Reviewerly/exHarmony huggingface dataset:
Lightweight Crowd Counting Network based on Depthwise Separable Convolution

Abstract Crowd counting on the image is a challenging problem. Many neural network-based methods usually use two-branch and multi-branch networks to extract high-level features of different scales or densities, and then merge these features by a fusion operation. Although these methods can reduce the error of crowd counting, it makes the amount of parameters is enormous, so that the efficiency of training and optimization of the model is low, and the calculation resource consumption is high. To this end, a residual network based on depthwise separable convolution is proposed for image crowd counting. The network can not only reduce the amount of calculation through depthwise separable convolution, but also deepen the network depth through the residual structure to extract more effective high-level features. The experiment proves that, compared with the start-of-the-art methods, the method in this paper dramatically reduces the parameter amount to 1.91 Million when the accuracy is comparable.
Design of Low cost Data Acquisition Circuit with Feature Extraction

Abstract Single channel Electrooculogram (EOG) data acquisition system is designed to observe the patterns of signal to confirm the presence of disease or disorder. Tracking the movements of the eyes are done for controlling applications such as wheelchair control, blink detection, word speller, etc for people with complete or partial disability. With the help of signals generated from the eyes, the pattern is studied. This paper lay upon designing of highly precised instrumentation part of single channel EOG with miniaturized circuit and low cost. Different amplifiers are used to remove noise and increase the gain of the signal. Arduino UNO will read the EOG signal as analog input and the signal is recorded using PLX DAQ software which is interfaced with Arduino. The EOG signal is taken for 15 adults and it is tested for the working of the circuit. The signal is further amplified and filtered using MATLAB software and thus the eye movements are visualized. The set of basic mathematical feature are derived for the filtered signal which can be used for future applications for differentiating various eye problems.
Attention Enhanced Multi-patch Deformable Network for Image Deblurring

End-to-end methods based on deep learning have gradually shown their convenience and advantages in non-uniform motion deblur. However, current deep models for deblurring still have some problems, which ignore the non-uniform deformation of motion blur kernel or global information, causing inadequately deblurring or artifacts. In this paper, we propose an enhanced network based on the four-layer multi-path hierarchy structure which divides image into multi-patches instead of down-sampling or other lossy way. Our network is combined with the self-attention module, and replace part of ordinary convolutions with deformable convolutions in the encoder-decoder backbone. For fewer artifacts and more deep feature details, perceptual loss is also applied in the training process. Compared with the results of prior frameworks, our method has improved nearly 0.4dB PSNR on the quantitative performance on GoPro testing data set, and also achieved clearer visual effect on several scenes.
Kitchen Appliance Recommendation based on User Profile and Interest Evolution Network

As a category of many products, kitchen appliances have unique labels, such as kitchen, food processing, cooking, kitchen hygiene, etc. At the same time, when users browse and purchase products, kitchen appliances have their own evolutionary process. It is a very meaningful task for users to recommend kitchen products that they are interested in. This article uses user profile containing potential interests and the interest evolution network to simulate the process of user interest changes, and then infer the category of kitchen appliance that users may be interested in next time, and in this process, a module is designed to narrow the distance between the interest state and the real interest to make the extracted interest state closer to reality.
Performance Assessment of Bi-facial PV Module Considering Cloud &amp; Albedo

The bifacial photovoltaic (PV) module has gained attraction in recent days for different applications. The objective of this work is to assess the performance of the bifacial solar module compared to the conventional ones with and without considering the cloud effect in the context of Bangladesh. A bifacial solar module has a structure that also absorbs solar irradiation through the rear surface along with the front module. In this paper, the total cumulative incident energy on both of the surface is calculated. The result shows that without considering the cloud, the monthly total energy shows an increment of 15.4% over mono-facial module for a fixed albedo of 0.16. After considering the cloud, the bifacial module harnesses 10.5% more energy than a mono-facial one for the same albedo. This energy accumulation can be increased up to 80% of the front side using different rear surfaces.
Mobile Robot Navigation Based on Regionalized Spatial Knowledge Representation and Reasoning

A regionalized environmental knowledge model (REKModel) is presented to describe the environment in the paper. The REKModel is a hierarchical structure in which small regions are grouped together to form superordinate regions. The REKModel is intrinsically hierarchical iterative and nested. Thus an extended nested-graph(ENG) is proposed to construct REKModel. An biomimetic navigation system for mobile robots is presented that is inspired by the fine-to-coarse planning heuristic, a human wayfinding strategy. A online fine-to-coarse pathfinding algorithm designed here allows robots to derives the route with decreasing the level of detail along the route. By using spatial information at different levels of detail for close and coarseness for distance, the memory load and plan complexity are all reduced. The simulation on MobileSim platform verifies the effectiveness and feasibility of the navigation system.
Analysis of GDP based on polynomial regression and BP neural network

GDP(Gross Domestic Product) is the important index for measuring economic development. Quantitative analysis and prediction for GDP can regulate the economic development trend and promote steady economic development. In this article, we used Jiangsu province as an example. Firstly, we determined to 19 indexes which could explain GDP potentially by national economic accounting theory, and a quadratic polynomial regression model was used between the GDP and the principal factor. Secondly, in order to efficiently predict the GDP, we established a BP neural network model with excellent parameter taking 19 indexes as independent variable and GDP as dependent variable. Finally, we came to the conclusion about the GDP of Jiangsu province, and some suggestions for development were given.
IRLAB-DAIICT@DravidianLangTech-EACL2021: Neural Machine Translation

This paper describes our team’s submission of the EACL DravidianLangTech-2021’s shared task on Machine Translation of Dravidian languages.We submitted our translations for English to Malayalam , Tamil , Telugu and also Tamil-Telugu language pairs. The submissions mainly focus on having adequate amount of data backed up by good preprocessing of it to produce quality translations,which includes some custom made rules to remove unnecessary sentences. We conducted several experiments on these models by tweaking the architecture,Byte Pair Encoding (BPE) and other hyperparameters.
Comprehensive Analysis of Cloud based Databases

Abstract With standard change in the computing architecture and distributed, wide range processing approach leverage the large data to be analyzed and extract the content. The recent advancement in Web technology enables the user to produce process and ingest content of any structure. Cloud computing helps to provide computing infrastructure, centralized storage and seamless services for organizations and users. In a digital world, data is core aspect of the economy, become part of government, enterprise, social sectors and every individual in a day to day life. Data analysis is gathering of strength in both research and production communities. There are many social networking sites, web, enterprise and IoT data require being stored in secured, centralized data store. It also demands data to be stored without any predefined schema and able to retrieve in near real time. When demand goes up application needs to be scaled to sustain for the demand. Similarly, data store should support scalability to support applications demand. Cloud providers and third-party organization offers numerous data storage, database options. This article highlights various available data storage options, suitability and limitations.
Movability assessment on physiotherapy for shoulder periarthritis via fine-grained 3D ResNet deep learning

We present a novel approach to movability assessment on physiotherapy for shoulder periarthritis via fine-grained 3D Residual Networks (R3D) deep learning. The unique deep neural networks is able to automatically extract the spatiotemporal features from the RGB-D videos. In our preliminary studies, we have a set of VR sports games customized for the immersive and interactive sports environment, to regulate the patient’s rehabilitation exercises. In this way, acquisition of RGB-D action videos can be more specific to the subject and defined movements; and fine-grained feature discrimination of the same subject can be better achieved from the longitudinal study, to increase the accuracy of therapeutic assessment.
Virtual Reality Environment as a Developer of Working Competences

Having the ability to do something efficiently and successfully is essential to any worker of any discipline, but who’s to say that a worker has or has not developed competence, Only the instructor? The worker? Both? If for example we select a driving school, who’s to say that a student is competent to drive? If he is going to drive it, we better be sure that not only the instructor, but the student feels competent enough to drive, otherwise, an accident could happen. In this paper, we will present the self-analysis of a group of 30 students from 3 different disciplines (IT, Art design, and Industrial Engineering) on the development of collaborative multidisciplinary work, problem analysis, and problem-solving competence along with this, we will present the comparative of the professor’s evaluation of those same competencies. Both of them gave substantial evidence that high development levels have been achieved.
Research on target tracking algorithm based on information entropy feature selection and example weighting

Abstract An improved algorithm based on information entropy feature selection and sample weighting is proposed to solve the drift problem when the multi-instance learning (MIL) target tracking algorithm is updating classifier. Algorithm based on Boosting framework, adopt the method of MIL as sample selection, the positive and negative samples collected around the target area, to extract the image feature is compressed and weak classifier. Based on maximum entropy principle from a number of weak classifier to select several optimal classifier, the resulting strong classifier can be used to track the location of the next frame target. The results show that the algorithm is robust to target occlusion and fast motion.
Classical and Quantum Brain

The purpose of the research reported in this article is to establish a structural explanation of physical phenomena observed or experimented on natural neural networks and especially on the brain. A simplified model based on double stochasticity properties, concentrates on the synaptic structure of any neuron. The higher the energy spent for the neural network functioning, the greater the number of used synapses. The notion of consciousness field intensity, by an analogy with electrical intensity, leads to considering mental mechanisms associated with the classical model behavior. The Gaussian formalism appears naturally that shows some conditions for an activated neuron to produce a spike signal while a principle of saturation gives an argument for the optimization of the number of synapses involved in this production. As frequently, the optimum identified previously is the place of oscillations and an analytical approach builds a Hamiltonian, the quantization of which results in the quantum harmonic oscillator of a neuron. This quantized neural model resumes all the features and results associated with this oscillator and shows the high relevance of the term “oscillation” to describe the neuron behavior in spite of the counter-intuitive nature of the model. The explained phenomena span from the strong entanglement of information with the brain physical medium, more precisely with the neuron synaptic structure, to the brain waves observed on any electroencephalogram of any living [human] being. The strangeness of this research is that its initial foundation, the electrical analogy, is coherent from the classical to the quantum brain models and probabilities or wave functions bear interpretations in terms of action potential or electrical oscillations.
Indoor Scene Recognition with Depth Estimation over Monocular RGB-D Sequences

Object detection and recognition has been a field of extensive research over the past years and still continues to be explored. In our work, we have implemented indoor scene recognition with depth estimation of the nearest object. Our proposed work has been experimented over the input images and videos taken from our college lab. SSD Inception V2 model has been extensively trained to work for different objects from lab setup and then depth estimation is done on monocular RGB images using encoder-decoder architecture. Our proposed method uses DeepLabV3 for semantic segmentation. Our proposed work shows that simple depth estimation architecture outperforms current CNN methods with relative error of 12.6%, rms error of 46.4% and average log error of 5.4%. The mean average precision of object detection module is 37%.
Introduction to the special section on advances in parallel and distributed computing, applications and technologies (VSI-pdcat3)

Completely Automated Public Turing Tests to Tell Computer and Humans Apart (CAPTCHA) is an approach used to distinguish between a self-active computer program and a human. This kind of CAPTCHA is used in the field of computing to discover a particular user interacting with the system is human or not. It requires someone to enter the correct sequence of digits, characters, or both the combinations. The average time taken to solve the current captcha displayed will take around 10 s approximately. Research findings also discover additionally that the numerous famous CAPTCHA procedures are not powerful or secure, further entangling the test of offering services acquired from robotic intervention yet available to individuals with disabilities. This leads to the formulation of new approaches like QR Code Scanning-to-Speech, Revamping, and Randomness. This technique is encrypted end-to-end by using Blowfish Fermat Little Theorem (BFLT). There are a total of 156 physical users (both visually impaired and visually fit) were analyzed to assess the potency of the proposed approach. Further, it is compared with the current highest development CAPTCHA to test the performance of the QR Code Scanning-to-Speech, Revamping, and Randomness. The security scrutiny proved that the proposed approach is robust and hindering Hidden Markov Model (HMM), Fuzzy solver, and recent eminent attacks.
Analytics in the Field of Patent Documents

The article describes a research wherein patent documents were examined and analyzed. The results of it are collected statistics such as the most productive authors for a certain period, the patent holders with the greatest number of patents, the most used programming languages, the most overlapping annotations and patent claims. To visualize this, charts, graphs and tables were presented.In the course of the research, it was devised a script that compares annotations and patent claims, which makes it possible to automate the task of searching for an earlier published document, which is a prototype for a new invention or utility model, and comparing it with a new pending one.
An Empirical Evaluation of Word Embedding Models for Subjectivity Analysis Tasks

It is a clearly established fact that good categorization results are heavily dependent on representation techniques. Text representation is a necessity that must be fulfilled before working on any text analysis task since it creates a baseline which even advanced machine learning models fail to compensate. This paper aims to comprehensively analyze and quantitatively evaluate the various models to represent text in order to perform Subjectivity Analysis. We implement a diverse array of models on the Cornell Subjectivity Dataset. It is worth noting that the BERT Language Model gives much better results than any other model but is significantly computationally expensive than the other approaches. We obtained state-of-the-art results on the subjectivity task by fine-tuning the BERT Language Model. This can open up a lot of new avenues and potentially lead to a specialized model inspired by BERT dedicated to subjectivity analysis.
A study on Medical Device Security Status in Medical Convergence Industry

Beyond informatization, the development of new technologies and the introduction of innovative technologies in the Fourth Industrial Revolution are the evolution of various industries. In the early information age, the introduction of various information systems has made it possible to manage and utilize large amounts of data in high quality, thus expanding the business of other industries faster and more reliably. However, in certain industries, various devices and equipments are dependent on the existing information system, and thus, time and cost difficulties are introduced to introduce a new system. Accordingly, existing security problems rather than new security flaws are coming to reality. The purpose of this study is to investigate and analyze the security status of existing medical devices according to the arrival of convergence environment in the medical industry.
Achieving of Magnus Effect with Agros Suite

When the rotating body gets into the ambiance flow appears the lifting force, called Magnus Effect. That lifting force may be controlled by changing the revolution speed of the body. That phenomenon uses in many engineering applications like wind turbines and marine ships. In this paper, the Magnus Effect simulation is achieved with Agros Suite, a multiplatform application for the solution of physical problems. The article presents the nature of the Magnus Effect and discusses possible applications in engineering. The research question is focused on demonstrating the Magnus Effect with Agros Suite and evaluating the computational power of the personal computer that runs the simulation. The simulation is made keeping in mind the possible application of the Agros Suite tools for Magnus-Effect-based wind generator control algorithms optimization. The simulation result analysis shows that Agros Suite is a reliable tool in accessing and simulation of such phenomena.
NRV: Leveraging Secure Multi-Party Computation for Lightweight BGP Security Enhancement

The Border Gateway Protocol (BGP) is the de facto standard interdomain routing protocol. A major problem affecting the operation of BGP is its failure to provide security guarantees. Despite some high-profile security extensions proposed, none of them has been largely deployed by Autonomous Systems (AS) in the global Internet. Previous studies show that three main factors hinder the adoption of BGP security solutions: limited benefits in partial deployment, computational overheads, and the trouble of coordinating among tens of thousands of independent ASes. In this paper, we present Neighbor Routes Validator (NRV), a lightweight prototype system of BGP security enhancement. Instead of depending on a single centralized authority, NRV focuses on neighboring ASes' self-driven collaborations that significantly reduce the scale of coordination. It aims to address real-world security issues of BGP and uses the privacy-preserving capability of Secure Multi-Party Computation (SMPC) to dispel ASes' privacy concerns. Security analyses and simulations demonstrate the feasibility of NRV, and we also argue that network operators have incentives to deploy it after weighing the pros and cons.
Cloudsim for a Hierarchical Load Balancing Strategy

For a cloud datacenter, the biggest issue is how to handle millions of requests coming dynamically from the end users. To distribute requests efficiently and effectively, there is a need to balance the load evenly among the Cloud federations. In this paper, we propose a load balancing strategy and apply it on two levels control in a datacenter: physicals machines and clusters. The algorithm proposed is based on hierarchical strategy. Simulations on CloudSim are used to check its performance and its influence on standard deviation of load, communication cost and number of task migrations. Comparing with an algorithm, that does not include different levels in load balancing decision; results indicate that our method not only gives good Cloud balancing but also ensures optimizing Standard Deviation of load, resources utilization and communication cost by reducing number of migrations in all the datacenter.
HONto: A Bottom-Up Knowledge Base from Textbooks for Recommending Contextually Relevant Documents

This research presents a recommender system designed on the basis of a bottom-up knowledge base from textbooks. While other ontologies that are usually applied to such tasks are hand-crafted, our automated approach is a possible answer to the knowledge acquisition bottleneck. We extract concept hierarchies from section titles and use co-occurrences in book sections as evidence for possible contextual relationships between the therein mentioned entities. Motivated by a legal use case of recommending upcoming changes in law, the design is targeting three major challenges: different abstraction levels between entities of legal documents and the parliament protocols announcing norm changes, as well as engineering an explainable retrieval mechanism using the knowledge base which can additionally offer decent usability despite a high-recall requirement. Although the system is developed for a specific legal use case, there are many aspects of general applicability in the fields of recommender systems, information retrieval and information extraction, entity resolution, explainable artificial intelligence and usability. We validate selected parts of the system design also on other applications, such as educational media research.
An anomaly detection parameter optimization algorithm for data center data

The storage and calculation of the larger amount of data in the cloud data center brings more abnormal data. In order to accurately and efficiently find anomalies in the data in the cloud data center, this paper proposes an anomaly detection parameter optimization algorithm for data center data. Methods Fuzzy C-means clustering (FCM) algorithm is used for anomaly detection. At the same time, in order to overcome the problem that the fuzzy clustering algorithm is sensitive to the initial value and easy to fall into the local optimum, the adaptive bat algorithm is used to optimize the FCM algorithm. And add distributed entropy and average distance to the algorithm, adaptively adjust the optimization ability of traditional bat algorithm. Finally, experiments prove the detection accuracy of the algorithm.