Reviewerly/exHarmony huggingface dataset:
Allowing for The Grounded Use of Temporal Difference Learning in Large Ranking Models via Substate Updates

We introduce a modification of an established reinforcement learning method to facilitate the widespread use of temporal difference learning for IR: interpolated substate temporal difference (ISSTD) learning. While reinforcement learning methods have shown success in document ranking, these contributions have relied on relatively antiquated policy gradient methods like REINFORCE. These methods bring associated issues like high variance gradient estimates and sample inefficiency, which presents significant obstacles when training deep neural retrieval models. Within the reinforcement learning community, there exists a substantial body of work on alternative methods of training which revolve around temporal difference updates, such as Q-learning, Actor-Critic, or SARSA, that resolve some of the issues seen in REINFORCE. However, temporal difference methods require the full size of the state to be modeled internally within the ranking model, which is unrealistic for deep full text retrieval or first stage retrieval. We therefore propose ISSTD, operating on the substate, or individual documents in the case of matching models, and interpolating the temporal difference updates to the rest of the state. We provide theoretical guarantees on convergence, enabling the drop in use of ISSTD for any algorithm that relies on temporal difference updates. Furthermore, empirical results demonstrate the robustness of this approach for deep neural models, outperforming the current policy gradient approach for training deep neural retrieval models.
Ultra Power Efficient Melior Quantum Multiplier with Reduced Ancilla and Garbage Outputs

Reversible Logic is an emerging field of research which finds its applications in low power computing, Nanotechnology and Quantum Computing. Reversible circuits should have one to one mapping i.e. one input can have only one output so that input vectors can be realized using output vectors. Reversible Circuits require Ancilla(constant inputs) and Garbage Outputs to retain reversibility. An efficient Reversible Circuit can be designed by optimizing their performance parameters. In this paper a \(4 \times 4\) Melior Quantum Multiplier has been proposed which consists of an optimized Partial Product Generation and Multi-Operand Addition using primitive Quantum gates to reduce the count of Ancilla and Garbage Outputs. This proposed multiplier shows an improvement of 21.73% and 18.18% reduction of Ancilla and Garbage Outputs respectively. This multiplier has been implemented in Cadence Virtuoso with average power dissipation of 106.79 nW at 45 nm technology node and used in the implementation of a Linear Phase FIR filter with an average power dissipation of 456.1 nW.
Improved Adaptive Grid Interactive Multiple Model Algorithm based on Maneuverability Division

The grid points of the Adaptive Grid Interactive Multiple Model (AGIMM) algorithm change slowly at the target high maneuvering and fluctuate when the target is moving at a constant speed. The paper proposes an improved AGIMM algorithm based on maneuverability of target. Firstly, divide the Maneuverability zones by Filter residuals. Secondly, according to different zones, the grid points are updated in different ways to speed up the convergence of the model when the target is high maneuvering, and reduce the fluctuation of the model when the target is low maneuvering. Realize the model update method according to the target maneuverability adaptive adjustment. The simulation shows that the improved algorithm significantly reduces the tracking error regardless of whether the target turns or not.
Toward Cooperation of Personal Health Devices and Smart Appliances in Japan Smart Homes

Smart home technologies are assisting elderly people to live independently and actively. Recently, the smart home healthcare market advances all over the world due to the increasing demand for connected health monitoring equipment. As a consequence, the interoperability problems of personal health standards/ecosystem and smart home protocols/ standards are rising. In the scope of this paper, a concept that supports the cooperation of the PCHA ecosystem and the ECHONET Lite protocol via a Data Distribution Platform is proposed and implemented.
WITHDRAWN: Thermodynamics and equilibrium evaluation of Cd (II)ion removal by adsorption onto Chitosan/Poly (Acryl amide -Acrylic Acid) Hydrogels

Due to the respective prominent benefits, researchers verified absorption as the most acceptable process of water treatment. hydrogel P(CH/AA-co-AM) is a plentiful and inexpensive material that has been employed substantially for many years as one of the adsorbents to eliminate the toxic heavy metals from the aqueous solution. hydrogel P(CH/AA-co-AM) effectively to eliminate diverse heavy metals from the aqueous solutions. Moreover, structural, surface and thermal features of the prepared materials were characterized using the technique: FT-IR, TEM, and FE-SEM. We performed the tests to Cd (II) uptake through hydrogel and effect of some experimental factors like temperature, initial Cd (II)ion concentration as well as the pH solution, also the absorption equilibrium has been characterized with the use of the Langmuir and Freundlich isotherm models. Results have shown immense potential, it was found that correlation coefficient value deal with of the Cd (II), while Langmuir equation was found not good correlation of Cd (II)ion. These data demonstrate that Hydrogels are efficient, inexpensive and environmental-friendly biomaterial to remove Cd (II) ion from the aqueous solution.
Tools and Techniques to Organize Distance Pedagogical Practice of Future Elementary School Teachers

The research objective is as follows: to develop pedagogical tools for organizing distance pedagogical practice of students in the fields of training 44.03.01 and 44.03.05 “Pedagogical Education”. The article describes results of a searching experiment, summarizes the authors’ experience of using open educational resources and web tools. Scientific originality of the study involves developing methodology of using open educational resources and web tools in students’ lesson planning process. The searching experiment results justify efficiency of the suggested methodology to organize distance pedagogical practice of students - future elementary school teachers. The problems arising during the process are revealed and possible solutions are suggested.
Analysis of high dynamic range and wide color gamut of UHDTV

With the development of TV technology, ultra high definition (UHD) industry is booming. UHD programme has gained great popularity. Compared with high definition television (HDTV) system, UHDTV system gets a boost in dynamic range and color gamut. However, improvements in brightness and color are mostly assessed subjectively. In view of above situation, this paper proposes UHD video dynamic range partition method and non-referenced assessment model of high dynamic range (HDR). Based on color gamut conversion algorithm, this paper also proposes wide color gamut (WCG) index and non-referenced assessment model of WCG. These models can give objective and precise evaluation of dynamic range and color gamut when analyzing PQ BT.2100 signal and SDR BT.2020 signal.
Noise processing and multi-task learning for far-field dialect classification

Deep learning has made great achievements in the field of speech recognition. With the popularization of embedded devices such as Intelligent speaker and the demand for dialect interaction scenes, it poses great challenges to far-field speech recognition and dialect language recognition. In order to solve the dialect language recognition of embedded devices in far-field speech recognition, we propose a deep learning neural network model with multi-task learning. First, we apply the AQPA(audio qualitative pre-analysis) method on the raw data of ten local Chinese dialects to reduce the influencing factors of steady-state and non-steady-state signals. Then we define dialect recognition as the main task and dialect area as the auxiliary task, using the multi-task learning method to improve the accuracy of dialect classification. The experimental results show that our approach improves accuracy with an average of 20% when compared with the single-task model without noise reduction.
A Novel Approach of Detecting Image Forgery Using GLCM and KNN

Photos have been utilized to report day-time occasions. They have frequently served as proof in audience chamber, in spite of the fact that picture takers are adequate to form composites of analogy images. This preparation is exceptionally pace expending and desires master information. Nowadays, in any case, effective computerized picture altering program makes picture adjustments direct. This undercuts our beliefs in photos and in specific investigation of images as prove for real universal vents. There are two sorts of methods for picture forensics: one is dynamic security, and the other is detached discovery. The most sorts of Picture imitation procedures are Picture Spicing; Copy-Move fraud is utilized mainly to examine the tempered photos. As the imitation of pictures proliferates day-by-day, it is indispensable to develop apparatuses for distingue between genuine and tempered images; therefore, we plan to analyze the copy-move imitation. This paper has proposed a combined method of feature collection using GLCM and training with KNN. Our method has given an 85.42% of accuracy level compared with other states of art papers in image forgery detection.
Tracking a Ground Moving Target with UAV Based on Interval Type-2 Fuzzy Logic

In recent years, tracking a ground moving target with an unmanned aerial vehicle (UAV) has played a key role in navigation, military reconnaissance, field rescue, and traffic monitoring tasks. However, due to the maneuvering uncertainty of the moving target and various uncertainties in the complex environment, it has brought many difficulties to the tracking task. In this paper, aiming at addressing the various uncertainties in the process of target tracking with UAV, we present a control strategy based on interval type-2 fuzzy logic. We design two interval type-2 fuzzy controllers to control the yaw angle and speed of the UAV, respectively. Images obtained from an embedded camera on the UAV platform are visually processed by the target recognition and tracking algorithms in the early stage to detect the pixel position of the target in every frame, which is used as the input of the controller we have designed. Through the analysis of the input and output data of controllers designed by us and the analysis of the tracking error, the reliability of control strategy proposed by us is proved. At last, a series of simulation experiments are implemented in ROS to demonstrate the performance of the target-tracking strategy we proposed.
Reproduction of "evaluating adversarial evasion attacks in the context of wireless communications" and "convolutional radio modulation recognition networks"

In this work, we reproduce several findings from references [1] and [2] which possess the same title. The authors of [2] trained a Convolutional Neural Network (CNN) to classify In-phase/Quadriture (IQ) signals to one of 11 possible modulation classes, while the authors of [1] performed a detailed analysis of the trade-off between disrupting an eavesdropper using the CNN from [2] and maintaining a low Bit Error Rate (BER) in a wireless transmit-receive chain.
Investigation and Analysis of Features in Decentralized Network Management of Minor Cryptocurrencies

Blockchain is a trustless interoperability platform for users and is managed in a decentralized structure. There are no controllers in this platform, and service continuity depends on the users present in the network. Surveys of such network have mostly focused on miners who are authorized to write to the ledger; however, the non-mining nodes that maintain the ledger and are operated by volunteers with no direct incentive are also important indicators of stability. Although there are several projects that are inspired by or forked from Bitcoin, it is not intuitive to assume that projects not pertaining to Bitcoin are running in the same manner. Compared with major currencies, networks running minor currency nodes, despite having the same infrastructure, are more challenging to focus owing to their unclear operational realities. Thus, learning from the characteristics of failed projects is as important as learning from successful ones. Therefore, in this study, we investigate the unique features in the network by analyzing blockchain-based minor currencies. We analyze 317 minor cryptocurrency nodes and report that they contain locality, we also report on the issues of blacklisted nodes.
Deep Regularized Discriminative Network

Abstract Traditional linear discriminant analysis (LDA) approach discards the eigenvalues which are very small or equivalent to zero, but quite often eigenvectors corresponding to zero eigenvalues are the important dimensions for discriminant analysis. We propose an objective function which would utilize both the principal as well as nullspace eigenvalues and simultaneously inherit the class separability information onto its latent space representation. The idea is to build a convolutional neural network (CNN) and perform the regularized discriminant analysis on top of this and train it in an end-to-end fashion. The backpropagation is performed with a suitable optimizer to update the parameters so that the whole CNN approach minimizes the within class variance and maximizes the total class variance information suitable for both multi-class and binary class classification problems. Experimental results on four databases for multiple computer vision classification tasks show the efficacy of our proposed approach as compared to other popular methods.
Application of Separate Modal Analysis and Scale-Invariant Feature Transform on Clinical Data for the Screening of Breast Cancer

Public health data indicate that breast cancer is a major health problem among women worldwide, measured by the occurrence, mortality, and economic cost of diagnosis. Digital Image Elasto Tomography (DIET) system is a non-invasive breast cancer screening approach based on induced mechanical sinusoidal vibrations to the breast surface. Fiducial markers were applied on the breast, and an array of five digital cameras captures surface oscillations. Scale Invariant Feature Transform (SIFT) algorithm used to extract interest points from the image for estimating the frequency of motion. After removing the background of pendant breast, SIFT features were extracted using the skin texture. The movement of the breast surface was tracked by using the SIFT points instead. Separate modal analysis was applied to the SIFT points for detecting the presence and location of the tumor. In the second case, separate modal analysis was applied using both SIFT points and fiducial markers on given clinical and phantoms data. Clinical data of two healthy and two affected patients with breast cancer were used for cancer detection by extracting SIFT features from the bare skin. Results were matching when compared with a clinical summary of the patient's details. Given results indicate the tumor's presence and location and removes empty spaces compared to only markers based system.
Anti-Gan: Discriminating 3D reconstructed and real faces for robust facial Identity in Anti-spoofing Generator Adversarial Network

3D face reconstruction is an attractive topic in computer vision. We have seen dramatic rise in its development recently. Now the state-of-the-art method can reconstruct a face from a single 2D face image freely, which brings a threat to facial security society. Since they are very similar in feature distributions, an efficient work to discriminate reconstructed face and real face is vital. Since Generative Adversarial Nets (GAN) has been proposed by Ian J. Goodfellow in 2014, it is extensively trained to approximate data distributions of many applications. For its adversarial mechanism, GAN shows a powerful generative ability to get the state of art. Inspired by its adversarial mechanism, we propose a similar framework called Anti-GAN to discriminate an adversarial dataset from real 3D face datasets and reconstructed face datasets. Considering the computation of backpropagation, G and D all adopt convolutional neural network architecture. Additionally, experiments show that Anti-GAN is a powerful way to distinguish real faces and reconstructed faces. At the same time, it can also offer robust features for a facial identity task.
EPT: An R package for ensemble patch transform

The primary focus of multiscale analysis is to interpret the temporal characteristics of one-dimensional signals and local spatial patterns of two-dimensional images according to the scale variability. There have been numerous studies for multiscale analysis in the literature, including spectral analysis, wavelet analysis, empirical mode decomposition, synchrosqueezed wavelet transforms, and variational mode decomposition. These methodologies have been widely applied in various areas and can be easily implemented by Matlab and R packages. Recently, ensemble patch transform has been developed for multiscale analysis of one-dimensional signals and two-dimensional images. For researchers and practitioners of multidisciplinary areas, we provide the R package EPT for an efficient implementation of the ensemble patch transform. This package flexibly designs various types of filters for identifying local characteristics and scale patterns hidden in a signal and the spatial characteristics of an image. The R package EPT also provides an effective tool for visualization and decomposition of signals and images.
Problems in the Area of Agile Methodologies

Working in distributed teams on digital platforms and software technology nowadays occurs in a more agile manner. This chapter will focus on the motivation for examining why agile methodology doesn't work efficiently in large enterprises having distributed agile software development model compared to small or medium enterprises. The chapter aims to unearth with the help of several known statistics and see if agile or distributed agile is always a success or not and explore the cascading impact on organizations when the agile implementation is not as per the expectation. Further on, the author tries to explore all factors and characteristics associated with distributed agile software development with the help of the literature, and thereafter, it intended to find an answer about the impact of distributed agile software development on team performance which has varying impact on organizations.
A Mathematical Recommendation Model to Rank Reviewers Based on Weighted Score for Online Review System

Recommendation systems play a very important role in business from several aspects. New systems, concepts are being evolved to enrich the business from different perspectives. Online reviews provide valuable information about products and services to consumers. Generally, online reviews are done on products to understand the usefulness or popularity of a product. However, it is being found that often fake reviews are given to increase the popularity of own product or to defame competitors’ products. This imposes research challenge to validate the reviews or Trustworthiness of reviewers. A recommendation model in online review system aims to filter the authenticated reviewers and then rank the top reviewers or emphasize on more impactful reviews. In this paper, a mathematical model is presented to rank the reviewers by assigning weighted scores based on certain parameters. A pre-processing technique is used before applying mathematical model to filter out the quality reviews. The pre-processing technique and data analysis are experimented on a real-life dataset to show the effectiveness of the proposed model.
Comment on “Quick Estimation of the Magnitude and Epicentral Distance Using the <i>P</i> Wave for Earthquakes in Iran” by Reza Heidari

Article Commentary| May 04, 2021 Comment on “Quick Estimation of the Magnitude and Epicentral Distance Using the P Wave for Earthquakes in Iran” by Reza Heidari Sahar Nazeri; Sahar Nazeri 1Department of Physics, University of Naples Federico II, Naples, Italy https://orcid.org/0000-0001-9205-6092 Search for other works by this author on: GSW Google Scholar Jamaledin Baniamerian; Jamaledin Baniamerian * 2Department of Earth Sciences, College of Sciences and Modern Technologies, Graduate University of Advanced Technology, Kerman, Iran *Corresponding author: j.baniamerian@kgut.ac.ir https://orcid.org/0000-0002-8496-3714 Search for other works by this author on: GSW Google Scholar Zaher Hossein Shomali Zaher Hossein Shomali 3Institute of Geophysics, University of Tehran, Tehran, Iran4Department of Earth Sciences, Uppsala University, Uppsala, Sweden https://orcid.org/0000-0001-6254-7560 Search for other works by this author on: GSW Google Scholar Bulletin of the Seismological Society of America (2021) 111 (3): 1661–1662. https://doi.org/10.1785/0120200167 Article history first online: 04 May 2021 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Sahar Nazeri, Jamaledin Baniamerian, Zaher Hossein Shomali; Comment on “Quick Estimation of the Magnitude and Epicentral Distance Using the P Wave for Earthquakes in Iran” by Reza Heidari. Bulletin of the Seismological Society of America 2021;; 111 (3): 1661–1662. doi: https://doi.org/10.1785/0120200167 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyBulletin of the Seismological Society of America Search Advanced Search Heidari (2016) studied the B−Δ methodology to rapidly estimate epicentral distance and earthquake magnitude employing 17 large earthquakes that occurred between 2002 and 2013 in Iran for use in an earthquake early warning system around the Tehran region. In the B−Δ approach, the epicentral distance (⁠Δ⁠, as unknown and dependent parameter) is estimated from the initial slope (coefficient B⁠, as known and independent parameter) of the acceleration waveform in a logarithm scale. Routinely, coefficient B results from a curve fitting, that is, regression, in the form of y=B... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
Single Image Super Resolution Enhancement Method Based on Fractal Interpolation of Iteration Function System

Abstract Based on the similarity and transitivity of local features of image data, self similarity and scale invariance of fractal features, fractal method of Iterated Function System is used to construct fractal interpolation function, and variable vertical scale factor is used to realize super-resolution enhancement of two-dimensional image data. The experimental results show that fractal interpolation method of iterated function system can keep the image details better, and the reconstruction error is smaller, which is better than bilinear interpolation and bicubic interpolation methods.