Reviewerly/exHarmony huggingface dataset:
The Atlas Structure of Images

Many operations of vision require image regions to be isolated and inter-related. This is challenging when they are different in detail and extent. Practical methods of Computer Vision approach this through the tools of downsampling, pyramids, cropping and patches. In this paper we develop an ideal geometric structure for this, compatible with the existing scale space model of image measurement. Its elements are apertures which view the image like fuzzy-edged portholes of frosted glass. We establish containment and cause/effect relations between apertures, and show that these link them into cross-scale atlases. Atlases formed of Gaussian apertures are shown to be a continuous version of the image pyramid used in Computer Vision, and allow various types of image description to naturally be expressed within their framework. We show that views through Gaussian apertures are approximately equivalent to the jets of derivative of Gaussian filter responses that form part of standard Scale Space theory. This supports a view of the simple cells of mammalian V1 as implementing a system of local views of the retinal image of varying extent and resolution. As a worked example we develop a keypoint descriptor scheme that outperforms previous schemes that do not make use of learning.
Trigger-Based Incremental Data Processing with Unified Sync and Async Model

In recent years, more and more applications in the cloud have needs to process large-scale on-line datasets, which evolve over time as new entries are added and existing entries are modified. Several programming frameworks, such as Percolator and Oolong, are proposed for such incremental data processing and can achieve efficient processing with an event-driven abstraction. However, these frameworks are inherently asynchronous, leaving the heavy burden of managing synchronization to applications' developers, which further significantly restricts their usabilities. In this study, we propose a trigger-based incremental computing framework in the cloud, called Domino, with both synchronous and asynchronous mechanisms to coordinate parallel triggers. With this new framework, both synchronous and asynchronous applications can be seamlessly developed. Use cases and extensive evaluation results confirm that it can deliver sufficient performance, and also is easy to use for incremental applications in large-scale distributed computing.
Locally Private Mean Estimation: Z-test and Tight Confidence Intervals

This work provides tight upper- and lower-bounds for the problem of mean estimation under differential privacy in the local-model, when the input is composed of n i.i.d. drawn samples from a Gaussian. Our algorithms result in a (1-\beta)-confidence interval for the underlying distribution's mean of length O( sigma *sqrt(log(n/beta)log(1/\beta))/(\epsilon*sqrt(n) ). In addition, our algorithms leverage on binary search using local differential privacy for quantile estimation, a result which may be of separate interest. Moreover, our algorithms have a matching lower-bound, where we prove that any one-shot (each individual is presented with a single query) local differentially private algorithm must return an interval of length Omega( sigma*sqrt(\log(1/\beta))/(\epsilon*sqrt(n)) ).
Perfect discrimination of nonorthogonal quantum states with posterior classical partial information

Some sets of nonorthogonal quantum states, ubiquitous in quantum cryptography, are shown to be perfectly discriminated if classical partial information about them is made available after they have been measured. Apart from deepening the understanding of quantum measurements, the results might help to strengthen the security of QKD protocols.
(In)Secure Android Debugging: Security analysis and lessons learned

Universal Serial Bus (USB) is currently one of the most popular standards that controls communication between personal computers (PCs) and their peripheral devices. Thus, it is important to establish whether such connections are properly secured especially when USB is used to connect devices like smartphones, tablets, etc. where sensitive user data can be potentially stored. For this reason, this paper evaluates security of the recent Android versions with respect to the USB-related attacks. In particular, we present a novel approach to compromise Android-based devices by exploiting Android Debug Bridge (ADB) protocol using Man in the Middle (MitM) attacks. Comprehensive analysis of those types of attacks have revealed five novel security vulnerabilities in the Android OS. Security gaps found in this paper cannot only be used to bypass the lock screen security and to gain unauthorized access to the user’s private data but also to enable future ADB attacks by incorporating a backdoor to bypass phone security at any time. We also developed a tool which exploits all discovered vulnerabilities and can serve as a security mean to assess current ADB implementations as well as future protocol improvements. By disclosing new security weaknesses we want to raise security awareness of the users, researches, security professionals, and developers related to the USB-related attacks and to the threat they pose not only to PCs but also to the USB devices.
Temporally Consistent Depth Estimation in Videos with Recurrent Architectures

Convolutional networks trained on large RGB-D datasets have enabled depth estimation from a single image. Many works on automotive applications rely on such approaches. However, all existing methods work on a frame-by-frame manner when applied to videos, which leads to inconsistent depth estimates over time. In this paper, we introduce for the first time an approach that yields temporally consistent depth estimates over multiple frames of a video. This is done by a dedicated architecture based on convolutional LSTM units and layer normalization. Our approach achieves superior performance on several error metrics when compared to independent frame processing. This also shows in an improved quality of the reconstructed multi-view point clouds.
An N-gram based model for predicting of word-formation in Assamese language

Word prediction is a technique which try to suggest the word by observing the previous input letters or words in any text editor. At present there is no such software or tool in Assamese which can predict the future word(s) of a sentence. This method helps the people who are not very much expert in typing and this research aims to reduce the gap between the people who are very much expert in typing with the people who are differently abled or the people who are not the daily users of the computer system. To predict the words, N-gram based models like unigram, bigram, trigram and quadrigram are used in this work. After doing two different level of experiments and testing maximum keystrokes saving (KS) 74.04% and 48.28% are found for preconfigured data-set and user-input data respectively. The results indicate a significant level of improvement towards sentence completion with the help of prediction method in Assamese language.
A Multi-Target Detection Algorithm Using High-Order Differential Equation

Dual-frequency Doppler radar is a popular tool for moving target tracking, which can support a large range of civilian and military applications. In this paper, a human localization algorithm based on a high-order differential equation and Doppler processing is proposed to increase the accuracy of Doppler radar target localization. The high-order differential equation is first proposed to process the radar echo signal and extract the instantaneous frequencies (IFs) of multiple targets. Then, based on the estimated IFs, the target localizations can be easily synthesized by the Doppler processing. Compared with the short-time Fourier transform algorithm and the fractional Fourier transform algorithm, the proposed algorithm has high-frequency estimation accuracy and can effectively separate the frequencies from multiple targets radar echo whether the frequency ambiguous issue occurs or not. A series of experiments is carried out to demonstrate the performance of the proposed algorithm.
Evolution of Shares in a Proof-of-Stake Cryptocurrency

Do the rich always get richer by investing in a cryptocurrency for which new coins are issued according to a Proof-of-Stake (PoS) protocol? We answer this question in the negative: Without trading, the investor shares in the cryptocurrency are martingales that converge to a well-defined limiting distribution, hence are stable in the long run. This result is robust to allowing trading when investors are risk-neutral. Then, investors have no incentive to accumulate coins and gamble on the PoS protocol, but weakly prefer not to trade.
Domain Specific NMT based on Knowledge Graph Embedding and Attention

In recent years, the encoder-decoder/attention-based Transformer architecture is superior to traditional methods and has been adopted as the core technology in Google services. But for specific areas, translation accuracy or BLEU scores are not good enough without domain knowledge. Typical problems include (1) domain entity such as subject/object translation error, and (2) relationship translation error, because lacking enough knowledge involved model and algorithms. This paper introduces the knowledge graph embedding and attention model based on the traditional encoder-decoder model. The KG embedded layer improve entities pair accuracy in diffident languages, by softly matching entities pair embedding in the encoder and decoder steps. The KG attention layer leverages entity relationship pair, by trained attention score from knowledge graph, just like business rules. The experiment proves Knowledge Graph Embedding and Attention (KGEA) can improve the ICT-NMT online production system, and also BLEU scores increase 6% with KG Embedding, 3% with KG Attention and total 7%, compared with general-purpose transformer, with semantic entities and relationship rules.
A Quantum Authorization Management Protocol Based on EPR-Pairs

Quantum authorization management (QAM) is the quantum scheme for privilege management infrastructure (PMI) problem. Privilege management (authorization management) includes authentication and authorization. Authentication is to verify a user’s identity. Authorization is the process of verifying that a authenticated user has the authority to perform a operation, which is more fine-grained. In most classical schemes, the authority management center (AMC) manages the resources permissions for all network nodes within the jurisdiction. However, the existence of AMC may be the weakest link of the whole scheme. In this paper, a protocol for QAM without AMC is proposed based on entanglement swapping. In this protocol, Bob (the owner of resources) authenticates the legality of Alice (the user) and then shares the right key for the resources with Alice. Compared with the other existed QAM protocols, this protocol not only implements authentication, but also authorizes the user permissions to access certain resources or carry out certain actions. The authority division is extended to fin-grained rights division. The security is analyzed from the four aspects: the outsider’s attack, the user’s attack, authentication and comparison with the other two QAM protocols.
Partnership as a Factor in the Effectiveness of Practice-Oriented Education of Students

The task of ensuring the effectiveness of practice-oriented secondary and higher professional education based on social partnership and network interaction of subjects of education in an innovative educational environment is set based on the requirements of the Federal State Educational Standard 3+ for the pedagogical and psychological specialties in higher education and modern (actualized) educational standards for the specialties “Construction and exploitation of buildings” (08.02.01) and “Primary school teaching” (44.02.02) in vocational professional education for the formation of general (general cultural) and professional competencies of students and graduates of institutions of secondary vocational and higher education. The aim of this study is to present the results of theoretical and experimental studies of activities and innovation platforms based on the institution of secondary vocational education, in conditions of the social-network educational partnership. The main methods used in this study were analyses, experiments, questioning and expert evaluation. The results of the experiments show the positive impact of partnerships on the levels of innovative activity of teachers and formation of general (general cultural) and professional competencies of students and graduates of secondary vocational education organizations. The study proves the role and importance of social-network educational partnerships in ensuring the effectiveness of practice-oriented education in modern professional schools, as it takes into account and reflects the characteristics, requirements and needs of the society and individual. It allows one to fulfill his role and take into account the interaction of organizations, institutions and individuals in ensuring the transfer and mastering of the content of professional education in organizations of secondary vocational and higher education.
A Differentiable Gaussian-like Distribution on Hyperbolic Space for Gradient-Based Learning.

Hyperbolic space is a geometry that is known to be well-suited for representation learning of data with an underlying hierarchical structure. In this paper, we present a novel hyperbolic distribution called \textit{pseudo-hyperbolic Gaussian}, a Gaussian-like distribution on hyperbolic space whose density can be evaluated analytically and differentiated with respect to the parameters. Our distribution enables the gradient-based learning of the probabilistic models on hyperbolic space that could never have been considered before. Also, we can sample from this hyperbolic probability distribution without resorting to auxiliary means like rejection sampling. As applications of our distribution, we develop a hyperbolic-analog of variational autoencoder and a method of probabilistic word embedding on hyperbolic space. We demonstrate the efficacy of our distribution on various datasets including MNIST, Atari 2600 Breakout, and WordNet.
Combining Hopfield neural networks, with applications to grid-based mathematics puzzles

Hopfield neural networks are useful for solving certain constrained set-selection problems. We establish that the vector fields associated with general networks of this type can be combined to produce a new network that solves the corresponding combination of set-selection/constraint problems, provided a relatively simple condition is satisfied. That is, we establish that just this one condition needs to be verified in order to be able to combine such networks. We introduce some generalizations of networks that exist in the literature, and, to demonstrate the usefulness of the work, we combine these networks to solve two well-known grid-based math puzzles (i.e. constraint problems): Kakuro and Akari (called Cross Sums and Light Up in North America). We present examples to illustrate the evolution of the solution process. We find that the difficulty rating of a Kakuro puzzle is strongly connected to the number of iterations used by the neural network solver.
AI-Chatbot Using Deep Learning to Assist the Elderly

Recently Bot and Chatbot, both Artificial Intelligence software systems, have appeared online. These create a conversation between a virtual agent and the user. This paper describes an Artificial Intelligent Chatbot conversing with elderly persons, with age-related problems. Chatbot: understands natural language and learns from interactions, increasing his knowledge; remembers commitments and medicines, connects remotely with doctors, family; controls transmission of physiological parameters; entertains the elder. Do this with machine learning algorithms. In order to learn functions with high-level abstractions, as Natural Language, we adopt deep architectures: composed of multiple levels of non-linear operations, such as neural nets with many hidden layers. We used the recently optimal learning algorithm (DBN, Deep Belief Network) proposed by Hinton et al. Experiments confirm its optimal training strategy, by initializing weights in a region near local minimum.
Digital Tumor-Collagen Proximity Signature Predicts Survival in Diffuse Large B-Cell Lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous tumor that originates from normal B-cells. A limited number of studies have investigated the role of acellular stromal microenvironment on outcome in DLBCL. Here, we propose a novel digital proximity signature (DPS) for predicting overall survival (OS) in DLBCL patients. We propose a novel end-to-end multi-task deep learning model for cell detection and classification and investigate the spatial proximity of collagen (type VI) and tumor cells for estimating the DPS. To the best of our knowledge, this is the first study that performs automated analysis of tumor and collagen on DLBCL to identify potential prognostic factors. Experimental results favor our cell classification algorithm over conventional approaches. In addition, our pilot results show that strongly associated tumor-collagen regions are statistically significant (p = 0.03) in predicting OS in DLBCL patients.
Soft Tissue Tumor Classification using Stochastic Support Vector Machine

Abstract As healthcare is becoming one of the most rapidly changing industries by the increasing type of diseases, technology plays an important role in helping medical staffs solve those medical problems. Soft tissue tumors are tumors in the musculoskeletal system that involve soft tissue (tissue other than bone tissue). It includes muscle tissue, nerves, blood vessels, fat, and connective tissue. This soft tissue tumor is divided into two, namely benign and malignant. To prevent any medical errors in classifying patients’ data, one machine learning called Stochastic Support Vector Machine is being studied. In this study, we will evaluate soft tissue tumor patients’ data in Nur Hidayah Hospital, Yogyakarta, Indonesia using Stochastic Support Vector Machine to see its accuracy. The result is encouraging that Stochastic Support Vector Machine works better than the original Support Vector Machine.
IMBUA: Identity Management on Blockchain for Biometrics-Based User Authentication

In centralized systems, privacy of user identity information relies on centralized entity, such as system administrator. Since centralized entity is responsible to distribute keys to all the users in the system, he can read and modify the identity information. Compromise of such centralized entity results into the entire system vulnerability. Therefore, centralized systems often failed to provide the data privacy and integrity. In addition, centralized systems are also vulnerable to single-point-of-failure. Blockchain based solution is a possible alternate to provide integrity and protect system from single-point-of-failure. But, the ledger of the blockchain, contains all the transactions information, is distributed and available to each member of the blockchain network. Hence, blockchain-based identity management for user authentication, while preserving privacy, is a challenging problem. This paper proposes a novel key management mechanism for blockchain-based identity management for user authentication. The rigorous analysis is presented to show that the proposed protocol is secure against various possible attacks.
Constraining long-distance allomorphy

Abstract This paper presents a cross-linguistic survey of non-local allomorphy and it develops a formal model that accounts for the observed patterns. The distance between the trigger and target of allomorphy in non-local patterns is much more conservative than expected. A model of Vocabulary Insertion is developed, where the limited distance follows from the basic linear computational properties of the PF-interface.
Fusion estimation for nonlinear multi-sensor networked systems with packet loss compensation and correlated noises

Purpose This paper aims to give the centralized and distributed fusion estimator for nonlinear multi-sensor networked systems with packet loss compensation and correlated noises and give the corresponding square-root cubature Kalman filter. Design/methodology/approach Based on the Gaussian approximation recursive filter framework, the authors derive the centralized fusion filter and using the projection theorem, the authors derive the centralized fusion smoother. Then, based on the fast batch covariance intersection fusion algorithm, the authors give the corresponding results for distributed fusion estimators. Findings Designing the fusion estimators for nonlinear multi-sensor networked systems with packet loss compensation and correlated noises is necessary. It is useful for general nonlinear systems. Originality/value Throughout the whole study, the main highlights of this paper are as follows: packet loss compensation and correlated noises are considered in nonlinear multi-sensor networked systems. There are no relevant conclusions in the existing literature; centralized and distributed fusion estimators are derived based on the above system; for the posterior covariance with compensation factor and correlated noises, a new square-root factor of the error covariance is derived; and the new square-root factor of the error covariance is used to replace the numerical implementation of the covariance in cubature Kalman filter (CKF), which simplified the problem in calculating the posterior covariance in CKF.
Xception-Based General Forensic Method on Small-Size Images

Developing universal forensic methods that can simultaneously identify multiple image operations to identify the authenticity and processing history of an image has attracted more and more attention. Although numerous forensics tools and methods emerge to detect the traces left by various image operations, the accuracy with current techniques still decreased significantly as the size of the investigated images reduced. To overcome this issue, especially for small-size or highly compressed images, we propose a method using Xception-based convolution neural network. While CNNs-based methods are able to learn features directly from data for classification task, they are not well suited for forensic problems directly in their original form. Hence, we have added magnified layer in the preprocessing layer. The input images are magnified by the nearest neighbor interpolation algorithm in the magnified layer, which can preserve the property of image operations better than other magnified tools, and then input them into the CNN model for classification. Finally, we get adaptive average pooling function from global average pooling to adapt to any size of input pictures. We evaluate the proposed strategy on six typical image processing operations. Through a series of experiments, we show that this approach can significantly improve classification accuracy to 97.71% when the images are of size 64 × 64. More importantly, it outperforms all the existing general purpose manipulation forensic methods.