Reviewerly/exHarmony huggingface dataset:
Closed sets of finitary functions between finite fields of coprime order

Abstract We investigate the finitary functions from a finite field $$\mathbb {F}_q$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:mi>q</mml:mi> </mml:msub> </mml:math> to the finite field $$\mathbb {F}_p$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:mi>p</mml:mi> </mml:msub> </mml:math> , where p and q are powers of different primes. An $$(\mathbb {F}_p,\mathbb {F}_q)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:msub> <mml:mi>F</mml:mi> <mml:mi>p</mml:mi> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>F</mml:mi> <mml:mi>q</mml:mi> </mml:msub> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> -linearly closed clonoid is a subset of these functions which is closed under composition from the right and from the left with linear mappings. We give a characterization of these subsets of functions through the invariant subspaces of the vector space $$\mathbb {F}_p^{\mathbb {F}_q\backslash \{0\}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mi>F</mml:mi> <mml:mi>p</mml:mi> <mml:mrow> <mml:msub> <mml:mi>F</mml:mi> <mml:mi>q</mml:mi> </mml:msub> <mml:mrow> <mml:mo>\</mml:mo> <mml:mrow> <mml:mo>{</mml:mo> <mml:mn>0</mml:mn> <mml:mo>}</mml:mo> </mml:mrow> </mml:mrow> </mml:mrow> </mml:msubsup> </mml:math> with respect to a certain linear transformation with minimal polynomial $$x^{q-1} - 1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mi>x</mml:mi> <mml:mrow> <mml:mi>q</mml:mi> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> . Furthermore we prove that each of these subsets of functions is generated by one unary function.
Tree-Based Algorithm for Stable and Efficient Data Clustering

The K-means algorithm is a well-known and widely used clustering algorithm due to its simplicity and convergence properties. However, one of the drawbacks of the algorithm is its instability. This paper presents improvements to the K-means algorithm using a K-dimensional tree (Kd-tree) data structure. The proposed Kd-tree is utilized as a data structure to enhance the choice of initial centers of the clusters and to reduce the number of the nearest neighbor searches required by the algorithm. The developed framework also includes an efficient center insertion technique leading to an incremental operation that overcomes the instability problem of the K-means algorithm. The results of the proposed algorithm were compared with those obtained from the K-means algorithm, K-medoids, and K-means++ in an experiment using six different datasets. The results demonstrated that the proposed algorithm provides superior and more stable clustering solutions.
Cooperative Education Strategies

The article presents the results of the Consumer Cooperation Forums of the Russian Federation on the problem of education, as centers for the creation of innovations. The relevance of this topic is due to the general trend in the development of higher education in Russia, namely, overcoming the lag and the development of new technologies in key areas of the country’s economy. The educational institution must comply with the socio-economic changes in the regions in which it is located. Also, an educational institution should build its activities taking into account world sociocultural and educational trends in the training of specialists capable of promptly mastering innovations in the field of professional activity. Information was collected and processed based on the information system of the Association of Educational Organizations of the Consumer Cooperation System. The results include the implementation of key projects that contribute to the development and strengthening of the position of educational organizations of PCs in the regions, in accordance with roadmaps.
Synchrotron X-ray radiation and the African earth sciences: A critical review

Synchrotron-based X-ray analytical techniques are gaining in prominence as a crucial yet highly specialized tool for advancing knowledge within the broad field of the Earth Sciences. In terms of global scientific competitiveness, African scientists are somewhat disadvantaged by not having access to their own synchrotron on the African continent. However, there currently exists a strong drive towards obtaining a synchrotron X-ray facility on the African continent and the purpose of this critical and timeous review is thus to highlight the importance of such equipment for the local earth science community (e.g., geologists, geochemists and palaeontologists). Our review shows that various high-level synchrotron X-ray spectroscopies have been successfully applied to African earth samples and that the outputs of these studies have measurable societal benefit in terms of improved understanding of fundamental earth processes (including the molecular level controls on earth climate cycles), improved characterization of important and economic ore commodities (including drill core samples), and improved understanding of the fate and mobility of deleterious elements in the surface environment (e.g. soils and waterways). Similarly, the high phase contrast and rapid acquisition capabilities of synchrotron X-ray computed tomography (sXCT) have added important insights into a wealth of samples derived from Africa's fossil inventory (>350 specimens). Not only these studies advance our understanding of all ancient life forms, shedding light on our origins, they contributed to the training of the next generation of palaeontologists and the dissemination of science in Africa. It is foreseen that this review will stimulate further use of synchrotron technologies by the local earth science community whilst concomitantly developing a strong scientific case for considering and incorporating the needs of the earth sciences when the time comes to develop an African synchrotron facility (i.e., the African Lightsource).
A Sound-Image Coding Method Inspired by an Acousto-Optic Electronic Piano

Abstract This paper firstly brings up a point that there seems to be a lack of a direct mapping bridge method for interconversion between sound and image, because many methods invented for scientific research on sound visualization are based on the sound characteristics of pitch, duration, loudness and timbre. Secondly, it designs an acousto-optic electronic piano which can possess both sound and color effects with the application of music visualization, and presents a sound-image coding method inspired by the previous design which can transform music note to picture pixel. Thirdly, it manages to transform a simple piece of music called “I am a painting master” into a corresponding picture with the mapping bridge method using Matlab software. More coding bits of the method should be added for complex music. The sound-image coding method exhibits a mapping bridge role for sound-image interactive design, which may help to find essential relationship between music and picture in mentality.
Optimized DWT SVD Based Image Watermarking Scheme Using Particle Swarm Optimization

The robust watermarking techniques have gained popularity over last decade as more and more digital contents are being shared over Internet. This increase in data transmission also poses greater risk of various attacks which hamper data authenticity, integrity and quality. Therefore, efficient mechanisms are required that can handle robust transmission of digital data while honoring the Digital Right Management (DRM) protection measures of copyrighted content. In this paper, we explore an optimized Particle Swarm Optimization (PSO) based watermarking scheme on digital images in the hybrid Discrete wavelet transform (DWT) and Singular value decomposition (SVD) transform. The watermark is embedded in the host images by using both single scaling and multiple scaling factors for optimizing the visual quality of the signed images viz-a-viz robustness. Eight gray-scale and six colored images are used to carry out the experiments. Comparison of the proposed watermarking scheme with other techniques ascertain the proposed scheme outperforms others and it also fulfills all requisite conditions to be a robust and optimized watermarking scheme.
Data augmentation process to improve deep learning-based NER task in the automotive industry field

Searching and extracting information in a textual sequence drawn from scientific articles, queries on a search engine and posts in a discussion forum necessitate a process called Named Entity Recognition (NER). Nevertheless, the data available to achieve this process diverge depending on their nature and field of study. In this article, we look at the performance of Named Entity Recognition systems, and their complexity and ability to process data from different backgrounds. A comparative study between several state-of-the-art approaches, applied to different types of data (search engine queries and discussion forum posts) related to the automotive industry field, is proposed in order to select the approach that will best suit our instance. To do this, we shall rely on the results of metrics evaluating machine learning models such as precision, recall and F-score.
Pipeline-Based Chunk Scheduling to Improve ABR Performance in DASH System

To deliver high quality video across different network conditions, the video chunks are explicitly fetched by client or proactively pushed by server in Dynamic Adaptive Streaming over HTTP (DASH) system. Unfortunately, on the one hand, the client fetch mechanism suffers from bandwidth wastage due to its stop-and-wait fashion when the network delay becomes large. On the other hand, the server push mechanism performs poorly because of its inflexibility in bitrate switching under fluctuating bandwidth. To address these inefficiencies, we propose a pipeline-based chunk scheduling scheme called PCS to auto-turn the sending time of each chunk. For a given ABR algorithm, PCS dynamically pre-schedules the chunk delivery according to the real-time network conditions. Using the pipelined-based chunk delivery, PCS flexibly adjusts the bitrate of each chunk and meanwhile avoids the unnecessary waiting time in the stop-and-wait transmission. The experimental results of testbed implementations show that PCS greatly improves the average bitrate of the state-of-the-art ABR algorithms by up to 26%, and reduces the rebuffer rate by up to 31%.
Dual-Direction Perception and Collaboration Network for Near-Online Multi-Object Tracking

Backward tracks have been exploited to improve performance of multi-object tracking (MOT). The existing method brings a stable similarity measurement but neglects unreliable detection. Exploiting predictions of forward tracks has emerged as a popular approach to tackle the task of tracking-by-detection. However, it's observed that missing detection has not been solved well enough which would significantly influence the tracking accuracy. Thus, obtaining more proposals from dual-direction tracking and predictions of tracks is concerned to address the problem of missing detection. In this paper, we propose a dual-direction perception and collaboration network (DPCNet) for MOT that exploits forward and backward tracking to collaboratively track objects. It collects candidates from the dual directions so that they can complement each other in different scenarios. Moreover, we propose a near-online tracking model based on DPCNet to improve the efficiency, which batches the tracking and makes forward and backward tracking in parallel. Experiments conducted on MOT challenge benchmarks demonstrate that the proposed method outperforms the state-of-the-arts.
Seismic Event Detection via Deep Multi-Task Learning

It is fundamental to detect seismic events reliably and efficiently when processing continuous waveform data recorded by seismic stations. Recently, convolutional neural network (CNN) based detecting methods have been proposed for seismic events detection and obtained great success in this area, where the learning of seismic event detecting network of all seismic stations is considered as one learning task and numerous labeled data need to be collected for training the detecting network. However, they tend to ignore the differences between seismic stations caused by geographic position. Moreover, due to a few seismic activities and high cost of manual data labeling, in some areas, the labeled data for seismic event detecting tasks is insufficient. Under this condition, these methods always encounter over-fitting problem leading to bad detection performance. In this paper, we propose a multi-task based framework based on convolutional neural network for accurate seismic event detection under the condition of insufficient labeled data. Specifically, we first cluster the seismic stations into several station clusters and treat the learning of seismic event detecting network of every station cluster as a learning task, and then we propose a deep multi-task network named detectMTIA among multiple tasks. Experimental results on a real-world seismic dataset with nine stations demonstrate the effectiveness of the proposed framework, especially when the labeled data is insufficient.
Improving Relation Classification by Incorporating Dependency and Semantic Information

Relation classification is the task of identifying relations between two entities in a sentence, which is an essential step in the standard NLP pipeline. Most of the previous models only make use of dependency or semantic features, which may result in the loss of vital information. In this paper, we propose a novel model that incorporates dependency and semantic information for relation classification. This is a neural network model using long short-term memory(LSTM), graph convolutional networks(GCN), and convolutional neural networks(CNN), named LGCNN. Concretely, it utilizes self-attention and LSTM to capture the local context in sentences. What's more, it uses graph convolution network to encode dependency information and takes advantage of convolution neural network to encode semantic information from the local context. Experiments on the SemEval-2010 Task 8 and KBP37 dataset demonstrate that our model is very effective in relation classification.
Visual Relationship Detection With A Deep Convolutional Relationship Network

Visual relationship is crucial to image understanding and can be applied to many tasks (e.g., image caption and visual question answering). Despite great progress on many vision tasks, relationship detection remains a challenging problem due to the complexity of modeling the widely spread and imbalanced distribution of {subject - predicate - object} triplets. In this paper, we propose a new framework to capture the relative positions and sizes of the subject and object in the feature map and add a new branch to filter out some object pairs that are unlikely to have relationships. In addition, an activation function is trained to increase the probability of some feature maps given an object pair. Experiments on two large datasets, the Visual Relationship Detection (VRD) and Visual Genome (VG) datasets, demonstrate the superiority of our new approach over state-of-the-art methods. Further, ablation study verifies the effectiveness of our techniques.
Continuous User Authentication Based on Deep Neural Networks

A user authentication method consists of a username, password, or any other related credential. These methods are mostly used only once to validate the user's identity at the start of session. However, one-time verification of user's identity is not resilient enough to provide adequate security all over the session. Such authentication methods should be adopted which can continuously verify that only genuine user is using the system resources for entire session. This research work has implemented a true continuous authentication system, based on keystroke dynamics, which tends to validate the user on each action by using the proposed robust recurrent confidence model(R-RCM). Moreover, the recurrent neural network(RNN) has been used to exploit the sequential nature of keystroke data. System has been tested with two experimental approaches and results are reported in mean genuine actions (ANGA) and imposter actions (ANIA).
Signatures of knowledge for Boolean circuits under standard assumptions

This paper constructs unbounded simulation sound proofs for boolean circuit satisfiability under standard assumptions with proof size O(n+d) bilinear group elements, where d is the depth and n is the input size of the circuit. Our technical contribution is to add unbounded simulation soundness to a recent NIZK of González and Ràfols (ASIACRYPT'19) with very small overhead. We give two different constructions: the first one is more efficient but not tight, and the second one is tight. Our new scheme can be used to construct Signatures of Knowledge based on standard assumptions that also can be composed universally with other cryptographic protocols/primitives. As an independent contribution we also detail a simple formula to encode Boolean circuits as Quadratic Arithmetic Programs.
Blockchain Technology for Healthcare Data

Nowadays, both medical institutions and individuals generate a large amount of data on health diagnostic. These data are critical and sensitive, so to share securely and storing is hard, conventional healthcare systems are facing multiple challenges including data breaching, medical record tampering, frauds, etc. In this paper, our aim is to extend the functionality of a Blockchain-based platform for healthcare information exchange to securely share data, maintaining privacy, and availability. Because, healthcare data contain a large amount of data, which is not feasible to store on Blockchain likes Ethereum chain, due to heavy cost. Moreover, a centralized database is prone to attack and failure leads to loss of data. Therefore, we propose a model of blockchain to use IPFS (InterPlanetry File System) for secure storation, sharing of data, and availability.
Turn up the heat!

Automated grading systems provide feedback to students in a variety of ways, but they typically focus on identifying incorrect program behaviors. Such systems provide notices of test case failures or runtime errors, but without debugging skills, students often become frustrated when they don't know where to start addressing these defects. Borrowing from work in software engineering research related to automated defect location, we leverage previous research describes using statistical fault localization (SFL) techniques to identify the probable locations of defects in student coding assignments. The goal is to use these SFL techniques to provide a scaffold for students, to direct their debugging efforts without giving too much guidance, and thus minimizing the learning associated with investigating the defects. After determining the "suspiciousness" for each line of code involved in the defect, we create a "heat map" visualization overlay onto their source code of the "suspiciousness" scores to visually guide a student's attention to parts of their code that are most likely to contain problems.
SimLoss: Class Similarities in Cross Entropy

One common loss function in neural network classification tasks is Categorical Cross Entropy (CCE), which punishes all misclassifications equally. However, classes often have an inherent structure. For instance, classifying an image of a rose as "violet" is better than as "truck". We introduce SimLoss, a drop-in replacement for CCE that incorporates class similarities along with two techniques to construct such matrices from task-specific knowledge. We test SimLoss on Age Estimation and Image Classification and find that it brings significant improvements over CCE on several metrics. SimLoss therefore allows for explicit modeling of background knowledge by simply exchanging the loss function, while keeping the neural network architecture the same. Code and additional resources are available at https://github.com/konstantinkobs/SimLoss
A general framework for privacy-preserving of data publication based on randomized response techniques

Privacy preserving is a paramount concern in publishing datasets that contain sensitive information. Preventing privacy disclosure and providing useful information to legitimate users for data analyzing/mining are conflicting goals. Randomized response is a class of techniques that perturbs each sensitive value in a certain way, so that personal privacy is protected while the large-trend of the entire dataset is still recoverable. However, existing randomized response techniques do not allow to flexibly configure the level of privacy protection, support only a few types of aggregate queries, and cannot achieve the best answer accuracy from perturbed data. These drawbacks impair the effectiveness of those techniques. This paper proposes a general framework based on randomized response techniques, which has good flexibility and extensibility, and can improve the effectiveness of randomized response methods. Our approach is validated by extensive experiments and comparison with existing randomized response and generalization methods.
AGAN: An Anatomy Corrector Conditional Generative Adversarial Network

The accurate segmentation of medical images has important consequences in clinical applications. Noisy and artefact-heavy images can result in erroneous image segmentation and often require expert understanding of the target anatomy by clinicians to interpret and compensate for missing and obfuscated data. This is especially true in ultrasound imaging where shadowing and speckle artefacts are common. We propose a novel approach to handle such artefacts using a conditional Generative Adversarial Network called Anatomical GAN (AGAN) that can correct anatomically-invalid pixel-wise segmentation and impose shape priors in carotid artery ultrasound images by learning the underlying structure of the arteries. These anatomically accurate outputs can then be used in the clinical work flow by clinicians or be further processed by other automated methods for assistance in clinical decision making. AGAN can be chained with any pixel-wise segmentation method and is generalisable for both anatomy and artefacts. Experimental results on a longitudinal ultrasound carotid artery dataset show that AGAN can correct anatomically-invalid segmentation masks obtained with different pixel-wise segmentation methods when other state-of-the-art methods fail.
A Facial Privacy Protection Framework Based on Component Difference and Template Morphing

Aiming to countermeasure facial privacy disclosure of the shared images in social media, a face privacy protection framework based on component difference and template morphing is proposed. For a shared facial image that requires privacy protection, its facial attributes are first detected, and then the most suitable face template is searched from a pre-built facial image template library. After that, the key points of the facial image and the face template are detected, and they are implemented for facial components segmentation. Finally, the facial components of two images are morphed according to the privacy protection level and the optimal morphing sequence determined by the component difference. Experiments and analysis are performed to an implementation of the framework. The results show that it can effectively protect the facial privacy meanwhile keep the visual quality of the image. It has great potential to be applied for privacy protection of the shared facial images in social media.