Reviewerly/exHarmony huggingface dataset:
Learning to Detect Head Movement in Unconstrained Remote Gaze Estimation in the Wild

Unconstrained remote gaze estimation remains challenging mostly due to its vulnerability to the large variability in head-pose. Prior solutions struggle to maintain reliable accuracy in unconstrained remote gaze tracking. Among them, appearance-based solutions demonstrate tremendous potential in improving gaze accuracy. However, existing works still suffer from head movement and are not robust enough to handle real-world scenarios. Especially most of them study gaze estimation under controlled scenarios where the collected datasets often cover limited ranges of both head-pose and gaze which introduces further bias. In this paper, we propose novel end-to-end appearance-based gaze estimation methods that could more robustly incorporate different levels of head-pose representations into gaze estimation. Our method could generalize to real-world scenarios with low image quality, different lightings and scenarios where direct head-pose information is not available. To better demonstrate the advantage of our methods, we further propose a new benchmark dataset with the most rich distribution of head-gaze combination reflecting real-world scenarios. Extensive evaluations on several public datasets and our own dataset demonstrate that our method consistently outperforms the state-of-the-art by a significant margin.
Blockchain Enabled Distributed Storage and Sharing of Personal Data Assets

Personal data are important information assets. Data sharing has potential of creating value to data owners as well as causing security and privacy concerns. Distributed storage solutions have emerged as an approach adhering to the Privacy-by-Design principles and in combination with blockchain technologies enable data and value exchange within communities of Internet users. The paper elaborates an approach for efficient distributed data storage and sharing, where access control is provided using the blockchain technologies and data searching and retrieval are facilitated using a knowledge base. A conceptual model and data management processes are elaborated and a prototype is developed. The prototype is used in experimental studies to compare data storage usage and data retrieval speed for the proposed approach and on-chain storage.
An Advanced Pruning Method in the Architecture of Extreme Learning Machines Using L1-Regularization and Bootstrapping

Extreme learning machines (ELMs) are efficient for classification, regression, and time series prediction, as well as being a clear solution to backpropagation structures to determine values in intermediate layers of the learning model. One of the problems that an ELM may face is due to a large number of neurons in the hidden layer, making the expert model a specific data set. With a large number of neurons in the hidden layer, overfitting is more likely and thus unnecessary information can deterioriate the performance of the neural network. To solve this problem, a pruning method is proposed, called Pruning ELM Using Bootstrapped Lasso BR-ELM, which is based on regularization and resampling techniques, to select the most representative neurons for the model response. This method is based on an ensembled variant of Lasso (achieved through bootstrap replications) and aims to shrink the output weight parameters of the neurons to 0 as many and as much as possible. According to a subset of candidate regressors having significant coefficient values (greater than 0), it is possible to select the best neurons in the hidden layer of the ELM. Finally, pattern classification tests and benchmark regression tests of complex real-world problems are performed by comparing the proposed approach to other pruning models for ELMs. It can be seen that statistically BR-ELM can outperform several related state-of-the-art methods in terms of classification accuracies and model errors (while performing equally to Pruning-ELM P-ELM), and this with a significantly reduced number of finally selected neurons.
Bot or not? User Perceptions of Player Substitution with Deep Player Behavior Models

Many online games suffer when players drop off due to lost connections or quitting prematurely, which leads to match terminations or game-play imbalances. While rule-based outcome evaluations or substitutions with bots are frequently used to mitigate such disruptions, these techniques are often perceived as unsatisfactory. Deep learning methods have successfully been used in deep player behavior modelling (DPBM) to produce non-player characters or bots which show more complex behavior patterns than those modelled using traditional AI techniques. Motivated by these findings, we present an investigation of the player-perceived awareness, believability and representativeness, when substituting disconnected players with DPBM agents in an online-multiplayer action game. Both quantitative and qualitative outcomes indicate that DPBM agent substitutes perform similarly to human players and that players were unable to detect substitutions. Notably, players were in fact able to detect substitution with agents driven by more traditional heuristics.
Experimental constraints on metal transport in fumarolic gases

Metals are efficiently transported in a gas phase and the gas composition strongly affects transport rates of these metals in the gas phase. Here we present a new approach to systematically investigate metal transport and deposition from volcanic gases of different compositions: We test the effect of sulfur and chlorine on the volatilization of 30 metal oxides and the phases deposited along a temperature gradient of 1250–300 °C in evacuated silica glass tubes in water-free systems. In total, we observe 30 different deposited phases, including oxides, sulfides and chlorides. The experimental results are compared with closed system and open system thermochemical equilibrium calculations. The closed system calculations provide insights into the metal speciation in the gas phase, and the open system calculations provide thermodynamic predictions of the temperature ranges over which metals deposited from the gas phase. Both approaches only partially reproduce the experimental observations of the deposited phases in the silica glass tubes. However, when we compare our experimental results with records from fumaroles on three different volcanoes (Kudriavy volcano, Russia, La Fossa crater on Vulcano, Italy, and Momotombo, Nicaragua), we find that our experiments agree very well with natural observations. We suggest that similar experimental investigations are powerful tools in the study of metal transport in ore forming systems involving gas phases, and results may be applied to various processes in terrestrial and planetary environments.
Color analysis and detection of Fe minerals in multi-mineral mixtures from acid-alteration environments

Iron electronic transitions produce intense colors in minerals with moderate to high Fe content, which phenomenon allows using color and visible spectroscopy to identify and quantify Fe-bearing minerals. This approach is especially useful for Mars investigation due to the limited analytical instruments available at Mars. The challenges are: (1) overlap between Fe electronic absorption bands of Fe-bearing minerals, (2) band wavelengths are modified by chemical and structural variables, and (3) particle size effects can modify mineral color. The magnitude of these difficulties, however, depends on the mineral mixture investigated. In this study, we further probe the potential of visible-range data for investigation of Fe-minerals. The samples were mineral mixtures from the Riotinto area (SW Spain) that resulted from acidic alteration and contained Fe in oxy-hydroxides, hydroxysulfates and phyllosilicates. Samples were studied using visible/near-infrared spectroscopy, X-ray diffraction and thermogravimetry. The wavelength range 320–1000 nm was investigated using spectral second derivatives and CIELAB color parameters. Hematite was readily detected, below X-ray diffraction detection limits, from the band at 520–560 nm. Jarosite was readily identified by the combined presence of bands at ~390 and ~ 440 nm. Thermogravimetric and CIELAB color analyses indicated goethite as the most common Fe-rich mineral in the samples. Correlations between CIELAB color parameters were controlled by goethite, where jarosite-rich specimens aligned with the correlations but some samples containing hematite plotted outside them. Quantification of goethite was attempted as calibrated against thermogravimetric data. Calibration was not possible below ~10 wt% goethite. Above this value, results from spectral second derivatives allowed quantification up to the maximum goethite content of ~35 wt% (linear correlation with R2 = 0.89). Goethite quantification with CIELAB color parameters was hindered by hematite. In samples with very low or no hematite, goethite quantification was possible with parameters L*10 (R2 = 0.83) and hab,10 (R2 = 0.65), also up to ~35 wt% goethite. Consequently, if accurate calibration is possible and depending on mineral mixture components, spectroscopic and CIELAB color parameters should allow goethite quantification in a wide concentration range.
A Fully 3D Cascaded Framework for Pancreas Segmentation

Convolutional Neural Networks (CNNs) have achieved remarkable results for many medical image segmentation tasks. However, segmenting small and polymorphous organs (e.g., pancreas) in 3D CT images is still highly challenging due to the complexity of such organs and the difficulties in 3D context information learning restricted by limited GPU memory. In this paper, we present a Fully 3D Cascaded Framework for pancreas segmentation in 3D CT images. We develop a 3D detection network (PancreasNet) to regress the locations of pancreas regions, and two different scales of a 3D segmentation network (SEVoxNet) to segment pancreas in a cascaded manner based on the detection results of PancreasNet. Experiments on the public NIH pancreas segmentation dataset show that we achieve 85.93% in the mean DSC and 75.38% in the mean JI, outperforming state-of-the-art results and with the fastest inference time ever reported (~200 times faster).
A weighted total variation based image denoising model using mean curvature

A number of variational models for image denoising have been proposed in the last few years in order to improve the denoising performance of the classical Rudin-Osher-Fatemi model. In this work, we propose an adaptive model which uses the mean curvature of an image surface to control the strength of smoothing. A fast method for noise level estimation is proposed to improve the effectiveness of the proposed model. We analytically study the convergence of the model. In addition, we also provide a solver based on split Bregman method for numerical realization. Numerical experiments demonstrate that the proposed model yields good performance compared with high-order variational models.
Sampling Rate Decay in Hindsight Experience Replay for Robot Control

Training agents via deep reinforcement learning with sparse rewards for robotic control tasks in vast state space are a big challenge, due to the rareness of successful experience. To solve this problem, recent breakthrough methods, the hindsight experience replay (HER) and aggressive rewards to counter bias in HER (ARCHER), use unsuccessful experiences and consider them as successful experiences achieving different goals, for example, hindsight experiences. According to these methods, hindsight experience is used at a fixed sampling rate during training. However, this usage of hindsight experience introduces bias, due to a distinct optimal policy, and does not allow the hindsight experience to take variable importance at different stages of training. In this article, we investigate the impact of a variable sampling rate, representing the variable rate of hindsight experience, on training performance and propose a sampling rate decay strategy that decreases the number of hindsight experiences as training proceeds. The proposed method is validated with three robotic control tasks included in the OpenAI Gym suite. The experimental results demonstrate that the proposed method achieves improved training performance and increased convergence speed over the HER and ARCHER with two of the three tasks and comparable training performance and convergence speed with the other one.
Love in Lyrics

Affectionate communication, the conveyance of closeness, care, and fondness for another, plays a key role in romantic relationships. While the pervasive use of digital technology for communication limits affectionate interaction through nonverbal cues -- a major channel of expression in face-to-face settings, there have been few approaches which scaffold couples' romantic text conversations. To bridge this gap, we propose a novel interactive system Lily which gives users inspirations to enrich their romantic expressions in text messaging. It first listens to users' original input and then recommends romantic lyrics holding the closest meaning in real-time during chats with partners. After a three-day empirical study, participants who are real-life couples reported that they not only received useful cues from Lily in terms of how to polish their affectionate expressions, but also learnt to enrich the conversation with topics enlightened by its recommendations. Based on our findings, we finally provide several design considerations for actual deployment of such an application.
MiniChain: A lightweight protocol to combat the UTXO growth in public blockchain

The current UTXO-based blockchains require the validators to keep the entire ever-growing UTXO set to verify transactions, which is unsuitable for ordinary machines since they occupy a large size of RAM in the long run, resulting in network centralizations. Recently, stateless blockchain technology has been proposed which uses the accumulator to combine the large UTXO set into one short, constant-sized commitment. However, the UTXO commitments in these methods are inefficient since the UXTO set is required dynamic addition and removal of elements as transactions are processed. In this work, we propose MiniChain, which replaces the UTXO set with two append-only data structures: STXO (Spent Transaction Outputs) set and TXO (Transaction Outputs) set. Thus, a valid UTXO must belong to the TXO set, but not in STXO set. Then, we construct a novel STXO commitment and TXO commitment by using a trapdoor-less RSA accumulator and a Merkle Mountain Range (MMR) respectively, greatly increasing the efficiency of accumulator. Besides, we introduce a cache mechanism, by storing the STXOs of latest N blocks, the transaction proof can be kept alive for a period of time, avoiding constantly recomputing proofs for unaccepted transactions. Our evaluation shows that (i) MiniChain only needs a fixed-size RAM and the disk usage grows very slow since only the block headers are stored; (ii) comparing to the state-of-the-art work, the performance of the accumulator update has been improved from O(n2) to O(n), enabling MiniChain to support a higher TPS.
Multi-Modality Generative Adversarial Networks with Tumor Consistency Loss for Brain MR Image Synthesis

Magnetic Resonance (MR) images of different modalities can provide complementary information for clinical diagnosis, but whole modalities are often costly to access. Most existing methods only focus on synthesizing missing images between two modalities, which limits their robustness and efficiency when multiple modalities are missing. To address this problem, we propose a multi-modality generative adversarial network (MGAN) to synthesize three high-quality MR modalities (FLAIR, T1 and T1ce) from one MR modality T2 simultaneously. The experimental results show that the quality of the synthesized images by our proposed methods is better than the one synthesized by the baseline model, pix2pix. Besides, for MR brain image synthesis, it is important to preserve the critical tumor information in the generated modalities, so we further introduce a multi-modality tumor consistency loss to MGAN, called TC-MGAN. We use the synthesized modalities by TC-MGAN to boost the tumor segmentation accuracy, and the results demonstrate its effectiveness.
Detecting breast cancer using artificial intelligence: Convolutional neural network

BACKGROUND: One of the most broadly founded approaches to envisage cancer treatment relies upon a pathologist’s efficiency to visually inspect the appearances of bio-markers on the invasive tumor tissue section. Lately, deep learning techniques have radically enriched the ability of computers to identify objects in images fostering the prospect for fully automated computer-aided diagnosis. Given the noticeable role of nuclear structure in cancer detection, AI’s pattern recognizing ability can expedite the diagnostic process. OBJECTIVE: In this study, we propose and implement an image classification technique to identify breast cancer. METHODS: We implement the convolutional neural network (CNN) on breast cancer image data set to identify invasive ductal carcinoma (IDC). RESULT: The proposed CNN model after data augmentation yielded 78.4% classification accuracy. 16% of IDC (-) were predicted incorrectly (false negative) whereas 25% of IDC (+) were predicted incorrectly (false positive). CONCLUSION: The results achieved by the proposed approach have shown that it is feasible to employ a convolutional neural network particularly for breast cancer classification tasks. However, a common problem in any artificial intelligence algorithm is its dependence on the data set. Therefore, the performance of the proposed model might not be generalized.
A note on the exclusion operator in multi-swarm PSO algorithms for dynamic environments

The exclusion operator is a key component in separating the search territory of each population in multi-population optimisation algorithms for unconstraint continues dynamic optimisation problems (DOPs) with the aim of maintaining the overall diversity of the population and avoiding redundant search. Although extensively used by the researchers, the role of exclusion has been barely studied in detail. Therefore, in this paper, we solely study the role of exclusion as a part of multi-population methods in DOPs. For this purpose, a comprehensive review of the various exclusion strategies reported in the literature is provided. Four strategies are also introduced to reduce the shortcomings of exclusion operator. Experimental results show that proposed strategies compared to other schemes such as reinitialized midpoint check, hill-valley detection with three checkpoints, and merging information of collided populations have the same or even higher ability to improve the performance of the multi-swarm PSO algorithms in moving peaks benchmark.
SPIDERS: Low-Cost Wireless Glasses for Continuous In-Situ Bio-Signal Acquisition and Emotion Recognition

We present a System for Processing In-situ Bio-signal Data for Emotion Recognition and Sensing (SPIDERS)- a low-cost, wireless, glasses-based platform for continuous in-situ monitoring of user's facial expressions (apparent emotions) and real emotions. We present algorithms to provide four core functions (eye shape and eyebrow movements, pupillometry, zygomaticus muscle movements, and head movements), using the bio-signals acquired from three non-contact sensors (IR camera, proximity sensor, IMU). SPIDERS distinguishes between different classes of apparent and real emotion states based on the aforementioned four bio-signals. We prototype advanced functionalities including facial expression detection and real emotion classification with a landmarks and optical flow based facial expression detector that leverages changes in a user's eyebrows and eye shapes to achieve up to 83.87% accuracy, as well as a pupillometry-based real emotion classifier with higher accuracy than other low-cost wearable platforms that use sensors requiring skin contact. SPIDERS costs less than $20 to assemble and can continuously run for up to 9 hours before recharging. We demonstrate that SPIDERS is a truly wireless and portable platform that has the capability to impact a wide range of applications, where knowledge of the user's emotional state is critical.
Geometry Structure Preserving Based GAN for Multi-Pose Face Frontalization and Recognition

Face frontalization is the process of converting a face image under arbitrary pose to an image with frontal pose. Benefited from significant improvement of generative adversarial networks (GAN), generative models can use face frontalization to overcome the problem of model degradation owing to the variation of head pose in face recognition. Existing GAN based models can generate a synthesis face image with the same identity as the input, while those models are hard to capture the geometry structure or facial patterns via pixel-wise constraint, e.g. face contour. In this paper, we propose a Geometry Structure Preserving based GAN, i.e. GSP-GAN, for multi-pose face frontalization and recognition. The generator of our model takes the form of a typical auto-encoder, where the encoder extracts identity feature and the decoder synthesizes the corresponding frontal face image. In this process, the perception loss constrains the generator to synthesize a face image with the same identity as the input image. Meanwhile, we adopt real frontal face images as extra input data during training process, where a L1 norm loss is utilized to construct a pixel-wise mapping from arbitrary pose image to frontal image. More importantly, for discriminator of our model, we use the self-attention block to preserve the geometry structure of a face. The discriminator consists of a series of parallel sub-discriminators that carry the global and local attention information. Compared with the state-of-the-art models on datasets of Multi-PIE, LFW and CFP, the proposed GSP-GAN can generate high-quality frontal images under arbitrary pose, and get satisfactory recognition performance.
Mapping Chemical Earth Program: Progress and challenge

There is a critical need to establish a global geochemical observation network to provide data for monitoring the chemical changes of the Earths near-surface environment. The International Centre on Global-scale Geochemistry, under auspices of UNESCO and Government of China, has initiated an International Scientific Cooperation Project called Mapping Chemical Earth. The project focuses on the establishment of Global Geochemical Observatory Network for documenting baselines and changes of nearly all natural chemical elements in the Earths surface and creating a digital Chemical Earth platform allowing anyone to access vast amounts of geochemical data through the Internet. A total area of about 37 million km2, nearly accounting for 27% of the global land, has been covered by global-/continental-scale sampling. Comparing the data of China, the US, Europe and Australia, the percentage of sites with toxic metals exceeding the risk limits of soil pollution according to “Environmental Quality Standard for Soil of China (GB 15618-1995)” to the total sample sites is 30.9%, 17.1%, 23.5% and 10.9% in Europe, China, USA, and Australia respectively. Comparing the China datasets of 15 years interval sampling between 1994, 1995 and in 2008–2012, toxic metals of As, Cd, Cr, Cu, Hg, Ni, Pb and Zn, particularly Cd at top soils significantly increase from 1990s to 2010s. The proportion of top soil samples exceeding the China Standard risk limit of 0.2 mg/kg Cd increases from 12.2% to 24.9%. The facts show that chemical changes of toxic metals induced by human activities can be well observed using catchment sediment sampling.