Reviewerly/exHarmony huggingface dataset:
Designing a Virtual Client for Requirements Elicitation Interviews

[Context and motivation] Role-playing offer experiential learning through the simulation of real-world scenarios; for this reason, it is widely used in software engineering education. In Requirements Engineering, role-playing is a popular way to provide students hands-on experience with requirements elicitation interviews. [Problem] However, managing a role-playing activity to simulate requirements elicitation interviews in a class is time consuming, as it often requires pairing students with student assistants or fellow classmates who act as either customers or requirement analysts as well as creating and maintaining the interview schedules between the actors. To make the adoption of role-playing activities in a class feasible, there is a need to develop a solution to reduce instructors' workload. [Principal ideas] To solve this problem we propose the use of VIrtual CustOmer (VICO), an intent-based, multimodal, conversational agent. VICO offers an interview experience comparable to talking to a human and provides a transcript of the interview annotated with the mistakes students made in it. The adoption of VICO will eliminate the need to schedule interviews as the students can interact with it in their free time. Moreover, the transcript of the interview allows students to evaluate their performance to refine and improve their interviewing skills. [Contribution] In this research preview, we show the architecture of VICO and how it can be developed using existing technologies, we provide an online rule-based initial prototype and show the practicality and applicability of this tool through an exploratory study.
Memetic algorithm-based path generation for multiple Dubins vehicles performing remote tasks

This paper formalises path planning problem for a group of heterogeneous Dubins vehicles performing tasks in a remote fashion and develops a memetic algorithm-based method to effectively produce the paths. In the setting, the vehicles are initially located at multiple depots in a two-dimensional space and the objective of planning is to minimise a weighted sum of the total tour cost of the group and the largest individual tour cost amongst the vehicles. While the presented formulation takes the form of a mixed-integer linear programme (MILP) for which off-the-shelf solvers are available, the MILP solver easily loses the tractability as the number of tasks and agents grow. Therefore, a memetic algorithm tailored to the presented formulation is proposed. The algorithm features a sophisticated encoding scheme to efficiently select the order of performing tasks. In addition, a path refinement technique that optimises detailed tours with the sequence of visits fixed is proposed to finally obtain further optimised trajectories. Comparative numerical experiments show the validity and efficiency of the proposed methods compared with the previous methods in the literature.
End-to-end Tibetan Speech Synthesis Based on Phones and Semi-syllables

Due to the 2D architecture of Tibetan characters, it is not convenient to treat the letters sequences as the input of the end-to-end speech synthesis system. The experiments are conducted based on phones and semi-syllables sequences respectively. In training and testing, the text is segmented into a sequence of syllables first, then syllables are transformed into phones and semi-syllables as the input sequence of the model. The results demonstrate the encoding and decoding alignment effect of Tibetan speech synthesis based on phones is better than that based on semi-syllables. In addition, the Highway network in the architecture plays a key role in the convergence of the model.
Avatar embodiment experiences to enhance mental health

Avatar embodiment defines the technologically induced illusion of seeing an avatar in the location where one's own physical body is normally experienced and having control over its body movements. There is large empirical evidence to support that embodying an avatar leads to important attitudinal, behavioral, and cognitive changes. These transformations can be used to improve health and psychological well-being. In this chapter, I first explain the phenomenon of avatar embodiment and the principal technologies that are used to create it. I then review the principal findings that demonstrate how avatar embodiment experiences cause cognitive, attitudinal, and behavioral modifications as well as the principal theories that explain why these effects occur. Next, I introduce the potential of avatar embodiment experiences to enhance mental health and summarize the principal studies in the field. Finally, I propose future research directions that should be explored to maximize the potential of avatar embodiment to improve mental health.
Belief Mosaics of Subjective Opinions

Subjective opinions are the belief arguments used with the operators of subjective logic. Binomial, multinomial and hypernomial opinions are three different classes of subjective opinions. The traditional visualisations of binomial and multinomial opinions are in the form of a point inside a simplex, which is equivalent to a barycentric coordinate system. However, hypernomial-opinions which are the most general class of subjective opinions cannot be visualised in the same way. This paper describes how subjectifve opinions including hypernomial opinions can be represented and visualised as mosaics of polygonal belief shapes within a simplex. Examples of belief mosaics are shown using binomial multinomial and hypernomial opinions which are also compared to their equivalent representation as Beta, Dirichlet and hyper-Dirichlet PDFs.
Health Assessment for Office Workers by Tracking the Wrist Motion Centric Activity

Continuous monitoring and improving the health condition of office worker plays an important role in the holistic development of the organization. Very often many office workers overlook the proper diet and other health problems due to sedentary lifestyle and hectic office schedule. This paper presents a supervised learning-based solution for activity monitoring of office workers using wearable devices. The activity classification models were developed from a laboratory study consisting of five different activities such as drinking water, eating food, smoking, rubbing eyes and talking on the phone. The machine learning model is trained on the data collected from the wrist motion of subjects. The feature matrix is selected from the measured raw data of accelerometer, gyroscope, magnetometer in all three x, y and z-axis. For classification, we studied the performance of SVM, kNN and decision tree. The 5-fold cross-validation results in SVM having the best accuracy of 98.7% in the present scenario. Thus, our qualitative finding indicates the potential use of the smartphone-based wearable solution for the assessment of bad habits and improving the health condition during office work.
Working Memory Augmentation for Improved Learning in Neural Adaptive Control

In this paper, we propose a novel control architecture, inspired from neuroscience, for adaptive control of continuous time systems. The objective here is to design control architectures and algorithms that can learn and adapt quickly to changes that are even abrupt. The proposed architecture, in the setting of standard neural network (NN) based adaptive control, augments an external working memory to the NN. The learning system stores, in its external working memory, recently observed feature vectors from the hidden layer of the NN that are relevant and forgets the older irrelevant values. It retrieves relevant vectors from the working memory to modify the final control signal generated by the controller. The use of external working memory improves the context inducing the learning system to search in a particular direction. This directed learning allows the learning system to find a good approximation of the unknown function even after abrupt changes quickly. We consider two classes of controllers for illustration of our ideas (i) a model reference NN adaptive controller for linear systems with matched uncertainty (ii) backstepping NN controller for strict feedback systems. Through extensive simulations and specific metrics we show that memory augmentation improves learning significantly even when the system undergoes sudden changes. Importantly, we also provide evidence for the proposed mechanism by which this specific memory augmentation improves learning.
Tracking objects with partial occlusion by background alignment

Visual object tracking is a challenging and fundamental research topic in the field of computer vision. Recent years, many subspace-learning based methods have been proposed for visual object tracking with promising results. These methods reconstruct candidate states by a set of basis vectors and select the best state with minimum reconstruction error. It is well known that the accuracy of reconstructed results is seriously affected by partial occlusion. Besides, updating with occlusions is likely to mislead the tracker to drift away. Existing methods either do not consider these situations or find occlusion regions only by current observation and reconstruction. In fact, occlusion regions usually come from the background regions in previous frames, which are neglected in existing methods. Under this assumption, a novel object tracking algorithm called Partial Occlusion by Background Alignment (POBA) is proposed, which aims to find the best candidate state with an accurate occlusion mask. The POBA tracker treats current observation as a combination of object appearance and occlusion regions. The object appearance is modelled by basis vectors through incremental PCA over grayscale images. Then the occlusion region is reconstructed from last frame under the assumption that the backgrounds between two consecutive frames are almost identical. Besides, most candidate states are different from the object obviously, which can be filtered by some predefined occlusion masks so that computational complexity can be further reduced. Finally, the POBA tracker was analyzed on 8 challenge sequences and evaluated on two challenging datasets including OTB2015 and Temple Color. It gets an AUC of 0.456, a success rate score of 0.538 and a precision score of 0.626 in OPE on OTB2015 dataset. All indicators are increased by more than 23% compared with the 6 classical trackers.
KQIs-Driven QoE Anomaly Detection and Root Cause Analysis in Cellular Networks

With the dense deployment of cellular networks, the enrichment of service types and the explosive growth of data volume, there is an increasing need of automatically detecting and diagnosing degraded performance cells for operators. Previous studies mainly focus on QoS (Quality of Service) based anomaly detection and root cause analysis, but the cellular network operators pay more attention to the userâ€™s QoE (Quality of Experience) degradation since QoE can better represent the userâ€™s actual service experience. To achieve online QoE anomaly detection and root cause analysis, in this paper, we present a new integrated anomaly detection and diagnosis framework. Firstly, QoE is expressed by the KQIs (Key Quality Indicators) vector of the userâ€™s three most common services with largest traffic. And then, QoE anomaly detection is processed by using two-step clustering method (SOM+K-mediods) to obtain QoE Anomaly Codebooks. Diagnosis is achieved by selecting KPIs (Key Performance Indicators) and learning their characteristic impact on KQIs to obtain Fault Cause Codebooks. The QoE Anomaly Codebooks are used to detect anomaly samples, and the Fault Cause Codebooks are applied to find the fault cause types. The designed framework has been evaluated with real 4G data. For comparison, three other two- step clustering algorithms are introduced, and results show that our framework can achieve the highest detection accuracy rate. Therefore, it can be directly used in real-world cellular networks for online fault detection and diagnosis.
Location Privacy Protection in Online Query based-on Privacy Region Replacement

In order to realize online location query based on privacy in the location-based service (LBS), a location privacy protection method based on privacy region replacement (PRR) was proposed to improve the query response time without reducing the degree of privacy protection in this paper. Firstly, a privacy region was generated according to the density of people and privacy requirements. Secondly, the privacy region was replaced into an anonymous region based on the personnel distribution in the privacy region. And then the coverage degree between the anonymous region and the user query region is calculated. Finally, a new query region was used to carry on the online query. Comparing with other methods such as Casper, Fragment, and ARC, the query response time was reduced at least 17.33% in PRR with the same degree of privacy protection. The results show that the quantity of services was improved effectively using the PRR in online query.
Multiple Machine Learning Algorithms Comparison for Modulation Type Classification for Efficient Cognitive Radio

In this paper the potential of improving channel utilization by signal modulation type classification based on machine learning algorithms has been studied. The classification has been performed between two popular digital modulations: BPSK and FSK in target application. Classification was based on three features available on a popular software defined radio transceiver AD9361: In-phase and quadrature components of the digital time domain signal and signal-to-noise ratio (SNR), measured as RSSI value. Data used for network training, validation and testing was generated by the Simulink model consisting mainly of modulator, transceiver AD9361 and AWGN to generate the signal with SNR ranging from 1 to 30 dB. Twenty-three supervised machine learning algorithms including K-nearest neighbor, Support Vector Machines, Decision Trees and Ensembles have been studied, evaluated and verified against the target application's requirements in terms of classification accuracy and speed. The highest average classification accuracy of 86.9% was achieved by Support Vector Machines with Fine Gaussian kernel, however with demonstrated classification speed of 790 objects per second it was considered unable to meet target application's real-time operation requirement of 2000 objects per second. Fine Decision Trees and Ensemble Boosted Trees have shown optimal performance in terms of both reaching classification speed of 1200000 objects per second and average classification accuracy of 86.0% and 86.3% respectively. Classification accuracy has been also studied as a function of SNR to determine the most accurate classifier for each SNR level. At the target application's demodulation threshold of 12 dB 87.0% classification accuracy has been observed for the Fine Decision Trees, 87.5% for both Fine Gaussian SVM and Coarse KNN. At SNR higher than 27 dB Fine Trees, Coarse KNN have reached 97.5% classification accuracy. The effects of data set size and number of classification features on classification speed and accuracy have been studied too.
Blockchain Technology and Renewable Energy Access: A Case for sub-Saharan Africa

As the developed world continues to advance technologically and closes the gap in deficiencies of existing economic, financial and environmentally ‘green’ systems, while consequently improving the socio-economic indices of their respective countries and citizens; a measure of introspection is not amiss in considering the challenges that beset their third world counterpart- Africa. The continent still struggles with issues that relate to energy poverty and overall access, as there are indicators that a large majority still have no access to modern, affordable and reliable energy services. The major constraints are identified as inadequate or unavailable financing, invalid or non-existent policies and largely unstable political environments. The SE4ALL mandate in accordance with the Paris Agreement stipulates the need for effective climate change policy, the imperative for improved energy efficiency and the need for inclusion. A dynamic approach is therefore required to provide energy access to the most vulnerable, especially in the rural and urban slums of sub-Saharan Africa. This paper suggests that blockchain technology is potentially that medium and therefore assesses the current advancements, challenges and its potential application within the regional energy context using the diffusion of innovation theory. The results suggest alternative platforms such as Iota being deployed in the near term, due to a lack of requisite network infrastructure and the financial handicap of the region. However, the emphasis on innovative and disruptive business models for harnessing renewable energy sources towards improved energy efficiency and overall socio-economic development, remains. The key is in the adoption of technology that is tailored specifically, towards a sustainable energy future for sub-Saharan Africa.
Transfer learning for multimodal dialog

Audio-Visual Scene-Aware Dialog (AVSD) is best understood as an extension of Visual Question Answering, the task of generating a textual answer in response to a textual question on multi-media content. In AVSD, the answer-relevant “context” is expanded to include past dialog turns, which we view as a specialized form of extra textual knowledge (in addition to the standard video features). We have developed a framework that uses hierarchical attention to fuse contributions from different modalities, and had shown how it can be used to generate textual summaries from multi-modal sources, specifically videos with accompanying commentary. In this paper, we transfer the algorithmic approach, models, and data from this background corpus of 2000 h of how-to videos to the AVSD task, and report our findings. Our approach uses dialog context, but makes no assumption about the ordering of the history. Our system achieves the best performance in both automatic and human evaluations in the 7th Dialog State Tracking Challenge (AVSD).
Information Retrieval System to Find Articles and Clauses in UUD 1945 Using Vector Space Model Method

Abstract This study aims to find articles and clauses from the 1945 Constitution (UUD 1945) using the Vector Space Model method that calculates the similarity of many documents. One document is represented by one clause from each article of the 1945 Constitution. The next step is pre-processing by deleting unnecessary words (stopwords) and changing it into basic words (stemmer) in the Indonesian language. Each document will be indexed to speed up query and simplify the weighting. Words weighting in documents is performed using the TF-IDF (Term Frequency-Inverse Document Frequency) algorithm by calculating the frequency of words in documents and all documents. The document search results will be presented in the ranking with the largest number of scoring appears at the top (descend sorting). The word search in this system more or less takes 90-100 milliseconds in 73 documents.
Hematologic parameters in pulmonary tuberculosis patients based on the microscopic sputum examination

This study aimed to analyze various hematological manifestations in tuberculosis (TB) patients using positive and negative smear sputum microscopy. This study was a retrospective study with cross-sectional design. The study subjects consisted of patients diagnosed with tuberculosis who were recruited consecutively at Rumah Sakit Islam Jakarta Sukapura hospital from 1st of January 2015 to 30 of June 2017. There were significant differences in hematological parameters, including leucocytes, erythrocytes, thrombocytes, and erythrocyte sedimentation rates between acid-fast Bacillus positive (+) and negative (−). Changes in the results from the hematological examination in pulmonary TB patients could be used as parameters for diagnosis, prognosis, and response to therapy.
Made to Measure

Metrics of human behaviour and effort lie at the heart of improving information interaction and retrieval. However, while some measurements have become predominant, such as precision and recall, there are many elements of information interaction where either measures have yet to be created, accepted, or widely used. This workshop seeks to tease out these areas, finding novel measures, or novel uses of existing measures, to create better experimental tools to improve our understanding of information interaction, and help develop better systems to support it.
A Topology Preserving Gridding Method for Vector Features in Discrete Global Grid Systems

Topological distortion seriously affects spatial cognition. To solve this problem caused by the integration of vector features in discrete global grid systems (DGGs), a topology-preserving gridding method for vector features is proposed. The method proposed determines the topological distortion according to the relationship between grid cells and then increases the local resolution of vector features by employing the multi-level resolution characteristic of DGGs, to repair three kinds of topological distortions. Experimental results show that the proposed method can effectively maintain the topological relationship between the original vector features, and the amount of data is stable, thus ensuring the correct integration of vector features in the DGGs.
Eliminating Search Intent Bias in Learning to Rank

Click-through data has proven to be a valuable resource for improving search-ranking quality. Search engines can easily collect click data, but biases introduced in the data can make it difficult to use the data effectively. In order to measure the effects of biases, many click models have been proposed in the literature. However, none of the models can explain the observation that users with different search intent (e.g., informational, navigational, etc.) have different click behaviors. In this paper, we study how differences in user search intent can influence click activities and determined that there exists a bias between user search intent and the relevance of the document relevance. Based on this observation, we propose a search intent bias hypothesis that can be applied to most existing click models to improve their ability to learn unbiased relevance. Experimental results demonstrate that after adopting the search intent hypothesis, click models can better interpret user clicks and substantially improve retrieval performance.
Enhancing Smartphone Lock Security using Vibration Enabled Randomly Positioned Numbers

In this age of information, we can't think a day without our cell phone which is a very important component in storing data which is mostly personal. In recent times we are using E-banking, E-shopping and personal messaging photo sharing, we use many social media applications etc. So it is a basic understanding that our mobile phone security system is very important. Now a days it's very common to have patterns, pins and gestures-based security options. Since it is very common to have one these systems on devices, attackers are always developing new ways to bypass these security systems using attacks like smudge detection, shoulder surfing, gesture recognition and using a dictionary attack (brute force method) any pattern or PIN can be exposed with given time. In our proposed model through randomizing PIN number position on the phone screen and use of vibration as pin input we have sufficiently secured the user's password from smudge attacks, shoulder surfing and gesture recognition also we have delayed the chance of being successful in a brute force attack as we have used vibration as a variable (and by limiting the attempt). In our proposed model we also designed the lock system app interface where the input buttons size and distance between two buttons are more than standard so that will help to reduce the human error and provides more comfort in use.