Reviewerly/exHarmony huggingface dataset:
Russian Doll Network: Learning Nested Networks for Sample-Adaptive Dynamic Inference

This work bridges recent advances in once-for-all (OFA) networks [1] and sample-adaptive dynamic networks. We propose a novel neural architecture dubbed as Russian doll network (RDN). Key differentiators of RDN are two-folds: first, a RDN topologically consists of a few nested sub-networks. Any smaller sub-network is completely embedded in all larger ones in a parameter-sharing manner. The computation flow of a RDN starts from the inner-most (and smallest) sub-network and sequentially executes larger ones according to the nesting order. A larger sub-network can re-use all intermediate features calculated at their inner sub-networks. This crucially ensures that each sub-network can conduct inference independently. Secondly, the nesting order of RDNs naturally plots the sequential neural path of a sample in the network. For an easy sample, much computation can be saved without much sacrifice of accuracy if an early-termination point can be intelligently determined. To this end, we formulate satisfying a specific accuracy-complexity tradeoff as a constrained optimization problem, solved via the Lagrangian multiplier theory. Comprehensive experiments of transforming several base models into RDN on ImageNet clearly demonstrate the superior accuracy-complexity balance of RDN.
En-Is Synthetic Parallel Astronomy Corpus with Injected Vocabulary

Synthetic parallel corpus (Icelandic-English) with astronomy terms injected into university abstracts (IPAC, https://arxiv.org/pdf/2108.05289v1.pdf). The data was created using the GreynirTerms utility tool (https://github.com/mideind/GreynirTerms). The sentences are injected with nouns from a glossary of astronomy and physics terms (https://idordabanki.arnastofnun.is/leit//ordabok/STJARNA). The sentences are grammatically correct, as the nouns are injected in the correct grammatical case, gender and number, but semantically they don't make sense. This synthetic data was originally used for training NMT models in order to teach them how to translate rare vocabulary, in fields where true parallel training data is hard to come by. ---Tilbuin samhliða malheild (islenska-enska) þar sem stjornufraeðihugtokum hefur verið skotið inn i utdraetti haskolaritgerða ur Skemmunni (IPAC, https://arxiv.org/pdf/2108.05289v1.pdf). Gognin voru buin til með utskiptingatolinu GreynirTerms (https://github.com/mideind/GreynirTerms). Setningarnar innihalda nafnorð tekin ur hugtakalista i stjornufraeði og eðlisfraeði (https://idordabanki.arnastofnun.is/leit//ordabok/STJARNA). Setningarnar eru malfraeðilega rettar, þar sem nafnorðin koma fyrir i rettri tolu, kyni og falli, en merkingarlega otaekar. Þessi gervigogn voru upprunalega notuð til þess að kenna velþýðingarlikonum að þýða sjaldgaefan orðaforða, a sviðum þar sem erfitt er að nalgast samhliða þjalfunargogn.
Study on Dynamic Equilibrium Factor based on Gauss-Cauchy Distribution in Artificial Bee Colony Algorithm

Based on the limitations of the traditional artificial bee colony algorithm in employed bee, onlooker bee and scout bee phase search strategy. The article proposes a dynamic balance factor strategy that uses the combination of Gauss-Cauchy distribution to further optimize the shortcomings of the original algorithm in global search and local search. The dynamic balance factor combines the characteristics of Gaussian distribution and Cauchy distribution, enriches the population diversity in the stage of scout bee and employed bee, and improves the accuracy of local search in the stage of onlooker bee. In the later stage of the algorithm, adaptive influence coefficients are introduced based on the characteristics of mathematical functions to change the algorithm&#x2019;s convergence speed, so as to realize the optimization of the algorithm&#x2019;s search breadth and accuracy, as well as the convergence speed. The results of simulation experiments show that the artificial bee colony algorithm base on Normal-Cauchy distribution (ABC-NCD) based on Gauss-Cauchy distribution is superior to other similar algorithms in terms of global search breadth and local convergence accuracy.
Recognizing Series-Parallel Matrices in Linear Time

A series-parallel matrix is a binary matrix that can be obtained from an empty matrix by successively adjoining rows or columns that are copies of an existing row/column or have at most one one-entry. Equivalently, series-parallel matrices are representation matrices of graphic matroids of series-parallel graphs, which can be recognized in linear time. We propose an algorithm that, for an m-by-n matrix A with k nonzeros, determines in expected time whether A is series-parallel or returns a minimal non–series-parallel submatrix of A. We complement the developed algorithm by an efficient [Formula: see text]implementation and report about computational results. History: Accepted by Andrea Lodi, Area Editor for Design &amp; Analysis of Algorithms–Discrete. Funding: This work was supported by Nederlandse Organisatie voor Wetenschappelijk Onderzoek [Grant OCENW.M20.151]. Supplemental Material: The software that supports the findings of this study is available at the GitHub software repository ( https://github.com/discopt/cmr-series-parallel ).
CuckoOnsai: An Efficient Memory Authentication Using Amalgam of Cuckoo Filters and Integrity Trees

The off-chip main memory data can be extracted or tampered by an adversary having physical access to a device. In modern secure designs, such tampering or data attacks can be prevented by storing the integrity tree built on top of encryption counters. However, such approaches have significant performance and on-chip storage overheads. This paper proposes CuckoOnsai, which uses the combination of a novel on-chip per core Cuckoo filter and an off-chip small integrity tree. Compared to the recent competing scheme, CuckoOnsai improves performance by 13.1% and reduces the space overheads and $\mathrm{ED}^{2}$ by 50% and 26.1%, respectively.
Public Key Encryption in the Cloud Computing Environments: Trust and untrust

With the development of data storage technology and the computerized process of an internet-based technology. Cloud computing systems allow users to store and process data virtually. Users also do not need to prepare a platform or supporting infrastructure, but only need to use existing platforms. For the establishment of a trust relationship between entities as an important part of establishing security mechanisms in the cloud computing environment. This article introduces a trust management model in cloud computing using public key encryption. Measurement and computation of trust, and the chain of trust where similarity of trust issues is recommended have been addressed to prevent fraudulent user behavior. This model is geared towards cloud users who make decisions about whether to use a cloud computing service provider by giving them confidence. The purpose of this article motivates us to try proposes new ideas and methods of trust management in cloud computing using public key encryption.
Movement Strategies in Virtual Reality: Exploring the Influence of 3D Endpoint Variability

Research using a kinematic approach has revealed that users often exhibit strategic biases in their movement behavior to minimize the effort required to reach a target. However, a recent exploration of these effects in a virtual reality (VR) environment yielded conflicting results, calling into question whether strategic patterns observed in movements to physical targets can be expected to generalize to VR environments. In the present study, we re-analyze the data from Clark and Riggs (2020) using principal component analysis (PCA) to empirically distinguish between alternative explanations for the unexpected results. Our findings clarify the source of these results for downward versus upward movements and provide a preliminary look at how adaptations to manage perception- and execution-related motor variability may impact users’ movement strategies in VR.
Automated Responsible Disclosure of Security Vulnerabilities

The disclosure of security vulnerabilities plays an important role in notifying vendors and the public about flaws in digital systems. Among the proposed disclosure approaches, the most utilized is Responsible Disclosure, which unfortunately suffers from several disadvantages such as fostering a false sense of security among the end-users, allowing arbitrary delays in the disclosure process, and forcing the party reporting a vulnerability to identify themselves, which has been exploited by vendors in the past through intimidation and malpractice. To address these issues, this paper presents an improved version of the Responsible Disclosure approach called Automated Responsible Disclosure (ARD) - a solution that leverages distributed ledgers and interledger technologies to automate the disclosure process while offering increased security, privacy, and transparency. A prototype implementation has been released as open-source software, and the evaluation of the solution shows that ARD is capable of addressing the key shortcomings in existing solutions and fostering more transparent vulnerability disclosure practices.
D-Flow: A Real Time Spatial Temporal Model for Target Area Segmentation

Semantic segmentation has attracted a large amount of attention in recent years. In robotics, segmentation can be used to identify a region of interest, or target area. For example, in the RoboCup Standard Platform League (SPL), segmentation separates the soccer field from the background and from players on the field. For satellite or vehicle applications, it is often necessary to find certain regions such as roads, bodies of water or kinds of terrain. In this paper, we propose a novel approach to real-time target area segmentation based on a newly designed spatial temporal network. The method operates under domain constraints defined by both the robot's hardware and its operating environment. The proposed network is able to run in real-time, working within the constraints of limited run time and computing power. This work is compared against other real time segmentation methods on a dataset generated by a Nao V6 humanoid robot playing soccer. In this case, the target area is defined as the artificial grass field. The method is also tested on a maritime dataset collected by a moving vessel, where the aim is to separate the ocean region from the rest of the image. This dataset demonstrates that the proposed model can generalise to a variety of vision problems.
Regulating cryptocurrencies checkpoints: Fighting a trench war with cavalry?

Abstract The rise of cryptocurrencies during the last decade has caused growing concerns among national and international regulators. One of the risks identified is that these instruments may constitute an innovative tool for criminals when laundering money. This risk has been confirmed by numerous recent cases which have underlined the criminogenic potential of cryptocurrencies. Through the V antimoney laundering (AML) Directive, the European legislator has first regulated this emerging issue. This legislation extends the AML duties to two players of the cryptocurrencies market: exchangers and wallet providers. This choice, however, does not exploit the opportunities offered by cryptocurrencies and fails to provide a customized regulatory framework. By maintaining a traditional regulatory approach centered on intermediaries it misses the key innovation of blockchain technology: disintermediation. Compared with traditional online money flows, intermediaries are not necessary nor fundamental in the cryptocurrencies environment. Failing to adapt to this reality, the Directive is employing chivalry to fight a trench war. To guarantee the integrity of this market, the policymaker has to abandon the traditional intermediary‐centred approach in favor of a strategy that seizes the new opportunities offered by blockchain. This paper advocates for a shift from an individual‐centered approach to financial crime control to a transaction‐centered one.
Evaluation model of personal information security awareness based on TOPSIS and k-means

Information security is an important issue in epidemic prevention and control, which is related to national stability and social harmony. A personal information security awareness evaluation model based on TOPSIS and K-means++ is proposed in this paper. Through the investigation of the citizens’ understanding of the information risk sources in the process of the collection, release, storage and processing of personal information by the government, we can understand the information security awareness of citizens under the prevention and control of the epidemic. According to the results of the survey, it is recommended to set the storage period of personal information, provide an effective mechanism for individuals to participate in information processing, and strengthen the information security training for government employees in information processing positions.
Bias Removal Blending to Create Panorama

Robust feature extraction and image stitching to create a panorama is a challenging task with various applications in computer vision and robotics. In order to describe and get the best conceivable stitching, this paper put forward an approach of panoramic image stitching and blending process which is divided into three steps. SIFT feature extraction, feature matching and image blending to generate a seamless panorama. The stitched images to create a panorama that have significant illumination changes at the stitched line appear to be unnatural. An image blending algorithm is developed in this paper, based on minimizing sharp edge error by a weighted matrix to solve the problem. First, panorama is generated using feature space of SIFT, so that the images are to be stitched should have maximum crucial features, and performs the rough matching process, followed by RANSAC algorithm for fine matches. Finally applied the different image blending techniques between three images and our method successfully minimizes the error to have a uniform illumination over panorama. The results are compared with the two blending processes. Results are demonstrated by visual assessment and quantitatively by calculating normalized edges. Experimental results show that our algorithm is effective and able to make sharp changes disappear at image joins. This method will be very useful for tracking aerial target near launchpad with multiple fixed cameras with same Field of View.
Susceptible Temporal Patterns Discovery for Electronic Health Records via Adversarial Attack

The recent advancements in deep neural networks (DNNs) are revolutionizing the healthcare domain. Although many studies try to build medical DNNs model based on historical Electronic Health Records (EHR) and have achieved promising performance in many clinical prediction tasks, recent studies show that DNNs are vulnerable to adversarial attacks. Much of the interest in adversarial examples has stemmed from their ability to shed light on possible limitations of DNNs. However, related research has been receiving sustained attention in computer vision community, how to design adversarial examples for EHR data remains a rarely investigated. To figure out this problem, we propose a novel approach for generating EHR adversarial examples, named as TSAttack, which explores temporal structure contained in EHR to achieve an effective and efficient attack. Based on the generated EHR adversarial examples, we further propose a procedure to discover susceptible temporal patterns (STP) in a patient’s medical records, which provide clinical decision support for dynamic monitoring. Extensive experiments on the real-world longitudinal EHR database MIMIC-III have demonstrated the effectiveness of our approach is yielding better performance in adversarial settings.
SocialBERT – Transformers for Online Social Network Language Modelling

The ubiquity of the contemporary language understanding tasks gives relevance to the development of generalized, yet highly efficient models that utilize all knowledge, provided by the data source. In this work, we present SocialBERT - the first model that uses knowledge about the author’s position in the network during text analysis. We investigate possible models for learning social network information and successfully inject it into the baseline BERT model. The evaluation shows that embedding this information maintains a good generalization, with an increase in the quality of the probabilistic model for the given author up to 7.5%. The proposed model has been trained on the majority of groups for the chosen social network, and still able to work with previously unknown groups. The obtained model is available for download and use in applied tasks ( https://github.com/karpovilia/SocialBert ).
Sports Video Extraction and Analysis Method Based on Cyclic Random Walk Algorithm

With the development of multimedia and network technology, sports videos have been widely produced and disseminated. Based on the cyclic random walk algorithm of deep learning, this paper establishes a cyclic machine random walk model for feature extraction and analysis of sports videos. Firstly, the rise of sports video and the necessity of video key frame analysis is introduced. Secondly, according to the random walk algorithm of deep learning, a cyclic random walk model is proposed, and several analysis methods of sports video are introduced. Finally, the key frame of sports video is extracted through code simulation to verify the effectiveness of the algorithm.
Gathering, Selecting and Preparing Unstructured Documents for Enterprise Information Extraction

A large amount of unstructured documents exists on the web incorporating data of paramount importance for the enterprises that can employ them to synthesize the past, to comprehend the present and to predict the future. However, it is worth noting that the unstructured nature of these documents made the handling and the extraction of knowledge from them a very critical issue. The current contribution is three-fold. First, we collect the unstructured documents which might be useful using general enterprise ontology. Then, we select the most suitable ones using specific ontologies that describe partial enterprise activities. Finally, we transform the kept documents into parsabale and requestable XML files that can be the corpus for future data extraction.
Single Picture Super-Resolution Using Generative Adversarial Network

Picture Super-resolution is a by and large analysed issue in computer vision, where the objective is to change over a low-resolution picture to a high-resolution picture. As of now, deep learning methods such as convolution neural systems and generative adversarial networks are being utilized to perform super-resolution with results competitive to the best in class. In this paper, a generative adversarial network, SRGAN, is proposed for super-resolution with a perceptual loss work comprising of an adversarial loss, mean squared loss, and content loss. The target of our usage is to get familiar with an end-to-end mapping between the low and high-resolution pictures furthermore, enhance the up-scaled picture for quantitative measurements as well as absolute quality. We at that point think about our outcomes with the present cutting-edge strategies in super-resolution, lead proof of idea division study to show that super-resolved pictures can be utilized as a compelling pre-processing step before division and approve the findings measurably.
3D Trajectory Planning of Underwater Vehicle Based on DPACO Algorithm

In trajectory planning space, the dynamic programming algorithm is characterized by capabilities of computing and optimization. However, in 3D trajectory planning, as the planning space expands, the number of grid points increases faster, and time complexity of the dynamic programming algorithm is $$O\left( {n^{3} } \right)$$ . It often leads to a “Curse of dimensionality” phenomenon, which lowers its computational efficiency drastically. To solve this problem, this paper divides the entire planning space into stages based on Bellman's optimality principle. A dynamic programming-ant colony algorithm is proposed by using ant colony algorithm in each stage for optimization, and dynamic programming algorithm for global planning. The global optimization ability of the algorithm is verified through its convergence analysis. Moreover, based on a comparative analysis of simulation experiments, it shows that the improved algorithm proposed in this paper is more efficient than the dynamic programming algorithm and ant colony algorithm alone, and can be used to plan a global optimal solution.
The development of an automatic answering service system By LINE BOT technology of the Research and Development Institute Sakon Nakhon Rajabhat University

The objective aims to develop the automatic answering service system of the Research and Development Institute, Sakon Nakhon Rajabhat University (RDI-SNRU) and to assess the efficiency of the RDI- SNRU auto answer service system. Dialog flow Is a tool for designing the questions and answers to the auto question answering system about various research grants. The Line Official Account is tool to develop news notification systems and the sending of report about research grants and automated question answering systems on research grants. The Black box Testing method was used to assess the efficiency application. The results show that the application can answer questions about research grants and the submission of evidence documents with various researches, This application can browse the research funds of Research and Development Institute each year, And also, can view the news releases of research funds, research funding request progress report draft and complete research report, display personnel information RDI-SNRU to contact personnel directly. Have calendar display show Recruiting research funds of RDI- SNRU. The result of the evaluation efficiency application is the best level.
A Novel Method on Summarization of Video Using Local Ternary Pattern and Local Phase Quantization

In last decade, Video Summarization (VS) approach is playing a pivotal role in the analysis of the Video contents. The methodologies involved in Video Summarization have wide range of applications in the field of defense for video surveillance, intrusion, object detection, Video Browsing, Content-based Video Retrieval and Storage etc. In this study, we have proposed video summarization techniques to extract the frames of interest. Then, video summarization has determined by the advanced texture descriptors. Local Ternary Pattern (LTP) & Local Phase Quantization (LPQ) are the texture descriptor methods used to provide an efficient video summarization process. These methodologies are in conformity with the elimination of redundant frames in a video as well as the maintenance of user defined number of distinctive images. Then apply the clustering process, which is an unsupervised machine learning algorithms, such as, Affinity Propagation and BIRCH, are utilized to cluster the similar frames into one group. These methodologies confirm that the summary of video denotes the most distinctive frames of the input video, which results the same importance to preserve the continuousness of the summarized video.