Reviewerly/exHarmony huggingface dataset:
CNN tracking based on data augmentation

Correlation filter based tracking methods have aroused increasing attention due to the appealing performance on tracking benchmark datasets. For each frame, a filter is trained to separate the object from its background. Considering that the object always undergoes challenging situations, the trained filter should consider both external and internal distractions. In this paper, we propose a data augmentation based robust visual tracking algorithm to better generalize the training data. Specifically, data augmentation technique is utilized to generate training samples to improve the robustness of the training filter. Then hierarchical convolutional neural network (CNN) features are utilized to encode the target and the augmented sample. Different from previous work, we exploit to use a hash matrix to reduce the dimension of the CNN features. Next, the correlation filter tracking method is employed. The tracking results of multiple hash features are combined to locate the target. Extensive experiments on five large scale datasets show that the proposed method achieves comparable results to state-of-the-art trackers.
"Erfahrung &amp; Erlebnis"

Bodily play systems are becoming increasingly prevalent, with research aiming to understand the associated player experience. We argue that a more nuanced lexicon describing "bodily play experience" can be beneficial to drive the field forward. We provide game designers with two German words to communicate two different aspects of experience: "Erfahrung", referring to experience where one is actively engaged in and gains knowledge from; and "Erlebnis", referring to a tacit experience often translated as "lived experience". We use these words to articulate a suite of design strategies for bodily play experiences by referring to past design work. We conclude by discussing these two aspects of experience in conjunction with two previously established perspectives on the human body. We believe this more nuanced lexicon can provide a clearer understanding for designers about bodily play allowing them to guide players in gaining the many benefits from such experiences.
Swatch-bits

The creation of e-textile swatches is a common practice for documenting material experiments, sharing techniques with other practitioners, and for concept ideation. The Creative Interactions Lab has developed a system that turns e-textile swatches into easily connectable "bits" so that swatches can move between being an ideation tool into a prototyping tool(kit). The benefit of this approach is that experimental swatches and ideas for their use can be easily tested in context. In this studio, participants will be invited to bring their own swatches and/or prototypes, will learn how to create modular e-textile swatch-bits, and then we will spend the afternoon making prototypes and will engage in hands-on activities with the modular swatches. The goal of the studio will be to share e-textile prototyping techniques, and to discuss the potential for modular swatches to be incorporated into e-textile prototyping processes.
Cyber-physical Support of Daily Activities

This article introduces RAS, a cyber-physical system that supports individuals with memory limitations to perform daily activities in their own homes. RAS represents a partnership between a smart home, a robot, and software agents. When smart home residents perform activities, RAS senses their movement in the space and identifies the current activity. RAS tracks activity steps to detect omission errors. When an error is detected, the RAS robot finds and approaches the human with an offer of assistance. Assistance consists of playing a video recording of the entire activity, showing the omitted activity step, or guiding the resident to the object that is required for the current step. We evaluated RAS performance for 54 participants performing three scripted activities in a smart home testbed and for 2 participants using the system over multiple days in their own homes. In the testbed experiment, activity errors were detected with a sensitivity of 0.955 and specificity of 0.992. RAS assistance was performed successfully with a rate of 0.600. In the in-home experiments, activity errors were detected with a combined sensitivity of 0.905 and a combined specificity of 0.988. RAS assistance was performed successfully for the in-home experiments with a rate of 0.830.
Debiasing Gender biased Hindi Words with Word-embedding

Word-embedding is a major machine learning technique for computational applications of languages. For a given corpus, the process of word-embedding is to embed each word onto multi-dimensional space such that semantic similarities between similar words are retained. While learning the similarity as encapsulated in the training corpus, the embedding process inadvertently captures many other inherent features present in the corpus. One such thing is the bias arising out of stereotyping present in almost all the corpus no matter how extensively used and trusted they are. We study this aspect of word-embedding in the context of Hindi language. We show that many gender-neutral words in Hindi are mapped to vectors which are inclined towards one gender or the other in multi-dimensional space. We propose a new algorithm of debiasing and demonstrate its efficacy in the context of Hindi language. Further, we build a SVM-based classifier that determines whether a gender-neutral word is classified as neutral or otherwise. We corroborate our claim with experimental results on large number of individual words. This work is first ever result on debiasing in Hindi Language and our new debiasing algorithm can be applicable in the context of any language.
Combining information from thresholding techniques through an evolutionary Bayesian network algorithm

Segmentation is an important task in image processing because it could affect the performance of other steps in image analysis. One of the most used methods for segmentation is thresholding which can be formulated as an optimization problem, and evolutionary algorithms (EAs) are alternatives commonly applied to solve it. Estimation of Distribution Algorithms (EDAs) is a branch of EAs that explores the search space by building a probabilistic model, such as Bayesian Networks (BNs). In this article is proposed a BN-based EDA for multilevel image segmentation called BNMTH . The proposed approach iteratively selects the combination of thresholding techniques that permits to find the best configuration of thresholds for a digital image, exploring the inter-dependencies between the decision variables (thresholds) and the different techniques. BNMTH is applied over a set of benchmark images and the results of the segmentation are qualitatively analyzed by using the Peak Signal-to-Noise Ratio (PSNR), the Structure Similarity Index (SSIM) and the Feature Similarity Index (FSIM). Besides, a statistical analysis is provided to compare BNMTH with other state-of-the-art optimization algorithms. The results show that BNMTH is a competitive approach for image segmentation, providing accurate results in almost all the cases. Moreover, the segmented images and the histograms show that the classes are accurately generated even in complex conditions.
Virtual reality as a platform for research in gambling behaviour

Research into gambling behaviour is an important aspect of mitigating the effects of problem gambling. However, the majority of existing studies are conducted in the laboratory, rather than in vivo, raising questions over the generalisability of results. Virtual reality (VR) is well established as an effective tool for exposure therapy, and has also been used in other experimental contexts: such work is often motivated by the ability of the platform to create ecologically valid conditions, and simulations that would be hard to create in real life. Whilst a small number of studies have used VR to evoke the urge to gamble, and also to conduct behavioural studies, no work has yet considered the differences in experience between laboratory and VR conditions. We approach this from the perspective of immersion, arousal and user experience, and conduct a within-subjects study (N = 48) in which participants play a gambling game both on a laptop in a laboratory, and on a virtual gaming machine located in a VR simulation of a betting shop. Our results show that participants reported higher levels of arousal, as well as higher levels of immersion in the game when playing in VR. There was also a significant difference in self-reported physical task workload in VR. Based on our results, we argue for further use of VR in experimental research related to gambling, as well as further study of the experience and responses of problem gamblers to VR simulations of real-world gambling.
The geological and hydrogeological framework of the Panabako, Kodjari, and Bimbilla formations of the Voltaian supergroup – Revelations from groundwater hydrochemical data

This research demonstrates the utility of groundwater hydrochemical data for facilitating lithological mapping in a sedimentary terrain. Factor analysis and Q-mode Hierarchical Cluster Analysis (HCA) were applied to groundwater hydrochemical data from the Nasia Basin in Northern Ghana to determine the major sources of hydrochemical variation and the main spatial groundwater associations (groups). These analyses were complemented by mass balance hydrochemical modeling from PHREEQC and petrographical analysis of rock samples from the area. Rock samples from the study area were subjected to petrographical and geochemical analyses from a petrographic microscope and Scanning Electron Microscope (SEM) respectively. Four principal factors were distinguished from the factor analysis, highlighting four principal processes responsible for the observed hydrochemistry in the area. These four factors cumulatively account for over 79% of the total variance in the dataset. Factor scores were computed from the factor loadings matrix through a regression process, to highlight the spatial variations in the significance of each of the factors in the terrain. On the basis of the parameter loadings, the first factor, which has been noted to be pervasive throughout the terrain, has been interpreted to represent silicate mineral weathering. Both the geochemical data and petrographical analysis indicate the presence of silicate minerals indicating the plausibility of silicate mineral weathering. The process of silicate mineral weathering appears to be highly pervasive throughout the basin, indicating high factor scores in places. The trend of spatial variations in the scores of factor 1 clearly mimics the known geology of the terrain, indicating that the geology is the main controlling factor in the hydrochemistry of groundwater in the basin. This pattern, exhibited by the factor scores indicates that where adequate groundwater hydrochemical data is available, factor analysis would prove useful in mapping out lithological variations in an area. Two of the remaining factors have been observed to represent other specific geogenic processes. Mass balance hydrochemical modeling suggests that albite may be in active dissolution, presenting pervasively negative saturation indices (undersaturation), while kaolinite is extensively supersaturated. Five groundwater associations distinguished from the Q-mode HCA display a spatial pattern which identifies with the underlying geology.
LVMapper: A Large-Variance Clone Detector Using Sequencing Alignment Approach

To detect large-variance code clones (i.e. clones with many modifications) in large-scale code repositories is difficult because most current tools can only detect almost identical or very similar clones. It has an important impact on downstream software applications such as bug detection, code completion, software analysis, etc. Recently, CCAligner made an attempt to detect the code clones with insertions or deletions in one place, which were called large-gap clones. Our contribution is to develop a novel and effective detection approach of large-variance clones to more general cases for not only the concentrated code modifications but also the scattered code modifications. A detector named LVMapper is proposed, borrowing and changing the approach of sequencing alignment in bioinformatics which can find two similar sequences with more differences. The ability of LVMapper was tested on 8 open source projects datasets, and the results show that LVMapper detected more than 5 times of large-variance clones compared with other state-of-the-art tools including CCAligner. Furthermore, our new tool also presents comparable recall for general Type-1, Type-2 and Type-3 clones with precision of 88.5% on the widely used benchmarking dataset BigCloneBench.
K-Anonymity inspired adversarial attack and multiple one-class classification defense

A novel adversarial attack methodology for fooling deep neural network classifiers in image classification tasks is proposed, along with a novel defense mechanism to counter such attacks. Two concepts are introduced, namely the K-Anonymity-inspired Adversarial Attack (K-A3) and the Multiple Support Vector Data Description Defense (M-SVDD-D). The proposed K-A3 introduces novel optimization criteria to standard adversarial attack methodologies, inspired by the K-Anonymity principles. Its generated adversarial examples are not only misclassified by the neural network classifier, but are uniformly spread along K different ranked output positions. The proposed M-SVDD-D consists of a deep neural architecture layer consisting of multiple non-linear one-class classifiers based on Support Vector Data Description that can be used to replace the final linear classification layer of a deep neural architecture, and an additional class verification mechanism. Its application decreases the effectiveness of adversarial attacks, by increasing the noise energy required to deceive the protected model, attributed to the introduced non-linearity. In addition, M-SVDD-D can be used to prevent adversarial attacks in black-box attack settings.
Paleostress and statistical analysis using quartz veins from mineralized and non-mineralized zones: Application for exploration targeting

Quartz vein orientations, thickness and spacing data are analyzed from 10 transects each of mineralized and non-mineralized zones of Gadag region, which is a gold-bearing terrain within the Dharwar Craton (Southern India). Paleostress analysis reveals that the veins emplaced when fluid pressure (Pf) varied from Pf>σ2 (intermediate principle stress) to Pf < σ2. Variation in ΔR′ is estimated in the mineralized and non-mineralized zone during Pf fluctuation (Pf > σ2 to Pf < σ2). Variation in driving pressure ratio (ΔR′) for transects in mineralized zone is greater than in non-mineralized zone. Fractal dimension (Dc), Weibull modulus (α), vein intensity (Vi) and coefficient of vein spacing (Cv(S)) are calculated from vein data of each transect. Dc and α values are lower in mineralized zone vis-à-vis non-mineralized zone, thus implying that the former is weaker and has higher growth rate than the latter. Moreover, Vi and mean thickness of veins are higher in the mineralized zone than the non-mineralized zone. These differences in paleostress and vein statistical distributions are exploited to propose a three-axis “mineralization potential plot” designated P-D-F plot. This plot integrates ΔR′, α, Dc and Cv(S) data from each transect and it is established that points representing transects (data) from mineralized zone lie closer to the origin (0,0,0) than those in non-mineralized domain. The distance between origin and the point representing the transect in the P-D-F plot gives the “mineralization potential parameter” (Md). Lower Md implies higher mineralization potential. It is concluded that comparative studies of paleostress and vein distribution in mineralized and non-mineralized zones of an area can be very useful and the P-D-F plot developed through this study can be applied for exploration targeting.
Multi-Object Tracking Hierarchically in Visual Data Taken From Drones

Visual understanding tasks on the drone platform have gained considerable attention recently due to the rapid development of drones. In this paper, we present a hierarchical multi-target tracker (HMTT) for visual data taken from drones. Our approach is specifically directed against sequences shot from drone's view with several stages hierarchically performed. The detector detects objects taken from different viewing angles and the detections are filtered to ensure the correctness. Moreover, we propose a method to locate the frames in the case of camera's fast move by two-norm of the homography matrix. Based on that, performance on Multi-Object Tracking is improved with the involvement of Single Object Tracking and a re-identification subnet. Our method participated in the Multi-Object Tracking Challenge (Task 4) of VisDrone2019 benchmark and achieved state-of-the-art performance.
Multi-agent informed path planning using the probability hypothesis density

An Informed Path Planning algorithm for multiple agents is presented. It can be used to efficiently utilize available agents when surveying large areas, when total coverage is unattainable. Internally the algorithm has a Probability Hypothesis Density (PHD) representation, inspired by modern multi-target tracking methods, to represent unseen objects. Using the PHD, the expected number of observed objects is optimized. In a sequential manner, each agent maximizes the number of observed new targets, taking into account the probability of undetected objects due to previous agents’ actions and the probability of detection, which yields a scalable algorithm. Algorithm properties are evaluated in simulations, and shown to outperform a greedy base line method. The algorithm is also evaluated by applying it to a sea ice tracking problem, using two datasets collected in the Arctic, with reasonable results. An implementation is provided under an Open Source license.
Blind Mesh Assessment Based on Graph Spectral Entropy and Spatial Features

With the wide applications of three-dimensional (3D) meshes in intelligent manufacturing, digital animation, virtual reality, digital cities and other fields, more and more processing techniques are being developed for 3D meshes, including watermarking, compression, and simplification, which will inevitably lead to various distortions. Therefore, how to evaluate the visual quality of 3D mesh is becoming an important problem and it is necessary to design effective tools for blind 3D mesh quality assessment. In this paper, we propose a new Blind Mesh Quality Assessment method based on Graph Spectral Entropy and Spatial features, called as BMQA-GSES. 3D mesh can be represented as graph signal, in the graph spectral domain, the Gaussian curvature signal of the 3D mesh is firstly converted with Graph Fourier transform (GFT), and then the smoothness and information entropy of amplitude features are extracted to evaluate the distortion. In the spatial domain, four well-performing spatial features are combined to describe the concave and convex information and structural information of 3D meshes. All the extracted features are fused by the random forest regression to predict the objective quality score of the 3D mesh. Experiments are performed successfully on the public databases and the obtained results show that the proposed BMQA-GSES method provides good correlation with human visual perception and competitive scores compared to state-of-art quality assessment methods.
Magnetically controllable photon blockade under a weak quantum-dot–cavity coupling condition

Quantum control of photons has become an area of great interest in the development of quantum technology. Here we explore how an external magnetic field can be exploited to control the statistical properties of photons in a bimodal cavity quantum electrodynamics (CQED) system. Our CQED system is composed of a bimodal micropillar Fabry-P\'erot cavity containing a single $V$-level quantum dot (QD) involving fine-structure-splitting neutral exciton transitions under the effect of the applied magnetic field in the presence of an incident pump laser field driving one cavity mode. With the introduction of the external magnetic field, not only is the conversion of polarization characteristics of photons achieved, but the statistics properties of photons are well engineered and the switching between photon bunching and antibunching is realized in the weak-coupling regime of CQED. The superiority of our system is manifested in the following aspects. (i) Using experimental parameters, we can manipulate the statistical properties of photons by appropriately tuning the strength of the magnetic field under the weak-coupling CQED condition, accompanied by either enhanced antibunching or a transition from antibunching to strong bunching of the light and vice versa. So we can arrive at selective photon statistics. (ii) The strong photon antibunching effect, under nonresonant scenarios but without the need to match the resonant or near-resonant conditions for the cavity and QD, can appear. Furthermore, we find that the multifrequency photon blockade can be generated in the present system, which is different from previous studies about photon blockade only for specific optical detuning. (iii) The high photon occupations or transmissions, simultaneously accompanied by strong photon antibunching in the system, are beneficial to the correlation measurement in practical experiments. This process works with a high quality of photon antibunching and bunching over a wide range of parameters and has potential applications in on-chip quantum information processing. It is hoped that the proposed scheme provides a possibility for the generation of tunable single-photon sources or controllable photon quantum gates.
MMP-Net: A Multi-Scale Feature Multiple Parallel Fusion Network for Single Image Haze Removal

Reducing the impact of hazy images on subsequent visual information processing is a challenging problem. In this paper, combining with atmospheric scattering model, we propose an end-to-end multi-scale feature multiple parallel fusion network called MMP-Net for single image haze removal. The MMP-Net includes three components: multi-scale CNN module, residual learning module and deep parallel fusion module. 1) In multi-scale CNN module, a multi-scale convolutional neural network (CNNs) is adopted to extract different scales features from whole to local, and these features are fused multiple times in parallel. 2) In residual learning module, residual blocks are introduced to deeply learn detailed features, which can recover more image details. 3) In deep parallel fusion module, those features from residual learning module are deeply merged with the fused features from CNNs, and finally used to recover a clean haze-free image via the atmospheric scattering model. The experimental results show that on the average of three datasets (SOTS, HSTS, and D-Hazy), proposed MMP-Net improves PSNR from 20.91db to 22.21db and SSIM from 0.8720 to 0.9023 over the best state-of-the-art DehazeNet method. What's more, MMP-Net gains the best subjective visual quality on real-world hazy images.