Reviewerly/exHarmony huggingface dataset:
FlexiShard: a Flexible Sharding Scheme for Blockchain based on a Hybrid Fault Model

One of the major bottlenecks of traditional Blockchain is its low throughput resulting in poor scalability. One way to increase throughput is to shard the network nodes to form smaller groups (shards). There are a number of sharding schemes in the literature with a common goal: nodes are split into groups to concurrently process different sets of transactions. Parallelism is used to enhance scalability, however with a trade-off in fault-tolerance; i.e., the smaller the shard size is, the better is the performance but higher is the fault probability. Contemporary sharding schemes use variants of Byzantine Fault Tolerance (BFT) protocol as their intra-shard consensus algorithms. BFT gives good performance when shard sizes are kept relatively small and maximum allowable faults is below some threshold. However, all these systems make rigid assumptions about their shard sizes and maximum allowable faults which may not be practical at times. In recent years, there have been more practical hybrid fault models in the literature which are better applicable to Blockchain (e.g., hybrid of Byzantine and alive-but-corrupt (abc) faults where the latter only compromises on safety) and corresponding consensus protocols that offer flexibility in choice of fault types and quorum sizes, e.g., Flexible Byzantine Fault Tolerance (Flexible BFT). In this paper, we present a new sharding scheme, FlexiShard, that uses Flexible BFT as its intra-shard consensus algorithm. FlexiShard leverages the notion of flexible Byzantine quorums and the hybrid fault model introduced in Flexible BFT that comprises of Byzantine and abc faults. Use of Flexible BFT allows flexibility in the choice of fault types and choosing shard sizes based on a range of allowable fault thresholds. Additionally, it allows to form shards that can tolerate more total faults than traditional BFT shards of similar size, and hence can deliver similar performance but with more fault-tolerance. To the best of our knowledge, FlexiShard is the first application of Flexible BFT and the hybrid fault model to Blockchain and its sharding. A theoretical analysis of FlexiShard is presented which demonstrates its flexibility and advantages over traditional sharding schemes.
Weakly Supervised Few-Shot Semantic Segmentation via Pseudo Mask Enhancement and Meta Learning

Few shot semantic segmentation has been proposed to enhance the generalization ability of traditional models with limited data. Previous works mainly focus on the supervised tasks, while limited amount of work is explored for the weakly supervised tasks. Weakly supervised semantic segmentation has become an active research area because weakly supervised labels effectively reduce the annotation cost of visual tasks. To this end, we propose a weakly supervised few-shot semantic segmentation model based on the meta learning framework, which utilizes prior knowledge and adjusts itself according to new tasks. Thereupon then, the proposed network is capable of both high efficiency and generalization ability to new tasks. In the pseudo mask generation stage, we develop a WRCAM method with the channel-spatial attention mechanism to refine the coverage size of targets in pseudo masks. In the few-shot semantic segmentation stage, the optimization based meta learning method is used to realize few-shot semantic segmentation by virtue of the refined pseudo masks. The experimental results show that the proposed method not only significantly outperforms weakly supervised SOTA methods, but also could be comparative to some supervised SOTA methods.
Application of Deep Learning models for Code Smell Prediction

Software developer always strives for quality of the software as it tends to be more robust and easier to maintain. Code smells play as a hinder to the quality of the software as they are the surface indication of deeper problem in the source code and hence required to be removed as early as possible. Refactoring is one of the methods to improve the quality of the software without affecting its external behavior. It removes bad smells present in the code as they are surface indication of deeper problem which may lead towards the failure of the software. In this work, we have proposed an approach to predict code smells in the source code using Deep learning algorithms. More specifically, we have trained three deep learning algorithms (CNN, LSTM and MLP) with open-source dataset. Information Gain feature selection algorithm is also applied to get the most prominent attributes for the code smell prediction. After evaluating the performance of these algorithms, results shows that CNN outperforms all other deep learning algorithms with the accuracy of 99% on the selected dataset. This study would be useful for software developer team as they can utilize CNN algorithm in code smell prediction. Results would also help in predicting the code smell which in turn will help in the removal of these smells to enhance the quality of the software.
Application of Natural Language Processing for Phishing Detection Using Machine and Deep Learning Models

Phishing scams are internet frauds that target people by sending them harmful links. Many victims ranging from individuals to big companies, have suffered numerous losses due to phishing, highlighting the increasing need to effectively detect and prevent a phishing attack as soon as it is received. This paper applied machine and deep learning models to detect phishing attacks by natural language processing of Uniform Resource Locators. Machine learning algorithms such as Logistic Regression and Multi-Naive Bayes were used Uniform Resource Locators for classification of legitimate and phishing. Additionally, Long Term Short Memory, Gated Recurrent Units, and Bidirectional Recurrent Neural Networks were used as Deep Learning models. Two of the used models are Long Term Short Memory and Gated Recurrent Units models possess significantly high training and validation scores with an overall accuracy of 95%. The Bidirectional Recurrent Neural Net using Gated Recurrent Units and Bidirectional Recurrent Neural Net using LTSM shows 97% accuracy. Therefore, using multiple deep learning models to predict whether URLs are phishing or legitimate is a significant assistance in reviewing websites. Further research for other parameters aside from using Uniform Resource Locators with different deep learning models can be used to improve the accuracy of phishing detection.
HHF: Hashing-Guided Hinge Function for Deep Hashing Retrieval

Deep hashing has shown promising performance in large-scale image retrieval. The hashing process utilizes Deep Neural Networks (DNNs) to embed images into compact continuous latent codes, then map them into binary codes by hashing function for efficient retrieval. Recent approaches perform metric loss and quantization loss to supervise the two procedures that cluster samples with the same categories and alleviate semantic information loss after binarization in the end-to-end training framework. However, we observe the incompatible conflict that the optimal cluster positions are not identical to the ideal hash positions because of the different objectives of the two loss terms, which lead to severe ambiguity and error-hashing after the binarization process. To address the problem, we borrow the Theory of Minimum-Distance Bounds for Binary Linear Codes to design the inflection point that depends on the hash bit length and category numbers and thereby propose Hashing-guided Hinge Function (HHF) to explicitly enforce the termination of metric loss to prevent the negative pairs unlimited alienated. Such modification is proven effective and essential for training, which contributes to proper intra- and inter-distances for clusters and better hash positions for accurate image retrieval simultaneously. Extensive experiments in CIFAR-10, CIFAR-100, ImageNet, and MS-COCO justify that HHF consistently outperforms existing techniques and is robust and flexible to transplant into other methods. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/JerryXu0129/HHF</uri> .
Information and library aspects of communication in the Siberian agricultural science

The article presents a generalized vision of the place and role of the Siberian Scientific Agricultural Library (SIBSAL), libraries and information units of scientific organizations of agricultural profile of Siberia in the scientific communications system. The dynamics of SIBSAL interaction with the libraries of regional scientific research institutions (SRI) in connection with changes in the political and economic life of the country in the 1990-s are considered. Approaches to changing the concept of methodological management of libraries and information departments of specialized organizations are presented. Influence of fundamental transformations, entailing the reform of the scientific field and digitalization of the society, on the change in SIBSAL status, its functions and the model of interaction with NRU is described. There are given the results of scientific research conducted in 2017-2021 to study the state and development of the information support system of agricultural science, during which data were collected on changes in the organizational and legal form of scientific institutions, their activities, individual information preferences of scientists, and also information about the current state of the information and library system of agricultural science of Siberia. In connection with the creation of a new interregional association, tasks are set to study the state of libraries and information units of the Agricultural Research Institute of the Far Eastern and Arctic region, which will expand the boundaries of communication in the agricultural information space.
Backward recursive Class Activation Map refinement for high resolution saliency map

The need for Explainable AI is increasing with the development of deep learning. The saliency maps derived from convolutional neural networks generally fail in localizing with accuracy the image features justifying the network prediction. This is because those maps are either low-resolution as for CAM, or smooth as for perturbation-based methods, or do correspond to a large number of widespread peaky spots as for gradient-based approaches. In contrast, our work proposes to combine the information from earlier network layers with the one from later layers to produce a high resolution Class Activation Map that is competitive with the previous art in term of insertion-deletion faithfulness metrics, while outperforming it in term of precision of class-specific features localization.
Programming Multi-robot Systems with X-KLAIM

Software development for robotics applications is still a major challenge that becomes even more complex when considering a Multi-Robot System (MRS). Such a distributed software has to perform multiple cooperating tasks in a well-coordinated manner to avoid unsatisfactory emerging behavior. This paper provides an approach for programming MRSs at a high abstraction level using the programming language X-Klaim. The computation and communication model of X-Klaim, based on multiple distributed tuple spaces, permits to coordinate with the same abstractions and mechanisms both intra- and inter-robot interactions of an MRS. This allows developers to focus on MRS behavior, achieving readable and maintainable code. The proposed approach can be used in practice through the integration of X-Klaim and the popular robotics framework ROS. We show the proposal’s feasibility and effectiveness by implementing an MRS scenario.
Improving Agility Using Big Data Analytics: The Role of Democratization Culture

Abstract Background: Big data analytics (BDA) is considered an enabler of organizational agility because it helps firms to sense market-based changes and improve decision making in a more informed and timely manner. However, in reality, only a handful of firms have achieved improvement in their outcomes by using BDA. To address this inconsistency, our study explores the conditions under which BDA use translates into agility. We particularly focus on organizational culture because in the pursuit of agility, culture is emphasized as a source of stability that allows firms to successfully adapt to the changing environment. Therefore, by assuming organizational culture as a contextual factor, this study examines the moderating effect of organizational culture on the link between BDA use and agility. Method: We employ a concept from data democratization called “democratization culture,” which values the willingness to share information and the acceptance of diversity. We also adopt collectivistic culture for comparison with democratization culture. Further, BDA use is decomposed into advanced and basic use based on the functions and BDA types. A model is proposed and empirically validated through survey data collected from 304 senior-level managers. Results: Our findings suggest that the moderating effects of democratization culture on agility are different depending on whether it is combined with advanced or basic BDA use. Conclusions: This study provides initial empirical evidence that contributes to the scarce research on the role of organizational culture in the link between BDA use and agility. Available at: https://aisel.aisnet.org/pajais/vol12/iss2/2/ Recommended Citation Hyun, Youyung; Kamioka, Taro; and Hosoya, Ryuichi (2020) "Improving Agility Using Big Data Analytics: The Role of Democratization Culture," Pacific Asia Journal of the Association for Information Systems: Vol. 12: Iss. 2, Article 2. DOI: 10.17705/1pais.12202 Available at: https://aisel.aisnet.org/pajais/vol12/iss2/2
Pedestrian Detection Using YOLOv5 For Autonomous Driving Applications

Object detection is a branch of computer vision that permits us to detect and classify objects inside image or video. Pedestrian detection is an important segment of object detection, which is one of the the trending issues of computer vision and self-driving cars. Deep learning techniques furnished significantly enhanced results in the area of pedestrian detection. However, most of the literature reveals that the models used for addressing either speed or accuracy. In this paper, we addressed the speed and accuracy in pedestrian detection for autonomous cars. Manual inspection is replaced with a deep learning method, augmented images are exposed to You Look Only Once version 5 (YOLOv5) with activation function which creates a tradeoff between speed of detection and accuracy. This model is the best influential object detection algorithm at the moment to detect pedestrians in public places. The proposed pedestrian detection model is trained with the dataset of 'CityPersons' with 2975 images out of total 5000 images. The experimental analysis proves that the proposed algorithm remarkably improvises the detection speed with 0.011sec/image which can apply to the real time environment with a 46.2% Miss-Rate (MR) on highly occluded city persons dataset among various occlusion levels of it.
A Novel Method of Clustering Using a Stochastic Approach

Cluster analysis of real-life data often encounters the challenges of noisy data or may rely heavily on the uncertainty of the main clustering variable owing to its stochastic nature, which has a potential influence on its performance. In this study, we propose a novel clustering technique that is efficient in dealing with noise and uncertainty in a dataset by adopting a stochastic approach that uses realistic values of data points by assuming a continuous probability distribution instead of exact values. By estimating the best-fit probability distribution of the clustering variable, the proposed method formulates the problem of determining the most homogeneous clusters by determining the optimum cluster partitions (OCP) as a mathematical programming problem (MPP). A computer-intensive dynamic programming technique was used to solve the MPP and determine the OCP, which minimized the sum of the weighted intracluster standard deviations. The proposed technique is then demonstrated in this study using univariate data that follows a normal distribution, which is a symmetric distribution, as well as the Weibull distribution, which is a skewed distribution. Numerical examples were also presented to illustrate the computational details of the proposed method. Finally, using both simulated and real datasets, a comparative analysis of the effectiveness of the proposed technique was performed against four advanced clustering methods: k-means, fuzzy c-means, expectation-maximization, and Genie++ hierarchical clustering. The results reveal that the proposed method works well and produces more efficient clusters than other methods.
Dual Task Learning by Leveraging Both Dense Correspondence and Mis-Correspondence for Robust Change Detection With Imperfect Matches

Accurate change detection enables a wide range of tasks in visual surveillance, anomaly detection and mobile robotics. However, contemporary change detection approaches assume an ideal matching between the current and stored scenes, whereas only coarse matching is possible in real-world scenarios. Thus, contemporary approaches fail to show the reported performance in real-world settings. To overcome this limitation, we propose SimSaC. SimSaC concurrently conducts scene flow estimation and change detection and is able to detect changes with imperfect matches. To train SimSaC without additional manual labeling, we propose a training scheme with random geometric transformations and the cut-paste method. Moreover, we design an evaluation protocol which reflects performance in realworld settings. In designing the protocol, we collect a test benchmark dataset, which we claim as another contribution. Our comprehensive experiments verify that SimSaC displays robust performance even given imperfect matches and the performance margin compared to contemporary approaches is huge.
Understanding the Dynamics of DNNs Using Graph Modularity

There are good arguments to support the claim that deep neural networks (DNNs) capture better feature representations than the previous hand-crafted feature engineering, which leads to a significant performance improvement. In this paper, we move a tiny step towards understanding the dynamics of feature representations over layers. Specifically, we model the process of class separation of intermediate representations in pre-trained DNNs as the evolution of communities in dynamic graphs. Then, we introduce modularity, a generic metric in graph theory, to quantify the evolution of communities. In the preliminary experiment, we find that modularity roughly tends to increase as the layer goes deeper and the degradation and plateau arise when the model complexity is great relative to the dataset. Through an asymptotic analysis, we prove that modularity can be broadly used for different applications. For example, modularity provides new insights to quantify the difference between feature representations. More crucially, we demonstrate that the degradation and plateau in modularity curves represent redundant layers in DNNs and can be pruned with minimal impact on performance, which provides theoretical guidance for layer pruning. Our code is available at https://github.com/yaolu-zjut/Dynamic-Graphs-Construction .
LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity

We propose transferability from Large Geometric Vicinity (LGV), a new technique to increase the transferability of black-box adversarial attacks. LGV starts from a pretrained surrogate model and collects multiple weight sets from a few additional training epochs with a constant and high learning rate. LGV exploits two geometric properties that we relate to transferability. First, models that belong to a wider weight optimum are better surrogates. Second, we identify a subspace able to generate an effective surrogate ensemble among this wider optimum. Through extensive experiments, we show that LGV alone outperforms all (combinations of) four established test-time transformations by 1.8 to 59.9% points. Our findings shed new light on the importance of the geometry of the weight space to explain the transferability of adversarial examples.
An Exploratory Approach to Intelligent Quiz Question Recommendation

With the rapid advancement of ICT, the digital transformation on education is greatly accelerating in various applications. As a particularly prominent application of digital education, quiz question recommendation is playing a vital role in precision teaching, smart tutoring, and personalized learning. However, the looming challenge of quiz question recommender for students is to satisfy the question diversity demands for students ZPD (the zone of proximal development) stage dynamically online. Therefore, we propose to formalize quiz question recommendation with a novel approach of reinforcement learning based two-sided recommender system. We develop a recommendation framework RTR (Reinforcement-Learning based Two-sided Recommender Systems) for taking into account the interests of both sides of the system, learning and adapting to those interests in real time, and resulting in more satisfactory recommended content. This established recommendation framework captures question characters and student dynamic preferences by considering the emergence of both sides of the system, and it yields a better learning experience in the context of practical quiz question generation.
Graph Neural Networks for Relational Inductive Bias in Vision-based Deep Reinforcement Learning of Robot Control

State-of-the-art reinforcement learning algorithms predominantly learn a policy from either a numerical state vector or images. Both approaches generally do not take structural knowledge of the task into account, which is especially prevalent in robotic applications and can benefit learning if exploited. This work introduces a neural network architecture that combines relational inductive bias and visual feedback to learn an efficient position control policy for robotic manipulation. We derive a graph representation that models the physical structure of the manipulator and combines the robot's internal state with a low-dimensional description of the visual scene generated by an image encoding network. On this basis, a graph neural network trained with reinforcement learning predicts joint velocities to control the robot. We further introduce an asymmetric approach of training the image encoder separately from the policy using supervised learning. Experimental results demonstrate that, for a 2-DoF planar robot in a geometrically simplistic 2D environment, a learned representation of the visual scene can replace access to the explicit coordinates of the reaching target without compromising on the quality and sample efficiency of the policy. We further show the ability of the model to improve sample efficiency for a 6-DoF robot arm in a visually realistic 3D environment.