Reviewerly/exHarmony huggingface dataset:
Small Face Detection Based On Feature Fusion

Abstract While great progress has been made in face detection, one of the remaining challenges is to detect the faces of small targets in images. The small target face we define, mainly includes the absolute size of the face is not greater than 32×32 pixels and the relative proportion of the face size is not greater than one tenth of the image size. To meet this challenge, we propose an effective face detector, called SmallFaceBoxes, which has a better performance in both speed and accuracy. Specifically, our method has a lightweight but powerful network structure consisting of small target face detection layers and multi-scale convolution layers. The design of small target face detection layers enables detector to increase the representation ability of small and medium-sized faces in the image and improve the detection accuracy of small faces. The multi-scale convolution layers aim at enriching the receptive fields and discretizing anchors over different layers to handle faces of various scales. In addition, according to the size of receptive field, we propose a strategy of setting Anchor in multiple feature layers, thus improving the recall rate of small face. As a consequence, our algorithm performs well on the wideface dataset.
Quantized Separable Residual Network for Facial Expression Recognition on FPGA

Facial expression recognition plays an important role in human machine interaction, and thus becomes an important task in cognitive science and artificial intelligence. In vision fields, facial expression recognition aims to identify facial expressions through images or videos, but there is rare work towards real-world applications. In this work, we propose a hardware-friendly quantized separable residual network and developed a real-world facial expression recognition system on a field programming gate array. The proposed network is first trained on devices with graphical processing units, and then quantized to speed up inference. Finally, the quantized algorithm is deployed on a high-performance edge device - Ultra96-V2 field programming gate array board. The complete system involves capturing images, detecting faces, and recognizing expressions. We conduct exhaustive experiments for comparing the performance with various deep learning models and show superior results. The overall system has also demonstrated satisfactory performance on FPGA, and could be considered as an important milestone for facial expression recognition applications in the real world.
Unsupervised 3D Human Pose Estimation in Multi-view-multi-pose Video

3D human pose estimation from a single 2D video is an extremely difficult task because computing 3D geometry from 2D images is an ill-posed problem. Recent popular solutions adopt fully-supervised learning strategy, which requires to train a deep network on a large-scale ground truth dataset of 3D poses and 2D images. However, such a large-scale dataset with natural images does not exist, which limits the usability of existing methods. While building a complete 3D dataset is tedious and expensive, abundant 2D in-the-wild data is already publicly available. As a consequence, there is a growing interest in the computer vision community to design efficient techniques that use the unsupervised learning strategy, which does not require any ground truth 3D data. Such methods can be trained with only natural 2D images of humans. In this paper we propose an unsupervised method for estimating 3D human pose in videos. The standard approach for unsupervised learning is to use the Generative Adversarial Network (GAN) framework. To improve the performance of 3D human pose estimation in videos, we propose a new GAN network that enforces body consistency over frames in a video. We evaluate the efficiency of our proposed method on a public 3D human body dataset.
iNote: A Low Cost Banknote Recognition System for Visually Impaired Persons

Paper currency recognition is an important area of pattern recognition and plays a vital role in day to day life of visually impaired persons. It has various potential applications including electronic banking, currency monitoring systems, money exchange machines, etc. Although there are many potential solutions for paper currency recognition system, those are either logically inefficient or economically infeasible. This paper describes a low cost automated hardware system for currency recognition using color sensors and unsupervised neural networks designed for visually impaired people. This device specifically can be used where paper currency notes have different colors for each class of notes. As a low cost solution, we have used minimal hardware components required to make the system workable. This device reads inputs from banknotes using color sensors and sends data to a trained neural network to predict the category of the note. The experimental results show that the built system is highly accurate.
Multiobjective and Multistakeholder Recommender Systems

As an effective information extraction tool, recommender systems(RSs) can effectively provide users with content strategies from a large amount of data. The traditional RS can discover the unknown products of users and satisfy their tastes. However, the preferences of other RS participants should also be considered, such as the platforms. The platform's objective is different from that of users, and they want to maximize profits. In this paper, we adopt a multiobjective model MSMO for multistakeholders, in which customer relevance and profit of the platform are taken into consideration. By applying four evolution techniques, we are able to find Pareto front as optimal solutions. The solutions can keep the balance among multiple stakeholders. Experiments on a real-world data set reveal that our proposed model can significantly promote profit with little sacrifice in individual preference.
Automatic Weapon Detection in Video Using Deep Learning

In recent years, terror attacks have become more common in public places like schools, banks, religious places and transportation system throughout the world. Automatic video surveillance techniques at these places aid the security personnel for identifying threats. Convolutional neural network-based approaches have been very successful in image/video cataloging and object recognition. In this work, Alex-net is used to identify terror weapons from the video feeds and classify them to aid further investigation by security personnel.
Literature Survey on Exploratory Analysis of Waste in IT Industries During Project Development Using Agile–Lean Practices

In the last two decades, Implementing and maintaining the standard of IT projects is a critical task. IT projects have not successfully facilitated standard due to cost involved in project completion, required resources along with inefficient risk and stakeholder. Agile approach is a customer-oriented approach which enhances the efficiency of software development process. This paper focuses on need of agile–lean model which focuses on two major concepts that are eliminating waste and improving flow of productivity during software development and maintenance and to be acknowledged positively by the IT organizations during project development.
Main empirical regularities of the phosphorus geochemistry in the hypergenesis

Research subject. The study investigates the distribution of phosphorus (as percentages of P 2 O 5 and P) and 4 phosphorus modules introduced by the authors - phosphorus-titanium FTM (P 2 O 5 /TiO 2 ), phosphorus-calcium FCM (P 2 O 5 /CaO), phos-phorus-iron FIM (P2O5/Fe 2 O 3 ) and phosphorus-general-iron FGiM (P 2 O 5 /(Fe2O 3 + FeO)) - in the main types of sedimentary rocks, such as psammites, pelites, carbonates, silicites, in volcanic-sedimentary rocks (tuffoids), as well as in soils, sediments and such specific rocks as nodules and weathering crusts. Methods. On the basis of a large data bank (several tens of thousands of silicate analyses), new clarks of phosphorus were calculated. Clark estimates were based on average median contents for all the above-mentioned rock groups. For the first time, lithochemistry methods were successfully applied to characterize the composition of phosphorites. Correlation analysis was used to identify forms of phosphorus in rocks. Only the most reliable linear correlation coefficients of phosphorus and phosphorus modules with rock-forming components were considered significant - with a significance level no greater than 0.01 and 0.05. Results and conclusions . In some cases, new estimates of clarks differed markedly from the estimates of our predecessors. For a number of objects, clarks were determined for the first time. Interpretation of the revealed relationships allowed us to identify the following main correlations and corresponding forms of phosphorus in sedimentary rocks and their analogues (soils and sediments): positive correlation with CaO - phosphorus in the form of accessory apatite or francolite dominated; positive correlation with Fe 2 O 3 - phosphorus sorbed on iron hydroxides dominates; positive correlation with MgO - phosphorus dominated in the composition of smectite or chlorite, initially sorbed on the clay substance of precipitation; positive correlation with TiO 2 - phosphate sorbed on leucoxene, dominates. Thus, in addition to the previously known calcium and iron hydroxide, a titanium geochemical barrier for phosphorus was first identified.
A Video Steganography Scheme Based on Post-Quantum Cryptography

This paper presents a novel video steganography scheme based Post-Quantum Cryptography encryption. The Post-Quantum Cryptography has an extraordinary security character which is going to become the mainstream encryption algorithm for next generation. Meanwhile, the video encoding bit-stream has a few available information redundant that could hide extra messages with small alterations. The proposed scheme combined merits above, it aims at practical engineering applications like UAV video detection, emergence image communication, etc. We made an example experiment with consideration of the information security and the image adaptation. The sample results show that it has excellent visual invisibility and huge messages inserting capacity as expected. And it is an innovative exploration in steganography and cryptography as well.
Multi-stream Fusion in Image Sets Comparison

A unique problem is considered: how to automatically determine whether two images (pictures, drawings, or photos) show the same 3D object pictured in different views. Similarity or equality of the objects seen despite the spotting, size or angle of view is one of the biggest challenges. Thus, a unique proposition of solving this task is proposed. In this complex solution image sets are compared and following question is answered: are these sets similar in the sense that they present an object of the same type? The approach uses CNN-like deep neural networks for object region masking, specific feature extraction and proper type classification, and similarity measures for data matching. Experimental validation is performed on a public car image database.
Research and Implementation of New Distributed Information Interactive Platform

Abstract The new distributed information interaction platform is based on the information system that is already in existence or under construction (or even to be built), so that the design and construction of the system can better meet the needs of various information(data) interactive processing in the future. In order to deal with the personalization problems that exist in various information systems, it provides custom means for the customization of event (business flow, workflow, data flow, etc.) processes and realizes a process automatic execution mechanism that is completely transparent to the operator.
Information Leakage in Backtesting

Testing the performance of statistical models with historical time series requires a careful handling of the data. Even if a dataset is seemingly completely separated in an in-sample and an out-of-sample set information may be leaked. Such leakage can lead to a significant overestimation of the out-of-sample performance of a predictive model. We provide experimental evidence to illustrate how randomised data splits lead to overfitting in the presence of time series structure. The experiment is set up in the framework of option replication, with real-world and simulated data.
A Strong Designated Verifier Signature Scheme with Hybrid Cryptographic Hard Problems

The security of the most of the existing strong designated verifier signature (SDVS) scheme are based on a single cryptographic hard problem. Although these schemes seem secure today, there is a possibility for an adversary to find a solution for this problem in the future and if this happens, then such SDVS schemes will no longer be secure. We develop a new SDVS scheme based on hybrid problems; factorization and discrete logarithm. The new developed scheme provides a greater security level than those previous designated SDVS schemes and satisfies the security properties and requirements of a SDVS scheme.
Victims of Cybercrime: Understanding the Impact Through Accounts

The technological changes of the last 30 years have facilitated a substantial increase in cybercrimes. The impact of these crimes on victims has not been the subject of extensive research. This paper based upon a British Home Office funded study draws upon the experience of 52 victims of computer misuse crime, which can be broadly grouped under hacking and computer virus related crimes. Drawing upon the interviews with these victims the researchers identified a continuum of three components founded upon the seriousness of the incident and the impact on the victim. These three categories included: incidents of inconvenience, crimes of inconvenience and serious crimes of personal violation or significant financial loss or fear of. The paper provides depth accounts of 15 of the 52 victims interviewed to illustrate this continuum.
Secure Fast Evaluation of Iterative Methods: With an Application to Secure PageRank

Iterative methods are a standard technique in many areas of scientific computing. The key idea is that a function is applied repeatedly until the resulting sequence converges to the correct answer. When applying such methods in a secure computation methodology (for example using MPC, FHE, or SGX) one either needs to perform enough steps to ensure convergence irrespective of the input data, or one needs to perform a convergence test within the algorithm, and this itself leads to a leakage of data. Using the Banach Fixed Point theorem, and its extensions, we show that this data-leakage can be quantified. We then apply this to a secure (via MPC) implementation of the PageRank methodology. For PageRank we show that allowing this small amount of data-leakage produces a much more efficient secure implementation, and that for many underlying graphs this 'leakage' is already known to any attacker.
Human Motion Capture Based on Incremental Dimension Reduction and Projection Position Optimization

Three-dimensional (3D) human motion capture is a hot researching topic at present. The network becomes advanced nowadays, the appearance of 3D human motion is indispensable in the multimedia works, such as image, video, and game. 3D human motion plays an important role in the publication and expression of all kinds of medium. How to capture the 3D human motion is the key technology of multimedia product. Therefore, a new algorithm called incremental dimension reduction and projection position optimization (IDRPPO) is proposed in this paper. This algorithm can help to learn sparse 3D human motion samples and generate the new ones. Thus, it can provide the technique for making 3D character animation. By taking advantage of the Gaussian incremental dimension reduction model (GIDRM) and projection position optimization, the proposed algorithm can learn the existing samples and establish the relevant mapping between the low dimensional (LD) data and the high dimensional (HD) data. Finally, the missing frames of input 3D human motion and the other type of 3D human motion can be generated by the IDRPPO.
A comparative study of different deep learning models for mid-term solar power prediction

Solar energy is an abundant and renewable form of energy that is the answer to the increasing energy requirements of today's world. Since the prediction of solar radiation shows some uncertainties depending on atmospheric parameters, a significant method of solar power forecasting for small and big businesses is necessary. In this paper based on Deep Learning algorithms, we put forward the models which can be employed to predict solar PV energy output in the energy industry. In this study, a comparison of different deep learning techniques is proposed. On average, we find that the models LSTM (Long short-term memory) and GRU (Gated recurrent unit) perform well. RNN, LSTM and GRU have been widely used in solar power forecasting. And their hybrid models have also improved their performance. The results show a comparative study of the three aforementioned models.
Efficient Pipelined Circuits for Histogram-based Tone Mapping of Nonlinear CMOS Image Sensors

Tone mapping is extensively researched to address the issue of displaying high dynamic range (DR) scenes on low DR displays. Even though several tone-mapping operators (TMOs) exist, not all are designed for hard real time. The operator has to be capable of scaling up the spatial resolution without compromising the frame rate. The implementation of a TMO should also be simple enough to embed in low-cost platforms for imaging systems. A computationally efficient, and well accepted, class of TMOs are global ones based on histograms. This work presents a method to implement TMOs that use histograms. This method is suitable for low-cost field-programmable gate arrays (FPGAs), using simple components such as adders, multipliers, and random access memories, and is particularly suited for a nonlinear CMOS image sensor (CIS) operating continuously in hard real time. The authors develop a fixed-point design, validated in bit-true fashion using Xilinx and Altera tools, from a background algorithm implemented using Matlab. Our generic design uses pipelined circuits and operates with low latency. The use of a hardware description language to model our circuits guarantees portability and modularity. Moreover, the complete TMO is generated from design parameters and a design template. The architecture is robust and scales well from kilopixel to megapixel formats. The circuits achieve 30 frames per second, at high definition resolutions, while occupying only a small fraction of the available logic elements in a low-cost FPGA device.
The importance of constructivism in the teaching social and humanitarian disciplines

The relevance of the topic is dictated by the inconsistency of the contemporary education system. On the one hand, the pedagogical community defends traditional teaching methods. On the other hand, there is a need to implement new methods that meet the current generation’s interests and worldview. The purpose of the present study is to identify the possibility of using constructivist elements in pedagogical activity, which are elements of the subject-to-subject paradigm of education. The need to implement elements of constructivism in educational activities is determined by the peculiarities of contemporary social life, in which the main role in the formation of everyday reality is played by virtual digital reality and information technologies. The conducted analysis was based on the studies directly related to the field of education and pedagogy, as well as works on philosophy, sociology, and psychology that explore the problems of education in present-day developments. The analysis of the contemporary educational system and the concept of constructivism in education are presented based on the conducted work. Study materials can be used to analyze and solve debatable issues of contemporary education and pedagogy.
Quantum Machine Learning with HQC Architectures using non-Classically Simulable Feature Maps

Hybrid Quantum-Classical (HQC) Architectures are used in near-term NISQ Quantum Computers for solving Quantum Machine Learning problems. The quantum advantage comes into picture due to the exponential speedup offered over classical computing. One of the major challenges in implementing such algorithms is the choice of quantum embeddings and the use of a functionally correct quantum variational circuit. In this paper, we present an application of QSVM (Quantum Support Vector Machines) to predict if a person will require mental health treatment in the future the tech world using the dataset from OSMI Mental Health Tech Surveys. We achieve this with non-classically simulable feature maps and prove that NISQ HQC Architectures for Quantum Machine Learning can be used alternatively to create good performance models in near-term real-world applications.
SimGQ: Simultaneously Evaluating Iterative Graph Queries

Graph processing frameworks are typically designed to optimize the evaluation of a single graph query. However, in practice, we often need to respond to multiple graph queries, either from different users or from a single user performing a complex analytics task. Therefore in this paper we develop SimGQ, a system that optimizes simultaneous evaluation of a group of vertex queries that originate at different source vertices (e.g., multiple shortest path queries originating at different source vertices) and delivers substantial speedups over a conventional framework that evaluates and responds to queries one by one. The performance benefits are achieved via batching and sharing. Batching fully utilizes system resources to evaluate a batch of queries and amortizes runtime overheads incurred due to fetching vertices and edge lists, synchronizing threads, and maintaining computation frontiers. Sharing dynamically identifies shared queries that substantially represent subcomputations in the evaluation of different queries in a batch, evaluates the shared queries, and then uses their results to accelerate the evaluation of all queries in the batch. With four input power-law graphs and four graph algorithms SimGQ achieves speedups of up to 45.67 × with batch sizes of up to 512 queries over the baseline implementation that evaluates the queries one by one using the state of the art Ligra system. Moreover, both batching and sharing contribute substantially to the speedups.
Automatically Gather Address Specific Dwelling Images Using Google Street View

Exciting research is being conducted using Google's street view imagery. Researchers can have access to training data that allows CNN training for topics ranging from assessing neighborhood environments to estimating the age of a building. However, due to the uncontrolled nature of imagery available via Google's Street View API, data collection can be lengthy and tedious. In an effort to help researchers gather address specific dwelling images efficiently, we developed an innovative and novel way of automatically performing this task. It was accomplished by exploiting Google's publicly available platform with a combination of 3 separate network types and postprocessing techniques. Our uniquely developed NMS technique helped achieve 99.4%, valid, address specific, dwelling images.