Reviewerly/exHarmony huggingface dataset:
Bayesian Spam Detection Framework on Mobile Device

Background: The spam message aimed at mobile devices has become a prevalent activity in recent years. About 76% of mobile users received spam messages of Ads, fraud messages and phishing links. It has led to a significant financial loss of 91.5 billion Yuan per year and this problem will be more severe in the coming future. Although the operators are making much effort against spam message, the number of spams are increasing day by day. Aim: Many schemes designed to detect and filter spam have been proposed in the research area. The Bayesian classification algorithm is one of the most popular schemes. Most of the spam detection schemes based on Bayesian are designed for communication providers. The scalability of filter policy is hard to control because the strict policy might filter the normal messages while some spam messages would not be detected due to the flexibility in the policy. Methods: The current mobile device has a robust computational capability so it can execute complex jobs. A spam detection framework is designed in the present study. Results: It can sniffer the coming messages in mobile device by hooking the Android SMS API, and send them to the filter module, -which is responsible to filter messages into normal or spam based on Bayesian classification. This is a light-weight framework of consuming low power, which is suitable for mobile devices. Extra experiments are conducted to prove their accuracy and efficiency. Conclusion: The results showed that it could filter the spam among receiving the messages in realtime and become more accurate by learning the users' feedback.
Beyond the <i>AJR</i>: “External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms”

Beyond the AJR: “External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms”Aimilia Gastounioti, PhD1 and Emily F. Conant, MD1Audio Available | Share
Design and Implementation of Automatic Evaluation System in College English Writing Teaching Based on ASP.Net

The development and application of an automatic evaluation system in College English writing teaching is conducive to the construction of online courses and the reform of teaching methods and means. This paper designs the framework and function of an automatic evaluation system in College English writing teaching establish the database of the system and finally realizes the automatic evaluation system in College English writing teaching by using asp.net, SQL Server, IIS, CSS, jQuery, and other technologies. The system mainly realizes the upload of teachers' courseware and teaching documents, students; independent learning, after class communication, homework submission, question answering, and testing functions, which can provide a reference for the construction of open teaching platforms in schools.
DIGGING DEEPER INTO QUALITY ASSESSMENT OF SOFTWARE REQUIREMENT SPECIFICATIONS

Software Requirements Specification (SRS) is considering a critical success factor for software projects development. Worthily, The SRS document is created at the beginning stage of the software project and is still an essential point of reference until the project is reaching the final product. Mainly, the SRS document was written via Natural Language (NL). Unfortunately, the NL has several problems such; ambiguity and incomplete. Consequently, it is vital to assess the quality of the SRS document to take any corrective actions during the initial stage of the project. This paper focuses on automated methods from three main perspectives; Firstly, digging the literature to find the most effective Quality Attributes (QA) and Quality Indicators (QI). Secondly, distinct the fully automated QA and QI. Finally, propose a framework that able to assess the quality of the SRS in a fully automated way.
Categorical Imperfections

The indexing systems used to systematise our knowledge about a domain tend to have an evaluative character: they represent some things as more important, general, complex, or central than others. They are also imperfect and can misrepresent something as more or less important, etc., than it really is. Such distortions mostly result from mistakes made due to lack of time or resources. In some cases they follow systematic patterns which can reveal the implicit judgements and values shared within a community who maintains and uses an index. I focus on the example of PhilPapers, the largest database of philosophy texts available, to show how the arrangement of categories and the way items are assigned to them, can effectively marginalise certain topics, authors, and entire traditions. I draw attention to such issues as: ordering, size and depth of categories, the use of miscellaneous categories, localising indexes in category names, and assigning items to some but not other categories. I suggest that such structuring of the index can have an impact on users, normalising marginalisation and contributing to the perpetuation of inequalities. I conclude by offering some suggestions for improvement which might help our databases flourish and become even more useful.
Extending the Step-Size Restriction for Gradient Descent to Avoid Strict Saddle Points

We provide larger step-size restrictions for which gradient descent-based algorithms (almost surely) avoid strict saddle points. In particular, consider a twice differentiable (nonconvex) objective function whose gradient has Lipschitz constant $L$ and that the set of points that obtain the maximum value of the spectral norm of the Hessian is measure zero. We prove that given one uniformly random initialization, the probability that gradient descent with a step-size up to $2/L$ will converge to a strict saddle point is zero. This extends previous results up to the sharp limit imposed by the convex quadratic case (provably converging to local minimizers). In addition, the arguments hold in the case when a learning rate schedule is given, with either a continuous decaying rate or a piecewise constant schedule. We show that the assumptions are robust in the sense that functions which do not satisfy the assumptions are meager with respect to analytic functions.
Designing Context-Aware Mobile Systems for Self-guided Exhibition Sites

This study examines the design of digital systems created to support users in self-guided exhibitions (i.e., sites without human personnel to support the users). We developed a location-aware smartphone guide called Aratag, which utilizes Bluetooth beacons to serve contextual information at the user’s request. Using this guide, we conducted a user study to investigate what types of content institutions perceive as relevant versus the kinds of content users actually find relevant. The study also contributes to our understanding of users’ attitudes toward using smartphones to support their self-guidance in exhibitions. Our results provide insights into designing for interplay between the physical setting of the exhibition and the digital platform, so as to inform the utility, desirability, and usability of mobile guides. Based on these findings, we present the following two design insights that should be considered when designing future mobile systems for self-guidance in exhibitions: 1) multi-level content to accommodate individual user interest by scaffolding information layers from glimpses to an increasingly immersive experience and 2) real-time location tracking with clear visual feedback.
Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser

Prior probability models are a central component of many image processing problems, but density estimation is notoriously difficult for high-dimensional signals such as photographic images. Deep neural networks have provided state-of-the-art solutions for problems such as denoising, which implicitly rely on a prior probability model of natural images. Here, we develop a robust and general methodology for making use of this implicit prior. We rely on a little-known statistical result due to Miyasawa (1961), who showed that the least-squares solution for removing additive Gaussian noise can be written directly in terms of the gradient of the log of the noisy signal density. We use this fact to develop a stochastic coarse-to-fine gradient ascent procedure for drawing high-probability samples from the implicit prior embedded within a CNN trained to perform blind (i.e., unknown noise level) least-squares denoising. A generalization of this algorithm to constrained sampling provides a method for using the implicit prior to solve any linear inverse problem, with no additional training. We demonstrate this general form of transfer learning in multiple applications, using the same algorithm to produce high-quality solutions for deblurring, super-resolution, inpainting, and compressive sensing.
Optimal parameter estimation of open quantum systems*

In quantum information technologies, quantum weak measurement is beneficial for protecting coherence of systems. In order to further improve the protection effect of quantum weak measurement on coherence, we propose an optimization scheme of quantum Fisher information (QFI) protection in an open quantum system by combing no-knowledge quantum feedback control with quantum weak measurement. On the basis of solving the dynamic equations of a stochastic two-level quantum system under feedback control, we compare the effects of different feedback Hamiltonians on QFI and find that via no-knowledge quantum feedback, the observation operator σ x (or σ x and σ z ) can protect QFI for a long time. Namely, no-knowledge quantum feedback can improve the estimation precision of feedback coefficient as well as that of detection coefficient.
Certified Compilation for Cryptography: Extended x86 Instructions and Constant-Time Verification

We present a new tool for the generation and verification of high-assurance high-speed machine-level cryptography implementations: a certified C compiler supporting instruction extensions to the x86. We demonstrate the practical applicability of our tool by incorporating it into supercop: a toolkit for measuring the performance of cryptographic software, which includes over 2000 different implementations. We show i. that the coverage of x86 implementations in supercop increases significantly due to the added support of instruction extensions via intrinsics and ii. that the obtained verifiably correct implementations are much closer in performance to unverified ones. We extend our compiler with a specialized type system that acts at pre-assembly level; this is the first constant-time verifier that can deal with extended instruction sets. We confirm that, by using instruction extensions, the performance penalty for verifiably constant-time code can be greatly reduced.
Citizens Acceptance of Online Services in Sri Lanka Police: Study on Police Clearance Online System

The Police, as the main law enforcement agency in Sri Lanka, assure a society which is free from fear of crimes & violence by her vision statement. Ensuring the state security is vital because threat to the security is, undoubtedly, vulnerability for everything. The Police Clearance Certificate (PCC) is one of the proactive measures to assure the security and social tranquillity. PCC must be free from fraud and errors. The criticism towards issuing agency was to delay of processes, bureaucracy and corruption. In order to expedite the clearance process, in 2015, it was introduced a E-governance approach. The aim of this study was to evaluate the efficiency and effectiveness of this new on-line system and reviewing the level of customer satisfaction. This is the first and only research made on this new Police Clearance Certificate Issuing System (PCCIS) in Sri Lanka. The data in this qualitative research were gathered through questionnaires, interviews, literature survey and observations. The samples were selected in a manner which can be generalized the results to the whole population. Though the Sri Lanka Police (SLP) assure the issuing of PCC within 14 days, this research revealed that 58% of the applicants don’t get it within that period. But it was revealed that the corruption and organizational bureaucracy have been remarkably reduced with the on-line mechanism. The attractiveness & user friendliness of the system is comparatively good. The recommendations, made by this study, will contribute for further enhancements. Unfortunately, majority of the people in Sri Lanka are not conversant with available police on-line services. Same time, unavailability of National Egovernment Platform and Data Warehouse, connecting the public & private agencies, is a big imperfection in the country.
Relating Lurie's problem, Hopfield's network and Alzheimer's disease

Alzheimer's disease is a degenerative brain disorder that affects millions of people around the world and still without cure. A very common application of Hopfield neural networks is to simulate a human memory as well as to evaluate problems of degeneration and memory loss. On the other hand, from the control area, one has Lurie's problem, which emerged in the 1940s and which still does not have a general solution. However many works and results came in an attempt to solve it. In this paper, the Hopfield's network is shown as a particular case of Lurie's problem, then one of the consequences of Alzheimer's disease, memory failure, is modeled using Hopfield's networks and nally a recent result of Lurie's problem is applied to the computationally modeled disease to correct the problem of memory loss. The correction is made using a controller via DK-iteration. Simulations are performed to validate the computational model of the disease and to demonstrate the effectiveness of the application of the recent Lurie's problem theorem. Therefore, in addition to the results presented, this work aims at encouraging the researches in the area, so that in the future, better diagnostic and treatment conditions will be achieved.
Scalable interferometric receiver for photon-efficient quantum communication

We describe a modular design for an optical receiver implementing projective measurements on multiple logical qubits encoded in the temporal degree of freedom of a single photon. Application to satellite quantum key distribution is discussed.
Working with RDF triples and worlds

In this chapter, we will see how to directly access Owlready’s RDF quadstore and how to create several isolated “worlds”, each having their own quadstore.
Data Covariance Learning in Aesthetic Attributes Assessment

Image Aesthetic Assessment (IAA) is a widely considered problem given its usefulness in a wide range of applications such as the evaluation image capture pipelines, sharing and storage techniques media, but the intrinsic mechanism of aesthetic evaluation is seldom been explored due to its subjective nature and the lack of interpretability of deep neural networks.Noticing that the photographic style annotations of images (i.e. the score of aesthetic attributes) are more objective and interpretable compared with the Mean Opinion Scores (MOS) annotations for IAA, we evaluate the problem of Aesthetic Attributes Assessment (AAA) as to provide complementary information for IAA.We firstly introduce the learning of data covariance in the field of Aesthetic Attributes Assessment and propose a regression model that jointly learns from MOS as well as the score of all aesthetic attributes at the same time.We construct our method by extending the scheme of data uncertainty learning and propose data covariance learning.Our method achieves the state-of-the-art performance on AAA without architectural design that trains in a totally end-to-end manner and can be easily extended to existing IAA methods.
PemanfaatanTeknologi Augmented Reality Edukasi Pengenalan Hewan Vivipar Ovipar Dan Ovovivipar Bagi Siswa Sekolah Dasar

AbstrakPenelitian ini memberikan informasi teknologi augmented reality (AR) pengenalan vivipar, ovipar, dan ovovivipar berbasis android sehingga dapat mengetahui perbedaan jenis hewan vivipar, hewan ovipar, dan hewan ovovivipar. Teknologi AR dapat digunakan sebagai edukasi pembelajaran yang menampilkan hewan ke dalam bentuk tiga dimensi bisa dilihat secara menyeluruh oleh siswa/siswi Sekolah Dasar. Metode yang digunakan pada penelitian ini adalah Metode Waterfall yang terdiri dari tahapan Analisa data, desain system, penulisan kode program, pengujian system dan penerapan program. Pembuatan marker yang dibentuk pada objek hewan vivipar, ovipar dan ovovivipar dalam menampilkan objek lebih menarik daripada marker hitam putih. Sehingga teknologi AR dapat digunakan sebagai media untuk memperkenalkan lebih informatif dan efektif. Hasil pengujian deteksi marker pada saat cahaya normal dan gelap jarak 4-10 waktu terbaik yaitu 1-3 detikAbstrackThis study provides information on augmented reality (AR) technology introduction of viviparous, ovipar, and ovovivipar based android so that it can find out the different types of viviparous animals, ovipar animals, and ovovivipar animals. AR technology can be used as a learning education that displays animals into three-dimensional form can be seen thoroughly by elementary school students. The method used in this research is Waterfall Method which consists of data analysis stage, system design, program code writing, system testing and program implementation. The creation of markers formed on viviparous animal objects, ovipar and ovovivipar in displaying objects is more interesting than black and white markers. So that AR technology can be used as a medium to introduce more informative and effective. Marker detection test results at normal light and dark distance of 4-10 the best time is 1-3 seconds.
GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images

We tackle a challenging blind image denoising problem, in which only single distinct noisy images are available for training a denoiser, and no information about noise is known, except for it being zero-mean, additive, and independent of the clean image. In such a setting, which often occurs in practice, it is not possible to train a denoiser with the standard discriminative training or with the recently developed Noise2Noise (N2N) training; the former requires the underlying clean image for the given noisy image, and the latter requires two independently realized noisy image pair for a clean image. To that end, we propose GAN2GAN (Generated-Artificial-Noise to Generated-Artificial-Noise) method that first learns a generative model that can 1) simulate the noise in the given noisy images and 2) generate a rough, noisy estimates of the clean images, then 3) iteratively trains a denoiser with subsequently synthesized noisy image pairs (as in N2N), obtained from the generative model. In results, we show the denoiser trained with our GAN2GAN achieves an impressive denoising performance on both synthetic and real-world datasets for the blind denoising setting; it almost approaches the performance of the standard discriminatively-trained or N2N-trained models that have more information than ours, and it significantly outperforms the recent baseline for the same setting, \textit{e.g.}, Noise2Void, and a more conventional yet strong one, BM3D.
ODIANLP’s Participation in WAT2020

This paper describes the ODIANLP submission to WAT 2020. We have participated in the English-Hindi Multimodal task and Indic task. We have used the state-of-the-art Transformer model for the translation task and InceptionResNetV2 for the Hindi Image Captioning task. Our submission tops in English->Hindi Multimodal task in its track and Odia English translation tasks. Also, our submissions performed well in the Indic Multilingual tasks.
WITHDRAWN: Teaching mobile application development (MAD) – The project centered learning method

The mobile devices become unavoidable in the everyday human life. Starting from the simple usage like calculator to the complex task like analytics mobile applications plays the greater role. Hence the educational professional bodies advised to introduce the course for teaching mobile application development in computer / information technology. This paper aims to bring out the implementation of project based learning (PBL) in the course mobile application development. The PBL has been implemented by gathering the basic information from students and dividing them into teams followed by teaching learning and evaluation. The project based learning gives considerable improvement in the knowledge levels of the students related to mobile application development.
Network Decomposition and Distributed Derandomization

This keynote talk will provide an overview of a recent line of work [Rozhoň and Ghaffari at STOC 2020; Ghaffari, Harris, and Kuhn at FOCS 2018; and Ghaffari, Kuhn, and Maus at STOC 2017], which presented the first efficient deterministic network decomposition algorithm as well as a general derandomization result for distributed graph algorithms. Informally, the derandomization result shows that any (locally-checkable) graph problem that admits an efficient randomized distributed algorithm also admits an efficient deterministic distributed algorithm. These results resolve several central and decades-old open problems in distributed graph algorithms.
Lifted Regression/Reconstruction Networks

In this work we propose lifted regression/reconstruction networks(LRRNs), which combine lifted neural networks with a guaranteed Lipschitz continuity property for the output layer. Lifted neural networks explicitly optimize an energy model to infer the unit activations and therefore—in contrast to standard feed-forward neural networks—allow bidirectional feedback between layers. So far lifted neural networks have been modelled around standard feed-forward architectures. We propose to take further advantage of the feedback property by letting the layers simultaneously perform regression and reconstruction. The resulting lifted network architecture allows to control the desired amount of Lipschitz continuity, which is an important feature to obtain adversarially robust regression and classification methods. We analyse and numerically demonstrate applications for unsupervised and supervised learning
Designing a Multitasking Interface for Object-aware AR applications

Many researchers and industry professionals believe Augmented Reality (AR) to be the next step in personal computing. However, the idea of an always-on context-aware AR device presents new and unique challenges to the way users organize multiple streams of information. What does multitasking look like and when should applications be tied to specific elements in the environment? In this exploratory study, we look at one such element: physical objects, and explore an object-centric approach to multitasking in AR. We developed 3 prototype applications that operate on a subset of objects in a simulated test environment. We performed a pilot study of our multitasking solution with a novice user, domain expert, and system expert to develop insights into the future of AR application design.
Deep Fault Prediction with Flexible Weighted Mining Based Alarm Correlation Analysis of Communication Networks

At present, most fault prediction methods of communication networks do not deeply mine the relationship between massive alarms, and are lack of deep feature engineering, so the prediction performance needs to be improved. We build a deep fault prediction model based on alarm correlation analysis and propose the feature interaction structure for feature engineering. For alarm correlation analysis, we propose flexible weighted sequential pattern mining (FWSPM) algorithm, which can flexibly determine the weight of alarms in different sequences and constrain the pruning process according to the weights. Then, we build a deep learning model based on feature interaction and introduce expert experience in the field of communication network management. Experimental results show that our model performs better in the historical alarms of the network management system than existing models. Besides, the proposed FWSPM method improves the performance of the proposed deep fault prediction model.