by domain experts corresponding to BTFSD, the performance of our method yields about 14% improvement over the state-of-the-art methods, CARE-GNN. Moreover, the interpretable results can give interesting intuitions about the fraud detection tasks.
A Genre-Specific Game Description Language for Game Boy RPGs

Existing game description languages (GDLs) aspire to generality, but their focus on the specification of low-level mechanics leaves game generators that target these GDLs in the awkward position of having to invent combinations of mechanics that work well together from scratch. As a result, many existing game generators are good at producing games that contain novel and surprising combinations of mechanics, but bad at generating games that are readily interpretable by players as cultural artifacts. To address this problem, we introduce the concept of a genre-specific game description language (GSGDL): a game description language that deliberately encodes assumptions about a particular genre of games as a cultural form. As a proof of concept, we demonstrate the use of an internal representation of game structure used by the game creation tool GB Studio as a GSGDL for top-down 2D roleplaying games targeting the Game Boy. The use of this GSGDL gives us leverage to rapidly iterate on game generation features targeting a specific game genre and platform; to work with an existing toolchain that offers graphical editing, code generation, and automated play testing; and to more readily generate games that are interpretable by players as examples of a particular cultural form.
Model design and parameter optimization of CNN for side-channel cryptanalysis

The side-channel cryptanalysis method based on convolutional neural network (CNNSCA) can effectively carry out cryptographic attacks. The CNNSCA network models that achieve cryptanalysis mainly include CNNSCA based on the VGG variant (VGG-CNNSCA) and CNNSCA based on the Alexnet variant (Alex-CNNSCA). The learning ability and cryptanalysis performance of these CNNSCA models are not optimal, and the trained model has low accuracy, too long training time, and takes up more computing resources. In order to improve the overall performance of CNNSCA, the paper will improve CNNSCA model design and hyperparameter optimization.The paper first studied the CNN architecture composition in the SCA application scenario, and derives the calculation process of the CNN core algorithm for side-channel leakage of one-dimensional data. Secondly, a new basic model of CNNSCA was designed by comprehensively using the advantages of VGG-CNNSCA model classification and fitting efficiency and Alex-CNNSCA model occupying less computing resources, in order to better reduce the gradient dispersion problem of error back propagation in deep networks, the SE (Squeeze-and-Excitation) module is newly embedded in this basic model, this module is used for the first time in the CNNSCA model, which forms a new idea for the design of the CNNSCA model. Then apply this basic model to a known first-order masked dataset from the side-channel leak public database (ASCAD). In this application scenario, according to the model design rules and actual experimental results, exclude non-essential experimental parameters. Optimize the various hyperparameters of the basic model in the most objective experimental parameter interval to improve its cryptanalysis performance, which results in a hyper-parameter optimization scheme and a final benchmark for the determination of hyper-parameters.Finally, a new CNNSCA model optimized architecture for attacking unprotected encryption devices is obtained-CNNSCAnew. Through comparative experiments, CNNSCAnew's guessing entropy evaluation results converged to 61. From model training to successful recovery of the key, the total time spent was shortened to about 30 min, and we obtained better performance than other CNNSCA models.
Mathematical Models for Calculation of Limits in Water Resources Management in Irrigation Systems

In the present article, algorithms for calculating limits in water resources management in irrigation systems were carried out and the problems of water resources management of the Basin Irrigation Systems Administration which were studied is characterized by the fact that it is necessary to solve the problems of collecting and processing information about the control object from large territories and to manage its links, which are located at large distances from each other.
UAV Detection using Web Application Approach based on SSD Pre-Trained Model

UAV development is being intensively developed by various groups to help overcome various types of problems. Object Detection is important in helping UAVs to do drone chasing and other competition that need visual approach based on image processing and deep learning. Unfortunately, the computational capabilities of the onboard processing unit that attached to the UAV are less than optimal for object detection due to storage and memory size constraints. This paper aims to create the new approach to improve the precision and recall during UAV detection by using web application to do real time detection. To decide a pre-trained model, it is necessary to compare which SSD pre-trained model is suitable to be deployed in this web application. The results obtained are that using the web application approach is better than the onboard processing approach with a high level of precision and recall with an average precision value of 0.85 and an average recall value of 0.837.
Privacy-Preserving Framework for Blockchain-based Stock Exchange Platform

This paper presents a privacy persevering framework for a decentralized stock exchange platform, ensuring anonymity and unlinkability of the investors' accounts and their respective trading activities.The proposed framework meets these privacy requirements by (i) anonymizing both the unique account identifier (NIN) and balance information through customized data generalization and distortion techniques and (ii) making trading transactions unlinkable to their original investors by ensuring that both the NIN and balance are k-anonymous; i.e., k accounts belonging to different investors have the same balance.Moreover, to ensure long-term unlinkability, the process of anonymization is repeated at regular time intervals (every trading session).In addition to anonymity and unlinkability characteristics, the proposed framework is augmented with traceability and non-repudiation features.The simulation experiments with several market sizes and types confirm the effectiveness of the proposed framework in achieving full k-anonymity.Furthermore, to assess the overhead of the proposed privacy algorithms on the trading execution time, we conduct several experiments considering different anonymity levels k.We compare the transaction execution time of our proposed platform against a traditional non-privacypreserving blockchain-based stock exchange.The obtained results for the worst-case scenarios show an acceptable execution time overhead.
Siamese Visual Tracking With Residual Fusion Learning

Multi-stage feature fusion is pretty effective for deep Siamese trackers to promote tracking performance. Unfortunately, conventional fusion approaches, such as weighted average, are so simple that they are inappropriate to combine the features with diverse characteristics. In addition, the fusion module is generally optimized along with Siamese network module, which may result in the performance degradation of the whole tracker. In this paper, we propose a novel feature fusion network for Siamese tracker by exploiting the expression capacity of residual fusion learning (SiamRFL). Specifically, the network employs the deep-layer features as direct input to semantically recognize the object from background, and refines the object state with local detail patterns by exploring the shallow-layer features through residual channel. The classification and the regression features can be fused respectively by deploying multiple fusion units. To avoid the degradation problem, we also present an ensemble training framework for our tracker, in which different loss functions are introduced to individually optimize the Siamese and the fusion modules. Compared to the baseline SiamRPN++ tracker, the proposed tracker achieves favorable gains by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.696\rightarrow 0.709$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.285\rightarrow 0.308$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.603\rightarrow 0.624$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.496\rightarrow 0.520$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.517\rightarrow 0.559$ </tex-math></inline-formula> on OTB100, VOT2019, UAV123, LaSOT and GOT10k datasets, outperforming other approaches by an obvious margin.
Capacity-Limited Decentralized Actor-Critic for Multi-Agent Games

This paper explores information-theoretic constraints on methods for multi-agent reinforcement learning (MARL) in mixed cooperative and competitive games. Within this domain, decentralized training has been employed to increase learning sample efficiency. However, these approaches do not explicitly discourage complex policies, which can lead to overfitting. To address this, we apply an information theoretic constraint onto agents' policies that discourages overly complex behaviour when it is not associated with a significant increase in reward. A second challenge in MARL is the non-stationarity of the environment introduced by other agents' changing policies. Previous methods in MARL have sought to reduce the impact of non-stationarity by inferring other agents' policies, but this can lead to over-fitting to previously observed behaviour. To avoid this, a similar information-theoretic constraint is applied onto the inference of other agents' policies, resulting in a more robust estimate. We evaluate the effects of these information-theoretic constraints on a test suite of multi-agent games, and report an overall improvement in performance, with greater improvements found in competitive domains compared to cooperative games.