Reviewerly/exHarmony huggingface dataset:
Kinect Validation of Ergonomics in Human Pick and Place Activities Through Lateral Automatic Posture Detection

In this paper we evaluate a system based on the Microsoft Kinect™ sensor, aimed at the automatic detection of risk postures during human work activities. We first introduce a pick and place task, where three different lateral standing subjects move light cardboard boxes from the various levels of a bookcase to its top, and then putting them back to their original places. They repeat the task over several work cycles and we capture all their natural movements in a continuous way using Kinect, storing the joint positions and the color images. Secondly, from the joint positions, our system detects specific risk postures following the definitions of the Rapid Upper Limb Assessment (RULA) method. Finally, we compare the posture detections by our system with the baseline detections made by a panel of five experts who used the captured color images. In our study we find that the experts have problems to distinguish among some RULA postures during a work cycle because of the narrow detection margin and the difficulty to perceive if a limb reached a certain position; which is particularly true for the cases of wrist and neck. This leads to a larger false positive rate and to a lower general accuracy, with our system detecting postures that experts do not. After applying a ±1° of relaxation to our system, which in negligible for human perception, we are able to reach an accuracy of 0.93 in the comparison with the baseline. Our results show the suitability of Kinect for lateral risk posture detection in pick and place activities.
Review of wheeled mobile robot collision avoidance under unknown environment

Recently, the working scenes of the robot have been emerging as diversity and complexity with gradually mature of robotic control technology. The challenge of robot adaptability emerges, especially in complicated and unknown environments. Among the numerous researches on improving the adaptability of robots, aiming at avoiding collision between robot and external environment, obstacle avoidance has drawn much attention. Compared to the global circumvention requiring the environmental information that is known, the local obstacle avoidance is a promising method due to the environment is possibly dynamic and unknown. This study is aimed at making a review of research progress about local obstacle avoidance methods for wheeled mobile robots (WMRs) under complex unknown environment in the last 20 years. Sensor-based obstacle perception and identification is first introduced. Then, obstacle avoidance methods related to WMRs' motion control are reviewed, mainly including artificial potential field (APF)-based, population-involved meta heuristic-based, artificial neural network (ANN)-based, fuzzy logic (FL)-based and quadratic optimization-based, etc. Next, the relevant research on Unmanned Ground Vehicles (UGVs) is surveyed. Finally, conclusion and prospection are given. Appropriate obstacle avoidance methods should be chosen based on the specific requirements or criterion. For the moment, effective fusion of multiple obstacle avoidance methods is becoming a promising method.
Differential Evolution with Distance-based Mutation-selection Applied to CEC 2021 Single Objective Numerical Optimisation

A Differential Evolution (DE) algorithm with distance-based mutation-selection, population size reduction, and an optional external archive (DEDMNA) is proposed and tested on the CEC 2021 benchmark suite. The three well-known mutation variants are chosen in combination with one crossover for this model. The distances of three newly generated positions are computed to select the most proper position to evaluate. In the proposed algorithm, an efficient linear population-size reduction mechanism is applied. Moreover, an archive is employed to store older effective solutions. The provided results show that the proposed variant of DEDMNA is able to solve 64 out of 160 optimisation problems. Moreover, DEDMNA outperforms the efficient adaptive j2020 variant in 102 problems, and it is worse only in 15 problems out of 160. From the comparison of DEDMNA with five state-of-the-art DE algorithms, the superiority of DEDMNA is obvious.
Video Rescaling Networks with Joint Optimization Strategies for Downscaling and Upscaling

This paper addresses the video rescaling task, which arises from the needs of adapting the video spatial resolution to suit individual viewing devices. We aim to jointly optimize video downscaling and upscaling as a combined task. Most recent studies focus on image-based solutions, which do not consider temporal information. We present two joint optimization approaches based on invertible neural networks with coupling layers. Our Long Short-Term Memory Video Rescaling Network (LSTM-VRN) leverages temporal information in the low-resolution video to form an explicit prediction of the missing high-frequency information for upscaling. Our Multi-input Multi-output Video Rescaling Network (MIMO-VRN) proposes a new strategy for downscaling and upscaling a group of video frames simultaneously. Not only do they outperform the image-based invertible model in terms of quantitative and qualitative results, but also show much improved upscaling quality than the video rescaling methods without joint optimization. To our best knowledge, this work is the first attempt at the joint optimization of video downscaling and upscaling.
Smart Contract based Distributed IoT Security: A Protocol for Autonomous Device Management

The proliferation of IoT devices in society demands a renewed focus on securing the use and maintenance of such systems. IoT-based systems have a significant impact on society and therefore such systems must have guaranteed resilience. We introduce a solution as a protocol that utilizes smart contracts on the Ethereum blockchain to enforce a security model that helps maintain distributed IoT networks in a healthy condition throughout their lifecycle. Our protocol enforces secured deployment, communication, management, and maintenance for hardened IoT devices. We additionally propose a smart contract solution for creating and handling generic IoT tasks. This solution enables users to manage devices by utilizing the Ethereum infrastructure for authentication, authorization, and communication. This design allows the devices to function autonomously by interpreting tasks received through smart contract transactions, without any direct human to device interaction. The result is a protocol that provides mechanisms for authentication and authorization, task provisioning, and audit trails.
Towards image denoising in the latent space of learning-based compression

Learning-based approaches to image compression have demonstrated comparable, or even superior performance when compared to conventional approaches in terms of compression efficiency and visual quality. A typical approach in learning-based image compression is through autoencoders, which are architectures consisting of two main parts: a multi-layer neural network encoder and a dual decoder. The encoder maps the input image in the pixel domain to a compact representation in a latent space. Consequently, the decoder reconstructs the original image in the pixel domain from its latent representation, as accurately as possible. Traditionally, image processing algorithms, and in particular image denoising, are applied to the images in the pixel domain before compression, and eventually even after decompression. The combination of the denoising operation with the encoder might reduce the computational cost while achieving the same performance in accuracy. In this paper, the idea of fusing the image denoising operation with the encoder is examined. The results are evaluated both by simulating the human perspective through objective quality metrics, and by machine vision algorithms for the use case of face detection.
Improving exploration efficiency of deep reinforcement learning through samples produced by generative model

Deep reinforcement learning (DRL) has made remarkable achievements in artificial intelligence. However, it relies on stochastic exploration that suffers from low efficiency, especially in the early learning stages, of which the time complexity is nearly exponential. To solve the problem, an algorithm, referred to as Generative Action Selection through Probability (GRASP), is proposed to improve exploration in reinforcement learning. The primary insight is to reshape exploration spaces to limit the choice of exploration behaviors. More specifically, GRASP trains a generator to generate the exploration spaces from demonstrations by generative adversarial network (GAN). And then the agent selects actions from new exploration spaces via modified ϵ-greedy algorithm to incorporate GRASP with existing standard deep reinforcement learning algorithms. Experiment results showed that deep reinforcement learning equipped with GRASP demonstrated significant improvements in simulated environments.
Inspection Path Planning for Aerial Vehicles via Sampling-based Sequential Optimization

Unmanned Aerial Vehicles (UAVs) commonly called drones are gaining interest for infrastructure inspection due to their ability to automize and monitor large areas more securely at a lower cost. Autonomous inspection and path planning are essential capabilities for the drone's autonomous flight. In this paper, we propose a novel inspection path planning method for achieving a complete and efficient inspection using drones. A point cloud generated from a 3D mapping service is used to represent complex inspection targets and provided as the input of the path planning method. The method is designed as a sampling-based sequential optimization to calculate and optimize an inspection path while considering the limitation of the sensors, inspection efficiency, and safety requirements of the drones. The proposed method is evaluated for both the use case of bridge inspection and power pylon inspection. A comparison between the proposed path search algorithm and TSP solver is made. Furthermore, the scalability of the method is assessed with different sizes of the inspection problem.
Game-Theoretic Fairness Meets Multi-party Protocols: The Case of Leader Election

Suppose that n players want to elect a random leader and they communicate by posting messages to a common broadcast channel. This problem is called leader election, and it is fundamental to the distributed systems and cryptography literature. Recently, it has attracted renewed interests due to its promised applications in decentralized environments. In a game theoretically fair leader election protocol, roughly speaking, we want that even a majority coalition cannot increase its own chance of getting elected, nor hurt the chance of any honest individual. The folklore tournament-tree protocol, which completes in logarithmically many rounds, can easily be shown to satisfy game theoretic security. To the best of our knowledge, no sub-logarithmic round protocol was known in the setting that we consider. We show that by adopting an appropriate notion of approximate game-theoretic fairness, and under standard cryptographic assumption, we can achieve $$(1-1/2^{\varTheta (r)})$$ -fairness in r rounds for $$\varTheta (\log \log n) \le r \le \varTheta (\log n)$$ , where n denotes the number of players. In particular, this means that we can approximately match the fairness of the tournament tree protocol using as few as $$O(\log \log n)$$ rounds. We also prove a lower bound showing that logarithmically many rounds are necessary if we restrict ourselves to “perfect” game-theoretic fairness and protocols that are “very similar in structure” to the tournament-tree protocol. Although leader election is a well-studied problem in other contexts in distributed computing, our work is the first exploration of the round complexity of game-theoretically fair leader election in the presence of a possibly majority coalition. As a by-product of our exploration, we suggest a new, approximate game-theoretic fairness notion, called “approximate sequential fairness”, which provides a more desirable solution concept than some previously studied approximate fairness notions.
Multi-domain collaborative feature representation for robust visual object tracking

Jointly exploiting multiple different yet complementary domain information has been proven to be an effective way to perform robust object tracking. This paper focuses on effectively representing and utilizing complementary features from the frame domain and event domain for boosting object tracking performance in challenge scenarios. Specifically, we propose common features extractor to learn potential common representations from the RGB domain and event domain. For learning the unique features of the two domains, we utilize a unique extractor for event based on Spiking neural networks to extract edge cues in the event domain which may be missed in RGB in some challenging conditions, and a unique extractor for RGB based on deep convolutional neural networks to extract texture and semantic information in RGB domain. Extensive experiments on standard RGB benchmark and real event tracking dataset demonstrate the effectiveness of the proposed approach. We show our approach outperforms all compared state-of-the-art tracking algorithms and verify event-based data is a powerful cue for tracking in challenging scenes.
Leaky Buddies: Cross-Component Covert Channels on Integrated CPU-GPU Systems

Graphics Processing Units (GPUs) are ubiquitous components used across the range of today’s computing platforms, from phones and tablets, through personal computers, to high-end server class platforms. With the increasing importance of graphics and video workloads, recent processors are shipped with GPU devices that are integrated on the same chip. Integrated GPUs share some resources with the CPU and as a result, there is a potential for microarchitectural attacks from the GPU to the CPU or vice versa. We consider the potential for covert channel attacks that arise either from shared microarchitectural components (such as caches) or through shared contention domains (e.g., shared buses). We illustrate these two types of channels by developing two reliable covert channel attacks. The first covert channel uses the shared LLC cache in Intel’s integrated GPU architectures. The second is a contention based channel targeting the ring bus connecting the CPU and GPU to the LLC. This is the first demonstrated microarchitectural attack crossing the component boundary (GPU to CPU or vice versa). Cross-component channels introduce a number of new challenges that we had to overcome since they occur across heterogeneous components that use different computation models and are interconnected using asymmetric memory hierarchies. We also exploit GPU parallelism to increase the bandwidth of the communication, even without relying on a common clock. The LLC based channel achieves a bandwidth of 120 kbps with a low error rate of 2%, while the contention based channel delivers up to 400 kbps with a 0.8% error rate. We also demonstrate a proof-of-concept prime-and-probe side channel attack that probes the full LLC from the GPU.
A phase unwrapping method suitable for high frequency fringe based on edge feature

Phase unwrapping (PU) technology is an important component of fringe projection profilometry (FPP). The key of PU is to obtain accurate fringe order. The existing PU methods are hard to make a tradeoff between efficiency and accuracy. To reduce the number of stripes, a phase unwrapping method based on edge feature (EFPU) is proposed. For this method, we make full use of the properties of wrapped phase map rather than auxiliary fringe patterns. We firstly analyze an edge detection operator suitable for wrapped phase map. Furthermore, in order to improve the stability of the proposed method, the edge correction method is proposed to correct edge point and the reverse correction method is presented to further optimize the fringe order. The experimental results show that the absolute phase map obtained by EFPU is similar to that of multi-wavelength phase-shifting (MWPS) method, but the number of fringe patterns is only one-third of MWPS.
A Quantum Technology-Based LiFi Security Using Quantum Key Distribution

Light fidelity (Li-Fi) is a technology that is used to design a wireless network for communication using light. Current technology based on wireless fidelity (Wi-Fi) has some drawbacks that include speed and bandwidth limit, security issues, and attacks by malicious users, which yield Wi-Fi as less reliable compared to LiFi. The conventional key generation techniques are vulnerable to the current technological improvement in terms of computing power, so the solution is to introduce physics laws based on quantum technology and particle nature of light. Here the authors give a methodology to make the BB84 algorithm, a quantum cryptographic algorithm to generate the secret keys which will be shared by polarizing photons and more secure by eliminating one of its limitations that deals with dependency on the classical channel. The result obtained is sequence of 0 and 1, which is the secret key. The authors make use of the generated shared secret key to encrypt data using a one-time pad technique and transmit the encrypted data using LiFi and removing the disadvantage of the existing one-time pad technique.
Movie recommendation system using enhanced content-based filtering algorithm based on user demographic data

Now-a-days people are consuming content in form of movies, series, etc. for entertainment. In this modern era, people always look up to entertainment and in that process, they waste their time in searching for movies. Everyone wants to watch good films that have great content. It takes lot of time to search for a movie they like. Recommendation system comes into play in such situations. It helps to people by recommending movies. This paper develops a Movie Recommendation System to recommend movies based on different parameters. The principal objective of the project is to construct a movie recommendation framework to prescribe pictures to users. There are many algorithms that help to build a recommendation system. Here, the Content-based algorithm has been employed to recommend movies based on the similarity with other films by analyzing the content of the movie. To find the similarity, the cosine similarity method has been used. Here, the cosine similarity has been computed by using linear kernel, where the parameters are taken by the result of TF-IDF vectorization. Then the most similar movies are recommended.
Hierarchical control of multi-agent reinforcement learning team in real-time strategy (RTS) games

Coordinated control of multi-agent teams is an important task in many real-time strategy (RTS) games. In most prior work, micromanagement is the commonly used strategy whereby individual agents operate independently and make their own combat decisions. On the other extreme, some employ a macromanagement strategy whereby all agents are controlled by a single decision model. In this paper, we propose a hierarchical command and control architecture, consisting of a single high-level and multiple low-level reinforcement learning agents operating in a dynamic environment. This hierarchical model enables the low-level unit agents to make individual decisions while taking commands from the high-level commander agent. Compared with prior approaches, the proposed model provides the benefits of both flexibility and coordinated control. The performance of such hierarchical control model is demonstrated through empirical experiments in a real-time strategy game known as StarCraft: Brood War (SCBW).
From LSAT: The Progress and Challenges of Complex Reasoning

Complex reasoning aims to draw a correct inference based on complex rules. As a hallmark of human intelligence, it involves a degree of explicit reading comprehension, interpretation of logical knowledge and complex rule application. In this paper, we take a step forward in complex reasoning by systematically studying the three challenging and domain-general tasks of the Law School Admission Test (LSAT), including analytical reasoning, logical reasoning and reading comprehension. We propose a hybrid reasoning system to integrate these three tasks and achieve impressive overall performance on the LSAT tests. The experimental results demonstrate that our system endows itself a certain complex reasoning ability, especially the fundamental reading comprehension and challenging logical reasoning capacities. Further analysis also shows the effectiveness of combining the pre-trained models with the task-specific reasoning module, and integrating symbolic knowledge into discrete interpretable reasoning steps in complex reasoning. We further shed a light on the potential future directions, like unsupervised symbolic knowledge extraction, model interpretability, few-shot learning and comprehensive benchmark for complex reasoning.
Reimagining Social Media Governance: Harm, Accountability, and Repair

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Parallel Training of Pre-Trained Models via Chunk-Based Dynamic Memory Management

The pre-trained model (PTM) is revolutionizing Artificial Intelligence (AI) technology. However, the hardware requirement of PTM training is prohibitively high, making it a game for a small proportion of people. Therefore, we proposed PatrickStar system to lower the hardware requirements of PTMs and make them accessible to everyone. PatrickStar uses the CPU-GPU heterogeneous memory space to store the model data. Different from existing works, we organize the model data in memory chunks and dynamically distribute them in the heterogeneous memory. Guided by the runtime memory statistics collected in a warm-up iteration, chunks are orchestrated efficiently in heterogeneous memory and generate lower CPU-GPU data transmission volume and higher bandwidth utilization. Symbiosis with the Zero Redundancy Optimizer, PatrickStar scales to multiple GPUs on multiple nodes. The system can train tasks on bigger models and larger batch sizes, which cannot be accomplished by existing works. Experimental results show that PatrickStar extends model scales 2.27 and 2.5 times of DeepSpeed, and exhibits significantly higher execution speed. PatricStar also successfully runs the 175B GPT3 training task on a 32 GPU cluster. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Tencent/PatrickStar</uri> .
Slow and Stale Gradients Can Win the Race

Distributed Stochastic Gradient Descent (SGD) when run in a synchronous manner, suffers from delays in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">runtime</i> as it waits for the slowest workers (stragglers). Asynchronous methods can alleviate stragglers, but cause gradient staleness that can adversely affect the convergence <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">error</i> . In this work, we present a novel theoretical characterization of the speedup offered by asynchronous methods by analyzing the trade-off between the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">error</i> in the trained model and the actual training <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">runtime</i> (wallclock time). The main novelty in our work is that our runtime analysis considers random straggling delays, which helps us design and compare distributed SGD algorithms that strike a balance between straggling and staleness. We also provide a new error convergence analysis of asynchronous SGD variants without bounded or exponential delay assumptions. Finally, based on our theoretical characterization of the error-runtime trade-off, we propose a method of gradually varying synchronicity in distributed SGD and demonstrate its performance on the CIFAR10 dataset.