Reviewerly/exHarmony huggingface dataset:
Cultural Resource Recommender System with Knowledge Graph Convolutional Network

Due to the abundance of cultural resources on the Internet, traditional information retrieval has not met people's requirements. Therefore, intelligent recommendation of cultural resources has become an valuable research. However, previous studies on recommender system fail to exploit the higher-order structural information in knowledge graphs fully. Therefore, this paper proposes an Improved Knowledge Graph Convolutional Network (KGCN-I) model, which expresses the semantic information in the knowledge graph by weighting the user's score on the relationship. We use a brand-new aggregation function to replace the original KGCN layer's aggregation function and expand each entity's receptive field in the knowledge graph by stacking multiple KGCN layers to capture the high-order personalized interests of users. We compare with the state-of-the-art baseline algorithms on two real datasets. Experiments show that our proposed model is superior to the baseline algorithms on the precision, recall and F1.
An AR-based Support System of Postcard Handwriting through Superimposing Drafts with Text Frames

This paper proposes a support system of postcard handwriting based on augmented reality(AR) technology. By superimposing drafts with text frames, the system assists a user to write easily and neatly an address on front of a postcard with balanced characters. The proposed system projected adjusted text of the address and frames at the correct position on the postcard, which is determined by number of inputted text. From a user experiment in comparison with the proposed system and old-fashioned style (writing while looking a sample), it is seen that the proposed system has high effectiveness, while font style and user interface are to be improved for more usability.
Learning Knowledge Graph Embedding with Batch Circle Loss

Knowledge Graph Embedding (KGE) is the process to learn low-dimension representations for entities and relations in knowledge graphs. It is a critical component in Knowledge Graph (KG) for link prediction and knowledge discovery. Many works focus on designing proper score function for KGE, while the study of loss function has attracted relatively less attention. In this paper, we focus on improving the loss function when learning KGE. Specifically, we find that the frequently used margin-based loss in KGE models seeks to maximize the gap between the true facts score f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</inf> and the false facts score f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</inf> and only cares about the relative order of scores. Since its optimization objective is f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</inf> - f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</inf> = m, increasing f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</inf> is equivalent to decreasing f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</inf> . Its optimization objective creates an ambiguous convergence status which impairs the separability of positive and negative facts in embedding space. Inspired by the circle loss that offers a more flexible optimization manner with definite convergence targets and is widely used in computer vision tasks, we further extend it into the KGE model with the presented Batch Circle Loss (BCL). BCL allows multiple positives to be considered per anchor (h, r) (or (r, t)) in addition to multiple negatives (as opposed to a single positive sample as used before in KGE models). By comparing with other approaches, the obtained KGE models using our proposed loss function and training method shows superior performance.
A Gas Cost Analytical Approach Based on Certificateless Key Encapsulation Protocol for Medicalized Blockchains

The emergence of blockchain decentralization has garnered considerable interest from the scientific and scholarly communities since it addresses scalability issues and provides security for its users. Since its inception, numerous encryption methods have adopted its methodology to develop medically applicable schemes. However, most proposed schemes involve time-consuming operations, while others rely on standard pairing libraries. Both approaches cause complexity delays, affecting the blockchain’s execution speed. In the case of blockchain digital signatures, participants’ cryptographic keys are tied to their assets instead of their identities. This contradicts the non-repudiation feature of public key digital signatures. Therefore, this article proposes a lightweight hybrid encryption protocol employing the key encapsulation technique to generate encapsulated keys for blockchain network participants. The proposed method allows users to generate encapsulated keys linked to their identities. In order to increase the speed of cryptographic execution on the blockchain, we evaluate the computational overhead of the proposed model using the pairing Ethereum library (Py-eth library). In addition, the Ethereum Improvement Proposals (EIPs) library measures the consumption of gas costs on the blockchain. Our performance analysis demonstrates that the proposed protocol achieves a lower computational cost with less gas consumption, accelerating blockchain transaction executions.
Optimized S2E Attention Block based Convolutional Network for Human Pose Estimation

Human pose estimation is a popular research area due to its wide range of application scenarios. The general works, on the other hand, concentrate on how to enhance the network’s width, depth, and resolution, which results in a sizable number of parameters that hinder practical implementation on real-time and resource-constrained devices. Furthermore, since networks are severely constrained by the unequal feature distribution, it is challenging to extract deep features. We propose an S2E-based attention module that is lightweight, easily scalable, and aims to achieve a balance between reignition accuracy and speed while using fewer computational resources. The optimized S2E attention model consists of two layers of compression modules and one layer of motivation modules. We apply this attention block to the classical ResNet-101 and HRNet network backbones to build our S2E Based S2E-ResNet-101 and S2E-HRNet structure. Comparative studies on the COCO dataset and the MPII dataset show that the S2E module consumes very few computational resources but shows significant improvement in prediction accuracy, achieving a better speed/accuracy tradeoff and being more practical than other state-of-the-art methods. Moreover, the visual output of the qualitative comparison experiments in chaotic pose recognition further demonstrates our model’s capacity to concentrate on a significantly more detailed area and prevent erroneous recognition brought on by posture crossover and occlusion. Overall, it can be seen that the S2E module is a simple but effective and easily scalable attention module, which is of tremendous practical value to the field of pose recognition.
A Novel ACM/ICPC-Based C++ Paperless Examination System

The ACM/ICPC is recognized as the largest and highest level international collegiate programming contest in the world, and it is one of the most important ways to train and select top students in computer science worldwide. Currently, C++ is the most popular and practical computer advanced programming language in the world. It is an important professional main course and one of the main tools for teaching programming and computer software development. It has become a programming course for universities and colleges. With further deepening of education reform, the paperless examination has become an important sign of the college examination reform, and its advantages have become more and more obvious. Also, the paperless examination has gradually become a trend of various examinations. Although there have been some paperless examination systems, but these systems usually can only automatically score four types of objective questions, including true and false questions, fill-in-the-blank questions, single-choice questions and multiple-choice questions. These paperless examination systems are still unable to conduct fair and impartial automatic scoring for programming questions which are however important subjective questions. In response to this problem, based on the ACM/ICPC platform, we propose a new design method for the C++ paperless examination system, which realizes the automatic scoring of C++ examinations. The system can greatly improve the flexibility, fairness, impartiality, credibility and work efficiency of the examination. Our design system can be an important platform for deepening the teaching reform of colleges and universities, promoting the innovation of C++ teaching, and improving the efficiency of college examinations. Therefore, it has a certain promotion and demonstration value.
Robo-Rage Against the Machine: Abuse, Sabotage, and Bullying of Robots and Autonomous Vehicles

This chapter analyzes emerging varieties of robot and autonomous entity sabotage, bullying, and manipulation by humans, placing them in context of security concerns as well as other kinds of aggression and abusive conduct. An increasing amount of this behavior can be considered in terms of self-defense, given the specific intrusions of these entities in personal spaces. However, other instances are more specifically expressions of aggression, in dramaturgical terms being “frontstage” actions with human or robotic entities as audiences and including “hate crimes.” Some abuse could be characterized as in keeping with robot-as-personage and others more reflecting robot-as-property themes. The chapter also puts robot-related violence into perspective with other social rebellions and protests, exploring how attacks against autonomous entities (or disseminated narratives and images of the attacks) often relate to externalizations of economic and symbolic violence.
Evaluating FPGA Acceleration on Binarized Neural Networks and Quantized Neural Networks

The gap between complex deep learning models and the limited computing capability of individual robots is one of the driving forces to hardware acceleration of image classification networks. Therefore, this paper presents two case studies using the field-programmable gate array (FPGA) acceleration on deep neural networks. The hardware platform is mainly composed of an AMD-Xilinx PYNQ-Z2 board as a computing device and a USB camera as the image/video sensor. The main applications in image/video detection and recognition of traffic signs and objects are performed in this work. Compared with the software computation, the results of the FPGA implementation show a significant speedup to the single road sign recognition using the Binarized Neural Networks and ≥ 50× speedup to the object detection using Quantized Neural Networks. This paper focuses on the preliminary research into hardware acceleration on neural networks. It demonstrates a great potential to improve the speed for image processing with FPGA, particularly to the time-constrained scenarios. The final goal of this project is to provide parameterizable designs for complicated neural networks with FPGA.
Using Extracted Emotion Cause to Improve Content-Relevance for Empathetic Conversation Generation

Empathetic conversation generation intends to endow the open-domain conversation model with the capability for understanding, interpreting, and expressing emotion. Humans express not only their emotional state but also the stimulus that caused the emotion, i.e., emotion cause, during a conversation. Most existing approaches focus on emotion modeling, emotion recognition and prediction, and emotion fusion generation, ignoring the critical aspect of the emotion cause, which results in generating responses with irrelevant content. Emotion cause can help the model understand the user’s emotion and make the generated responses more content-relevant. However, using the emotion cause to enhance empathetic conversation generation is challenging. Firstly, the model needs to accurately identify the emotion cause without large-scale labeled data. Second, the model needs to effectively integrate the emotion cause into the generation process. To this end, we present an emotion cause extractor using a semi-supervised training method and an empathetic conversation generator using a biased self-attention mechanism to overcome these two issues. Experimental results indicate that our proposed emotion cause extractor improves recall scores markedly compared to the baselines, and the proposed empathetic conversation generator has superior performance and improves the content-relevance of generated responses.
Superconducting Qubits as Musical Synthesizers for Live Performance

In the frame of a year-long artistic residency at the Yale Quantum Institute in 2019, artist and technologist Spencer Topel, quantum physicists Kyle Serniak and Luke Burkhart, and producer Florian Carle collaborated to create Quantum Sound, a distinctive piece of music created and performed directly from measurements of superconducting quantum devices. Using analogue- and digital-signal-processing sonification techniques, the team transformed GHz-frequency signals from experiments inside dilution refrigerators into audible sounds. The project was performed live at the International Festival of Arts and Ideas in New Haven, Connecticut on June 14, 2019, as a structured improvisation using the synthesis methods described in this chapter. At the interface between research and art, Quantum Sound represents an earnest attempt to produce a sonic reflection of the quantum realm.
A language processing-free unified spam detection framework using byte histograms and deep learning

In this paper, we established a unified deep learning-based spam filtering method. The proposed method uses the message byte-histograms as a unified representation for all message types (text, images, or any other format). A deep convolutional neural network (CNN) is used to extract high-level features from this representation. A fully connected neural network is used to perform the classification using the extracted CNN features. We validate our method using several open-source text-based and image-based spam datasets.We obtained an accuracy higher than 94% on all datasets.
RemOD: Operational Drift-Adaptive Intrusion Detection

The critical infrastructure’s (CI) environment is complex and dynamic in nature. The normal behaviour of physical devices changes due to time-dependent operational features and infrastructure component needs. The sensors capturing the changed device behaviour generates measurements in a different operating range due to the time dependent variation in the normal behaviour. Such normal variation in the sensors measurements are called operational drift (OD). The state-of-the-art process-level intrusion detection systems (IDSs) are based on offline training, which leads to repeated false alarms for the ODs. Frequently retraining the offline-based IDS model may be a solution, but it’s costly and challenging. To overcome the limitation of offline training, we propose an online learning-based IDS named RemOD. Instead of retraining the entire model, RemOD can adapt the ODs to update itself in online fashion. Updating the RemOD for ODs significantly reduces the false alarms in such dynamic environments. We validate the proposed method on two benchmark datasets: SWaT (dynamic environment) and C-town (stationary environment). On SWaT dataset, RemOD generates 6.88 times lower false alarms than the baseline methods such as PASAD.
Encrypted Image Fuzzy Search Scheme

Traditional cloud image data retrieval can not provide the function of cloud encryption retrieval. Storing images in non credit cloud servers is easy to expose user images and privacy information. Most of the existing encrypted image search schemes add image encryption technology and improve search accuracy, but this leads to the vast majority of existing schemes requiring strong computing performance. With the increase of storage and computing costs, it is difficult for cloud servers to adopt existing technical solutions at a relatively high rate of economic return. In this paper, a simplified existing search algorithm is proposed, which realizes fuzzy query and reduces the requirements of computing performance. Combined with the access control function of attribute encryption based on ciphertext strategy, it ensures the safe sharing of image data in multi-user environment, and can better meet the real needs in real environment.
WiFi-Based Human Pose Image Generation

This paper tackles a new challenge: how to generate human pose images from wireless signals? Although the optical camera can capture optical images, it is easily restricted by bad lighting. The wireless signals do not rely on visible lights. However, the low-resolution characteristics make previous works can only generate a rough skeleton of human posture, missing a lot of detailed visual information, such as background, appearance, etc. Since the visual information usually maintains unchanged for a period and the wireless signals can capture the movements of the human, in this paper, we propose a framework to generate the target human pose images by combining the wireless signals with an initial optical image. We utilize multiple wireless devices to collect the WiFi signals and a camera to capture the initial optical image. Then a data preprocessing component is designed to preprocess the wireless and vision data. Finally, a deep learning model learns to generate the human pose images from the processed wireless signals and the initial optical image. We conduct experiments to evaluate our proposed framework and results show that it achieves higher accuracy than the state-of-the-art WiFi-based pose estimation method and better visual quality than the state-of-the-art human generation method.
Performance Estimation in V2X Network

5G networks have developed to be a new technology which is helping telecom networks advance faster. Further-more,as compared to earlier networks, 5G networks improve performance of the network while bringing down network complexity.Deep learning is a method for detecting anomalies in wireless networks, doing predictive analytics, predicting lifetime value,and other tasks.Its efficiency of training examples determines its performance [1]. Deep Learning is built to operate with massive datasets and employs a sophisticated datasets algorithm fo rmodel training. As outliers in the raw data diminish model reliability,the project’s goal is to lessen the influence of outliers in these models.
Probability Estimation of Distributed Photovoltaic Power Prediction Based on KMC-optimized Copula

Because of the natural randomness of solar energy and the unique property of distributed photovoltaic (DPV), the uncertainty of its output affects the safe and stable operation of the distribution network. DPV power probability prediction can provide more comprehensive prediction information, and it is one of the most important measures to solve the problem of large-scale DPV integration. Therefore, a DPV probability prediction algorithm based on k-means clustering(KMC)-optimized Copula is proposed in this paper. Firstly, the weather types are classified by the clustering method. Then, according to the characteristics of each weather attribute, the optimal representative weight of the Copula function is constructed via Genetic Algorithm (GA), and the KMC-optimized Copula algorithm is proposed to quantify the dynamic power space correlation between concentrated photovoltaic (CPV) and DPV. Finally, the DPV prediction value is dynamically expressed by the CPV power in the coming prediction duration, and the conditional prediction probability estimation of the DPV power is obtained. The case study shows that the proposed KMC-optimized Copula Model is of great importance for distributed power predictionand compared with the normal Copula model, the model performances of point prediction and probability prediction are both improved under different weather types.
Sign Language Apprehension using Convolution Neural Networks

Sign language is one of the oldest and most natural forms of language for communication. Most people don’t know sign language and practitioners are veritably delicate to come by, thus we’ve come up with a real time system using neural networks for finger spelling grounded American subscribe Language(ASL). In our system, the hand is first passed through a complication filter and after the filter is applied the hand is passed through a classifier which predicts the class of the hand gestures. Sign language isn’t enough for communication of people with hearing capability or people with speech disability. The gestures made by the people with disabilities get mixed or disordered for someone who has noway learned this language. Communication should be in both ways. In this paper, we introduce Sign Language recognition using American Sign Language. The user must be suitable to capture images of hand gestures using a web camera in this analysis, and the system must prognosticate and show the name of the captured image. The captured image undergoes a series of processing steps which include multiple Computer Vision ways similar as the conversion to gray-scale, threshold operation. Convolutional Neural Network(CNN) is used to train our model and identify the images. Our model has achieved accuracy about 94%.
AutoCoMet: Smart Neural Architecture Search via Co-Regulated Shaping Reinforcement

Designing suitable deep model architectures for AI-driven on-device apps and features, at par with evolving mobile hardware and complex target scenarios, is difficult. Though Neural Architecture Search (NAS/AutoML) has made this easier by automated architecture learning from data saving substantial manual effort, yet it has major limitations, in context of mobile devices, including model-hardware alignment, prohibitive search times and divergence from primary target objective(s). So, we propose AUTOCOMET that can learn the most suitable DNN architecture optimized for varied types of device hardware and task contexts, ≈ 3× faster. Our novel co-regulated shaping reinforcement controller together with the high fidelity hardware meta-behavior predictor produces a smart, fast NAS framework that adapts to context via a generalized formalism for any kind of multi-criteria optimization.
Modulation and Pulse Shaping Filter Classification of Raw Baseband Samples Based on Convolutional Neural Network

The convolutional neural network (CNN) is a machine learning methodology that was successfully implemented in many domains, including electromagnetics and wireless communications. This paper investigates the use of CNN in modulation and pulse shaping filter classification, which can be used in future cognitive radios to facilitate the reception of unknown signals. The focus of this paper is on the conversion of raw baseband samples to a format which complies with the CNN input. The AlexNet CNN architecture was selected and trained on six different datasets. Obtained results show that CNN can be used to classify modulation schemes and the raised cosine filter roll-off factor in noisy samples even without carrier synchronization.