Reviewerly/exHarmony huggingface dataset:
LDC-IL: The Indian repository of resources for language technology

Abstract This paper introduces the Government of India Initiative on linguistic data creation in Indian languages. The Linguistic Data Consortium for Indian Languages (LDC-IL) is a fully funded Government of India scheme established in 2007 to cater to the needs of linguistic resources required for the development of language technology in Indian languages. LDC-IL worked silently for more than a decade with a team of around regular fifty people and involving thousands of resource persons, covering twenty major languages of India. Part of the output of LDC-IL was launched in April 2019 by the Hon’ble Vice President of India. This paper, the first introductory paper in an academic journal, aims to give a brief of the works done by LDC-IL and how these works are crucial in the development of language technology for Indian languages. Within a short span of eight months of its release, the language resources released by LDC-IL have been procured and utilized by scores of industry and academic bodies and individual researchers, including major industry leaders like Microsoft, Google, Samsung etc.
IoT in Online Banking

As we know, there are so many changes arriving right now ion the banking industries which are really complex industries. Every day, huge amount of data is processed and gathered. With this increase in size, it is becoming more difficult for banking institutions to manage this data and handle other segments of their business. This paper presents the scope of IoT in the banking domain and how various transformations could potentially bring game changing reforms in the traditional methodology. Banking institutions need to integrate IoT in their systems to increase their market share by providing services catered to a clients need based on the data that’s being processed in real time. In future, IoT will be able to create such technologies which will be able to connect physical objects so that objects can do their own intelligent decision making.
Context-aware Music Recommender System Based on Implicit Feedback

This paper proposes a method for recommending music items without explicit feedback. Context and content features are used as auxiliary information to compensate for implicit feedback. The recent development of communication technology and portable electronic devices has changed the way of consuming music. We can access a vast amount of music items via online music streaming services. As a result, finding appropriate music items from enormous resources gets to be difficult for users. To help users discover their favorite music items, recommender systems in the music domain have been studied. This paper focuses on two challenges specific to music recommender systems: the difficulty of obtaining explicit feedback such as ratings, and the importance of making use of context information. To handle the context information as auxiliary information to compensate for implicit feedback, this paper employs FMs (Factorization Machines), in which the context information is treated as features. Utilizing the merit of FMs that can easily introduce features, this paper also introduces content features in addition to context features. As it is known that using low-level content features directly is not effective because of the semantic gap, this paper proposes two types of abstract content features: UGP (user genre profile) and UCP (user content profile). The effectiveness of the proposed method and the effect of negative sampling methods are evaluated in terms of MPR with #nowplaying-rs and LFM-1b dataset. The result of the experiment shows that the proposed method outperforms wALS (weighted Alternating Least Squares), which is one of the popular music recommendation algorithms based on matrix factorization. The characteristics of the proposed sampling methods are investigated with different settings of the parameters and the ratio of negative samples. As for the effectiveness of each feature, it is found that a feature is effective when JS (Jensen-Shannon) divergence of popularity distribution among different feature values is large. It is also shown that the UCP and UGP cluster labels are more effective than using content features directly.
Interactive convolutional learning for visual tracking

Recent years have witnessed significant improvements of ensemble trackers based on independent models. However, existing ensemble trackers only combine the responses of independent models and pay less attention to the learning process, which hinders their performance from further improvements. To this end, we propose an interactive learning framework to strengthen the independent models in the learning process. Specifically, in the interactive network, we force convolutional filter models to interact with each other by sharing their responses during the learning. The interaction between the convolutional filter models can mine hard samples and prevent easy samples from overwhelming them, which improve their discriminative capacity. In addition, to achieve a more accurate target location, we develop a fusion mechanism based on the confidences of the independent predictions. We evaluate the proposed method on five public datasets including OTB-2013, OTB-2015, VOT-2016, VOT-2017, Temple-Color-128, and LaSOT. The comprehensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods.
Enabling accuracy-aware Quantum compilers using symbolic resource estimation

Approximation errors must be taken into account when compiling quantum programs into a low-level gate set. We present a methodology that tracks such errors automatically and then optimizes accuracy parameters to guarantee a specified overall accuracy while aiming to minimize the implementation cost in terms of quantum gates. The core idea of our approach is to extract functions that specify the optimization problem directly from the high-level description of the quantum program. Then, custom compiler passes optimize these functions, turning them into (near-)symbolic expressions for (1) the total error and (2) the implementation cost (e.g., total quantum gate count). All unspecified parameters of the quantum program will show up as variables in these expressions, including accuracy parameters. After solving the corresponding optimization problem, a circuit can be instantiated from the found solution. We develop two prototype implementations, one in C++ based on Clang/LLVM, and another using the Q# compiler infrastructure. We benchmark our prototypes on typical quantum computing programs, including the quantum Fourier transform, quantum phase estimation, and Shor's algorithm.
Source-free Domain Adaptation via Distributional Alignment by Matching Batch Normalization Statistics

In this paper, we propose a novel domain adaptation method for the source-free setting. In this setting, we cannot access source data during adaptation, while unlabeled target data and a model pretrained with source data are given. Due to lack of source data, we cannot directly match the data distributions between domains unlike typical domain adaptation algorithms. To cope with this problem, we propose utilizing batch normalization statistics stored in the pretrained model to approximate the distribution of unobserved source data. Specifically, we fix the classifier part of the model during adaptation and only fine-tune the remaining feature encoder part so that batch normalization statistics of the features extracted by the encoder match those stored in the fixed classifier. Additionally, we also maximize the mutual information between the features and the classifier's outputs to further boost the classification performance. Experimental results with several benchmark datasets show that our method achieves competitive performance with state-of-the-art domain adaptation methods even though it does not require access to source data.
BERT Model Applications: Question Answering System

We are surrounded by massive amounts of information present in the form of documents, images, blogs, websites, and more. In most cases, we always look for a direct answer instead of reading the entirety of lengthy documents. Question answering systems are generally being used for this purpose. These systems scan thorough a corpus of documents and provide you with the relevant answer or paragraph. It is part of the computer science discipline in the field of information retrieval and NLP, which focuses on building systems that automatically extract an answer to questions posed by humans or machines in a natural language.
[Paper] Personalized Recommendation of Tumblr Posts Using Graph Convolutional Networks with Preference-aware Multimodal Features

Tumblr is a popular micro-blogging service on which users can share posts comprising text and images. This paper presents a method for personalizing post recommendations for each user from a large number of posts. Specifically, we develop a supervised multi-variational auto encoder considering user preference (SMVAE-UP). SMVAE-UP can extract relationships between text and image features by considering class information representing a user's preference for each post; thus, preference-aware multimodal features can be calculated. Furthermore, for each target user, a network that enables comparison between a user and posts in the same feature space is constructed using the preference-aware multimodal features and metadata on posts. By applying graph convolutional networks (GCNs) to the network constructed for each target user, an accurate recommendation matching each user's preferred posts becomes feasible. Experimental results for real-world datasets including six users and 99,844 posts show the effectiveness of our method.
Improved Lightweight Cloud Storage Auditing Protocol for Shared Medical Data

Now, it is common for patients and medical institutions to outsource their data to cloud storage. This can greatly reduce the burden of medical information management and storage and improve the efficiency of the entire medical industry. In some cases, the group-based cloud storage system is also very common to be used. For example, in an medical enterprise, the employees outsource the working documents to the cloud storage and share them to the colleagues. However, when the working documents are outsourced to the cloud servers, how to ensure their security is a challenge problem for they are not controlled physically by the data owners. In particular, the integrity of the outsourced data should be guaranteed. And the secure cloud auditing protocol is designed to solve this issue. Recently, a lightweight secure auditing scheme for shared data in cloud storage is proposed. Unfortunately, we find this proposal not secure in this paper. It’s easy for the cloud server to forge the authentication label, and thus they can delete all the outsourced data when the cloud server still provide a correct data possession proof, which invalidates the security of the cloud audit protocol. On the basis of the original security auditing protocol, we provide an improved one for the shared data, roughly analysis its security, and the results show our new protocol is secure.
Digital interfaces of historical newspapers: opportunities, restrictions and recommendations

Many libraries offer free access to digitised historical newspapers via user interfaces. After an initial period of search and filter options as the only features, the availability of more advanced tools and the desire for more options among users has ushered in a period of interface development. However, this raises a number of open questions and challenges. For example, how can we provide interfaces for different user groups? What tools should be available on interfaces and how can we avoid too much complexity? What tools are helpful and how can we improve usability? This paper will not provide definite answers to these questions, but it gives an insight into the difficulties, challenges and risks of using interfaces to investigate historical newspapers. More importantly, it provides ideas and recommendations for the improvement of user interfaces and digital tools.
Stewardship and analysis of big Earth observation data

Big Earth observation (EO) data have played an important role in fields such as quantifying global forest changes (Hansen et al., 2014), tracking global urban expansion (Liu et al., 2019), and mapp...
Sequential motor learning transfers from real to virtual environment

Abstract Background Skill acquisition of motor learning between virtual environments (VEs) and real environments (REs) may be related. Although studies have previously examined the transfer of motor learning in VEs and REs through the same tasks, only a small number of studies have focused on studying the transfer of motor learning in VEs and REs by using different tasks. Thus, detailed effects of the transfer of motor skills between VEs and REs remain controversial. Here, we investigated the transfer of sequential motor learning between VEs and REs conditions. Methods Twenty-seven healthy volunteers performed two types of sequential motor learning tasks; a visually cued button-press task in RE (RE task) and a virtual reaching task in VE (VE task). Participants were randomly assigned to two groups in the task order; the first group was RE task followed by VE task and the second group was VE task followed by RE task. Subsequently, the response time in RE task and VE task was compared between the two groups respectively. Results The results showed that the sequential reaching task in VEs was facilitated after the sequential finger task in REs. Conclusions These findings suggested that the sequential reaching task in VEs can be facilitated by a motor learning task comprising the same sequential finger task in REs, even when a different task is applied.
WITHDRAWN: Identification of optimized features using nature-inspired meta-herustics based optimizations in breast cancer detection

Breast cancer is one of the significant reasons for death among women in today’s world. As early detection saves the patient, much research work is going on in this area to diagnose the disease in the early stages. Traditional machine learning models can effectively diagnose breast cancer even though still improvement is needed to get the most accurate results. Swarm intelligence algorithms help in giving an accurate diagnosis. In this paper, different swarm intelligence algorithms like Particle Swarm optimization, Ant colony, Firefly algorithm and Bat optimization were implemented. The performance of these algorithms was evaluated using the Naive Bayes classifier. The results show that particle swarm optimization and firefly algorithm has generated the highest accuracy of 98%.
The Development of Ethnocultural Competence of University Students During COVID-19 Pandemic in Russia

The paper concerns the importance of ethnocultural competence development of students in a polyethnic university environment.The study considers the value of ethnocultural competence from the point of view of creating conditions for positive ethnocultural interaction and forming students' ability to follow cross-cultural behavior standards.The purpose of the research was to define whether an online format is effective in providing for ethnocultural competence development, taking into account the students' opinions.The article analyzes the ways to improve the consequences caused by COVID-19 in the sphere of cross-cultural communication of students with a diverse ethnocultural background that study mostly online or have blended learning at Southern Federal University, Russia.The negative impact of the pandemic period on the process of ethnocultural competence development is observed considering technological and methodological drawbacks of online education.The research was based on the results of digital surveys, tests, questionnaires, and online class interviews, and involved 120 students of different majors.The study examines the attitude of students to the difficulties they have in the process of ethnocultural communication and takes into consideration their preferences concerning online and offline forms of education.The recommendations for improving the development of ethnocultural competence in online format are given.
A Novel Driving Behavior Learning and Visualization Method With Natural Gaze Prediction

Driving behavior analysis is vital for the advanced driving assistance system, aiming to improve driving behavior and decrease traffic accidents.Most existing driving behavior learning methods focus on either vehicle sensor information or driver's attention information, and provide a classification result on the current time data samples.The visualization of driving behavior on time series data samples could give an understanding and review of the driver's continuous actions.However, there has been little progress in combining the multi-modal vehicle and driver information on driving behavior learning and visualization.A multi-information driving behavior learning and visualization method with natural gaze prediction is proposed in this paper, which automatically integrates driver's gaze direction estimated from face camera, and various vehicle sensor data collected from on-board diagnostics (OBD) system.To accurately estimate the eye gaze under large head movement, a novel head pose-free eye gaze prediction method without calibration is proposed based on global and local scale sparse encoding, which treats the direction mapping as small gaze region classification.To understand driving behavior more intuitively, the latent features that represent different driving behaviors are extracted by FastICA from the fused time series data, and mapped into RGB color space for distinguished visualization.Experimental results demonstrate the effectiveness of the proposed method, and show that the proposed method performs better than the compared methods.
An Outlier Robust Finite Impulse Response Filter With Maximum Correntropy

Non-Gaussian noise is common in industrial applications, and it is a severe challenge to existing state estimators.In this paper, a novel robust maximum correntropy finite impulse response (MCFIR) filter is proposed to deal with the state estimation problem in the linear state-space system corrupted by outliers.The filter operates as a finite memory form, and thus it obtains superior immunity to noise statistics and process uncertainties than existing Kalman-like robust filters.Gaussian correntropy is adopted to generate a new cost function, which improves the filter robustness to outlier interference.We derive an unbiased MCFIR filter that ignores noise statistics and propose an improvement bias-constrained MCFIR filter to achieve better estimate accuracy.To improve the filtering performance degradation caused by improper kernel size, an adaptive kernel size algorithm is further proposed, which adjusts the bandwidth within a specific range adaptively and achieves significant improvement in the MCFIR filter.An illustrative example based on moving target tracking is presented to evaluate the performance of the proposed filter, and simulation results confirmed that the MCFIR filter obtained superior immunity to outliers than the existing robust filters.
The Evolution of Software Design Practices Over a Decade: A Long Term Study of Practitioners.

We present the results of a survey of 248 software practitioners conducted in three phases ten years apart.The goal of the study is to uncover trends in the practice of software design and the adoption patterns of modeling languages such as UML.The first phase was conducted in April-December 2007 and included 113 responses.The second phase was conducted in March-November 2017 and included 115 responses.The third phase is a post-survey study was conducted in November 2018 and included additional questionnaires with 20 participants.All survey phases were conducted online, employed identical solicitation mechanisms, and included the same set of questions.The survey results are analyzed within each phase and across phases.We present the results and analysis of the data identifying upward and downward trends in design and modeling practices.The results indicate a significant increase in the use of well-defined and formal modeling languages, as well as a marked increase in the adoption of Domain-Specific Languages.This is also reflected in a significant increase in the adoption of forward engineering methodologies.A key motivation for this uptake is a concern that programming languages and platforms may become quickly outdated.Unfortunately, there has been a consistent dissatisfaction with modeling tools features, particularly their ability to support effective communication and collaboration.This is mirrored by increasing dissatisfaction with modeling tools usability and learnability.Future projections of this study suggest that diversity in modeling languages and tools is likely to continue to grow, as well as the increase in reliance on models for automated artifacts generation.As such, model and tool interoperability is likely to become an even greater concern for the years to come.The results of this study can help researchers, practitioners, and educators to focus efforts on issues of relevance and significance to the profession.Specifically, this research will advocate to build better software modeling tools and promote modeling to the educators.
Multibyte Electromagnetic Analysis Based on Particle Swarm Optimization Algorithm

This paper focuses on electromagnetic information security in communication systems. Classical correlation electromagnetic analysis (CEMA) is known as a powerful way to recover the cryptographic algorithm’s key. In the classical method, only one byte of the key is used while the other bytes are considered as noise, which not only reduces the efficiency but also is a waste of information. In order to take full advantage of useful information, multiple bytes of the key are used. We transform the key into a multidimensional form, and each byte of the key is considered as a dimension. The problem of the right key searching is transformed into the problem of optimizing correlation coefficients of key candidates. The particle swarm optimization (PSO) algorithm is particularly more suited to solve the optimization problems with high dimension and complex structure. In this paper, we applied the PSO algorithm into CEMA to solve multidimensional problems, and we also add a mutation operator to the optimization algorithm to improve the result. Here, we have proposed a multibyte correlation electromagnetic analysis based on particle swarm optimization. We verified our method on a universal test board that is designed for research and development on hardware security. We implemented the Advanced Encryption Standard (AES) cryptographic algorithm on the test board. Experimental results have shown that our method outperforms the classical method; it achieves approximately 13.72% improvement for the corresponding case.