Reviewerly/exHarmony huggingface dataset:
Two-stage training algorithm for AI robot soccer

In multi-agent reinforcement learning, the cooperative learning behavior of agents is very important. In the field of heterogeneous multi-agent reinforcement learning, cooperative behavior among different types of agents in a group is pursued. Learning a joint-action set during centralized training is an attractive way to obtain such cooperative behavior; however, this method brings limited learning performance with heterogeneous agents. To improve the learning performance of heterogeneous agents during centralized training, two-stage heterogeneous centralized training which allows the training of multiple roles of heterogeneous agents is proposed. During training, two training processes are conducted in a series. One of the two stages is to attempt training each agent according to its role, aiming at the maximization of individual role rewards. The other is for training the agents as a whole to make them learn cooperative behaviors while attempting to maximize shared collective rewards, e.g. , team rewards. Because these two training processes are conducted in a series in every time step, agents can learn how to maximize role rewards and team rewards simultaneously. The proposed method is applied to 5 versus 5 AI robot soccer for validation. The experiments are performed in a robot soccer environment using Webots robot simulation software. Simulation results show that the proposed method can train the robots of the robot soccer team effectively, achieving higher role rewards and higher team rewards as compared to other three approaches that can be used to solve problems of training cooperative multi-agent. Quantitatively, a team trained by the proposed method improves the score concede rate by 5% to 30% when compared to teams trained with the other approaches in matches against evaluation teams.
Two Fall-Related and Kinematic Data-Based Approaches for an Instrumented Conventional Cane

Stimulating the usability and acceptability of canes within the geriatric community is crucial for fall prevention. Clinical evidences support the use of canes to increase the user's balance and reduce the fall risk. Its nonuse is correlated with a higher number of falls. This fateful event draws tragic consequences such as death or injuries that might affect the victim's psychology, reducing their confidence and mobility. However, users tend to put aside canes and canes' acceptability is reduced. To increase their usability and acceptability, appellative and time-effective technology-based strategies, such as automatic fall detection systems, should be incorporated. First, steps toward a fall-related approach demand the ability to identify that a fall event is eminent relying only on information derived from the device. This study proposes and compares two fall event approaches. A machine learning framework determines the best models and features for three classification problems: fall event, fall phase, and fall category. The long short-term memory (LSTM) is the most suitable classifier for fall event and phase classification problems, requiring the first 32 and 17 features, respectively, ranked by the Relief-F and minimum-redundancy maximum-relevancy methods (accuracy >99% and >95%). K-nearest neighbors with the first 40 features ranked by the Relief-F method presented the best performance for fall category classification (accuracy >74%). A finite-state machine (FSM) combined accelerometer and gyroscope's data to detect the cane's fall with a clear proximity to the LSTM performance (accuracy >99%). Both approaches detect a fall before its occurrence with mean lead times higher than 373 (LSTM) and 266 ms (FSM).
Optimal Partitioning of Non-Convex Environments for Minimum Turn Coverage Planning

In this letter, we tackle the problem of planning an optimal coverage path for a robot operating indoors. Many existing approaches attempt to discourage turns in the path by covering the environment along the least number of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coverage lines</i> , i.e., straight-line paths. This is because turning not only slows down the robot but also negatively affects the quality of coverage, e.g., tools like cameras and cleaning attachments commonly have poor performance around turns. The problem of minimizing coverage lines however is typically solved using heuristics that do not guarantee optimality. In this work, we propose a turn-minimizing coverage planning method that computes the optimal number of axis-parallel (horizontal/vertical) coverage lines for the environment in polynomial time. We do this by formulating a linear program (LP) that optimally partitions the environment into axis-parallel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ranks</i> (non-intersecting rectangles of width equal to the tool width). We then generate coverage paths for a set of real-world indoor environments and compare the results with state-of-the-art coverage approaches.
Authoring-By-Doing: An Event-Based Interaction Module for Virtual Reality Scenario Authoring Framework

Virtual reality (VR) and augmented reality (AR) have already shown their advantages in industrial use during these last years. Nevertheless, the authoring and editing process of virtual and augmented contents are still time-consuming and even more for complex industrial scenarios. Therefore, it is important to simplify the authoring process. For this purpose, we are working on a framework called INTERVALES. This model aims to ease virtual and augmented environments and complex operation orchestration. We present here an evolution of our framework and an interaction module using events allowing us to build scenarios using authoring by doing and making it more interoperable with others frameworks.
Boost Transformer with BERT and copying mechanism for ASR error correction

The accuracy of Automatic Speech Recognition (ASR) is critical to speech-based products, such as subtitling, speech translation, spoken dialogue. We aim to enhance ASR accuracy by correcting errors in ASR hypothesis with sequence-to-sequence (seq2seq) models. In this paper, we propose to boost Transformer-based ASR error correction by fusing the pre-trained BERT [1] in encoder and copying mechanism in decoder, which exploits externally well-learned token representation and copying correct tokens in ASR transcript respectively. In addition, we leverage Text-to-Speech (TTS) synthesized data and ASR 5-best hypotheses to augment the training data and make the data more diverse. We evaluate our approach on two internal test sets and two public ASR test sets. Experimental results show that the proposed approach decreases the average Character Error Rate (CER) from 9.36% to 7.30% compared with ASR hypothesis without correction, and outperforms Transformer-based model by a large margin.
VäriWig: Interactive Coloring Wig Module

We present the design and prototype of VäriWig, an interactive coloring wig module, which allows traditional synthetic wigs to change colors depending on several factors, such as head movements, music rhythms and other external synchronizations. A large number of customized optical fibers coupled with individually controlled digital tricolor light-emitting diodes are employed to instantly change the colors of specific locks of hair placed all around the head. VäriWig was designed to be seamlessly blended with the traditional wig, even while inactive and is envisioned to be used in art shows and other entertaining social events.
Scalable multi-task Gaussian processes with neural embedding of coregionalization

Multi-task regression attempts to exploit the task similarity in order to achieve knowledge transfer across related tasks for improving the quality of prediction and alleviating the demand of big data. The application of Gaussian process (GP) in this scenario yields the non-parametric yet informative Bayesian multi-task regression paradigm. Multi-task GP (MTGP) provides not only the prediction mean but also the associated prediction variance to quantify uncertainty, thus gaining popularity in various scenarios. The linear model of coregionalization (LMC) is a well-known MTGP paradigm which exploits the dependency of tasks through linear combination of several independent and diverse GPs. The LMC however suffers from high model complexity and limited model capability when handling complicated multi-task cases. To this end, we develop the neural embedding of coregionalization that transforms the latent GPs into a high-dimensional latent space to induce rich yet diverse behaviors. Furthermore, we use advanced variational inference as well as sparse approximation to devise a tight and compact evidence lower bound (ELBO) for higher quality of scalable model inference. Extensive numerical experiments have been conducted to verify the higher prediction quality and better generalization of our model, named NSVLMC, on various real-world multi-task datasets and the cross-fluid modeling of unsteady fluidized bed.
Lean to Fly: Leaning-Based Embodied Flying can Improve Performance and User Experience in 3D Navigation

When users in virtual reality cannot physically walk and self-motions are instead only visually simulated, spatial updating is often impaired. In this paper, we report on a study that investigated if HeadJoystick, an embodied leaning-based flying interface, could improve performance in a 3D navigational search task that relies on maintaining situational awareness and spatial updating in VR. We compared it to Gamepad, a standard flying interface. For both interfaces, participants were seated on a swivel chair and controlled simulated rotations by physically rotating. They either leaned (forward/backward, right/left, up/down) or used the Gamepad thumbsticks for simulated translation. In a gamified 3D navigational search task, participants had to find eight balls within 5 min. Those balls were hidden amongst 16 randomly positioned boxes in a dark environment devoid of any landmarks. Compared to the Gamepad, participants collected more balls using the HeadJoystick. It also minimized the distance travelled, motion sickness, and mental task demand. Moreover, the HeadJoystick was rated better in terms of ease of use, controllability, learnability, overall usability, and self-motion perception. However, participants rated HeadJoystick could be more physically fatiguing after a long use. Overall, participants felt more engaged with HeadJoystick, enjoyed it more, and preferred it. Together, this provides evidence that leaning-based interfaces like HeadJoystick can provide an affordable and effective alternative for flying in VR and potentially telepresence drones.
A Medical Pre-Diagnosis System for Histopathological Image of Breast Cancer

This paper constructs a novel intelligent medical diagnosis system, which can realize automatic communication and breast cancer pathological image recognition. This system contains two main parts, including a pre-training chatbot called M-Chatbot and an improved neural network model of EfficientNetV2-S named EfficientNetV2-SA, in which the activation function in top layers is replaced by ACON-C. Using information retrieval mechanism, M-Chatbot instructs patients to send breast pathological image to EfficientNetV2-SA network, and then the classifier trained by transfer learning will return the diagnosis results. We verify the performance of our chatbot and classification on the extrinsic metrics and BreaKHis dataset, respectively. The task completion rate of M-Chatbot reached 63.33%. For the BreaKHis dataset, the highest accuracy of EfficientNetV2-SA network have achieved 84.71%. All these experimental results illustrate that the proposed model can improve the accuracy performance of image recognition and our new intelligent medical diagnosis system is successful and efficient in providing automatic diagnosis of breast cancer.
Spoken Language Diarization Using an Attention based Neural Network

Spoken language diarization (SLD) is a task to perform automatic segmentation and labeling of the languages present in a given code-switched speech utterance. Inspiring from the way humans perform SLD (i.e capturing the language specific long term information), this work has proposed an acoustic-phonetic approach to perform SLD. This acoustic phonetic approach consists of an attention based neural network modelling to capture the language specific information and a Gaussian smoothing approach to locate the language change points. From the experimental study, it has been observed that the proposed approach performs better when dealing with code-switched segment containing monolingual segments of longer duration. However, the performance of the approach decreases with decrease in the monolingual segment duration. This issue poses a challenge in the further exploration of the proposed approach.
Correlation Clustering with Global Weight Bounds

Given a set of objects and nonnegative real weights expressing “positive” and “negative” feeling of clustering any two objects together, min-disagreement correlation clustering partitions the input object set so as to minimize the sum of the intra-cluster negative-type weights plus the sum of the inter-cluster positive-type weights. Min-disagreement correlation clustering is \(\mathbf {APX}\)-hard, but efficient constant-factor approximation algorithms exist if the weights are bounded in some way. The weight bounds so far studied in the related literature are mostly local, as they are required to hold for every object-pair. In this paper, we introduce the problem of min-disagreement correlation clustering with global weight bounds, i.e., constraints to be satisfied by the input weights altogether. Our main result is a sufficient condition that establishes when any algorithm achieving a certain approximation under the probability constraint keeps the same guarantee on an input that violates the constraint. This extends the range of applicability of the most prominent existing correlation-clustering algorithms, including the popular Pivot, thus providing benefits, both theoretical and practical. Experiments demonstrate the usefulness of our approach, in terms of both worthiness of employing existing efficient algorithms, and guidance on the definition of weights from feature vectors in a task of fair clustering.
ACSL: Adaptive correlation-driven sparsity learning for deep neural network compression

Deep convolutional neural network compression has attracted lots of attention due to the need to deploy accurate models on resource-constrained edge devices. Existing techniques mostly focus on compressing networks for image-level classification, and it is not clear if they generalize well on network architectures for more challenging pixel-level tasks, e.g., dense crowd counting or semantic segmentation. In this paper, we propose an adaptive correlation-driven sparsity learning (ACSL) framework for channel pruning that outperforms state-of-the-art methods on both image-level and pixel-level tasks. In our ACSL framework, we first quantify the data-dependent channel correlation information with a channel affinity matrix. Next, we leverage these inter-dependencies to induce sparsity into the channels with the introduced adaptive penalty strength. After removing the redundant channels, we obtain compact and efficient models, which have significantly less number of parameters while maintaining comparable performance with the original models. We demonstrate the advantages of our proposed approach on three popular vision tasks, i.e., dense crowd counting, semantic segmentation, and image-level classification. The experimental results demonstrate the superiority of our framework. In particular, for crowd counting on the Mall dataset, the proposed ACSL framework is able to reduce up to 94% parameters (VGG16-Decoder) and 84% FLOPs (ResNet101), while maintaining the same performance of (at times outperforming) the original model.
Registration of Land and Building Certificate Ownership using Blockchain Technology

Currently, the certificate ownership system in Indonesia is still done manually. The system has weaknesses and loopholes that can be exploited by irresponsible parties. Some examples of problems that may arise are the loss of physical certificates. To overcome this, a land and building certificate ownership recording system is built using blockchain technology. The development of this system aims to reduce the problems that arose in the manual system. The use of blockchain technology is intended to maintain data integrity. The system utilizes one of the main properties of blockchain technology, the anti-tamper properties that maintain data integrity from parties who try to tamper with stored data. The system is decentralized by involving several peers who run the blockchain network. The technology used to build the blockchain network is Hyperledger Fabric. This is a permissioned blockchain, using a consensus method that does not require large computing resources. The developed system facilitates basic processes in the form of certificate ownership transfer transactions through the functional requirements. The system also improves data transparency by providing various information retrieval features. The developed system is tested with various tests to ensure that the functional and non-functional requirements are met. The system built can be used as a system for recording ownership of land and building certificates, as it is able to record ownership of certificates without problems and facilitates checking and the failsafe methods and can reduce problems in the previous system, for example loss of certificates.
Visual Representation Learning for Preference-Aware Path Planning

Autonomous mobile robots deployed in outdoor environments must reason about different types of terrain for both safety (e.g., prefer dirt over mud) and deployer preferences (e.g., prefer dirt path over flower beds). Most existing solutions to this preference-aware path planning problem use semantic segmentation to classify terrain types from camera images, and then ascribe costs to each type. Unfortunately, there are three key limitations of such approaches - they 1) require preenumeration of the discrete terrain types, 2) are unable to handle hybrid terrain types (e.g., grassy dirt), and 3) require expensive labelled data to train visual semantic segmentation. We introduce Visual Representation Learning for Preference-Aware Path Planning (VRL-PAP), an alternative approach that overcomes all three limitations: VRL-PAP leverages un-labelled human demonstrations of navigation to autonomously generate triplets for learning visual representations of terrain that are viewpoint invariant and encode terrain types in a continuous representation space. The learned representations are then used along with the same unlabelled human navigation demonstrations to learn a mapping from the representation space to terrain costs. At run time, VRL-PAP maps from images to representations and then representations to costs to perform preference-aware path planning. We present empirical results from challenging outdoor settings that demonstrate VRL-PAP 1) is successfully able to pick paths that reflect demonstrated preferences, 2) is comparable in execution to geometric navigation with a highly detailed manually annotated map (without requiring such annotations), 3) is able to generalize to novel terrain types with minimal additional unlabeled demonstrations.
Detecting Ransomware Attacks Distribution Through Phishing URLs Using Machine Learning

During the last few years, phishing and ransomware seem to be the most widespread type of threat and a rapidly growing hazard to organizations. Nowadays, ransomware is injected via phishing emails, poorly informed users appear without much thought to click on links and attachments, believing that the emails are valid hence any defence action must concentrate on e-mail security. Ransomware is a malicious code or software that encrypts files in the victim’s system in a simplified way, and the perpetrators are demanding a lot of ransom for it. Ransomware can inflict enormous harm to companies, resulting in a lack of production and sometimes economic difficulties. Most importantly, there is a lack of documents that may reflect hundreds of hours of work or customer information that is important to the effective operation of the organization. Traditional defence methods that depend on malware signatures and fundamental protection rules have been found to be ineffective towards ransomware threats. In reality, attackers are developing their malware to compromise conventional Web and email security set-ups that are vulnerable. A thorough review of the organization’s defensive measures should address the ransomware problem and learn how they are truly capable of reacting to the current threats. The paper goal is to see how using this approach helps to prevent malicious malware and uses it as a self-defence tool through machine learning techniques. Research was done on several uniform resource locator (URL) samples and the findings show that we can say the difference between malicious and benign URL.
Review of Quantum Computing

Quantum computer is a computational framework based on Quantum Mechanism, which is receiving more and more attentions during the last few decades. It has reached remarkable success on some specific tasks comparing with classical computers. In this paper, we summarize the latest progress of this area, including fundamental knowledge, varying state-of-the-art quantum algorithms and physical implementation of quantum computers. This paper discusses the reason of quantum computer's powerful computing ability — the correlation effect between quantum entangled states. Several possible schemes of quantum computer are introduced. It is pointed out that the research of quantum computer needs the combination of the most advanced micro physical technology and computer technology.
Neural Basket Embedding for Sequential Recommendation

Next basket prediction from historical purchases is quite a complex task, even for e-commerce datasets with a low number of items that are being purchased repeatedly. Neural approaches are not much better in predicting next purchases than simple heuristics. This paper focuses on the challenge of how to encode baskets into efficient neural embedding with low reconstruction error while maintaining the similarity of baskets in the latent space. In our representation, replacing a product with a similar product or increasing quantity will not change the embedding of the basket much. We believe that good basket representation is critical for subsequent prediction. Our analysis shows that state-of-the-art next basket prediction approaches have limitations in their representation of baskets. We would like to focus on this aspect in our future research.
Predicting risk of satellite collisions using machine learning

Active collision avoidance has become an important task in space operations nowadays, and hundreds of alerts corresponding to close encounters of a satellite and other space objects are typically issued for a satellite in Low Earth Orbit every week. Such alerts are provided in the form of conjunction data messages, and only about two actionable alerts per spacecraft and week remain to be resolved after analyzing all cases. Therefore, building fully automated techniques for predicting the collision risk can help make the process of avoiding collisions less costly, as the number of false positives could be substantially reduced. In this paper, we present our machine learning techniques for this task which we exploited in the Collision Avoidance Challenge organized by the European Space Agency, in which we took the seventh place as the DunderMifflin Team (out of 97 registered participants).