Reviewerly/exHarmony huggingface dataset:
Semantically Contrastive Learning for Low-Light Image Enhancement

Low-light image enhancement (LLE) remains challenging due to the unfavorable prevailing low-contrast and weak-visibility problems of single RGB images. In this paper, we respond to the intriguing learning-related question -- if leveraging both accessible unpaired over/underexposed images and high-level semantic guidance, can improve the performance of cutting-edge LLE models? Here, we propose an effective semantically contrastive learning paradigm for LLE (namely SCL-LLE). Beyond the existing LLE wisdom, it casts the image enhancement task as multi-task joint learning, where LLE is converted into three constraints of contrastive learning, semantic brightness consistency, and feature preservation for simultaneously ensuring the exposure, texture, and color consistency. SCL-LLE allows the LLE model to learn from unpaired positives (normal-light)/negatives (over/underexposed), and enables it to interact with the scene semantics to regularize the image enhancement network, yet the interaction of high-level semantic knowledge and the low-level signal prior is seldom investigated in previous methods. Training on readily available open data, extensive experiments demonstrate that our method surpasses the state-of-the-arts LLE models over six independent cross-scenes datasets. Moreover, SCL-LLE's potential to benefit the downstream semantic segmentation under extremely dark conditions is discussed. Source Code: https://github.com/LingLIx/SCL-LLE.
Haze Risk Assessment Based on Improved PCA-MEE and ISPO-LightGBM Model

With the economic development in China, haze risks are frequent. It is important to study the urban haze risk assessment to manage the haze disaster. The haze risk assessment indexes of 11 cities in Fenwei Plain were selected from three aspects: the sensitivity of disaster-inducing environments, haze component hazards and the vulnerability of disaster-bearing bodies, combined with regional disaster system theory. The haze hazard risk levels of 11 cities in Fenwei Plain were evaluated using the matter-element extension (MEE) model, and the indicator weights were determined by improving the principal component analysis (PCA) method using the entropy weight method, and finally, five haze hazard risk assessment models were established by improving the particle swarm optimization (IPSO) light gradient boosting machine (LightGBM) algorithm. It is used to assess the risk of affected populations, transportation damage risk, crop damage area risk, direct economic loss risk and comprehensive disaster risk before a disaster event occurs. The experimental comparison shows that the haze risk index of Xi’an city is the highest, and the full index can improve the evaluation accuracy by 4–16% compared with only the causative factor index, which indicates that the proposed PCA-MEE-ISPO-LightGBM model evaluation results are more realistic and reliable.
Scalability of parametric down-conversion for generating higher-order Fock states

Spontaneous parametric down-conversion (SPDC) is the most widely-used method to generate higher-order Fock states ($n\geq 2$). Yet, a consistent performance analysis from fundamental principles is missing. Here we address this problem by introducing a framework for state fidelity and generation probability under the consideration of losses and multimode emission. With this analysis we show the fundamental limitations of this process as well as a trade-off between state fidelity and generation rate intrinsic to the probabilistic nature of the process. This identifies the parameter space for which SPDC is useful when generating higher-order Fock states for quantum applications. We experimentally investigate the strong pump regime of SPDC and demonstrate heralded Fock states up to $\lvert n\rangle =4$.
Efficient Pairing-Free Identity-Based ADS-B Authentication Scheme With Batch Verification

Automatic dependent surveillance-broadcast (ADS-B) is an emerging air traffic surveillance technology that overcomes the limitations of today's radar technology and enhances the air traffic control. To provide authenticity, integrity in ADS-B and to improve the computation, communications efficiency, in this paper, we propose a new, efficient, and secure pairing-free ADS-B authentication scheme with Batch Verification in ID-based framework. The proposed scheme is proven secure and is more efficient than the existing schemes.
A Tangled Web: Should Online Review Portals Display Fraudulent Reviews?

Consumers rely on review platforms when deciding where to stay, where to eat, what movies to watch, or even which doctor to use. This is great for consumers, but it has makes online review platforms a target for fraud. Review platforms have responded by developing tools and algorithms to identify potentially fraudulent reviews. But the question remains: What should platforms do with fraudulent reviews after detecting them? Our research answers this question using randomized experiments and large-scale data analysis from Yelp’s review platform. Our results show that after detecting fraudulent reviews, platforms should keep them on their platforms, but should display them with a flag that identifies them as potentially fraudulent. Doing so will increase consumers' trust in the platform by demonstrating that the platform takes fraud serious and will also penalize dishonest businesses. Together, these results provide strong managerial and policy guidance to developing truthful, transparent, and accountable online ecosystems. Our research topic is particularly timely given the presence of misinformation on technology platforms, the incentives of actors to exploit anonymity to manipulate consumer beliefs, and the influence these actions can have on consumer trust.
An Improved Brain Storming Optimization Algorithm for Estimating Parameters of Photovoltaic Models

Estimating parameters for various photovoltaic (PV) models is of great importance in simulating, evaluating, and controlling PV systems. To achieve the effective and accurate parameters of PV models, this paper presents an improved brain storming optimization (IBSO). In IBSO, a new individuals' generation scheme is developed to balance the global and local search capability in the entire iterative process. Furthermore, an improved individual clustering scheme is developed to decrease the computational cost of K-means in the original BSO. In addition, a self-adaptive individuals' update scheme is introduced to further balance the global and local exploration capability. The proposed IBSO is employed to identify the parameters of different PV models, i.e., single diode, double diode, and PV module. The experimental results show that the IBSO can generate an extremely promising performance compared with other state-of-the-art algorithms, especially in terms of accuracy and reliability.
Embodiment into a robot increases its acceptability

Abstract Recent studies have shown how embodiment induced by multisensory bodily interactions between individuals can positively change social attitudes (closeness, empathy, racial biases). Here we use a simple neuroscience-inspired procedure to beam our human subjects into one of two distinct robots and demonstrate how this can readily increase acceptability and social closeness to that robot. Participants wore a Head Mounted Display tracking their head movements and displaying the 3D visual scene taken from the eyes of a robot which was positioned in front of a mirror and piloted by the subjects’ head movements. As a result, participants saw themselves as a robot. When participant’ and robot’s head movements were correlated, participants felt that they were incorporated into the robot with a sense of agency. Critically, the robot they embodied was judged more likeable and socially closer. Remarkably, we found that the beaming experience with correlated head movements and corresponding sensation of embodiment and social proximity, was independent of robots’ humanoid’s appearance. These findings not only reveal the ease of body-swapping, via visual-motor synchrony, into robots that do not share any clear human resemblance, but they may also pave a new way to make our future robotic helpers socially acceptable.
SuperTML: Two-Dimensional Word Embedding for the Precognition on Structured Tabular Data

Tabular data is the most commonly used form of data in industry according to a Kaggle ML and DS Survey. Gradient Boosting Trees, Support Vector Machine, Random Forest, and Logistic Regression are typically used for classification tasks on tabular data. DNN models using categorical embeddings are also applied in this task, but all attempts thus far have used one-dimensional embeddings. The recent work of Super Characters method using two-dimensional word embeddings achieved state-of-the-art results in text classification tasks, showcasing the promise of this new approach. In this paper, we propose the SuperTML method, which borrows the idea of Super Characters method and two-dimensional embeddings to address the problem of classification on tabular data. For each input of tabular data, the features are first projected into two-dimensional embeddings like an image, and then this image is fed into fine-tuned two-dimensional CNN models for classification. The proposed SuperTML method handles the categorical data and missing values in tabular data automatically, without any need to pre-process into numerical values. Comparisons of model performance are conducted on one of the largest and most active competitions on the Kaggle platform, as well as on the top three most popular data sets in the UCI Machine Learning Repository. Experimental results have shown that the proposed SuperTML method have achieved state-of-the-art results on both large and small datasets.
Illusion of Causality in Visualized Data

Students who eat breakfast more frequently tend to have a higher grade point average. From this data, many people might confidently state that a before-school breakfast program would lead to higher grades. This is a reasoning error, because correlation does not necessarily indicate causation - X and Y can be correlated without one directly causing the other. While this error is pervasive, its prevalence might be amplified or mitigated by the way that the data is presented to a viewer. Across three crowdsourced experiments, we examined whether how simple data relations are presented would mitigate this reasoning error. The first experiment tested examples similar to the breakfast-GPA relation, varying in the plausibility of the causal link. We asked participants to rate their level of agreement that the relation was correlated, which they rated appropriately as high. However, participants also expressed high agreement with a causal interpretation of the data. Levels of support for the causal interpretation were not equally strong across visualization types: causality ratings were highest for text descriptions and bar graphs, but weaker for scatter plots. But is this effect driven by bar graphs aggregating data into two groups or by the visual encoding type? We isolated data aggregation versus visual encoding type and examined their individual effect on perceived causality. Overall, different visualization designs afford different cognitive reasoning affordances across the same data. High levels of data aggregation by graphs tend to be associated with higher perceived causality in data. Participants perceived line and dot visual encodings as more causal than bar encodings. Our results demonstrate how some visualization designs trigger stronger causal links while choosing others can help mitigate unwarranted perceptions of causality.
Question-type Driven Question Generation

Question generation is a challenging task which aims to ask a question based on an answer and relevant context.The existing works suffer from the mismatching between question type and answer, i.e. generating a question with type how while the answer is a personal name.We propose to automatically predict the question type based on the input answer and context.Then, the question type is fused into a seq2seq model to guide the question generation, so as to deal with the mismatching problem.We achieve significant improvement on the accuracy of question type prediction and finally obtain state-of-the-art results for question generation on both SQuAD and MARCO datasets.
Deep Sensing of Breathing Signal During Conversational Speech

In this paper, we show the first results on the estimation of breathing signal from conversational speech using deep learning algorithms.Respiratory diseases such as COPD, asthma, and respiratory infections are common in the elderly population and patients in health care monitoring and medical alert services in general.In this work, we compare algorithms for the estimation of a known respiratory target signal, measured by respiratory belt transducers positioned across the rib cage and abdomen, from conversational speech.We demonstrate the estimation of the respiratory signal from speech using convolutional and recurrent neural networks.The estimated breathing pattern gives respiratory rate, breathing capacity and thus might provide indications of the pathological condition of the speaker.Evaluation of our model on our database of breathing signal and speech yielded a sensitivity of 91.2 % for breath event detection and a mean absolute error of 1.01 breaths per minute for breathing rate estimation.
LiDAR Data Integrity Verification for Autonomous Vehicle

Deterministic perception of the surrounding environment is both crucial and a challenging task for autonomous vehicles. A wide range of sensors, including LiDAR, RADAR, cameras, and so on, are used to build the perception layer of an autonomous vehicle. Many interfaces, such as OBD-II, Wi-Fi, Bluetooth, cellular networks, etc., have been introduced in autonomous vehicles to control various functionalities, including V2X communications, over-the-air updates, security, remote vehicle-health monitoring, and so on. These interfaces are introducing new attack surfaces that can be exploited via external as well as internal attacks. Attackers have successfully demonstrated how to exploit these attack surfaces by crafting attack vectors to launch both insider and external attacks. The sensor and sensor data are also vulnerable to both external and insider attacks. Developing safeguards against these attacks is a steppingstone toward the design and development of reliable autonomous vehicles. For instance, failure to detect and localize sensor data tampering can result in an erroneous perception of the environment and lead to wrong path-planning and control decisions. In this paper, we propose a novel semi-fragile data hiding-based technique for real-time sensor data integrity verification and tamper detection and localization. Specifically, the proposed data hiding-based method relies on 3-dimensional quantization index modulation (QIM)-based data hiding to insert a binary watermark into the LiDAR data at the sensing layer, which is used for integrity verification and tamper detection and localization at the decision-making unit, e.g., the advanced driver assistance system (ADAS). The performance of the proposed scheme is evaluated on a benchmarking LiDAR dataset. The impact of information hiding on the object-recognition algorithm is also evaluated. Experimental results indicate that the proposed method can successfully detect and localize data tampering attacks, such as fake object insertion (FOI) and target object deletion (TOD). Robustness to noise-addition attacks is also evaluated.
Estimation of shortwave solar radiation using the artificial neural network from Himawari-8 satellite imagery over China

In this study, we use an artificial neural network (ANN) method to estimate the downward surface shortwave radiation (DSSR) over China from Himawari-8 geostationary satellite data. As the training data of the DSSR estimation algorithm, ground-observed DSSR (GOS) data is compiled to complete the ANN method. GOS data from 89 stations over mainland China in 2016 are divided into training, testing and validation samples with a proportion of 3:1:1, in order to perform the DSSR estimation and accuracy validation. As a result, estimated DSSR from Himawari-8 data in 2016 shows good consistency with validation samples of ground observed DSSR, holding the determination coefficient and root mean square error of 0.90 and 88.86 W m−2 for the hourly mean DSSR, and 0.96 and 24.46 W m−2 for the daily mean DSSR, respectively. To investigate the spatio-temporal variation of daily DSSR in China, we performed the first empirical orthogonal function (EOF) analysis based on the ANN-derived DSSR estimates. The spatial pattern of the first EOF mode reveals a larger DSSR variation in northeast and northwest China, in contrast to the smallest variation appeared in the southwest. The hourly ANN-derived DSSR analysis shows a peak point at noon (local time) in each region, particularly in the Tibetan Plateau. In terms of the monthly and annual DSSR spatial distribution, the regions with higher elevations, such as Northwest, Tibetan Plateau and Inner Mongolia Plateau in China, have larger DSSR than other regions. In contrast, relatively low DSSR appears in the southwest and the northeast of China through the year.
Data augmentation using back-translation for context-aware neural machine translation

A single sentence does not always convey information required to translate it into other languages; we sometimes need to add or specialize words that are omitted or ambiguous in the source languages (e.g., zero pronouns in translating Japanese to English or epicene pronouns in translating English to French).To translate such ambiguous sentences, we exploit contexts around the source sentence, and have so far explored context-aware neural machine translation (NMT).However, a large amount of parallel corpora is not easily available to train accurate context-aware NMT models.In this study, we first obtain large-scale pseudo parallel corpora by back-translating target-side monolingual corpora, and then investigate its impact on the translation performance of context-aware NMT models.We evaluate NMT models trained with small parallel corpora and the large-scale pseudo parallel corpora on IWSLT2017 English-Japanese and English-French datasets, and demonstrate the large impact of the data augmentation for context-aware NMT models in terms of BLEU score and specialized test sets on ja→en 1 and fr→en.
Adaptive Cuckoo Search based optimal bilateral filtering for denoising of satellite images

A satellite image transmitted from satellite to the ground station is corrupted by different kinds of noises such as impulse noise, speckle noise and Gaussian noise. The traditional methods of denoising can remove the noise components but cannot preserve the quality of the image and lead to over-blurring of the edges in the image. To overcome these drawbacks, this paper develops an optimized bilateral filter for image denoising and preserving the edges using different nature inspired optimization algorithms which can effectively denoise the image without blurring the edges in the image. Denoising the image using a bilateral filter requires the decision of the control parameters so that the noise is removed and the edge details are preserved. With the help of optimization algorithms such as Particle Swarm Optimization (PSO), Cuckoo Search (CS) and Adaptive Cuckoo Search (ACS), the control parameters in the bilateral filter are decided for optimal performance. It is observed that the proposed Adaptive Cuckoo Search based bilateral filter denoising gives better results in terms of Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Feature Similarity Index (FSIM), Entropy and CPU time in comparison to traditional methods such as Median filter and RGB spatial filter.
AFA: Adversarial fingerprinting authentication for deep neural networks

With the vigorous development of deep learning, sharing trained deep neural network (DNN) models has become a common trend in various fields. An urgent problem is to protect the intellectual property (IP) rights of the model owners and detect IP infringement. DNN watermarking technology, which embeds signature information into the protected model and tries to extract it from the plagiarism model, has been the main approach of IP verification. However, the existing DNN watermarking methods have to be robust to various removal attacks since their watermarks are single in form or limited in quantity. Meanwhile, the process of adding watermarks to the DNN models will affect their original prediction abilities. Moreover, if the model has been distributed before embedding the watermarks, its IP cannot be correctly recognized and protected. To this end, we propose AFA, a new DNN fingerprinting technology aiming at extracting the inherent features of the model itself instead of embedding fixed watermarks. The features we selected as model fingerprints are a set of specially-crafted adversarial examples called Adversarial-Marks, which can transfer much better to the models that are derived from the original model than to other irrelative models. We also design a new IP verification scheme to identify a remote model’s ownership. Experimental results show that our mechanism works well for common image classification models, and it can be easily adapted to other deep neural networks.
A Novel Image Encryption Approach Based on a Hyperchaotic System, Pixel-Level Filtering with Variable Kernels, and DNA-Level Diffusion

With the rapid growth of image transmission and storage, image security has become a hot topic in the community of information security. Image encryption is a direct way to ensure image security. This paper presents a novel approach that uses a hyperchaotic system, Pixel-level Filtering with kernels of variable shapes and parameters, and DNA-level Diffusion, so-called PFDD, for image encryption. The PFDD totally consists of four stages. First, a hyperchaotic system is applied to generating hyperchaotic sequences for the purpose of subsequent operations. Second, dynamic filtering is performed on pixels to change the pixel values. To increase the diversity of filtering, kernels with variable shapes and parameters determined by the hyperchaotic sequences are used. Third, a global bit-level scrambling is conducted to change the values and positions of pixels simultaneously. The bit stream is then encoded into DNA-level data. Finally, a novel DNA-level diffusion scheme is proposed to further change the image values. We tested the proposed PFDD with 15 publicly accessible images with different sizes, and the results demonstrate that the PFDD is capable of achieving state-of-the-art results in terms of the evaluation criteria, indicating that the PFDD is very effective for image encryption.
FasterSeg: Searching for Faster Real-time Semantic Segmentation

We present FasterSeg, an automatically designed semantic segmentation network with not only state-of-the-art performance but also faster speed than current methods. Utilizing neural architecture search (NAS), FasterSeg is discovered from a novel and broader search space integrating multi-resolution branches, that has been recently found to be vital in manually designed segmentation models. To better calibrate the balance between the goals of high accuracy and low latency, we propose a decoupled and fine-grained latency regularization, that effectively overcomes our observed phenomenons that the searched networks are prone to collapsing to low-latency yet poor-accuracy models. Moreover, we seamlessly extend FasterSeg to a new collaborative search (co-searching) framework, simultaneously searching for a teacher and a student network in the same single run. The teacher-student distillation further boosts the student model’s accuracy. Experiments on popular segmentation benchmarks demonstrate the competency of FasterSeg. For example, FasterSeg can run over 30% faster than the closest manually designed competitor on Cityscapes, while maintaining comparable accuracy.