Reviewerly/exHarmony huggingface dataset:
CNN-based multilingual handwritten numeral recognition: A fusion-free approach

Numeral recognition plays a vital role in making automated systems like postal address sorting and license plate recognition. In a multilingual country like India, more often, the multiple languages are mixed while writing. Numeral recognizer systems which can handle more than one language are very much useful to recognize numerals of various scripts. Handwritten numeral recognition is much more complicated than the printed one because of the different writing styles. Few multilingual works have been reported earlier, but these systems are either trained for each language individually or fusion of similar shaped classes of performed. In either case, multiple classes exist for a single digit. In this work, we have developed a script independent numeral recognition system for multilingual handwritten digits which is independent of fusion and has only 10 classes corresponding to every single numeric digit. This work is first of its kind in which we have addressed the problems of multilingual numeral recognition systems. Exhaustive experiments are done with numeral datasets of 8 Indic and non-Indic scripts. We have obtained the accuracy of 96.23% collectively for all the eight scripts. This obtained high accuracy is promising and demonstrates the hypothesis that multilingual handwritten numeral recognition is void with CNN.
A New Video Steganography Scheme Based on Shi-Tomasi Corner Detector

Recent developments in the speed of the Internet and information technology have made the rapid exchange of multimedia information possible. However, these developments in technology lead to violations of information security and private information. Digital steganography provides the ability to protect private information that has become essential in the current Internet age. Among all digital media, digital video has become of interest to many researchers due to its high capacity for hiding sensitive data. Numerous video steganography methods have recently been proposed to prevent secret data from being stolen. Nevertheless, these methods have multiple issues related to visual imperceptibly, robustness, and embedding capacity. To tackle these issues, this paper proposes a new approach to video steganography based on the corner point principle and LSBs algorithm. The proposed method first uses Shi-Tomasi algorithm to detect regions of corner points within the cover video frames. Then, it uses 4-LSBs algorithm to hide confidential data inside the identified corner points. Besides, before the embedding process, the proposed method encrypts confidential data using Arnold's cat map method to boost the security level. Experimental results revealed that the proposed method is highly secure and highly invisible, in addition to its satisfactory robustness against Salt & Pepper noise, Speckle noise, and Gaussian noise attacks, which has an average Structural Similarity Index (SSIM) of more than 0.81. Moreover, the results showed that the proposed method outperforms state-of-the-art methods in terms of visual imperceptibility, which offers excellent peak signal-to-noise ratio (PSNR) of average 60.7 dB, maintaining excellent embedding capacity.
Decentralized Privacy-Preserving Data Aggregation Scheme for Smart Grid Based on Blockchain

As a next-generation power system, the smart grid can implement fine-grained smart metering data collection to optimize energy utilization. Smart meters face serious security challenges, such as a trusted third party or a trusted authority being attacked, which leads to the disclosure of user privacy. Blockchain provides a viable solution that can use its key technologies to solve this problem. Blockchain is a new type of decentralized protocol that does not require a trusted third party or a central authority. Therefore, this paper proposes a decentralized privacy-preserving data aggregation (DPPDA) scheme for smart grid based on blockchain. In this scheme, the leader election algorithm is used to select a smart meter in the residential area as a mining node to build a block. The node adopts Paillier cryptosystem algorithm to aggregate the user's power consumption data. Boneh-Lynn-Shacham short signature and SHA-256 function are applied to ensure the confidentiality and integrity of user data, which is convenient for billing and power regulation. The scheme protects user privacy data while achieving decentralization, without relying on TTP or CA. Security analysis shows that our scheme meets the security and privacy requirements of smart grid data aggregation. The experimental results show that this scheme is more efficient than existing competing schemes in terms of computation and communication overhead.
High-resolution real-time 360∘ 3D surface defect inspection with fringe projection profilometry

Since a slight variance in production processes can make the entire production run defective, defect inspections are indispensable procedures in manufacturing processes to ensure high quality of each item before entering the next manufacturing step. Three-dimensional (3D) optical shape measurement technologies are widely applied for surface defect inspection of complex workpieces because of its high-accuracy and digitization. However, the complex surface structure and position of the test object can pose serious challenges, making inspections still relatively slow, expensive, and complicated in implementation and maintenance. In this work, we propose a real-time 360∘ 3D surface defect inspection approach based on fringe projection profilometry without any auxiliary equipment for position control. Firstly, a multi-view 3D measurement based on geometric constraints is employed to acquire high-accuracy depth information from different perspectives. Then, a cycle-positioning-based registration scheme with the establishment of the pose-information-matched 3D standard digital model is proposed to realize rapid alignment of the measured point cloud and the standard model. Finally, a minimum 3D distance search is driven by a dual-thread processing mechanism for simultaneous scanning and detecting to quantify and locate 3D surface defects in real time. Experimental results show that our method can accurately identify the surface defects of complicated objects in real time in an extremely simple (hand-held) manner, saving a lot of operational expenses on precision alignment and position-orientation adjustment. The proposed method holds tremendous potential for quality control in many facets of industry, such as product development, testing, and manufacturing.
The Best Defense Is a Good Offense: Adversarial Attacks to Avoid Modulation Detection

We consider a communication scenario, in which an intruder tries to determine the modulation scheme of the intercepted signal. Our aim is to minimize the accuracy of the intruder, while guaranteeing that the intended receiver can still recover the underlying message with the highest reliability. This is achieved by perturbing channel input symbols at the encoder, similarly to adversarial attacks against classifiers in machine learning. In image classification, the perturbation is limited to be imperceptible to a human observer, while in our case the perturbation is constrained so that the message can still be reliably decoded by the legitimate receiver, which is oblivious to the perturbation. Simulation results demonstrate the viability of our approach to make wireless communication secure against state-of-the-art intruders (using deep learning or decision trees) with minimal sacrifice in the communication performance. On the other hand, we also demonstrate that using diverse training data and curriculum learning can significantly boost the accuracy of the intruder.
FISSA: Fusing Item Similarity Models with Self-Attention Networks for Sequential Recommendation

Sequential recommendation has been a hot research topic because of its practicability and high accuracy by capturing the sequential information. As deep learning (DL) based methods being widely adopted to model the local and dynamic preferences beneath users’ behavior sequences, the modeling of users’ global and static preferences tends to be underestimated that usually, only some simple and crude users’ latent representations are introduced. Moreover, most existing methods hold an assumption that users’ intention can be fully captured by considering the historical behaviors, while neglect the possible uncertainty of users’ intention in reality, which may be influenced by the appearance of the candidate items to be recommended. In this paper, we thus focus on these two issues, i.e., the imperfect modeling of users’ global preferences in most DL-based sequential recommendation methods and the uncertainty of users’ intention brought by the candidate items, and propose a novel solution named fusing item similarity models with self-attention networks (FISSA) for sequential recommendation. Specifically, we treat the state-of-the-art self-attentive sequential recommendation (SASRec) model as the local representation learning module to capture the dynamic preferences beneath users’ behavior sequences in our FISSA, and further propose a global representation learning module to improve the modeling of users’ global preferences and a gating module that balances the local and global representations by taking the information of the candidate items into account. The global representation learning module can be seen as a location-based attention layer, which is effective to fit in well with the parallelization training process of the self-attention framework. The gating module calculates the weight by modeling the relationship among the candidate item, the recently interacted item and the global preference of each user using an MLP layer. Extensive empirical studies on five commonly used datasets show that our FISSA significantly outperforms eight state-of-the-art baselines in terms of two commonly used metrics.
A Proof-of-Quality-Factor (PoQF)-Based Blockchain and Edge Computing for Vehicular Message Dissemination

Blockchain applications in vehicular networks can offer many advantages, including decentralization and improved security. However, most of the consensus algorithms in blockchain are difficult to be implemented in vehicular ad hoc networks (VANETs) without the help of edge computing services. For example, the connectivity in VANET only remains for a short period of time, which is not sufficient for highly time-consuming consensus algorithms, e.g., Proof of Work, running on mobile-edge nodes (vehicles). Other consensus algorithms also have some drawbacks, e.g., Proof of Stake (PoS) is biased toward nodes with a higher amount of stakes and Proof of Elapsed Time (PoET) is not highly secure against malicious nodes. For these reasons, we propose a voting blockchain based on the Proof-of-Quality-Factor (PoQF) consensus algorithm, where the threshold number of votes is controlled by edge computing servers. Specifically, PoQF includes voting for message validation and a competitive relay selection process based on the probabilistic prediction of channel quality between the transmitter and receiver. The performance bounds of failure and latency in message validation are obtained. This article also analyzes the throughput of block generation, as well as the asymptotic latency, security, and communication complexity of PoQF. An incentive distribution mechanism to reward honest nodes and punish malicious nodes is further presented and its effectiveness against the collusion of nodes is proved using the game theory. Simulation results show that PoQF reduces failure in validation by 11% and 15% as compared to PoS and PoET, respectively, and is 68 ms faster than PoET.
ADRL: A Hybrid Anomaly-Aware Deep Reinforcement Learning-Based Resource Scaling in Clouds

The virtualization concept and elasticity feature of cloud computing enable users to request resources on-demand and in the pay-as-you-go model. However, the high flexibility of the model makes the on-time resource scaling problem more complex. A variety of techniques such as threshold-based rules, time series analysis, or control theory are utilized to increase the efficiency of dynamic scaling of resources. However, the inherent dynamicity of cloud-hosted applications requires autonomic and adaptable systems that learn from the environment in real-time. Reinforcement Learning (RL) is a paradigm that requires some agents to monitor the surroundings and regularly perform an action based on the observed states. RL has a weakness to handle high dimensional state space problems. Deep-RL models are a recent breakthrough for modeling and learning in complex state space problems. In this article, we propose a Hybrid Anomaly-aware Deep Reinforcement Learning-based Resource Scaling (ADRL) for dynamic scaling of resources in the cloud. ADRL takes advantage of anomaly detection techniques to increase the stability of decision-makers by triggering actions in response to the identified anomalous states in the system. Two levels of global and local decision-makers are introduced to handle the required scaling actions. An extensive set of experiments for different types of anomaly problems shows that ADRL can significantly improve the quality of service with less number of actions and increased stability of the system.
Context-aware Graph Embedding for Session-based News Recommendation

Online news recommender systems aim to make personalized recommendations according to user preferences, which require modeling users' short-term reading interest. However, due to the limited logged user interactions in practice, news recommendation at session-level becomes very challenging. Existing methods on session-based news recommendation mainly focus on extracting features from news articles and sequential user-item interactions, but they usually ignore the semantic-level structural information among news articles and do not explore external knowledge sources. In this paper, we propose a novel Context-Aware Graph Embedding (CAGE) framework for session-based news recommendation, which builds an auxiliary knowledge graph to enrich the semantic meaning of entities involved in articles, and further refines the article embeddings by graph convolutional networks. Experimental results on a real-world news dataset demonstrate the effectiveness of our method compared with the state-of-the-art methods on session-based news recommendation.
Human-robot interaction for robotic manipulator programming in Mixed Reality

The paper presents an approach for interactive programming of the robotic manipulator using mixed reality. The developed system is based on the HoloLens glasses connected through Robotic Operation System to Unity engine and robotic manipulators. The system gives a possibility to recognize the real robot location by the point cloud analysis, to use virtual markers and menus for the task creation, to generate a trajectory for execution in the simulator or on the real manipulator. It also provides the possibility of scaling virtual and real worlds for more accurate planning. The proposed framework has been tested on pick-and-place and contact operations execution by UR10e and KUKA iiwa robots.
SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud

3D vehicle detection based on point cloud is a challenging task in real-world applications such as autonomous driving. Despite significant progress has been made, we observe two aspects to be further improved. First, the semantic context information in LiDAR is seldom explored in previous works, which may help identify ambiguous vehicles. Second, the distribution of point cloud on vehicles varies continuously with increasing depths, which may not be well modeled by a single model. In this work, we propose a unified model SegVoxelNet to address the above two problems. A semantic context encoder is proposed to leverage the free-of-charge semantic segmentation masks in the bird's eye view. Suspicious regions could be highlighted while noisy regions are suppressed by this module. To better deal with vehicles at different depths, a novel depth-aware head is designed to explicitly model the distribution differences and each part of the depth-aware head is made to focus on its own target detection range. Extensive experiments on the KITTI dataset show that the proposed method outperforms the state-of-the-art alternatives in both accuracy and efficiency with point cloud as input only.
Candidate Obfuscation via Oblivious LWE Sampling

We present a new, simple candidate construction of indistinguishability obfuscation (iO). Our scheme is inspired by lattices and learning-with-errors (LWE) techniques, but we are unable to prove security under a standard assumption. Instead, we formulate a new falsifiable assumption under which the scheme is secure. Furthermore, the scheme plausibly achieves post-quantum security. Our construction is based on the recent "split FHE" framework of Brakerski, Döttling, Garg, and Malavolta (EUROCRYPT '20), and we provide a new instantiation of this framework. As a first step, we construct an iO scheme that is provably secure assuming that LWE holds and that it is possible to obliviously generate LWE samples without knowing the corresponding secrets. We define a precise notion of oblivious LWE sampling that suffices for the construction. It is known how to obliviously sample from any distribution (in a very strong sense) using iO, and our result provides a converse, showing that the ability to obliviously sample from the specific LWE distribution (in a much weaker sense) already also implies iO. As a second step, we give a heuristic contraction of oblivious LWE sampling. On a very high level, we do this by homomorphically generating pseudorandom LWE samples using an encrypted pseudorandom function.
Blockchain Technology: Consensus Protocol Proof of Work and Proof of Stake

The core technology used for many of the cryptocurrencies is blockchain. The extensive research attention has been received by the blockchain, a distributed ledger technology. The peer to peer network and the cryptography are playing a major role in blockchain technology. In addition to that, the consensus protocols are also playing an important role in the fundamentals of the blockchain technology. In blockchain systems, the security and the fault tolerance were guaranteed by the consensus protocols. There are two broad categories of the blockchain consensus protocols. They are Probabilistic finality consensus and the absolute finality consensus protocols. In this paper, the consensus protocols of the above two categories and the strengths and the weakness of blockchain types that could be applicable are presented.
Question-driven summarization of answers to consumer health questions

Abstract Automatic summarization of natural language is a widely studied area in computer science, one that is broadly applicable to anyone who needs to understand large quantities of information. In the medical domain, automatic summarization has the potential to make health information more accessible to people without medical expertise. However, to evaluate the quality of summaries generated by summarization algorithms, researchers first require gold standard, human generated summaries. Unfortunately there is no available data for the purpose of assessing summaries that help consumers of health information answer their questions. To address this issue, we present the MEDIQA-Answer Summarization dataset, the first dataset designed for question-driven, consumer-focused summarization. It contains 156 health questions asked by consumers, answers to these questions, and manually generated summaries of these answers. The dataset’s unique structure allows it to be used for at least eight different types of summarization evaluations. We also benchmark the performance of baseline and state-of-the-art deep learning approaches on the dataset, demonstrating how it can be used to evaluate automatically generated summaries.
Improving Face Recognition from Hard Samples via Distribution Distillation Loss

Large facial variations are the main challenge in face recognition. To this end, previous variation-specific methods make full use of task-related prior to design special network losses, which are typically not general among different tasks and scenarios. In contrast, the existing generic methods focus on improving the feature discriminability to minimize the intra-class distance while maximizing the inter-class distance, which perform well on easy samples but fail on hard samples. To improve the performance on hard samples, we propose a novel Distribution Distillation Loss to narrow the performance gap between easy and hard samples, which is simple, effective and generic for various types of facial variations. Specifically, we first adopt state-of-the-art classifiers such as Arcface to construct two similarity distributions: a teacher distribution from easy samples and a student distribution from hard samples. Then, we propose a novel distribution-driven loss to constrain the student distribution to approximate the teacher distribution, which thus leads to smaller overlap between the positive and negative pairs in the student distribution. We have conducted extensive experiments on both generic large-scale face benchmarks and benchmarks with diverse variations on race, resolution and pose. The quantitative results demonstrate the superiority of our method over strong baselines, e.g., Arcface and Cosface. Code will be available at https://github.com/HuangYG123/DDL.
Suggesting natural method names to check name consistencies

Misleading names of the methods in a project or the APIs in a software library confuse developers about program functionality and API usages, leading to API misuses and defects. In this paper, we introduce MNire, a machine learning approach to check the consistency between the name of a given method and its implementation. MNire first generates a candidate name and compares the current name against it. If the two names are sufficiently similar, we consider the method as consistent. To generate the method name, we draw our ideas and intuition from an empirical study on the nature of method names in a large dataset. Our key finding is that high proportions of the tokens of method names can be found in the three contexts of a given method including its body, the interface (the method's parameter types and return type), and the enclosing class' name. Even when such tokens are not there, MNire uses the contexts to predict the tokens due to the high likelihoods of their co-occurrences. Our unique idea is to treat the name generation as an abstract summarization on the tokens collected from the names of the program entities in the three above contexts.