Reviewerly/exHarmony huggingface dataset:
Integration of vis-NIR and pXRF spectroscopy for rapid measurement of soil lead concentrations

Heavy metals accumulate in soil over time and, with changing land use, humans may be exposed to elevated contaminant concentrations. It is therefore important to delineate contaminated sites in the most efficient and accurate manner. Sensors, such as portable X-ray fluorescence (pXRF) and visible near-infrared (vis-NIR) spectroscopy predict metal concentrations more rapidly and in a less hazardous manner compared to traditional laboratory analytical methods. The current study explored the potential for integrating vis-NIR and pXRF outputs to improve lead predictions in fine- (&amp;lt;62.5 µm) and whole-fraction (&amp;lt;2 mm) soil samples. A multi-stage approach was taken to compare different data treatments and combination methods for the prediction of whole-fraction lead content. Data treatment included principal component analysis, and combination methods included concatenation of pXRF and vis-NIR spectra before modelling, and Granger–Ramanathan model averaging of pXRF and vis-NIR model outputs. The most accurate predictions of whole-fraction lead were obtained by Granger–Ramanathan model averaging of vis-NIR Cubist predictions and Compton-normalised pXRF output: Lin’s Concordance Correlation Coefficient (LCCC) = 0.95, root mean square error (RMSE) = 86.4 mg kg–1, Bias &amp;lt; 0.001 mg kg–1 and ratio of performance to inter-quartile range (RPIQ) = 0.37. The most suitable modelling method was then used to predict fine-fraction lead, which provided a similarly accurate model fit (LCCC = 0.94, RMSE = 84.2 mg kg–1, Bias &amp;lt; 0.001 mg kg–1 and RPIQ = 0.34), indicating the potential to reduce the number of samples required for fine-fraction processing. In addition, the quality of the prediction interval estimates was examined – an important aspect in modelling which is underutilised in current literature related to soil spectroscopy.
ELPephants: A Fine-Grained Dataset for Elephant Re-Identification

Despite many possible applications, machine learning and computer vision approaches are very rarely utilized in biodiversity monitoring. One reason for this might be that automatic image analysis in biodiversity research often poses a unique set of challenges, some of which are not commonly found in many popular datasets. Thus, suitable image datasets are necessary for the development of appropriate algorithms tackling these challenges. In this paper we introduce the ELPephants dataset, a re-identification dataset, which contains 276 elephant individuals in 2078 images following a long-tailed distribution. It offers many different challenges, like fine-grained differences between the individuals, inferring a new view on the elephant from only one training side, aging effects on the animals and large differences in skin color. We also present a baseline approach, which is a system using a YOLO object detector, feature extraction of ImageNet features and discrimination using a support vector machine. This system achieves a top-1 accuracy of 56% and top-10 accuracy of 80% on the ELPephants dataset.
Distortion-Adaptive Salient Object Detection in 360$^\circ$ Omnidirectional Images

Image-based salient object detection (SOD) has been extensively explored in the past decades. However, SOD on 360° omnidirectional images is less studied owing to the lack of datasets with pixel-level annotations. Toward this end, this paper proposes a 360° image-based SOD dataset that contains 500 high-resolution equirectangular images. We collect the representative equirectangular images from five mainstream 360° video datasets and manually annotate all objects and regions over these images with precise masks with a free-viewpoint way. To the best of our knowledge, it is the first public available dataset for salient object detection on 360° scenes. By observing this dataset, we find that distortion from projection, large-scale complex scene and small salient objects are the most prominent characteristics. Inspired by the founding, this paper proposes a baseline model for SOD on equirectangular images. In the proposed approach, we construct a distortion-adaptive module to deal with the distortion caused by the equirectangular projection. In addition, a multi-scale contextual integration block is introduced to perceive and distinguish the rich scenes and objects in omnidirectional scenes. The whole network is organized in a progressively manner with deep supervision. Experimental results show the proposed baseline approach outperforms the top-performanced state-of-the-art methods on 360° SOD dataset. Moreover, benchmarking results of the proposed baseline approach and other methods on 360° SOD dataset show the proposed dataset is very challenging, which also validate the usefulness of the proposed dataset and approach to boost the development of SOD on 360° omnidirectional scenes.
A verifiable secret sharing scheme with combiner verification and cheater identification

In threshold secret sharing (SS) scheme, widely known as (k, n) threshold secret sharing, a secret S is divided into n shares and distributed to participants by a dealer in such a way that S can successfully be retrieved by a combiner from k or more shares collected from participants, but fewer than k shares will get no information about the secret. Verifiable secret sharing (VSS) scheme adds verifiable feature to secret sharing mechanism to check whether the retrieved information is the original secret or not. It helps to avoid mishap before using the secret. VSS may also helps to identify the participant submitting wrong information. Cheating detection and cheater identification are two well known terms in verifiable secret sharing. The former finds whether the right secret is retrieved and the later detects the participant(s), who has/have submitted wrong share. In the current work we have proposed a verifiable secret sharing scheme with cheater identification facility. The scheme also facilitates combiner verification by the participants to check whether share submission request is coming from a valid combiner or not. Security analysis of the proposed scheme have proved its immunity against different types of attacks.
Semi-supervised Learning for Sentiment Classification using Small Number of Labeled Data

Sentiment analysis is an essential task to gain insights over a huge amount of opinions and thoughts. Timeliness of data is important in making major decision. However, the manual data labeling method is slow and expensive, it also cannot cope with this enormous amount of data. We investigated the literature of sentiment analysis and discovered most of the works using manual data labeling. We propose semi-supervised learning as a method that helps to reducing the effort and time needed in data labeling as it uses a combination of small amount of labeled data and a large pool of unlabeled data for model training. In our work, we trained semi-supervised deep neural network with different settings and compared the model performances to a baseline, the supervised deep neural network trained with same number of labeled data. From the results, we can see that the unlabeled data is useful in improving the data performances. But it is not a guarantee, the unlabeled data must be handled with care otherwise degraded the model performances.
Feature extraction optimization of JPEG steganalysis based on residual images

JPEG steganalysis is a technique for detecting the existence of secret information hidden in JPEG images. At present, JPEG steganalysis based on residual images has been widely studied due to its low complexity and good performance. Due to the use of different quality factors in the JPEG image, the features need to be scaled appropriately according to this quality factor to obtain better detection performance. However, the current method of selecting the appropriate scale is usually based on empirical formulas or experiments. To solve this problem, we propose a novel adaptive scale adjustment algorithm for feature extraction filters. This algorithm can adaptively adjust the scale of the feature extraction filter according to the quality factor of JPEG images for better performance. In addition, we also propose a new weighted histogram method. Before calculating the histogram features of the residual image, we do not perform the rounding operation as usual, and different weights are assigned according to the distance between each input and histogram bins. The experimental results on BossBase set show that the proposed method can improve the performance of steganalysis.
Facial Landmark Detection via Attention-Adaptive Deep Network

Facial landmark detection is a key component of the face recognition pipeline as well as facial attribute analysis and face verification. Recently convolutional neural network-based face alignment methods have achieved significant improvement, but occlusion is still a major source of a hurdle to achieve good accuracy. In this paper, we introduce the attentioned distillation module in our previous work Occlusion-adaptive Deep Network (ODN) model, to improve performance. In this model, the occlusion probability of each position in high-level features are inferred by a distillation module. It can be learnt automatically in the process of estimating the relationship between facial appearance and facial shape. The occlusion probability serves as the adaptive weight on high-level features to reduce the impact of occlusion and obtain clean feature representation. Nevertheless, the clean feature representation cannot represent the holistic face due to the missing semantic features. To obtain exhaustive and complete feature representation, it is vital that we leverage a low-rank learning module to recover lost features. Considering that facial geometric characteristics are conducive to the low-rank module to recover lost features, the role of the geometry-aware module is, to excavate geometric relationships between different facial components. The role of attentioned distillation module is, to get rich feature representation and model occlusion. To improve feature representation, we used channel-wise attention and spatial attention. Experimental results show that our method performs better than existing methods.
Bitcoin Options Pricing Using LSTM-Based Prediction Model and Blockchain Statistics

Although Bitcoin and other cryptocurrencies continue to attract attention, the inaccurate pricing of Bitcoin options has resulted in an inefficient crypto derivative market. In this paper, we adopt a multiple input LSTM-based prediction model in conjunction with the Black-Scholes (BS) model to address this challenge in Bitcoin option pricing. We discuss the relationship between on/off-chain transactions and predict the implied price volatility of the next 30 days, which is the main factor in the BS model. First, we analyze the Blockchain statistics and social network trends of Bitcoin as inputs to our model, including the liveness of Blockchain wallet, the scale of active Blockchain nodes and the impact factor of Google, Reddit, Twitter to name a few. Next, we implement the LSTM-based prediction model and evaluate it in various historical window sizes and network parameters. Finally, we compare the performance with the baseline model without Blockchain statistics inputs. The experimental results show that the proposed multi-input LSTM-based prediction model provides the risk-informed pricing of the Bitcoin call options and our Blockchain statistics reduce the root-mean-square error (RMSE) by up to 46.2%.
Traceability in Permissioned Blockchain

In this paper, we propose the traceability of assets in a permissioned blockchain connected with a permissionless blockchain. We make traceability of assets in the permissioned blockchain be defined and be expressed as a hidden Markov model. There exists no dishonest increase and decrease of assets in this model. The condition is called balance. As we encrypt this model with fully homomorphic encryption and apply the zero knowledge proof of plaintext knowledge, we show that the trace-ability and balance of the permissioned blockchain are able to be proved in zero knowledge to the permissionless blockchain with concealing the asset allocation of the permissioned blockchain.
Speech Encryption Algorithm Based on Nonorthogonal Quantum State with Hyperchaotic Keystreams

With the advancement in modern computational technologies like cloud computing, there has been tremendous growth in the field of data processing and encryption technologies. In this contest there is an increasing demand for successful storage of the data in the encrypted domain to avoid the possibility of data breach in shared networks. In this paper, a novel approach for speech encryption algorithm based on quantum chaotic system is designed. In the proposed method, classical bits of the speech samples are initially encoded in nonorthogonal quantum state by the secret polarizing angle. In the quantum domain, encoded speech samples are subjected to bit-flip operation according to the Controlled–NOT gate followed by Hadamard transform. Complete superposition of the quantum state in both Hadamard and standard basis is achieved through Hadamard transform. Control bits for C-NOT gate as well as Hadamard gate are generated with a modified <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>L</mml:mi><mml:mover accent="true"><mml:mrow><mml:mi>u</mml:mi></mml:mrow><mml:mrow><mml:mo>˙</mml:mo></mml:mrow></mml:mover></mml:mrow></mml:math>-hyperchaotic system. Secret nonorthogonal rotation angles and initial conditions of the hyperchaotic system are the keys used to ensure the security of the proposed algorithm. The computational complexity of the proposed algorithm has been analysed both in quantum domain and classical domain. Numerical simulation carried out based on the above principle showed that the proposed speech encryption algorithm has wider keyspace, higher key sensitivity and robust against various differential and statistical cryptographic attacks.
Conversational Repair in Chatbots for Customer Service: The Effect of Expressing Uncertainty and Suggesting Alternatives

Due to the complexity of natural language, chatbots are prone to misinterpreting user requests. Such misinterpretations may lead the chatbot to provide answers that are not adequate responses to user request – so called false positives – potentially leading to conversational breakdown. A promising repair strategy in such cases is for the chatbot to express uncertainty and suggest likely alternatives in cases where prediction confidence falls below threshold. However, little is known about how such repair affects chatbot dialogues. We present findings from a study where a solution for expressing uncertainty and suggesting likely alternatives was implemented in a live chatbot for customer service. Chatbot dialogues (N = 700) were sampled at two points in time – immediately before and after implementation – and compared by conversational quality. Preliminary analyses suggest that introducing such a solution for conversational repair may substantially reduce the proportion of false positives in chatbot dialogues. At the same time, expressing uncertainty and suggesting likely alternatives does not seem to strongly affect the dialogue process and the likelihood of reaching a successful outcome. Based on the findings, we discuss theoretical and practical implications and suggest directions for future research.
Fast Reaction to Sudden Concept Drift in the Absence of Class Labels

A data stream can be considered as a sequence of examples that arrive continuously and are potentially unbounded, such as web page visits, sensor readings and call records. One of the serious and challenging problems that appears in a data stream is concept drift. This problem occurs when the relation between the input data and the target variable changes over time. Most existing works make an optimistic assumption that all incoming data are labelled and the class labels are available immediately. However, such an assumption is not always valid. Therefore, a lack of class labels aggravates the problem of concept drift detection. With this motivation, we propose a drift detector that reacts naturally to sudden drifts in the absence of class labels. In a novel way, the proposed detector reacts to concept drift in the absence of class labels, where the true label of an example is not necessary. Instead of monitoring the error estimates, the proposed detector monitors the diversity of a pair of classifiers, where the true label of an example is not necessary to determine whether components disagree. Using several datasets, an experimental evaluation and comparison is conducted against several existing detectors. The experiment results show that the proposed detector can detect drifts with less delay, runtime and memory usage.
Joint Decision of Anti-Spoofing and Automatic Speaker Verification by Multi-Task Learning With Contrastive Loss

Automatic speaker verification (ASV) is an emerging biometric verification technique with more and more applications. However, both verification accuracy and anti-spoofing should be considered carefully before putting ASV into practice, where anti-spoofing is also called replay detection in which voice is recorded, stored and replayed to deceive ASV systems. Cascaded decision of anti-spoofing and ASV is a straightforward solution to tackle the two issues. In this paper, joint decision of anti-spoofing and ASV was investigated in a multi-task learning framework with contrastive loss in order to improve the cascaded decision approach. A modified triplet loss was firstly constructed to supervise deep neural networks to extract embedding vectors containing information of both speaker identity and spoofing. The embedding vectors were subsequently taken as input features by back-end classifiers towards speaker and spoofing classification. The experimental results on both ASVspoof 2017 and ASVspoof 2019 showed that the proposed joint decision approach with triplet loss outperformed the corresponding baselines, a recent work on joint decision with Gaussian back-end fusion and our previous joint decision approach with cross-entropy loss.
Boosting dark channel dehazing via weighted local constant assumption

In dark channel based methods, local constant assumption is widely used to make the algorithms invertible. It inevitably introduces outliers since the assumption can not perfectly avoid depth discontinuities and meanwhile cover enough pixels. Unfortunately, because of the limitation of the dark channel prior, which only confirms the existence of dark things but does not specify their locations or likelihoods, no fidelity measurement is available in refinement. Therefore, the outliers are either under-corrected or over-corrected. In this paper, we go deeper than the dark channel theory to overcome this problem. We split the concept of dark channel into dark pixel and local constant assumption, and then, propose a novel weight map. With such effort, our method shows improvements on quality, robustness and speed. The theory is even simpler and more intuitive than the original, leading to the refreshingly concise algorithm. In the last, we show that the results can be ever-improved by scribbles, which indicate better dark pixel locations.
Integrated photonic quantum walks

Quantum walks (QWs) are one of the fundamental concepts in quantum information science, being an essential component for various new applications. Implementing QWs using integrated photonic waveguide lattices allows for realizing long evolution times, for exploring novel lattice geometries, and even for controllable coupling to the environment. This tutorial gives an introduction to the field of on-chip QWs of single photons and photon pairs featuring non-classical two-photon correlations.
Modulation of Body Representation Impacts on Efferent Autonomic Activity

Abstract The afferent branch of the autonomic nervous system contributes with interoception to the multimodal sensory correlation continuously needed to update our representation of the body. To test whether the modulation of body representation would have an impact on the efferent branch of the autonomic nervous system, nonspecific skin conductance has been measured in three rubber hand illusion (RHI) experiments, controlled with asynchronous brush-stroking and incongruent fake hand position. Nonspecific skin conductance standard deviation (SCSD) computed along the whole 90 sec of stroking was found to be increased by the illusion and to correlate with all the typical measures of embodiment. Computing SCSD in shorter time windows strongly enhanced the difference between illusion and controls. The highest difference was found in the 10–55 sec window, being the 14–34 sec window as the most informative one. The higher correlations with the validated measures of embodiment (all but the proprioceptive drift) were found for time windows ranging between 35 and 65 sec. The SCSD was no longer significantly higher when the RHI was repeated twice (two trials each iteration), but it was still significantly higher in synchronous stroking even when considering only the second trial. However, after the first iteration of the RHI paradigm, the effect of the embodiment on nonspecific skin conductance response results to be attenuated, suggesting that novelty in presentation of the RHI can contribute to the effect on nonspecific skin conductance response. Results candidate SCSD as a noninvasive, cheap, easy, and objective measure of embodiment, especially sensible to onset and strength of the illusion. Alike the already known enhanced autonomic reaction to a threatening, SCSD does not interfere with the collection of other behavioral measures. Correlations and their dynamics, presence of the effect in the second presentation of the setup but relative low robustness against multiple repetition, suggest that the increased fluctuations of skin conductance caught by SCSD are not just the effect of different presented sensory stimuli but more likely a stronger arousal response to the novelty of the updated perceptual status.
Detecting Hand Posture in Piano Playing Using Depth Data

Abstract We present research for automatic assessment of pianist hand posture that is intended to help beginning piano students improve their piano-playing technique during practice sessions. To automatically assess a student's hand posture, we propose a system that is able to recognize three categories of postures from a single depth map containing a pianist's hands during performance. This is achieved through a computer vision pipeline that uses machine learning on the depth maps for both hand segmentation and detection of hand posture. First, we segment the left and right hands from the scene captured in the depth map using per-pixel classification. To train the hand-segmentation models, we experiment with two feature descriptors, depth image features and depth context features, that describe the context of individual pixels' neighborhoods. After the hands have been segmented from the depth map, a posture-detection model classifies each hand as one of three possible posture categories: correct posture, low wrists, or flat hands. Two methods are tested for extracting descriptors from the segmented hands, histograms of oriented gradients and histograms of normal vectors. To account for variation in hand size and practice space, detection models are individually built for each student using support vector machines with the extracted descriptors. We validate this approach using a data set that was collected by recording four beginning piano students while performing standard practice exercises. The results presented in this article show the effectiveness of this approach, with depth context features and histograms of normal vectors performing the best.