Reviewerly/exHarmony huggingface dataset:
Tagging of Regional Websites using Fuzzy Logic

Meta tags of websites are a key factor for improving the search engine results. The problem with website tagging is that a lot of developers, especially regional website developers, tend to overlook this fact. This leads to a decrease in the number of hits of the webpage during search engine results. This paper proposes a model for recommending tags for a website in a regional language. Fuzzy logic is used to provide the results of tag recommendation on a variety of text data sets, with the help of different fuzzy logic operators. The novelty of this paper is that the tag set for a web page in a regional language (Hindi) is recommended. The text is first translated to English and the frequency of the words is represented using a fuzzy membership function, which forms the first feature. A second novel feature is the use of a list of common words for every particular category. A combination of these features is used to generate the output. The model gives an output between 0-1 for each tag denoting the relevance of the tag. This generates the list of top tags recommended for a website, from which the user can select tags according to the user's preference.
Active Learning and Deep Learning for the Cold-Start Problem in Recommendation System: A Comparative Study

Recommendation systems, which are employed to mitigate the information overload e-commerce users face, have succeeded in aiding customers during their online shopping experience. However, to be able to make accurate recommendations, these systems require information about the items for sale and about users’ individual preferences. Making recommendations to new customers, who have no prior data in the system, is therefore challenging. This scenario, called the “cold-start problem,” hinders the accuracy of recommendations made to a new user. In this paper, we introduce the popular users personalized predictions (PUPP-DA) framework to address cold starts. Soft clustering and active learning are used to accurately recommend items to new users in this framework. Additionally, we employ deep learning algorithms to improve the overall predictive accuracy. Experimental evaluation shows that the PUPP-DA framework results in high performance and accurate predictions. Further, focusing on frequent, or so-called popular, users during our active-learning stage clearly benefits the learning process.
Efficient Min-Cost Real Time Action Recognition using Pose Estimates

The following topics are dealt with: learning (artificial intelligence); feature extraction; diseases; neural nets; image classification; deep learning (artificial intelligence); convolutional neural nets; photovoltaic power systems; mobile robots; Internet.
A Two-Phase Object Detection Solution for Aerial Images

This paper proposes a two-phase solution for aerial inspecting. First phase focuses on removing images with no abnormal, modified YOLOv3 is used in this phase. Second phase focuses on target locating and identifying, modified SSD is applied in this phase. The experiment result shows the modified YOLOv3 get 2.2% higher accuracy than original design, and the miss rate of detecting images with no abnormal is only 2.6%.
Domain Generalization vs Data Augmentation: An Unbiased Perspective

In domain generalization the target domain is not known at training time. We show that a style transfer based data augmentation strategy can be implemented easily and outperforms the current state of the art domain generalization methods. Moreover, we observe that those methods, even if combined with the described data augmentation, do not take advantage of it, indicating the need of new generalization solutions.
Speaker Recognition for Device Controlling using MFCC and GMM Algorithm

Biometric technology is widely used to identify a smart home device controller with access control to the system. Sound Abstract is one of the biometric technologies used because human speech is different and unique. Generally, a smart home device controller based on sound can be controlled by everyone so that a speaker who should not have access rights to the system will still execute his voice command. The solution to this problem is a sound control system that can identify one speaker's voice with other speakers registered on the system to control smart home devices and reject commands from foreign speakers who are not registered on the system to secure a voice control system is formed. The Mel-Frequency Cepstrum Coefficient (MFCC) method, capable of capturing the characteristics of different human voices and is unique; the output of the MFCC is modeled and classified using GMM (Gaussian Mixture Model) on each cepstrum subject so that the modeling results can identify the voice of the speaker registered on the system listed or the voice of foreign speakers not registered with the system. The accuracy of the system built can identify the voice of the speaker registered on the system by 98.1% and reject the voice of the speaker who is not registered on the system by 91.6%.
Fisher Information Approximations in Policy Gradient Methods

Natural policy gradient (NPG)—the covariant gradient on the manifold of parameterized policies—is the foundation of state-of-the-art policy optimization algorithms. The update direction in NPG-based algorithms is found by preconditioning the usual gradient with the inverse of the Fisher information matrix (FIM). Estimation and approximation of the FIM and FIM-vector products (FVP) are therefore of crucial importance for enabling applications of the NPG-based algorithms to problems with high-dimensional policy parameterizations. This chapter provides an overview of well-established online and offline estimation methods as well as surveys more recent developments such as the expectation approximation technique based on the Kronecker-factored approximate curvature (KFAC) method and extensions thereof. The trade-offs introduced by the approximations in the context of policy gradient methods are discussed.
Privacy preserved secured outsourced cloud data access control scheme with efficient multi-authority attribute based signcryption

Privacy preserved outsourced data access control is a hard task under the control of thirdâparty storage server. To overcome obstacles in the third party based scenario, Attribute-based signcryption system with bilinear pairing tool is one of the most suitable methods in cloud. It maintains the basic features of security like, authenticity, confidentiality, public verifiability, owner privacy, etc. Although, this method has some challenges like a centralized authority used for user secret key generation for de-signcryption operation, and lack in competent attribute revocation. To overcome the issues, we have proposed a scheme of attribute revocable privacy preserved outsourced based data access control mechanism using Attribute-based signcryption. The proposed method allows multi-authorities for assigning both attribute and secret keys for users along with trusted certified authority, which provides security parameters. The analysis of the proposed method shows less computation cost in decryption and authentication verification. The almost same performance and efficiency is found while comparing with the existing schemes after adding new features.
Model structure on the universe of all types in interval type theory

Abstract Model categories constitute the major context for doing homotopy theory. More recently, homotopy type theory (HoTT) has been introduced as a context for doing syntactic homotopy theory. In this paper, we show that a slight generalization of HoTT, called interval type theory (⫿TT), allows to define a model structure on the universe of all types, which, through the model interpretation, corresponds to defining a model structure on the category of cubical sets. This work generalizes previous works of Gambino, Garner, and Lumsdaine from the universe of fibrant types to the universe of all types. Our definition of ⫿TT comes from the work of Orton and Pitts to define a syntactic approximation of the internal language of the category of cubical sets. In this paper, we extend the work of Orton and Pitts by introducing the notion of degenerate fibrancy, which allows to define a fibrant replacement, at the heart of the model structure on the universe of all types. All our definitions and propositions have been formalized using the Coq proof assistant.
A Multiscale Approach to Detect the Semantics of Locomotion without Positioning Information

The detection of locomotion patterns and trajectory semantics has long relied on the positioning information. However, positioning information can be unavailable in specific scenarios. In addition, the position values are sensitive to rotation and hence a bias can exist during the training process in deep learning models. This paper introduces an alternative model that classifies trajectories based on accelerometers, instead of positioning systems. We built up a convolutional neural network that inputs the degree of velocity and turning angles at several time scales and converts this information into a semantic class. We examined the model in a simulated environment and also a benchmark task. This model has exhibited a competitive performance even compared with those models based on positioning information.
Enhanced Lightweight Model for Detection of Phishing URL Using Machine Learning

Due to the increase in the use of the Internet and the advancement of technology, more people are browsing on the Internet. No need to mention that most people are not well aware of basic precautions, preventive measures that have to be taken while performing transactions over the net. This lack of knowledge among most people is well exploited by the phishers or attackers to acquire valuable private information from the user. Those security threats can cause user’s mobile or computer to be hacked by the remote person usually called a hacker by executing some malware in the computer or mobile when the user clicks on the links purposely sent to the user. The phisher or attacker or hackers use sophisticated technics to cheat the user or to make scam in the virtual world by creating a fake site which looks almost similar to the original one. Therefore, if proper attention is not provided while filing user credentials or clicking on unknown links, those the security of the user can be compromised, and its private information can be sent to the person who can take advantage of it. Phishing is new kind of an attack which is prevalent nowadays on the Internet to jeopardize privacy of the user and subsequently of an organization. Therefore, many works are going on to detect and identify the phishing sites, so user can be saved from entering his personal information and to prevent security attacks. Nut at the same time the way of designing phishing links is also evolving by improving content of the link in such way that the detection rate would below. This paper is presenting a detection system which gives emphasis on boosting algorithm. At the time, various other classifiers are used on same set of datasets to evaluate the performance of the Boosting algorithm. We implemented five classification algorithms and some unusual features from the URLs. The experimental results exhibit better accuracy and other parameters of the Boosting algorithm as compared to other traditional classifiers.
A Novel Method to Evaluate Irradiance in PV Field without Irradiance Sensors

In order to develop scientific research on the performance analysis and faults detection for monitoring status in photovoltaic (PV) array, an accurate and reliable irradiance evaluation are required. The mathematical expressions for irradiance evaluation at module level and array level are presented by the measured current and voltage of the PV module and PV array. The proposed method is verified with three typical weathers and a big dataset of one year. The results of module level show that the proposed method has performed good R2 errors of 0.9957 on a sunny day, 0.9978 on a cloudy day and 0.9898 on a day with low irradiance. The average percentage errors are 4.9%., 7.4% and 8.3% respectively. At the array level, the average error for one year is achieved by 6.51%., and 50% of average irradiance ranges are from 200 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> to 600 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> during the whole year. It is proved to be rarely affected by uncertainties from weather changes. The proposed method is suitable to calculate irradiance and obtain input energy of PV array in engineering application.
DefogNet: A Single-Image Dehazing Algorithm with Cyclic Structure and Cross-Layer Connections

Inspired by the application of CycleGAN networks to the image style conversion problem Zhu et al. (2017), this paper proposes an end-to-end network, DefogNet, for solving the single-image dehazing problem, treating the image dehazing problem as a style conversion problem from a fogged image to a nonfogged image, without the need to estimate a priori information from an atmospheric scattering model. DefogNet improves on CycleGAN by adding a cross-layer connection structure in the generator to enhance the network’s multiscale feature extraction capability. The loss function was redesigned to add detail perception loss and color perception loss to improve the quality of texture information recovery and produce better fog-free images. In this paper, the novel Defog-SN algorithm is presented. This algorithm adds a spectral normalization layer to the discriminator’s convolution layer to make the discriminant network conform to a 1-Lipschitz continuum and further improve the model’s stability. In this study, the experimental process is completed based on the O-HAZE, I-HAZE, and RESIDE datasets. The dehazing results show that the method outperforms traditional methods in terms of PSNR and SSIM on synthetic datasets and Avegrad and Entropy on naturalistic images.
Assess the Prevalence of Hypercalcemia among Immobilized Patients

Immobilization hypercalcemia is a diagnosis in which excessive bone metabolism abnormalities and conditions related to reduced movement such as medullar lesions or vascular cases.Investigation needs a more evaluation to find out other causes of hypercalcemia.Incidence rate of immobilization hypercalcemia is 11-22% in spinal cord injury group and 20-30% in immobilized patients secondaryto fractures in 2 case series .Usually itoccurs 4-6 weeks post trauma (1 week-16weeks)may stay elevated for up to 12 months based onmobilization.Aim-To determine the prevalence of hypercalcemia among immobilized patients.Materials and Methods-An analytical research approach and a cross-sectional research design were used.The study was conducted in Acharya Vinoba Bhave Rural Hospital; Sawangi Meghe Wardha.The samples were selected using purposive sampling technique, sample size was 30 and blood test done for screening after written informed consent.There were total 30 patients, blood samples which were carried out during studyperiod, it was concluded that out of 30 immobilized patients 14 patients is having hypercalcemia in immobilized state.The study found that there was a person suffering from hypercalcemia in their immobilized state.Hence this study helps in treatment of hypercalcemia during their immobilized state of the patients.From this study it is concluded that 46.67 % of the patients suffering from hypercalcemia.
Extracting Temporal Features by Key Points Transfer for Effective Action Recognition

The extraction of temporal features in video is an essential task for effective action recognition. Previous networks utilizes optical flow as effective temporal features, which utilizes positional relationship between pixels to extract temporal features. However, not all the pixels are meaningful in a video frame, with most pixels related to background information rather than the action itself. In this paper, we propose a novel temporal feature extraction model, Key points inter-Frame Transfer Module (KFTM), which extracts the temporal feature by extracting the transfer of multiple key points along the temporal axis. Such information can be equivalent to the temporal feature of the video since it also represents the positional relationship between pixels. Yet such method is more efficient due to the use of key points. Meanwhile, to extract the temporal feature more effectively, we add the attention mechanism which pays more attention to the transfer of key points most relevant to the action. Our proposed module obtains competitive performance on both UCF101 and HMDB51 datasets with 96.49% accuracy on UCF101 and 77.48% accuracy on HMDB51 datasets.
CPU vs GPU Performance of MATLAB Clustering Algorithms

Clustering is one of machine learning’s tasks when given objects must be split into specific groups based on distance between them. Its applications include different fields such as pattern matching, data compression and image analysis. Many programing languages allow to create clustering algorithms, though using already implemented ones is much easier. MATLAB includes a few of them. Knowing the performance of MATLAB’s cluster analysis algorithms may help choose the more optimal hardware for a given problem.
Human-Robot Teaming and Coordination in Day and Night Environments

As robots are sharing work spaces with human, human-robot teamwork is becoming increasingly important. It is foreseeable that the daily work team will be composed of human and robots. The integration of the appropriate decision-making process is an essential part to design and develop the team. If robots can understand the activities and intents of human, it is convenient for a person to cooperate with robots in a natural manner. This paper proposes a system that enables robots to understand human pose and execute given command. The system provides two options for different hardware systems: the first one is suitable for powerful computational units; the second model is compact and efficient on a normal robot platform. In order to enrich application scenarios, we propose a method to extract human pose from thermal images so that our system can be used in all-weather scenario. In addition, we collected extensive training data and trained a MLP neural network to classify several human poses. The experimental results show the accuracy and efficiency of the proposed MLP neural network in day and night environments.
Transfer Learning for Related Languages: Submissions to the WMT20 Similar Language Translation Task

In this paper, we describe IIT Delhi’s submissions to the WMT 2020 task on Similar Language Translation for four language directions: Hindi Marathi and Spanish Portuguese. We try out three different model settings for the translation task and select our primary and contrastive submissions on the basis of performance of these three models. For our best submissions, we fine-tune the mBART model on the parallel data provided for the task. The pre-training is done using self-supervised objectives on a large amount of monolingual data for many languages. Overall, our models are ranked in the top four of all systems for the submitted language pairs, with first rank in Spanish -> Portuguese.
To Study the Effect of Varying Camera Parameters in the Process of Matching and Reconstruction

In computer vision, there are many methods of reconstructing the image point. 3D reconstruction from digital image sequence of scene or object is a difficult and important task in computer vision. However, such a reconstruction requires a large computational effort for finding the point of correspondence between different views. Furthermore, the accuracy should not be reduced in case of noisy data. There is one important technique to digitization of physical object which is binocular stereo vision. From two subsequent digital images of the physical object taken from different viewpoints, we can make a 3D virtual model for the physical object by using this approach. Basically, the common processes for binocular stereo vision comprise digital image acquisition, camera’s calibration, feature point extractions, feature points matching and 3D reconstructions. In this paper, we have discussed the problem of varying camera parameter in the process of matching and reconstruction. we have the studied the problem and how it affects when the camera parameter varies in the process of matching; two types of parameter are as follows: intrinsic parameter and extrinsic parameter; image setup and some equation help to set the parameter, and a robust algorithm is proposed for reconstructing free-form space.
An Approach for Estimating the Opportunity Cost Using Temporal Association Rule Mining and Clustering

Timely identification of newly emerging trends is needed in business process. Data mining techniques are best suited for the classification, useful patterns extraction and predications which are very important for business support and decision making. Some research studies have also extended the usage of this concept in inventory management to determine opportunity cost based on association rules. Yet, not many research studies have considered the application of the data mining approach on evaluating penalty cost which is also a significant factor to the manager for optimal inventory control. In this paper, two different cases for evaluating penalty cost based on cross-selling effect are presented. An example is illustrated to validate the results.
Task–Driven Image–to–Image Translation for Automotive Applications

Image-to-image translation attempts at mapping an image from a given domain into a different domain. In this paper we introduce a novel image-to-image translation paradigm, where the translation itself is performed by a system of generative adversarial networks (GANs), guided by a set of pretrained networks, called task networks. The underlying hypothesis is that these task networks can guide the translation process of the GAN generator to focus on those aspects of the image which matter the most for the tasks in question. Moreover, our work is motivated by a real-life situation, created by the need to adapt existing, duly trained networks to work on images acquired with a different sensor than the one used to acquire the images used to train the networks. In order to optimize the image domain translation from the new sensor to the old one, we use the output loss of the trained neural networks to guide the training process of the image-to-image translation engine, thus forcing it to create images which improve the performances of the trained, guiding networks. Experimental results presented therein confirm the usefulness of our approach for an automotive application within the context of driver monitoring systems. More specifically we show that guiding the learning of the image-to-image translation module with the loss of a fixed neural network trained to estimate the driver eyelid opening, improves the performances of the latter when being fed with images output by the translation module.