Reviewerly/exHarmony huggingface dataset:
Design of unmanned swarm tactics for an urban mission

Military planners envision a future of unmanned vehicle swarms that can self-organize to provide superior intelligence and overwhelming effects over a widely dispersed battlefield greatly multiplying the effectiveness of the manned forces. Deploying swarms requires new tactics to take advantage of this capability. Working with military experts we developed a suite of swarm tactics for unmanned air and ground vehicles supporting a full Company with a mission to secure an objective in an urban area. The air vehicles create and maintain perimeter security around the objective. They map the area and maintain tracks on all vehicles and people in the perimeter. They also provide persistent, stealthy communication relay support to all the ground forces. The ground vehicles surveil the key intersections, provide scout and rear security to the squads and scout out alternative routes through the city. The unmanned vehicles execute decoy operations and their behaviors are designed to mask the actual task they are performing through seemingly random movement and regular swapping of tasks. These tactics were implemented in software and evaluated in a 3D model of an urban area taken from a city in the Midwest and implemented in Unity. This paper describes the tactics, algorithms, and experimental setup and reports the results.
Blind Deblurring of Saturated Images Based on Optimization and Deep Learning for Dynamic Visual Inspection on the Assembly Line

Image deblurring can improve visual quality and mitigates motion blur for dynamic visual inspection. We propose a method to deblur saturated images for dynamic visual inspection by applying blur kernel estimation and deconvolution modeling. The blur kernel is estimated in a transform domain, whereas the deconvolution model is decoupled into deblurring and denoising stages via variable splitting. Deblurring predicts the mask specifying saturated pixels, which are then discarded, and denoising is learned via the fast and flexible denoising network (FFDNet) convolutional neural network (CNN) at a wide range of noise levels. Hence, the proposed deconvolution model provides the benefits of both model optimization and deep learning. Experiments demonstrate that the proposed method suitably restores visual quality and outperforms existing approaches with good score improvements.
Learnae: Distributed and Resilient Deep Neural Network Training for Heterogeneous Peer to Peer Topologies

Learnae is a framework proposal for decentralized training of Deep Neural Networks (DNN). The main priority of Learnae is to maintain a fully distributed architecture, where no participant has any kind of coordinating role. This solid peer-to-peer concept covers all aspects: Underlying network protocols, data acquiring/distribution and model training. The result is a resilient DNN training system with no single point of failure. Learnae focuses on use cases where infrastructure heterogeneity and network unreliability result to an always changing environment of commodity-hardware nodes. In order to achieve this level of decentralization, new technologies had to be utilized. The main pillars of this implementation are the ongoing projects of IPFS and IOTA. IPFS is a platform for a purely decentralized filesystem, where each node contributes local data storage. IOTA aims to be the networking infrastructure of the upcoming IoT reality. On top of these, we propose a management algorithm for training a DNN model collaboratively, by optimal exchange of data and model weights, always using distribution-friendly gossip protocols.
Context-Based Object Recognition: Indoor Versus Outdoor Environments

Object recognition is a challenging problem in high-level vision. Models that perform well for the outdoor domain, perform poorly in the indoor domain and the reverse is also true. This is due to the dramatic discrepancies of the global properties of each environment, for instance, backgrounds and lighting conditions. Here, we show that inferring the environment before or during the recognition process can dramatically enhance the recognition performance. We used a combination of deep and shallow models for object and scene recognition, respectively. Also, we used three novel topologies that can provide a trade-off between classification accuracy and decision sensitivity. We achieved a classification accuracy of 97.91%, outperforming the performance of a single GoogLeNet by 13%. In another experiment, we achieved an accuracy of 95% to categorise indoor and outdoor scenes by inference.
Perception Evaluation: A New Solar Image Quality Metric Based on the Multi-fractal Property of Texture Features

Next-generation ground-based solar observations require good image quality metrics for post-facto processing techniques. Based on the assumption that texture features in solar images are multi-fractal which can be extracted by a trained deep neural network as feature maps, a new reduced-reference objective image quality metric, the perception evaluation is proposed. The perception evaluation is defined as cosine distance of Gram matrix between feature maps extracted from high resolution reference image and that from blurred images. We evaluate performance of the perception evaluation with simulated and real observation images. The results show that with a high resolution image as reference, the perception evaluation can give robust estimate of image quality for solar images of different scenes.
Requirement Elicitation Framework for Child Learning Application - A Research Plan

Requirements elicitation are the initial stages in requirements engineering and are crucial stages. Failure in the steps of requirements elicitation can lead to a lack of definition of user needs for the application so that it has an impact on the quality and usability of the software produced. That will also affect the application of children's education, where without involving children in the elicitation process, it can make the software built not by the age development needs of the child. Failure in requirements elicitation occurs because of lack of knowledge of analysts in communicating and choosing elicitation techniques. Both components have a close relationship with the type of users who will become respondents. Nowadays, requirements elicitation framework tends to pay more attention to adults and parents, while for children it is still not found. Children have different characteristics with adults and parents, so it needs to be considered in carrying out approaches or techniques in performing elicitation. The selection of elicitation techniques that are not by the characteristics of children can cause failure in exploring the needs of children for child learning applications built. Based on these problems, we need a requirements elicitation framework in children's education applications. The requirements for the elicitation framework are expected to make it easier for child learning application developers to get application requirements that are appropriate for the child's developmental age.
A core Erlang semantics for declarative debugging

One of the main advantages of declarative languages is their clearly established formal semantics, that allows programmers to reason about the properties of programs and to establish the correctness of tools. In particular, declarative debugging is a technique that analyses the proof trees of computations to locate bugs in programs. However, in the case of commercial declarative languages such as the functional language Erlang, sometimes the semantics is only informally defined, and this precludes these possibilities. Moreover, defining semantics for these languages is far from trivial because they include complex features needed in real applications, such as concurrency. In this paper we define a semantics for Core Erlang, the intermediate language underlying Erlang programs. We focus on the problem of concurrency and show how a medium-sized-step calculus, that avoids the details of small-step semantics but still captures the most common program errors, can be used to define an algorithmic debugger that is sound and complete.
Unsupervised Sentiment Analysis of Objective Texts

Unsupervised learning is an emerging approach in sentiment analysis. In this paper, we apply unsupervised word and document embedding algorithms, Word2Vec and Doc2Vec, to medical and scientific text. We use SentiWordNet as the benchmark measures. Our empirical study is done on the Obesity NLP Challenge data set and four Science subgroups from Reuters 20 Newsgroups. Our results show that Word2Vec demonstrates a reliable performance in sentiment analysis of the text, whereas Doc2Vec requires more detailed studies.
On the verification of system-level information flow properties for virtualized execution platforms

The security of embedded systems can be dramatically improved through the use of formally verified isolation mechanisms such as separation kernels, hypervisors, or microkernels. For trustworthiness, particularly for system-level behavior, the verifications need precise models of the underlying hardware. Such models are hard to attain, highly complex, and proofs of their security properties may not easily apply to similar but different platforms. This may render verification economically infeasible. To address these issues, we propose a compositional top-down approach to embedded system specification and verification, where the system-on-chip is modeled as a network of distributed automata communicating via paired synchronous message passing. Using abstract specifications for each component allows to delay the development of detailed models for cores, devices, etc., while still being able to verify high-level security properties like integrity and confidentiality, and soundly refine the result for different instantiations of the abstract components at a later stage. As a case study, we apply this methodology to the verification of information flow security for an industry-scale security-oriented hypervisor on the ARMv8-A platform and report on the complete verification of guest mode security properties in the HOL4 theorem prover.
A Personalized Differential Privacy Protection Method for Repeated Queries

As the emergence and development of application requirements such as data analysis and data publication, how to prevent sensitive information from disclosure has become a great challenge. Traditional differential privacy providing the same level of privacy protection for all users has serious limitations. Moreover there is privacy flaw in dealing with repeat query attacks. In this paper, we propose a personalized differential privacy protection method for repeated queries, generating a new privacy protection specification according to the data privacy protection requirements, query user privilege and the number of same queries. The proposed differentiated privacy protection is achieved by adding random noise with different distribution characteristics to the query results. Simulation experiments show that the proposed method can solve the privacy leakage threat caused by repeated query attacks effectively, and can provide strong personalized differential privacy protection for multi-user aggregate queries in data analysis and data publication.
Planning with Real-Time Collision Avoidance for Cooperating Agents under Rigid Body Constraints

In automated warehouses, path planning is a crucial topic to improve automation and efficiency. This kind of planning is usually computed off-line knowing the planimetry of the warehouse and the starting and target points of each agent. However, this global approach is not able to manage unexpected static/dynamic obstacles and other agents moving in the same area. For this reason in multi-robot systems global planners are usually integrated with local collision avoidance algorithms. In this paper we use the Voronoi diagram as global planner and the Velocity Obstacle (VO) method as collision avoidance algorithm. The goal of this paper is to extend such hybrid motion planner by enforcing mechanical constraints between agents in order to execute a task that cannot be performed by a single agent. We will focus on the cooperative task of carrying a payload, such as a bar. Two agents are constrained to move at the end points of the bar. We will improve the original algorithms by taking into account dynamically the constrained motion both at the global and at the collision avoidance level.
On Optimally Partitioning Variable-Byte Codes

The ubiquitous Variable-Byte encoding is one of the fastest compressed representation for integer sequences. However, its compression ratio is usually not competitive with other more sophisticated encoders, especially when the integers to be compressed are small which is the typical case for inverted indexes. This paper shows that the compression ratio of Variable-Byte can be improved by 2× by adopting a partitioned representation of the inverted lists. This makes Variable-Byte surprisingly competitive in space with the best bit-aligned encoders, hence disproving the folklore belief that Variable-Byte is space-inefficient for inverted index compression. Despite the significant space savings, we show that our optimization almost comes for free, given that: we introduce an optimal partitioning algorithm that does not affect indexing time because of its linear-time complexity; we show that the query processing speed of Variable-Byte is preserved, with an extensive experimental analysis and comparison with several other state-of-the-art encoders.
Research on personal intelligent scheduling algorithms in cloud computing based on BP neural network

With the maturity of virtualization technology, cloud computing has brought us new computing and service models. Adopting a resource pool solution based on cloud computing architecture, the virtualized resources can be uniformly managed and deployed to achieve the purpose of automated and intellige nt management of information systems. However, because cloud servers have some features that are different from ordinary hosts, existing intrusion detection technologies cannot be directly applied to cloud computing. This paper focuses on the real-time dispatching of people. The main innovation of this paper is to apply BP neural network technology to the real-time dispatching of intelligent people, establish the time prediction model of intelligent dispatching of people based on BP neural network, and design the intelligent dispatching algorithm of people based on BP neural network, and use examples to analyze the opposition to verify the feasibility of the algorithm. The results show that the algorithm improves the classification accuracy of neural networks and shortens the training time of samples. It improves the efficiency of intelligent dispatch detection, the accuracy of results and the efficiency of explicit algorithm.
A Probabilistic Graph-Based Method to Improve Recommender System Accuracy

The last two decades have seen a surge of data on the Web which causes overwhelming users with huge amount of information. Recommender systems (RSs) help users to efficiently find desirable items among a pool of items. RSs often rely on collaborating filtering (CF), where history of transactions are analyzed in order to recommend items. High accuracy, and low time and implementation complexity are most important factors for evaluating the performance algorithms which current methods have the shortage of all or some of them. In this paper, a probabilistic graph-based recommender system (PGB) is proposed based on graph theory and Markov chain with improved accuracy and low complexity. In the proposed method, selecting each item for recommendation is conditioned by considering recommended items in the previous steps. This approach uses a probabilistic model to consider the items which are likely to be preferred by users in the future. Experimental results performed on two real-world datasets including Movielens and Jester, demonstrate that the proposed method significantly outperforms several traditional and state-of-the-art recommender systems.
Automatic Windowing for MRI With Convolutional Neural Network

This paper presents a fast, high-precision, and fully automatic windowing method based on deep convolutional neural network (CNN) for magnetic resonance imaging (MRI).Displaying a magnetic resonance (MR) image with a data depth of 12/16 bits on regular 8-bit monitors usually needs a windowing process to remap the full range of pixel intensity to a subrange.However, adaptively and automatically adjusting the windowing parameters of MR images under various viewing conditions is a challenging problem in medical image processing due to the low contrast and high grayscale range.We present a novel method based on the deep CNN's to estimate the windowing parameters that can match the adjustment of human experts precisely and quickly.The network acts as a typical end-to-end mapping function that takes the raw pixels of the MR images as input and directly outputs the corresponding estimation of the optimal windowing parameters.To speed up the inference, we utilize a space-to-depth (STD) conversion to reduce the spatial resolution of input images, and thus the computing burden of the inference process.The extensive experiments on the dataset annotated by clinicians show that the proposed method can accurately predict the optimal windowing parameters of an MR image with a size of 1024×1024 in less than 0.01 s.Due to the high effectiveness and efficiency of the proposed method, it is highly applicable for various clinical and research purposes.
E-commerce Personalization with Elasticsearch

Personalization techniques are constantly gaining traction among e-commerce retailers, since major advancements have been made at research level and the benefits are clear and pertinent. However, effectively applying personalization in real life is a challenging task, since the proper mixture of technology, data and content is complex and differs between organizations. In fact, personalization applications such as personalized search remain largely unfulfilled, especially by small and medium sized retailers, due to time and space limitations. In this paper we propose a novel approach for near real-time personalized e-commerce search that provides improved personalized results within the limited accepted time frames required for online browsing. We propose combining features such as product popularity, user interests, and query-product relevance with collaborative filtering, and implement our solution in Elasticsearch in order to achieve acceptable execution timings. We evaluate our approach against a publicly available dataset, as well as a running e-commerce store.
Color Image Enhancement Using a Multiscale Morphological Approach

Color image enhancement has been widely applied in a variety of applications from different scientific areas. On the other hand, mathematical morphology is a theory that deals with describing shapes using sets and, therefore, it provides a number of useful tools for image enhancement. Despite its utility, one of the challenges of this theory, when applied to color images, is to determine an order between the components of the image. Color images are represented by multidimensional data structures, which implies that there is no natural order between their components. In this work we propose an image enhancement method for color images that uses the extension of the multiscale mathematical morphology with different color spaces and ordering methods. The experiments carried out show that the proposed method generates competitive results using different ordering methods in terms of both local and global contrast, as well as the color quality of the image.
An Application of Hybrid Swarm Intelligence Algorithms for Dengue Outbreak Prediction

Dengue fever is a hazardous infectious disease which is channeled by Aedes mosquito. A serious infection of dengue may lead to a potentially lethal complication, known as severe dengue, which includes Dengue Haemorrhagic Fever and shock syndrome. In recent decades, this disease becomes a global burden which has grown dramatically around the world. Unfortunately, until today, a specific anti-viral medicine for dengue is still undiscovered. Therefore, it is a huge responsibility to the community in finding an effective solution to prevent a widespread of this disease in advance. Concerning this matter, this study presents an application of hybrid Swarm Intelligence (SI) algorithms for a dengue outbreak prediction. For simulation purposes, a monthly dengue cases time series data in the area of Indonesia were employed, which are fed to four hybrid SI algorithms, namely Moth Flame Optimization (MFO), Grey Wolf Optimizer (GWO), Firefly Algorithm (FA) and Artificial Bee Colony (ABC) algorithm. These algorithms are individually hybrid with Least Squares Support Vector Machines. Guided by Mean Square Error (MSE) and Root Mean Square Percentage Error (RMSPE), findings of the study indicate that the identified hybrid algorithms were able to produce competitive result, with a slightly favor to ABCLSSVM.
Extraversion Measure for Crowd Trajectories

In this paper, we propose an approach to estimate and quantify the degree of extraversion for crowd motion based on individual trajectories. Extraversion is a typical personality that is often observed in human behaviors. We present a composite motion descriptor, which integrates the basic motion information and social metrics, to describe the extraversion of each individual in a crowd. In order to train a universal scoring function that can measure the degrees of extraversion, we incorporate the active learning technique with the relative attribute approach based on the social grouping behavior in crowd motions. In addition, we demonstrate the performance of the proposed method by measuring the degree of extraversion for real individual trajectories in a crowd and analyzing crowd scenes from a real-world dataset.
A New Method of Latin-to-Balinese Script Transliteration based on Noto Sans Balinese Font and Dictionary Data Structure

This research aims to provide a new method of Latin-to-Balinese script transliteration based on the Noto Sans Balinese font since so far no research provides its technological state of the art. This multi-disciplines collaboration research produced the first known method utilizing the Noto Sans Balinese font that can be elaborated into: 1) the establishment of the MVC design pattern and its implementation; 2) the identification of 17 types of particular words; 3) the accomodation of those particular words by the dictionary data structure; 4) the establishment of the accuracy measurement for the next new method development; and 5) the accuracy improvement compared to other methods utilizing Bali Simbar font. This method can be utilized as a core of learning application to provide the knowledge of transliteration, as a part of Balinese Language learning program in Indonesia, especially in Bali Province where its Balinese Language was considered as a mandatory subject from elementary to high school. This method used MVC architectural pattern, where Model handles a repository for the particular words, View handles application User Interface, and Controller handles transliteration algorithm based on string pattern matching. Through the experiment, accuracy above 91% (138 of 151 cases) has been achieved by this method on the testing cases of The Balinese Alphabet document. On the same testing cases, that result outperformed the existing method Transliterasi Aksara Bali based on the Bali Simbar font, with its accuracy a little above 68% (103 of 151 cases). In the future work, this method could be enhanced by: 1) accommodating remain and future rules and examples inside and outside the testing document that recently cannot be handled or gave incorrect result; and 2) enriching the particular words repository.