Reviewerly/exHarmony huggingface dataset:
Unifying paragraph embeddings and neural collaborative filtering for hybrid recommendation

Collaborative filtering is one of widely used recommendation techniques. Despite the effectiveness of matrix factorization for collaborative filtering; however, the inner product operator, combining the multiplication of latent features linearly, may not be sufficient to capture the complex structure of user interaction ratings. On the other hand, we argue that there is a great deviation between user ratings and their real interest preference. In this paper, we propose a novel hybrid recommendation algorithm. It adopts neural networks to exploit user–item ratings for collaborative filtering, which is endowed a high level of non-linearity for capturing the complex structure of user interaction ratings. At the same time, it exploits item embeddings to capture the content feature for auxiliary information, which solves the cold start problem to some extent. In particular, we introduce paragraph embeddings to represent user reviews and item descriptions, and design two neural networks to capture the sentiment of user reviews and the content feature of items, respectively. And then, we treat these embeddings as attention weights of users and items, and unify them with user–item ratings to model the hybrid recommendation system. Extensive experiments on Amazon product dataset demonstrates that our algorithm performs better on rating prediction than other state-of-the-art algorithms.
Reinforcement Learning Based Rate Adaptation for 360-Degree Video Streaming

The 360-degree video streaming has higher bandwidth requirements compared with traditional video to achieve the same user-perceived playback quality. Since users only view part of the entire videos, viewport-adaptive streaming is an effective approach to guarantee video quality. However, the performance of viewport-adaptive schemes is highly dependent on the bandwidth estimation and viewport prediction. To overcome these issues, we propose a novel reinforcement learning (RL) based viewport-adaptive streaming framework called RLVA, which optimizes the 360-degree video streaming in viewport prediction, prefetch scheduling and rate adaptation. Firstly, RLVA adopts t location-scale distribution rather than Gaussian distribution to describe the viewport prediction error characteristic more accurately and achieve the tile viewing probability based on the distribution. Besides, a tile prefetch scheduling algorithm is proposed to update the tiles according to the latest prediction results, which further reduces the adverse effect of prediction error. Furthermore, the tile viewing probabilities are treated as input status of RL algorithm. In this way, RL can adjust its policy to adapt to both of the network conditions and viewport prediction error. Through extensive evaluations, the simulation results show that the proposed RLVA outperforms other viewport-adaptive methods by about 4.8%-66.8% improvement of Quality of Experience (QoE) and effectively reduces the impact of viewport prediction errors.
Self-play reinforcement learning with comprehensive critic in computer games

Self-play reinforcement learning, where agents learn by playing with themselves, has been successfully applied in many game scenarios. However, the training procedure for self-play reinforcement learning is unstable and more sample-inefficient than (general) reinforcement learning, especially in imperfect information games. To improve the self-play training process, we incorporate a comprehensive critic into the policy gradient method to form a self-play actor-critic (SPAC) method for training agents to play computer games. We evaluate our method in four different environments in both competitive and cooperative tasks. The results show that the agent trained with our SPAC method outperforms those trained with deep deterministic policy gradient (DDPG) and proximal policy optimization (PPO) algorithms in many different evaluation approaches, which vindicate the effect of our comprehensive critic in the self-play training procedure.
Transfer reinforcement learning-based road object detection in next generation IoT domain

The landscape of fifth generation (5G) and beyond 5G (B5G)-enabled Internet of Things(IoT) is expected to seamlessly and ubiquitously connect everything, which includes 5G, cloud computing, artificial intelligence and other cutting-edge technologies to realize truly intelligent applications in smart cities. In this paper, we present an important key technology for smart city, which is a road target recognition algorithm for smart city applications and designs a set of corresponding programs to assist automatic drivers, pedestrians and visually impaired people in road safety, or to manage city infrastructure. The system can connect robots in cars, wearable devices and body area network in pedestrians or blind people. A target recognition algorithm based on scene fusion is designed to recognize the specific target in the road environment, and transfer reinforcement learning method is used to improve the accuracy and real-time performance of target recognition. The system provides them with travel assistance, identify dangerous or useful objects for them through high-performance target recognition services. It can collect the road visual scene data by road cameras and transmit it to edge devices for training model. The model is collaborated trained in the edge devices and aggregated by the cloud. Based on the transfer reinforcement learning method, the vision-based road target recognition has been implemented, and the accurate and reliable target recognition can be realized. Many details of experiments verify the effectiveness of our technology.
UAV flight control sensing enhancement with a data-driven adaptive fusion model

Accurate sensing is essential for achieving reliable control of unmanned aerial vehicles (UAVs). In prior works, the unscented Kalman filter (UKF) has shown superior performance in both estimation accuracy and computation efficiency, which makes it suitable for onboard sensing enhancement. However, the prediction accuracy of existing filter-based statistical models is generally assumed to be invariant of the flight conditions. To avoid deterioration in the estimation performance, this paper proposes a novel data-driven adaptive fusion state space model for quantifying the prediction uncertainty of the system model. Based on the multi-output Gaussian process regression (GPR), a rule for tuning the noise parameter of the statistical model is provided based on the estimated variance. The sparse GPR model is utilized to incorporate available features and obtain high estimation accuracy under dynamic operation conditions. Simulation results have illustrated the superior performance of the proposed approach in state estimation for the UAV.
Lexical alignment is affected by addressee but not speaker nativeness

Abstract Interlocutors tend to refer to objects using the same names as each other. We investigated whether native and non-native interlocutors’ tendency to do so is influenced by speakers’ nativeness and by their beliefs about an interlocutor's nativeness. A native or non-native participant and a native or non-native confederate directed each other around a map to deliver objects to locations. We manipulated whether confederates referred to objects using a favored or disfavored name, while controlling for confederates’ language behavior. We found evidence of audience design for native and non-native addressees: participants were more likely to use a disfavored name after a non-native confederate used that name than after a native confederate used that name; this tendency did not differ between native and non-native participants. Results suggest that both native and non-native speakers can adapt to the language of non-native partners through non-automatic, goal-directed mechanisms of alignment during cognitively demanding communicative tasks.
Improved Fully Convolutional Network for Digital Image Region Forgery Detection

With the rapid development of image editing techniques, the image splicing behavior, typically for those that involve copying a portion from one original image into another targeted image, has become one of the most prevalent challenges in our society. The existing algorithms relying on hand-crafted features can be used to detect image splicing but unfortunately lack precise location information of the tampered region. On the basis of changing the classifications of fully convolutional network (FCN), here we proposed an improved FCN that enables locating the spliced region. Specifically, we first insert the original images into the training dataset that contains tampered images forming positive and negative samples and then set the ground truth masks of the original images to be black images. The purpose of forming positive and negative samples is to guide the improved FCN to distinguish the differences between the original images and spliced images. After these steps, we conducted an experiment to verify our proposal, and the results reveal that the improved FCN really can locate the spliced region. In addition, the improved FCN achieves improved performance compared to the already-existing algorithms, thereby providing a feasible approach for digital image region forgery detection.
Enhancing and Evaluating an Impact of OCR and Ontology on Financial Document Checking Process

This research objective is to increase the efficiency of financial document auditing process for reimbursement by using Optical Character Recognition (OCR) and Ontology. On the researched system, user can use the system for checking completeness of document completeness, in accordance with the disbursement accounting standards, with digital photo after paid immediately. In the past it took more than a week to submit the evidence and make an account disbursement document. This research can reduce the time since the occurrence of the program. (Transaction) up to the creation of documents with more than 50% disbursement.
Federated Ecology: Steps Toward Confederated Intelligence

Welcome to the second issue of IEEE Transactions on Computational Social Systems (TCSS) this year. First, I am grateful to report that, as of February 7, 2021, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Citescore</i> of TCSS has leapfrogged back to 5.8, a new high, which indicates the high quality and relevance of IEEE TCSS in the field of social computing and computational social systems research. Many thanks to all of you for your great effort and support.
Face recognition framework based on effective computing and adversarial neural network and its implementation in machine vision for social robots

In recent years, with the continuous breakthrough of computer vision technology, the accuracy of object detection and target recognition has been improved by leaps and bounds. Face recognition is one of the important research directions in the field of computer vision, which is widely used in mobile payment, safe city, criminal investigation and other fields. Traditional face recognition methods need to extract face image features manually. The extracted features are greatly affected by subjective factors, and time-consuming and laborious. Deep learning is the most important technology in the field of computer vision at present. Compared with traditional face recognition methods, it can extract more essential features of face image without manual participation. In this paper, we build a face recognition system based on neural computing model and the principle of neural network. The experimental results show that the proposed method has high detection rate and short processing time.
Deep center-based dual-constrained hashing for discriminative face image retrieval

With the advantages of low storage cost and extremely fast retrieval speed, deep hashing methods have attracted much attention for image retrieval recently. However, large-scale face image retrieval with significant intra-class variations is still challenging. Neither existing pairwise/triplet labels-based nor softmax classification loss-based deep hashing works can generate compact and discriminative binary codes. Considering these issues, we propose a center-based framework integrating end-to-end hashing learning and class centers learning simultaneously. The framework minimizes the intra-class variance by clustering intra-class samples into a learnable class center. To strengthen inter-class separability, it additionally imposes a novel regularization term to enlarge the Hamming distance between pairwise class centers. Moreover, a simple yet effective regression matrix is introduced to encourage intra-class samples to generate the same binary codes, which further enhances the hashing codes compactness. Experiments on four large-scale datasets show the proposed method outperforms state-of-the-art baselines under various code lengths and commonly-used evaluation metrics.
Decentralized and Lightweight Approach to Detect Eclipse Attacks on Proof of Work Blockchains

Clients of permissionless blockchain systems, like Bitcoin, rely on an underlying peer-to-peer network to send and receive transactions. It is critical that a client is connected to at least one honest peer, as otherwise the client can be convinced to accept a maliciously forked view of the blockchain. In such an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">eclipse attack</i> , the client is unable to reliably distinguish the canonical view of the blockchain from the view provided by the attacker. The consequences of this can be catastrophic if the client makes business decisions based on a distorted view of the blockchain transactions. In this paper, we investigate the design space and propose two approaches for Bitcoin clients to detect whether an eclipse attack against them is ongoing. Each approach chooses a different trade-off between average attack detection time and network load. The first scheme is based on the detection of suspicious block timestamps. The second scheme allows blockchain clients to utilize their natural connections to the Internet (i.e., standard Web activity) to gossip about their blockchain views with contacted servers and their other clients. Our proposals improve upon previously proposed eclipse attack countermeasures without introducing any dedicated infrastructure or changes to the Bitcoin protocol and network, and we discuss an implementation. We demonstrate the effectiveness of the gossip-based schemes through rigorous analysis using original Internet traffic traces and real-world deployment. The results indicate that our protocol incurs a negligible overhead and detects eclipse attacks rapidly with high probability, and is well-suited for practical deployment.
Hybrid attention-based Long Short-Term Memory network for sarcasm identification

Sentiment analysis of people’s opinion is used in a lot of business and decision-making scenarios. Although social media is an informal medium in which to express one’s opinions, it is being used in many business and decision-making scenarios now. Social media posts contain a lot of sarcastic statements that affect the automatic extraction of the correct sentiment of the post, as sarcasm can flip the overall polarity of the sentence. Sarcasm is a bitterly cutting form of irony to be unpleasant to somebody or to make fun of them. Therefore, identifying sarcastic statements from the users’ posts has become an important task to extract the actual sentiments from informal statements regarding an event or a person. In this work, we propose a hybrid attention-based Long Short Term Memory (HA-LSTM) network to identify sarcastic statements. This HA-LSTM network is different than the existing LSTM model, as the proposed HA-LSTM network combines 16 different linguistic features in their hidden layers. The proposed HA-LSTM network is validated with three benchmark datasets. The combination of 16 different linguistic features shows an improvement in the performance of the model in comparison with other state-of-the-art models with an improvement of up to 2% in terms of F1-score with three different gold standard datasets.
A3: Assisting Android API Migrations Using Code Examples

The fast-paced evolution of Android APIs has posed a challenging task for Android app developers. To leverage Android's frequently released APIs, developers must often spend considerable effort on API migrations. Prior research and Android official documentation typically provide enough information to guide developers in identifying the API calls that must be migrated and the corresponding API calls in an updated version of Android (what to migrate). However, API migration remains a challenging task since developers lack the knowledge of how to migrate the API calls. There exist code examples, such as Google Samples, that illustrate the usage of APIs. We posit that by analyzing the changes of API usage in code examples, we can learn API migration patterns to assist developers with API Migrations. In this paper, we propose an approach that learns API migration patterns from code examples, applies these patterns to the source code of Android apps for API migration, and presents the results to users as potential migration solutions. To evaluate our approach, we migrate API calls in open source Android apps by learning API migration patterns from code examples. We find that our approach can successfully learn API migration patterns and provide API migration assistance in 71 out of 80 cases. Our approach can either migrate API calls with little to no extra modifications needed or provide guidance to assist with the migrations. Through a user study, we find that adopting our approach can reduce the time spent on migrating APIs, on average, by 29%. Moreover, our interviews with app developers highlight the benefits of our approach when seeking API migrations. Our approach demonstrates the value of leveraging the knowledge contained in software repositories to facilitate API migrations.
DOC-BLOCK: A Blockchain Based Authentication System for Digital Documents

With the rapid growth in the sector of information technology and easy access to cheap and advanced office instruments in the market, the faking of important documents has become a matter of concern nowadays. Therefore, the need for verification and authentication practices of various important documents in the form of banking documents, government documents, transaction documents, educational certificates etc is also increasing. However, various challenging and tedious processes have made document verification very complex and time-consuming which motivated us to conduct this research. In this paper, we present a decentralized web application for digital document verification using Ethereum blockchain-based technology in P2P cloud storage to enhance the verification process by making it more open, transparent, and auditable. The proposed model includes several methods such as public/private key cryptography, online storage security, digital signatures, hash, peer-to-peer networks and proof of work which has made the verification of any uploaded documents for any organization or authority faster and convenient with just a click. Furthermore, respective hash values are also assigned to each individual document. Our proposed model successfully meets up all the criteria for a digital document verification system by alleviating the gaps and difficulties in the traditional methods in document verification.
Light Field View Synthesis via Aperture Disparity and Warping Confidence Map

This paper presents a learning-based approach to synthesize the view from an arbitrary camera position given a sparse set of images. A key challenge for this novel view synthesis arises from the reconstruction process, when the views from different input images may not be consistent due to obstruction in the light path. We overcome this by jointly modeling the epipolar property and occlusion in designing a convolutional neural network. We start by defining and computing the aperture disparity map, which approximates the parallax and measures the pixel-wise shift between two views. While this relates to free-space rendering and can fail near the object boundaries, we further develop a warping confidence map to address pixel occlusion in these challenging regions. The proposed method is evaluated on diverse real-world and synthetic light field scenes, and it shows better performance over several state-of-the-art techniques.
MFSR: A novel multi-level fuzzy similarity measure for recommender systems

There is nowadays explosive growth and diversity of information due to the development of the internet. Thus, decision making in various fields has faced different challenges. Recommender systems by identifying the interests of users, data filtering and data management, offer personalized services to users. This is beneficial for marketing and user satisfaction. Recommender system has always faced challenges such as cold start, sparsity, scalability, accuracy, and quality. Collaborative Filtering (CF) as one of the most successful methods used in recommender systems is based on the similarity between users. We argue that similarity is a fuzzy notion and we get more realistic results in recommender systems by using fuzzy logic. Fuzzy logic deals better with uncertainty and is an effective method to identify ambiguities and uncertainty in measuring the similarity of items and users. In this paper, we present a new multi-level fuzzy similarity measure for recommender systems, called MFSR, which is based on popularity and significance. In order to improve the accuracy and quality of recommendations, we also propose a hierarchical structure for calculation of the similarity. To evaluate the contribution of this work, we use MAE, F1, recall, and precision. The MAE value based on the proposed similarity measure and the hierarchical structure is equal to 0.423 and outperforms the PIP and NHSM respectively by %4 and %13. Also, using the proposed similarity measure and the hierarchical structures, we obtain F1 value equal to 0.654, which outperforms the PIP and NHSM respectively by %17 and %20. We have also observed an improvement in recall and precision using the proposed approach. The results show that the proposed method (MFSR) performs better than similar methods in recent years such as PIP and NHSM.
Cosine metric supervised deep hashing with balanced similarity

Deep supervised hashing takes prominent advantages of low storage cost, high computational efficiency and good retrieval performance, which draws attention in the field of large-scale image retrieval. However, similarity-preserving, quantization errors and imbalanced data are still great challenges in deep supervised hashing. This paper proposes a pairwise similarity-preserving deep hashing scheme to handle the aforementioned problems in a unified framework, termed as Cosine Metric Supervised Deep Hashing with Balanced Similarity (BCMDH). BCMDH integrates contrastive cosine similarity and Cosine distance entropy quantization to preserve the original semantic distribution and reduce the quantization errors simultaneously. Furthermore, a weighted similarity measure with cosine metric entropy is designed to reduce the impact of imbalanced data, which adaptively assigns weights according to sample attributes (pos/neg and easy/hard) in the embedding process of similarity-preserving. The experimental results on four widely-used datasets demonstrate that the proposed method is capable of generating hash codes of high quality and improve large-scale image retrieval performance.