Reviewerly/exHarmony huggingface dataset:
An Approach to Teach Nao Dialogue Skills

Endowing social robots with natural interaction abilities, such as following a dialogue that gives the human user a sense of natural interaction, is a current interest in many areas. We are interested in developing the dialogue skills of the Nao robot for its potential use in treatment of children with special educational needs and elder people at risk of isolation. Corpora based dialog system development approaches are not adequate for personalization. In our approach we propose a teacher and introspection approach that may be able to produce highly personalized and entertaining dialog systems. The introspection module would run in the background using generative randomized systems creating new dialog pathways from the patterns learnt by direct teaching interaction.
Bacterial Resistance Algorithm. An Application to CVRP

This work considers an approach called Bacterial Antibiotic Resistance Algorithm (BARA) in which a bacteria colony represents a set of candidates solutions subjected to the presence of an antibiotic as a pressure factor for separating good and wrong answers. In our terms, the classification allows us to have two groups: resistant and non-resistant bacteria. Then, by using genetic variation mechanisms (conjugation, transformation, and mutation), it is expected that non-resistant bacteria may improve their defense capability to enhance their probability of survival. The proposed algorithm implements and evaluates instances of the Capacitated Vehicle Routing Problem (CVRP). Results are comparable to those obtained in similar approaches.
PREDIKSI POLA KELULUSAN MAHASISWA MENGGUNAKAN TEKNIK DATA MINING CLASSIFICATION EMERGING PATTERN

Mercu Buana University is one of the private universities in Indonesia, especially in DKI Jakarta, which has a large number of students and a number of graduations. However, the University management has difficulty predicting a pattern and graduation rate from existing student data in each academic year. Most researchers use data mining techniques to find a regularity of patterns or relationships set on large data. In this paper, to predict patterns and analyze student graduation rates researchers use data mining by focusing on the classification process using emerging pattern algorithms on the timeliness of student studies. In this study the data used came from combined data between student master data and graduation data. The results of testing the data carried out by researchers in the data mining application produce graduation patterns with various variations according to the learning attributes used, namely gender, class, study program, lecture system and student GPA.&#x0D; By using the results of testing this study, it is expected that the resulting data can help the management of the University as a basis for analysis in planning the teaching and learning process strategy to increase the graduation rate on time and as a support for the management of Mercu Buana University
On general Bayesian inference using loss functions

Bissiri et al. (2016) propose a framework for general Bayesian inference using loss functions which connect parameters with data, and the updated posterior distribution is characterized through a set of axioms. The result, which is restricted to finite probability spaces, is extended in this paper to spaces which are subsets of the real line.
Removing Software Development Waste to Improve Productivity

One way to improve productivity is to reduce waste: objects, properties, conditions, activities, or processes that consume resources without benefiting stakeholders. However, reducing waste can be very challenging. People quickly acclimate to wasteful practices and waste is often hidden by bureaucracy, multitasking, poor prioritization, and invisible cognitive processes. To better understand software development waste, we conducted an in-depth study of waste at Pivotal Software, a large American software development organization, known for using and evolving Extreme Programming. This chapter describes the different kinds of waste we discovered and recommends a variety of strategies for waste removal.
Inference of an Extended Short Fiber Bundle Atlas Using Sulcus-Based Constraints for a Diffeomorphic Inter-subject Alignment

We present a new framework for the creation of an extended atlas of short fiber bundlesShort fiber bundles between 20 and 80 mm length. This method uses a Diffeomorphic inter-subject alignmentDiffeomorphic inter-subject alignment procedure including information of cortical foldings and forces the accurate match of the sulci that have to be circumvented by the U-bundlesU-bundle. Then, a clustering is performed to extract the most reproducible bundles across subjects. First results show an increased number of U-bundlesU-bundle consistently mapped in the general population compared with previous atlases created from the same database. Future analysis over this new extended Brain atlasBrain atlas may improve our understanding of the relationship between the folding pattern and the U-bundleU-bundle variability. The ultimate aim will be the possibility to detect abnormal configurations induced by developmental issues.
VR Training System of Step Timing for Baseball Batter Using Force Stimulus

Conventionally, correct motion in sports training and rehabilitation has been taught directly from a trainer. However, oral or gesture presentations are difficult based on what the trainer considers to be the accurate motion. We propose a motion timing presentation method using force stimulus, which has high relevance to the target motion. In this study, we developed a timing presentation training system for a baseball batter using force stimulus. The force stimulus is presented according to the pitching motion reproduced on a virtual reality (VR) system to present the timing stimulus repeatedly. The stimulus is used for presenting the timing of the initial foot step motion, which is the first phase of batting. For verification of the timing presentation effect of the training system, we measured the initial batting timing of the baseball beginner. As a result, it was found that the foot step could be started very close to the target timing using force stimulus presentation.
Using Three-Frame Difference Algorithm to Detect Moving Objects

Detection technology for moving targets is an important direction in computer vision research. This paper proposes a method to improve moving target detection, which is a three-frame difference algorithm combining edge information. This difference algorithm can detect moving objects with high accuracy. For the real-time image captured by the camera, the algorithm not only can reduce the processing time, but also judge the moving object with the fastest speed and the highest accuracy. In the process of the algorithm, Expansion and corrosion in mathematical morphology are used to remove noise from images. Due to the detection of moving targets, there will inevitably be some interference factors, and these factors will have a more serious impact on the detection sample. In order to eliminate the influence of noise on the processing results, the algorithm first divides the image into binary image, expansion and corrosion treatment of the image, and then according to the different pixel points between the three frame images, the shape of the object can be determined by the information of space and time, so as to identify the moving object, give an alarm and save the image to the local area.
LQG controller design for a quadrotor UAV based on particle swarm optimisation

This paper deals with the modelling and the linear quadratic Gaussian (LQG) control design of a quadrotor unmanned aerial vehicle (UAV) using different particle swarm optimisation (PSO) variants. Such a PSO-designed LQG controller is optimised in order to stabilise the position and the heading of the studied vertical take-off and landing (VTOL) quadrotor. Both canonical and recent variants of PSO algorithm, with linearly decreasing of inertia weight (PSO-In) and perturbed updating strategy (PSO-gbest), are considered for the systematically design and tuning of the LQG weighting matrices. These effective control parameters of the LQG approach represent the decision variables of the PSO-based LQG optimisation problem. Such an optimisation problem is formulated to minimise various performance time-domain criteria, like the integral of absolute error (IAE) and the maximum overshoot (MO) index, under nonlinear constraints related to the step responses of the closed-loop quadrotor dynamics. All proposed PSO algorithms are compared with each other and with the well known harmony search algorithm (HSA) and water cycle algorithm (WCA) metaheuristics for the stabilisation problem of the position and heading dynamics of the VTOL drone. Demonstrative simulation results are carried out in order to show the effectiveness of the proposed PSO variants-tuned LQG control approach.
Histogram Equalization Image Enhancement Based on FPGA Algorithm Design and Implementation

Histogram equalization is a method of image enhancement which was widely used in the image processing. The algorithm is traditionally implemented with the CPU or DSP, but with the improvement of image resolution and frame rate, CPU and DSP is hard to meet the needs of real-time video enhancement. This paper propose a histogram equalization method based on FPGA, It use the way of parallel pipeline module, which improve the real-time performance of the algorithm. In this paper, a video enhancement experiments has been carried out to verify the real-time performance, with resolution of 1600 × 1200 image and 60 frames per second. real-time image enhancement algorithm was implemented on a FPGA development board, and the experimental results are given, the results show that it is suitable for the high resolution video real-time enhancement processing for low contrast image.
SemiStarGAN: Semi-supervised Generative Adversarial Networks for Multi-domain Image-to-Image Translation

Recent studies have shown significant advance for multi-domain image-to-image translation, and generative adversarial networks (GANs) are widely used to address this problem. However, to train an effective image generator, existing methods all require a large number of domain-labeled images, which may take time and effort to collect for real-world problems. In this paper, we propose SemiStarGAN, a semi-supervised GAN network to tackle this issue. The proposed method utilizes unlabeled images by incorporating a novel discriminator/classifier network architecture—Y model, and two existing semi-supervised learning techniques—pseudo labeling and self-ensembling. Experimental results on the CelebA dataset using domains of facial attributes show that the proposed method achieves comparable performance with state-of-the-art methods using considerably less labeled training images.
Tourism Web Filtering and Analysis Using Naïve Bay with Boundary Values and Text Mining

This paper proposes a technique to filter tourism websites and datamining to analyze keywords to improve searching on tourism websites as Search Engine Optimization (SEO). This work focuses on tourist sites or attractions, hotel or accommodation, and restaurant in the tourist provinces as a core keywords. Content in websites is retrieved from Google with queries of 11 Thai famous tourism provinces. From all retrieved results, filtering using Naïve Bayes algorithm with Boundary Values is performed to detect only relevant content as 6,171 filtered websites (66.55%) from 9,273 retrieved websites. From keyword analysis method, we compared three methods including (1) keywords from Apriori algorithm, (2) keywords from frequent terms within websites, and (3) keywords by frequency of terms from the ontology. The experiment results are conclusive that keywords from frequent terms within websites performed best. The keywords are usable to customize the websites to improve search ranking.
Image De-fencing Based on Binary Morphology

Fences are widely used in our daily life, which can be seen everywhere especially in gardens, highways, zoos, etc. While the fence forms an isolation zone and provides security protection, it also brings drawbacks. For example, people can't remove the fence when taking pictures, resulting in separated scenery which reduce the aesthetic experience. Image de-fencing aims at generating images without fence, thus presenting the original appearance of the scenery. In this paper, we propose a method of fence detection based on binary morphology. By using different structural elements to obtain image information, our method can extract fences and marked them with mask. Then input the image with mask into the existing inpainting network which can generate the final de-fencing image. Our method requires neither complicated algorithm, nor time-consuming training network. It is very simple to implement, fast, and produces nearly identical results compared to more complex methods.
Technical aspects for counting and timing photons with a digital oscilloscope

Photon counting and timing are generic tasks in many photonics laboratories. However, the cost of a commercial photon counter system can be a limiting factor to establish a new laboratory. Homemade photon counters can present a cost-saving solution, but they can also present a demanding side project not always part of the main research. Modern digital oscilloscopes are available in most universities and can provide a simple solution to measure photon statistics. Here, we describe the technicalities and limitations for counting and timing photons using a digital oscilloscope.
Using Pre-Computed Knowledge for Goal Allocation in Multi-Agent Planning

Many real-world robotic scenarios require performing task planning to decide courses of actions to be executed by (possibly heterogeneous) robots. A classical centralized planning approach has to find a solution inside a search space that contains every possible combination of robots and goals. This leads to inefficient solutions that do not scale well. Multi-Agent Planning (MAP) provides a new way to solve this kind of tasks efficiently. Previous works on MAP have proposed to factorize the problem to decrease the planning effort i.e. dividing the goals among the agents (robots). However, these techniques do not scale when the number of agents and goals grow. Also, in most real world scenarios with big maps, goals might not be reached by every robot so it has a computational cost associated. In this paper we propose a combination of robotics and planning techniques to alleviate and boost the computation of the goal assignment process. We use Actuation Maps (AMs). Given a map, AMs can determine the regions each agent can actuate on. Thus, specific information can be extracted to know which goals can be tackled by each agent, as well as cheaply estimating the cost of using each agent to achieve every goal. Experiments show that when information extracted from AMs is provided to a multi-agent planning algorithm, the goal assignment is significantly faster, speeding-up the planning process considerably. Experiments also show that this approach greatly outperforms classical centralized planning.
Volcano-Independent Seismic Recognition: detecting and classifying events of a given volcano using data from others

Modern seismic networks provide a huge amount of data received in real-time, being impossible the manual identification of relevant events useful to monitor the activity of the volcano. Thus, many volcano observatories are interested in tools to perform an online, automatic analysis of the seismic activity. Machine Learning area provides various of Volcano-Seismic Recognition (VSR) systems designed to classify seismic events in real-time. However, only a few approaches can also detect them in a continuous data streams. Most of those VSR systems are based on the 2-step supervised paradigm: 1. A training database (X-DB) of a given volcano ’X’ is prepared with hundreds of events manually detected and classified according to their physical origin. 2. Statistical models are built analysing this DB, and are later used to automatically identify events in new data recorded at the volcano X. This supervised procedure is the major drawback to achieve a fast deployment of a VSR system for another volcano Y, as the preparation of its own Y-DB takes considerable time, and requires qualified operators and previous recordings, which is difficult for volcanoes without recent activity or which haven’t been monitored. In order to overcome these limitations, the EU-funded project ’VULCAN.ears’ focused on real-time, Volcano-Independent VSR (VI.VSR) approaches. It proposes alternative solutions based on state-of-the-art technologies as universal DBs and models, waveform standardisation and parallel architectures. Recent results obtained by mixing DBs from Popocatépetl, Colima, Deception and Arenal active volcanoes will be presented. We apply VULCAN.ears technologies to evaluate VSR systems on joint DBs built with data of several volcanoes. We also use volcano-independent models to automatically classify events of another volcano, analysing how the recognition accuracy varies as the training DB becomes more complex. All tests are carried out by an easy to use, user-friendly graphical application (geoStudio). All these achievements produce new insights useful to redesign the next-generation, portable and robust VSR systems.
Fast pornographic image recognition using compact holistic features and multi-layer neural network

The paper presents an alternative fast pornographic image recognition using compact holistic features and multi-layer neural network (MNN). The compact holistic features of pornographic images, which are invariant features against pose and scale, is extracted by shape and frequency analysis on pornographic images under skin region of interests (ROIs). The main objective of this work is to design pornographic recognition scheme which not only can improve performances of existing methods (i.e., methods based on skin probability, scale invariant feature transform, eigenporn, and Multilayer-Perceptron and Neuro-Fuzzy (MP-NF)) but also can works fast for recognition. The experimental outcome display that our proposed system can improve 0.3% of accuracy and reduce 6.60% the false negative rate (FNR) of the best existing method (skin probability and eigenporn on YCbCr, SEP), respectively. Additionally, our proposed method also provides almost similar robust performances to the MP-NF on large size dataset. However, our proposed method needs short recognition time by about 0.021 seconds per image for both tested datasets.
The Impact of Transformational Leadership on Organizational Performance Case Study: The University of Jordan

This research aimed to examine the impact of transformational leadership on organisational performance at the University of Jordan. A questionnaire was distributed to a sample of (280) administrative employees at the University of Jordan. Data were analysed using SPSS software. Information about respondents was analysed using descriptive analysis. While multiple regression was used to determine whether the four dimensions of transformational leadership; idealised influence, inspirational motivation, intellectual stimulation and individualised consideration have a significant impact on organisational performance. The findings revealed that the transformational leadership had a significant impact on organisational performance along with idealised influence and intellectual stimulation. However, individualised consideration and inspirational motivation did not have a significant impact on organisational performance according to the respondents' ratings.
Groups Versus Coalitions: On the Relative Expressivity of GAL and CAL

Group Announcement Logic (GAL) and Coalition Announcement Logic (CAL) were proposed to study effects of public announcements by groups of agents on knowledge in multiagent systems. Both logics have operators that quantify over such announcements. In GAL, it is possible to express that 'a group of agents G has a (truthful) announcement such that after this announcement, some property A holds'; for example, A may involve some agents in G gaining additional knowledge, while agents outside G remain ignorant. In CAL, the meaning of the coalition announcement operator is subtly different: it says that 'G has an announcement such that, whatever else the agents outside G announce simultaneously, some property A is guaranteed to hold after the joint announcement'. It has been open for some time whether GAL and CAL are equally expressive. We show that this is not the case: there is a property expressible in GAL that is not expressible in CAL. It is still an open question whether CAL is subsumed by GAL, or whether the two logics have incomparable expressive power.
Deploying a Shareholder Rights Management System onto a Distributed Ledger

This work demonstrates how a multi-company shareholder rights management system has been implemented using Distributed Ledger Technology (DLT). In this demo, we use a permissioned blockchain to store our corporate data, such as the list of all registered companies, each company's shareholders and how many shares everyone holds. It is assumed that the nodes of the blockchain are controlled by the main stakeholder agents but we show that users who do not run a node can still use multiple websites to access company information. On top of this, we show our system can be used to allow any shareholder to participate in elections for company matters. Lastly, we describe how we designed our system's architecture so that it could be implemented even on a public blockchain.
Extraction of Message Sequence Charts from Software Use-Case Descriptions

Girish Palshikar, Nitin Ramrakhiyani, Sangameshwar Patil, Sachin Pawar, Swapnil Hingmire, Vasudeva Varma, Pushpak Bhattacharyya. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers). 2019.
High-performance of the Multiplication over the Quadratic Extension in Montgomery Domain for the Pairing Cryptosystems

In our paper we propose an architecture to calculate the operation of multiplication over $\mathbb{F}_{p^{2}}$ in the tower extension field arithmetic for pairing. This tower used in pairings based cryptography. Pairing is becoming the most popular for Public Key Cryptosystems. For the same security level the cryptosystem pairing use much smaller key length than other cryptosystems like RSA. We propose an efficient and optimized architectures that considerably accelerate the calculation of Miller algorithm in pairing-based cryptography. In our architecture we use the Montgomery multiplication based on the systolic architecture published in [17]. We propose an efficient design to implement multiplication over $\mathbb{F}_{p^{2}}$ . In this work our architecture is designed with VHDL in order to use the component flexible DSP of Xilinx FPGAs. This design of multiplication is scalable and depends on the size of words.
Interval Chi-Square Score (ICSS): Feature Selection of Interval Valued Data

In this paper, a novel feature ranking criterion suitable for interval valued feature selection is proposed. The proposed criterion simulates the characteristics of the well known statistical criterion - chi-square in selecting the interval valued features effectively and hence called as Interval Chi-Square Score. Moreover, the paper also highlights the alternative approach proposed for computing the frequency of the distribution of interval valued data. For experimentation purpose, two standard benchmarking interval valued datasets are used with a suitable symbolic classifier for classification. The performance of the proposed ranking criterion is evaluated in terms of accuracy and the results are comparatively better than the contemporary interval valued feature selection methods.