Reviewerly/exHarmony huggingface dataset:
Research on the Application of Semantic Segmentation of driverless vehicles in Park Scene

Semantic segmentation is widely used in the establishment of semantic map, but a good semantic segmentation model still has the disadvantage of insufficient real-time performance for driverless applications, especially in the park. In this paper, an improved PFPN (Panoptic Feature Pyramid Network) network model is proposed to reduce the time of semantic segmentation. In this algorithm, the instance segmentation branch function in the original PFPN is trimmed and the small target feature layer extracted from the semantic segmentation branch is pruned, so as to reduce the semantic segmentation time of the model. In order to verify the correctness and feasibility of the model, a test environment is set up in the campus scene. The experimental results show that the improved lightweight semantic segmentation model achieves better segmentation results, and the average image segmentation time is reduced by 22.2%.
Blockchain-based Dynamic Group Management for Multiple Keywords Searchable Encryption Technology

Searchable Encryption (SE) provides data online search function to users while protecting the data security and the user's query privacy. It has challenges of high computational and storage overhead that should be mitigated for a practical SE system. In this paper, we propose a blockchain-based user management scheme for SE system that can decentralize bandwidth resource consumption. Meanwhile it prevents single point failure and enhances the security protection in order to prevent identity leakage, loss, tampering brought by TTP. It leverages blockchain to offer non-central dynamic user management capability. Our theoretical analysis validates the feasibility and security. Further, experiments are carried out to demonstrate the SE effectiveness.
Assets on the Blockchain: An Empirical Study of Tokenomics

Digital tokens linked to financial and economic ventures may have multiple functions and uses. In this work, we examine the relationship between various token functions and the market price of the corresponding token. We consider 86 venture related blockchain tokens, and develop the analysis through a stepwise testing of four hypotheses using panel ordinary least squares with cluster-robust standard errors. We find that token functions are statistically significant in relation to token prices. In the absence of an established legal framework, we argue that our results complements recent regulatory actions identifying tokens to be investment contracts in a common venture.This is an original manuscript of an article published by Elsevier in Information Economics and Policy, available online: https://doi.org/10.1016/j.infoecopol.2020.100881.
Video emotion recognition based on Convolutional Neural Networks

Abstract The existing video sentiment analysis methods only obtain features from the spatial and temporal signals of the video for sentiment classification, and cannot solve the difficulty of not knowing which emotion contributes the most to the entire video sentiment analysis in the video sentiment analysis. To solve this problem, a neural network with video frame weight vector is proposed. First, the video frame feature is obtained through the reel neural network, and then the weight vector layer is used to calculate the weight of the feature, and finally the frame feature with weight is put into the LSTM Training to obtain a video sentiment analysis model. We verified on the BAUM-1s data set. The results show that this method is better than existing methods in accuracy.
An Advanced Fourier Descriptor Based on Centroid Contour Distances

Abstract In order to distinguish two shapes with similar centroid contour distances. This paper proposes a novel descriptor-AFD-CCD (advanced Fourier descriptor based on centroid contour distances), which is based on FD-CCD ( Fourier descriptor based on centroid contour distance ) descriptor. FD-CCD only provides the distance information from the centroid point to the contour point, and the lack of direction information affects the accuracy of matching. The algorithm uses circularity and rectangularity to supplement discriminability, with low computational complexity. Tests were performed on three datasets. The experimental results show that AFD-CCD has further improved the matching accuracy while ensuring that the matching speed does not decrease.
Optimal Selection of Pyramid Pooling Components for Convolutional Neural Network Classifier

There has been an increasing number of researches on deep learning applications in screening eye diseases. Since most of the screening tools will be deployed for rural areas that lack modern medical equipment, it is best if the screening algorithm can be deployed on a mobile platform. Thus, a lightweight deep learning model very much fits the mobile platform as it requires low memory storage and imposes a low computational burden on the hardware. Therefore, a modified ShuffleNet V1 network is proposed to screen eye diseases using fundus images. A spatial pyramid pooling module is integrated at the exit flow of the network, such that the memory usage remains relatively the same but with improved classification accuracy. The better performance can be attributed to the exit module that better extracts the features from various scales rather than a simple global average pooling operator. The best mean accuracy of 0.7564 is obtained when maximum down-pooling operators are used with a kernel set of 2, 4, and 7. Furthermore, a wrong selection of hyper-parameters such as the case of kernel set 5, 6, and 7 also with maximum pooling operators return the lowest accuracy of just 0.7011. Therefore, an optimal selection of kernel set and down pooling operators is vital in improving classification performance. The proposed lightweight model can be further improved by using a separable convolution scheme, which a factorized version of a regular convolution.
Performance optimisation Method of PBFT Consensus for Supply Chain Integration SVM

The Hyperledge Fabric permissioned consortium chain based on the Practical Byzantine Fault Tolerance (PBFT), the interference of the dishonest node as the primary node to the consensus process caused the performance problems in the application of the supply chain. To solve this problem, a consensus performance optimisation method that integrates Support Vector Machine (SVM) and PBFT is proposed in this research. Firstly, SVM is used to evaluate the trust of consensus nodes in blockchain network. By selecting a consensus node with a high degree of trust as the primary node to reduce the possibility of dishonest nodes as the primary node. Then, introduce a dynamic adjustment credit incentive mechanism to the blockchain network by reducing the consensus trust of malicious nodes and inactive nodes in the blockchain, which will improve the security of the supply chain system. Finally, the experimental results show that, comparing with the existing methods, the consensus node trust evaluation and credit incentive mechanism have improved the throughput and latency of the supply chain blockchain application.
Analysis of the Effects of the Viscous Thermal Losses in the Flute Musical Instruments

This article presents the third part of a larger project whose final objective is to study and analyse the effects of viscous thermal losses in a flute wind musical instrument. After implementing the test bench in the first phase and modelling and validating the dynamic behaviour of the simulator, based on the previously implemented test bench (without considering the losses in the system) in the second phase, this third phase deals with the study of the viscous thermal losses that will be generated within the resonator of the flute. These losses are mainly due to the friction of the air inside the resonator with its boundaries and the changes of the temperature within this medium. They are mainly affected by the flute geometry and the materials used in the fabrication of this instrument. After modelling these losses in the frequency domain, they will be represented using a system approach where the fractional order part is separated from the system’s transfer function. Thus, this representation allows us to study, in a precise way, the influence of the fractional order behaviour on the overall system. Effectively, the fractional behavior only appears much below the 20 Hz audible frequencies, but it explains the influence of this order on the frequency response over the range [20–20,000] Hz. Some simulations will be proposed to show the effects of the fractional order on the system response.
FIRE 2020 EDNIL Track: Event Detection from News in Indian Languages

The goal of FIRE 2020 EDNIL track was to create a framework which could be used to detect events from news articles in English, Hindi, Bengali, Marathi and Tamil. The track consisted of two tasks: (i) Identifying a piece of text from news articles that contains an event (Event Identification). (ii) Creating an event frame from the news article (Event Frame Extraction). The events that were identified in Event Identification task were Man-made Disaster and Natural Disaster. In Event Frame Extraction task the event frame consists of Event type, Casualties, Time, Place, Reason.
Generating Attribution Maps with Disentangled Masked Backpropagation

Attribution map visualization has arisen as one of the most effective techniques to understand the underlying inference process of Convolutional Neural Networks. In this task, the goal is to compute an score for each image pixel related to its contribution to the network output. In this paper, we introduce Disentangled Masked Backpropagation (DMBP), a novel gradient-based method that leverages on the piecewise linear nature of ReLU networks to decompose the model function into different linear mappings. This decomposition aims to disentangle the attribution maps into positive, negative and nuisance factors by learning a set of variables masking the contribution of each filter during back-propagation. A thorough evaluation over standard architectures (ResNet50 and VGG16) and benchmark datasets (PASCAL VOC and ImageNet) demonstrates that DMBP generates more visually interpretable attribution maps than previous approaches. Additionally, we quantitatively show that the maps produced by our method are more consistent with the true contribution of each pixel to the final network output.
Utilization artificial intelligence to improve creativity skills in society 5.0

Abstract The purpose of this research is to take advantage of Artificial Intelligence which is used to increase Creativity Skills in the era of society 5.0. Data collection is done to see subtle problems and develop solutions. Technique data collection is a literature study. Creativity Skill is one of the abilities that students must have to answer challenges, namely the era of society 5.0. The era of society 5.0 is an era where new values created by technological developments can minimize human responsibility and future economic problems. In this case, Artificial Intelligence is a way to maximize students in social change which requires someone to be innovative, creative by emphasizing contextual learning in which students must be active. Artificial intelligence (AI) enables machines to learn from experience, adapt to new inputs and perform human-like tasks. Most of the AI you hear about today - from computers playing chess to adult cars - rely heavily on deep learning and comparing natural language. Using this technology, computers can complete specific tasks by processing large amounts of data and managing patterns in the data so that it is hoped that the use and development of Artificial Intelligence can increase creativity skills to face opportunities and challenges in the era of 5.0 society.
A Few Good Counterfactuals: Generating Interpretable, Plausible and Diverse Counterfactual Explanations

Counterfactual explanations are an important solution to the Explainable AI (XAI) problem, but good, “native” counterfactuals can be hard to come by. Hence, the popular methods generate synthetic counterfactuals using “blind” perturbation, by manipulating feature values to elicit a class change. However, this strategy has other problems, notably a tendency to generate invalid data points that are out-of-distribution or that involve feature-values that do not naturally occur in a given domain. Instance-guided and case-based methods address these problems by grounding counterfactual generation in the dataset or case base, producing synthetic counterfactuals from naturally-occurring features, and guaranteeing the reuse of valid feature values. Several instance-guided methods have been proposed, but they too have their shortcomings. Some only approximate grounding in the dataset, or do not readily generalise to multi-class settings, or are limited in their ability to generate alternative counterfactuals. This paper extends recent case-based approaches by presenting a novel, general-purpose, case-based solution for counterfactual generation to address these shortcomings. We report a series of experiments to systematically explore parametric variations on common datasets, to establish the conditions for optimal performance, beyond the state-of-the-art in instance-guided methods for counterfactual XAI.
Toward a Unified View of Dynamic Information Security Behaviors

Extant information systems security research identified and examined a variety of individual as well as organizational factors influencing information security behaviors, but rarely offered sufficient theoretical insight into the interaction of the individual factors with the organizational context in impacting information security behaviors. To fill this gap, this study proposes a theoretical framework that builds on the concepts of organizational culture and sensemaking to show that: 1) information security behaviors are outcomes of sensemaking; and 2) sensemaking is enabled as well as constrained by organizational culture. This study further epitomizes that information security diagnosing, solving, and performing behaviors emerge as outcomes of sensemaking about information security during the organization's interactions with technology. Theoretical and pragmatic contributions of this framework and future research directions are also demonstrated.
Forum Size and Content Contribution per Person: A Field Experiment

Promoting contribution of content is a key challenge for platforms that support the collective creation or transfer of knowledge. We study the role of forum size (number of people in a forum) on contribution of content per person with a field experiment on a massive open online course (MOOC). We find that larger forums elicit more contribution per person. The number of questions and other help-seeking threads posted per person was unchanged by size, but replies and other more conversational posts increased sharply. Most of the positive effect of size was in a subset of socially responsive subjects. The implication of social responsiveness driving our results is that the unequal distribution of contribution on online platforms is unlikely to be easily changed: if more contributions are elicited from infrequent contributors, the greatest contributors would contribute even more due to there being more to respond to.
Piper: A Unified Machine Learning Pipeline for Internet-scale Traffic Analysis

Machine learning has been applied to network traffic analysis for a variety of purposes, including botnet detection. To improve the computational efficiency, several architectures have been proposed to consolidate processes common across multiple applications that use the same traffic data. However, when introducing conventional architectures to real-world traffic analysis at Internet scale, the amount of input traffic data and the variety of output features to represent global access patterns become new challenges. To address the challenges, we have developed Piper, a machine learning pipeline, that consolidates diversified machine learning applications in a highly efficient manner. On top of the consolidated architecture, Piper employs two novel techniques: (1) selective sampling to reduce traffic data efficiently while maintaining prediction performance, and (2) a set of enriched features to extract temporal and spatial characteristics in global traffic. For the evaluation, we have been deploying Piper to detect botnets from internet backbone traffic over nine months. The evaluation has confirmed the effectiveness of Piper in terms of computational performance, prediction performance, and lead time to detect botnets.
Verification of dynamic bisimulation theorems in Coq

Over the last years, the study of logics that can update a model while evaluating a formula has gained in interest. Motivated by many examples in practice such as hybrid logics, separation logics and dynamic epistemic logics, the ability to update a model has been investigated from a more general point of view. In this work, we formalize and verify in the proof assistant Coq, the bisimulation theorems for a particular family of dynamic logics that can change the structure of a relational model while evaluating a formula. Our framework covers update operators to perform different kinds of modifications on the accessibility relation, the valuation and the evaluation point of a model. The benefits of this formalization are twofold. First, our results apply for a wide variety of dynamic logics. Second, we argue that this is the first step towards the development of a modal logic library in Coq, which allows us to mechanize many relevant results.
Sosed

In this paper, we present Sosed, a tool for discovering similar software projects. We use fastText to compute the embeddings of subtokens into a dense space for 120,000 GitHub projects in 200 languages. Then, we cluster embeddings to identify groups of semantically similar subtokens that reflect topics in source code. We use a dataset of 9 million GitHub projects as a reference search base. To identify similar projects, we compare the distributions of clusters among their subtokens. The tool receives an arbitrary project as input, extracts subtokens in 16 most popular programming languages, computes cluster distribution, and finds projects with the closest distribution in the search base. We labeled subtoken clusters with short descriptions to enable Sosed to produce interpretable output.
Computational localization of attack regions through a direct observation of the audio waveform

This article addresses the computational estimation of attack regions in audio recordings. Previous attempts to do so were based on the reduction of the audio waveform into an envelope curve, which decreases its temporal resolution. The proposed approach detects the attack region directly from the audio waveform. The attack region is modeled as a line starting from a low-amplitude point and intersecting one of the local maxima according to two principles: (1) maximizing the slope, while favoring, at the same time, a higher peak if the slope remains only slightly lower and (2) dismissing initial attack regions of relatively low amplitude. The attack start position is fine-tuned by intersecting the attack slope with the audio waveform. The proposed method precisely pinpoints the attack region in cases where it is unambiguously observable from the waveform itself. In such cases, previous methods selected a broader attack region due to the loss of temporal resolution. When attack regions are less evident, the proposed method's estimation remains within the range of results provided by other methods. Applied to the prediction of judgments of P-center localization [Danielsen, Nymoen, Anderson, Câmara, Langerød, Thompson, and London, J. Exp. Psychol. Hum. Percept. Perform. 45, 402–418 (2019)], the proposed method shows a significant increase in precision, at the expense of recall.
Applying Software Agents to Make City Traffic Management Smarter

With the emergence of IoT technologies, autonomous vehicles, and introduction of 5G networking, the idea of a smart city is considerably more achievable than before. One of the ways of reaching this goal would be improvement of traffic management. In this contribution, we suggest how an agent-based system can optimize vehicular travel, thus reducing CO\(_2\) emission, decreasing time spent in traffic jams, and improving overall city travel experience. We propose the design of an agent system describes how agents communicate and, experimentally, validates the design in two use case scenarios.
Emotion Recognition from Facial Biometric System Using Deep Convolution Neural Network (D-CNN)

Emotion recognition is an essential field of research in social robots, medical applications, neuromarketing, E-learning, monitoring, entertainment, law and games in human-to-machine interactions. In face-to-face interactions, facial expressions convey non-verbal information among humans, which is an important task in human–machine interfaces. Since the early 1990s, it has attracted attention from researchers. Classical approaches to machine learning often involve complex processes of extraction of features and poor results. Significant research has been carried out over the past decade in the development of different methods of extraction, tracking and analysis of facial features. The analysis includes measuring facial feeling and recognition of expression by extracting options for classification victimization ancient machine learning algorithms and could be a complicated and long task in comparison to deep learning techniques. As per the literature survey, using machine learning algorithms, the achieved accuracy rate ranges between 75 to 90% for mostly four to six emotions from the facial data set. In this paper, a deep CNN, a recent advancement in deep learning, is employed, which might accurately interpret linguistics information out there in the face in an automatic manner while not hand-designing the feature descriptors to acknowledge seven completely different emotions, viz. neutral, anger, fear, happy, disgust, surprise and sad. Categorical cross-entropy loss and Adam optimizer algorithms are applied to coach D-CNN with a powerful classification power. This D-CNN is applied on well-known JAFFE data set which consists of seven emotions and ten subjects and could achieve an accuracy of 97.53%. Compared to machine learning, the number of parameters in D-CNN decreases intensively, accelerating the overall performance improvement and making this network well suited for real-time applications.
Extending MUD to Smartphones

The tremendous success of the IoT is overshadowed by severe security risks introduced by IoT devices and smartphone apps to control them. Therefore, academia and industry increasingly acknowledge the use of in-network security approaches, such as IETF Manufacturer Usage Description (MUD), to restrict undesired communication. However, actual communication patterns of smart homes are not sufficiently covered by such policy-based approaches. In this paper, we propose to enforce MUD on authenticated smartphones to efficiently filter malicious traffic close to its origin and hinder further spreading. Such enforcement allows us to successfully mitigate the threat of malicious apps and IoT devices in smart home networks.