Reviewerly/exHarmony huggingface dataset:
Efficient Algorithm for the Linear Complexity of Sequences and Some Related Consequences

The linear complexity of a sequence s is one of the measures of its predictability. It represents the smallest degree of a linear recursion which the sequence satisfies. There are several algorithms to find the linear complexity of a periodic sequence s of length N (where N is of some given form) over a finite field F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> in O(N) symbol field operations. The first such algorithm is The Games-Chan Algorithm which considers binary sequences of period 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> , and is known for its extreme simplicity. We generalize this algorithm and apply it efficiently for several families of binary sequences. Our algorithm is very simple, it requires βN bit operations for a small constant β, where N is the period of the sequence. We make an analysis on the number of bit operations required by the algorithm and compare it with previous algorithms. In the process, the algorithm also finds the recursion for the shortest linear feedback shift-register which generates the sequence. Some other interesting properties related to shift-register sequences, which might not be too surprising but generally unnoted, are also consequences of our exposition.
Equivalent Keys of a Nonlinear Filter Generator Using a Power Residue Symbol

The existence of equivalent keys for a secret key is an inseparable topic in cryptography. Especially for pseudorandom number generators for cryptographic applications, equivalent keys are not only a specific pair of keys that generate the same sequence but includes the one that gives simply the phase-shifted sequence. In this paper, the authors especially focus on a kind of nonlinear filter generator (NLFG) defined by using a power residue calculation over an odd characteristic. Generally speaking, an evaluation of NLFGs has conducted by the randomness of the sequence itself and the security of keys. Though the previous evaluations of the randomness of the target NLFG are studied and proven theoretically, the security aspects as a cryptosystem still have not discussed. Therefore, this paper would like to begin a new security evaluation by focusing on the existence of equivalent keys for the NLFG. As a result, the authors first show that sequences generated by the NLFG are classified into several types of sequences depending on the choice of a certain parameter. Owing to this, it is found that there exist equivalent keys concerning the parameter corresponding to the above. At the same time, we show that the equivalent keys are possible to eliminate by giving the restriction on the corresponding parameter adequately.
Channel-Wise Spatial Attention with Spatiotemporal Heterogeneous Framework for Action Recognition

Recent years have witnessed the effective of attention network based on two-stream for video action recognition. However, most methods adopt the same structure on spatial stream and temporal stream, which produce amount redundant information and often ignore the relevance among channels. In this paper, we propose a channel-wise spatial attention with spatiotemporal heterogeneous framework, a new approach to action recognition. First, we employ two different network structures for spatial stream and temporal stream to improve the performance of action recognition. Then, we design a channel-wise network and spatial network inspired by self-attention mechanism to obtain the fine-grained and salient information of the video. Finally, the feature of video for action recognition is generated by end-to-end training. Experimental results on the datasets HMDB51 and UCF101 shows our method can effectively recognize the actions in the video.
Low Latency and Area Efficient Very Large Scale Integration Architecture of 2-Dimensional Bicubic Interpolation using Carry Save Adder Based Fast Multiplier

Presently, real-time image processing is gaining increasing popularity, specifically in the field of satellite imaging and medical imaging. Naturally, researchers are increasingly being inclined to design dedicated hardware for imaging methods to aid real-time processing in a cost-effective way. Likewise, several VLSI architectures have already been designed for a variety of imaging methods. But, in spite of being a significant and popular imaging method, the image interpolation has no such notable dedicated VLSI architecture so far. Image interpolation has vast applicability ranging from image inpainting to image registration. In all such applications, the image interpolation part has been performed by software-based implementations. To alleviate the issue, a dedicated VLSI architecture 2D bicubic interpolation has been designed. The proposed bicubic architecture is based on state-of-the-art VLSI architectures for performing square and cube operations following vedic mathematics. Furthermore, all the adders used in the proposed design are judiciously developed to increase the speed retaining an acceptable area and power consumption. The proposed architecture is realized in the xilinx vivado 18.2 tool. The simulation results exhibit satisfactory performances. The quality of the proposed bicubic architecture has also been successfully tested by on board testing using the xilinx ZCU104 board to verify the viability of the design to be applied in real-time imaging applications.
Sights, titles and tags

The paper focuses on calculating suitable place names and descriptive tags for large photo collections of visually interesting sights. The core dataset analyzed contains 45 million crowd-sourced geotagged pictures of the Panoramio database. We present several methods for analysis along with machine learning experiments for tag recommendation and suggest a manually built taxonomy of tag categories, based on the analysis of most widely used taglike words in the photo titles, along with their popularities. The methods, selected tags and the taxonomy can be used for building different tourism applications for visually interesting sights.
Adaptive Online Learning for Human-Robot Teaming in Dynamic Environments

Robotic and vehicular autonomy in contested, dynamic environments has historically been limited to teleoperation and simple programmed behaviors due to the low survivability of available AI and machine-learning techniques in the face of novel situations. Here we report that recent few-shot machine-learning models trained using interactive, human-centered, vehicular simulations can enable collaborative learning that is both adaptive (dynamically recognizing unfamiliar environmental conditions) and online (learning at each time step). Specifically, we show that our human-machine teaming approach enables simulated vehicles to anticipate novel adversities imposed in real time, both externally by their terrain and internally by their own mechanics, using only images captured by their front-facing cameras. We conclude by discussing the implications of our work for enhancing the future survivability of human-robot teams in large-scale, cluttered, contested environments.
A TWO-DIMENSIONAL LOGIC FOR TWO PARADOXES OF DEONTIC MODALITY

Abstract In this paper, we axiomatize the deontic logic in Fusco (2015), which uses a Stalnaker-inspired account of diagonal acceptance and a two-dimensional account of disjunction to treat Ross’s Paradox and the Puzzle of Free Choice Permission. On this account, disjunction-involving validities are a priori rather than necessary. We show how to axiomatize two-dimensional disjunction so that the introduction/elimination rules for boolean disjunction can be viewed as one-dimensional projections of more general two-dimensional rules. These completeness results help make explicit the restrictions Fusco’s account must place on free-choice inferences. They are also of independent interest, as they raise difficult questions about how to “lift” a Kripke frame for a one-dimensional modal logic into two dimensions.
Image Tampering Detection for Splicing based on Rich Feature and Convolution Neural Network

Image splicing is a widely used image tampering method. The detection of these methods has also been widely concerned by researchers. We propose a detection method based on rich feature and convolution neural network. In order to avoid the interference of image content on classification, we use a high pass filter to preprocess the image, then a well-designed novel convolutional neural network is used to classify the images. In the experiment, the network performed better than traditional methods on the Columbia image splicing detection evaluation dataset.
Implementation of dynamic page generation for stream data by SuperSQL

SuperSQL is an extension of SQL that allows you to structure the output of relational databases by writing your own queries and to express various layouts. However, this method is not suitable for data with high update frequency, such as stream data, because the information in the database refers to the data at the time of SuperSQL execution. In this study, we propose an implementation of a web page generation function that asynchronously updates a web page with the latest information for frequently updated data, using PipelineDB and SuperSQL, both of which are DBMSs capable of processing streams. You can specify the dynamic part of the stream by specifying the stream in the "decorator" which is a feature of SuperSQL. At the same time, you can specify "pull" and "push" in the stream decorator to select how the dynamic part is updated. This makes it possible to create a web page that displays the latest stock prices at any time in a page that displays a list of stock prices.
Natural phenomena simulating algorithms

Natural phenomena inspired algorithms are those that simulates models of natural phenomena. Those models are idealizations of natural phenomena and different idealizations results in different algorithm. It is very interesting to see how natural phenomena inspire algorithms, which are employed for solving hard problems, works by simulating respective phenomenon. In this work, we will describe in detail how such algorithms can be created systematically and describe some of the most successful algorithms after formally presenting the general structure of optimization problems. The general structure of natural phenomena inspired algorithms is discussed with possible enhancements to it so as to increase the power of those algorithms. Generally, all the natural phenomena inspired algorithms follow the same structure, while natural phenomena are extremely complex and this complexity makes room for various enhancements in those algorithms. Although any enhancements require intensive research, but the presented enhancements are supported by nature. The open problems and research direction for future research is also presented.
On Learning Parametric Non-Smooth Continuous Distributions

With the eventual goal of better understanding learning rates of general continuous distributions, we derive the first essentially min-max optimal estimators and learning rates for several natural classes of parametric non-smooth continuous distributions under KL divergence. In particular, we show that unlike the folk theorem of 1/2n learning-rate increase per distribution parameter, non-smooth distribution exhibit a wide range of learning rates.
Smart Products in Smart Manufacturing Systems: An Opportunity to Utilize AR?

The evolution of manufacturing throughout time has allowed our industrial world to develop and ever faster create various products in large numbers using a myriad of industrial manufacturing processes. However, like everything in the world, time moves forward and the evolution does not stop but accelerate. Therefore, the implementation of Augmented Reality (AR) through the use of the internet of things, low cost sensors, and free programming languages, are all contributing factors to further advanced the industry. These implementations will allow for faster data processing, quicker error recovery time, loss and error prevention, a wider range of employees at different skill levels to be productive in a manufacturing environment. The implementation of an Arduino based sensor system into the manufacturing process enables data streaming to an AR device. This system allows for instant data streaming and visualization to the user/operator with information pertaining to the status of the product ('state'), key developments, and changes in the product/or system, any associated errors, and the best way to proceed with resolving them. The AR enabled system with its capability to streaming relevant information prevents losses associated with product quality issues and/or process down-time among a myriad of other applications. Preliminary research shows the ability to collect data from the product in addition to data processing on an external device to later be used on an augmented reality device.
Evolutionary Programming Based Deep Feature and Model Selection for Visual Data Classification

Deep Learning (DL) has made significant changes to a large number of research areas in recent decades. For example, several astonishing Convolutional Neural Network (CNN) models have been built by researchers to fulfill image classification needs using large-scale visual datasets successfully. Transfer Learning (TL) makes use of those pre-trained models to ease the feature learning process for other target domains that contain a smaller amount of training data. Currently, there are numerous ways to utilize features generated by transfer learning. Pre-trained CNN models prepare mid-/high-level features to work for different targeting problem domains. In this paper, a DL feature and model selection framework based on evolutionary programming is proposed to solve the challenges in visual data classification. It automates the process of discovering and obtaining the most representative features generated by the pre-trained DL models for different classification tasks.
UAV Pre-Study for In-Air-Capturing Maneuver

Implementation of reusable launch vehicle (RLV) missions is a major goal in current aerospace research. One conceptual idea to return a booster stage is the so-called “in-air capturing” (IAC), where a winged stage is captured by an aerial vehicle and towed back to the final destination. This approach has the advantage that a towed winged stage does not need a propulsion system or fuel reserves to arrive to a destination point, compared to alternative approaches as demonstrated by SpaceX. The concept of capturing the RLV in air is based on earlier IAC missions for satellite photo capsules and the probe-and-drogue refueling method. A key difference is that in an air-to-air-refueling procedure a highly dynamic fighter tries to connect to a trailing drogue or rigid boom from a sluggish tanker. Opposite to this procedure, it can be assumed that in IAC case two aerodynamically sluggish aircraft need to be coupled. The challenge is to build up a formation which enables a connection between a gliding RLV and a dynamic coupling device trailing from a large aircraft. To investigate this IAC approach, the German Aerospace Center (DLR) built a scaled demonstration system with smaller unmanned aerial vehicles (UAV) to research different aspects of IAC flight tests. Based on the assumption that at an IAC approach would involve two large aerodynamically sluggish systems, a third highly dynamic vehicle should be introduced to enable a safe and reliable connection. Therefore, the trailing system, which is known from the air-to-air refueling, was modified by DLR with aerodynamic control surfaces and an independent flight control system. This enables a dynamic and independent motion of the coupling device relative to the towing aircraft, as well as the RLV. This paper gives an overview about IAC investigations of DLR, which are validated in experimental flight test demonstrations with scaled unmanned systems. The focus of this work is on building up the formation, from the rough approach with GNSS up to the final approach, where the global, absolute localization is supported by an image based relative position estimation of the coupling device. A major aspect in the formation implementation is the active coupling device (ACD). Therefore, the paper will show the construction of the ACD, its functionality and operation during the formation flight and a validation of its behavior at the flight demonstration. The first flight test results show that our research is heading in the right direction and further tests are expected to provide comprehensive results for the validation of the IAC concept.
Towards a Relational Treating of Language and Logical Systems

Generally speaking, there are two categories of semantics theory: model-theoretic approach and proof-theoretic approach. In the first part of this paper, I will briefly analyze some inadequacies related to these two approaches, and promote an alternative relational approach, which bases semantic notions on relations between expressions. A brief discussion in general for this alternative will be provided. In the second part, I will provide a solid mathematical framework to the study of logical meanings, and show its connection with the other two approaches.
A Novel Approach for User Specified Cryptographic Calculation for Securing Authentication

Authentication is major among the foremost security criterion whereas subject to key of the cataloged services to the supposed end user. The participating choices of cloud computing umpteen institutions practices cloud storage for lock away their imperative data. End users hold on info remotely among the cloud through the end user and may well be accessed mistreatment patron as and once prescribed. Procuring these essence info from the unwarranted connection of the users is among in every of the dominant controversy that causes slash in the expansion of prescribed technology among the corporations. To reinforce security in authentication we offer a unique technique during this paper. During this technique we have a tendency to use user specified cryptographic calculation for securing authentication. This authentication relies on random technique for data securing.
Lexicon-Based Sentiment Analysis

The sentiment analysis is an emerging field of natural language processing which is based on human–computer interaction, information retrieval and distilling sentiments from the ever-increasing online social data. It involves identifying the words or phrases in the underlying text express positive, negative or neutral attitude. The objective of this paper is to extract the editorial text of a leading newspaper and classify the sentiments expressed at different levels, namely paragraph level, sentence level and word level into positive, negative or neutral.
Translating video into language by enhancing visual and language representations

It is a fundamental task of translating videos into natural language automatically by computer. At present, the models for video description based on deep learning have made a great breakthrough. However, the static information loss is serious during encoding stage for motion feature of videos, and the linguistic feature from LSTM network lack personalized expression, leading to inappropriate words and poor semantics in generation sentences. In this work, a model with enhanced features of visual and language is proposed to address the challenges. First, static features of video frames from the first LSTM layer are incorporated, then fed into another LSTM layer according by frame sequence. Second, the feature of word is combined with the output of LSTM network for predicted probability of candidate word on each time step. The experimental results demonstrate effectiveness of the proposed approach with competitive performance compared with other state-of-the-art methods on various metrics.
A Geometric Connectionist Machine for Word-Senses

In this chapter, we follow the design principles to precisely spatialize tree structured hypernym relations among word-senses onto word embeddings. Our target is to promote each word-embedding into a ball in a higher dimension space (\(\mathscr {N}\)-Ball) such that the configuration of these balls precisely captures tree-structured hypernym relations. Each \(\mathscr {N}\)-Ball represents a word-sense. One \(\mathscr {N}\)-Ball is contained by another \(\mathscr {N}\)-Ball, if and only if the word-sense represented by the first \(\mathscr {N}\)-Ball is a hyponym of the word-sense represented by the second \(\mathscr {N}\)-Ball.
Towards Faster Matching Algorithm Using Ternary Tree in the Area of Genome Mapping

In area of precision medicine there is a need to map long sequences of DNA, which are represented as strings of characters or numbers. Most of the computer programs used for genome mapping use suffix-based data structures, but those are much more suitable for mapping of short DNA sequences represented as strings over small alphabets. The most crucial parameters of data structure used for DNA mapping are time to fill the data structure, search time and system resources needed, especially memory, as the amount of data from scanning process can be really large. This article will describe implementation of memory optimized Ternary Search Tree (TST) for indexing of positions of labels obtained by Bionano Genomics DNA imaging device. BNX file parser with alphabet encoding functions is described and performance results from experiments with presented software solution on real data from Bionano Genomics Saphyr device are also included.
Human Activities of Daily Living Recognition with Graph Convolutional Network

A rapidly growing population presents many challenges to healthcare and security surveillance around the world. Human activity recognition is one of the active research areas to recognizing and understanding the various activities. Many researchers are finding and representing the details of human body gestures to determine human activity or action. The result, however, is still unsatisfactory due to the inclusion of irrelevant images. The model is rather rudimentary and it does not specific enough for representing the meaning of images. In this paper, we propose a methodology for human activities of daily living recognition with 4 steps (1) processes including text-based embedding concept, (2) semi-supervised graph node, (3) graph convolution network, and (4) measurement and evaluation. The experimental results indicate that our proposed approach offers significant performance improvements in data set 2 in 10-fold, with the maximum of 79.34%.
Study on Extrapolation Technique of Firefinder Radar Based on Meteorological Conditions

Given the meteorological conditions, especially the issue of low extrapolation accuracy of firefinder radar under high wind speed, we propose a new ballistic trajectory model that takes crosswind and range wind into consideration, to set up the state equations of projectile flight and measurement equations. We also adopt an Extended Kalman Filter (EKF) to estimate the parameters through a great deal of datum from trajectory simulation in different fire parameters, and then we yield the simulation results by contrasting and analyzing the Circular Error Probable (CEP) through Fourth-order Ronge-Kutta equations to extrapolate. It is proven by the simulation results that EKF, in consideration of meteorological conditions, is able to enhance the extrapolation accuracy of the firefinder radar.