Reviewerly/exHarmony huggingface dataset:
Towards Constant-Depth Circuits for Dynamic Simulations of Materials on Quantum Computers

Author(s): Bassman, Lindsay; Beeumen, Roel Van; Younis, Ed; Smith, Ethan; Iancu, Costin; Jong, Wibe A de | Abstract: Dynamic simulation of materials is a promising application for near-term quantum computers. Current algorithms for Hamiltonian simulation, however, produce circuits that grow in depth with increasing simulation time, limiting feasible simulations to short-time dynamics. Here, we present a method for generating circuits that are constant in depth with increasing simulation time for a subset of one-dimensional materials Hamiltonians, thereby enabling simulations out to arbitrarily long times. Furthermore, by removing the effective limit on the number of feasibly simulatable time-steps, the constant-depth circuits enable Trotter error to be made negligibly small by allowing simulations to be broken into arbitrarily many time-steps. Composed of two-qubit matchgates on nearest-neighbor qubits, these constant-depth circuits are constructed based on a set of multi-matchgate identity relationships. For an $N$-spin system, the constant-depth circuit contains only $\mathcal{O}(N^2)$ CNOT gates. When compared to standard Hamiltonian simulation algorithms, our method generates circuits with order-of-magnitude fewer gates, which allows us to successfully simulate the long-time dynamics of systems with up to 5 spins on available quantum hardware. This paves the way for simulations of long-time dynamics for scientifically and technologically relevant quantum materials, enabling the observation of interesting and important atomic-level physics.
Khasi Speech Recognition using Hidden Markov Model with Different Spectral Features: A Comparison

In this work, various feature extraction techniques are implemented for a speech data corpus in Khasi Language. This paper provides a comparative performance analysis of widely used spectral features like Mel-Frequency Cepstrum Coefficients (MFCC), Perceptual Linear Prediction (PLP), Linear Predictive Cepstrum Coefficient (LPCC), Linear Predictive Coefficient (LPC) and Linear Predictive Reflection Coefficients (LPREFC). For each features we have used coefficient dimensions of static, delta and acceleration evaluated for the recognition of words spoken in Khasi language, a language spoken in the Khasi and Jaintia hills districts of Meghalaya. A recorder based continuous speech database has been collected as per the standard procedure and data processing was done using wave surfer. From the analysis, it was observed that for sampling rate of 16 KHz and 8 KHz the word recognition accuracy of MFCC and PLP features are better than LPCC, LPC and LPREFC features under all circumstances.
Theedhum Nandrum@Dravidian-CodeMix-FIRE2020: A Sentiment Polarity Classifier for YouTube Comments with Code-switching between Tamil Malayalam and English.

Theedhum Nandrum is a sentiment polarity detection system using two approaches--a Stochastic Gradient Descent (SGD) based classifier and a Long Short-term Memory (LSTM) based Classifier. Our approach utilises language features like use of emoji, choice of scripts and code mixing which appeared quite marked in the datasets specified for the Dravidian Codemix - FIRE 2020 task. The hyperparameters for the SGD were tuned using GridSearchCV. Our system was ranked 4th in Tamil-English with a weighted average F1 score of 0.62 and 9th in Malayalam-English with a score of 0.65. We achieved a weighted average F1 score of 0.77 for Tamil-English using a Logistic Regression based model after the task deadline. This performance betters the top ranked classifier on this dataset by a wide margin. Our use of language-specific Soundex to harmonise the spelling variants in code-mixed data appears to be a novel application of Soundex. Our complete code is published in github at this https URL.
Encyclopaedia Britannica; or, A dictionary of arts and sciences, compiled upon a new plan in which the different sciences and arts are digested into distinct treatises or systems; and the various technical terms, etc., are explained as they occur in the order of the alphabet : Preservation Lab Treatment Report

These are volumes one and two of a three volume set of Encyclopaedia Britannica. Both volumes were received by the Preservation Lab for stabilization and rehousing. Both books are full leather bindings with components worn and either detached or semi-detached.
APPLICATION OF AUGMENTED REALITY (AR) IN BULGARIAN SCHOOL AND ITS POTENTIAL IMPACT ON THE FUTURE OF BULGARIAN EDUCATION

The fastest evolution of technology has changed the face of education, especially when technology was combined with adequate pedagogical foundations. This combination has created new opportunities for improving the quality of teaching and learning experiences. Augmented Reality (AR) is one of the latest technologies that offer a new way to educate. Due to the rising popularity of mobile devices globally, the widespread use of AR on smartphones and tablets has become a growing phenomenon.Augmented reality appears as an emerging technology that promises to make "educational immersion" available to practically everyone. Augmented Reality (AR) is a technology that permits to overlay computer graphics onto the real world. AR interfaces enhance the real-world experience, unlike other computer interfaces that draw users away from the real world and onto the screen. Using advanced technology an empty space is turned into a very rich educational experience. AR interface is a visualization technology that can take advantage of the limitations offered by other visual means of communication for learning. Nowadays, Bulgarian schools are changed slowly: the vast majority of teachers continue to bore students with ineffective, non-interactive approaches to education that result in little more than the teacher's notes becoming the students' notes without passing through the their minds of either. True learning is experiential. Students learn best by doing, not by reading or listening to lectures. The more senses are involved (sound, sight, touch, emotions, etc.), the more powerful the learning experience. That's why today's best teachers are those pioneering individuals who take the effort to engage their students in meaningful activities that reach students at multiple levels.To the moment, there is a lack of studies with focus on investigating factors such as: the use, advantages, limitations, effectiveness, challenges, and features of augmented reality in Bulgarian educational settings. This paper presents Augmented Reality (AR), how it can apply to Bulgarian school and training, and the potential impact on the future of Bulgarian education.
THE KNOWLEDGE GRAPH APPLICATION IN LANGUAGE LEARNING STUDY CASE: CHINESE LANGUAGE

J. Naranjo1, V. Stoffová2, C. Zhu3 1Eotvos Lorand University (HUNGARY) 2Trnava University (SLOVAKIA) 3Lanzhou University of Technology (CHINA)
A Parameter-free Clustering Algorithm based K-means

Clustering is one of the relevant data mining tasks, which aims to process data sets in an effective way.This paper introduces a new clustering heuristic combining the E-transitive heuristic adapted to quantitative data and the k-means algorithm with the goal of ensuring the optimal number of clusters and the suitable initial cluster centres for k-means.The suggested heuristic, called PFK-means, is a parameter-free clustering algorithm since it does not require the prior initialization of the number of clusters.Thus, it generates progressively the initial cluster centres until the appropriate number of clusters is automatically detected.Moreover, this paper exposes a thorough comparison between the PFK-means heuristic, its diverse variants, the E-Transitive heuristic for clustering quantitative data and the traditional kmeans in terms of the sum of squared errors and accuracy using different data sets.The experiments results reveal that, in general, the proposed heuristic and its variants provide the appropriate number of clusters for different real-world data sets and give good clusters quality related to the traditional k-means.Furthermore, the experiments conducted on synthetic data sets report the performance of this heuristic in terms of processing time.
Sistem Pendukung Keputusan Untuk Rekomendasi Topik Skripsi Dengan Metode Fuzzy AHP

The ability to produce a scientific work both journals, scientific articles and thesis is needed to support the competence of undergraduate students in a university. The existence of a thesis course in a number of universities is one of the efforts to train students in producing a scientific work. To arrive at the stage of making a thesis, a student must first study various kinds of courses in each study program. Choosing a thesis topic is one of the most difficult stages in the preparation of a thesis. This is because there are still many students who do not understand and know their interest and ability in one of the courses which will later be used as a topic in thesis preparation. The focus of this research is a decision support system that is able to answer student problems in determining thesis topics according to their abilities. The method used is a waterfall within the SDLC (system development life cycle) framework starting from system requirement, data collection, design, implementation, testing, and maintenance. The result of this research is the ability of the decision support system to provide recommendations for thesis topics according to th e abilities of students. The greatest weight is for A 5 students for Computer Networking interest of 1,410 while the smallest weight is for A 4 students for Multimedia interest of 0. 080 .
IMPLEMENTING INSTAGRAM APP AS AN EDUCATIONAL TOOL IN HIGHER EDUCATIONAL ESTABLISHMENTS (CREATIVE APPROACH)

The coronavirus pandemic with Universities worldwide moving on-line and students starting to feel deprived of communication and interaction has made educational establishments rearrange their teaching methods and turn to various means of interactive learning once not seeing as educational tools. In a tech-savvy community with most of the students being avid proficient Instagram users the former has become an integral part of today’s life, yet the interest to this platform as a possible e-learning component of distance learning programs at the University level was long not approved and neglected. The article investigates Instagram properties and gives guidelines on how to implement the application for educational purposes, serving as both supplementary and independent teaching tool. It analyses the application’s potential as a complementary educational element used in the classroom to encourage blended learning in a mixed educational environment. Furthermore it introduces approach to the application under consideration as the core element of the overall on-line study course. The author goes into study of Instagram efficiency in teaching foreign languages at its all levels, namely mastering pronunciation, speaking, listening, spelling, writing and reading skills. Moreover, the article investigates motivational aspect, ways to bring teaching outside the classroom and make e-learning more interactive. The author considers the means of encouraging students into collaborative activities, promoting interpersonal skills and peer feedback in the environment where e-learning is the only educational option. The article suggests ways to assess students’ Instagram activity and offers the system of evaluating their advance in studying the information presented by the lecturer on Instagram page. It proves beneficial effects of Instagram as an educational tool, which offers students the learning process that they enjoy and which makes studying engrossing and insightful.
NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video

We present a novel framework named NeuralRecon for real-time 3D scene reconstruction from a monocular video. Unlike previous methods that estimate single-view depth maps separately on each key-frame and fuse them later, we propose to directly reconstruct local surfaces represented as sparse TSDF volumes for each video fragment sequentially by a neural network. A learning-based TSDF fusion module based on gated recurrent units is used to guide the network to fuse features from previous fragments. This design allows the network to capture local smoothness prior and global shape prior of 3D surfaces when sequentially reconstructing the surfaces, resulting in accurate, coherent, and real-time surface reconstruction. The experiments on ScanNet and 7-Scenes datasets show that our system outperforms state-of-the-art methods in terms of both accuracy and speed. To the best of our knowledge, this is the first learning-based system that is able to reconstruct dense coherent 3D geometry in real-time.
EU-Citizen.Science offer funding for 10 training modules

This new call from EU-Citizen.Science provides funding for the development, implementation and testing of 10 training modules on the topic of citizen ...
Insider Risk Management

The world of the modern workplace offers innovative technology that employees love. It empowers them to communicate, collaborate, and produce with agility. In this world, trusting your employees is the key to creating a dynamic, inclusive workspace and increasing productivity. But with trust also comes risk, a risk that an employee may negligently breach that trust by inadvertently leaking confidential information and corporate communications. Or an employee maliciously violates that trust by stealing intellectual property.
Towards a Unified Approach to Single Image Deraining and Dehazing

We develop a new physical model for the rain effect and show that the well-known atmosphere scattering model (ASM) for the haze effect naturally emerges as its homogeneous continuous limit. Via depth-aware fusion of multi-layer rain streaks according to the camera imaging mechanism, the new model can better capture the sophisticated non-deterministic degradation patterns commonly seen in real rainy images. We also propose a Densely Scale-Connected Attentive Network (DSCAN) that is suitable for both deraining and dehazing tasks. Our design alleviates the bottleneck issue existent in conventional multi-scale networks and enables more effective information exchange and aggregation. Extensive experimental results demonstrate that the proposed DSCAN is able to deliver superior derained/dehazed results on both synthetic and real images as compared to the state-of-the-art. Moreover, it is shown that for our DSCAN, the synthetic dataset built using the new physical model yields better generalization performance on real images in comparison with the existing datasets based on over-simplified models.
SAR Image Recording Strategy to Improve Characteristics

The low information consistency and serious interference caused by synthetic aperture radar (SAR) imaging characteristics and target attributes affect the accuracy of SAR image information description, which reduces the performance of SAR image registration. In this work, the polar speeded up robust features (PSURF) descriptor is proposed for SAR image registration. In PSURF, according to the advantages of pole space in the relationship between pixels, a quantitative model of information change based on pole space is established to improve the integrity, consistency, and separability of image features. Firstly, the support region is transformed into polar coordinates. Then, the image gray level changes in the radial and angular directions are quantified with different scales. Finally, the PSURF descriptor based on the scale of keypoints is constructed covering the omnidirectional space.
Perancangan Sistem Informasi Arsip Kontrak Vendor pada PT PLN (Persero) UPDK Keramasan

The filing process carried out at PT PLN (Persero) UPDK Keramasan is a process that supports the storage and search of SPK contract data (contract data valued above 1M) and SPJ (contract data valued below 1M) against vendors. The storage of SPK and SPJ contract files is still done manually, by writing in a ledger and copying them into Microsoft Excel. After that, the file is printed and stored. This method makes archive management not optimal. Therefore, it is necessary to design an archive information system that is useful for assisting PT PLN in data storage and retrieval. The design is carried out by applying process modeling using DFD and database designing using ERD. The purpose of this research is to design a prototype of an information system that can be used by further researchers in making applications that are used to facilitate company activities, especially in the field of data archiving of PT PLN's SPK and SPJ contracts against vendors. The result of this research is a prototype design of an archival information system at PT PLN for vendors.
Towards Efficient Big Data Storage With MapReduce Deduplication System

In the big data era, there is a high requirement for data storage and processing. The conventional approach faces a great challenge, and de-duplication is an excellent approach to reduce the storage space and computational time. Many existing approaches take much time to pinpoint the similar data. MapReduce de-duplication system is proposed to attain high duplication ratio. MapReduce is the parallel processing approach that helps to process large number of files in less time. The proposed system uses two threshold two divisor with switch algorithm for chunking. Switch is the average parameter used by TTTD-S to minimize the chunk size variance. Hashing using SHA-3 and fractal tree indexing is used here. In fractal index tree, read and write takes place at the same time. Data size after de-duplication, de-duplication ratio, throughput, hash time, chunk time, and de-duplication time are the parameters used. The performance of the system is tested by college scorecard and ZCTA dataset. The experimental results show that the proposed system can lessen the duplicity and processing time.
Products and Movie Recommendation System for Social Networking Sites

Recommendation systems are an integral part of information filtering system in data science, that are widely used in order to identify the pattern a user would likely choose on the basis of the previous choices of the user as well as from studying the pattern in which others have chosen. For a fact, the recommendation can never be a cent percent correct at providing recommendations to the user but can be close enough to please them to a certain extent. Thus, the same is widely used in the industries these days to get higher profit and have a good hold in the market. The data scientists of every company design some algorithm that studies the information from the social network and clusters the data. There can be a single algorithm for classification like k-Means clustering or Hidden Markov model or can be done by bagging and boosting techniques. With this technique of displaying the movies or products into the profile of a particular customer, they not only increase their business but also enhances the customer experiences but there are several issues related to the standard techniques like the cold start problem, shrill attack, etc. thereby increasing the scope of research in this field. This work deals with both Collaborative Filtering and Content-Based Filtering to form a product and movie recommendation system for the social networking sites that shows the effectiveness of collaborative filtering and portrays the challenges faced by content-based filtering.
Web Based e-Posyandu at Posyandu Teratai Putih Bahalayung Village using CodeIgniter Framework

Posyandu, which stands for Integrated Health Family Planning Service Post, is a basic health activity organized for people who are assisted by health workers, one of which is the Posyandu Teratai Putih Bahalayung Village. There are several obstacles in this manual system, including Posyandu cadres who often experience difficulties in the data search process, this is because these cadres may not remember the location of the data being searched for or do not understand the data written by other cadres. The system development method used is the Waterfall methodology, from the requirements analysis stage to system testing. For system requirements analysis, the data taken is posyandu data from 2019. For the system design stage, using ERD and DFD, namely Context Diagram and DFD Level 1. For the implementation stage of the system using the Codeigniter framework, programming languages ??PHP, HTML, and Javascript, and the MySQL query language. And the last stage for testing the system uses the Black Box Testing technique.
Computer Research Project Management

Most research project managers, laboratories directors, young researchers at the beginning stage of thesis or professional research projects leaders are well effective at dealing with planned, scheduled eventsthey know how to function in conducting their research projects according to traditional knowledge areas of classical processes lied to time, cost, human resources, risk, stakeholders, and quality management.Unfortunately, they may have little specific training in selecting the best thematic of research.Indeed, they have no experience in identifying adequate research problems.Despite their motivation for the selected project and research thematic, they don't well master research problematic and how to deal with: Literature for the selected thematic of research: (Sources, Documents, reports and technical folders): List of problems encountered during the research theme conducting and how to make profit of the obtained solutions approaches for these kinds of research problems.-How to decide if this research theme and the list of connected problems are already resolved or not by any other research team.This paper aims to develop this idea and finally to propose ontology named "Onto-Research-Project" that formalizes all the domain knowledge of computer research projects.Our final goal is to propose an approach for historical research projects reusing.The output of this approach is a computer research project memory.In this way, we have to make use and to restructure the knowledge obtained from the research computer projects stored in the database "HAL-Archives-Nouvelles".
Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution Pipeline

Imaging is usually a mixture problem of incomplete color sampling, noise degradation, and limited resolution. This mixture problem is typically solved by a sequential solution that applies demosaicing (DM), denoising (DN), and super-resolution (SR) sequentially in a fixed and predefined pipeline (execution order of tasks), DM$\to$DN$\to$SR. The most recent work on image processing focuses on developing more sophisticated architectures to achieve higher image quality. Little attention has been paid to the design of the pipeline, and it is still not clear how significant the pipeline is to image quality. In this work, we comprehensively study the effects of pipelines on the mixture problem of learning-based DN, DM, and SR, in both sequential and joint solutions. On the one hand, in sequential solutions, we find that the pipeline has a non-trivial effect on the resulted image quality. Our suggested pipeline DN$\to$SR$\to$DM yields consistently better performance than other sequential pipelines in various experimental settings and benchmarks. On the other hand, in joint solutions, we propose an end-to-end Trinity Pixel Enhancement NETwork (TENet) that achieves state-of-the-art performance for the mixture problem. We further present a novel and simple method that can integrate a certain pipeline into a given end-to-end network by providing intermediate supervision using a detachable head. Extensive experiments show that an end-to-end network with the proposed pipeline can attain only a consistent but insignificant improvement. Our work indicates that the investigation of pipelines is applicable in sequential solutions, but is not very necessary in end-to-end networks. \RR{Code, models, and our contributed PixelShift200 dataset are available at \url{https://github.com/guochengqian/TENet}