Reviewerly/exHarmony huggingface dataset:
Visual Analysis of Large‐Scale Protein‐Ligand Interaction Data

Abstract When studying protein‐ligand interactions, many different factors can influence the behaviour of the protein as well as the ligands. Molecular visualisation tools typically concentrate on the movement of single ligand molecules; however, viewing only one molecule can merely provide a hint of the overall behaviour of the system. To tackle this issue, we do not focus on the visualisation of the local actions of individual ligand molecules but on the influence of a protein and their overall movement. Since the simulations required to study these problems can have millions of time steps, our presented system decouples visualisation and data preprocessing: our preprocessing pipeline aggregates the movement of ligand molecules relative to a receptor protein. For data analysis, we present a web‐based visualisation application that combines multiple linked 2D and 3D views that display the previously calculated data The central view, a novel enhanced sequence diagram that shows the calculated values, is linked to a traditional surface visualisation of the protein. This results in an interactive visualisation that is independent of the size of the underlying data, since the memory footprint of the aggregated data for visualisation is constant and very low, even if the raw input consisted of several terabytes.
Assessing Uranium Pollution Levels in the Rietspruit River, Far West Rand Goldfield, South Africa

The Rietspruit is located in Gauteng Province, South Africa, within the Witwatersrand Basin. The basin is noted for its vast gold deposit. The river extends for about 60 km before joining the Vaal River. The aim of this study was to determine the concentration of uranium in the Rietspruit and the factors that influenced the concentration of U at each of the sites. The source of uranium in the river is the discharge from the gold mine and the mine wastes. Inductively coupled plasma mass spectrometry was used for water and sediment analysis in order to determine the concentration of U. High concentration of U was found in the river water and sediment, which is above the permissible limit of U. The water is used for irrigation of farmlands, cattle watering and for human consumption despite the high concentration of uranium in it. Ingestion of uranium is dangerous to human health. Due to the toxic nature of uranium, consumption of the water for domestic use and agriculture purpose must be discouraged.
An Integrated Approach to a Safety Argumentation for AI-Based Perception Functions in Automated Driving

Developing a stringent safety argumentation for AI-based perception functions requires a complete methodology to systematically organize the complex interplay between specifications, data and training of AI-functions, safety measures and metrics, risk analysis, safety goals and safety requirements. The paper presents the overall approach of the German research project “KI-Absicherung” for developing a stringent safety-argumentation for AI-based perception functions. It is a risk-based approach in which an assurance case is constructed by an evidence-based safety argumentation.
A novel OTP based tripartite authentication scheme

Purpose Because of the continued use of mobile, cloud and the internet of things, the possibility of data breaches is on the increase. A secure authentication and authorization strategy is a must for many of today’s applications. Authentication schemes based on knowledge and tokens, although widely used, lead to most security breaches. While providing various advantages, biometrics are also subject to security threats. Using multiple factors together for authentication provides more certainty about a user’s identity; thus, leading to a more reliable, effective and more difficult for an adversary to intrude. This study aims to propose a novel, secure and highly stable multi-factor one-time password (OTP) authentication solution for mobile environments, which uses all three authentication factors for user authentication. Design/methodology/approach The proposed authentication scheme is implemented as a challenge-response authentication where three factors (username, device number and fingerprint) are used as a secret key between the client and the server. The current scheme adopts application-based authentication and guarantees data confidentiality and improved security because of the integration of biometrics with other factors and each time new challenge value by the server to client for OTP generation. Findings The proposed authentication scheme is implemented on real android-based mobile devices, tested on real users; experimental results show that the proposed authentication scheme attains improved performance. Furthermore, usability evaluation proves that proposed authentication is effective, efficient and convenient for users in mobile environments. Originality/value The proposed authentication scheme can be adapted as an effective authentication scheme to accessing critical information using android smartphones.
CNN-based parameter selection for fast VVC intra-picture encoding

This paper presents two new methods for fast VVC intra-picture encoding. Both are based on an approach that uses a CNN for blockadaptive parameter estimation. The parameters restrict the multitype-tree (MTT) partitionings tested by the encoder. The methods aim for an improvement of the approach by further constraints with additional parameters. Adding parameters increases the time required for training data generation exponentially. This raises the question which parameters to add and how. To explore further partitioning restrictions, the first method adds parameters controlling the block sizes the MTT can start from. Although this leads to four parameters, we can exploit that some of their combinations are invalid. To investigate whether testing fewer prediction and transform modes is feasible, the second method adds a single parameter that restricts their number jointly. The paper evaluates hypothetical and actual encoding time reductions for VTM-10.2. The first method outperforms our other and other existing method: The encoding time decreases by 50% with a bit rate increase of 0.7%.
Baseball Swing Pose Estimation Using OpenPose

This study explores usefulness of using the human pose estimation technique in sport. Since the expansion of deep learning techniques, human pose estimation became an important field of computer vision, it can be used in many applications like pose analysis, correction, training session, etc. The proposed method is used to estimate whether a baseball hitter performs a good swing. The hitter's limb coordinates are detected by the OpenPose model which is a real time multi-person detection system. The coordinates are used to calculate hip distance and limb angles, then the distance and angles are applied with our custom rules. The custom rules are made by researches and coaching experience in order to evaluate the swing of baseball hitter. Each rule is awarded differ points by its importance which we assumed. The goal of this study is using technology assistance in sport coaching scenario.
Anytime informed path re-planning and optimization for human-robot collaboration

Robots working in proximity of humans often need to change their motion to avoid collisions and interference with the operators. This paper uses a path re-planning approach to change the robot path online when the human operator is in the robot way. The method exploits a set of pre-computed paths to compute a new feasible path in case of obstruction to enhance the trajectory’s readability. Moreover, the algorithm iteratively optimizes the current solution in an anytime fashion to deal with strict computing time requirements. Experimental results show the method’s effectiveness in a collaborative cell, compared with industry best practices.
Artificial Intelligence, Chaos, Prediction and Understanding in Science

Machine learning and deep learning techniques are contributing much to the advancement of science. Their powerful predictive capabilities appear in numerous disciplines, including chaotic dynamics, but they miss understanding. The main thesis here is that prediction and understanding are two very different and important ideas that should guide us about the progress of science. Furthermore, it is emphasized the important role played by that nonlinear dynamical systems for the process of understanding. The path of the future of science will be marked by a constructive dialogue between big data and big theory, without which we cannot understand.
SMAC-Seg: LiDAR Panoptic Segmentation via Sparse Multi-directional Attention Clustering

Panoptic segmentation aims to address semantic and instance segmentation simultaneously in a unified framework. However, an efficient solution of panoptic segmentation in applications like autonomous driving is still an open research problem. In this work, we propose a novel LiDAR-based panoptic system, called SMAC-Seg. We present a learnable sparse multi-directional attention clustering to segment multi-scale foreground instances. SMAC-Seg is a real-time clustering-based approach, which removes the complex proposal network to segment instances. Most existing clustering-based methods use the difference of the predicted and ground truth center offset as the only loss to supervise the instance centroid regression. However, this loss function only considers the centroid of the current object, but its relative position with respect to the neighbouring objects is not considered when learning to cluster. Thus, we propose to use a novel centroid-aware repel loss as an additional term to effectively supervise the network in order to differentiate each object cluster with its neighbours. Our experimental results show that SMAC-Seg achieves state-of-the-art performance among all real-time deployable networks on both large-scale public SemanticKITTI and nuScenes panoptic segmentation datasets.
KRAN: Knowledge Refining Attention Network for Recommendation

Recommender algorithms combining knowledge graph and graph convolutional network are becoming more and more popular recently. Specifically, attributes describing the items to be recommended are often used as additional information. These attributes along with items are highly interconnected, intrinsically forming a Knowledge Graph (KG). These algorithms use KGs as an auxiliary data source to alleviate the negative impact of data sparsity. However, these graph convolutional network based algorithms do not distinguish the importance of different neighbors of entities in the KG, and according to Pareto’s principle, the important neighbors only account for a small proportion. These traditional algorithms can not fully mine the useful information in the KG. To fully release the power of KGs for building recommender systems, we propose in this article KRAN, a Knowledge Refining Attention Network, which can subtly capture the characteristics of the KG and thus boost recommendation performance. We first introduce a traditional attention mechanism into the KG processing, making the knowledge extraction more targeted, and then propose a refining mechanism to improve the traditional attention mechanism to extract the knowledge in the KG more effectively. More precisely, KRAN is designed to use our proposed knowledge-refining attention mechanism to aggregate and obtain the representations of the entities (both attributes and items) in the KG. Our knowledge-refining attention mechanism first measures the relevance between an entity and it’s neighbors in the KG by attention coefficients, and then further refines the attention coefficients using a “richer-get-richer” principle, in order to focus on highly relevant neighbors while eliminating less relevant neighbors for noise reduction. In addition, for the item cold start problem, we propose KRAN-CD, a variant of KRAN, which further incorporates pre-trained KG embeddings to handle cold start items. Experiments show that KRAN and KRAN-CD consistently outperform state-of-the-art baselines across different settings.
Binary Label Learning for Semi-supervised Feature Selection

Semi-supervised feature selection methods jointly exploit the labelled and unlabeled samples when selecting the features. Under the semi-supervised learning scenario, the number of labelled data significantly impacts the feature selection performance. In this paper, we introduce the label learning with binary hashing to the research field of feature selection and propose a novel Semi-supervised Feature Selection with Binary Label Learning (SFS-BLL) model. Specifically, we learn the binary hash codes as the pseudo labels by specially imposing binary hash constraints on the spectral embedding process to increase the number of labels. Meanwhile, we propose a self-weighted sparse regression module which exploits the learned labels and given manual labels together with importance differentiation to guide the feature selection process. Finally, we develop an effective discrete optimization method based on the Alternating Direction Method of Multipliers (ADMM) to iteratively optimize the binary labels and the feature selection matrix. Extensive experiments on widely tested benchmarks demonstrate the superiority of the proposed method from various aspects.
Application of Monte Carlo stochastic optimization (MOST) to deep learning

In this paper, we propose a new optimization method based on the Monte Carlo method. The proposed method is applied to several benchmark problems, and the result of applying it to the optimization of neural network is reported. Deep machine learning using neural networks is one of the important keywords to promote innovation in today’s advanced information society. Therefore, research on large-scale, high-speed, and high-precision algorithms has been actively conducted. The author has developed an optimization method in which the search region for multivariate parameters constituting the objective function is divided into two regions for each parameter, the integral values of each divided region are numerically calculated by the Monte Carlo method, the magnitude of each integral value is compared, and the optimum point is judged to be in a small region. The proposed method was applied to 50 variable benchmark functions (Schwefel and Ackley Functions), and was compared with the results of genetic algorithm (GA) which was a representative of existing optimization methods. As a result, it was confirmed that the proposed method is faster and more accurate than GA. In addition, the proposed method is applied to machine learning by neural networks, specifically XOR gate circuits and IRS classification problems, and verified. The neural network optimized by MOST reproduced teacher data and test data faster and more accurately than conventional Adam and genetic algorithms.
Biologically motivated learning method for deep neural networks using hierarchical competitive learning

This study proposes a novel biologically motivated learning method for deep convolutional neural networks (CNNs). The combination of CNNs and backpropagation learning is the most powerful method in recent machine learning regimes. However, it requires a large amount of labeled data for training, and this requirement can occasionally become a barrier for real world applications. To address this problem and use unlabeled data, we introduce unsupervised competitive learning, which only requires forward propagating signals for CNNs. The method was evaluated on image discrimination tasks using the MNIST, CIFAR-10, and ImageNet datasets, and it achieved state-of-the-art performance with respect to other biologically motivated methods in the ImageNet benchmark. The results suggest that the method enables higher-level learning representations solely based on the forward propagating signals without the need for a backward error signal for training convolutional layers. The proposed method could be useful for a variety of poorly labeled data, for example, time series or medical data.
Deterministic symbolic regression with derivative information: General methodology and application to equations of state

Abstract Symbolic regression methods simultaneously determine the model functional form and the regression parameter values by generating expression trees. Symbolic regression can capture the complexity of real‐world phenomena but the use of deterministic optimization for symbolic regression has been limited due to the complexity of the search space of existing formulations. We present a novel deterministic mixed‐integer nonlinear programming formulation for symbolic regression that incorporates derivative constraints through auxiliary expression trees. By applying the chain rule to mathematical operations, binary expression trees are capable of representing the calculation of first and second derivatives. We apply this formulation to illustrative examples using derivative information to show increased model discrimination capability. In addition, we perform a case study of a thermodynamic equation of state to gain insight on valid functional forms with thermodynamics‐based constraints on the first and second derivatives.
Virtual Reality and Augmented Reality in Education

Educational environment has faced and continues to face a lack of personnel, especially in fields of activity difficult to be understood like mathematics or physics. For this reason, schools are required to teach their students in classes with more students, being difficult for teachers to explain and exercise with every student individually. This involves blocking the activity during class of both the teacher and the other students. For a better understanding, in all science courses, the teachers usually spend multiple hours on the same subject, at the same time. After completing the subject, students need practice for memorizing and often need the teacher to help with homework. Because an active individual teacher, even virtual, is easier to be understood than a written instruction, the authors propose the use of virtual environments as a Virtual Reality/Augmented Reality (VR/AR) teacher. The use of augmented reality glasses offers a number of advantages, including: the possibility of working at the same time while following the instructions, reviewing the course material or following a teacher resolving an exercise without having to interrupt or taking a long time to resolve the homework. This paper presents the possibilities of using virtual and augmented reality devices such as Google Glass or Oculus Rift for individual teaching classes.
Artificial Intelligence for Low Level Suspicious Activity Detection

Human activity detection using video surveillance system is the processing of consecutive video frames and analysing any suspicious activity occurring in the video. Detection of human activities comes under the areas of artificial intelligence with image analysis and computer vision as sub-domain. The paper deals with detection of harmful or troublesome human activities using the matching of features obtained by SIFT, so that the further suspicious action can be prevented by alerting the concerned system. The paper narrates about the detection of suspicious activity such as holding a gun, wielding a knife, or punching based on their features. The proposed approach analyses the video frame by frame by observing the suspicious object as well as its activities in the video, and implements SIFT for extraction of features and mean shift algorithm (MSA) for object tracking. This paper gives an apparent idea of developing the system detecting suspicious activities of humans.
A Novel Hierarchical Situation Awareness Model for CBTC Using SVD Entropy and GRU With PRD Algorithms

As a typical and special cyber-physical system, Communication-Based Train Control (CBTC) systems require a different security model from the traditional Information Technology(IT) systems.In this paper, we propose a novel hierarchical situation awareness model for CBTC systems.The two major characteristics of the model are that it can achieve rapid dimensionality reduction of multi-source heterogeneous data, and it can also deal with complex situation variations (such as hacker attacks) by learning, classifying and predicting critical data from the physical layer, network layer and application layer of CBTC systems.The Singular Value Decomposition (SVD) entropy algorithm and Gated Recurrent Unit (GRU) neural network with Progressive Residuals Detecting (PRD) algorithm are proposed to support our hierarchical model.When the singular value entropy threshold is set to 0.85, the SVD entropy algorithm can effectively compress the training and classification time by about 80% while keeping the classification accuracy stable.The mean absolute error between the observed Movement Authority (MA) value and the MA value predicted by the GRU algorithm is 23.96, which can be set as a threshold.Then the PRD algorithm is proposed to continuously calculate the progressive residuals between the two adjacent groups of observations and predictions, and only when the residuals of both two adjacent groups are greater than the threshold, the possible abnormal data can be detected.The denial of service (Dos) attack, Probing attack and data tampering attack are introduced to verify our hierarchical model and the simulation results demonstrate that our model can achieve real-time and precise system situation awareness and alerting.
Quantum Cryptography as an Approach for Teaching Quantum Physics

Quantum physics is not only a fundamental physical theory but recently it promises big technological advances in the so-called 2nd quantum revolution, especially in quantum information. These advances rely on the basic research done since about 1980 while looking for a clarification of the fundamentals of quantum physics. The corresponding experiments and insights paved the way for quantum cryptography which is the first development in quantum information leaving the laboratory and going into practice. These developments should have its influence on teaching physics at school level in order to give students insight into a fascinating and fundamental part of modern physics. Herewith they can experience the fundamental notions of quantum physics: non-determinism, superposition and uncertainty. To achieve this goal an approach exploiting recent results of quantum cryptography and combining it intimately with the fundamentals seems promising. In addition such an approach also permits to introduce students with help of Dirac notation to mathematical structures of quantum physics. This might additionally support understanding quantum concepts without retreating to vague metaphors or descriptions. Therefore, teaching quantum information, especially quantum cryptography, at school may serve for motivating students and at the same time impart insight into physics research and the nature of physics. We present a corresponding teaching–learning proposal that was conducted with teacher students.