Reviewerly/exHarmony huggingface dataset:
FPGA Implementation of AES Encryption Optimization Algorithm

To improve the confidentiality of data transmission and the efficiency of encryption and decryption, this paper uses FPGA to improve the traditional AES encryption algorithm. The scheme uses pipeline technology to optimize the structure of AES algorithm, including the key expansion part and the encryption and decryption part. Then, the AES encryption algorithm in 128bit initial key mode is implemented, and the bit width conversion module is written, so that the encryption part can be connected with other types of bus. The experimental analysis uses Quartus tool to compile AES algorithm, and establish entity PCB circuit to test in real environment. The experimental results show that the optimization algorithm can complete the encryption and decryption function, and improve the data throughput and clock frequency.
Deep Learning Inspired Vision based Frameworks for Drone Detection

Drone detection technology is a new frontier in defence systems. With increasing incidences of crimes and terroristic attacks using commercial drones, detection of unauthorized drones is critical for timely responses from law enforcement agencies. In this paper, the issues of unavailability of benchmark dataset and performance metrics for drone detection are addressed and three single shot detectors, based on YOLOv4, YOLOv5 and DETR architectures are presented. A maximum of 99% average precision (AP) with an average Intersection over Union (IOU) of 84% was achieved. The precision-recall curves corroborate the generalization and fitness of the trained detection models.
<i>P</i>-wave picking for earthquake early warning: refinement of a<i>T</i><i>pd</i>method

SUMMARY Detecting P-wave onsets for online processing is an important component for real-time seismology. As earthquake early warning systems around the world come into operation, the importance of reliable P-wave detection has increased, since the accuracy of the earthquake information depends primarily on the quality of the detection. In addition to the accuracy of arrival time determination, the robustness in the presence of noise and the speed of detection are important factors in the methods used for the earthquake early warning. In this paper, we tried to improve the P-wave detection method designed for real-time processing of continuous waveforms. We used the new Tpd method, and proposed a refinement algorithm to determine the P-wave arrival time. Applying the refinement process substantially decreases the errors of the P-wave arrival time. Using 606 strong motion records of the 2011 Tohoku earthquake sequence to test the refinement methods, the median of the error was decreased from 0.15 to 0.04 s. Only three P-wave arrivals were missed by the best threshold. Our results show that the Tpd method provides better accuracy for estimating the P-wave arrival time compared to the STA/LTA method. The Tpd method also shows better performance in detecting the P-wave arrivals of the target earthquakes in the presence of noise and coda of previous earthquakes. The Tpd method can be computed quickly, so it would be suitable for the implementation in earthquake early warning systems.
Text Sentiment Analysis Based on CNN-BiLSTM-Attention Model

To solve the problem that the neural network structure used in the current text sentiment analysis task cannot extract the important features of the text, a model of feature fusion of Convolutional Neural Network(CNN) and Bidirectional Long Short-Term Memory(BiLSTM) is proposed. The CNN is used to extract the local features of the text vector, and the BiLSTM is used to extract the global features related to the text context. The problem that the single CNN model ignores the semantic and grammatical information of words in the context is solved, and the problem of gradient disappearance or gradient diffusion in traditional Recurrent Neural Network(RNN) is effectively avoided. After preprocessing the corpus, the text is expressed as a two-dimensional word vector matrix, and then the local information features are extracted by using the CNN. Then, it is used as the input of BiLSTM to learn the sequence relationship between words and sentences. Then attention mechanism is introduced to highlight important information. The influence on text emotion. Experimental results show that the proposed feature fusion model effectively improves the accuracy of text classification.
Toward Automated Threat Modeling of Edge Computing Systems

Edge computing brings processing and storage capabilities closer to the data sources, to reduce network latency, save bandwidth, and preserve data locality. Despite the clear benefits, this paradigm brings unprecedented cyber risks due to the combination of the security issues and challenges typical of cloud and Internet of Things (IoT) worlds. Notwithstanding an increasing interest in edge security by academic and industrial communities, there is still no discernible industry consensus on edge computing security best practices, and activities like threat analysis and countermeasure selection are still not well established and are completely left to security experts.In order to cope with the need for a simplified yet effective threat modeling process, which is affordable in presence of limited security skills and economic resources, and viable in modern development approaches, in this paper, we propose an automated threat modeling and countermeasure selection strategy targeting edge computing systems. Our approach leverages a comprehensive system model able to describe the main involved architectural elements and the associated data flow, with a focus on the specific properties that may actually impact on the applicability of threats and of associated countermeasures.
YOLO Target Detection Algorithm with Deformable Convolution Kernel

In order to solve the problem of inaccurate detection of targets with large scale and shape changes and small targets by the YOLO target detection algorithm, a YOLO target detection algorithm with deformable convolution kernel is proposed. The algorithm adds deformable convolution to the network, and designs three progressive embedding schemes, so that the network can adaptively change the receptive field of feature points according to the shape of the target, thereby extracting features more effectively and improving detection accuracy; by adjusting the backbone network the structure reduces the calculation amount of the algorithm. Tested on the VOC data set, the results show that the algorithm can effectively improve the accuracy of target detection, and the detection speed meets the real-time requirements.
It’s a Matter of Style: Detecting Social Bots through Writing Style Consistency

Social bots are computer algorithms able to produce content and interact with other users on social media autonomously, trying to emulate and possibly influence humans’ behavior. Indeed, bots are largely employed for malicious purposes, like spreading disinformation and conditioning electoral campaigns. Nowadays, bots’ capability of emulating human behaviors has become increasingly sophisticated, making their detection harder. In this paper, we aim at recognizing bot-driven accounts by evaluating the consistency of users’ writing style over time. In particular, we leverage the intuition that while bots compose posts according to fairly deterministic processes, humans are influenced by subjective factors (e.g., emotions) that can alter their writing style. To verify this assumption, by using stylistic consistency indicators, we characterize the writing style of more than 12,000 among bot-driven and human-operated Twitter accounts and find that statistically significant differences can be observed between the different types of users. Thus, we evaluate the effectiveness of different machine learning (ML) algorithms based on stylistic consistency features in discerning between human-operated and bot-driven Twitter accounts and show that the experimented ML algorithms can achieve high performance (i.e., F-measure values up to 98%) in social bot detection tasks.
Towards Aspect Based Components Integration Framework for Cyber-Physical System

Cyber-Physical Systems (CPS) comprise interactive computation, networking, and physical processes. The integrative environment of CPS enables the smart systems to be aware of the surrounding physical world. Smart systems, such as smart health care systems, smart homes, smart transportation, and smart cities, are made up of complex and dynamic CPS. The components integration development approach should be based on the divide and conquer theory. This way multiple interactive components can reduce the development complexity in CPS. As reusability enhances efficiency and consistency in CPS, encapsulation of component functionalities and a well-designed user interface is vital for the better end-user's Quality of Experience (QoE). Thus, incorrect interaction of interfaces in the cyber-physical system causes system failures. Usually, interface failures occur due to false, and ambiguous requirements analysis and specification. Therefore, to resolve this issue semantic analysis is required for different stakeholders’ viewpoint analysis during requirement specification and components analysis. This work proposes a framework to improve the CPS component integration process, starting from requirement specification to prioritization of components for configurable. For semantic analysis and assessing the reusability of specifications, the framework uses text mining and case-based reasoning techniques. The framework has been tested experimentally, and the results show a significant reduction in ambiguity, redundancy, and irrelevancy, as well as increasing accuracy of interface interactions, component selection, and higher user satisfaction.
Joint Dynamic Manifold and Discriminant Information Learning for Feature Extraction

Neighborhood reconstruction is a good recipe to learn the local manifold structure. Representation-based discriminant analysis methods normally learn the reconstruction relationship between each sample and all the other samples. However, reconstruction graphs constructed in these methods have three limitations: 1) they cannot guarantee the local sparsity of reconstruction coefficients; 2) heterogeneous samples may own nonzero coefficients; and 3) they learn the manifold information prior to the process of dimensionality reduction. Due to the existence of noise and redundant features in the original space, the prelearned manifold structure may be inaccurate. Accordingly, the performance of dimensionality reduction would be affected. In this article, we propose a joint model to simultaneously learn the affinity relationship, reconstruction relationship, and projection matrix. In this model, we actively assign neighbors for each sample and learn the inter-reconstruction coefficients between each sample and their neighbors with the same label information in the process of dimensionality reduction. Specifically, a sparse constraint is employed to ensure the sparsity of neighbors and reconstruction coefficients. The whitening constraint is imposed on the projection matrix to remove the relevance between features. An iterative algorithm is proposed to solve this method. Extensive experiments on toy data and public datasets show the superiority of the proposed method.
Measuring Global, Direct, and Diffuse Irradiance Using a Reference Cell Array

We report on a field trial of a novel method for measuring global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DHI) using an array of reference cells with no moving parts. The technique, similar to previous work on the multi-pyranometer array concept, uses multiple reference cells facing different directions together with an analysis model that fits the data from the sensors to determine GHI, DNI, and DHI. Preliminary results show good agreement between results obtained from the reference cell array and those from reference instruments which include a pyranometer, pyrheliometer, and shaded pyranometer. The results show the potential utility of using a reference cell array to add capability to resource assessment campaigns and solar power plant meteorological stations at a low cost.
Performance of Deep Learning Methods in DF Based Cooperative Communication Systems

In recent years, deep learning (DL) based communication systems have gained great importance. In this paper, channel estimation for Rayleigh fast fading channels is proposed by applying three different deep learning algorithms which are multilayer perceptron (MLP), convolutional neural network (CNN), and long-short term memory (LSTM). Firstly, the maximum likelihood (ML) estimation is implemented by employing the Monte Carlo simulation program. Training and test samples for models are generated by combining the complex data consisting of real and virtual parts obtained at the channel output. For training process, channel output is used for the input of DL models whereas the channel coefficient matrix is used for the output of the models. DL algorithms have shown good performance comparable to ML estimation in terms of bit error rate (BER). From the simulation results, DL algorithms can be seen a promising method for channel estimation in cooperative communication.
Social Relation Analysis from Videos via Multi-entity Reasoning

Videos contain rich semantic information. Analyzing social relations in video semantics can help machines interpret the behavior of human beings. However, most of the work related to social relationship recognition is based on still images, while video-based social relationship analysis tasks are less concerned. Here we propose a Multi-entity Relation Reasoning (MRR) framework that can be used for recognizing or predicting social relations in videos. To capture temporal features and contextual cues in videos, and use richer information to represent the person in the video, we track each person's appearance timeline and design a multi-entity representation method to build a social relationship knowledge graph. Then we use graph attention networks to gather information from the entity's neighborhood. Besides, situation information is helpful to identify relationships, we design a situation information extraction module to generate situation embedding from the video clip. Finally, a decoder is adopted to predict relationships between character entities. We evaluate the model on the MovieGraphs dataset and verify the effectiveness of the proposed framework.
A Subjective and Behavioral Assessment of Affordances in Virtual Architectural Walkthroughs

Immersive technologies, such as VR, offer first-person experiences using depth perception and spatial awareness that elucidate a sense of space impossible with traditional visualization techniques. This paper looks beyond the visual aspects and towards understanding the experiential aspects of two popular uses of VR in 3D architectural visualization: a “passive walkthrough” and an “interactive walkthrough”. We designed a within-subject experiment to measure the user-perceived quality for both experiences. All participants (N = 34) were exposed to both scenarios and afterwards responded to a post-experience questionnaire; meanwhile, their physical activity and simple active behaviors were also recorded. Results indicate that while the fully immersive-interactive experience rendered a heightened sense of presence in users, overt behaviors (movement and gesture) did not change for users. We discuss the potential use of subjective assessments and user behavior analysis to understand user-perceived experiential quality inside virtual environments, which should be useful in building taxonomies and designing affordances that best fit these environments.
e-Reside: A Case Study on Blockchain for IoT in Home Automation Using Raspberry Pi

Today, we are living in a technological world that it is necessary to use more advanced technologies to control devices remotely from different locations. Earlier electronic devices were controlled through Wi-Fi, Bluetooth, and Zigbee in the restricted areas, but with the help of advanced technologies like Internet of Things (IoT) and cloud, we can extend it to such a level where we can control the devices even from remote areas. Raspberry Pi is one such technology which made a strong influence to control any electronic devices even from the remote areas. The technology designed can expand in allowing multiplicity of devices that can be controlled and also at a very low cost. Usage of Raspberry Pi device on home automation and its benefits need to be observed. Ever-growing connected ecosystem provisioned the industry to change the way we interact with the traditional brick and mortar houses to such an extent that the user is entirely in control of his home’s comfort and security. For the useful utilization of power very effectively, certain human errors happening unintentionally need to be reduced, like to switch off home appliances; the home automation system has to be designed in such a way that they need to control all electronic devices of a house remotely in a secured and safe manner. The smart home is one of the most mainstream applications which can improve the inhabitant’s life quality. Nevertheless, the security and protection problems of the house owner bring developing difficulties, while an ever-increasing number of information are gathered and shared by the various IoT gadgets inside the smart home. Blockchain for an IoT is a developing enchanting innovation strategy that gives a private, secure, and decentralized instrument for the information utilization in smart home.
Neuron Manifold Distillation for Edge Deep Learning

Although deep neural networks show their extraordinary power in various object detection tasks, it is very challenging for them to be deployed on resource constrained devices or embedded systems due to their high computational cost. Efforts such as model partition, pruning or quantization have been used at an expense of accuracy loss. Recently proposed knowledge distillation (KD) aims at transferring model knowledge from a well-trained model (teacher) to a smaller and faster model (student), which can significantly reduce the computational cost, memory usage, and prolong the battery lifetime. In this work, we propose a novel neuron manifold distillation (NMD), where the student models not only imitate teacher’s output activations, but also learn the feature geometry structure of the teacher. Our approach produces a high-quality, compact, and lightweight student model. We conduct comprehensive experiments with different distillation configurations over multiple datasets, and the proposed method demonstrates a consistent improvement in accuracy-speed trade-offs for the distilled model.
Manga-MMTL: Multimodal Multitask Transfer Learning for Manga Character Analysis

In this paper, we introduce a new pipeline to learn manga character features with visual information and verbal information in manga image content. Combining these set of information is crucial to go further into comic book image understanding. However, learning feature representations from multiple modalities is not straightforward. We propose a multitask multimodal approach for effectively learning the feature of joint multimodal signals. To better leverage the verbal information, our method learn to memorize the content of manga albums by additionally using the album classification task. The experiments are carried out on Manga109 public dataset which contains the annotations for characters, text blocks, frame and album metadata. We show that manga character features learnt by the proposed method is better than all existing single-modal methods for two manga character analysis tasks.
Towards a Guide for Risk Management Integration in Agile Software Projects

Several risks are inherent to software development, such as those related to schedule, budget, and stakeholder expectations. In agile software development methods, risks management is typically carried out implicitly, through practices such as small increments, job visibility and expectations management that tend to keep risks under control and minimize their impact. However, for many software development contexts, such as in highly regulated domains, as finance, healthcare or automotive, for example, only these implicit practices may not be sufficient. However, the practices commonly used for project risk management were developed in the context of traditional project management environments and are not adapted to the agile software development values and principles. Thus, this paper presents a guide to agile risk management in software projects. The structure of the guide is defined based on standards and reference models and its content is developed based on the analysis of the state of the art and the traditional risk management literature. A web tool is also developed to facilitate access and understanding of the guide’s content. A preliminary evaluation of the Guide content is performed through an Expert Panel. The results of this preliminary evaluation raise initial indications that the content of the guide is comprehensive and can be applied to different contexts of software development.
A Sensor Fused Rear Cross Traffic Detection System Using Transfer Learning

Recent emerging automotive sensors and innovative technologies in Advanced Driver Assistance Systems (ADAS) increase the safety of driving a vehicle on the road. ADAS enhance road safety by providing early warning signals for drivers and controlling a vehicle accordingly to mitigate a collision. A Rear Cross Traffic (RCT) detection system is an important application of ADAS. Rear-end crashes are a frequently occurring type of collision, and approximately 29.7% of all crashes are rear-ended collisions. The RCT detection system detects obstacles at the rear while the car is backing up. In this paper, a robust sensor fused RCT detection system is proposed. By combining the information from two radars and a wide-angle camera, the locations of the target objects are identified using the proposed sensor fused algorithm. Then, the transferred Convolution Neural Network (CNN) model is used to classify the object type. The experiments show that the proposed sensor fused RCT detection system reduced the processing time 15.34 times faster than the camera-only system. The proposed system has achieved 96.42% accuracy. The experimental results demonstrate that the proposed sensor fused system has robust object detection accuracy and fast processing time, which is vital for deploying the ADAS system.
Modelling supra-classical logic in a Boltzmann neural network: I representation

Abstract This paper looks at the representation of supra-classical, non-monotonic (SCNM) logic by an artificial neural network. It identifies the features of defeasiblity in this logic related to inference in the context of common-sense reasoning. It considers the machine characteristics that make a representation possible, with reference to previous literature. We describe a theoretical environment for investigating the representation and provide experimental evidence confirming that a Boltzmann machine is a suitable network representation. A Boltzmann machine can learn an input distribution corresponding to a preference relation and explicitly retrieve appropriate model states, constituting one-to-many mappings, entailed by the uncertain information contained in a premiss. The place of the Boltzmann machine in knowledge representation is discussed. In future papers, this neural network model of SCNM logic will serve as an experimental gateway for exploration of typicality and belief revision.