Reviewerly/exHarmony huggingface dataset:
Development of a self-learning automotive comfort function: an adaptive gesture control with few-shot-learning

With numerous new comfort functions such as infotainment or navigation systems being added to modern car cockpits, touchscreens gradually grew to be the primary input system for automotive UI. However, screen-based UI navigation requires extended visual attention and can thus be a dangerous distraction while driving. In this paper, we describe an innovative few-shot-learning real-time gesture control system that allows the driver to define custom dynamic hand gestures by recording a small set of samples for each gesture. By employing a deep convolutional neural network to extract 2D hand poses from a continuous stream of depth data, we can make use of a k-NN classifier to match gestures with a small set of previously recorded samples. Utilizing a state-of-the-art network architecture, our pose estimation network achieves an average keypoint distance accuracy of 87%–98.6%. Based on that, our overall system is able to classify gestures from as few as three samples with an accuracy of 93.69%. The system runs at 20fps in an unoptimized Python implementation and therefore meets the real-time requirements to a sufficient amount.
Accurate and fast speckle interferometry in severe illumination conditions

Illumination conditions are an important factor affecting the accuracy of speckle interferometry. High-intensity illumination could cause the speckle images to be overexposed due to the limited dynamic range of the camera, degrading the fringe quality. This study proposes a high-dynamic phase reconstruction method enabling accurate speckle interferometry by means of speckle image fusion. Integrating with phase shifting, a real-time and robust speckle interferometric measurement routine is established by using a heterogeneous concurrency computing. To show the performance, the proposed method is exemplarily applied to shearography with both simulation and experimental studies. Results suggest that the proposed image fusion scheme is effective in dealing with the non-uniform illumination. The proposed linear fusion operator is accordance with the requirement of phase extraction for temporal phase shifting by balancing the image quality and computational efficiency. The innovative method facilitates the use of speckle interferometry in severe conditions where uniform illumination cannot be achieved due to surface conditions.
Illumination invariant face recognition using contourlet transform and convolutional neural network

The face is a dominant biometric for recognizing a person. However, face recognition becomes challenging when there are severe changes in lighting conditions, i.e., illumination variations, which have been shown to have a more severe effect on recognition performance than the inherent differences between individuals. Most of the existing methods for tackling the problem of illumination variation assume that illumination lies in the large-scale component of a facial image; as such, the large-scale component is discarded, and features are extracted from small-scale components. Recently, it has been shown that large-scale component is also important; in addition, small-scale component contains detrimental noise features. Keeping this in view, we introduce a method for illumination invariant face recognition that exploits large-scale and small-scale components by discarding the illumination artifacts and detrimental noise using ContourletDS. After discarding the unwanted components, local and global features are extracted using a convolutional neural network (CNN) model; we examined three widely employed CNN models: VGG-16, GoogLeNet, and ResNet152. To reduce the dimensions of local and global features and fuse them, we employ linear discriminant analysis (LDA). Finally, ridge regression is used for recognition. The method was evaluated on three benchmark datasets; it achieved accuracies of 99.7%, 100%, and 79.76% on Extended Yale B, AR, and M-PIE, respectively. The comparison reveals that it outperforms the state-of-the-art methods.
Depth Map Super-Resolution via Cascaded Transformers Guidance

Depth information captured by affordable depth sensors is characterized by low spatial resolution, which limits potential applications. Several methods have recently been proposed for guided super-resolution of depth maps using convolutional neural networks to overcome this limitation. In a guided super-resolution scheme, high-resolution depth maps are inferred from low-resolution ones with the additional guidance of a corresponding high-resolution intensity image. However, these methods are still prone to texture copying issues due to improper guidance by the intensity image. We propose a multi-scale residual deep network for depth map super-resolution. A cascaded transformer module incorporates high-resolution structural information from the intensity image into the depth upsampling process. The proposed cascaded transformer module achieves linear complexity in image resolution, making it applicable to high-resolution images. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art techniques for guided depth super-resolution.
Boring Problems Are Sometimes the Most Interesting

Abstract In a recent position paper, Turing Award Winners Yoshua Bengio, Geoffrey Hinton, and Yann LeCun make the case that symbolic methods are not needed in AI and that, while there are still many issues to be resolved, AI will be solved using purely neural methods. In this piece I issue a challenge: Demonstrate that a purely neural approach to the problem of text normalization is possible. Various groups have tried, but so far nobody has eliminated the problem of unrecoverable errors, errors where, due to insufficient training data or faulty generalization, the system substitutes some other reading for the correct one. Solutions have been proposed that involve a marriage of traditional finite-state methods with neural models, but thus far nobody has shown that the problem can be solved using neural methods alone. Though text normalization is hardly an “exciting” problem, I argue that until one can solve “boring” problems like that using purely AI methods, one cannot claim that AI is a success.
Bayesian Attribute Bagging-Based Extreme Learning Machine for High-Dimensional Classification and Regression

This article presents a Bayesian attribute bagging-based extreme learning machine (BAB-ELM) to handle high-dimensional classification and regression problems. First, the decision-making degree (DMD) of a condition attribute is calculated based on the Bayesian decision theory, i.e., the conditional probability of the condition attribute given the decision attribute. Second, the condition attribute with the highest DMD is put into the condition attribute group (CAG) corresponding to the specific decision attribute. Third, the bagging attribute groups (BAGs) are used to train an ensemble learning model of extreme learning machines (ELMs). Each base ELM is trained on a BAG which is composed of condition attributes that are randomly selected from the CAGs. Fourth, the information amount ratios of bagging condition attributes to all condition attributes is used as the weights to fuse the predictions of base ELMs in BAB-ELM. Exhaustive experiments have been conducted to compare the feasibility and effectiveness of BAB-ELM with seven other ELM models, i.e., ELM, ensemble-based ELM (EN-ELM), voting-based ELM (V-ELM), ensemble ELM (E-ELM), ensemble ELM based on multi-activation functions (MAF-EELM), bagging ELM, and simple ensemble ELM. Experimental results show that BAB-ELM is convergent with the increase of base ELMs and also can yield higher classification accuracy and lower regression error for high-dimensional classification and regression problems.
Identifying latent variables in Dynamic Bayesian Networks with bootstrapping applied to Type 2 Diabetes complication prediction

Predicting complications associated with complex disease is a challenging task given imbalanced and highly correlated disease complications along with unmeasured or latent factors. To analyse the complications associated with complex disease, this article attempts to deal with complex imbalanced clinical data, whilst determining the influence of latent variables within causal networks generated from the observation. This work proposes appropriate Intelligent Data Analysis methods for building Dynamic Bayesian networks with latent variables, applied to small-sized clinical data (a case of Type 2 Diabetes complications). First, it adopts a Time Series Bootstrapping approach to re-sample the rare complication class with a replacement with respect to the dynamics of disease progression. Then, a combination of the Induction Causation algorithm and Link Strength metric (which is called IC*LS approach) is applied on the bootstrapped data for incrementally identifying latent variables. The most highlighted contribution of this paper gained insight into the disease progression by interpreting the latent states (with respect to the associated distributions of complications). An exploration of inference methods along with confidence interval assessed the influences of these latent variables. The obtained results demonstrated an improvement in the prediction performance.
Use of Deep Learning Methods for People Counting in Public Transport

This paper uses deep learning in the field of public transport, where it is of immense interest to know the occupation of vehicles for their coordination and scheduling. Counting people on a bus or train is very often still done manually, whereas this paper presents a modern approach with just the use of camera images and deep learning. The people counter presented in this work consists of two parts, in each of which a neural network has been optimized. In the first step, persons are recognized by means of a person detector while this information is used in the second step for counting people. For the detection of people at the bus entrance, RetinaNet was selected as the model and optimized. The output of the person detector was then used to optimize a novel architecture of a neural network. This allows determining the number of people getting on and off a bus in a video.
Shoeprint Image Retrieval Based on Dual Knowledge Distillation for Public Security Internet of Things

In order to implement rapid retrieval of large-scale crime scene investigation shoeprint image (SPI) in the intelligent mobile terminal of Public Security Internet of Things (PSIoT), a novel dual knowledge distillation (DKD) network is designed by fusing spatial attention (SA) distillation and feature distillation to solve its problems of limited computing and storage capacity. First, ResNet50 was modified by adding a new SA module and hash layer as the teacher model, and a light convolutional neural network (CNN) with the same SA and hash layer is designed as the student model. Then, the attention distillation loss function is constructed to distill the SA knowledge in the teacher module to the student module to improve the ability of the convolutional layer at the front end of the student network to capture the underlying visual features of the SPI. Finally, the feature distillation loss function is constructed to distill the semantic knowledge in the teacher module to the student module to improve the ability of the student module to express high-level semantics of the SPI. we compare our method on two data sets of SPID and FID-300 with other state-of-the-art methods in the SPI retrieval domain. The experimental results show that our method can improve the SPI retrieval baseline by a large margin and better than other methods.
Basketball self training shooting posture recognition and trajectory estimation using computer vision and Kalman filter

Abstract Self-shooting training is one of the fundamental criteria for success in basketball. Particularly, young players increase their performance with regular training. However, the training process becomes painful and time-consuming without a coach since the incorrect shooting posture causes missing shots, leading to reluctance. In this research, a self-shooting posture algorithm is developed to track the movement of basketball players and give them feedback about their position, angle, and basketball projectile trajectory information. The proposed algorithm uses computer vision techniques and Kalman filter to detect the best projectile trajectory using initial conditions such as acceleration due to gravity the initial velocity at the angle of launch having certain horizontal distance to the rim and the rim distance from the ground The acceleration of both gravity and air drag are altered by predefined parameters, including the drag coefficient basketball mass ball radius and silhouette area The proposed algorithm provides the shooting angle in real-time by placing the projectile angle on to the cropped image of the player posture and draws the projectile trajectory towards the basketball hoop According to the results, the players having a specified height can achieve the best shooting at the angle with air drag force. On the other hand, if there is no air resistance, the best shooting angle is deviated significantly. The other stats that are a total time of travel, maximum horizontal distance, maximum height and the time until the top are also given along with the results.
Understanding emojis: Cultural influences in interpretation and choice of emojis

Emojis have been widely used in computer-mediated communication. This study examined how cultural identification with individualism and collectivism influences Americans' interpretations and choices of emojis commonly used in South Korea. We also investigated the moderating effects of intercultural communication competence (ICC) and emotional intelligence (EI). Results from a sample of college students in the U.S. (N = 369) showed that the identification with individualism and collectivism played a significant role in interpreting and selecting appropriate emojis. Moreover, ICC and EI significantly moderated the relationships. These findings enhance understanding of cross-cultural differences in understanding emojis in CMC.
Ransomware: Pay to Play?

Ransomware is fairly new, and many folks don’t realize that it will affect them. Just as with shoplifting, we all pay.
TCTracer: Establishing test-to-code traceability links using dynamic and static techniques

Abstract Test-to-code traceability links model the relationships between test artefacts and code artefacts. When utilised during the development process, these links help developers to keep test code in sync with tested code, reducing the rate of test failures and missed faults. Test-to-code traceability links can also help developers to maintain an accurate mental model of the system, reducing the risk of architectural degradation when making changes. However, establishing and maintaining these links manually places an extra burden on developers and is error-prone. This paper presents TCTracer , an approach and implementation for the automatic establishment of test-to-code traceability links. Unlike existing work, TCTracer operates at both the method level and the class level, allowing us to establish links between tests and functions, as well as between test classes and tested classes. We improve over existing techniques by combining an ensemble of new and existing techniques that utilise both dynamic and static information and exploiting a synergistic flow of information between the method and class levels. An evaluation of TCTracer using five large, well-studied open source systems demonstrates that, on average, we can establish test-to-function links with a mean average precision (MAP) of 85% and test-class-to-class links with an MAP of 92%.
GSAP: A Hybrid GRU and Self-Attention Based Model for Dual Medical NLP Tasks

This paper proposes a hybrid Gated Recurrent Unit (GRU) and Self-Attention based model, named GSAP, for dual medical related NLP tasks. GSAP stacks three famous neural network units: GRU, self-attention and pooling of Convolutional Neural Network (CNN) to improve the accuracy. In the GSAP, GRU is first adopted to comprehend sentences. Second, the Self-Attention layer helps the model to focus on key points of inputs. Finally, the pooling layer eases the outfitting and upgrades the system accuracy. The proposed GSAP is applied to two different medical NLP tasks: medical QA matching and smoking status classification and demonstrates outstanding results. In the smoking prediction, GSAP obtains an accuracy around 80%. Regarding to the medical QA matching task, GSAP upgrades the accuracy up to around 90%.
B-DP: Dynamic Collection and Publishing of Continuous Check-In Data with Best-Effort Differential Privacy

Differential privacy (DP) has become a de facto standard to achieve data privacy. However, the utility of DP solutions with the premise of privacy priority is often unacceptable in real-world applications. In this paper, we propose the best-effort differential privacy (B-DP) to promise the preference for utility first and design two new metrics including the point belief degree and the regional average belief degree to evaluate its privacy from a new perspective of preference for privacy. Therein, the preference for privacy and utility is referred to as expected privacy protection (EPP) and expected data utility (EDU), respectively. We also investigate how to realize B-DP with an existing DP mechanism (KRR) and a newly constructed mechanism (EXPQ) in the dynamic check-in data collection and publishing. Extensive experiments on two real-world check-in datasets verify the effectiveness of the concept of B-DP. Our newly constructed EXPQ can also satisfy a better B-DP than KRR to provide a good trade-off between privacy and utility.
Sequential modeling, monitoring, and forecasting of streaming web traffic data

In this paper we introduce strategies for modeling, monitoring, and forecasting sequential web traffic data using flows from the Fox News website. In our analysis we consider a family of Poisson-gamma state space (PGSS) models that can accurately quantify the uncertainty exhibited by web traffic data, can provide fast sequential monitoring and prediction mechanisms for high frequency time intervals, and are computationally feasible when structural breaks are present. As such, we extend the family of PGSS models to include the state augmented (sa-)PGSS model whose state evolution structure is flexible and responsive to sudden changes. Such adaptability is achieved by augmenting the state vector of the PGSS model with an additional state variable for a time-varying discount factor. We develop an efficient particle-based estimation procedure that is suitable for sequential analysis, allowing us to estimate dynamic state variables and static parameters via closed-form conditional sufficient statistics. We compare the performance of the PGSS family of models against viable alternatives from the literature and argue that, especially in the presence of structural breaks, our proposed approach yields superior sequential model fit and predictive performance while preserving computational feasibility. We provide additional insights by designing a simulation study that mimics potential web traffic data patterns.
Values Embedded in Legal Artificial Intelligence

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Increasingly, governments are</b> using technological systems in the application and administration of law <xref ref-type="bibr" rid="ref6" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[6]</xref> , <xref ref-type="bibr" rid="ref22" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[22]</xref> . For example, officials use computer systems to sentence criminal defendants, approve or deny government benefits, predict the location of future crimes, and disallow border entry <xref ref-type="bibr" rid="ref6" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[6]</xref> , <xref ref-type="bibr" rid="ref22" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[22]</xref> . In each instance, technology is used to make substantive decisions about law, individual legal rights, or allocation of government resources. Let us refer to any automation system used in the administration or application of law as a “legal technological system.” Notably, some of these legal technological systems use machine learning and other artificial intelligence techniques to achieve their goals <xref ref-type="bibr" rid="ref13" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[13]</xref> . Let us refer to those as “legal AI systems.”
Planning for potential: efficient safe reinforcement learning

Abstract Deep reinforcement learning (DRL) has shown remarkable success in artificial domains and in some real-world applications. However, substantial challenges remain such as learning efficiently under safety constraints. Adherence to safety constraints is a hard requirement in many high-impact application domains such as healthcare and finance. These constraints are preferably represented symbolically to ensure clear semantics at a suitable level of abstraction. Existing approaches to safe DRL assume that being unsafe leads to low rewards. We show that this is a special case of symbolically constrained RL and analyze a generic setting in which total reward and being safe may or may not be correlated. We analyze the impact of symbolic constraints and identify a connection between expected future reward and distance towards a goal in an automaton representation of the constraints. We use this connection in an algorithm for learning complex behaviors safely and efficiently. This algorithm relies on symbolic reasoning over safety constraints to improve the efficiency of a subsymbolic learner with a symbolically obtained measure of progress. We measure sample efficiency on a grid world and a conversational product recommender with real-world constraints. The so-called Planning for Potential algorithm converges quickly and significantly outperforms all baselines. Specifically, we find that symbolic reasoning is necessary for safety during and after learning and can be effectively used to guide a neural learner towards promising areas of the solution space. We conclude that RL can be applied both safely and efficiently when combined with symbolic reasoning.
A Sampling-Based Unfixed Orientation Search Method for Dual Manipulator Cooperative Manufacturing

The case of dual manipulators with shared workspace, asynchronous manufacturing tasks, and independent objects is named a dual manipulator cooperative manufacturing system, which requires collision-free path planning as a vital issue in terms of safety and efficiency. This paper combines the mathematical modeling method with the time sampling method in the classification of robot path-planning algorithms. Through this attempt we can achieve an optimal local search path during each sampling period interval. Our strategy is to build the corresponding non-linear optimization functions set based on the motion characteristics of the dual manipulator system. In this way, the path-planning problem can be turned into a purely mathematical problem of solving the non-linear optimization programming equations set. The spatial geometric analysis is used to linearize the predicted dual-manipulator minimum distance equation, thus linearizing the non-linear optimization equations set. Finally, this system of linear optimization equations will be mapped directly into a virtual Euclidean space and then solved intuitively using the spatial geometry theory. By simulation and comparing with the previous strategies, we find that the planning results of the newly proposed planning strategy are smoother and have shorter deviations as well as a higher algorithmic efficiency in terms of spatial geometric properties.