Reviewerly/exHarmony huggingface dataset:
Privacy-enhanced remote data integrity checking with updatable timestamp

Remote data integrity checking (RDIC) enables clients to verify whether the outsourced data is intact without keeping a copy locally or downloading it. Nevertheless, the existing RDIC schemes do not support the pay-as-you-go (PAYG) payment model, where the payment is decided by the volume and duration of the outsourced data. Specifically, none of the existing works have considered the client's control over changes in storage duration. In this paper, we propose an RDIC scheme to simultaneously check the data content and storage duration represented by an updatable timestamp via the third-party auditor (TPA). Also, our proposed scheme achieves indistinguishable privacy (IND-privacy) against TPA for both data content and timestamp. To bind the content and timestamp in the authenticator and support efficient timestamp update, we construct the authenticator with the randomizable structure-preserving signature (SPS). Additionally, we utilize the Groth-Sahai proof and range proof to provide the IND-privacy and guarantee the timestamp validation in the auditing phase. We formalize the definition and security model and provide the formal proof of our scheme. We also present the theoretical and experimental performance analysis to demonstrate that our scheme is comparable to the previous RDIC schemes which do not consider the storage time.
An Innovative Teaching with Serious Games through Virtual Reality Assisted Language Learning

Research indicates that today's language learners are expected to spend as many, if not more, hours engaging in simulated and serious games than they are in formal face-to-face instruction in classrooms. According to what did in this study, within a computer game-like virtual reality games, the researcher creates an environment in which the learner as a player is challenged and rewarded, and obtain results/feedback to learn pronunciation part of English learning (particularly vowel /ι/ and /i/ and vowel /υ/ and /u/ words). To do this, this study investigated the effect of Virtual Reality (VR) games on the pronunciation of 36 low-intermediate levels Iranian young Foreign Language learners (EFL), aged between 6 to 12 years at Sharif University of Technology, Tehran, Iran. To achieve the goals of the study, after taking a pronunciation pre-test via Speech ace Browser, 18 students participated in the experimental and 18 participated in the control group to demonstrate the applicability of the virtual reality games. During the study, the researcher taught learners different subjects in 10 ninety-minute-long sessions through the VR games for the experimental group. Finally, the researcher gave the post-test whose results of the t-test was conducted to examine a potential relationship between the pretest and the posttest of the experimental group in comparison to control group. Findings showed that there was a significant relationship between these two groups which indicated that the VR group had higher achievement in the case of using VR games. This study has useful applications for EFL teachers, EFL instructors, Syllabus and curriculum developers, game-based learning institutes, and game designers.
Double-Base Chains for Scalar Multiplications on Elliptic Curves

Double-base chains (DBCs) are widely used to speed up scalar multiplications on elliptic curves. We present three results of DBCs. First, we display a structure of the set containing all DBCs and propose an iterative algorithm to compute the number of DBCs for a positive integer. This is the first polynomial time algorithm to compute the number of DBCs for positive integers. Secondly, we present an asymptotic lower bound on average Hamming weights of DBCs $$\frac{\log n}{8.25}$$ for a positive integer n. This result answers an open question about the Hamming weights of DBCs. Thirdly, we propose a new algorithm to generate an optimal DBC for any positive integer. The time complexity of this algorithm is $$\mathscr {O}\left( \left( \log n\right) ^2 \log \log n\right) $$ bit operations and the space complexity is $$\mathscr {O}\left( \left( \log n\right) ^{2}\right) $$ bits of memory. This algorithm accelerates the recoding procedure by more than 6 times compared to the state-of-the-art Bernstein, Chuengsatiansup, and Lange's work. The Hamming weights of optimal DBCs are over 60% smaller than those of NAFs. Scalar multiplication using our optimal DBC is about 13% faster than that using non-adjacent form on elliptic curves over large prime fields.
Availability of substances for use in personal vaporisers on three online cryptomarkets

Personal vaporisers are gaining popularity as an alternative route of administration for a range of substances. Online cryptomarkets are becoming increasingly popular among people who use substances due to their perceived anonymity, ease of use, and reduced risk of violence compared to traditional face-to-face dealers. We examined the diversity of substances marketed for use in a personal vaporiser on these marketplaces. Vaping related listings were extracted from three online cryptomarkets (‘Agartha’, ‘Cryptonia’, and ‘Tochka’) using The Onion Router browser. Data collection occurred between October and November 2019. We identified 1929 listings from 201 unique sellers. The top product on Agartha, Cryptonia, and Tochka were vape cartridges prefilled with the e-liquid (70.4 %, 39.4 %, 52.3 % respectively). The most common substance in these products was cannabis oil (96.1 %, 82.1 %, 87.8 %), followed by synthetic cannabinoids (3.7 %, 9.7 %, 9.8 %) and psychedelic substances (0.2 %, 6.4 %, 1.2 %). Vendors were primarily from the USA. Many products offered worldwide shipping (96.3 %, 42.4 %, 51.2 %). Vaping products listed on online cryptomarkets in 2019 primarily contained cannabis oils. Future studies should continue to examine cryptomarkets to identify emerging trends of substances that can be used in personal vaporisers.
A Modified Logistic Regression for Positive and Unlabeled Learning

The positive and unlabeled learning problem is a semi-supervised binary classification problem. In PU learning, only an unknown percentage of positive samples are known, while the remaining samples, both positive and negative, are unknown. We wish to learn a decision boundary that separates the positive and negative data distributions. In this paper, we build on an existing popular probabilistic positive unlabeled learning algorithm and introduce a new modified logistic regression learner with a variable upper bound that we argue provides a better theoretical solution for this problem. We then apply this solution to both simulated data and to a simple image classification problem using the MNIST dataset with significantly improved results.
Histopathological image classification through discriminative feature learning and mutual information-based multi-channel joint sparse representation

Histopathological image classification is a very challenging task because of the biological heterogeneities and rich geometrical structures. In this paper, we propose a novel histopathological image classification framework, which includes the discriminative feature learning and the mutual information-based multi-channel joint sparse representation. We first propose a stack-based discriminative prediction sparse decomposition (SDPSD) model by incorporating the class labels information to predict deep discriminant features automatically. Subsequently, a mutual information-based multi-channel joint sparse model (MIMCJSM) is presented to jointly encode the common component and particular components of the discriminative features. Especially, the main advantage of the MIMCJSM is the construction of a joint dictionary using a mutual information criterion, which contains a common sub-dictionary and three particular sub-dictionaries. Based on the joint dictionary, the MIMCJSM captures the relationship of multi-channel features, which can improve discriminative ability of joint sparse representation coefficients. Finally, the joint sparse representation coefficients of different levels can be aggregated using the spatial pyramid matching (SPM) model, and the linear support vector machine (SVM) is used as the classifier. Experimental results on ADL and BreaKHis datasets demonstrate that our proposed framework consistently performs better than popular existing classification frameworks. Additionally, it can show promising strong-robustness performance for histopathological image classification.
Research and optimization of sports injury medical system under the background of Internet of things

Abstract With the popularization of Internet of things (IoT) knowledge and the wide dissemination of technology, IoT technology medical and health industry has brought far‐reaching influence. This article studies the application of IoT technology in sports injury medical system, and expounds the application of telemedicine, mobile diagnosis, and user data management in medical system supported by IoT technology. First, the application layer, network layer, and perceptual layer structure of the IoT are used to plan the overall hierarchy of the sports injury medical system, and then the system is divided into three modules, which are the user physiological parameter acquisition module, the user physiological parameter processing module, and the motion damage diagnosis result module. Finally, the performance of the system is detected from packet loss rate and concurrency quantity, and it is concluded that the medical system of sports injury can meet the requirements of field diagnosis based on the background of IoT, and the user's satisfaction is found to be greatly improved by the results of user's investigation.
Copyright Protection Application Based on Blockchain Technology

In the traditional copyright protection platform, there are problems such as copyright information security and high registration cost. With the rapid development of blockchain technology, blockchain technology, as a new technology architecture, has the characteristics of decentralization, nontamperability, traceability, openness and transparency, and has a natural combination with copyright protection, which is expected to solve the existence of traditional copyright protection. The pain point of this paper is to apply the blockchain technology to the copyright management platform. It is hoped that in this way, the problems of poor security, difficult rights, complicated transaction procedures, etc. in the traditional copyright management platform are solved. An attempt to combine chain technology with copyright protection.
A novel local binary pattern based blind feature image steganography

Steganography methods in general terms tend to embed more and more secret bits in the cover images. Most of these methods are designed to embed secret information in such a way that the change in the visual quality of the resulting stego image is not detectable. There exists some methods which preserve the global structure of the cover after embedding. However, the embedding capacity of these methods is very less. In this paper a novel feature based blind image steganography technique is proposed, which preserves the LBP (Local binary pattern) feature of the cover with comparable embedding rates. Local binary pattern is a well known image descriptor used for image representation. The proposed scheme computes the local binary pattern to hide the bits of the secret image in such a way that the local relationship that exists in the cover are preserved in the resulting stego image. The performance of the proposed steganography method has been tested on several images of different types to show the robustness. State of the art LSB based steganography methods are compared with the proposed method to show the effectiveness of feature based image steganography
Learning discriminative domain-invariant prototypes for generalized zero shot learning

Zero-shot learning (ZSL) aims to recognize objects of target classes by transferring knowledge from source classes through the semantic embeddings bridging. However, ZSL focuses the recognition only on unseen classes, which is unreasonable in realistic scenarios. A more reasonable way is to recognize new samples on combined domains, namely Generalized Zero Shot Learning (GZSL). Due to the fact that the source domain and target domain are disjoint and have unrelated classes potentially, ZSL and GZSL often suffer from the problem of projection domain shift. Besides, some semantic embeddings of prototypes are very similar, which makes the recognition less discriminative. To circumvent these issues, in this paper, we propose a novel method, called Learning Discriminative Domain-Invariant Prototypes (DDIP). In DDIP, both target and source domains are combined and projected into a hyper-spherical space, which is automatically learned by a regularized dictionary learning. In addition, an orthogonal constraint is employed to the latent hyper-spherical space to ensure all the class prototypes, including seen classes and unseen classes, to be orthogonal to each other to make them more discriminative. Extensive experiments on four popular benchmark and a large-scale datasets are conducted on both GZSL and standard ZSL settings, and the results show that our DDIP can outperform the state-of-the-art methods.
Dynamics of on-chip asymmetrically coupled semiconductor lasers

We investigate the dynamics of asymmetrically coupled semiconductor lasers on photonic integrated circuits in experiment and theory. The experimental observations are explained using a rate-equation model for coupled lasers incorporating a saturable coupling waveguide. We perform a bifurcation analysis of the coupled laser dynamics, focusing on the effects of the coupling phase and the dynamical difference between passive and saturable coupling waveguides. For a passive waveguide, we find a bifurcation scenario closely resembling the well-known optical injection setup, which is largely insensitive to the coupling phase. When the coupling waveguide is saturable, the dynamics become increasingly complex and unpredictable, with a strong phase-dependence. Our results show the possibility of a simple layout for reproducible laser dynamics on a chip.
The CMMI-Dev Implementation Factors for Software Quality Improvement

Refer to CMMI-Institute Appraisal result, there are 6 (six) companies in Indonesia that have achieved CMMI-Dev v1.3 maturity level 3, one of them is XYZ Corporation. Initially, XYZ Corporation has set a target to achieve CMMI-Dev level 2. As advised by the consultant, XYZ Corporation has a possibility to achieve level 3 by completing several parts of level 3 requirements that had not been met yet. XYZ Corporation faced a significant problem in progress implementation, particularly in documents standardization. This is due to each division has a different document format. XYZ Corporation also needs to improve its current processes that are still in ongoing phase of improvement process to achieve better standardized. This research aims to find out "The factors that affects the success of CMMI-Dev implementation based on its Current Conditions of XYZ Corporation". The approach of this research in finding the factors that influence the implementation of CMMI-Dev by means Qualitative and Quantitative approach. A qualitative method, by means of questionnaires, is used to prioritize the main factor. A quantitative method is used to get ranking of the factors that affecting successfulness of CMMI-Dev implementation in which has been validated by questionnaire's results. This research finding expects that the factors could be applied by other companies which desire to implement CMMI-Dev.
A Closer Look at Disentangling in β-VAE

In many data analysis tasks, it is beneficial to learn representations where each dimension is statistically independent and thus disentangled from the others. If data generating factors are also statistically independent, disentangled representations can be formed by Bayesian inference of latent variables. We examine a generalization of the Variational Autoencoder (VAE), β-VAE, for learning such representations using variational inference. β -VAE enforces conditional independence of its bottleneck neurons controlled by its hyperparameter β. This condition is in general not compatible with the statistical independence of latents. By providing analytical and numerical arguments, we show that this incompatibility leads to a non-monotonic inference performance in β -VAE with a finite optimal β .
Smart devices employment in teaching and learning: reality and challenges in Jordan universities

Abstract The aim of this study was to investigate the reality of smart devices’ employment in teaching and learning in Jordanian universities from the perspective of instructors. A random sample of (364) instructors were targeted. A structured interview was used to collect data and qualitative analysis was performed to reveal results. Obtained results showed that (68.1%) of respondents were against technology use specially inside the class, whereas (31.9%) were in-favor. Although a high percentage were against technology use, yet most of them use the technology by some means. Distraction, misuse, and lack of skills were the most commonly reported drawbacks. On the other hand, enriched interaction and excitement were the main aspects encouraging instructors to employ the technology in the teaching process.
Offline Handwritten Text Recognition using Convolutional Recurrent Neural Network

Offline handwriting recognition is an image-based sequence recognition task within computer vision. Traditional approaches rely on lexical segmentation, complex feature extraction techniques and considerable knowledge in the domain of linguistics. This paper presents a novel approach to handwriting recognition by using Convolutional Recurrent Neural Network combined with Connectionist Temporal Classification. The implemented method has the advantage of not being dependent on lexical segmentation and manual feature extraction. Moreover, applied methods are symbolic and character independent, making the model globally trainable and suitable to be applied to multiple languages.
Local Frequency Domain Transformer Networks for Video Prediction

Video prediction is commonly referred to as forecasting future frames of a video sequence provided several past frames thereof. It remains a challenging domain as visual scenes evolve according to complex underlying dynamics, such as the camera's egocentric motion or the distinct motility per individual object viewed. These are mostly hidden from the observer and manifest as often highly non-linear transformations between consecutive video frames. Therefore, video prediction is of interest not only in anticipating visual changes in the real world but has, above all, emerged as an unsupervised learning rule targeting the formation and dynamics of the observed environment. Many of the deep learning-based state-of-the-art models for video prediction utilize some form of recurrent layers like Long Short-Term Memory (LSTMs) or Gated Recurrent Units (GRUs) at the core of their models. Although these models can predict the future frames, they rely entirely on these recurrent structures to simultaneously perform three distinct tasks: extracting transformations, projecting them into the future, and transforming the current frame. In order to completely interpret the formed internal representations, it is crucial to disentangle these tasks. This paper proposes a fully differentiable building block that can perform all of those tasks separately while maintaining interpretability. We derive the relevant theoretical foundations and showcase results on synthetic as well as real data. We demonstrate that our method is readily extended to perform motion segmentation and account for the scene's composition, and learns to produce reliable predictions in an entirely interpretable manner by only observing unlabeled video data.
Asset management information system for higher education

Abstract Asset management is the process of recording, collecting data, reporting and documenting assets at a specific time based on existing provisions, proper asset management can support operational activities especially in the maintenance, repair, and procurement of assets for the organization. This study aims to design an asset management information system for the process of managing asset data and integrated asset depreciation calculation methods for universities. The methodology used in this research is Scrum, which can accommodate changes during the system design process, by taking samples based on the business process of asset management in one of the private universities. This study produced a web-based asset management information system prototype that can assist in managing asset data and calculating asset value depreciation.
Practical Challenges and Recommendations of Filter Methods for Feature Selection

Feature selection, the process of identifying relevant features to be incorporated into a proposed model, is one of the significant steps of the learning process. It removes noise from the data to increase the learning performance while reducing the computational complexity. The literature review indicated that most previous studies had focused on improving the overall classifier performance or reducing costs associated with training time during building of the classifiers. However, in this era of big data, there is an urgent need to deal with more complex issues that makes feature selection, especially using filter-based methods, more challenging; this in terms of dimensionality, data structures, data format, domain experts’ availability, data sparsity, and result discrepancies, among others. Filter methods identify the informative features of a given dataset to establish various predictive models using mathematical models. This paper takes a new route in an attempt to pinpoint recent practical challenges associated with filter methods and discusses potential areas of development to yield better performance. Several practical recommendations, based on recent studies, are made to overcome the identified challenges and make the feature selection process simpler and more efficient.
Multi-Dimension Topic Mining Based on Hierarchical Semantic Graph Model

Topic mining of scientific literature can accurately capture the contextual structure of a topic, track research hotspots within a field, and improve the availability of information about the literature. This paper introduces a multi-dimensional topic mining method based on a hierarchical semantic graph model. The main innovations include (1) the hierarchical extraction of feature terms and construction of a corresponding semantic graph and (2) multi-dimensional topic mining based on graph segmentation and structure analysis. The process of semantic graph construction is based primarily on hierarchical feature term extraction, which can effectively reveal the hierarchical structural distribution of feature terms within documents. Our graph model also takes into account the complementarity of content- and context-related feature terms in documents while avoiding the loss of textual information. In addition, the multi-dimensional features of the topic can be mined effectively via an in-depth analysis of the constructed graph, resulting in a quantitative visualization of the many-to-many association between the topic and feature terms. A variety of experiments on existing document datasets demonstrate that the proposed approach is able to outperform state-of-the-art methods in terms of accuracy and efficacy.