Reviewerly/exHarmony huggingface dataset:
Weight Compression-Friendly Binarized Neural Network

The resources of edge devices in AIoT systems are usually constrained with size and power. The computational complexity of neural network models in these edge devices has become a major concern. The most compact form of deep neural networks is binarized neural network (BNN), which adopts binary weights and exclusive NOR (XNOR) operations as binary convolution. In this paper, we propose weight compression-friendly BNN to save hardware resources by reducing memory space. The proposed technique does not binarize weights just according to the signs of weights, but fully considers compression efficiency in the training of the BNN model. The experiments are performed by using the binary version of a 6-layer convolutional neural network (CNN) and MNIST case. The results show that the proposed technique can achieve more than 25% reduction in memory space with accuracy loss of 1 %, or more than 35% memory reduction with about 2.5% accuracy drop for MNIST classification. The weight compression method does not destroy the regular structure of neural networks, so the proposed technique is very fit for processor-based BNN hardware accelerators.
A Brief Review on Deep Learning in Application of Communication Signal Processing

This paper presents a brief review on deep learning in application of communication signal processing. As an important branch of artificial intelligence technology, deep learning schemes are independent of the underlying object model and can extract meaningful features from the observed data. When applied to communication signal processing, deep learning algorithms perform the following main functions: symbol detection, anti-interference and modeling the channel information. Furthermore, several challenges confronted by application of deep learning technology in communication signal processing are proposed as well.
Analytical radiative flux model via convolution integral and image plane mapping

Real-time radiative flux distribution simulation is necessary for a central receiver system in solar thermal power generation applications. In this paper, an improved analytical model (iCauchy model) and three intrinsic features of the analytical methods are proposed for more authentic simulation purpose. A convolution integral against the effective reflecting surface silhouette is conducted on the image plane and a closed form expression is deduced. Then this expression is projected to the receiver and final result is obtained. Experiments and comparisons show that this model can achieve more accurate and authentic simulations of the flux spots than the Cauchy model. The iCauchy model provides a new view of analytical models. As a result, it will contribute to fast monitoring and forecasting the solar irradiation distribution on the receiver, thus help resolve the optimization problems in solar thermal power research.
An efficient local stereo matching method based on an adaptive exponentially weighted moving average filter in SLIC space

Rapidly obtaining accurate dense disparity maps has been the focus of stereo matching research. At present, approaches that achieve superior disparity maps require a large amount of computation, which is not suitable for practical applications. To address this issue, this paper proposes an efficient local matching method based on an adaptive exponentially weighted moving average filter and simple linear iterative clustering segmentation algorithm. First, an effective matching cost is introduced to adaptively integrate absolute intensity difference with Census transform, which is robust against texture free and luminance variate. Following this, during the cost aggregation, the exponentially weighted moving average filter and the SLIC segmentation are combined to handle the problems of computing consumption and adaptive expansion of the cost aggregation window. Finally, the dense disparity map is obtained by a winner-takes-all approach and disparity refinement. To demonstrate its efficiency and validity, the method is quantitatively tested and compared to existing approaches on the Middlebury benchmark. The results show that it has a non-occlusion accuracy of 90.66% and an average runtime of 7.01 s on the 2014 Middlebury dataset. Compared with existing competitive methods, the proposed method achieves superior matching results with a lower time cost.
Exemplar-based Learning for Recognition &amp; Annotation of Human Actions

Human action recognition is an active research topic in computer vision. It is a challenging task to model various actions, varying with time resolution, visual appearance and others. For each action category, a large collection of similar actions is learned. This requires training a neural network with a large number of videos. Each action is described as a set of similarities between its instances and candidate exemplars. Then the most discriminative video is chosen. The experiment results on a publicly available dataset known as the KTH dataset. The project is expected to separate the human from the background in the video and identify what action is performed by him/her.
A New Anomaly Detector of Flare Pilot Based on Siamese Network

The flare pilot is the key to ensure that the flare system is ignited at any time. At present, domestic and foreign companies mainly use thermocouples, flame ionization detectors and other sensors to detect the working condition of the flare pilot. These electronic components have the hysteretic and vulnerable problems due to extreme heat, thermal shock and vibration, which may well further lead to the failure to ignite the flare gas with flare pilot in time and cause production accidents. In view of the above problems, this paper proposes an anomaly detector of flare pilot based on deep learning technology. First of all, we made an anomaly detection database for flare pilot, which was shot in a domestic petrochemical company. Then, we designed a specific siamese network based on the triplet loss function for learning the similarity between the samples, in order to apply to the curing scene of the flare pilot. Finally, it is determined whether the flare pilot is abnormal according to the similarity between the input picture and the positive and negative samples. Experimental results demonstrate that the proposed method can be effectively used for the anomaly detection of flare pilot.
Virtual and Augmented Reality: Enhancing the learning experience in higher education in the U.A.E. Current standing &amp; research directions

In addition to the established and widespread technological tools and trends of the past decades, such as the surge of mobile computing, high-speed networks and social media, new technologies utilizing the increased power and capabilities of modern computers is developing rapidly. Perhaps, no better example of this can be found than the rapid developments in Virtual and Augmented Reality (VR/AR), that have recently made the step from the laboratories and specialized, bespoke training applications of the past, to the mainstream. Advances in VR/AR have opened the floodgates to digital internships, virtual labs, and novel collaborative and experiential learning. The magnitude and impact of these emerging technologies is also evident on the significant interest in their application and use in various commercial, professional, and industrial contexts. Examples of these include, but are not limited to, the entertainment industries, specialized training, corporate demonstrations and conferencing, and prototyping and modelling in the technical and engineering areas. Since universities play a vital role in moulding tomorrow's talents, integrating such technologies can help them not only by supporting teaching and learning, but also by contributing to related research and enhancing the learning technology frameworks in their entirety. The aim of this research paper is to briefly discuss the position of these emerging technologies in the educational sector in general, and explore what their role, impact, and structure may be in the educational systems of the future with a focus on higher education in the U.A.E. Eventually, it will contribute by suggesting areas of interest for its future use in higher education in the U.A.E.
Online AUC Optimization for Sparse High-Dimensional Datasets

The Area Under the ROC Curve (AUC) is a widely used performance measure for imbalanced classification arising from many application domains where high-dimensional sparse data is abundant. In such cases, each d dimensional sample has only k non-zero features with k ≪ d, and data arrives sequentially in a streaming form. Current online AUC optimization algorithms have high per-iteration cost O(d) and usually produce non-sparse solutions in general, and hence are not suitable for handling the data challenge mentioned above. In this paper, we aim to directly optimize the AUC score for high-dimensional sparse datasets under online learning setting and propose a new algorithm, FTRL-AUC. Our proposed algorithm can process data in an online fashion with a much cheaper per-iteration cost O(k), making it amenable for high-dimensional sparse streaming data analysis. Our new algorithmic design critically depends on a novel reformulation of the U-statistics AUC objective function as the empirical saddle point reformulation, and the innovative introduction of the “lazy update” rule so that the per-iteration complexity is dramatically reduced from O(d) to O(k). Furthermore, FTRL-AUC can inherently capture sparsity more effectively by applying a generalized Follow-The-Regularized-Leader (FTRL) framework. Experiments on real-world datasets demonstrate that FTRL-AUC significantly improves both run time and model sparsity while achieving competitive AUC scores compared with the state-of-the-art methods. Comparison with the online learning method for logistic loss demonstrates that FTRL-AUC achieves higher AUC scores especially when datasets are imbalanced. Experiments on real-world datasets demonstrate that FTRL-AUC significantly improves both run time and model sparsity while achieving competitive AUC scores compared with the state-of-the-art methods. Comparison with the online learning method for logistic loss demonstrates that FTRL-AUC achieves higher AUC scores especially when datasets are imbalanced.
Optical constants and diffuse reflectance of opaque minerals: A modeling study using magnetite

We use radiative transfer (RT) models, based upon the Hapke (1993) model, to estimate the imaginary refractive index of magnetite from laboratory reflectance measurements. We used a RT program coupled with a least-squares algorithm to fit measured reflectance data using complex refractive indices of magnetite estimated here and literature values. We included differing representations of the grain size distribution for modeling the measured reflectance of the magnetite samples. Best-fitting models were obtained when using the complex indices of refraction estimated from a specific grain size fraction to fit the same grain size of reflectance data. Magnetite complex refractive indices taken from reported literature studies resulted in the poorest fits to the measured reflectance data. We investigated the multiple-scattering behavior of magnetite using Fresnel's equation and complex refractive indices from literature values and our own estimates. For both we found the reflection coefficient is <1% after four reflections suggesting that multiple scattering is minimal. We also calculated the transmission via the Beer-Lambert law using the same sets of refractive indices. For both, the initial interface transmission had a comparable value near 80%. However, as the distance through the material increases the discrepancy between the two refractive indices had substantial influence. For the literature values the transmission was reduced to <1% after a distance of 8 μm at all wavelengths, whereas for the estimated values the transmission remained ≥75% at this distance. Magnetite, when viewed in a petrographic thin section (~30 μm thick), is opaque. This suggests that the optical constants estimated via the Hapke approach are not realistic. We compared the calculated Fresnel reflectance using one literature value to the measured reflectances and found that the overall spectral shape was similar to the magnetite diffuse reflectance measurements. However, the magnetite diffuse reflectance is only 30–40% of the calculated Fresnel reflectance. We speculate this may be due to the granular surfaces scattering light into a non-specular angle. Hapke-like models have been successfully applied for estimating optical constants of transparent materials. However, the present study finds that such models may not be appropriate for determining the optical constants of low-reflectance, opaque materials, as the results are not comparable to values of optical constants reported in the literature.
Research on the method of improving community governance capability based on block-chain technology

In recent years, the rapid development of blockchain technology has gradually expanded from the financial field to the social field, bringing new opportunities and challenges for community governance innovation. In this paper, we try to find a way to use blockchain technology to solve some problems of community governance. So, a “private blockchain+consortium blockchain” concept is proposed for helping improve the capability of community governance, for the purpose of fulfilling the needs of community residents during the period of COVID-19 epidemic. This paper is concluded with the advantages and limitations of the application of blockchain technology in the community governance on epidemic prevention.
Design of robot system for radioactive source detection based on ROS

In order to reduce radiation hazards, a mobile robot for detecting and grasping radioactive sources is designed. The robot is based on the ROS platform, uses the radioactive source detector to locate the radioactive source and moves to the radioactive source, and uses the mechanical gripper carried by the body to grab the corresponding radioactive source. In order to improve its stability and adaptability, a remote real-time control method is used to realize the motion capture of the detection robot. The robot operates the radioactive source, reducing the environmental pollution of the open-air radioactive source, and at the same time avoiding the radiation damage to the corresponding staff.
Self-calibrated Attention Neural Network for Real-World Super Resolution

Single Image Super-Resolution in practical scenarios is quite challenging, because of more complex degradation than bicubic downsampling and diverse degradation differences among devices. To solve this problem, we develop a novel super resolution network with large receptive field called SCA-SR. The contributions mainly contain the following four points. First, we introduce self-calibrated convolutions to low-level vision task for the first time to significantly enlarge the receptive field of SR model. Second, Cutblur methods are used to improve the generalization of model. Third, long skip connection was used in model design to improve the convergence of deep model structure. Fourth, we use both self-ensemble and model-ensemble to improve the robustness of model and reduce the noise introduced by individual model. According to the preliminary results of AIM 2020 Real Image Super-Resolution Challenge, our solution ranks third in both $$\times $$ 2 and $$\times $$ 3 tracks.
Intelligent Travel Route Suggestion System Based on Pattern of Travel And Difficulties

Abstract People travel a lot to new places for career, business, education, travel, health and for various other reasons. Travellers always search to find shortest and safest route to reach the destination. They majorly consider distance, modes of transport and safety as major parameters. Various algorithms, mechanisms are proposed earlier to serve the purpose. However existing algorithms, mechanisms suffer from various shortcomings like unable to analyze the suggestions and filtering the database. The existing models use collaborative or content filtering algorithms. These methods do not collect the past user experience. The main objective of this paper is to help the travellers from their source to target from a database with best possible way. Proposed method provides the information from the travellers and problems faced by them during their travel. Based on the reported issues and experiences, new path will be enrooted to the destination. In the proposed model C4.5 decision tree algorithm is used to collect data from previous user experience, minimum redundancy maximum relevance (MRMR) and features selection algorithm is used to reduce execution time and increase system accuracy. The experimental outcome of this paper is to improve the system accuracy and performance.
Revealed preference in argumentation: Algorithms and applications

Argumentative agents in AI are inspired by how humans reason by exchange of arguments. Given the same set of arguments possibly attacking one another (Dung's AA framework) these agents are bound to accept the same subset of those arguments (aka extension) unless they reason by different argumentation semantics. However humans may not be so predictable, and in this paper we assume that this is because any real agent's reasoning is inevitably influenced by her own preferences over the arguments. Though such preferences are usually unobservable, their effects on the agent's reasoning cannot be washed out. Hence by reconstructing her reasoning process, we might uncover her hidden preferences, which then allow us to predict what else the agent must accept. Concretely we formalize and develop algorithms for such problems as uncovering the hidden argument preference relation of an agent from her expressed opinion, by which we mean a subset of arguments or attacks she accepted from a given AA framework; and uncovering the collective preferences of a group from a dataset of individual opinions. A major challenge we addressed in this endeavor is to deal with “answer sets” of argument preference relations which are generally exponential or even infinite. So we start by developing a compact representation for such answer sets called preference states. Preference revelation tasks are then structured as derivations of preference states from data, and reasoning prediction tasks are reduced to manipulations of derived preference states without enumerating the underlying (possibly infinite) answer sets. We also apply the presented results to two non-trivial problems: learning preferences over rules in structured argumentation with priorities – an open problem so far; and analyzing public polls in apparently deeper ways than existing social argumentation frameworks allow.
Two-Branch Convolutional Sparse Representation for Stereo Matching

Supervised learning methods have been used to calculate the stereo matching cost in a lot of literature. These methods need to learn parameters from public datasets with ground truth disparity maps. Due to the heavy workload used to label the ground truth disparities, the available training data are limited, making it difficult to apply these supervised learning methods to practical applications. The two-branch convolutional sparse representation (TCSR) model is proposed in the paper. It learns the convolutional filter bank from stereo image pairs in an unsupervised manner, which reduces the redundancy of the convolution kernels. Based on the TCSR model, an unsupervised stereo matching cost (USMC), which does not rely on the truth ground disparity maps, is designed. A feasible iterative algorithm for the TCSR model is also given and its convergence is proven. Experimental results on four popular data sets and one monocular video clip show that the USMC has higher accuracy and good generalization performance.
Effect Implementation of Information Technology Software On Improving Performance Capacity Academic and Non Academic Service Sunan Ampel Islamic University of Surabaya

Abstract This research was conducted to examine the effect of applying information technology software to increase the capacity of academic and non-academic performance at the Sunan Ampel State Islamic University in Surabaya as a work support tool. The aim is to apply a software that is able to identify the needs of increasing performance capacity at Sunan Ampel State Islamic University in Surabaya, both academic and non-academic, so that systematic control and evaluation of all academic and non-academic operations can be carried out. The research respondents were 60 students and employees from the Faculty of Science and Technology of the Sunan Ampel State Islamic University in Surabaya. This research uses a quantitative approach with the analysis technique used is path analysis with SmartPLS software-based data processing and the overall results support the hypothesis. Based on the results of the study stated that Information Technology has a positive and significant effect on performance, Information Technology has a positive and significant effect on academic services, Information Technology has a positive and significant effect on non-academic services, but performance has no effect on academic and non-academic services. The conclusion of the research is the influence of the application of information technology software can improve academic and non-academic performance and services at the Sunan Ampel State Islamic University of Surabaya
Modified Auto Regressive Technique for Univariate Time Series Prediction of Solar Irradiance

The integration of renewable resources has increased in power generation as a means to reduce the fossil fuel usage and mitigate its adverse effects on the environment. However, renewables like solar energy are stochastic in nature due to its high dependency on weather patterns. This uncertainty vastly diminishes the benefit of solar panel integration and increases the operating costs due to larger energy reserve requirement. To address this issue, a Modified Auto Regressive model, a Convolutional Neural Network and a Long Short Term Memory neural network that can accurately predict the solar irradiance are proposed. The proposed techniques are compared against each other by means of multiple error metrics of validation. The Modified Auto Regressive model has a mean absolute percentage error of 14.2%, 19.9% and 22.4% for 10 minute, 30 minute and 1 hour prediction horizons. Therefore, the Modified Auto Regressive model is proposed as the most robust method, assimilating the state of the art neural networks for the solar forecasting problem.