Reviewerly/exHarmony huggingface dataset:
PeerRank: Robust Learning to Rank With Peer Loss Over Noisy Labels

User-generated data are extensively utilized in learning to rank as they are easy to collect and up-to-date. However, the data inevitably contain noisy labels attributed to users&#x2019; annotation mistakes, lack of domain knowledge, system failure, etc., making building a robust model challenging. On account of the remarkable nature of deep neural networks in fitting datasets, the noisy labels significantly degrade the performance of learning-to-rank algorithms. To cope with this problem, previous studies have put forward several methods for label de-noising. However, they are either susceptible to the noise distribution on datasets, raising the demand for clean data or incurring more computational costs. Moreover, most of them are tough to extend to different scenarios. This paper proposes a simple yet effective framework named PeerRank that can be applied in broad applications such as click-through rate prediction and commercial web search in learning-to-rank tasks. PeerRank is a robust, effective, and adaptable framework that can couple with numerous models with theoretical guarantees. Extensive experiments on three public real-world datasets with thirteen point-wise base models and four semi-synthetic generation datasets with four pair-wise base models show the consistent improvement of PeerRank. The results comparing PeerRank with seven classic and state-of-the-art de-noising methods validate the advantages of PeerRank framework for learning to rank over noisy labels.
Multi-sensor measurement fusion based on minimum mixture error entropy with non-Gaussian measurement noise

In this paper, a novel multi-sensor measurement fusion method is proposed based on minimum error entropy criterion for nonlinear state estimation in non-Gaussian measurement noise. Through cubature-quadrature transform, a mixture error entropy cost function is defined to capture the high order statistics of multi-source measurement error. Based on the centralized measurement model, a measurement fusion information filter is developed by minimizing the mixture error entropy cost. The fixed point iteration approach is employed to calculate the estimates, and the square root implementation of the proposed filter is also provided. Further, the convergence conditions of the proposed information filter are derived. Simulations are performed with different dimensional measurements to demonstrate the effectiveness of the proposed algorithm. It is shown that, the estimation performance of the proposed measurement fusion information filter is better than that of traditional Kalman and Huber's filter based fusion methods against outliers and Gaussian mixture noises.
Deep Learning based Real-Time Face Detection and Gender Classification using OpenCV and Inception v3

Automatic object identification and recognition of the objects are done through CCTV cameras. In this approach, the entire footage is stored which leads to more data storage requirements. Apparently, the cost for data storage is more. This paper proposes the technique in which CCTV cameras record the data only when the motion is detected in the footage. Therefore, the proposed algorithm reduces the cost of the data storage device and will be implemented in home, office, garden, and cabin etc. to Figure out the unusual activity. Face and Gender is the most basic information on human beings. It is of great significance in the field of face recognition. However, due to factors such as lighting, poses and expressions, gender classification has the problem of low accuracy. In this paper, Haar cascades are used to detect the human face from the real time image and the detected face is cropped and fed to the Inception V3 which performs gender classification. The IMDB datasets are used in this research work for training and testing. In addition, real-time face detection and gender classification were also performed. From the results, the accuracy for predicting the gender classification is 97.4 %.
Learning Discrete Representations From Reference Images for Large Scale Factor Image Super-Resolution

Image super-resolution (SR) task aims to recover high-resolution (HR) images from degraded low-resolution (LR) images, which has achieved great progress due to the recent advances of deep neural networks. Due to severe information loss of the LR images, it is more challenging to reconstruct high quality HR images at large scale factors, i. e., higher than 4× . Traditional reference image based SR methods usually perform patch matching to locate detailed texture from HR reference images which could provide fine details from similar image contents. But it suffers from difficulties in achieving good matching in the largely downscaled image space or feature space due to the ill-posed nature between LR and HR mapping. In this paper, we tackle this problem by exploiting fine details contained in reference HR images. Inspired by vector quantization (VQ), we propose a simple yet effective auto-encoder convolutional neural network (CNN) module to learn discrete representations of images. Furthermore, we propose to progressively learn pairs of cross-scale discrete feature representations using paired LR and HR reference images. The coarser scale of the discrete representation is responsible for encoding the global image structure while the paired finer scale of the discrete representation takes charge of capturing missing details in the finer image scale. During inference, continuous features of the test LR image are used as queries to retrieve finer scale discrete representations (value) by searching the nearest coarser scale discrete representations (key). Then, the queries and retrieved values are combined to progressively recover the HR image. Experimental results indicate that when compared with the state-of-the-art image SR models, the proposed method can achieve advanced performance in terms of both objective quality and subjective quality. The code will be available on URL: https://github.com/sunwj/refsr.
Using parallel pre-trained types of DCNN model to predict breast cancer with color normalization

Breast cancer is the most common among women, and it causes many deaths every year. Early diagnosis increases the chance of cure through treatment. The traditional manual diagnosis requires effort and time from pathological experts, as it needs a joint experience of a number of pathologists. Diagnostic mistakes can lead to catastrophic results and endanger the lives of patients. The presence of an expert system that is able to specify whether the examined tissue is healthy or not, thus improves the quality of diagnosis and saves the time of experts. In this paper, a model capable of classifying breast cancer anatomy by making use of a pre-trained DCNN has been proposed. To build this model, first of all the image should be color stained by using Vahadane algorithm, then the model which combines three pre-trained DCNN (Xception, NASNet and Inceptoin_Resnet_V2) should be built in parallel, then the three branches should be aggregated to take advantage of each other. The suggested model was tested under different values of threshold ratios and also compared with other models.The proposed model on the BreaKHis dataset achieved 98% accuracy, which is better than the accuracy of other models used in this field.
Data-Driven Teaching Model Design of College English Translation Using Intelligent Processing Technology

DDL (data-driven learning) advocates a deep integration of modern information technology and resources based on corpora and search engines with education and teaching, opening up a new path for college English translation instruction. The concept and characteristics of DDL theory are discussed in this paper. This system proposes an automatic scoring model of English translation based on mixed vector space, which comprehensively analyzes the target translation from two aspects: grammatical errors and semantic relevance, in order to address the shortcomings of current algorithms. The findings of this study show that using a combination of English fragmentation translation and adaptive resource push, learners can learn English reading using mobile terminals in fragmented learning situations, as well as receive targeted training for weak links in English reading in fragmented learning time and space, improving their learning efficiency and effect.
Quantum Sampling for Finite Key Rates in High Dimensional Quantum Cryptography

It has been shown recently that the framework of quantum sampling, as introduced by Bouman and Fehr, can lead to new entropic uncertainty relations highly applicable to finite-key cryptographic analyses. Here we revisit these so-called sampling-based entropic uncertainty relations, deriving newer, more powerful, relations and applying them to source-independent quantum random number generators and high-dimensional quantum key distribution protocols. Along the way, we prove several interesting results in the asymptotic case for our entropic uncertainty relations. These sampling-based approaches to entropic uncertainty, and their application to quantum cryptography, hold great potential for deriving proofs of security for quantum cryptographic systems, and the approaches we use here may be applicable to an even wider range of scenarios.
I-MMCCN: Improved MMCCN for RGB-T Crowd Counting of Drone Images

Crowd counting is a critical technique in many artificial intelligent applications, such as security monitoring and automatic transportation management. However, due to the variations in object scales, illumination and image quality, crowd counting from drone images is full of challenges. To fully delve the information hidden in the multi-modal RGB-T images shot by drones for crowd counting, we proposed a hard examples mining module and a novel Block Mean Absolute Error loss (BMAE) to improve Multi-Modal Crowd Counting Network (MMCCN). With the local structural supervision introduced by BMAE loss, the network can incorporate local spatial correlation within each block and focus on the local pattern of people. Besides, BMAE is more similar to the evaluation metrics. By combining our proposed hard example mining module and BMAE loss with MMCCN, we obtain our Improved MMCCN, named as I-MMCCN. Experiments on the DroneRGBT dataset verify the effectiveness of our I-MMCCN. It achieves 1.01 MAE and 1.48 RMSE lower than MMCCN on DroneRGBT validation set.
Self-Adaptive Feature Transformation Networks for Object Detection in low luminance Images

Despite the recent improvement of object detection techniques, many of them fail to detect objects in low-luminance images. The blurry and dimmed nature of low-luminance images results in the extraction of vague features and failure to detect objects. In addition, many existing object detection methods are based on models trained on both sufficient- and low-luminance images, which also negatively affect the feature extraction process and detection results. In this article, we propose a framework called Self-adaptive Feature Transformation Network (SFT-Net) to effectively detect objects in low-luminance conditions. The proposed SFT-Net consists of the following three modules: (1) feature transformation module, (2) self-adaptive module, and (3) object detection module. The purpose of the feature transformation module is to enhance the extracted feature through unsupervisely learning a feature domain projection procedure. The self-adaptive module is utilized as a probabilistic module producing appropriate features either from the transformed or the original features to further boost the performance and generalization ability of the proposed framework. Finally, the object detection module is designed to accurately detect objects in both low- and sufficient- luminance images by using the appropriate features produced by the self-adaptive module. The experimental results demonstrate that the proposed SFT-Net framework significantly outperforms the state-of-the-art object detection techniques, achieving an average precision (AP) of up to 6.35 and 11.89 higher on the sufficient- and low- luminance domain, respectively.
Leveraging Simulation and Virtual Reality for a Long Term Care Facility Service Robot During COVID-19

Providing care to seniors and adults with Developmental Disabilities (DD) presents challenges associated with care, companionship, medication intake, and fall monitoring among others. Currently, measures to prevent the spread of COVID-19 have seen restricted access to those living in long-term care facilities (LTCFs). While technologies such as robotics and virtual reality (VR) have seen advances in overcoming the aforementioned challenges, the restrictions have impacted research and development relying on human participants. Recently, the use of synthetic data for training motion detection algorithms and virtual worlds has been gaining momentum as an alternative continue for simulating robot and human interactions instead of relying on public databases and physical locations. Here, we propose the development of VR robot simulator for Aether™, a socially assistive mobile robot created to help seniors and people living with DD to achieve a higher degree of independence. For example, Aether™can assist caregivers by alleviating the burden of care by monitoring the LTCF for tripping hazards, open doors and cabinets. Our simulator allows configuring the virtual Aether™robot to navigate a virtual environment and detect upper limb gestures performed by a virtual avatar. Our preliminary results indicate that the virtual sensor has detection equivalent to the real sensor, thus ensuring that the simulated data is transferable for real-world testing.
Listen to Social Media Users: Mining Chinese Public Perception of Autonomous Vehicles after Crashes

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Geochemical studies of snow over mineral accumulations under conditions of their overlap by sedimentary rocks of the platform cover

In geochemical exploration, the seasonal snow cover is a convenient natural material for identifying under deep covered mineral accumulations. Snow has shielding and accumulating properties sufficient for the occurrence of dispersion of geochemical halos. The snow horizon of geochemical sampling is protected from atmospheric and landscape factors of replenishment of its chemical composition for most of the winter. The filtrate of snow melted water is cleaned of large-sized solid material, and chemical compounds are in the dissolved and finely dispersed solid phase of submicron dimension. Therefore, in terms of the physical and chemical properties of the analyzed substance, snow geochemical survey is close to selective methods. Test surveys were carried out on single profiles of snow sampling at two ore occurrences (Au, Pb-Zn) and an oil field within the West Siberian plate. Concentrations of a wide range of chemical elements in snowmelt water filtrate were determined by the ICP-MS method. As a result of statistical processing of geochemical data, a low variability of concentrations of chemical elements has been established. Correlation and regression analyzes demonstrate the presence of linear dependences in the distribution of trace elements, which reflect the ability of elements to form easily soluble and hardly soluble occurrence forms. The variability of the local geochemical background is mainly due to the content of hardly soluble phases that entered the snow layer during its deposition on the ground. Presumably, they are represented by particles of aeolian dust, needles and scales of tree bark captured by snow from tree and shrub crowns. In the area of study with high heterogeneity of elementary landscapes, the influence of this factor is comparable in amplitude to the contrast of superimposed geochemical halos. Normalization of the concentrations of trace elements by the sum of rare earth elements (REE) allows to strengthen the useful geochemical signal. Geochemical anomalies detected in snow in space quite accurately coincide with the position of mineral deposits buried the platform cover terrains, and also find confirmation in the chemical composition of soils and soil air. The good potential of snow metallometric surveying for prospecting of deep-seated mineral resources needs to be confirmed by studying the vertical and lateral migration of chemical elements in the snow cover.
A Framework for the Evaluation of the Cultural Heritage Information Ontology

The intelligent management of built cultural heritage, including heritage buildings, requires common semantics in the form of standardized ontologies to achieve semantic interoperability. Foundational ontologies should be reused when building new ontologies, as they provide high-level terms; however, candidate foundational ontologies should be evaluated for quality and pitfalls. Simple metrics (e.g., number of concepts) are easy to obtain with existing tools. Complex metrics such as quality of ontology structure, functional adequacy, transferability, reliability, compatibility, maintainability, and operability, are defined in recent ontology evaluation frameworks; however, these do not evaluate interoperability features. The paper proposes an improved framework for an automated ontology evaluation based on the OQuaRE framework. Our approach improved some of the metrics of the OQuaRE framework and introduced three metrics for assessing the interoperability of the ontology in question (Externes, Composability, and Aggregability). In the experimental section, the framework is validated in an evaluation of cultural heritage information ontology (CIDOC CRM—ISO 12217:2014) with the use of new software for ontology evaluation. The detailed results reveal that the ontology is minimally acceptable and that the improved evaluation framework efficiently integrated interoperability metrics. Recommendations for the improvement of the cultural heritage information ontology are described in the Discussion and Conclusions section.
Policy-Approximation Based Deep Reinforcement Learning Techniques: An Overview

Until recently, Deep Reinforcement Learning was restricted to innovations in games like Atari, Dota2. Despite surpassing the benchmarks established by their human counterparts in multiple games, these methods could not scale to real-life and industrial automation tasks. The main reason for this was the essential requirement of complex and continuous action control and sophisticated physics of the domain involved in these tasks. Because of these reasons, most of the incumbent solutions for such applications involved the invent of custom planning algorithms. The design of such sophisticated custom solutions required complete knowledge to the dynamics of the domain and its derivatives and hence were not scalable. Policy-based DRL has democratized this space, as now deep reinforcement learning agents could be trained to learn similar sophisticated policies just by learning from the data generated by interacting with these systems or their respective simulations. This has led to significant innovations in real-life and high-value control automation applications like autonomous vehicles, drones, and industrial robots. Therefore, in this paper, we present an overview of different types of policy-approximation based technique in Deep Reinforcement Learning that are the basis of many advanced control automation systems.
Depth Relevance and Hyperformalism

Formal symptoms of relevance usually concern the propositional variables shared between the antecedent and the consequent of provable conditionals. Among the most famous results about such symptoms are Belnap's early results showing that for sublogics of the strong relevant logic R, provable conditionals share a signed variable between antecedent and consequent. For logics weaker than R stronger variable sharing results are available. In 1984, Ross Brady gave one well-known example of such a result. As a corollary to the main result of the paper, we give a very simple proof of a related but strictly stronger result.
Comparison of Static and Time-Sequential Features in Automatic Fluency Detection of Spontaneous Speech

There have been lots of previous studies on disfluency detection. However, most of them focus on lexical cues, and little emphasis is placed on how diverse acoustic features contribute to improving the performance. We describe a framework for automatic fluency evaluation of spontaneous speech. We investigate not only lexical features extracted from transcription, but also consider time-sequential and static acoustic features from audio data, including energy and voicing-related features. This work tries to reveal how diverse acoustic features contribute to the performance of speech fluency evaluation. The proposed framework was evaluated with the Corpus of Spontaneous Japanese using LSTMs and DNN architectures. Evaluation results showed that when detecting fluent speech, combining four time-sequential acoustic features with static lexical features achieved the best performance. When detecting disfluent speech, on the other hand, static jitter/shimmer features helped to improve the precision at relatively high lower bounds.
A Super-resolution Method of Remote Sensing Image Using Transformers

This paper proposes a Transformers-based super-resolution method for remote sensing images. Firstly, a remote sensing image super-resolution network based on convolutional neural network and Transformer module is constructed; then the training data is used to train the remote sensing image super-resolution network and the optimized network parameters are obtained; finally, the trained remote sensing image super-resolution network is used to super-resolve low-resolution remote sensing images to obtain high-resolution remote sensing images. Experiments are conducted on the public remote sensing dataset (UC Mercedes) and compared with several traditional super-resolution algorithms. The results show that the present algorithm is highly automated and has improved in both accuracy and efficiency.
Supervised discrete hashing for hamming space retrieval

Recently, Hamming space retrieval, which realizes the foremost effective constant-time search, is incredibly prevalent in large-scale approximate neighbor retrieval since its high computational potency. However, notwithstanding the exciting progress, the accuracy of extant hashing strategies for Hamming space retrieval continues to be faraway from satisfactory. To handle this issue, this research proposes a novel discrete hashing method named Supervised Hamming Hashing (SHH). Specifically, supervision is carefully tailored to reduce the semantic classification loss, which incorporates label regression strategies and asymmetric similarity preserving scheme. Further, a unique hash code fusion strategy and a customized discrete optimization algorithm are designed for optimizing hash codes, thus heightening the potency and precision of the hash codes. A large number of experiments carried out on three benchmarks have corroborated the advantageous performance of SHH in Hamming space retrieval.