Reviewerly/exHarmony huggingface dataset:
Smartphone based accurate touch operations on an AR desktop

Recently, we have seen and used touch-based operations as a popular interface for smart phones and tablet PCs. As the interface, we interact with a device through touching the physical surface of the screen. The touch-based manner gives simple and intuitive interface for users but is not always convenient for several users to operate a single device cooperatively. Also, in some cases, manipulating a screen without direct touch is rather desirable. Because touchless operations prevent viruses from infecting the users. It is especially true in the current situation of COVID-19 pandemic. In order to implement such touchless interfaces, researchers have proposed augmented reality (AR) based desktops or keyboards. Most of such AR based interfaces, however, require interactions with virtual objects in the air, which are not accurate. Therefore, such interfaces are rarely used in practice. In this paper, we propose a new operation manner of AR based desktop. In our operation manner, the user can perform operations on his or her smartphone as an AR object displayed in the virtual space on the screen. The smartphone has its own AR marker, which specifies the coordinate of it, and gives the reference point for displaying the cursor. The cursor is always displayed at the center of the smartphone and represents the home position of fingers. Thus, even if the virtual screen is displayed with wide size such as one of a desktop PC, a user moves his or her fingers along with his or her smartphone to the location of a specific icon on the screen, and then can give any operations constituting of single touch or double touch, such as tapping, flicking, pinching or swiping. Notice here that the user physically operates the smartphone although he or she feels like operating the virtual screen through directly touching it. The physical operations on the smartphone contribute to intuitive manipulation and providing actual feelings. They also help the users perform accurate operations. In order to demonstrate the effectiveness of our manipulation manner, we have implemented a prototype system and have conducted numerical experiments on it. The prototype system recognizes an AR marker attached on the users’ smartphones by external web cameras and decides their locations, so that the user can apply any operations of fingertips to the AR objects on the virtual screen. In the experiment, the screen is displayed in front of each user, who can identify and synchronize movement of his or her smartphone with the cursor. Once the cursor reaches the target position, each user can apply one of the following six operations: tap to launch apps, double-tap/drag to move app icons, pinch-out/pinch-in to zoom in and out, and flick to create or switch screens. We focused how the users could grasp the touch operation intuitively and manipulate the system as they intended. As the results, we have confirmed that the users can feel as if they touch the actual screen in the virtual space and manipulate the prototype system as intended.
Meta-Voice: Fast few-shot style transfer for expressive voice cloning  using meta learning

The task of few-shot style transfer for voice cloning in text-to-speech (TTS) synthesis aims at transferring speaking styles of an arbitrary source speaker to a target speaker's voice using very limited amount of neutral data. This is a very challenging task since the learning algorithm needs to deal with few-shot voice cloning and speaker-prosody disentanglement at the same time. Accelerating the adaptation process for a new target speaker is of importance in real-world applications, but even more challenging. In this paper, we approach to the hard fast few-shot style transfer for voice cloning task using meta learning. We investigate the model-agnostic meta-learning (MAML) algorithm and meta-transfer a pre-trained multi-speaker and multi-prosody base TTS model to be highly sensitive for adaptation with few samples. Domain adversarial training mechanism and orthogonal constraint are adopted to disentangle speaker and prosody representations for effective cross-speaker style transfer. Experimental results show that the proposed approach is able to conduct fast voice cloning using only 5 samples (around 12 second speech data) from a target speaker, with only 100 adaptation steps. Audio samples are available online.
QuoteR: A Benchmark of Quote Recommendation for Writing

It is very common to use quotations (quotes) to make our writings more elegant or convincing. To help people find appropriate quotes efficiently, the task of quote recommendation is presented, aiming to recommend quotes that fit the current context of writing. There have been various quote recommendation approaches, but they are evaluated on different unpublished datasets. To facilitate the research on this task, we build a large and fully open quote recommendation dataset called QuoteR, which comprises three parts including English, standard Chinese and classical Chinese. Any part of it is larger than previous unpublished counterparts. We conduct an extensive evaluation of existing quote recommendation methods on QuoteR. Furthermore, we propose a new quote recommendation model that significantly outperforms previous methods on all three parts of QuoteR. All the code and data of this paper are available at https://github.com/thunlp/QuoteR.
ADPCM with nonlinear prediction

Many speech coders are based on linear prediction coding (LPC), nevertheless with LPC is not possible to model the nonlinearities present in the speech signal. Because of this there is a growing interest for nonlinear techniques. In this paper we discuss ADPCM schemes with a nonlinear predictor based on neural nets, which yields an increase of 1-2.5dB in the SEGSNR over classical methods. This paper will discuss the block-adaptive and sample-adaptive predictions.
Match Your Words! A Study of Lexical Matching in Neural Information  Retrieval

Neural Information Retrieval models hold the promise to replace lexical matching models, e.g. BM25, in modern search engines. While their capabilities have fully shone on in-domain datasets like MS MARCO, they have recently been challenged on out-of-domain zero-shot settings (BEIR benchmark), questioning their actual generalization capabilities compared to bag-of-words approaches. Particularly, we wonder if these shortcomings could (partly) be the consequence of the inability of neural IR models to perform lexical matching off-the-shelf. In this work, we propose a measure of discrepancy between the lexical matching performed by any (neural) model and an 'ideal' one. Based on this, we study the behavior of different state-of-the-art neural IR models, focusing on whether they are able to perform lexical matching when it's actually useful, i.e. for important terms. Overall, we show that neural IR models fail to properly generalize term importance on out-of-domain collections or terms almost unseen during training
The Effectiveness of Digital Learning Media in Mobile Programming Courses Using Case-Based Learning

This study aimed at developing digital learning media in mobile programming courses using a case-based learning model.The research method used was research and development (R&D) with the ADDIE research model (analysis, design, development, implementation, and evaluation).The results revealed that digital learning media in the mobile programming course was very good.It was based on an individual test score of 94%, the small group test score of 92.4%, and the large group test result of 90.4%.All results were in the very good category.The effectiveness test results using the gain normality test (n-gain) are 0.754.It means the effectiveness of digital learning media was categorized as effective.
Understanding the Decentralization of DPoS: Perspectives From Data-Driven Analysis on EOSIO

Recently, many Delegated Proof-of-Stake (DPoS)-based blockchains have been widely used in decentralized applications, such as EOSIO, Tron, and Binance Smart Chain. Compared with traditional PoW-based blockchain systems, these systems achieve a higher transaction throughput and are well adapted to large-scale scenes in daily applications. Decentralization is a key element in blockchain networks. However, little is known about the evolution of decentralization in DPoS-based blockchain networks. In this paper, we conduct a systematic analysis on the decentralization of DPoS with data from up to 135,000,000 blocks in EOSIO, the first successful DPoS-based blockchain system. We characterize the decentralization evolution of the two phases in DPoS, namely block producer election and block production. Moreover, we study the voters with similar voting behaviors and propose methods to discover abnormal mutual voting behaviors in EOSIO. The analytical results show that our methods can effectively capture the decentralization evolution and abnormal voting phenomena in the system, which also have reference significance for other DPoS-based blockchains.
Linear Regression, Covariate Selection and the Failure of Modelling

It is argued that all model based approaches to the selection of covariates in linear regression have failed. This applies to frequentist approaches based on P-values and to Bayesian approaches although for different reasons. In the first part of the paper 13 model based procedures are compared to the model-free Gaussian covariate procedure in terms of the covariates selected and the time required. The comparison is based on seven data sets and three simulations. There is nothing special about these data sets which are often used as examples in the literature. All the model based procedures failed. In the second part of the paper it is argued that the cause of this failure is the very use of a model. If the model involves all the available covariates standard P-values can be used. The use of P-values in this situation is quite straightforward. As soon as the model specifies only some unknown subset of the covariates the problem being to identify this subset the situation changes radically. There are many P-values, they are dependent and most of them are invalid. The P-value based approach collapses. The Bayesian paradigm also assumes a correct model but although there are no conceptual problems with a large number of covariates there is a considerable overhead causing computational and allocation problems even for moderately sized data sets. The Gaussian covariate procedure is based on P-values which are defined as the probability that a random Gaussian covariate is better than the covariate being considered. These P-values are exact and valid whatever the situation. The allocation requirements and the algorithmic complexity are both linear in the size of the data making the procedure capable of handling large data sets. It outperforms all the other procedures in every respect.
Consistent Style Transfer

Recently, attentional arbitrary style transfer methods have been proposed to achieve fine-grained results, which manipulates the point-wise similarity between content and style features for stylization. However, the attention mechanism based on feature points ignores the feature multi-manifold distribution, where each feature manifold corresponds to a semantic region in the image. Consequently, a uniform content semantic region is rendered by highly different patterns from various style semantic regions, producing inconsistent stylization results with visual artifacts. We proposed the progressive attentional manifold alignment (PAMA) to alleviate this problem, which repeatedly applies attention operations and space-aware interpolations. The attention operation rearranges style features dynamically according to the spatial distribution of content features. This makes the content and style manifolds correspond on the feature map. Then the space-aware interpolation adaptively interpolates between the corresponding content and style manifolds to increase their similarity. By gradually aligning the content manifolds to style manifolds, the proposed PAMA achieves state-of-the-art performance while avoiding the inconsistency of semantic regions. Codes are available at https://github.com/computer-vision2022/PAMA.
FastSurferVINN: Building Resolution-Independence into Deep Learning Segmentation Methods -- A Solution for HighRes Brain MRI

Leading neuroimaging studies have pushed 3T MRI acquisition resolutions below 1.0 mm for improved structure definition and morphometry. Yet, only few, time-intensive automated image analysis pipelines have been validated for high-resolution (HiRes) settings. Efficient deep learning approaches, on the other hand, rarely support more than one fixed resolution (usually 1.0 mm). Furthermore, the lack of a standard submillimeter resolution as well as limited availability of diverse HiRes data with sufficient coverage of scanner, age, diseases, or genetic variance poses additional, unsolved challenges for training HiRes networks. Incorporating resolution-independence into deep learning-based segmentation, i.e., the ability to segment images at their native resolution across a range of different voxel sizes, promises to overcome these challenges, yet no such approach currently exists. We now fill this gap by introducing a Voxelsize Independent Neural Network (VINN) for resolution-independent segmentation tasks and present FastSurferVINN, which (i) establishes and implements resolution-independence for deep learning as the first method simultaneously supporting 0.7-1.0 mm whole brain segmentation, (ii) significantly outperforms state-of-the-art methods across resolutions, and (iii) mitigates the data imbalance problem present in HiRes datasets. Overall, internal resolution-independence mutually benefits both HiRes and 1.0 mm MRI segmentation. With our rigorously validated FastSurferVINN we distribute a rapid tool for morphometric neuroimage analysis. The VINN architecture, furthermore, represents an efficient resolution-independent segmentation method for wider application
QAC: Quantum-computing Aided Composition

In this chapter I will discuss the role of quantum computing in computer music and how it can be integrated to better serve the creative artists. I will start by considering different approaches in current computer music and quantum computing tools, as well as reviewing some previous attempts to integrate them. Then, I will reflect on the meaning of this integration and present what I coined as QAC (Quantum-computing Aided Composition) as well as an early attempt at realizing it. This chapter will also introduce The QAC Toolkit Max package, analyze its performance, and explore some examples of what it can offer to realtime creative practice. Lastly, I will present a real case scenario of QAC in the creative work Disklavier Prelude #3.
Preliminary experimental results of a stacked 3D cyclic chaotic neural network reservoir integrated circuit

A cyclic chaotic neural network reservoir (CNNR) circuit is implemented on a stacked three-dimensional (3D) cyclic neural network integrated circuit (IC) system, which was originally designed and fabricated for a deep feedforward neural network. We create a circuit emulator for a neuron chip used in the 3D integrated system according to the measurement results obtained from the chip. We preliminary evaluate the performance of the cyclic CNNR circuit for a waveform classification task using the emulator.
Corrigendum to “Petrology and geochemistry of multiphase post-granitic dikes: A case study from the Gabal Serbal area, Southwestern Sinai, Egypt”

Article Corrigendum to “Petrology and geochemistry of multiphase post-granitic dikes: A case study from the Gabal Serbal area, Southwestern Sinai, Egypt” was published on January 1, 2022 in the journal Open Chemistry (volume 20, issue 1).
SparseDet: Improving Sparsely Annotated Object Detection with Pseudo-positive Mining

Training with sparse annotations is known to reduce the performance of object detectors. Previous methods have focused on proxies for missing ground truth annotations in the form of pseudo-labels for unlabeled boxes. We observe that existing methods suffer at higher levels of sparsity in the data due to noisy pseudo-labels. To prevent this, we propose an end-to-end system that learns to separate the proposals into labeled and unlabeled regions using Pseudo-positive mining. While the labeled regions are processed as usual, self-supervised learning is used to process the unlabeled regions thereby preventing the negative effects of noisy pseudo-labels. This novel approach has multiple advantages such as improved robustness to higher sparsity when compared to existing methods. We conduct exhaustive experiments on five splits on the PASCAL-VOC and COCO datasets achieving state-of-the-art performance. We also unify various splits used across literature for this task and present a standardized benchmark. On average, we improve by $2.6$, $3.9$ and $9.6$ mAP over previous state-of-the-art methods on three splits of increasing sparsity on COCO. Our project is publicly available at https://www.cs.umd.edu/~sakshams/SparseDet.
A novel technique for random number generation using CMOS image sensor

Random Number Generators (RNGs) have been used in several traditional fields such as simulation, gaming, cryptography, etc. Random numbers are used in cryptography to generate passwords. The strength of a password depends not just on its length and complexity but mainly on its unpredictability. Strong passwords lower the overall risk of a security breach. Hence, we propose a True Random Number Generation (TRNG) using images from a Complementary Metal Oxide Semiconductor (CMOS) image sensor to provide unpredictability to the passwords. The proposed system is based on the dark pixel values of the image from the CMOS image sensor. Dark pixels are used to eliminate the requirement of light. The Image Fusion Algorithm is used in this system to combine more image files into a single image file. The process of combining more image files into one image file increases unpredictability. The TRNG does not require any additional resources, such as a light source for providing true randomness. The images received from the CMOS image sensor are processed and dark pixel values are combined to produce the initial seed value. A pseudo-random number generator algorithm is used to enhance the randomness of the initial seed value. This TRNG mobile application can be used in any android mobile phone and this can also be used in any electronic device that has a CMOS image sensor with it. The effectiveness of the system can be analyzed by statistical analysis suite.
ETHICAL LEADERSHIP AND ITS IMPACT ON IMPROVING JOB PERFORMANCE: - AN EXPLORATORY STUDY OF BAGHDAD INTERNATIONAL AIRPORT

The current research aims to test the relationship between moral leadership as an independent variable in improving job performance in a sample of Baghdad International Airport, as well as diagnosing the level of interest of Baghdad International Airport in research variables and their dimensions, to come up with a set of recommendations that contribute to improving the practice and adoption of these variables And based on the novelty of the variables and their urgent importance for the airport and its staff on the one hand, and society on the other, and in order to serve the objectives of the research, the descriptive exploratory approach was used for that purpose, as the application of the current research included Baghdad International Airport (Iraqi Airways Company, the Iraqi Civil Aviation Authority) and it was The research sample whose opinions about the research variables should be investigated included (200) of (department managers, divisional officials and administrative units), distributed to them exclusively and comprehensively. I retrieved (182) of them, and when they were unpacked, the valid ones for statistical analysis were (179) views. While the research used the statistical programs (AMOS V.25 - SPSS V.26) to analyze the primary data, with the adoption of descriptive and inferential statistics methods represented in (normal distribution test, exploratory factor analysis, confirmatory factor analysis, test of validity and reliability, arithmetic mean, deviation Standard, relative difference coefficient, relative importance, Pearson correlation coefficient, multiple linear regression coefficient, ANOVA test) to test the seven research hypotheses emanating from the main questions. and the Iraqi Civil Aviation Authority), or according to the type of department at Baghdad International Airport on the existence of a joint impact of ethical leadership by influencing the productivity effectiveness, especially through serving others and achieving justice and honesty, to improve the job performance of teams working in the field of tourism in particular and for society in general, to put Research some of the necessary recommendations and the necessary mechanisms to avoid cases of procrastination Ethical
Category theory with examples in probability theory

The basic concepts of category theory are developed and examples of them are presented to illustrate them using measurement theory and probability theory tools. Motivated by Perrone's workarXiv:1912.10642 where notes on category theory are developed with examples of basic mathematics, we present the concepts of category, functor, natural transformation, and products with examples in the probabilistic context. The most prominent examples of the application of Category Theory to Probability Theory are the Lawvere (available at ncatlab.org/nlab/files/lawvereprobability1962.pdf.) and Giry (avaible at https://doi.org/10.1007/BFb0092872) approaches. However, there are few categories with objects as probability spaces due to the difficulty of finding an appropriate condition to define arrows between them
A Study on Suitability Analysis of 6ㆍ3 Private Education Expenses Reduction Policy through School Education Competitiveness

The purpose of this study is to investigate the suitability analysis of MB government’s 6 ․ 3 private education expenses reduction policy. As the private education expense increased, every government tried to make a policy for reducing private education expenses. But it is acknowledged that the 6·3 private education expenses reduction policy enforced by the MB government showed the effect of reduction in private education expenses to some extent. This study will analyze the 6.3 policy mainly by focusing on the concept of school education competitiveness. The method of this study includes literature study and research study with Likert-type 5 point scale. According to the results, the policies on the adequacy of 6 ․ 3 private education expenses reduction evaluated above 3.5 are the enhancement of the transparency of private educational institute expenses(3.80), the service quality of EBS college scholastic ability test lectures(3.68), the educational service of after school(3.67), the quality of English education and narrow the gap(3.63), and the project of non-private-education school(3.59). The policies evaluated below 3.0 are the science olympiad and improvement of election type gifted education candidate(2.96), and national academic achievement test(2.76). The other 12 policies are marked between 3.0 and 3.5. The groups discussed by the policy adequacy are statistically significant. Based on this result, it is strongly recommended that the private education expenses reduction policy in MB government should be considered as an important policy for reducing private education expenses.