Reviewerly/exHarmony huggingface dataset:
First-Order Difference Bare Bones Particle Swarm Optimizer

The Bare Bones Particle Swarm Optimization (BBPSO), because of its implementation simplicity, has been a popular swarm-based metaheuristic algorithm for solving optimization problems. However, as found in its many variants, their search behaviors were not considered in the design. Instead of employing heuristics, we formulate a low complexity particle swarm optimizer, called the First-Order Bare Bones Particle Swarm Optimizer (FODBB), whose behavior obeys the principle of first-order difference equations. The search trajectory can be constructed in a prescribed manner together with decreasing random searches that enable particles to explore the search space more completely. This characteristic thus allows for a wider search coverage at initial iterations and consequently improves the search performance. A comparative evaluation with recently reported BBPSO algorithms was conducted and experimental results indicate that the proposed optimizer outperforms others in a majority of benchmark optimization functions.
Representing planning autonomy in agent organizational models

Organizations are key elements in a multi-agent system, since they promote cooperation between agents by constraining their possible behaviors. However, constraining behavior means diminishing the agents autonomy. In human organizations, agents have different degrees of autonomy: autonomous and more adaptable agents coexist with non-autonomous agents that just follow organizational rules. This work will propose an explicit representation for planning autonomy in agents organizations, particularly using the MOISE organizational model. We propose the use of a domain specification, based on the planning formalism and on goal types. We implemented our representation using the JaCaMo framework in a proof-of-concept scenario showing how a planning autonomous agent can use the SHOP planner to achieve an organizational goal.
Comparative Analysis of Hindi Text Summarization for Multiple Documents by Padding of Ancillary Features

There is an enormous amount of textual material, and it is only growing every single day. The data available on Internet comprised of Web pages, news articles, status updates, blogs which are unstructured. There is a great need to reduce much of these text data to shorter, focused summaries that capture the salient details so that the user can navigate it more effectively as well as check whether the larger documents contain the information that we are looking for. Text summary is generating a shorter version of the original text. The need of summarization arises because every time it is not possible to read the detailed document due to lack of time. Automatic text summarization methods are greatly needed to address the ever-growing amount of text data available online both to better help discover relevant information and to consume relevant information faster. To address the issue of time constraint, an extractive text summarization technique has been proposed in this research work which selects important sentences from a text document to get a gist of information contained in it. A fuzzy technique has been used to generate extractive summary from multiple documents by using eight and eleven feature sets. The eleven feature set combines the existing eight features (term frequency-inverse sentence, length of sentence in the document, location of sentence in document, similarity between sentences, numerical data, title overlap, subject object verb (SOV) qualifier, lexical similarity) and three ancillary features (proper nouns, hindi cue phrase, thematic words). It was seen that applying fuzzy technique with eleven features gave better results for summarization than the same using eight features. The precision increases in the range of 3–5% for different datasets. Datasets used were Hindi news articles from online sources.
Mid Roll Advertisement Placement Using Multi Modal Emotion Analysis

In recent years, owing to the ever-increasing consumer base of video content over the internet, promoting business via advertising between the videos has become a powerful strategy. Mid roll ads are the video ads that are played between the content of a video being watched by the user. While a lot of research has already been done in the field of analyzing the context of the video to suggest relevant ads, little has been done in the field of effective placement of the ads so that it does not deteriorate users' experience. In this paper, we are proposing a new model to suggest at which particular spot in a video, an advertisement should be placed such that most people will watch more of the ad. This is done using emotion, text, action, audio and video analysis of different scenes of a video under consideration.
Learning Relational-Structural Networks for Robust Face Alignment

Unconstrained face alignment usually undergoes extreme deformations and severe occlusions, which likely gives rise to biased shape prediction. Most existing methods simply exploit shape structure by directly concatenating all landmarks, which leads to losses of facial details in extreme deformation regions. In this paper, we propose a relational-structural networks (RSN) approach to learn both local and global feature representation for robust face alignment. To achieve this goal, we built a structural branch network to disentangle the local geometric relationship among neighboring facial sub-regions. Moreover, we develop a reinforcement learning approach to reason the robust iterative process. Our RSN generates three candidate shapes. Then a Q-net evaluates three candidate shapes by a reward function, which select the best shape to re-initialize network input to alleviate the local optimization problem of cascade regression methods. Authentic experimental results indicate that our approach consistently outperforms the most state-of-the-art methods on widely evaluated challenging datasets.
Development of a Wirelessly-Powered Wearable System for Finger Tracking

In this paper, the development of a system capable of tracking finger motion is presented. The proposed wearable tracking unit is placed on the finger and it is wirelessly powered without using cables or batteries, thus enhancing freedom of movement. The system is comprised by two sections: a measurement unit and a wireless power transfer apparatus. The measurement unit consists of an electronic board able to measure, acquire and transmit sensors signals; a stretch sensor and an inertial measurement unit are integrated on the measurement unit. Furthermore, the wireless power transfer apparatus is implemented by means of inductively-coupled resonant circuits. A prototype of the developed system is characterized by experimental tests. Results show the feasibility of the proposed approach. The measuring system can track finger movement with a sample rate of 30 Hz, while the wireless power transfer system demonstrates the capability of transferring 67.72 mW, necessary to power the measurement unit, at a maximum distance of about 8 cm.
Automatic Augmentation by Hill Climbing

When learning from images, it is desirable to augment the dataset with plausible transformations of its images. Unfortunately, it is not always intuitive for the user how much shear or translation to apply. For this reason, training multiple models through hyperparameter search is required to find the best augmentation policies. But these methods are computationally expensive. Furthermore, since they generate static policies, they do not take advantage of smoothly introducing more aggressive augmentation transformations. In this work, we propose repeating each epoch twice with a small difference in data augmentation intensity, walking towards the best policy. This process doubles the number of epochs, but avoids having to train multiple models. The method is compared against random and Bayesian search for classification and segmentation tasks. The proposal improved twice over random search and was on par with Bayesian search for 4% of the training epochs.
Intelligent Voice Agent and Service (iVAS) for Interactive and Multimodal Question and Answers

This paper describes MITRE’s Intelligent Voice Agent and Service (iVAS) research and prototype system that provides personalized answers to government customer service questions through intelligent and multimodal interactions with citizens. We report our novel approach to interpret a user’s voice or text query through Natural Language Understanding combined with a Machine Learning model trained on domain-specific data and interactive conversations to disambiguate and confirm user intent. We also describe the integration of iVAS with voice or text chatbot interface.
Multi-region System Modelling by using Genetic Programming to Extract Rule Consequent Functions in a TSK Fuzzy System

This paper aims to build a fuzzy system by means of genetic programming, which is used to extract the relevant function for each rule consequent through symbolic regression. The employed TSK fuzzy system is complemented with a variational Bayesian Gaussian mixture clustering method, which identifies the domain partition, simultaneously specifying the number of rules as well as the parameters in the fuzzy sets. The genetic programming approach is accompanied with an orthogonal least square algorithm, to extract robust rule consequent functions for the fuzzy system. The proposed model is validated with a synthetic surface, and then with real data from a gas turbine compressor map case, which is compared with an adaptive neuro-fuzzy inference system model. The results have demonstrated the efficacy of the proposed approach for modelling system with small data or bifurcating dynamics, where the analytical equations are not available, such as those in a typical industrial setting.
Algorithm and Realization of Robotic end Orientation Equivalent Similarity Transformation Based on Rodrigues

In the process of robotic pose measurement, error estimation and online correction, the relative pose transformation and calibration between coordinate frames are required. This paper presented a solution algorithm for measuring and precision evaluating the robot end orientation. Robotic frames transformation relationship was investigated. In view of the measuring space restricted problems of a robot end orientation, the unified description model of the robot end tool orientation transformation and the 3D coordinate measurement parameters of the space target are established. The algorithm of the robot end orientation using multi-point measurement is given with the coordinate frame equivalent similarity transformation. This algorithm utilizes the multi-point laser measurement values of the space rigid body to implement the spatial point coordinate center registration. By the least square optimization algorithm based on Rodrigues matrix transformation, the orthogonal rotation matrix of the orientation relative transformation can be obtained. At last the exact estimation of the robot end orientation z-y-x Euler angle and the orientation error are realized.
Using Augmented Reality Techniques to Simulate Training and Assistance in Electric Power

In combination with the augmented reality (AR) technology and Hololens device, an electric power work assist system is developed to provide a more realistic and convenient training experience for electric power training. The training effect was improved, and help guidance information was provided in the actual operation of electric power work. Experimental results show that difficulty of electric power work is reduced and the efficiency of actual work is improved.
Performance Study of OpenMP and Hybrid Programming Models on CPU–GPU Cluster

Optimizing complex code of scientific and engineering applications is a challenging area of research. There are many parallel and distributed programming frameworks which efficiently optimize the code for the performance. In this study, we did a comparison study of the performance of parallel computing models. We have used irregular graph algorithms such as Floyd’s algorithm (shortest path problems) and Kruskal’s algorithm (minimum spanning tree problems). We have considered OpenMP and hybrid [OpenMP + MPI] on CPU cluster and MPI + CUDA programming strategies on the GPU cluster to improve the performance on shared–distributed memory architecture by minimizing communication and computation overlap overhead between individual nodes. A single MPI process per node is used to launch small chunks of large irregular graph algorithm on various nodes on the cluster. CUDA is used to distribute the work between the different GPU cores within a cluster node. Results show that from the performance perspective GPU, implementation of graph algorithms is effective than the CPU implementation. Results also show that hybrid [MPI + CUDA] parallel programming framework for Floyd’s algorithm on GPU cluster yields an average speedup of 19.03 when compared to the OpenMP and a speedup of 15.96 is observed against CPU cluster with hybrid [MPI + OpenMP] frameworks. For Kruskal’s algorithm, average speedup of 27.26 is observed when compared against OpenMP and a speedup of 20.74 is observed against CPU’s cluster with hybrid [MPI + OpenMP] frameworks.
How human age affects the signature’s curvature, density and amplitude to wavelength ratio and its potential application for countering document falsification

By comprehensive investigating of 3540 signatures from 86 males and 23 females over a specific period, we identified that a signature’s curvature and multiplication of density with time change exponentially with age, while the amplitude to wavelength ratio likely changes inversely with time. The subjects’ ages are between 30 and 63 for females and 36 and 68 for males and they had the same educational background, occupation and institution. Their signatures were collected from the same types of documents stored in several libraries. Combination of three characterizations enable in estimating the year when a document was signed. Ten signatures that have been randomly selected from two subjects showed that the bias in prediction of signing year was less than 3 years. The method is potential for countering the crime associated with falsification of documents created in the past by the same person, as a complement to the already standard methods.
Delving into the Impact of Saliency Detector: A GeminiNet for Accurate Saliency Detection

Although plenty of saliency detection methods based on CNNs have shown impressive performance, we observe that these methods adopt single-scale convolutional layers as saliency detectors after extracting features to predict saliency maps, which will cause serious missed detection especially those targets having small scales, irregular shapes and sporadic locations in complex scenario of multi-target graphs. In addition, the edges of salient objects predicted by these methods are often confused with their background, causing these partial regions to be very blurred. In order to deal with these issues, we delved into the impact of diverse unified detectors based on convolutional layers and nearest neighbor optimization on saliency detection. It was found that (1) the flattened design contributes to the improvement of accuracy, but due to the inherent characteristics of convolutional layers, it is not the effective way to solve the problems; (2) Nearest neighbor optimization is beneficial to remove background regions from salient objects and restore the missing sections while refining their boundaries, yielding a more reliable final prediction. With the progress of these studies, we built a GeminiNet for accurate saliency detection. Quantitative and qualitative experiments on six benchmark datasets demonstrate that our proposed GeminiNet performs favorably against the state-of-the-art methods under different evaluation metrics.
Determining How Citations Are Used in Citation Contexts

Citations have been classified based on their textual contexts w.r.t. their worthiness, function, polarity, and importance. To the best of our knowledge, so far citations have not automatically been classified by their grammatical role, that is, whether the citation (1) is grammatically integrated in the sentence, (2) is annotated directly after the occurrence of author names, (3) backs up a concept, (4) backs up a claim, or (5) is not appropriate because the context is incomplete or noisy.We argue that determining such classes for citation contexts is useful for a variety of tasks, such as improved citation recommendation and scientific impact quantification. In this paper, we propose this classification scheme, as well as a machine-learning-based approach to determine the classes automatically. Our evaluation reveals that the classification performance varies significantly between the citation types.
Research on Accounting Practical Teaching System under the Background of "Internet +"

With the rapid development of information technology, "Internet +" has become a buzzword in this era, which has caused earth-shaking changes in all walks of life. Because enterprises are deeply affected by "Internet +", university education must also keep up with the times. The pace of accounting, accounting profession is no exception, must rely on the Internet to build an innovative, intelligent accounting practice teaching system, in order to meet the needs of accounting in all walks of life.
Weakly Supervised Learning by a Confusion Matrix of Contexts

Context consideration can help provide more background and related information for weakly supervised learning. The inclusion of less documented historical and environmental context in researching diabetes amongst Pima Indians uncovered reasons which were more likely to explain why some Pima Indians had much higher rates of diabetes than Caucasians, primarily due to historical, environmental and social causes rather than their specific genetic patterns or ethnicity as suggested by many medical studies. If historical and environmental factors are considered as external contexts when not included as part of a dataset for research, some forms of internal contexts may also exist inside the dataset without being declared. This paper discusses a context construction model that transforms a confusion matrix into a matrix of categorical, incremental and correlational context to emulate a kind of internal context to search for more informative patterns in order to improve weakly supervised learning from limited labeled samples for unlabeled data. When the negative and positive labeled samples and misclassification errors are compared to “happy families” and “unhappy families”, the contexts constructed by this model in the classification experiments reflected the Anna Karenina principle well - “Happy families are all alike; every unhappy family is unhappy in its own way”, an encouraging sign to further explore contexts associated with harmonizing patterns and divisive causes for knowledge discovery in a world of uncertainty.
Automatic encoding of a target position relative to a natural scene

We previously showed that the brain automatically represents a target position for reaching relative to a large square in the background. In the present study, we tested whether a natural scene with many complex details serves as an effective background for representing a target. In the first experiment, we used upright and inverted pictures of a natural scene. A shift of pictures significantly attenuated prism adaptation of reaching movements as long as they were upright. In one-third of participants, adaptation was almost completely cancelled whether the pictures were upright or inverted. It was remarkable that there were two distinct groups of participants, one who relies fully on the allocentric coordinate and the other who depended only when the scene was upright. In the second experiment, we examined how long it takes for a novel upright scene to serve as a background. A shift of the novel scene had no significant effects when it was presented for 500 ms before presenting a target, but significant effects were recovered when presented for 1,500 ms. These results show that a natural scene serves as a background against which a target is automatically represented once we spend 1,500 ms in the scene.NEW & NOTEWORTHY Prism adaptation of reaching was attenuated by a shift of natural scenes as long as they were upright. In one-third of participants, adaptation was fully canceled whether the scene was upright or inverted. When an upright scene was novel, it took 1,500 ms to prepare the scene for allocentric coding. These results show that a natural scene serves as a background against which a target is automatically represented once we spend 1,500 ms in the scene.
A Comparative Study of Certain Classifiers for Bharatanatyam Mudra Images' Classification using Hu-Moments

India is rich in culture and heritage where various traditional dances are practiced. Bharatanatyam is an Indian classical dance, which is composed of various body postures and hand gestures. This ancient art of dance has to be studied under guidance of dance teachers. In present days there is a scarcity of Bharatanatyam dance teachers. There is a need to adopt technology to popularize this dance form. This article presents a 3-stage methodology for the classification of Bharatanatyam mudras. In the first stage, acquired images of Bharatanatyam mudras are preprocessed to obtain contours of mudras using canny edge detector. In the second stage, Hu-moments are extracted as features. In the third stage, rule-based classifiers, artificial neural networks, and k-nearest neighbor classifiers are used for the classification of unknown mudras. The comparative study of classification accuracies of classifiers is provided at the end. The work finds application in e-learning of ‘Bharatanatyam' dance in particular and dances in general and automation of commentary during concerts.
TPOS Tagging Method Based on BiLSTM_CRF Model

Part of speech (POS) tagging determines the attributes of each word, and it is the fundamental work in machine translation, speech recognition, information retrieval and other fields. For Tibetan part-of-speech (TPOS) tagging, a tagging method is proposed based on bidirectional long short-term memory with conditional random field model (BiLSTM_CRF). Firstly, the designed TOPS tagging set and manual tagging corpus were used to get word vectors by embedding Tibetan words and corresponding TPOS tags in continuous bag-of-words (CBOW) model. Secondly, the word vectors were input into the BiLSTM_CRF model. To obtain the predictive score matrix, this model using the past input features and future input feature information respectively learned by forward long short-term memory (LSTM) and backward LSTM performs non-linear operations on the softmax layer. The prediction score matrix was input into the CRF model to judge the threshold value and calculate the sequence score error. Lastly, a Tibetan part of speech tagging model was got based on the BiLSTM_CRF model. The experimental results indicate that the accuracy of TPOS tagging model based on the BiLSTM_CRF model can reach 92.7%.
Signature Analysis for Forgery Detection

Forgery of signature has become very common, and the need for identification and verification is vital in security and resource access control. There are three types of forgery: random forgery, simple or casual forgery, expert or skilled or simulated forgery. The main aim of signature verification is to extract the characteristics of the signature and determine whether it is genuine or forgery. There are two types of signature verification: static or offline and dynamic or online. In our proposed solution, we use offline signature analysis for forgery detection which is carried out by first acquiring the signature and then using image pre-processing techniques to enhance the image. Feature extraction algorithms are further used to extract the relevant features. These features are used as input parameters to the machine learning algorithm which analyses the signature and detects for forgery. Performance evaluation is then carried out to check the accuracy of the output.