Reviewerly/exHarmony huggingface dataset:
Informative Path Planning for Active Tracking of Agile Targets

This paper proposes a method to generate informative trajectories for a mobile sensor that tracks agile targets. The goal is to generate a sensor trajectory that maximizes the tracking performance, captured by a measure of the covariance matrix of the target state estimate. The considered problem is a combination of estimation and control, and is often referred to as informative path planning (IPP). When using nonlinear sensors, the tracking performance depends on the actual measurements, which are naturally unavailable in the planning stage. The planning problem hence becomes a stochastic optimization problem, where the expected tracking performance is used in the objective function. The main contribution of this work is an approximation of the problem based on deterministic sampling of the predicted target distribution. This is in contrast to prior work, where only the most likely target trajectory is considered. It is shown that the proposed method greatly improves the ability to track agile targets, compared to a baseline approach.
Context-based bidirectional-LSTM model for sequence labeling in clinical reports

Recurrent Neural Network (RNN) models have been widely used for sequence labeling applications in different domains. This paper presents an RNN-based sequence labeling approach with the ability to learn long-term labeling dependencies. The proposed model has been successfully used for a Named Entity Recognition challenge in healthcare: anatomical phrase labeling in radiology reports. The model was trained on labeled data from a radiology report corpus and tested on two independent datasets. The proposed model achieved promising performance in comparison with other state-of-the-art context-driven sequence labeling approaches.
Fast Hamming distance computation for 2D art recognition on VLIW-architecture in case of Elbrus platform

In the paper we consider computational optimization of recognition system on Very Long Instruction Word architecture. Such architecture is aimed to a broad parallel execution and low energy consumption. We discuss VLIW features on the example of Elbrus-based computational platform. In the paper we consider system for 2D art recognition as the example. This system is able to identify a painting on acquired image as a painting from the database, using local image features constructed from YACIPE-keypoints and their RFD-based binary color descriptors, created as a concatenation of RFD-like descriptors for each channel. They are computed fast, while the 2D art database is quite large, so in our case more than a half of execution time consumes descriptor comparison using Hamming distance during image matching. This operation can be optimized with the help of low-level optimization considering special architecture features. In the paper we show efficient usage of intrinsic functions for Elbrus-4C processor and memory access with array prefetch buffer, which is specific for Elbrus platform. We demonstrate the speedup up to 11.5 times for large arrays and about 1.5 times overall speedup for the system without any changes in intermediate computations.
RETRACTED: Prediction methods of ecological civilization outlook based on distributed algorithm of factor graph

Evaluation index set was constructed according to connotation of evaluation system for ecological civilization construction so as to establish an evaluation model for ecological civilization construction of factor graph distribution for input layer, hidden layer, and output layer which are respectively 10, 3, and 2. Relevant data of 15 cities at different levels in China are collected so as to be subject to standardization for input learning matrix. Error precision was close to 10-5 through 5000 times of training, thus ecological civilization construction of Wanjiang urban belt is evaluated. Empirical result shows that ecological civilization construction of Wanjiang urban belt belong to grade III, which means that it is in development period. Therefore, economic development needs to be conducted in stable carbon emission level. Economic development mode needs to be further changed; national proprietary brand needs to be supported; scientific and technological innovation needs to be driven; resource output rate needs to be improved.
The Design and Implementation of RTSP/RTP Multimedia Traffic Identification Algorithm

In recent years, multimedia traffic has been growing rapidly in the network, and it is of great significance to effectively supervise and manage multimedia traffic. This paper not only analyzes some specific multimedia transmission protocols, but also studies their communication characteristics. We proposes a multimedia traffic identification framework as well, which has a great advantage in scalability. In addition, multimedia traffic identification algorithms for specific RTSP, RTP/RTCP traffic, which have high identification accuracy, are described and validated in this paper. More importantly, a solution is put forward, aiming to solve the problem that the start and end of the stream cannot be determined when the multimedia stream adopts UDP transmission.
Exploiting NoSQL Document Oriented Data Using Semantic Web Tools

The web has experienced a quantitative explosion of digital data handled by companies manipulating numerous heterogeneous management systems that encapsulate large web sites destined for a wide audience. In fact, some web users treat this big amount of data with NoSQL databases while others prefer to use semantic web technologies, which make the communication between the web applications a very hard aim. The previous raison has motivated us to bridge the conceptual gap between them in order to make NoSQL data machine-readable and allow web applications to exchange information easily. Our main contribution is to generate RDF format from NoSQL database model with a specific focus on MongoDB as the most used document oriented database in order to make the NoSQL data available on the triplestores and to carry out some operations not supported by NoSQL systems.
Consistency of the maximum likelihood and variational estimators in a dynamic stochastic block model

We consider a dynamic version of the stochastic block model, in which the nodes are partitioned into latent classes and the connection between two nodes is drawn from a Bernoulli distribution depending on the classes of these two nodes.The temporal evolution is modeled through a hidden Markov chain on the nodes memberships.We prove the consistency (as the number of nodes and time steps increase) of the maximum likelihood and variational estimators of the model parameters, and obtain upper bounds on the rates of convergence of these estimators.We also explore the particular case where the number of time steps is fixed and connectivity parameters are allowed to vary.
Fast and Stable Computation of the Tchebichef’s Moments Using Image Block Representation and Clenshaw’s Formula

In this paper, we propose a new method of stable computation of the discrete orthogonal moments of Tchebichef and its inverse. In this method, we have combined two main concepts. The first concept is Clenshaw’s recurrence formula to accelerate the calculation process of the Tchebichef moments. The second concept is the partitioning of the image into a set of blocks of fixed sizes where each block is processed independently. This method is meant to accelerate the computation time and improve the quality of images reconstruction. In order to demonstrate the efficiency, the stability and the precision of the proposed method compared to the direct method based of Clenshaw’s recurrence formula, some simulations have been performed on different types of images.
The Hybrid Body and Sonic-Cyborg Performance in Why Should Our Bodies End at the Skin?

In "A Cyborg Manifesto," Donna Haraway describes how by the late twentieth century, humans have become hybridized with machines. While society has long been concerned with the ever-growing encroachment of technology into human activity, Haraway challenges this concern, proposing instead a kinship between organism and machine. The result is the cyborg, a hybrid body that fluidly transcends mechanical and organic boundaries. Why Should Our Bodies End at the Skin? for sensor-equipped dancer, robotic percussion, sound exciters, and live sound processing, explores these ideas of intersectionality and fluidity between organism and machine by connecting human action and mechanical tasks. This paper describes the creative framework and associated technologies involved in the development of the piece.
High-Performance Fault Diagnosis Schemes for Efficient Hash Algorithm BLAKE

Augmenting the security of cryptographic algorithms by protecting them against side-channel active attacks (and natural faults) is essential in cryptographic engineering. BLAKE algorithm is an efficient hash function which has been developed based on Bernstein's ChaCha stream cipher. Because of the fact that Google has chosen ChaCha along with Bernstein's Poly1305 message authentication code as a replacement for RC4 in TLS for Internet security, BLAKE's implementation is of paramount importance. In this paper, we present high-performance fault detection schemes for BLAKE. Specifically, for the round function, two fault diagnosis approaches are developed and analyzed in terms of error detection capability and overhead. Through our injection-based error simulations, we show that the error coverage of almost 100% can be achieved for the proposed approaches. In addition, through hardware platform benchmarks, we show that the proposed architectures have implementations which reach acceptable area/delay overheads. The proposed high-performance fault diagnosis approaches will make the hardware implementations of BLAKE more reliable.
Finding Topic Experts in the Twitter Dataset Using LDA Algorithm

In microblogging services like Twitter, the expert judgment problem has gained increasing attention in social media. Twitter is a new type of social media that provides a publicly available way for users to publish 140-character short messages (tweets). However, previous methods cannot be directly applied to twitter experts finding problems. They generally rely on the assumption that all the documents associated with the candidate experts contain tacit knowledge related to the expertise of individuals. Whereas it might not be directly associated with their expertise, i.e., who is not an expert, but may publish/re-tweet a substantial number of tweets containing the topic words. Recently, several attempts use the relations among users and twitter list for expert finding. Nevertheless, these strategies only partially utilize such relations. To address these issues a probabilistic method is developed to jointly exploit three types of relations (i.e., follower relation, user-list relation and list-list relation) for finding experts. LDA algorithms are used for finding topic experts. LDA is based upon the concept of searching for a linear combination of variables (predictors) that best separates two classes (targets). Semi-supervised graph-based ranking approach (SSGR) to offline calculate the global authority of users. Then, the local relevance between users and the given query is computed. Then, the rank of all the users is found and the top-N users with the highest-ranking scores. Therefore, the proposed approach can jointly exploit the different types of relations among users and lists for improving the accuracy of finding experts on a given topic on Twitter.
Research on the human brain, the external brain and the public external brain

Information technology such as Internet, Internet of Things and mobile communication is the infrastructure of information communication under network digital space. With increasingly completion of information technology, people can instantly access and get information in digital network space. The cognition and learning of the human brain can achieve real-time synchronization with cyberspace. Digital cyberspace is like an external brain and a public external brain, and they are part of human brain's memory and thinking. This paper defines the human brain, the external brain and the public external brain, and summarizes attributes and relationships of the three, moreover proposes human cognition and action models in the digital age to understand human behavior. This model provides an engineering cognitive perspective and thinking method for understanding human behavior and social activities in the digital age.
Depth information aided constrained correlation filter for visual tracking

In this paper, we proposed a novel visual tracking system by constructing the Constrained Correlation Filter (CCF) with Depth Information. More specifically, in our proposed system, to avoid the boundary effects and the fixed shape assumption of conventional Discriminative Correlation Filter (DCF), the shape of target is extracted from depth image provided by RGB-D sensor to construct the CCF, which may prevent the filter from being disturbed by the background noise at the learning stage and enlarge the search region. Moreover, in order to avoid the drifting problem, the update of the model is stopped once part of the target is occluded. The feature weighting coefficients, which reflect the discriminability of the feature channels, are used at the location stage to improve the discriminability. The experimental results show that our method is capable of achieving state-of-the-art performance on Princeton RGB-D tracking benchmark among all public tracking algorithms.
Approaches to Realize the Potential of Autonomous Underwater Systems in Concept Development and Experimentation

Recent NATO reports highlight the rapid progress being made in the development of autonomous underwater systems. In contrast, national reports indicate that their benefits are not being fully realized in a timely manner in operational scenarios. One approach to improve NATO's adoption of these systems is to provide guidance in the NATO concept development and experimentation process specially aimed at articulating autonomous system behaviors and allowing efficient experimentation with their capabilities. This position paper reviews the latest techniques and approaches for articulating and testing autonomous system capabilities in industry, academia and within NATOs national militaries. Discussed techniques focus on encouraging and developing understanding and trust in the commander and operator stakeholder communities as well improving the efficiency of autonomous system testing. Potential future guidance and the structure of these activities within the existing NATO CD&E framework are presented for further discussion.
Reduction of Dimensionality of Feature Vectors in Subject Classification of Text Documents

Within a paper we investigate the influence of dimensionality reduction of feature vector (PCA and random projection) on the results of subject classification of text documents in Polish. Two state of the art methods of text representation have been applied, i.e. bag of words consisting of the most frequent 1000 lemmatized nouns and the emerging fastText based on the word embedding dimensionality equal to 100. The methods have been evaluated on four corpora in Polish (two different data sets, divided into training and testing set in different proportions). Results show that PCA gives better accuracy in all analyzed cases. In case of fastText, a significant reduction of up to 10 times is possible without loss of quality regardless the quality of corpus. To analyse this phenomenon, we have performed a set of experiments in which we train fastText with different values of word embedding dimensionality (from 2 to recommended 100). Experiments show that even the dimension 3–10 (depending on the quality of data) allows to achieve very good accuracy.
Wearable sensing systems for biomechanical parameters monitoring

The use of wearable sensing systems in clinical rehabilitation has demonstrated high potential in improving the trainings effectiveness and reducing mobility recovery time. The monitoring of biomechanical parameters in upper limbs provides valuable supports to neurological patient’s recovery trainings, where the self-awareness of human body must be restored. After preliminary activities of the author’s group, which led to the GoldFinger prototype for HMI applications, the development of the sensing glove described in this paper has been carried out. Here, small dimensions and lightness are accompanied by flexibility of supports, reliable connectors and interfaces and low power consumption.
Agile Requirements Validation in Brazilian Software Development Companies: A Survey

Background: In Agile Software Development context, Requirements Engineering (RE) is an important process that happens continuously during the iterations of a product. To be able to deliver value, the teams should perform requirements validation to assure that they meet user’s expectations, and also foster the collaboration between stakeholders and developers. Despite this understanding, there are still few studies that provide empirical data that make it possible to generalize the aspects, practices, and difficulties found by the teams that perform validation of agile requirements. Goal: The goal of this work is to understand how the requirements validation activities are being put in practice by companies of the Brazilian software industry that adopt agile methodologies. Method: We carried out an online survey, involving 117 participants. Our instrument focused on identifying the most used requirements artefacts, and what are the main difficulties in adopting these practices. Results: The results of our study reported that (i) user stories and prototypes are the most used artefacts, (ii) teams usually validate requirements by running systematic refinement meetings, and (iii) the development team and Product Owner are the most popular roles that attend to these sessions. Conclusion: The analysis reveals that agile teams are running requirements validation sessions as part of their development processes but they still face general RE problems, like the lack of stakeholders engagement or stakeholders that have different business visions of the same product. These results also contribute with information that allows future studies focused on the improvement of agile requirements validation.
Mining Source Code Improvement Patterns from Similar Code Review Works

Code review is key to ensuring the absence of potential issues in source code. Code reviewers spend a large amount of time to manually check submitted patches based on their knowledge. Since a number of patches sometimes have similar potential issues, code reviewers need to suggest similar source code changes to patch authors. If patch authors notice similar code improvement patterns by themselves before submitting to code review, reviewers' cost would be reduced. In order to detect similar code changes patterns, this study employs a sequential pattern mining to detect source code improvement patterns that frequently appear in code review history. In a case study using a code review dataset of the OpenStack project, we found that the detected patterns by our proposed approach included effective examples to improve patches without reviewers' manual check. We also found that the patterns have been changed in time series; our pattern mining approach timely achieves to track the effective code improvement patterns.
CALCULATION OF THE NEGENTROPY AND WEIGHT COEFFICIENTS OF MULTICRITERIA ESTIMATES ON THE BASIS OF FUZZY SETS

This article is a logical continuation of the material published in the journal “Informatics and Education” № 6-2018. The authors consider the issues of predicting learning outcomes based on the calculation of negentropy, which is proposed to be considered as an integral information indicator characterizing the quality of student learning. The work can be divided into three stages and is based on the construction of the hierarchical structure of the criteria-based quality system. At the first stage, a questionnaire was developed for the survey of expert teachers, in which it was necessary to note, from the point of view of importance, the criteria of the first and second levels those influence the process of emergent learning. At the second stage, using an expert rationing method, an array of expert estimates was processed, representing an example of a fuzzy set. As a result, weighting coefficients were obtained according to the criteria of the first and second levels. At the third stage, a dichotomous assessment of the third level criteria was carried out and the integrated values of negentropy were calculated for the three directions of training and for the model situation. The algorithms proposed in the article can be used both to assess the quality of already developed online courses, products or educational processes with their use, and to predict learning outcomes based on expert assessment.
Security Analysis in Context-Aware Distributed Storage and Query Processing in Hybrid Cloud Framework

Recent studies have shown that several government and business organizations experience huge data breaches. Data breaches increase in a daily basis. The main target for attackers is organization sensitive data which includes personal identifiable information (PII) such as social security number (SSN), date of birth (DOB) and credit card /debit card (CCDC). The other target is encryption/decryption keys or passwords to get access to the sensitive data. The cloud computing is emerging as a solution to store, transfer and process the data in a distributed location over the Internet. Big data and internet of things (IoT) increased the possibility of sensitive data exposure. Most methods used for the attack are hacking, unauthorized access, insider theft and false data injection on the move. Most of the attacks happen during three different states of data life cycle such as data-at-rest, data-in-use, and data-in-transit. Hence, protecting sensitive data at all states particularly when data is moving to cloud computing environment needs special attention. The main purpose of this research is to analyze risks caused by data breaches, personal and organizational weaknesses to protect sensitive data and privacy. The paper discusses methods such as data classification and data encryption at different states to protect personal and organizational sensitive data. The paper also presents mathematical analysis by leveraging the concept of birthday paradox to demonstrate the encryption key attack. The analysis result shows that the use of same keys to encrypt sensitive data at different data states make the sensitive data less secure than using different keys. Our results show that to improve the security of sensitive data and to reduce the data breaches, different keys should be used in different states of the data life cycle.
A Hybrid Probabilistic Estimation Method for Photovoltaic Power Generation Forecasting

Because of stochastic nature of weather conditions, the predictability of photovoltaic (PV) power generation is poor. Compared with the point prediction, the probabilistic prediction of PV power generation can provide more information about the underlying uncertainties, which is beneficial to the stability and safety of grid dispatching and power system. Based on random forest (RF), fuzzy C-means (FCM), sparse Gaussian process (SPGP), improved grey wolf optimizer (IMGWO) algorithm, a hybrid probabilistic estimation method, in this paper, is proposed to predict the probability of PV power generation for every hour in one day. RF algorithm is firstly used to reduce multidimensional input variables. And according to the weather patterns, FCM method is adopted to divide data and get the similar samples. Finally, a hybrid forecasting method combines SPGP and IMGWO is applied to forecast the test data. With the simulation and experimental results, the validity and reliability of the proposed model (IMGWOSP) is verified. The results show that the proposed model has improved both accuracy and practicability, so the stability and safety of grid dispatching and power system can be improved.