Reviewerly/exHarmony huggingface dataset:
Scaling Up Bit-Flip Quantum Error Correction

Quantum error correction comes from the marriage of quantum mechanics with the theory of error-correcting codes, which is still at the rudimentary stage tackling only 1 qubit to date. As, unlike the classical world, the extent of error in the quantum world is substantially high and unavoidable, scaling up the quantum error correction from 1 qubit to higher is of utmost significane. Therefore, to achive the scaling up, we present a general quantum error correction code that can be adapted for any number of quantum errors (bit-flip error to be specific in our case). This generalized code for bit-flip error correction realizes a delicate blending of using controlled-NOT gates with the notion of quantum entanglement. We augment the blending with majority voting in the final stage of the error correction. We demonstrate efficacy of our proposed error correction code through real experimentation in IBM Qiskit. Our experimental results up to 3-qubit bit-flip errors in the available Qiskit infrastructure confirm a total escape from errors using our proposed error correction code.
Video Super Resolution Using Temporal Encoding ConvLSTM and Multi-Stage Fusion

Video super resolution is a challenging task and has attracted the attention of many researchers in recent years. In this paper, we propose a multi-stage spatio-temporal feature fusion network. Different from existing methods that only aggregate features from temporal branch once at a specified s tage of network, the proposed network is organized in a multi-stage manner so that the temporal correlation in features at different stages of the network can be fully exploited. Furthermore, we propose the temporal encoding convLSTM to effectively capture the temporal information at the end of each stage. Experiments on vid4 and viemo-90K demonstrate the effectiveness of the proposed method.
Deep Learning Based EBCOT Source Symbol Prediction Technique for JPEG2000 Image Compression Architecture

In this work, an efficient and robust learning-based JPEG2000 architecture is proposed. It uses machine learning techniques for predicting and encoding the decision bit in the embedded block coding with optimized truncation (EBCOT) process. First, we apply non-locally weighted ridge regression to predict the quantized wavelet coefficients in the LL subband. Then, during the EBCOT process, we perform inter/intra subband prediction and inter/intra bit plane symbol prediction to estimate the activity of the decision bit using the deep learning architecture. Then, the binary prediction result is treated as an additional context and the decision bit is eventually coded using an advanced context-based adaptive binary arithmetic coder. Simulations show that the proposed framework provides the same visual quality as conventional codecs with as much as 30% bitrate savings.
Disparity compensation of light fields for improved efficiency in 4D transform-based encoders

Efficient light field encoders take advantage of the inherent 4D data structures to achieve high compression performance. This is accomplished by exploiting the redundancy of co-located pixels in different sub-aperture images (SAIs) through prediction and/or transform schemes to find a m ore compact representation of the signal. However, in image regions with higher disparity between SAIs, such scheme's performance tends to decrease, thus reducing the compression efficiency. This paper introduces a reversible pre-processing algorithm for disparity compensation that operates on the SAI domain of light field data. The proposed method contributes to improve the transform efficiency of the encoder, since the disparity-compensated data presents higher correlation between co-located image blocks. The experimental results show significant improvements in the compression performance of 4D light fields, achieving Bjontegaard delta rate gains of about 44% on average for MuLE codec using the 4D discrete cosine transform, when encoding High Density Camera Arrays (HDCA) light field images.
On the quadratic reciprocity law

In the number theory, the law of quadratic reciprocity is a theorem about modular arithmetic that gives conditions for the solvability of quadratic equations modulo prime numbers. It noticed by Euler and Legendre and proved by Gauss. In this paper, we will study the quadratic reciprocity law theorem where the Euler Criterion and Legendre Symbol are involved. The application of quadratic reciprocity law theorem is given in cryptography, where the Quadratic Residuosity Problem considered as a hard mathematical problem for Goldwasser Micali Randomized Public Key Cryptosystem. This system will be discussed with the details in this paper.
Device Independent Key Generation Using Decoy State

It is a common belief that the unavailability of perfect single-photon source and detector will limit the feasibility of Quantum key distribution (QKD). A perfect device independent protocol can overcome this limitation in a long distance secure communication. This paper proposes to minimize the detector dependency at receiver’s end by introducing a device independent key generation protocol. Here, impurity is embedded in the data string using a decoy source. A one way function known to both Alice and Bob is guided to determine the subsequent basis for generating the key and the impurity. Since the basis of both key as well as impurity (decoy state) is fixed by this function, it provides self checking mechanism for both the legitimate users, thereby providing better results in key generation process by some imperfect devices at the receiver’s end.
Variational Autoencoder with Embedded Student-t Mixture Model for Authorship Attribution

Traditional computational authorship attribution describes a classification task in a closed-set scenario.Given a finite set of candidate authors and corresponding labeled texts, the objective is to determine which of the authors has written another set of anonymous or disputed texts.In this work, we propose a probabilistic autoencoding framework to deal with this supervised classification task.Variational autoencoders (VAEs) have had tremendous success in learning latent representations.However, existing VAEs are currently still bound by limitations imposed by the assumed Gaussianity of the underlying probability distributions in the latent space.In this work, we are extending a VAE with an embedded Gaussian mixture model to a Student-t mixture model, which allows for an independent control of the "heaviness" of the respective tails of the implied probability densities.Experiments over an Amazon review dataset indicate superior performance of the proposed method.
Efficient Bandwidth Management Using Geo Nearest Datacenter for Inter DCN and Optimal Path for Intra DCN to Optimize Bandwidth Cost

Cloud services are getting much attraction these days among different types of stakeholders. To satisfy their demand, cloud service providers often establish geographically distributed DCs or make contract with third party DC providers. In this work, we consider the problem of resource allocation for enterprise customers considering both inter and intra data center network (DCN) jointly. Therefore, cloud service providers need to find an optimal solution to establish a connection from Point of Presences (PoPs) to the DCs (inter DCN) and from the entry points to the servers within each DC (intra DCN) to minimize the bandwidth cost. Here, a novel mixed-integer linear programming (MILP) formulation is presented to minimize the inter DC bandwidth cost by selecting a DC which is geo-nearest from the demand generating PoP and the intra DC bandwidth cost by using the optimal routes from the entry points to the servers. Efficiency of our approach is analyzed through inter and intra DC bandwidth cost and number of links used.
Evaluation of Hyperbolic Attention in Histopathology Images

We bring together into a common framework three key ideas - multi-scale medical image analysis, the attention mechanism, and hyperbolic embeddings. The formulation and evaluation of hyperbolic-attention models for multi-scale medical image analysis have not been previously explored. In this paper, we evaluate a hyperbolic-attention model on two classification tasks using histopathology image datasets. The experiments show improvement compared to other commonly used models. Our method directly captures the multi-scale structure of histopathology images, and we speculate that the hyperbolic attention mechanism naturally singles out one or more structures at one or more scales that are most discriminatory.
Aspect of join ingress authority for civic directory

Abstract T Nowadays, Cloud computing is a major and emerging platform that provides various IT related services. The two major factors which inhibit the growth of cloud computing are privacy and security. Hackers can break into public or private cloud environment and steal the data. They can use it with malicious intent such as either use or sell the sensitive information obtained from the business cloud environment. Hence, user’s safety depends upon security level in cloud service providers. For an enterprise, there are enormous amount of data is stored and most of them are sensitive information which can affect huge number of people or an entire business organization. Networks are the medium through which all tasks are performed. For access control, being one of the classic research topics, various schemes have been proposed and implemented. In existing key policy, when the key which provides access to the data falls into the wrong hands, they can use it with malicious intent such as steal or alter the data or sell the sensitive information obtained from the business cloud environment. Using our proposed algorithm, we can able to provide a collaborative access control scheme for public cloud storage. This system is implemented using encryption method called Attribute Based Encryption. Encrypting data ensures that even if the data falls into the wrong hands, it is useless as long as its keys remain secure. Even when the hacker gets access to the key, he cannot access the data as only the assigned users can access the data using the key.
Sparse fooling images: Fooling machine perception through unrecognizable images

Fooling images are potential threats to deep neural networks (DNNs). These images cannot be recognized by humans as natural objects, e.g., dogs and cats. However, they are misclassified by DNNs as natural object classes with high confidence scores. Despite their original design concept, existing fooling images, if closely examined, can be seen to retain some features that are characteristic of the target objects. Hence, DNNs can react to these features. In this study, we evaluate whether fooling images with no characteristic pattern of natural objects, either locally or globally, can exist. As a minimal case, we introduce single-color images with a few pixels altered, called sparse fooling images (SFIs). We first prove that SFIs always exist under mild conditions for linear and nonlinear models and reveal that complex models are more likely to be vulnerable to SFI attacks. Using two SFI generation methods, we demonstrate that in deeper layers, SFIs have features similar to those of natural images. Therefore, they fool DNNs successfully. Among the other layers, we discover that the max-pooling layer causes vulnerability to SFIs. The defense against SFIs and transferability are also discussed. This study highlights a new vulnerability of DNNs by introducing a novel class of images that are distributed extremely far from natural images.
An Embedding-based Medical Note De-identification Approach with Minimal Annotation

Medical note de-identification is critical for protecting private information. The task demands the complete removal of all patient names and other sensitive information such as addresses and phone numbers from medical records. Accomplishing this goal is challenging, with many variations in the medical note formats and string representations. Existing deidentification approaches include pattern matching where extensive dictionary lists are constructed a prior; and entity tagging, which trains on a large word-wise annotated corpus. This motivates us to study an alternative to the existing approaches with a reduced annotation burden. In this work, we propose a novel approach that implicitly accounts for the language territory of sensitive information. Specifically, our approach incorporates a contextualized word embedding module and a multilayer perceptron to simultaneously infer the similarity of sensitive and non-sensitive vocabularies to a constructed landmark set, providing an overall sparsely supervised classification. To demonstrate the rationale, we present the principle and work pipeline with the task of name removal, but the proposed method applies to other strings as well. On a large cohort of hybrid clinical reports, including various forms of consulting, on-treatment-visit, and follow-up notes, we achieved >0.99 accuracies in our constructed training, validation, and testing sets. The sensitivity and specificity were 1.0 and 0.9973 respectively for two randomly selected reports, comparing favorably to the benchmark Stanford NER tagger, which achieved 0.8529 and 0.9969.
Information hiding algorithm based on edge detection and holographic watermarking

Abstract In order to improve the robustness of the image watermarking and reduce the influence of the overall image quality of the carrier when watermark is embedded on the carrier, an edge detection method combined with holographic watermarking was proposed to realize the hiding of watermark information. The carrier image is processed by edge detection, the watermark embedding area is segmented in the carrier image, and the suitable embedding area is filtered according to the size of the watermark; the original watermark is processed by a four-step phase shift algorithm to generate a holographic watermark, and the holographic watermark is embedded in the selection The embedding area is completed to embed the watermark, and the carrier image which the watermark is embedded in is carrier image with watermark. The watermark extraction method is to perform the same edge detection segmentation and region screening on the watermark carrier image, find the embedded area of the watermark, extract the holographic watermark, perform holographic inverse transformation to obtain the original watermark image, and complete the watermark recovery. The algorithm which is based on edge detection and holographic watermarking reduces the influence of embedded watermark on the carrier image, increases the flexibility and concealment of embedded position, and improves the robustness of the image when it is under geometric attacks.
Computational Hardness of Collective Coin-Tossing Protocols

Ben-Or and Linial, in a seminal work, introduced the full information model to study collective coin-tossing protocols. Collective coin-tossing is an elegant functionality providing uncluttered access to the primary bottlenecks to achieve security in a specific adversarial model. Additionally, the research outcomes for this versatile functionality has direct consequences on diverse topics in mathematics and computer science. This survey summarizes the current state-of-the-art of coin-tossing protocols in the full information model and recent advances in this field. In particular, it elaborates on a new proof technique that identifies the minimum insecurity incurred by any coin-tossing protocol and, simultaneously, constructs the coin-tossing protocol achieving that insecurity bound. The combinatorial perspective into this new proof-technique yields new coin-tossing protocols that are more secure than well-known existing coin-tossing protocols, leading to new isoperimetric inequalities over product spaces. Furthermore, this proof-technique’s algebraic reimagination resolves several long-standing fundamental hardness-of-computation problems in cryptography. This survey presents one representative application of each of these two perspectives.
CRM Becomes Seriously Ill

This is a less technical chapter devoted to a CRM management problem of poor performance of an organization such a call center or a technical support department. We explore a technology that can detect this performance and a root cause for it, in terms of We explore the phenomenon of Distributed Incompetence (DI), which is an opposite to Distributed Knowledge and occurs in various organizations such as customer support. In a DI organization, a team of employees is managed in a way that, being rational, impresses a customer or an external observer with total irrationality and incompetence, an inability to get things done. In most cases, the whole organization or individual team members gain from DI by means of refusing customer compensation while avoiding other obligations. We investigate DI in a variety of organizations to analyze its commonality as well as specific DI features for organizations and communities. A discourse-level analysis to detect DI in textual descriptions of customers and observers is outlined. We report a detected DI rate in financial organizations and propose a solution to handle it, such as a chatbot.
Informational Model of Optical Signals and Images in Machine Vision Systems

The rapid development and use of optical systems in measurement, navigation, and space technology to obtain accurate and detailed information about an object of observation is accompanied by the problems of transmitting high quality information through the optical system and processing of the obtained data. Integration of artificial intelligence systems in industry requires the creation and improvement of objective assessment and self-assessment systems. This is especially designed for automated recognition and classification systems. The problem of the object movement registration also contains some peculiarities such as background and main signal separation, noise influence and main objects selecting. Information about data quality is a set of properties that reflects the degree of suitability of specific information. It contains the data about objects and their relationship to achieve the goals of user requirements.
Interpretation Area-Guided Detection of Adversarial Samples

Deep learning systems are known to be vulnerable to adversarial samples, which are implemented to change the prediction results by adding small perturbations to benign samples. It is significant to defend against an adversarial attack in critical fields such as automatic drive. In this paper, we propose an interpretation area-guided detection method of adversarial samples, which can improve the performance of the typical feature squeezing method by combining the generated interpretation results. Specifically, we divide the input image into two main parts, the interpretation part, and the non-interpretation part. Then we only squeeze the non-interpretation part, which can reduce the side-effect for benign samples. We evaluate our approach on two widely used datasets, and the results demonstrate that our approach outperforms the original feature squeezing method.
Detecting Errors in Cognitive Models Using Visualization Metaphors of Fuzzy Cognitive Maps

Verification of cognitive models is one of the most important stages in their construction, since reliability of results of subsequent modeling largely depends on the successful implementation of verification. The paper considers the problem of verifying cause-and-effect relationships in cognitive models based on the use of fuzzy cognitive maps. It is noted that increasing the effectiveness of cognitive model verification is possible by activating analyst's cognitive potential. The most natural way of such activation is to increase cognitive clarity of the model through the use of visualization capabilities. For this purpose, a number of metaphors for visualizing fuzzy cognitive maps have been proposed, aimed at increasing their cognitive clarity during verification. Each of the metaphors is focused on the visualization of a certain type of fragments of a fuzzy cognitive map potentially containing errors, redundancy or incompleteness and therefore of interest from the point of view of verification. The first considered visualization metaphor is intended to display the cycles that are part of a cognitive graph. The second metaphor focuses on the mapping of transitive paths between concepts. Finally, the third metaphor is aimed at eliminating cognitive model incompleteness, which consists in the lack of relationships between some concepts. Examples are given of applying the proposed visualization metaphors to increase cognitive clarity of the visual image of the verified fuzzy cognitive map.
A Fusion of Java Domain Knowledge Base and Siamese Network for Java API Recommendation

The following topics are dealt with: program testing; Internet; security of data; Internet of Things; formal specification; mobile computing; data mining; learning (artificial intelligence); blockchains; electronic commerce.
Research Programs Based on Machine Intelligence Games

Games have played a significant role throughout the history of artificial intelligence and robotics. Machine intelligence games are examined here from a methodological perspective, focusing on their role as generators of research programs. These research programs are schematized in terms of framework building, subgoaling, and outcome appraisal processes. The latter process is found to involve a rather intricate system of rewards and penalties, which take into account the double allegiance of participating scientists, trading and sharing interchanges which occur in multidisciplinary research environments, in addition to expected industrial payoffs, and a variety of other research benefits in the way of outreach and dissemination of results, recruitment of junior researchers and students’ enrolment. Examples used here to illustrate these various aspects of the outcome appraisal process include RoboCup and computer chess, Go, Poker and video-games. On the whole, a reflection on research programs that are based on machine game playing opens a window on central features of the complex systems of rewards and penalties that come into play to appraise machine intelligence investigations.
Static mode of the mathematical model of an electric multipole with memresistive branches in conditions of interval uncertainty

Abstract This article discusses a method for obtaining a mathematical model of an electric multipole with memresistive branches in finite parameter increments for a large signal mode when calculating a direct current based on a generalized model obtained earlier. A computational algorithm for calculating nanoelectronic circuits based on methods of interval arithmetic is also given.
Optimal fiber optic scheme for sub-SQL quantum receiver realization

Abstract Practical implementation of high-precision quantum measurements is an important problem in modern science. One of the main parts of the quantum receiver is the optical scheme. We developed and tested several optical circuits based on different types of interferometers, namely Sagnac-based scheme, Mach-Zehnder-based scheme, and Michelson-based scheme. All these schemes are assembled with optical fibers and fiber-optic components, since the fiber-optic implementation is closest to application in practical devices. Schemes were evaluated according to two main criteria: extinction and interference stability. On the basis of the obtained data, it can be concluded that the most suitable is the scheme based on the Mach-Zehnder interferometer. In continuous mode, we were able to obtain an interference extinction about 30 dB with acceptable temporal stability.