Reviewerly/exHarmony huggingface dataset:
Distance-Bounding, Privacy-Preserving Attribute-Based Credentials

Distance-bounding anonymous credentials could be used for any location proofs that do not need to identify the prover and thus could make even notoriously invasive mechanisms such as location-based services privacy-preserving. There is, however, no secure distance-bounding protocol for general attribute-based anonymous credentials. Brands and Chaum’s (EUROCRYPT’93) protocol combining distance-bounding and Schnorr identification comes close, but does not fulfill the requirements of modern distance-bounding protocols. For that, we need a secure distance-bounding zero-knowledge proof-of-knowledge resisting mafia fraud, distance fraud, distance hijacking and terrorist fraud. Our approach is another attempt toward combining distance bounding and Schnorr to construct a distance-bounding zero-knowledge proof-of-knowledge. We construct such a protocol and prove it secure in the (extended) DFKO model for distance bounding. We also performed a symbolic verification of security properties needed for resisting these attacks, implemented in Tamarin. Encouraged by results from Singh et al. (NDSS’19), we take advantage of lessened constraints on how much can be sent in the fast phase of the distance-bounding protocol and achieve a more efficient protocol. We also provide a version that does not rely on being able to send more than one bit at a time which yields the same properties except for (full) terrorist fraud resistance.
Linear time identification of local and global outliers

Anomaly detection methods differ in their time complexity, sensitivity to data dimensions, and their ability to detect local/global outliers. The recently proposed algorithm FiRE is a ‘sketching’ based linear-time algorithm for identifying global outliers. This work details FiRE.1, an extended implementation of FiRE that fares well on local outliers as well. We provide an extensive comparison with 18 state-of-the-art anomaly detection algorithms on a diverse collection of 1000 annotated datasets. Five different evaluation metrics have been employed. FiRE.1’s performance was particularly remarkable on datasets featuring a large number of local outliers. In the sequel, we propose a new “outlierness” criterion to infer the local or global identity of outliers.
Probabilistic Tractable Models in Mixed Discrete-Continuous Domains

Abstract We study the problem of the unsupervised learning of graphical models in mixed discrete-continuous domains. The problem of unsupervised learning of such models in discrete domains alone is notoriously challenging, compounded by the fact that inference is computationally demanding. The situation is generally believed to be significantly worse in discrete-continuous domains: estimating the unknown probability distribution of given samples is often limited in practice to a handful of parametric forms, and in addition to that, computing conditional queries need to carefully handle low-probability regions in safety-critical applications. In recent years, the regime of tractable learning has emerged, which attempts to learn a graphical model that permits efficient inference. Most of the results in this regime are based on arithmetic circuits, for which inference is linear in the size of the obtained circuit. In this work, we show how, with minimal modifications, such regimes can be generalized by leveraging efficient density estimation schemes based on piecewise polynomial approximations. Our framework is realized on a recent computational abstraction that permits efficient inference for a range of queries in the underlying language. Our empirical results show that our approach is effective, and allows a study of the trade-off between the granularity of the learned model and its predictive power.
Degradation Model Learning for Real-World Single Image Super-Resolution

It is well-known that the single image super-resolution (SISR) models trained on those synthetic datasets, where a low-resolution (LR) image is generated by applying a simple degradation operator (e.g., bicubic downsampling) to its high-resolution (HR) counterpart, have limited generalization capability on real-world LR images, whose degradation process is much more complex. Several real-world SISR datasets have been constructed to reduce this gap; however, their scale is relatively small due to laborious and costly data collection process. To remedy this issue, we propose to learn a realistic degradation model from the existing real-world datasets, and use the learned degradation model to synthesize realistic HR-LR image pairs. Specifically, we learn a group of basis degradation kernels, and simultaneously learn a weight prediction network to predict the pixel-wise spatially variant degradation kernel as the weighted combination of the basis kernels. With the learned degradation model, a large number of realistic HR-LR pairs can be easily generated to train a more robust SISR model. Extensive experiments are performed to quantitatively and qualitatively validate the proposed degradation learning method and its effectiveness in improving the generalization performance of SISR models in practical scenarios.
Deep-Learning LSTM Mechanism and Wearable Devices based Virtual Fitness-Coach Information System for Barbell Bench Press

This study aims to design and develop a virtual fitness-coach information system for barbell bench press based on deep-learning Long Short Term Memory (LSTM) mechanism and wearable devices. We utilizes a set of three-axis accelerometers, gyroscopes and Electromyography (EMG) sensing modules to design our proposed wearable devices. Through computer and smartphone, the analysis and real-time assessment of the weight training in barbell free bench press can be performed to avoid injury in weight training and improve the quality of training performance.In this study, 21 subjects are recruited to use our proposed wearable devices for weight training in barbell free bench press. In the training, the subject's physiological signals and videos are captured, and the subject's signals are extracted according to the 11 most common kinds of errors marked by the fitness instructor, including 7 posture errors and 4 kinds of muscle force errors. After the extracted signal is normalized, the data is fed for the Recurrent Neural Network (RNN) training through the Long Short Term Memory (LSTM) to classify the weight training errors. The experimental results show that the classification threshold used in the classification has the best classification result when set at 0.5, and the overall average accuracy, accuracy, recall rate, F1 Score, FPR and FNR are 91.84%, 89.25%, 88.17%, 88.18%, 6.50% and 11.83%, respectively. We found that in some categories, because the sensors are not powerful enough to capture the characteristics of the errors, the accuracy is low. While the overall accuracy of the other categories is higher than 85%.In order to accelerate the training speed of LSTM, we also try to use the common factor extraction analysis to reduce the data of accelerometers and gyroscopes from 24 to 18, 12 and 6 dimensions for training. When the total dimension including EMG is 30 dimensions, there is not much difference in the accuracy when the dimension is reduced to 24 or 18. However when it is reduced to 12 dimensions, the evaluation metrics are reduced to below 70%, and the False Negative Rate (FNR) has risen sharply to 30.21%. We therefore choose to reduce the training data from 30 dimensions to 18 dimensions to maintain recognition accuracy and to accelerate LSTM training.To verify the feasibility of our Virtual Fitness-Coach Information System, we have further recruited 5 subjects for user satisfaction survey of the instant voice feedback and our wearable devices. The users show relatively high satisfiaction about our instant feedback system in the following aspects: helpfulness, clearance, reliability, correctness, and performance. The users also feel relatively comfortable for our wearable devices and suggest further simplification of our wearable devices for ease of wearing.
Knowledge reuse in software projects: Retrieving software development Q&amp;A posts based on project task similarity

Software developers need to cope with a massive amount of knowledge throughout the typical life cycle of modern projects. This knowledge includes expertise related to the software development phases (e.g., programming, testing) using a wide variety of methods and tools, including development methodologies (e.g., waterfall, agile), software tools (e.g., Eclipse), programming languages (e.g., Java, SQL), and deployment strategies (e.g., Docker, Jenkins). However, there is no explicit integration of these various types of knowledge with software development projects so that developers can avoid having to search over and over for similar and recurrent solutions to tasks and reuse this knowledge. Specifically, Q&amp;A sites such as Stack Overflow are used by developers to share software development knowledge through posts published in several categories, but there is no link between these posts and the tasks developers perform. In this paper, we present an approach that (i) allows developers to associate project tasks with Stack Overflow posts, and (ii) recommends which Stack Overflow posts might be reused based on task similarity. We analyze an industry dataset, which contains project tasks associated with Stack Overflow posts, looking for the similarity of project tasks that reuse a Stack Overflow post. The approach indicates that when a software developer is performing a task, and this task is similar to another task that has been associated with a post, the same post can be recommended to the developer and possibly reused. We believe that this approach can significantly advance the state of the art of software knowledge reuse by supporting novel knowledge-project associations.
Classification of 9-dimensional trilinear alternating forms over GF(2)

Let V be a finite-dimensional vector space over a finite field and let f be a trilinear alternating form over V. For such forms, we introduce two new invariants. Together with a generalized radical polynomial used for classification of forms in dimension 8 over GF(2), they are sufficient to distinguish between all trilinear alternating forms in dimension 9 over GF(2). To prove the completeness of the list of forms, we computed their groups of automorphisms. There are 31 degenerate and 317 nondegenerate forms. We point out some forms with either small or large automorphism group.
Common Mistakes When Using the Command Pattern and How to Avoid Them

Command, is a behavioral pattern from the Gang of Four catalog that allows us to structure an application with respect to primitive actions that can be easily managed and executed. The main idea is to decouple the objects that invoke actions from the objects that know how to perform them, by encapsulating everything that is needed for executing the actions in corresponding command objects. The application can comprise different classes of command objects that realize different actions. The different classes of command objects implement the same interface. Therefore, command objects can be passed as parameters to other objects that use them to execute the respective actions, without knowing how this is actually done.
Detection of Ovarian Malignancy from Combination of CA125 in Blood and TVUS Using Machine Learning

Carcinoma of the ovary keeps on being one of the main causes of death from gynecologic malignancies on the earth. Women determined to have beginning phase (1/2) ovarian disease have considerably better endurance rates contrasted with those analyzed in later stages. The speculation behind screening for ovarian malignant growth is that previous recognition of the illness will bring about more women being analyzed at less propelled stages and that this will convert into lower generally death rates for the ovarian disease. Both transvaginal ultrasound and the Cancer Antigen 125 in blood have been used to screen for early ovarian malignant growth. The aims of our study are to the detection of ovarian malignancy in primary stage and thereby reduce the mortality due to ovarian cancer. In this study, we have used a real-world dataset, namely PLCO to anticipate the discoveries from CA-125 and TVUS joined screening utilizing machine learning models. We obtained a critical presentation from the model as far as accuracy, precision, and recall with the estimations of 80, 86, and 90.25% individually. Our work will support the doctors and the suspected to analyze ovarian threats in the beginning and decreased the complication and mortality due to ovarian malignancy.
Downhole Lithological Profile Reconstruction Based on Chemical Composition of Core Samples and Drill Cuttings Measured with Portable X-ray Fluorescence Spectrometer

The reconstruction of a lithological profile based on geophysical logs of chemical composition provided by geochemical gamma-gamma well logging probes has been increasingly used for geophysical interpretation. A chemical profile, analogous to the measurements mentioned above, can be determined based on measurements made with a portable X-ray fluorescence spectrometer (pXRF). This paper presents a methodology for determining the mineral composition of drilled, clastic, as well as clay-rich rocks on the basis of both inexpensive and timesaving pXRF measurements as well as models combining the results of chemical composition analysis with results of mineral composition analysis (XRD). The results of chemical composition analysis obtained with a portable XRF spectrometer were calibrated based on a detailed analysis produced with ICP-OES and ICP-MS methods. A significant advantage of the proposed method is the possibility to apply it with regard to drill cuttings as well as archival cores. However, considerable discrepancies in the results obtained were identified while comparing the results of chemical composition analysed directly on the core and milled material. The analysed material comprised Carboniferous rocks derived from three boreholes located in Poland: Kobylin-1 as well as Biesiekierz-1 and -2. It was possible to directly compare the lithological profile obtained based on measurements taken on drill cuttings with the results of the lithological interpretation of a geochemical probe log.
A User Group Classification Model Based on Sentiment Analysis Under Microblog Hot Topic

User classification based on sentiment analysis makes it easier for researchers to understand the sentiment behavior characteristic of different groups. Since the current user classification method does not consider the fact of user’s sentiment fluctuation, this paper proposes a user group classification model takes into account the change of user’s sentiment. Firstly, the sentiment analysis of Microblog comments base on the Microblog sentiment dictionary. Calculate the user’s temporal sentiment vector and the user’s sentiment feature vector according to the rules made in this paper. Finally, k-means clustering is used to classify user based on the user’s sentiment feature vector. Experimental results validate the accuracy of the proposed model.
BCVerifier: A Tool to Verify Hyperledger Fabric Ledgers

The blockchain technology has been popular not only in cryptoassets but also in various fields of enterprise systems, where smart contracts between multiple stakeholders play a significant role. Hyperledger Fabric is one of the leading open-source enterprise blockchain platforms, which employs a unique consensus algorithm comprising three phases: endorsement, ordering, and committing. In this paper, three potential issues in Hyperledger Fabric are addressed when it is applied to enterprise systems: local alteration of ledgers, short of endorsers faced especially with private data, and auditing requirements. To solve and mitigate the issues, a blockchain verification tool bcverifier is proposed. This paper presents six requirements for the tool, the basic design of the tool, and how the design satisfies the requirements. It also clarifies the properties which the tool verifies in the ledger: immutability, consensus, and consistency in Hyperledger Fabric ledgers.
RETRACTED: Research on color of medical rehabilitation products based on embedded system and artificial intelligence

The world's third passing and incapacity sickness can prompt enormous challenges in restoration patients. In addition to the fact that it is a costly conventional treatment strategy, bringing about a lack of actual specialists. Presently, most multi-level of opportunity robot restoration of the upper appendages aren't appropriate for family recovery because of monstrous plan issue appendage recovery robot dependent on implanted framework recovery robot can perform three free development: elbow flexion/augmentation, wrist pivot, and wrist flexion/expansion, and utilize two-way recuperation techniques. The mechanical structure of hardware for recovery is a convenient skeleton in lightweight. The sensor framework uses a little gyrator, an inserted framework utilized in a portion of the control frameworks. It is entirely appropriate for the group of recovery. At last, in two of the test, went to test the dependability and precision of the control framework. The test results, the gadget for recovery, shows that it is sheltered and dependable.
IoT based Child Safety Management using Raspberry Pi and RFID Technology

Abstract The protection and welfare of children is becoming more necessary to create a society that is greater and stronger. Therefore, the protection measures of kids must be strengthened to eliminate difficulties for kids. With this in mind, several tools and systems are employed to maintain the child’s safety environment. Improving intelligence agencies in this field, in this paper a system for children’s safety is proposedfor children safety purpose.We develop anIoT based child safety using raspberry. Students having a RFID based cards which used for authentication.Whenever student enters in school bus the Raspberry sends a message notification to parents and the principal.
ReLink: Open Information Extraction by Linking Phrases and Its Applications

Recently, many Open IE systems have been developed based on using deep linguistic features such as dependency-parse features to overcome the limitations presented in older Open IE systems which use only shallow information like part-of-speech or chunking. Even though these newer systems have some clear advantages in their extractions, they also possess several issues which do not exist in old systems. In this paper, we analyze the outputs from several popular Open IE systems to find out their strength and weaknesses. Then we introduce ReLink, a novel Open IE system for extracting binary relations from open-domain text. Its working model is based on identifying correct phrases and linking them in the most proper way to reflect their relationship in a sentence. After establishing connections, it can easily extract relations by using several pre-defined patterns. Despite using only shallow linguistic features for extraction, it does not have the same weakness that existed in older systems, and it can also avoid many similar issues arising in recent Open IE systems. Our experiments show that ReLink achieves larger Area Under Precision-Recall Curve compared with ReVerb and Ollie, two well-known Open IE systems.
Quantum Algorithms for Nonlinear Equations in Fluid Mechanics

In recent years, significant progress has been made in the development of quantum algorithms for linear ordinary differential equations as well as linear partial differential equations. There has not been similar progress in the development of quantum algorithms for nonlinear differential equations. In the present work, the focus is on nonlinear partial differential equations arising as governing equations in fluid mechanics. First, the key challenges related to nonlinear equations in the context of quantum computing are discussed. Then, as the main contribution of this work, quantum circuits are presented that represent the nonlinear convection terms in the Navier–Stokes equations. The quantum algorithms introduced use encoding in the computational basis, and employ arithmetic based on the Quantum Fourier Transform. Furthermore, a floating-point type data representation is used instead of the fixed-point representation typically employed in quantum algorithms. A complexity analysis shows that even with the limited number of qubits available on current and near-term quantum computers (&lt;100), nonlinear product terms can be computed with good accuracy. The importance of including sub-normal numbers in the floating-point quantum arithmetic is demonstrated for a representative example problem. Further development steps required to embed the introduced algorithms into larger-scale algorithms are discussed.
<i>GlaxoSmithKline v. Teva</i>: Holding a Generic Liable for an Artificial Act of Inducement

Biotechnology Law ReportVol. 39, No. 6 The Holman ReportGlaxoSmithKline v. Teva: Holding a Generic Liable for an Artificial Act of InducementBy Christopher M. HolmanBy Christopher M. HolmanSearch for more papers by this authorPublished Online:2 Dec 2020https://doi.org/10.1089/blr.2020.29209.cmhAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetailsCited byGovernment Involvement in Pharmaceutical Development Can Come Back to Haunt a Drug Company Christopher M. Holman3 February 2021 | Biotechnology Law Report, Vol. 40, No. 1 Volume 39Issue 6Dec 2020 InformationCopyright 2020, Mary Ann Liebert, Inc., publishersTo cite this article:By Christopher M. Holman.GlaxoSmithKline v. Teva: Holding a Generic Liable for an Artificial Act of Inducement.Biotechnology Law Report.Dec 2020.425-439.http://doi.org/10.1089/blr.2020.29209.cmhPublished in Volume: 39 Issue 6: December 2, 2020PDF download
Application of innovative technology "Media Synergy" in the process of training specialists at the technical university in the conditions of digitalization of education

The article is devoted to the innovative technology of upbringing students "Media synergy", which was used for four years of training teachers at the technical University in a prolonged experiment. Special attention is paid to the analysis of educational technologies that are used in higher foreign and domestic professional education, including information, health-saving, subject, media, upbringing, in the conditions of digitalization of all spheres of life in modern society. The article substantiates a set of conceptual ideas that allowed us to determine the criteria of the spiritual and ethical component of the existential period of students. Models of the "Media synergy" technology were developed; the results of the application of innovative technology were summarized, which allowed to realize the main goal in the professional education of teachers – to overcome the pseudo-virtual picture of the world in the minds of students through synergetic relationships between equal partners and through interaction based on anti-manipulative techniques in the media space.