Reviewerly/exHarmony huggingface dataset:
Safe Human-Robot Collaboration via Collision Checking and Explicit Representation of Danger Zones

This paper deals with safe human-robot collaboration in the context of speed and separation monitoring paradigm. The core of the approach is to continuously track the separation distance between the robot and the human. The robot speed is then adjusted according to the perceived distance so that it will be able to stop before eventually come into contact with the human. We present an approach that aims at maximizing the productivity of the robot, i.e., its speed, while keeping the prescribed safety requirements satisfied. The method is based on explicit representation of danger zones – regions around the robot, where safety requirements are violated. The motion is then generated such that the robot moves as fast as possible, while its danger zone still does not collide with human operators. The approach is validated within an experimental study. Note to Practitioners—This article was motivated by the problem of maximizing productivity of the robotic manipulator while ensuring the safety of human collaborator. The increase in productivity is achieved by a faster traversal of predefined paths without compromising the safety of the human, which is specifically defined by industrial standard. The approach requires limited knowledge on robot’s dynamical properties. More precisely, we only need the braking time as a “lumped” representation of robot’s inertia. The underlying optimization problem is conveniently resolved by introducing danger zones that allow for intuitive visualization and geometrical representation of the regions around the robot that must be avoided. On the other hand, the method assumes the representation of humans via typical geometric primitives, which can be obtained using of-the-shelf depth perception systems. The solution to the problem reduces to a repeated collision checking between danger zones and the human. Such an approach turns out to be suitable for real-time implementation due to availability of fast and efficient collision checking algorithms/libraries.
PicassoNet: Searching Adaptive Architecture for Efficient Facial Landmark Localization

Since recent facial landmark localization methods achieve satisfying accuracy, few of them enable fast inference speed, which, however, is critical in many real-world facial applications. Existing methods typically employ complicated network structure and predict all the key points through uniform computation, which is inefficient since individual facial part might take different computation to obtain the best performance. Taking both accuracy and efficiency into consideration, we propose the PicassoNet, a lightweight cascaded facial landmark detector with adaptive computation for individual facial part. Different from the conventional cascaded methods, PicassoNet integrates refinement submodules into a single network with group convolution, where each convolution group predicts landmarks from an individual facial part. Note that the groups' structures are flexible in the training process. Then, a novel grouping search algorithm is proposed to optimize the group division. With formulating the optimization as a network architecture search (NAS) problem, the grouping search adaptively allocates computation to each group and obtains an efficient structure. In addition, we propose a boundary-aware loss to optimize along tangent and normal of facial boundaries, instead of optimizing along horizontal and vertical as the conventional loss (L2, SmoothL1, WingLoss, and so on) do. The novel loss improves the joint locations of predicted keypoints. Experiments on three benchmark datasets AFLW, 300W, and WFLW show that the proposed method runs over 6× times faster than the state of the arts and meanwhile achieves comparable accuracy.
The ICT Impact on Bank Performance: The Case of Vietnam

Bank digital transformation boosts the development of information and communication technology (ICT) in the banking and financial systems worldwide. This study investigates the impact of ICT development on the performance of the banking system in a frontier market. For the period from 2009 to 2020, the empirical evidence is based on balanced panel data of 39 banks in Vietnam. The result shows that bank profitability and ICT index have a significant positive nexus. This beneficial impact is particularly evidenced in the bank’s digital transformation. Hence, the beneficial impact on the return on IT infrastructure investment surpasses the original installation expenses. The findings suggest that ICT advances help the banks perform better when transforming from traditional to digital systems.
Analysis on Blockchain Vulnerabilities &amp; Attacks on Wallet

Blockchain is considered one of the Secured systems even it has flaws and Vulnerabilities which leads to big threats, steal of cryptocurrencies through hackings. Many Big organizations witnessed these threats which cost them shutdown the companies. Whether its Public or Private blockchain both have recently subjected to attacks and identified to be ineffective in certain situations. Significant development has been remarked recently to provide robust solution for Blockchain vulnerabilities. We survey some of attacks occurred on blockchain systems from last few years and also analyse some of these vulnerabilities. We also tried to cover or summarize some of academic achievements done for the enhancement of Blockchain Security. Our main focus on the blockchain vulnerabilities occurs in wallets or smart contracts.
Identification of Missing Person Using Fusion of KNN and SVM Approach

Each day an enormous number of people get missing from kids to senior citizens because of some mental illness or Alzheimer’s or Dementia. Out of all, most of them are trackless. Therefore, an artificial intelligence-based technique is proposed in this paper. It helps to find missing persons and can be used by the government officials like police or the common people. If a person goes missing, relatives of that person can visit the nearest police station and provide the details of the missing person which includes a photo (picture). When any person got such a missing person then, they can capture a picture of that person and upload it in our proposed model. Now, our proposed model tries to find that match in our database using the face recognition encoding technique. If a match is found, the proposed model will inform police officers along with the current location of the missing person.
PIWS: Private Intersection Weighted Sum Protocol for Privacy-Preserving Score-Based Voting With Perfect Ballot Secrecy

This article proposes private intersection weighted sum (PIWS), a scalable, fair, and privacy-preserving intersection weighted sum protocol and applies it to voting scenarios. The PIWS protocol can privately calculate the intersection of identity index sets maintained by each participant and can privately calculate the weighted sum of the data associated with the identity indexes of the intersection set. After the execution of the protocol, both parties can only know the weighted sum, but not any additional information, such as any identity index or associated data of the other party. The PIWS protocol is very suitable for the privacy-preserving weighted voting scenarios and has three novel characteristics. First, it does not require as many semitrusted tally clerks as other protocols, which greatly reduces the deployment, communication, and calculation costs involved. It only requires the distributed deployment of voting servers and weight servers that are honest but curious. This is consistent with the deployment framework of the future big data application backgrounds. Second, perfect privacy protection and ballot secrecy are achieved. That is, the voting terminal or polling station provides encryption services for ballots immediately after each ballot is cast. All voting information is then expressed in ciphertext throughout the weighting and counting processes, until the final result of the weighted vote is passed to the voting server in the ciphertext. After decryption, the voting server only knows the results of the voting and it has no knowledge of the content or preference of the ballots, the privacy of the voters, or even the process of counting the votes. This design avoids the disclosure of voter privacy and ballot information, and the ciphertext form also prevents malicious users from cheating or tampering with voter or ballot information during the counting process. To better explain the security of our protocol, we present the provable security of the protocol under the honest-but-curious model and show the formal verification obtained using the Tamarin prover software. Third, our protocol not only achieves the function of an optional weighted voting protocol but also is relatively lightweight and efficient. The efficiency analysis results of the deployed voting system in terms of communication, storage, and calculation show that the protocol meets the requirements applicable to real-world applications. In summary, PIWS is superior to existing voting protocols in terms of function, security, and efficiency, and can be harmoniously applied to model updating of federated learning, consensus building of blockchain systems, or decision-making in artificial intelligence.
Automating Reinforcement Learning With Example-Based Resets

Deep reinforcement learning has enabled robots to learn motor skills from environmental interactions with minimal to no prior knowledge. However, existing reinforcement learning algorithms assume an episodic setting, in which the agent resets to a fixed initial state distribution at the end of each episode, to successfully train the agents from repeated trials. Such reset mechanism, while trivial for simulated tasks, can be challenging to provide for real-world robotics tasks. Resets in robotic systems often require extensive human supervision and task-specific workarounds, which contradicts the goal of autonomous robot learning. In this paper, we propose an extension to conventional reinforcement learning towards greater autonomy by introducing an additional agent that learns to reset in a self-supervised manner. The reset agent preemptively triggers a reset to prevent manual resets and implicitly imposes a curriculum for the forward agent. We apply our method to learn from scratch on a suite of simulated and real-world continuous control tasks and demonstrate that the reset agent successfully learns to reduce manual resets whilst also allowing the forward policy to improve gradually over time.
Convolutional Neural Network—A Practical Case Study

The convolutional neural networks had enormous success in the classification of images, and networks such as “AlexNet”, “VGG”, “Inception” and “ResNet” were references for this purpose. This way, it is intended to verify which networks were more successful in the “Imagenet” dataset challenge. Then, it was verified their success when classifying videos through the “Kinetics400” and “UCF101” datasets and, finally, to conclude if the success in the classification of images can also evidence a possible success in the classification of videos. To this end, the margin of error of the networks mentioned above is compared. The two networks with the lowest margin of error are selected, and these networks are studied to classify videos. Thus, if these networks are successful, they can receive input videos from sensors and accurately identify the human activity present in the video. It should be noted that the networks “ResNet” and “Inception” had very satisfactory success rates, above 70%, showing success in the approach adopted.
Survey of Reinforcement Learning based on Human Prior Knowledge

At present, task planning is mainly solved by rules or operational research methods. The time complexity and space complexity of these methods increase exponentially with the growth of problem size. Thus, it is challenging to solve large-scale problems. Moreover, it is helpless to solve dynamic task planning problems. Reinforcement learning (RL) is often used to solve the dynamic planning problem of continuous decision making. RL agent constantly interacts with the environment to achieve the expected goal. However, the RL method meets challenges when handling the problem with ample state space. More sampling and exploration are needed to update the strategy gradient, and the convergence speed is slow. Humans ensure a quick start of learning by using prior knowledge, which reduces the exploration time of problems. Thus, we review the methods which combine human prior knowledge with RL through temporal node classification of human prior knowledge combined with RL. In this way, agents effectively reduce the sampling and exploration of the environment and eventually get the optimal strategy faster. Finally, we propose the development direction.
A new statistical image watermark detector in RHFMs domain using beta-exponential distribution

The detection of watermarks can be achieved by statistical approaches. How to select robust modeling object, appropriate statistical model, and decision rules is one of the major issues in statistical image watermark detection. In this paper, we propose a new image watermark detector in robust fast radial harmonic Fourier moments (FRHFMs) magnitudes domain, wherein the Beta-exponential distribution model and locally most powerful (LMP) decision rule are used. We first investigate the statistical modeling of the robust FRHFMs magnitudes by the Beta-exponential distribution. It is shown that the Beta-exponential distribution model fits the empirical data more accurately than the formerly employed statistical distributions, such as the Cauchy, Weibull, BKF, and Exponential, do. Motivated by the statistical modeling results, we design a blind image watermark detector in FRHFMs magnitudes domain by using Beta-exponential distribution and LMP test. Also, we utilize the Beta-exponential model to derive the closed-form expressions for the watermark detector. We provide comparative experimental results to alternative approaches to demonstrate the advantages of the proposed image watermark detector.
A Fast Sea-sky Line Detection Method Based on EDLines

As the dividing line between the sky area and the water surface area, sea-sky line (SSL) can not only be used to extract the area where the target may exist, but also can be used to stabilize the image collected on the sailing unmanned surface vehicle (USV). The image obtained by the ship-borne camera contains a lot of interference due to factors such as waves and reflections. Existing methods cannot guarantee the accuracy, robustness, and real-time requirements of SSL detection at the same time. In our method, the pixels where the gradient is a local peak in the vertical direction are chosen as the key points of detection and a novel and effective pixel connection rule is proposed. The proposed method is tested on visual images collected by USV. While ensuring accuracy and robustness, the average processing time of a single frame of image is less than that of the existing methods and meets real-time requirement.
Semantic Detection of Vehicle Violation Video Based on Computer 3D Vision

In order to study the semantic detection accuracy of 3D vehicle accident video, an accident detection method combining 2D image and 3D information was proposed. The 3D semantic bounding box generated by the 3D detection and tracking task of the vehicle is used to extract the motion features of the vehicle, it includes the trajectory of the vehicle and the dimension and orientation of the 3D bounding frame, and the 3D semantic bounding frame is used to establish the evaluation index of the accident detection. The experimental results show that the average loss function of each batch of 1000 images is calculated by the stochastic gradient descent method to update the parameter values. The learning rate was set to 0.001 in the first 30,000 iterations and 0.0001 in the last 10,000 iterations. The MOTA of the CEM algorithm is 78.4%, FP is 1.1%, and FN is 3.5%, and the MOTA of the 3-DCMK algorithm is 88.6%, FP is 0.9%, and FN is 1.9%. The MOTA of this method is 89.3%, FP is 0.9%, and FN is 1.2%. The 3D target semantic detection of vehicle accident video has stability and accuracy.
Understanding Cross-lingual Pragmatic Misunderstandings in Email Communication

Communication tools such as email facilitate communication and collaboration between speakers of different languages, who use two primary strategies-English as a common language and machine translation (MT) tools-to help them overcome language barriers. However, each of these communication strategies creates its own challenges for cross-lingual communication. In this paper, we compare how people's interpretations of an email sender's social intention, and their evaluation of the email and the senders, differ when using a common language versus MT in email communication. We conducted an online experiment in which monolingual native English speakers read and rated request emails written by native English speakers, emails written by bilingual Chinese speakers in English, and emails written in Chinese then machine-translated into English. We found that participants interpreted the social intentions of the email sender less accurately for machine-translated emails than for emails written by non-native speakers in English. Participants also rated the senders and emails less positively overall for machine-translated emails compared to emails written by non-native speakers in English. Based on these findings, we suggest design possibilities that could better aid multilingual communication.
Replicated layout for in-memory database systems

Scanning and filtering are the foundations of analytical database systems. Modern DBMSs employ a variety of techniques to partition and layout data to improve the performance of these operations. To accelerate query performance, systems tune data layout to reduce the cost of accessing and processing data. However, these layouts optimize for the average query, and with heterogeneous data access patterns in parts of the data, their performance degrades. To mitigate this, we present CopyRight, a layout-aware partial replication engine that replicates parts of the data differently and lays out each replica differently to maximize the overall query performance. Across a range of real-world query workloads, CopyRight is able to achieve 1.1X to 7.9X faster performance than the best non-replicated layout with 0.25X space overhead. When compared to full table replication with 100% overhead, CopyRight attains the same or up to 5.2X speedup with 25% space overhead.
Search for Similarity Transformation Between Image Point Clouds Using Geometric Algebra for Conics

We introduce a novel way of searching for the similarity transformation of 2D point clouds using Geometric Algebra for Conics (GAC). In our approach, we do not represent the image objects by their contour but, instead, by the ellipse fitted into the contour points. Such representation makes the consequent similarity search fast and memory-saving. Examples of application on the real object images are also included.
Automated interpretable discovery of heterogeneous treatment effectiveness: A COVID-19 case study

Testing multiple treatments for heterogeneous (varying) effectiveness with respect to many underlying risk factors requires many pairwise tests; we would like to instead automatically discover and visualize patient archetypes and predictors of treatment effectiveness using multitask machine learning. In this paper, we present a method to estimate these heterogeneous treatment effects with an interpretable hierarchical framework that uses additive models to visualize expected treatment benefits as a function of patient factors (identifying personalized treatment benefits) and concurrent treatments (identifying combinatorial treatment benefits). This method achieves state-of-the-art predictive power for COVID-19 in-hospital mortality and interpretable identification of heterogeneous treatment benefits. We first validate this method on the large public MIMIC-IV dataset of ICU patients to test recovery of heterogeneous treatment effects. Next we apply this method to a proprietary dataset of over 3000 patients hospitalized for COVID-19, and find evidence of heterogeneous treatment effectiveness predicted largely by indicators of inflammation and thrombosis risk: patients with few indicators of thrombosis risk benefit most from treatments against inflammation, while patients with few indicators of inflammation risk benefit most from treatments against thrombosis. This approach provides an automated methodology to discover heterogeneous and individualized effectiveness of treatments.
<scp>XCode</scp> : Towards Cross-Language Code Representation with Large-Scale Pre-Training

Source code representation learning is the basis of applying artificial intelligence to many software engineering tasks such as code clone detection, algorithm classification, and code summarization. Recently, many works have tried to improve the performance of source code representation from various perspectives, e.g., introducing the structural information of programs into latent representation. However, when dealing with rapidly expanded unlabeled cross-language source code datasets from the Internet, there are still two issues. Firstly, deep learning models for many code-specific tasks still suffer from the lack of high-quality labels. Secondly, the structural differences among programming languages make it more difficult to process multiple languages in a single neural architecture. To address these issues, in this article, we propose a novel Cross -language Code representation with a large-scale pre-training ( XCode ) method. Concretely, we propose to use several abstract syntax trees and ELMo-enhanced variational autoencoders to obtain multiple pre-trained source code language models trained on about 1.5 million code snippets. To fully utilize the knowledge across programming languages, we further propose a Shared Encoder-Decoder (SED) architecture which uses the multi-teacher single-student method to transfer knowledge from the aforementioned pre-trained models to the distilled SED. The pre-trained models and SED will cooperate to better represent the source code. For evaluation, we examine our approach on three typical downstream cross-language tasks, i.e., source code translation, code clone detection, and code-to-code search, on a real-world dataset composed of programming exercises with multiple solutions. Experimental results demonstrate the effectiveness of our proposed approach on cross-language code representations. Meanwhile, our approach performs significantly better than several code representation baselines on different downstream tasks in terms of multiple automatic evaluation metrics.
Optical Neuromorphic Processor at 11 TeraOPs/s based on Kerr Soliton Crystal Micro-combs

We demonstrate a universal optical vector convolutional accelerator operating at 11 Tera-OPS, generating convolutions of images of 250,000 pixels with 8-bit resolution for 10 kernels simultaneously. We use the same hardware to form a deep optical CNN with ten output neurons, achieving successful recognition of full 10 digits with 88% accuracy. Our approach is scalable and trainable for applications to unmanned vehicle and real-time video recognition.