Reviewerly/exHarmony huggingface dataset:
Effective utilization of labeled data from related tasks using graph contrastive pretraining

Contrastive pretraining techniques for text classification has been largely studied in an unsupervised setting. However, oftentimes labeled data from related tasks which share label semantics with current task is available. We hypothesize that using this labeled data effectively can lead to better generalization on current task. In this paper, we propose a novel way to effectively utilize labeled data from related tasks with a graph based supervised contrastive learning approach. We formulate a token-graph by extrapolating the supervised information from examples to tokens. Our formulation results in an embedding space where tokens with high/low probability of belonging to same class are near/further-away from one another. We also develop detailed theoretical insights which serve as a motivation for our method. In our experiments with $13$ datasets, we show our method outperforms pretraining schemes by $2.5\%$ and also example-level contrastive learning based formulation by $1.8\%$ on average. In addition, we show cross-domain effectiveness of our method in a zero-shot setting by $3.91\%$ on average. Lastly, we also demonstrate our method can be used as a noisy teacher in a knowledge distillation setting to significantly improve performance of transformer based models in low labeled data regime by $4.57\%$ on average.
Blockchain Graphs (BCGs) to Support Right-To-Be-Forgotten “RTBF” in Blockchains

Due to immutability of the blockchains a person’s right to be forgotten (RTBF) is difficult to accommodate in a blockchain implementation. As a step to solve the immutability problem, Chameleon Hash functions (CHF) are designed to protect contents of a chain of blocks, i.e., ensure the immutability of every block’s contents. Any change of data can invalidate the chain as CHF functions would not allow linking in the same chains. A trapdoor (td) provides a way computationally to be able to change the contents, and create collision again, thus allowing the blockchain to be validated again. Also, Ephemeral trapdoor (etd) is added to allow a transient key for more resiliency so that the data can be protected against those who may know the trapdoor but now no longer are stakeholders for some reason. This is akin to changing the passwords for logins or changing a lock of your house. We proposed a summary of our previous work (MAP-ABCH) that implements a majority consensus among the stake holder creating combined ownership for the blockchain-stakeholders. At this point, there is no support of RTBF which is a feature juxtaposed to immutability. In this paper, we proposed an extension for MAP-ABCH multi-scale local interaction to create a global solution which would then support both immutability when needed, and also allow RTBF. It gives preference to RBTF, i.e., that our technique for the first time favors those who have the right to their data in the blockchain instead of those who are stakeholders or have somehow accessed personal data. This creates a reasonable possibility that those actors with malicious intent can be proven accountable when the data has been requested to be forgotten yet they somehow have a copy of the contents through surreptitious means. These malicious actors will need to explain why the data is in their possession when it has been forgotten by the person who holds the data. This is one of the intent and motivation of our proposed solution.
Web 2.0 technologies adoption barriers for external contacts and participation: a case study of federal establishment of Africa

Ever since the evolution of web technology in the 1990's, the web traffic got subjected to many developments. Web 1.0 and 2.0 are some of the innovations that made the world as a global village and increased the communication among various players in the world. Compared to the former, the latter has more features that opened gates for communication networks across the world. In spite of the sea change that Web 2.0 brought among the federal establishments of Nigeria, full level of adoption is did not occur due to numerous hindrances. The study outcome revealed that majority of the federal establishment does not use the technology for external collaboration. The research also shows that majority of the establishments still use collaboration tools such as letters, telephone and email as means of collaboration with external audience. This is because of the hindrances that are not facilitating the smooth flow of technology adoption.
Prostate Cancer Prediction Using Feedforward Neural Network Trained with Particle Swarm Optimizer

Prostate cancer has been one of the most commonly diagnosed cancers in men and one of the leading causes of death in the United States. Because of the complexity of the masses, radiologists are unable to diagnose prostate cancer properly. Many prostate cancer detection methods have been established in the recent past, but they have not effectively diagnosed cancer. It is worth noting that most current studies employ machine learning techniques, especially when creating prediction models from data. Despite its possible benefits compared to standard statistical analyses, these methods break down the problem statements into different parts and combine their results at the final stage. This makes complexity, and the prediction accuracy not consistently high. In this paper, the Feedforward Neural Networks (FNNs) is trained by using Particle Swarm Optimizer (PSO) and the FNNPSO framework is applied to the prediction of prostate cancer. PSO is one of the novel metaheuristics and frequently used for solving several complex problems. The experimental results are evaluated using the mean, best, worst, and standard deviation (Std.) values of the fitness function and compared with other learning algorithms for FNNs, including the Salp Swarm Algorithm (SSA) and Sine Cosine Algorithm (SCA). The experimental finding shows that the FNNPSO framework provides better results than the FNNSSA and FNNSCA in FNN training. Moreover, FNN trained with PSO is also shown to be better accurate than other trained methods to predict prostate cancer.
Impact of university E-Learning environment on value orientations of students

Transferring values to future generations have become one of the important aims of education. This study aims to evaluate students’ views on the role of the online learning environment on the value orientations of university students. This study was carried out using the case study method, one of the qualitative research methods. The study group of the research consists of 25 students studying at various universities in Kazakhstan in the fall semester of the 2021-2022 academic year. The researchers created a semi-structured interview form to determine the role of the online learning environment on the value orientations of university students. As a result of the research, students found online learning advantageous in terms of gaining self-confidence, responsibility, a comfortable life, adaptability, respect for others, and acceptance of norms. They stated that the online learning environment is disadvantageous in terms of success, happiness, excitement, friendship, sharing, cultural and universal values.&#x0D; Keywords: E-Learning Environment; Kazakhstan; university environment; values; value orientations.
Evaluation of Human Pose Estimation in 3D with Monocular Camera for Clinical Application

State of the art in the area of image processing and machine learning shows significant advances in the estimation of human posture in 2D and 3D. However, it has not been accurately reported whether the use of these methodologies provides per joint plane of motion information within the range of error of clinical measurements (5º). The purpose of this work was to select a method for estimating human posture in 3D with a monocular camera from the state of the art and to statistically compare it with clinical measurements in the angular variables. A discriminative method for estimating human posture was trained with the Human3.6M database, and results were obtained from the performance of the posture estimation model in 3D with a monocular camera. Subsequently, angular variables were obtained by plane of movement per predicted joint. Our results show that are joints with less than 5º of error in a plane of movements, such as the knee and elbow in the sagittal plane and the wrist in the frontal plane. On the other hand, in other joints such as the hip and ankle, its results are dependent on the action, the time periods of the evaluated actions, and the view of the camera.
Convolutional Neural Networks

In the previous chapters, we looked at fully connected networks and all the problems you encounter while training them. The network architecture we used, one where each neuron in a layer is connected to all the neurons in the previous and next layers, is not good at many fundamental tasks like image recognition, speech recognition, time series prediction, and many more. Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are the most advanced architectures used today. This chapter looks at convolution and pooling, the basic building blocks of CNNs. We also discuss a complete, although basic, implementation of CNNs in Keras. RNNs are discussed, although briefly, in the next chapter.
Taking stock of the science of reading: A conversation with Amanda Goodwin

Amanda Goodwin, co-editor of Reading Research Quarterly, talks with Kappan about the research findings presented in two special issues of journal focused on the science of reading. The science of reading is a broad body of research, she explains, that includes not just alphabetics, phonics, and word reading, which receive the most attention in the media. Actual science of reading research also incorporates findings related to motivation, dyslexia, digital texts, and multilingualism. In addition, effective reading instruction does not follow a simple prescription but leaves room for teacher discretion.
Quantum teleportation of two-qubit states via W-State and GHZ-like States

Abstract We investigate the criteria for two-qubit states that could be transmitted via W-state. The appropriate criterias are entangled states which have maximum number of terms equal to number of channel terms and maximum of two different constants. We have also shown quantum teleportation of a particular type of two-qubit state via three-qubit GHZ-like states, with Alice possesses the first qubit of the channel and the rest belongs to Bob. GHZ-like state is an entangled state which has different characteristic from GHZ state. It is stronger against particle loss than GHZ state. Furthermore, GHZ-like states which can teleport a particular type of two-qubit state are GHZ-like states which have two terms.
CNN performance dependence on linear image processing

Over-exposure happens often in daily-life photography due to the range of light far exceeding the capabilities of the limited dynamic range of current imaging sensors. Correcting overexposure aims to recover the fine details from the input. Most of the existing methods are based on manual image pixel manipulation, and therefore are often tedious and time-consuming. In this paper, we present the first convolutional neural network (CNN) capable of inferring the photo-realistic natural image for the single over-exposed photograph. To achieve this, we propose a simple and lightweight Over-Exposure Correction CNN, namely OEC-cnn, and construct a synthesized dataset that covers various scenes and exposure rates to facilitate training. By doing so, we effectively replace the manual fixing operations with an end-toend automatic correction process. Experiments on both synthesized and real-world datasets demonstrate that the proposed approach performs significantly better than existing methods and its simplicity and robustness make it a very useful tool for practical over-exposure correction. Our code and synthesized dataset will be made publicly available.
Van Inwagen’s Approach to Relations and the Theory of O-Roles

In “Names for Relations,” Van Inwagen presents a semantic problem about names for non-symmetric relations, based on the presupposition that there are converse relations. This paper first provides an analysis of Van Inwagen’s problem and then addresses it by appealing to o-roles such as ‘agent,’ ‘patient,’ ‘theme,’ instrument, etc., and by distinguishing sparse and abundant levels of properties and relations. At the sparse level, o-roles are appealed to in order to tackle the problem of relational order and reject converse relations. At the abundant level, converse relations are recovered in the light of the sequential character of thinking, speaking and writing, and Van Inwagen problem is solved by giving due consideration to the order in which relata are assigned o-roles and referred to.
Distributed MHT with Passive Sensors

This paper focuses on two challenges in multi-target tracking with passive sensors. The first is the well-known observability problem whereby individual sensor measurements are insufficient to localize targets. The second is the need to relax the usual small-target assumption of at most one measurement per target per scan. Indeed, in some applications such as passive sonar, there are repeated measurements, i.e. multiple detections per target per scan of one sensor. We examine these challenges in a multi-sensor setting and describe the advantages of a distributed MHT solution architecture, with measurement-space tracking following by multi-sensor Cartesian tracking using a robust Cartesian initialization scheme. In the presence of repeated measurements, there are (at least) two viable processing architectures. In both cases we leverage a recently developed generalization to the MHT recursion. We study the relative merits of the two alternative solutions.
Hierarchical Clustering Approach for Region Analysis of Contraceptive Users

Through increasing the use of contraceptives to limit births, the Family Planning (KB) Program is one of the government's efforts to control the rate of population growth. Klaten Districts is one of the regencies in Central Java Province with a relatively high number of births and relatively low coverage of active family planning. This study aimed to determine the grouping of sub-districts and these characteristics in the Klaten Districts in 2020. The method used in this study was a hierarchical cluster analysis method, with the best method being the centroid method. In this study obtained 3 clusters with cluster 1 consisting of 23 sub-districts, cluster 2 consists of 2 sub-districts and cluster 3 with 1 sub-district. The cluster characteristics based on the highest number of users of contraceptive methods are cluster 1-contraceptives injection, cluster 2- contraception implant, and IUDs in cluster 3
Health City Collaboration Model Through the Program No Gross City (Kotaku) in Coastal Areas Makassar City, Indonesia

Slums are a problem faced by almost all major cities in Indonesia and even large cities in other developing countries. The study of slum settlements (slum) generally covers three aspects, first the physical condition, the other socio-economic conditions of the community living in the agreement, and the three impacts by the two states. These physical conditions, among others, can be seen from the very stable state of the building with low-quality construction, non-patterned, and non-hardened road network, public sanitation, and non-functioning drainage, and waste that has not been adequately managed. (1) (2) The study aimed to form a collaboration model for Healthy Cities through a Program of No Slum Cities in the Coastal Area of Makassar City. Method This study uses a quantitative descriptive research method as an approach to Secondary Data Analysis (ADS). ADS is a method using secondary data as the primary data source. Utilizing the secondary data in question is to use a statistical test technique that is appropriate for obtaining the desired information from a mature body of material derived from certain agencies or institutions (such as PUPR, departments, or educational institutions) to be systematically and objectively processed. Observations found that the tendency towards collaboration with various sectors to reduce the number of slums in settlements. As for the indicators used, there are 7 + 1 from the City program without slums. The conclusions of the study were to reduce the number of slums in Makassar, especially in the coastal areas of Makassar City with an approach, namely by improving housing, environmental roads, drainage, garbage, fire fighting, and green open space by involving various sectors.
Hybrid neural network of analysis to increase the accuracy of the approximator

In recent years, there has been a need for shorter analysis techniques in order to reduce the time required for design optimization. For this purpose, approximation by polynomial regression, which is a mathematical representation of the analytical structure, can be considered, but the difference from the real structure is large and the error is large. In addition, an approximator based on machine learning requires a lot of time to create training data, so a method that can be evaluated accurately even with a small amount of training data is essential. Therefore, by combining a neural network approximator that mimics polynomial regression and a neural network trained on errors with unknown governing equations, we have shown that the accuracy can be improved even with a small amount of training data.
A Daytime Free-Space Quantum-Optical Link using Atomic-Vapor Spectral Filters

We establish a free-space quantum-optical link that leverages narrow (∼1 GHz) linewidth rubidium-based bandpass filters to drastically reduce solar background. To show its utility, we demonstrate the BB84 quantum key distribution protocol under daytime conditions.
Horizon Pharma, Inc. v. Dr. Reddy's Labs. Inc. United States Court of Appeals of the Federal Circuit, 2021 No. 2019-1607, 2021 WL 48428 (Non-precedential)

Biotechnology Law ReportVol. 40, No. 2 As presented by Chris HolmanHorizon Pharma, Inc. v. Dr. Reddy's Labs. Inc. United States Court of Appeals of the Federal Circuit, 2021 No. 2019-1607, 2021 WL 48428 (Non-precedential)Edited for Biotechnology Law Report by CHRISTOPHER M. HOLMAN*Published Online:30 Mar 2021https://doi.org/10.1089/blr.2021.29220.cmhAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 40Issue 2Apr 2021 InformationCopyright 2021, Mary Ann Liebert, Inc., publishersTo cite this article:Horizon Pharma, Inc. v. Dr. Reddy's Labs. Inc. United States Court of Appeals of the Federal Circuit, 2021 No. 2019-1607, 2021 WL 48428 (Non-precedential).Biotechnology Law Report.Apr 2021.129-132.http://doi.org/10.1089/blr.2021.29220.cmhPublished in Volume: 40 Issue 2: March 30, 2021Online Ahead of Print:March 2, 2021PDF download
Speckle and Rician Noise Removal from Medical Images and Ultrasound Images

In medical images, medical images are corrupted by different types of noise. It is important to get a precise picture and accurately observe the correspondence. Removing noise from medical images has become a very difficult problem in the field of the medical image. The most well-known noise reduction method, which is usually based on the local statistics of medical images, is efficient because of the noise reduction of medical images. In paper, an efficient and simple method for noise reduction from medical images is presented. The paper proposes a filtering system to combine both the Median filter and Gaussian filter to remove the Speckle noise form Medical and Ultrasound images. The image quality is measured through statistical quantities: Peak signal to noise ratio (PSNR). Experimental results show that the proposed system removes Speckle noise from medical images.
Research on English Translation Based on Recursive Deep Neural Network

research-article Research on English Translation Based on Recursive Deep Neural Network Share on Author: Bo Li Bei Jing Polytechnic, China Bei Jing Polytechnic, ChinaSearch about this author Authors Info & Claims AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced ManufactureOctober 2021 Pages 483–487https://doi.org/10.1145/3495018.3495104Online:14 March 2022Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Status of E-resources in Select University Libraries of Haryana

Electronic resources have become an important part of a library collection. The value and use of these resources have increased significantly, especially in the prevailing COVID-19pandemic period. The article discusses a study conducted to explore the status of e-resources in select university libraries of Haryana.This study is based on primary data collected using a self-designed questionnaire from three well-established, multi-disciplinary state universities of Haryana, namely Kurukshetra University (KUK), Maharshi Dayanand University (MDU), and Chaudhary Devi Lal University (CDLU).The study did find Vivekananda Library of MDU to be equipped with maximum hardware infrastructural facilities as compared to other two university libraries. All the three university libraries are automated and using LIBSYS software. E-ShodhSindhu is subscribed by all the university libraries. Libraries of KUK and MDU provide links of open access databases such as Directory of Open Access Repositories (DOAR), Directory of Open Access Books (DOAB), Directory of Open Access Journals (DOAJ) and National Digital Library of India (NDLI). MDU library and CDLU library are subscribing maximum e-book databases. E-journals are subscribed on perpetual access basis with archives by libraries of MDU and CDLU. The study further found that all university libraries take the feedback from their users regarding continuation or discontinuation of e-resources. Results of the study also reveal that only MDU library has permanent full-time librarian whereas other two university libraries are looked after by professor-in-charge of their universities, given the additional charge of librarian. The universitiesâ€™ libraries of Haryana need to subscribe more e-resources and appoint full-time librarian so as to better serve their users in the post COVID-19, new normal situation.