Reviewerly/exHarmony huggingface dataset:
Cost-Effective Anomaly Detection for Blockchain Transactions Using Unsupervised Learning

If the user performs an online transaction, it is disseminated to the blockchain network (BN) and irrevocably implemented. But if a malicious user signs a fake deal, it cannot be half-done far along, thus causing a loss of resources for the customer. Hence, to reduce the processing power and bandwidth, we propose to design a cost-effective anomaly detection for blockchain transactions using unsupervised learning techniques in this paper. In this technique, the nodes in the BN are grouped into various clusters. If any node of a cluster has missed transactions, it can be reconstructed using the erasure coding technique. Then the transaction history is grouped based on the address. The time series data features are extracted by applying the rolling window aggregation to detect abnormal transactions. Then, K-means clustering is used to perform anomaly detection. By simulation results, we show that the proposed approach is cost-effective.
Literature Survey: Sign Language Recognition Using Gesture Recognition and Natural Language Processing

The deaf communities prevalent in India are still struggling for Indian Sign Language to gain the status of a minority language. A system is required that translates Indian Sign Language to the corresponding English language excerpt. For this, the visual, as well as non-visual input of Sign Language signs, have to be processed, translated into English words, and then these words have to be put together into a grammatically correct and meaningful sentence (or sentences). The researchers have worked on processing input which can be sensor-based, image-based, with videos in their entirety, or sampling videos after fixed intervals of time to decide the trajectories of motions. The input could be of any form, i.e., a hardware system for recognizing hand movements, images, or video format. This paper focuses on state-of-the-art literature that identifies areas of interest in the non-visual inputs, image frames, and video frames to determine the features for a particular hand gesture. The literature survey also takes into account the approaches considered by researchers across different sign languages like American Sign Language, Taiwanese Sign Language, etc. which will help to develop a perspective for Indian Sign Language. This paper also reviews previous research work that has been conducted to translate a video to the English language using Natural Language Processing techniques such as the Viterbi algorithm, tokenization, part-of-speech tagging, and parsing.
A Systematic Review of Coevolution in Real-Time Strategy Games

Real-time strategy (RTS) games are a subgenre of strategy video games. Due to their importance in practical decision-making and digital entertainment over the last two decades, many researchers have explored different algorithmic approaches for controlling agents within RTS games and learning effective strategies and tactics. Among the techniques, coevolutionary algorithms proved to be one of the most popular and successful algorithms for developing such games, in which players can compete or cooperate to achieve the given game’s mission. However, as many alternative designs exist with their analysis and the applications reported in diverse publications, a review covering the evolution of such algorithms would be valuable for researchers and practitioners in this domain. This paper aims to provide a systematic review by highlighting why and how coevolution is used in RTS games and analysis of the recent work. The review conducted follows procedural steps to identify, filter, analyse and discuss the existing literature. This structured review articulates the purposes of using coevolution in RTS games and highlights several open questions for future research in this domain.
Follow Your Path: A Progressive Method for Knowledge Distillation

Deep neural networks often have huge number of parameters, which posts challenges in deployment in application scenarios with limited memory and computation capacity. Knowledge distillation is one approach to derive compact models from bigger ones. However, it has been observed that a converged heavy teacher model is strongly constrained for learning a compact student network and could make the optimization subject to poor local optima. In this paper, we propose ProKT, a new model-agnostic method by projecting the supervision signals of a teacher model into the student’s parameter space. Such projection is implemented by decomposing the training objective into local intermediate targets with approximate mirror descent technique. The proposed method could be less sensitive with the quirks during optimization which could result in a better local optima. Experiments on both image and text datasets show that our proposed ProKT consistently achieves superior performance comparing to other existing knowledge distillation methods.
ICDAR 2021 Competition on Scientific Table Image Recognition to LaTeX

Tables present important information concisely in many scientific documents. Visual features like mathematical symbols, equations, and spanning cells make structure and content extraction from tables embedded in research documents difficult. This paper discusses the dataset, tasks, participants' methods, and results of the ICDAR 2021 Competition on Scientific Table Image Recognition to LaTeX. Specifically, the task of the competition is to convert a tabula r image to its corresponding Open image in new window source code. We proposed two subtasks. In Subtask 1, we ask the participants to reconstruct the Open image in new window structure code from an image. In Subtask 2, we ask the participants to reconstruct the Open image in new window content code from an image. This report describes the datasets and ground truth specification, details the performance evaluation metrics used, presents the final results, and summarizes the participating methods. Submission by team VCGroup got the highest Exact Match accuracy score of 74% for Subtask 1 and 55% for Subtask 2, beating previous baselines by 5% and 12%, respectively. Although improvements can still be made to the recognition capabilities of models, this competition contributes to the development of fully automated table recognition systems by challenging practitioners to solve problems under specific constraints and sharing their approaches; the platform will remain available for post-challenge submissions at https://competitions.codalab.org/competitions/26979.
Research on Intelligent Target Recognition Integrated With Knowledge

With the development of artificial intelligence technology, intelligent weapon systems that can automatically identify, lock on and strike targets have gradually appeared and can replace humans in executing simple decision-making commands. Target detection is a key part of intelligent weapons. At present, large-scale target detection has serious challenges such as long-tail data distributions, severe occlusion, and category ambiguity. The main detection algorithms only detect each independent area without considering the key semantic dependencies between objects. It has become a hot trend to apply deep learning to prior knowledge to form a model. This article uses both internal and external knowledge to instill a target detection system with human reasoning capabilities. Commonly used external embedded knowledge includes geometric relations, attributes, locations, etc. They have a common shortcoming in that they require large amounts of labeled data, and the integration costs are huge. The purpose of this article is to construct a general external prior knowledge module to guide network learning. By paying attention to the characteristics of each object in different semantic contexts, the characteristics of each object are adaptively enhanced, and the high-level semantics of all categories evolve on a global scale. Internal knowledge uses a convolutional attention module that can learn spatial and channel information at multiple scales. The experimental results show the superiority of our knowledge-YOLOv5. The proposed method achieved 1.7%, 2.2%, 1.1%, and 0.7% improvements over YOLOv5s, YOLOv5m, VOLOv51, and YOLOv5x, respectively, on the COCO data sets; and the proposed method also achieves a 0.9% improvement on the self-built data set. The trained lightweight model Knowledge-YOLOv5s is deployed on an NVIDIA Jetson TX2 through TensorRT acceleration, and the real-time detection frame is 20 ms, which meets the real-time detection requirements. This system can also be used as a module of an intelligent weapon system, which has certain referential significance for autonomous weapons and unmanned combat systems.
EgoRenderer: Rendering Human Avatars from Egocentric Camera Images

We present EgoRenderer, a system for rendering full-body neural avatars of a person captured by a wearable, egocentric fisheye camera that is mounted on a cap or a VR headset. Our system renders photorealistic novel views of the actor and her motion from arbitrary virtual camera locations. Rendering full-body avatars from such egocentric images come with unique challenges due to the top-down view and large distortions. We tackle these challenges by decomposing the rendering process into several steps, including texture synthesis, pose construction, and neural image translation. For texture synthesis, we propose Ego-DPNet, a neural network that infers dense correspondences between the input fisheye images and an underlying parametric body model, and to extract textures from egocentric inputs. In addition, to encode dynamic appearances, our approach also learns an implicit texture stack that captures detailed appearance variation across poses and viewpoints. For correct pose generation, we first estimate body pose from the egocentric view using a parametric model. We then synthesize an external free-viewpoint pose image by projecting the parametric model to the user-specified target viewpoint. We next combine the target pose image and the textures into a combined feature image, which is transformed into the output color image using a neural image translation network. Experimental evaluations show that EgoRenderer is capable of generating realistic free-viewpoint avatars of a person wearing an egocentric camera. Comparisons to several baselines demonstrate the advantages of our approach.
Graph Convolutional Network for Word Sense Disambiguation

Word sense disambiguation (WSD) is an important research topic in natural language processing, which is widely applied to text classification, machine translation, and information retrieval. In order to improve disambiguation accuracy, this paper proposes a WSD method based on the graph convolutional network (GCN). Word, part of speech, and semantic category are extracted from contexts of the ambiguous word as discriminative features. Discriminative features and sentence containing the ambiguous word are used as nodes to construct the WSD graph. Word2Vec tool, Doc2Vec tool, pointwise mutual information (PMI), and TF-IDF are applied to compute embeddings of nodes and edge weights. GCN is used to fuse features of a node and its neighbors, and the softmax function is applied to determine the semantic category of the ambiguous word. Training corpus of SemEval-2007: Task #5 is adopted to optimize the proposed WSD classifier. Test corpus of SemEval-2007: Task #5 is used to test the performance of WSD classifier. Experimental results show that average accuracy of the proposed method is improved.
Comparison of Multidimensional Hyperspectral Image with SIFT Image Mosaic Methods for Mosaic Better Accuracy

Hyperspectral images can offer a lot of clarity by blending both spectral and spatial data. Details are for the researcher. A multidimensional paper in this paper, the hyperspectral image mosaic solution, was suggested to properly assemble hyperspectral images. This approach is a synthesis of texture details of the single gray picture, the hyperspectral spatial details image, and location details obtained during the purchase phase. This method is used in the world of medicine. Image and experimental findings hyperspectral suggest that this technique is useful compared to other image mosaic approaches based on the line segment function of scale-invariant feature transform (SIFT).
Semi-lexical languages: a formal basis for using domain knowledge to resolve ambiguities in deep-learning based computer vision

Human vision is able to compensate imperfections in sensory inputs from the real world by reasoning based on prior knowledge about the world. Deep learning has had a significant impact on computer vision due to its inherent ability in handling imprecision, but the absence of a reasoning framework based on domain knowledge limits its ability to interpret complex scenarios. We propose semi-lexical languages as a formal basis for reasoning with imperfect tokens provided by the real world. The power of deep learning is used to map the imperfect tokens into the alphabet of the language, and symbolic reasoning is used to determine the membership of input in the language. Semi-lexical languages have bindings that prevent the variations in which a semi-lexical token is interpreted in different parts of the input, thereby leaning on deduction to enhance the quality of recognition of individual tokens. We present case studies that demonstrate the advantage of using such a framework over pure deep learning and pure symbolic methods.
Coding for Straggler Mitigation in Federated Learning

We present a novel coded federated learning (FL) scheme for linear regression that mitigates the effect of straggling devices while retaining the privacy level of conventional FL. The proposed scheme combines one-time padding to preserve privacy and gradient codes to yield resiliency against stragglers and consists of two phases. In the first phase, the devices share a one-time padded version of their local data with a subset of other devices. In the second phase, the devices and the central server collaboratively and iteratively train a global linear model using gradient codes on the one-time padded local data. To apply one-time padding to real data, our scheme exploits a fixed-point arithmetic representation of the data. Unlike the coded FL scheme recently introduced by Prakash et al., the proposed scheme maintains the same level of privacy as conventional FL while achieving a similar training time. Compared to conventional FL, we show that the proposed scheme achieves a training speed-up factor of 6.6 and 9.2 on the MNIST and Fashion-MNIST datasets for an accuracy of 95% and 85%, respectively.
Morphological Cross Entropy Loss for Improved Semantic Segmentation of Small and Thin Objects

Image segmentation often faces a trade-off between using higher resolution images to detect fine details, such as the edges of objects or thin structures, and lower resolution images which is more suitable for accurate segmentation of massive objects. Because low resolution images require less resources, accurate detection of small objects is often less prioritized in trying to achieve the highest accuracy. In this paper, we propose to improve the segmentation of small and thin objects by convolutional neural networks by adding a morphological element to the loss function used for training the segmentation network. The approach is tested on a traffic sign segmentation problem using the Cityscapes dataset with a training set of 2 979 images and is shown to have an advantage over the popular cross-entropy (CE) and a ground truth (GT) affinity map weighted CE as it yields higher global IoU and, more importantly, an IoU gain among the smaller traffic signs with no additional computational resources.
IoT Security and New Trends of Solutions

This chapter focuses on the security issues of the Internet of Things (IoT). We hope to explore the development direction of IoT security through discussion of technology. The IoT can make individuals more closely connected, but it may spread individual or local insecurity factors to a wider range. Specifically, we will discuss IoT security in detail with six sections. The first section will discuss the foundation of IoT security. Then, we will discuss the security issues in perception layer. This layer is the core of IoT and its security is a prerequisite for information security and privacy protection of IoT. The third section is IoT security in the network layer and then we will discuss the security in IoT applications. In terms of security in application layer, we will discuss security technologies and security strategies for IoT applications. In particular, as more and more applications are running with cloud service, we will focus on the security issues of cloud computing applications. Section 5 discusses some case studies of IoT security, and Sect. 6 discusses possible future research directions for IoT security. We hope that this whole chapter could give readers a preliminary understanding of the security of the IoT and try to think about the relationship between technology and the human society in the IoT.
A novel signal processing algorithm for measurement while drilling (MWD) system

Measurement while drilling (MWD) is a widely used signal transmission technology in the drilling process. At present, the domestic pulse identification method is relatively simple, using the basic threshold method to identify, only set the threshold which will leading a number of missing detection mud pulse in the complex working conditions. In this paper, a novel data processing algorithm is proposed for measurement while drilling system. The method designed in this paper identifies the start time of data frame by using correlation coefficient and synchronization head model, then, based on the principle that the amplitude of mud pulse signal is larger than that of noise pulse signal, the maximum pulse signal in the length of a group of mud pulse series is extracted as mud pulse signal. The simulation is executed using the data from the actual drilling process and the results show the effectiveness of the proposed method.
Simulation-Based Bi-Selective Opening Security for Public Key Encryption

Selective opening attacks (SOA) (for public-key encryption, PKE) concern such a multi-user scenario, where an adversary adaptively corrupts some fraction of the users to break into a subset of honestly created ciphertexts, and tries to learn the information on the messages of some unopened (but potentially related) ciphertexts. Until now, the notion of selective opening attacks is only considered in two settings: sender selective opening (SSO), where part of senders are corrupted and messages together with randomness for encryption are revealed; and receiver selective opening (RSO), where part of receivers are corrupted and messages together with secret keys for decryption are revealed.
Neural Augmentation of Kalman Filter with Hypernetwork for Channel Tracking

We propose Hypernetwork Kalman Filter (HKF) for tracking applications with multiple different dynamics. The HKF combines generalization power of Kalman filters with expressive power of neural networks. Instead of keeping a bank of Kalman filters and choosing one based on approximating the actual dynamics, HKF adapts itself to each dynamics based on the observed sequence. Through extensive experiments on CDL-B channel model, we show that the HKF can be used for tracking the channel over a wide range of Doppler values, matching Kalman filter performance with genie Doppler information. At high Doppler values, it achieves around 2dB gain over genie Kalman filter. The HKF generalizes well to unseen Doppler, SNR values and pilot patterns unlike LSTM, which suffers from severe performance degradation.
Understanding practitioners’ strategies to handle test smells: a multi-method study

Test smells are poor design and implementation choices that can affect the test code's understanding and maintainability. Recent studies show the industry is not aware of the test smells concept, while software engineers commonly encounter obstacles to maintain test code. This study investigated test creation and maintenance strategies through developers' perception of eight test smells types. We surveyed 87 software testers and interviewed eight to understand their view on test smells in practice. Our results show that most participants use manual strategies for creating and maintaining test cases. Based on data captured from software testers' perspectives, this study contributes with possible directions and treatments to analyze test smells, seeking to understand how the test affects and potential solutions for test smells.
A Privacy-Preserving Enforced Bill Collection System using Smart Contracts

Maintaining a balance between anonymity and traceability is a fundamental issue in privacy-preserving systems. Isshiki et al. proposed an identity management system based on group signatures in which a service provider anonymously determines whether or not users of the service are legitimate members, and only a bill collector can identify users for the purposes of sending them invoices. It is particularly worth noting that, under the Isshiki system, the service provider is not required to manage personal information such as user lists, which allows the system to outperform other in terms of preserving user privacy and managing personal information leakage risk. It is also noteworthy that the Isshiki system only considers cases in which the bill collector identifies users who have used the service and that, in fact, identified users who ignore invoices can use the service for free. In this paper, we extended the Isshiki system by adding a smart contract-enabled enforcement bill collection functionality. Under this functionality, deposits made by users who do not pay a service fee are automatically transferred to the bill collector. Because of their centralized structure, group signatures are not suitable to blockchain systems, therefore, the proposed system employs accountable ring signatures as building blocks. The privacy-preserving enforced bill collection system is implemented using the accountable ring signature scheme developed by Bootle et al. and Ethereum smart contracts. To reduce the gas costs associated with running smart contracts, the smart contract is not run unless the user ignores an invoice, and basic procedures are run via an off-chain channel. To avoid the use of heavy cryptographic algorithms in carrying out the accountable ring signature scheme for running smart contracts, we employed standard elliptic curve digital signature algorithm (ECDSA) signatures without especially changing the state to be verified in smart contracts.
A Uniform Framework for Anomaly Detection in Deep Neural Networks

Deep neural networks (DNN) can achieve high performance when applied to In-Distribution (ID) data which come from the same distribution as the training set. When presented with anomaly inputs not from the ID, the outputs of a DNN should be regarded as meaningless. However, modern DNN often predict anomaly inputs as an ID class with high confidence, which is dangerous and misleading. In this work, we consider three classes of anomaly inputs, (1) natural inputs from a different distribution than the DNN is trained for, known as Out-of-Distribution (OOD) samples, (2) crafted inputs generated from ID by attackers, often known as adversarial (AD) samples, and (3) noise (NS) samples generated from meaningless data. We propose a framework that aims to detect all these anomalies for a pre-trained DNN. Unlike some of the existing works, our method does not require preprocessing of input data, nor is it dependent to any known OOD set or adversarial attack algorithm. Through extensive experiments over a variety of DNN models for the detection of aforementioned anomalies, we show that in most cases our method outperforms state-of-the-art anomaly detection methods in identifying all three classes of anomalies.