Reviewerly/exHarmony huggingface dataset:
Assessing the Impact of Cognitive Assistants on Mental Workload in Simple Tasks

Abstract Cognitive assistants, such as Apple’s Siri or Amazon’s Alexa, are increasingly becoming routine elements of our daily lives, yet the effects of such devices on the humans using them has been largely unstudied. While much research has proposed new intelligent systems for specific contexts, few studies have characterized the dynamics between cognitive assistants and humans. In an effort to address this gap, this research study investigates the effect of cognitive assistants on mental workload. Participants perform the Wisconsin Card Sorting Task while working with a fake cognitive assistant (Mary) and concurrently completing a secondary speed and accuracy test. Performance with the cognitive assistant was found to be higher but with no significant difference in mental workload. Participants working with the cognitive assistant were found to be less frustrated than those completing the task without it.
Real-Time Binocular Vision Implementation on an SoC TMS320C6678 DSP

In recent years, computer binocular vision has been commonly utilized to provide depth information for autonomous vehicles. This paper presents an efficient binocular vision system implemented on an SoC TMS320C6678 DSP for real-time depth information extrapolation, where the search range propagates from the bottom of an image to its top. To further improve the stereo matching efficiency, the cost function is factorized into five independent parts. The value of each part is pre-calculated and stored in the DSP memory for direct data indexing. The experimental results illustrate that the proposed algorithm performs in real time, when processing the KITTI stereo datasets with eight cores in parallel.
A Memetic and Adaptive Continuous Ant Colony Optimization Algorithm

This paper proposes two new variants of the Continuous Ant Colony Optimization algorithm, ACOR. The first variant, called the Adaptive ACOR (AACOR), uses the relative diversity of the solutions in the algorithm’s archive to adapt its parameters. The second variant, called the memetic AACOR (MAACOR), uses a local search operator to improve the performance of AACOR. Both variants were tested on the 22 IEEE CEC 2011 real-world optimization problems and compared with ACOR and two state-of-the-art optimization methods. The results demonstrate the merits of the proposed approaches.
Improving Cuckoo Search Algorithm With Mittag-Leffler Distribution

Abstract Cuckoo search (CS), as one of the recent nature-inspired metaheuristic algorithms, has proved to be an efficient approach due to the combination of Lévy flights, local search capabilities and guaranteed global convergence. CS uses Lévy flights in global random walk to explore the search space. The Lévy step is taken from the Lévy distribution which is a heavy-tailed probability distribution. In this case, a fraction of large steps are generated, which plays an important role in enhancing search capability of CS. Besides, although many foragers and wandering animals have been shown to follow a Lévy distribution of steps, investigation into the impact of other different heavy-tailed probability distributions on CS is still insufficient up to now. Based on the above considerations, we are motivated to apply the well-known Mittag-Leffler distribution to the standard CS algorithm, and proposed an improved cuckoo search algorithm (CSML) in this paper, where a more efficient search is supposed to take place in the search space thanks to the long jumps. In order to verify the performance of CSML, experiments are carried out on a test suite of 20 benchmark functions. In terms of the observations and results analysis, CSML can be regarded as a new potentially promising algorithm for solving optimization problems.
Dynamic Data Management Strategy on Cloud Network by Fog Computing Model

This paper proposes a data management platform using a fog computing model. It enables to construct of dynamic user data plane adapted to diverse range and several kinds of functions for these users. In particular, this paper implements a sample recommendation application of movie information being able to control the data on fog nodes. However, this paper does not include the quantitative evaluation applying to application services.
Age estimation in human face by fractal directional code method

This paper reveals various constraints in digital way of estimating the age of human facial image. The age estimation involves analyzing the facial features and classifying the age in accordance to the extracted feature values. The biometric features such as the facial parts (eye, mouth, chin, forehead, hair), texture, color and shape provide essential aging details. In the proposed method, the fractal features are extracted using fractal directional code method to retain the significant details present in the input image. The fractal features along with the local features are used to train the system. The deep neural network with three layers is used to train the system. The scaled conjugate gradient back propagation trains the system and the age is classified into seven age groups (0–10, 11–19, 20–29, 30–39, 40–49, 50–59 and 60–69). Higher accuracy is achieved by concocting the biometric features and the fractal features.
Performance Analysis of Queries with Hive Optimized Data Models

The processing of structured data in Hadoop is achieved by Hive, a data warehouse tool. It is present on top of Hadoop and helps to analyze, query, and review the Big Data. The execution time of the queries has drastically reduced by using Hadoop MapReduce. This paper presents the detailed comparison of various optimizing techniques for data models like partitioning and bucket methods to improve the processing time for Hive queries. The implementation is done on data from New York Police Portal using AWS services for storage. Hive tool in Hadoop ecosystem is used for querying data. Use of partitioning has shown remarkable improvement in terms of execution time.
A New Steganalysis Method Using Densely Connected ConvNets

Steganography is an ancient art of communicating a secret message through an innocent-looking image. On the other hand, steganalysis is the counter process of the steganography, which targets to detect hidden trace within a given image. In this paper, a new approach to steganalysis is presented to learn prominent features and avoid loss of stego signals. The proposed model uses diverse sized filters to capture all useful steganalytic features through a densely connected convolutional network. Moreover, there is no fully connected network in the proposed model, which allows testing any size of images regardless of the image size used for training. To justify the applicability of the proposed scheme, it has been shown experimentally that the proposed scheme outperforms most of the related state-of-the-art methods.
Robust Rotation Interpolation Based on SO(n) Geodesic Distance

A novel interpolation algorithm for smoothing of successive rotation matrices based on the geodesic distance of special orthogonal group SO(n) is proposed. The derived theory is capable of achieving optimal interpolation and owns better accuracy and robustness than representatives.
Computational Cognition for Mission Command and Control Decisions Facing Risk in Unknown Environments

Abstract Systems operating in hazardous and remote environments is both desirable from a scientific perspective and incredibly difficult from an engineering and logistical perspective. This paper develops a novel two-system top-down computational-cognition-based agent and investigates its ability to respond to unanticipated situations in a hazardous environment. A simulated space mission is performed in which a Martian settlement site is constructed by a team of robots. In order to evaluate the agent’s ability to respond to unanticipated mission scenarios, “black swan” events are added to the simulation. We found that the agent was able to respond well to black swan events that changed the mission conditions in a detectable way, but black swans that were either undetectable or gave the agent inaccurate mission information increased the likelihood of failure. This work presents an interesting first step towards autonomous systems that are more resilient when facing hazardous or unknown environments. Additionally, the techniques presented here can handle large, complex problems with very minimal computational resources.
Noise Detection in Imbalanced Classes Using Adaptive Boosting

One of the problems frequently encountered in machine learning is the imbalanced class problem. Since the classification algorithms in the literature show bias according to the number of observations of the classes, various methods have been developed in order to solve this problem. Existing methods in literature can lead to various problems such as generation of noise while allowing the solution of this problem. In this study, synthetic minority oversampling technique (SMOTE), which is widely used in imbalanced classification problems, is discussed with Boosting. Using the proposed approach, the noise problem after SMOTE is solved with the help of Boosting procedure. This approach was implemented using C4.5 decision tree method on 5 different datasets. As a result of the application, the average performance of F1 score, AUC and G mean increased.
Teaching Commonsense and Dynamic Knowledge to Service Robots

Incorporating commonsense and coping with dynamic knowledge are key capabilities of service robots to efficiently interact with humans. In the presented system, we demonstrate how to equip service robots with commonsense knowledge and the dynamic reasoning capabilities of Answer Set Programming (ASP). We investigated the response of our system to basic human needs and evaluated the viability and scalability of the combination of the commonsense knowledge database ConceptNet 5 and the ASP solver Clingo. Our results show the flexibility and versatility of our approach. Further, we identified the need for research on scalability in case of environments that are abundant with objects.
Impact of Inertia Weight and Cognitive and Social Constants in Obtaining Best Mean Fitness Value for PSO

The performance of an Inertia Weight-based Particle Swarm Optimization (IWPSO) is relied on parameters like inertia weight, cognitive, and social coefficients. This paper is investigating effective range of inertia weights and relationship between cognitive and social coefficients over different swarm sizes for selective iterations and dimensions in assessing performance of IWPSO. The experimental results show that the inertia weights in the range 0.1–0.8 and when cognitive coefficient is less than social coefficient, the results are more aspiring.
Fast and Generalized Adaptation for Few-Shot Learning

Many Few-Shot Learning research works have two stages: pre-training base model and adapting to novel model. In this paper, we propose to use closed-form base learner, which constrains the adapting stage with pre-trained base model to get better generalized novel model. Following theoretical analysis proves its rationality as well as indication of how to train a well-generalized base model. We then conduct experiments on four benchmarks and achieve state-of-the-art performance in all cases. Notably, we achieve the accuracy of 87.75% on 5-shot miniImageNet which approximately outperforms existing methods by 10%.
A Novel Autonomous Scaled Electric Combat Vehicle

Abstract Current literature pertaining to multi-steerable mobile platforms and the progression of military vehicles in the past few decades suggest a lack of effort in pursuing advanced technologies in this joint area. As a result, a novel 1:6 scaled electric combat vehicle prototype that features eight independently driven and steerable wheels is designed and developed in this paper. The intent is to create a scaled model for future autonomous vehicle navigation and control research in off road terrains. Starting with the mechanical design, this paper discusses the details of the chassis, suspension, driving and steering systems development. The electronics necessary for vehicle actuation is implemented with custom nodes and topics created for hardware communication within the Robot Operating System (ROS). Lastly, path planning and obstacle avoidance abilities are implemented to achieve autonomous navigation. The result of this work is a fully functional and instrumented robotic platform with a modular software architecture. Vehicle design analysis, performance and autonomous navigation abilities are experimentally tested with promising results.
DXNet: An Encoder-Decoder Architecture with XSPP for Semantic Image Segmentation in Street Scenes

Semantic image segmentation plays a crucial role in scene understanding tasks. In autonomous driving, the driving of the vehicle causes the scale changes of objects in the street scene. Although multi-scale features can be learned through concatenating multiple different atrous-convolved features, it is difficult to accurately segment pedestrians with only partial feature information due to factors such as occlusion. Therefore, we propose a Xiphoid Spatial Pyramid Pooling method integrated with detailed information. This method, while connecting the features of multiple atrous-convolved, retains the image-level features of target boundary information. Based on the above methods, we design an encoder-decoder architecture called DXNet. The encoder is composed of a deep convolution neural network and two XSPP modules, and the decoder decodes the advanced features through up-sampling operation and skips connection to gradually restore the target boundary. We evaluate the effectiveness of our approach on the Cityscapes dataset. Experimental results show that our method performs better in the case of occlusion, and the mean intersection-over-union score of our model outperforms some representative works.
SentiPhraseNet: An extended SentiWordNet approach for Telugu sentiment analysis

Sentimental Analysis in English language is a relatively easier task to perform as it has a predefined set of rules followed and accepted universally. But, when it comes to Indian languages, there isn’t a benchmark dataset. Moreover, if a data set exists, it cannot be validated as a similar sentence may differ in the meaning as the regional languages are very unpredictable and have no proper rules. In this paper, we used a Rule-Based Approach to build SentiPhraseNet. Here, we obtained the sentiment using SentiPhraseNet and validated the results using ACTSA which is an annotated corpus data set.
Two Stream Convolutional Neural Networks for Anomaly Detection in Surveillance Videos

In this paper we proposeJamadandi, Adarsh a deep learning framework to identify anomalous eventsKotturshettar, Sunidhi in surveillance videos. Anomalous eventsMudenagudi, Uma are those which do not adhere to normal behaviour. We propose to use two discriminatively trained Convolutional Neural Networks, to capture the spatial and temporal features of videos, the classification scores obtained from the two streams are later fused to assign one final score. Since our approach is scenario-based, this eliminates the need for adopting a particular definition of anomaly. We show that the Two Stream CNNs perfectly capture the intricacies involved in modelling a video data by demonstrating the framework on airport and mall surveillance datasets respectively. We achieve a final test accuracy of 99.1% for spatial stream and 91% for temporal stream for airport scenario and an accuracy of 94.7% for spatial and 90.1% for the temporal stream for the mall scenario. Our framework can be easily implemented in real-time and is capable of detecting anomaly in each frame fed by a live surveillance system.
A COMPARATIVE ANALYSIS OF CRYPTOCURRENCY CONSENSUS ALGORITHMS

The aim of this work is to analyze the major existing cryptocurrency consensus algorithms considering a number ofattributes that may play a significant role in the long-term sustainability of a cryptocurrency ecosystem and to comparativelyevaluate
Speaker verification normalization sequence kernel based on Gaussian mixture model super-vector and Bhattacharyya distance

Due to the accessibility and economy of human speech, speaker verification has become the research hotspot in the field of biometric authentication. A novel normalization sequence kernel based on Bhattacharyya distance clustering and within class covariance normalization was proposed in this paper. In this kernel, the high computation complexity and channel interference susceptibility that commonly exist in speaker verification could be restrained. In our method, we calculated the Bhattacharyya distance between pairs of Gaussian mixture models first. And then, a clustering algorithm was designed according to Gaussian mixture model’s Bhattacharyya distance to obtain clustering center models. Maximum a posteriori was applied on these clustering center models to generate super-vectors immediately following. The sequence kernel was generated based on Bhattacharyya distance transformation and super-vectors. Finally, within class covariance normalization was utilized to restrain the channel distortion in kernel space. We adopted support vector machine as classifier to decide the target speaker. The experiment results on TIMIT corpus and NIST 2008 SRE showed that our proposed kernel has superior recognition accuracy and better robustness.
Knowledge Fusion: Introduction of Concepts and Techniques

The Internet contains a huge amount of data information, relying only on human resources can no longer obtain the complete and useful information from it. The research of knowledge graph provides a means to obtain large amounts of knowledge from massive texts and images, which plays an important role in people's daily life, such as QA, search, recommendation. The effective organization and integration of knowledge obtained from different sources into a knowledge base is the researching difficulty and hot spot to be solved in the knowledge graph construction in currently. This paper summarizes the various methods and research progress involved in knowledge fusion from three aspects, that is, open network knowledge fusion and multiple knowledge base knowledge fusion, and knowledge evaluation. We hope it can provide some useful guidance and inspiration for the research of knowledge fusion and knowledge graph construction of future network.
Incentive Mechanism for Bitcoin Mining Pool Based on Stackelberg Game

Bitcoin is the most popular cryptocurrency all over the world. Existing mining pool systems do not consider the cost of miners. In this paper, we propose a novel pool mining mechanism based on Stackelberg game to incentivize the rational miners in Bitcoin mining pool. Through both theoretical analysis and simulations, we demonstrate that the proposed mechanism achieve computational efficiency, individual rationality, and profitability. Moreover, we show that the Stackelberg game has a unique Equilibrium.
A Structure-Based De-Anonymization Attack on Graph Data Using Weighted Neighbor Match

Nowadays, many large-scale graph datasets, such as social networks data, are often published for scientific research, information sharing and data mining. Privacy protection issues in publishing datasets have also received widespread attention. Usually, in order to preserve the individual privacy, the datasets will be anonymized before released. Many researchers have proposed various of De-anonymization algorithms to re-identify nodes in the anonymized dataset which is an effective way to evaluate the anonymity of the published datasets. There are many problems when the de-anonymization algorithms apply to the anonymized datasets. The method might require high-quality seeds, or had a low noise tolerance, so that the de-anonymization process can only achieve poor results. In this paper, we propose a structure-based weighted neighborhood matching algorithm, named SWNM, which have a comprehensive consideration of local and global features when calculating the nodes similarity between anonymized nodes and auxiliary nodes. SWNM also using a dynamic similarity matrix and weighted neighborhood matching algorithm to ensure a nice noise tolerance and de-anonymization accuracy. Finally, experiments on real graph datasets show that the SWNM has better performance than baseline method.
A Novel k-Anonymization Approach to Prevent Insider Attack in Collaborative Social Network Data Publishing

Social network data analysts can retrieve improved results if mining operations are performed on collaborative social network data instead of independent social network data. The collaborative social network can be constructed by joining data of all social networking sites. This data may contain sensitive information about individuals in its original form and sharing of such data, as it is, may violate individual privacy. Hence, various techniques are discussed in literature for privacy preserving publishing of social network data. However, these techniques suffer from the insider attack, performed by colluding data provider(s) to breach the privacy of the social network data contributed by other data providers. In this paper, we propose an approach that offers protection against the insider attack in the collaborative social network data publishing scenario. Experimental results demonstrate that our approach preserves data utility while protecting collaborated social network data against the insider attack.
Rule based English-Vietnamese bilingual terminology extraction from Vietnamese documents

Bilingual terminologies are important resources for natural language processing as well as for human use. The automatic acquisition of bilingual terminologies is mostly based on bilingual corpora. However, monolingual corpora could also be a good source for extracting bilingual terms. In fact, as English is used for international publications, we often find English words together with their translation in monolingual documents written in other languages. In this paper, we propose a rule-based approach for automatically extracting English-Vietnamese bilingual terminology from Vietnamese monolingual documents. Experiments on a medical corpus and on Wikipedia documents show that the extracted bilingual term candidates are of high quality.
Development and Research of Marker Isolation Schemes from Low-Frequency PLC Signal

One of the aspects of application of low-frequency PLC technology for remote control of led dimmable lighting devices is considered. To transmit control commands, a modified method of amplitude modulation is used, which has a minimal impact on the power supply network. The peculiarity of this method is the use as markers of half - wave voltage with an amplitude of 10...15% less than the amplitude of the half-wave voltage of the network, and as an information base-the half-wave voltage of the network.One of the main components of the lamp is the scheme of marker allocation from the mains voltage. The marker is needed to obtain information from the PLC signal by the microcontroller of the lamp. Difficulties of the selection marker are associated with insignificant difference from the usual modulated half waves, and with the instability of the level of the mains voltage.Two alternative analog circuits for marker selection are proposed, their modeling and experimental testing are carried out. Graphs of transients in the schemes are obtained and analyzed. The scheme allowing adapting to instability of a level of network voltage and at the same time providing the required sensitivity for detection of a marker is chosen.