Reviewerly/exHarmony huggingface dataset:
RestoreNet: a deep learning framework for image restoration in optical multi-aperture imaging system

Imaging blur is an inevitable problem because of the low response to medium frequency in optical multi-aperture imaging system, in which Wiener filtering is usually used in implementing image restoration to obtain clear highresolution images. Recent notable developments in the field of deep learning have opened up exciting avenues inspiring us to use data-driven approach for image deblurring in optical multi-aperture imaging system. In this paper, a deep learning framework named RestoreNet, which is based on a U-shaped convolution neural network, is proposed to replace the general Wiener filtering for image restoration. Numerical simulation and experiment results show that RestoreNet could recover the imaging map from system successfully, just like Wiener filtering does. However, RestoreNet only needs one dataset containing a few images for training, and shows strong image restoration ability without the point spread function or optical transfer function of system in testing, as well as the priori information of object and noise. As a result, RestoreNet is an effective alternative in image restoration of the optical multi-aperture imaging system.
Implementation Approach of Indian Language Gujarati Grammar's Concept “sandhi” using the Concepts of Rule-based NLP

The term ‘language’ in NLP has to be understood as natural languages like Gujarati, Hindi, English etc., which we use in daily life to communicate. Most of the NLP research has been centered on English & other European Languages. NLP research concerning the Indian language like Gujarati is commenced in the last few years. The centre of attention of this paper is to demonstrate the road map of implementation of Gujarati grammar's concept “sandhi ”. In our words sandhi is a word segmentation process & it is present in most of the South Asian language, such as Devnagri, Sanskrit, Hindi, and Gujarati & even in Chinese & Thai languages.” Sandhi leads to phonetic transformation at word boundaries of a written chunk (small part), and the sounds at the end of word join together to form a single chunk of the character sequence.” Our main spotlight is on rule-based implementation of “sandhi”. Similar to every Indian scripting language Gujarati language (Grammar) also has its own specified rules of composition for combining the consonants, vowels and modifiers. We have identified certain rules by which we accomplish the practical implementation of “sandhi ”. There are many sandhi rules available, each denoting a unique combination of phonetic transformations, documented in the grammatical tradition of Gujarati. The Sandhi does not make any syntactic or semantic changes to the words implicated. Sandhi is an elective operation that depends only on the alertness of the writer.
Proses Komunikasi melalui Media Visual Fotografi sebagai Ilustrasi

Artwork generally contains a message or idea to be communicate to others as a viewer. From the various situations that can be captured in a visual appearance of the work of Photography, many things can be delivered into an idea that can have different meanings depending on the impression captured by the photographer and also depending on the interpretation of the viewer.&#x0D; However, considering the work of Photography as a visual work in two-dimensional form, of course there are advantages and disadvantages of expressing ideas or meanings that the photographer might want to deliver, so maybe there is different perception, and not everybody can clearly understand the intent and impression the photographer means. Different interpretations from the photographer's point of view and the point of view of the observer are actually relatively reasonable, considering that each person with different emotional experiences and from different points of view can produce different reactions.&#x0D; With the visual appearance of the photography work, many people use this visual information to explain some information. Either in articles, in stories, or in a work procedures or processes. So that the photo works will be a tool to support, to clear up and clarify information. This journal will discuss about photography as a visual media that support an information commonly known as Illustration Photography.
Mobile Apps and Sustainable Development of Voluntary Organizations

ICT support in voluntary organizations is an interesting emergent field of research. The voluntary organizations are aware about the use of technology in their activities but the complexity of technologies, lack of technological knowledge, lack of funds and lack of standards are big obstacles. There is a need to transform the operation and structure of these organizations, as the roles and functions of voluntary organizations have significantly expanded in recent years. The current research objective is to investigate the current scenario of the implementation of ICT by the voluntary organizations. The objectives of the study were to study the usage of ICT, impact of ICT and satisfaction level of ICT in voluntary organizations, also to study the issues and challenges they are facing. The study was conducted in and around Pune. Questionnaires were managed to various voluntary organizations and the results of this are analyzed. Based on the investigation, recommendations are made and mobile application using open source technologies is suggested.
Spotify at TREC 2020: Genre-Aware Abstractive Podcast Summarization.

This paper contains the description of our submissions to the summarization task of the Podcast Track in TREC (the Text REtrieval Conference) 2020. The goal of this challenge was to generate short, informative summaries that contain the key information present in a podcast episode using automatically generated transcripts of the podcast audio. Since podcasts vary with respect to their genre, topic, and granularity of information, we propose two summarization models that explicitly take genre and named entities into consideration in order to generate summaries appropriate to the style of the podcasts. Our models are abstractive, and supervised using creator-provided descriptions as ground truth summaries. The results of the submitted summaries show that our best model achieves an aggregate quality score of 1.58 in comparison to the creator descriptions and a baseline abstractive system which both score 1.49 (an improvement of 9%) as assessed by human evaluators.
REVEALING AUDITOR AND AUDITEE SATISFACTION IN THE EVOLUTION OF ACCOUNTING SOFTWARE (PHENOMENOLOGY STUDY IN THE REGIONAL GOVERNMENT OF SOUTH NIAS DISTRICT)

The development of the evolution of accounting software has made significant changes to the ease of financial information and decision making for leaders in each organizational unit, especially for accountants who work in the audit department (as auditors) and in accounting (as auditees). The purpose of this study is to determine the essence of satisfaction for auditors and auditors behind the evolution of accounting software. This research is a qualitative research with interpretive phenomenology method. The research data were obtained through in-depth interviews and observations. The results showed that each informant has his own perception about the evolution of accounting software. Informants (auditors and auditees) consider that the evolution of accounting software provides convenience and satisfaction in the process of completing their work as accountants, especially in presenting data quickly and accurately. Furthermore, informants have confidence in carrying out their duties as accountants even though they are not from an accounting education background because accounting software provides applications that are easy to learn and familiar to ordinary people. Accountants are required to be professional in their fields because their work is not just debiting, posting and printing financial reports because accounting software has not been able to carry out some of the functions of accountants in validating transaction data, making analyzes, conducting reconciliation, evaluating and recommendations, therefore accountants are required to have competence and expertise to support optimal performance. As a recommendation from the informant to the leaders of each organization that even though the evolution of accounting software has provided job satisfaction and helped the work processes of accountants, to achieve good and optimal performance is largely determined by the principle of the right man on the right place.
Conformer-Kernel with Query Term Independence at TREC 2020 Deep Learning Track.

We benchmark Conformer-Kernel models under the strict blind evaluation setting of the TREC 2020 Deep Learning track. In particular, we study the impact of incorporating: (i) Explicit term matching to complement matching based on learned representations (i.e., the “Duet principle”), (ii) query term independence (i.e., the “QTI assumption”) to scale the model to the full retrieval setting, and (iii) the ORCAS click data as an additional document description field. We find evidence which supports that all three aforementioned strategies can lead to improved retrieval quality.
Shape-Based Video Watermarking Robust Against Affine Attacks

The abstract should summarize the contents of the paper in short terms. With the increasing use of the Internet and other digital data, there is a strong need to protect the illegal duplication, editing, or modifying of these data. One such approach to meet this demand is to use watermarking. Such mark must be transparent to legitimate users of the media yet robust against geometric attacks or any image processing. This paper presents a new model, based on shape, for rendering video watermarking scheme resistant to geometric attacks and image processing. This is achieved by means of a new image transform to rotation, scaling, and translation (RST) based on ideas from shape theory and the utilization of the scale-invariant feature transform (SIFT) detector for locating robust feature points. Indeed any detector that produces such robust feature points can be used. This model was first developed and applied to standard image processing based on a new image transform into such RST invariant domain (Morris and Muhi El-Ddin in Proceedings of IPV08, 2008 [1]; El-Ddin in Watermarking schemes robust against affine attacks. Ph. D. Thesis, Claremont Graduate University, Los Angeles, 2008 [2]; Muhi El-Ddin et al. in Proceedings of the Fifth International Conference on Information Technology: New Generations (ITNG 2008), pp. 795–800, 2008 [3]; Muhi El-Ddin in Proceedings of the 41st Annual Asilomar Conference on Signals, Systems, and Computers, pp. 565–569, 2007 [4]). In this work, we utilize the shape theory and the curvelet transform to introduce an RST invariant map from a video scene to a set of universal video watermarking prisms. In this invariant domain, we have embedded a watermark into the low frequency component of the curvelet transforms of each of the three universal videos and assured RST invariance.
Achievements and Impact of the Big Data Value Public-Private Partnership: The Story so Far

Abstract The European contractual Public-Private Partnership on Big Data Value (BDV PPP) has played a central role in the implementation of the revised Digital Single Market strategy, contributing to multiple pillars, including “Digitising European Industry”, “Digital Skills”, “Building the European Data Economy” and “Developing a European Data Infrastructure”. The BDV PPP and the Big Data Value Association have also played a pivotal role in the European Artificial Intelligence and Data Strategies launched by the European Commission in 2018. This chapter provides an overview and an in-depth analysis of the impact of the PPP by mid-2019, with a focus on the achievements and the overall impact since the launch of the PPP.
SPEECH RECOGNITION ROBOT

Speech recognition software has advanced greatly since it had been first invented, but still it's several big problems that prevent it from being employed exclusively as a way of transcription. one in every of the foremost basic speech recognition problems is thatthe quality of the input devices getting used. if a microphone isn't sensitive enough or is overly sensitive then it can create audio information that's difficult for the software to decipher. Difference in pronunciation, accents, and speaking cadence combine to make one in every of the more pervasive speech recognition problems. When one word is often pronounced in several ways, the software can become confused and misinterpret what's being said. the identical happens when an individual speaks slower or faster than the program expects. an analogous problem stems from background that may be problematic to filtrate the most speech and might cause inaccurate translations when included within the speech processing. Speech is one amongst the foremost important way of communication for people. Using speech as an interface for processes has become important. This project it's implemented to regulate a robot with speech comments. The speech recognition system is trained in such a way that it recognizes defined commands and also the design system will navigate supported the instructions through the speech commands. The received voice signal Is identified and also the voice signals are quantified, sampled and also the discretization process takes place. To the get feature extraction of the speech signal we use MEL-Frequency Cepstrum Coefficients (MFCC) method and KNN (Nearest neighbor classifier).
Classification of the streaming approximability of Boolean CSPs

A Boolean constraint satisfaction problem (CSP), Max-CSP$(f)$, is a maximization problem specified by a constraint $f:\{-1,1\}^k\to\{0,1\}$. An instance of the problem consists of $m$ constraint applications on $n$ Boolean variables, where each constraint application applies the constraint to $k$ literals chosen from the $n$ variables and their negations. The goal is to compute the maximum number of constraints that can be satisfied by a Boolean assignment to the $n$~variables. In the $(\gamma,\beta)$-approximation version of the problem for parameters $\gamma \geq \beta \in [0,1]$, the goal is to distinguish instances where at least $\gamma$ fraction of the constraints can be satisfied from instances where at most $\beta$ fraction of the constraints can be satisfied. In this work we completely characterize the approximability of all Boolean CSPs in the streaming model. Specifically, given $f$, $\gamma$ and $\beta$ we show that either (1) the $(\gamma,\beta)$-approximation version of Max-CSP$(f)$ has a probabilistic streaming algorithm using $O(\log n)$ space, or (2) for every $\varepsilon > 0$ the $(\gamma-\varepsilon,\beta+\varepsilon)$-approximation version of Max-CSP$(f)$ requires $\Omega(\sqrt{n})$ space for probabilistic streaming algorithms. Previously such a separation was known only for $k=2$. We stress that for $k=2$, there are only finitely many distinct problems to consider. Our positive results show wider applicability of bias-based algorithms used previously by [Guruswami-Velingker-Velusamy APPROX'17], [Chou-Golovnev-Velusamy FOCS'20] by giving a systematic way to explore biases. Our negative results combine the Fourier analytic methods of [Kapralov-Khanna-Sudan SODA'15], which we extend to a wider class of CSPs, with a rich collection of reductions among communication complexity problems that lie at the heart of the negative results.
KEBIJAKAN SELEKSI DALAM MENDUKUNG STANDAR KOLEKSI DI PERPUSTAKAAN SEKOLAH

The school library is a library in a school to support the teaching and learning activities and objectives of the parent school. In terms of the collection, the library has a standard, both from the number of collections as well as the depth of the collection. There needs to be the selection policy as part of a collection development policy so that these standards are fulfilled. This paper uses literature study method. This paper provides guidance to the school librarian about the things that need to be considered in making the selection policy. Selection policy must consider the amount of collection, type of collection, the suitability of the curriculum, language, and so forth.
LumNet

High-quality lighting positively influences visual performance in humans. The experienced visual performance can be measured using desktop luminance and hence several lighting control systems have been developed for its quantification. However, the measurement devices that are used to monitor the desktop luminance in existing lighting control systems are obtrusive to the users. As an alternative, ceiling-based luminance projection sensors are being used recently as these are unobtrusive and can capture the direct task area of a user. The positioning of these devices on the ceiling requires to estimate the desktop luminance in the user's vertical visual field, solely using ceiling-based measurements, to better predict the experienced visual performance of the user. For this purpose, we present LUMNET, an approach for estimating desktop luminance with deep models through utilizing supervised and self-supervised learning. Our model learns visual representations from ceiling-based images, which are collected in indoor spaces within the physical vicinity of the user to predict average desktop luminance as experienced in a real-life setting. We also propose a self-supervised contrastive method for pre-training LUMNET with unlabeled data and we demonstrate that the learned features are transferable onto a small labeled dataset which minimizes the requirement of costly data annotations. Likewise, we perform experiments on domain-specific datasets and show that our approach significantly improves over the baseline results from prior methods in estimating luminance, particularly in the low-data regime. LUMNET is an important step towards learning-based technique for luminance estimation and can be used for adaptive lighting control directly on-device thanks to its minimal computational footprint with an added benefit of preserving user's privacy.
RANCANG BANGUN SISTEM INFORMASI AKUNTANSI PEMBELIAN BAHAN BAKU SECARA KREDIT PADA PT. PROMETAMA SARANA GRAHA TANGERANG

Now a days information technology is not only rapidly developing, but also often changes very quickly. Almost every second new discoveries are always found with the aim of improving or perfecting the previous technology. In the developca of economics and technology when the use of computers feels more needed in order to facilitate human work and be more time efficient. PT. Prometama Sarana Graha requires a system that can support data processing of raw material purchases. For this reason, the author tries to make a final assignment regarding the design of accounting information systems for purchasing raw materials in credit at PT. Prometama Sarana Graha which is currently not computerized. System for purchasing raw materials at PT. Prometama Sarana Graha Still manual, from the process of ordering raw materials to making reports. Computerization of the system is the best solution to solve the problems that exist in PT. Prometama Sarana Graha, And with a computerized system can be achieved an effective and efficient activity in supporting activities at PT. Prometama Sarana Graha, and can reduce the buildup of archives at PT. Prometama Sarana Graha.
LibGuides: Hot Topics: Gender: Websites

Though the terms sex and gender are often been used interchangeably, it is important to know the differences, as our understanding of gender becomes more speciifc and encompasses more than the two biological sexes. In general, gender refers more to the ro
On Benchmarking Quantum Heuristics

Present an outline of important aspects for benchmarking quantum algorithms, in particular quantum heuristics. Using quantum approximate optimization algorithm as an example, we demonstrate how the choice of cost Hamiltonian, of mixer, and of initial states can affect the performance. We also discuss how realistic aspects of near-term quantum hardware will influence the algorithm performance.
Secured Multikeyword Search over Encrypted Cloud Data Based On Quality and Usability

Searchable encryption permits to upload encrypted documents to a remote honest-but-suspicious server and query that data at the server without the papers having to be decrypted first. With the advent of cloud computing, data owners are encouraged to move their sophisticated data management systems from local sites to commercial public clouds for greater flexibility and cost savings. However, in order to safeguard data privacy, sensitive data must be encrypted before being outsourced, rendering traditional data utilization based on plaintext keyword search outdated. As a result, implementing an encrypted cloud data search service is critical. Given the huge number of data users and documents in the cloud, it is critical for the search service to support multi-keyword queries and result similarity ranking in order to meet the effective data retrieval requirement. In this paper propose the Secured Multikeyword Search over Encrypted Cloud Data, which is based On Quality and Usability of cloud data transmission and storage. Further we used triple DES (Data Encryption Standard) Algorithm of encryption and decryption key for secure authentication process. Here we used different key sizes are used in cryptographic process. Our analysis shows that the suggested approach is secure against adaptive chosen-keyword attacks. This solution is highly efficient and ready to be applied in real-world cloud storage systems, and it also achieves better encryption and decryption execution speed. .
Imbalance Data Classification to Identify Fraudulent Transactions

Data is considered to be the oil or fuel for the next generation. Data mining is one ofthe most widely used methods to extract hidden information from large datasets. Themain goal of mining is knowledge discovery from databases, which in known as KDD.Mining and discovery is quite similar in the domain of data mining. We discoverknowledge by doing mining from the databases.Now the question is how to learn from the dataset. The answer is that there is someclassification algorithm for the data mining filed and these are Support Vector Machinealgorithm, Decision tree algorithm, Artificial Neural Network algorithm andAdaboost algorithm etc. We train the dataset using these algorithms whichclassifies for us. Depending on the same or different scenarios, these algorithms'accuracy could be different. Most of the time this problem occurs for bi class datasets,and it also can occur for multi-class datasets as well. Another important term issupervised and unsupervised learning. In supervised learning class labels are knownand at unsupervised learning class labels are unknown. And about our dataset this issupervised learning because which mentioned algorithm we have used those are bestfor supervised learning.
Blockchain Technology for Supply Chain, Health Care, Intellectual Property Rights, E-voting

Blockchain technology (BT) is probably going to produce ground-breaking changes in supply chains, healthcare, IPR etc. BT provide better transparent methods to remove various issues faced in these areas. It improves the benefits by adapting the BT in various fields. It improves health management, identify fake drugs sold in the market, etc. The improved technology of BT can be used in various legal areas like improve IP rights management, working of IP offices etc. In this paper we look into the brief about how BT can be used in the above area.