Reviewerly/exHarmony huggingface dataset:
Long-range person and vehicle detection

Automatic detection and tracking of persons and vehicles can greatly increase situational awareness in many military applications. Various methods for detection and tracking have been proposed so far, both for rule-based and learning approaches. With the advent of deep learning, learning approaches generally outperform rule-based approaches. Pre-trained neural networks on datasets like MS COCO can give reasonable detection performance on military datasets. However, for optimal performance it is advised to optimize the training of these pre-trained networks with a representative dataset. In typical military settings, it is a challenge to acquire enough data, and to split the training and test set properly. In this paper we evaluate fine-tuning on military data and compare different pre- and post-processing methods. First we compare a standard pre-trained RetinaNet detector with a fine-tuned version, trained on similar objects, which are recorded at distances different than the distance in the test set. On the aspect of distance this train set is therefore out-of-distribution. Next, we augment the training examples by both increasing and decreasing their size. Once detected, we use a template tracker to follow the objects, compensating for any missing detections. We show the results on detection and tracking of persons and vehicles in visible imagery in a military long range detection setting. The results show the added value of fine-tuning a neural net with augmented examples, where final network performance is similar to human visual performance for detection of targets, with a target area of tens of pixels in a moderately cluttered land environment.
Evolutionary-Based Image Encryption with DNA Coding and Chaotic Systems

In order to obtain encrypted image with optimal correlation coefficient, we propose a new evolutionary-based image encryption scheme in this paper, based on DNA coding, chaotic systems, and a genetic algorithm (GA). Firstly, the DNA encoding operation is performed over the original plain image. Then logistic mapping and hyper-chaotic systems are used to create the number of encrypted images over the DNA matrices. We fix these encrypted images as the initial population for the GA, and the GA is applied to them to determine the best one based on the fitness function. The correlation coefficient is used to define the fitness function in this paper. Finally, after the evolution, the encrypted image with the lowest correlation coefficient will be obtained as the final result. The novelty of this research is in using the logistic map over the DNA matrix of the original image directly, which will make the initial group of encrypted images more secure and with lower initial fitness for the GA. Therefore, the scheme will achieve high fitness in fewer iterations and retain the efficiency of the encryption. Experimental results confirm that the proposed scheme not only has a good encryption effect, but also has the ability of resisting statistical analysis attacks, brute force attacks, and differential attacks.
Development of a Vision Training System Using an Eye Tracker by Analyzing Users’ Eye Movements

Eye movements are important but, in Japan, not tested so. However, there are some reports that training eye movement can improve developmental and learning disorders. In the U.S. or EU countries, these tests and training are generally performed as medical procedures by optometrists. But these depend on their experience and knowhow. In this study, we develop an eye movement training system using a gaming eye tracker. First, we proposed a calibration method to realize highly precise eye tracking during training. Next, we developed a prototype of an eye movement training system. Then, we measured and analyzed the data of eye movements, and proposed some parameters with individual differences and high reliability for the test and the analysis of the effects of training. The results of the experiment demonstrated effectiveness of the system.
Extended Abstract: Science Fiction and Social Justice: Teaching Students to Consider Ethics, Sustainability, and Diversity Issues

To encourage students to analyze technologies, instructors of technical communication can use science fiction texts to discuss ethics, sustainability, and diversity issues.
A Hybrid Network for Facial Age Progression and Regression Learning

Facial age transformation is an attractive application on an entertainment or amusement robot. With this application, the robot can transform an input face to the same face but in different ages. We propose a new algorithm for age transformation. Due to recent progresses made by state-of-the-art deep learning approaches, the facial age progression and regression has become an attractive research topic in the fields of computer vision. Many existing approaches require paired data which refer to the face images of the same person at different ages. As the cost of collecting such paired datasets is expensive, some emerging approaches have been proposed to learn the facial age manifold from unpaired data. However, the images generated by these approaches suffer from the weakness or loss in generating some age traits, for example wrinkles and creases. We propose a hybrid network that is composed of a generator and two discriminators. The generator is trained to disentangle the age from the identity of the face so that it can generate a face of the same identity as of the input face but at a different age. One of the discriminator is designed for handling multitasks, including the identification of real vs. fake (generated) faces and the classification of the identities and ages of the faces. The other discriminator is designed to make the latent space satisfy the requirement so that the generated image can be made more realistic. Experiments show that the proposed network can generates better facial age images with more age traits compared with other state-of-the-art approaches.
Who Wrote Dido, Queen of Carthage?

The title page of the 1594 Dido, Queen of Carthage states that it was "Written by Christopher Marlowe and Thomas Nash. Gent," and several centuries of speculation on authorship have followed. In 2008, Martin Wiggins claimed that the "available evidence" proved joint authorship to be beyond doubt, a position reiterated in the second volume of British Drama (2012), edited by Wiggins and Catherine Richardson. The authors of this essay disagree, finding instead that Marlowe wrote the play text and Nashe did not contribute. Our focus is upon the evidence, and we approach the problem of authorship in two, complementary ways. The first approach evaluates the verbal evidence (words, phrases, spellings) that has been proposed as indicating Nashe’s presence; this approach employs analysis of words in context, comparing Dido with the works of Marlowe and Nashe. The second approach is quantitative; it assesses previous quantitative work on the authorship problem before offering new quantitative tests for likenesses between segments of Dido and the works of Marlowe and Nashe. Our two approaches agree: we find no evidence for Nashe’s hand in the play. We suggest instead that Dido influences Nashe, most notably in what he wrote after the publication of Dido, but quite possibly before as well. We believe it should now be possible for students of the play to ignore the statement on the 1594 title page and approach Dido as Marlowe’s unaided work.
Impact of Demonetization in India using Opinion Mining over Twitter Data

In recent years, the utilization of Internet has turned out to be one of the everyday activities in our life. Social networks constitute a noteworthy segment of the Web and made an upheaval. It incorporates social media, forum conversations, blogs and micro-blogs like twitter. Due to this, large numbers of comments are produced on daily basis. So, nowadays most of the researchers or analyzers are concentrating on extracting significant data from social networks in order to understand the public viewpoint. This research has been reached out outside the computer science to cover other areas like business, political and social science. Hence, Sentiment analysis and Opinion mining are popular field of research in Data mining. This paper delineates various aspects of sentiment analysis in detail inclusive of important concepts, classification, process, importance, challenges and applications. The following paper presents experiment on sentiment analysis of public opinion on demonetization in India. Sentiment analysis is performed on tweets related to demonetization in India extracted from twitter. Polarity of the opinion is observed through the experimental analysis. Through the outcome of this analysis, the sentiments of the citizens that are determined help the government in improving their decisions and work for the welfare of the citizens.
An Overview of Machine Learning

Artificial intelligence sounds pretty interesting, doesn’t it? It’s exciting to create a thinking machine that can do whatever you need it to do. And you don’t need to worry about learning something new and extravagant—all you need to do is learn to program.
Mobile crowdsourcing in crop production for farmers in rural areas of the south kivu (DRC)

A lack of an effective way to collect farm produce data, record farm input expenses, as well as expenditure on farm chemicals and receive information from other stakeholders (e.g. agriculture advisers) are some challenges encountered by farmers on their daily basis [1]. Gaining access to information and communication technologies can alleviate some of their problems and allow them to benefit from earlier unexploited opportunities [2]. In this research, we studied the factors that influenced farmers in the adoption of mobile crowdsourcing portals for agriculture purposes. A model was used to measure the perception of farmers about the technology after having used the technology for one season. The results showed that there is a strong relationship between the multiple independent factors in the model find the dependent variable intention to use for mobile crowdsourcing portals for agriculture. We conclude that mobile crowdsourcing application is perceived highly in enhancing the agricultural development in remote areas with regards to data accessibility, agriculture development in crop production and providing support in decision-making processes.
Deck Archetype Prediction in Hearthstone

Hearthstone is a competitive, online Collectible Card Game, in which players construct their own 30-card decks from hundreds of available cards. Different decks differ wildly in terms of their strategy, from very agressive decks that seek to attack the opponent early, to decks relying on certain combinations of cards, to decks that are focused on responding to the opponent’s and ending the game slowly. The player community has therefore given names to different deck archetypes, depending on the strategy they pursue. When playing the game, knowing which archetype the opponent’s deck is likely to have helps inform a player on how they should adapt their own strategy to best counter the opponent’s. In this paper we introduce the problem of predicting a player’s deck archetype from minimal information, in particular only from the actions they performed on their first turn. We discuss the relevance of this problem, and how it can help players adapt to the opponent’s strategy, as well as information that can be learned from it. While the information was intentionally chosen to be minimal, due to the nature of the game it still varies in size from game to game, which presents an additional challenge. We describe different approaches to handle this information and their performance applied to this problem, comparing standard statistical methods with Recurrent Neural Networks, and their relative trade-offs, in particular with regards to training time.
The Development of Innovative Learning Material with Project and Multimedia for Redox Titration

The study is aimed to provide an innovative learning material with project based learning that are integrated with multimedia which will be used as a teaching support for active learning in improving student competence in chemistry.Research procedures are consisted of making standard teaching materials that contain mini project and multimedia packages for teaching redox titration topic of Analytical Chemistry, integration of multimedia and hyperlink to the main learning topic, standardization of the developed learning material, and the use of instructional materials as a support in studying chemistry topics in the class.The results of the study show that innovative teaching materials are equipped with projects and multimedia has been developed for redox titration topic.Teaching materials from the development results proved to be sufficient in helping the students to learn chemistry independently.The learning pattern has changed to be centered on the students so as to intensify teaching and learning activities.
Spatial general autoregressive model-based image interpolation accommodates arbitrary scale factors

This paper proposed a novel image interpolation algorithm with an arbitrary upscaling factor based on the spatial general autoregressive model. First, to accommodate arbitrary scale factors, a non-integer mapping method was modulated into the spatial general autoregressive model, which was employed to model the piecewise stationary pattern with a higher description capacity than autoregressive models. A gradient angle guided extension method was utilized to extend the spatial general autoregressive model, and more pixels in the neighborhood were included to estimate the parameters of the spatial general autoregressive model. To realize the high-accuracy estimation of the model parameters, a regularization method via an elastic network was adopted to maintain the complexity of the object function in a reasonable state and address the overfitting problem. We also introduced an iterative curvature method to refine the interpolation result of those image blocks with large variances of gray intensities. Experiments on 25 images were conducted with integer and non-integer magnification factors to systematically verify the objective and subjective measures of the proposed method. The visual artifacts were effectively suppressed by the proposed method, and a flexible interpolation method for arbitrary scale factors was implemented.
Perspective-aware Distillation-based Crowd Counting

Recently, with the progress of deep learning technologies, crowd counting has been rapidly developed. In this work, we first introduce a perspective-aware density map generation method that is able to produce ground-truth density maps from point annotations to train crowd counting model to accomplish superior performance than prior density map generation techniques. Besides, leveraging our density map generation method, we propose an iterative distillation algorithm to progressively enhance our model with identical network structures. In experiments, we demonstrate that, with our simple convolutional neural network architecture strengthened by our proposed training algorithm, our model is able to outperform or be comparable with the state-of-the-art methods.
New iteration parallel-based method for solving graph NP-complete problems with reconfigurable computer systems

Abstract The paper presents a method of creating parallel-pipeline programs for reconfigurable computer systems based on iteration paralleling for the solution of graph NP-complete problems, which is different from others for processing the irregular sets with the elements accessed not by their addresses (like in arrays) but by values, the names of vertices and the graph arcs. It shows that the new iteration-parallel method is more efficient than the layer-parallel method when the available computing resource and the size of the solved graphs increase due to the significant reduction of the commutation connections.
A Novel Heterogeneous Multi-Agent System for Production Processes Based on the Market Economy

In this article, an approach is presented in which transports in a production facility are performed by self-controlling and self-managing agents. In this context, the organization is carried out by a decentralized multi-agent system and is roughly oriented on the model of the market economy. It is to be shown that a system of heterogeneous transport resources can organize itself independently and are self-optimized, fulfilling all requirements to transport parts to the respective processing points at the right time. The heterogeneous system shall be represented in this consideration by three different transport resources including the human worker. Different aspects of the market economy are implemented via cost-benefit functions of the agents and in decision making processes. The benefits of those aspects for the overall system e.g. workload are investigated.
Chatterbot: Technologies, Tools and Applications

Chatbot is used to chat with live user and assist human to seek information and perform skilled task instantly. Later then many chatbot came, some of the voice-based chatbots (as like assistant) are SIRI, CORTANA, BIXBY, ALEXA, NATASHA, IBM WATSON and many more. There was no game changing in the chatbot technologies, it is obvious that they evolved from the very simple pattern matching systems towards complicated patterns combined with a set of concept and categories in a subject area or domain in which the knowledge is enabling computer reasoning. This application working is very simple because it is already known in advance. The techniques which will be improving the chatbots are artificial intelligence, machine learning with natural language processing. This chapter proposal is representative of the significant technologies, tools and methods in Chatbots in the last decade.
Agent-based Learning for Auto-Navigation within the Virtual City

Virtual reality is a field that can be widely explored for sustainability requirements. In this work, a virtual city environment was developed with intend to help city navigation especially by personnel with special abilities, mainly while traveling. They may need assistance to perform such daily tasks. The goal is to build an agent that can be trained to help guide daily commute from a given source to destination, optimizing for a minimum possible time as well as avoiding multiple obstacles. Real-time traffic and crowd were simulated to train learning agents. Apart from using the shortest path, factors such as following traffic rules and avoiding accidents with obstacles are also considered. The agent must be trained in order to avoid heavy traffic and change its path to reach the goal taking minimum time, not just the shortest distance. By using machine learning gents and simulations, training of the agent using reinforcement learning has been achieved. Identification of certain unsafe paths and behaviors have also been considered for simulation and learning purposes. Custom scenarios to mitigate the risks before actual accidents happen have been created for understanding and further resolution. Agent-based learning for successful space exploration is established on the relationship parameter between actions and the virtual environment. Initially, the agent explores the virtual city on its own and interacts with numerous components of the environment. The main objective of the agent is to reach its goal following the reward system, as used in a game environment. Maximization of the score using these parameters helps in training the agent. Route learning is also considered to facilitate backtracking wherein the obstacles are placed in the route. The virtual environment, so created, then has an agent that depicts that shorted, safe and accident-free path for a person to reach his or her destination from a given source point. The city has numerous parameters of an actual live city of building, paths, and unruly movements, amongst others.
Programming with Tensorflow

So far, we’ve learned how Python can be used for lucrative machine learning with the help of its numerous libraries, which were created for just that purpose. We also know that Jupyter Notebook is a solid development environment that can be used to build and run large programs.
Fuzzy Logic Classifier and Conditional Responses Algorithm for Gestural Input Game

This paper presents hand gesture classification using fuzzy logic, with an application to the Rock-Paper-Scissors game. It includes a comparison between a simple threshold classifier and the Fuzzy Logic classifier. The real-time gesture recognition is implemented for the Leap Motion input device. Although the human's game gesture pattern may seem random, some recent work suggests how it is possible to predict an opponent's gesture, from a previous gesture, with better-than-random success. Based on this, we designed a conditional-response algorithm making the behavior of the virtual player more similar to a real human. Using networking tools, we enable game play remotely against other players. The game was experimentally evaluated with eleven players, reaching an average online classification accuracy of 97.35% for hand pattern recognition. The evaluation metrics are represented in confusion matrices.
Basis Representations and Arithmetics

The main topic of the present chapter is a detailed study of the interaction between theoretical results on various types of bases used to represent finite extension fields \(E = {\text{GF}}(q^{n} )\) over \({\text{GF}}(q)\) and their application to efficiently performing the arithmetic operations in E, with particular emphasis on hardware implementations. Such questions are of fundamental importance for various practical applications, in particular, in Cryptography, Coding Theory, and Signal Processing. We also briefly discuss an alternative approach to the arithmetics of finite fields, namely discrete logarithms, which is likewise useful in some applications.
Shift Register Sequences

In Chapter 8, we already encountered linear feedback shift registers in connection with the hardware implementation of arithmetics in GF(2n): In the present chapter, we will study the sequences produced by such devices—that is, shift register sequences—in detail. As we shall see, these sequences are just the solutions of linear recurrence relations over GF(2): In view of this connection, we will consider shift register sequences over F = GF(q) for general q, and initially even over arbitrary fields F. Even though shift registers (as hardware devices) are of little practical interest for other fields but GF(2), they are a useful concept for visualizing linear recurrences over general fields. In the first two sections, we present some fundamental results which are valid over arbitrary fields F, including characterizations of shift register sequences and results about (ultimately) periodic sequences. After that, we shall restrict ourselves to the case of finite fields; in particular, we will consider shift register sequences associated with irreducible polynomials in Sect. 9.3. In the subsequent two sections, we discuss two applications, namely the construction of pseudo-random sequences and of some cyclic difference sets. We then return to the general theory by considering the notions of the linear complexity and, more strongly, the linear complexity profile of an arbitrary sequence with entries from GF(q). We will describe methods for determining these quantities, in particular the Berlekamp-Massey algorithm. Finally, in Sect. 9.9, we give a further application and study the so-called GMW-sequences, a class of periodic binary sequences which combine good randomness properties with a comparatively high linear complexity.