Reviewerly/exHarmony huggingface dataset:
Child–display interaction: Lessons learned on touchless avatar-based large display interfaces

Abstract During the last decade, touchless gestural interfaces have been widely studied as one of the most promising interaction paradigms in the context of pervasive displays. In particular, avatars and silhouettes have proved to be effective in making the touchless capacity of displays self-evident. In this paper, we focus on a child–display interaction approach to avatar-based touchless gestural interfaces. We believe that large displays offer an opportunity to stimulate children’s experiences and engagement; for instance, learning about art is very engaging for children but can bring a number of challenges. Our study aims to contribute to the literature on both pervasive displays and child–computer interaction by reporting the results of a study involving 107 children aged 2 to 10 years. The main purposes of this study were to discover: (1) whether an avatar (movable or immovable) provides interactions that are intuitive for children and therefore help to overcome so-called “affordance blindness”; (2) whether an avatar-based touchless interface makes children’s experiences engaging and enjoyable therefore improving recall of content provided through the interaction (learning about art). The study unveiled relevant outcomes in terms of affordance blindness and two-handed interactions. We provide evidence indicating that chronological age influences the style of child–avatar interaction. Finally, it is suggested that avatars could facilitate the development of new effective educational technologies for young children.
Auto Malicious Websites Classification Based on Naive Bayes Classifier

In recent years, the spread of malicious websites has had an increasingly serious impact on people's lives. To solve this problem, in this paper, we mainly focus on achieving the factor analysis of the website category and accurate identification of unknown information, so as to classify the benign and others, which help users avoid the risk of malicious websites. In the procedure, Naïve Bayes and other effective methods are used to calculate probability to test and train the model of website classification. Our result shows the response of benign and other tests well compares to others, suggesting that the Naïve Bayes method is more suitable to solve the differentiation of good websites, which accuracy can be reached to 90 percent. Besides, the training model of that classification is more accurate in that datasheet. Furthermore, if the data is more abundant and the technical bottleneck can be solved, the realization of highest accuracy of website classification using Naïve Bayes is possible.
Towards AI Conversing: FloodBot using Deep Learning Model Stacks

Talking to the electronic device and getting the required information at a minimal time has become today's norm. Although AI-powered conversational agents have percolated the commercial market, their use in a communal setting is still evolving. We postulate that the deployments of chatbots in disaster-prone areas can be beneficial to watch, monitor, and warn people during the crisis. Furthermore, the successful implementation of such technology can be life-saving. In this work, we discuss our deployment of a real-time flood monitoring chatbot called FloodBot. We collect, annotate and visually parse images from potentially hazardous areas. We detect the flood conditions and identify objects in harm's way by stacking deep learning models such as a convolutional neural network (CNN), single-shot multi-box object detection (SSD). We then feed the image contents to a knowledge base of our artificially intelligent FloodBot and explore its AI-Conversing power using end to end memory network. We also showcase the power of cross-domain transfer learning and model fusion techniques. In this work, we discuss our deployment of a real-time flood monitoring chatbot called FloodBot. We collect, annotate and visually parse images from potentially hazardous areas. We detect the flood conditions and identify objects in harm's way by stacking deep learning models such as a convolutional neural network (CNN), single-shot multi-box object detection (SSD). We then feed the image contents to a knowledge base of our artificially intelligent FloodBot and explore its AI-Conversing power using end to end memory network. We also showcase the power of cross-domain transfer learning and model fusion techniques.
An Introduction to Linguistic Summaries

First we provide the fundamental ideas involved in linguistic summaries, next we look at the quantification of the information contained in a linguistic summary and finally we look at the formulation of summaries involving rich concepts.
Anchor-Free One-Stage Online Multi-object Tracking

Current multi-object tracking (MOT) algorithms are dominated by the tracking-by-detection paradigm, which divides MOT into three independent sub-tasks of target detection, appearance embedding, and data association. To improve the efficiency of this tracking paradigm, this paper presents an anchor-free one-stage learning framework to perform target detection and appearance embedding in a unified network, which learns for each point in the feature pyramid of the input image an object detection prediction and a feature representation. Two effective training strategies are proposed to reduce missed detections in dense pedestrian scenes. Moreover, an improved non-maximum suppression procedure is introduced to obtain more accurate box detections and appearance embeddings by taking the box spatial and appearance similarities into account simultaneously. Experiments show that our MOT algorithm achieves real-time tracking speed while obtaining comparable tracking performance to state-of-the-art MOT trackers. Code will be released to facilitate further studies of this problem.
Using dynamically inferred invariants to analyze program runtime complexity

Being able to detect program runtime complexity can help identify security vulnerabilities such as DoS attacks and side-channel information leakage. In prior work, we use dynamic invariant generation to infer nonlinear numerical relations to represent runtime complexity of imperative programs. In this work, we propose a new dynamic analysis approach for learning recurrence relations to capture complexity bounds for recursive programs. This approach allows us to efficiently infer simple linear recurrence relations that represent nontrivial, potentially nonlinear, complexity bounds. Preliminary results on several popular recursive programs show that we can learn precise recurrence relations capturing worst-case complexity bounds such as O(n log n) and O(cn).
Query Expansion for Bangla Search Engine Pipilika

Query expansion aims to extend the user query with related terms for improving the search results of a search engine like Google, Pipilika (a Bangla search engine), etc. In this paper, a query expansion method has been proposed for Pipilika to select expanded words that will be added with a search key to make the search result more accurate and efficient. Collected documents from corpus data have been ranked using BM25 document ranking method. The method selects additional words from ranked documents that have top syntagmatic relations with the search key. It generates similar words for each additional word and searches keywords using Word2Vec CBOW model which has a more semantic similarity with original words. It adds these similar words with search key and additional words. Finally, an expanded query will be generated. This can help the search engine to capture more user requirements for better search results. The calculated F1 score of the method is 0.74.
Joint Deep Recurrent Network Embedding and Edge Flow Estimation

The two most important tasks of network analysis are network embedding and edge flow estimation. The network embedding task seeks to represent each node as a continuous vector, and the edge flow estimation seeks to predict the flow direction and amount along each edge given some known flows of edges. In the past works, they are always studied separately, while their inner connection are completely ignored. In this paper, we fill this gap by building a joint learning framework for both node embedding and flow amount learning. We firstly use a long short-term memory network (LSTM) model to estimate the embedding of a node from its neighboring nodes’ embeddings, meanwhile we use the same LSTM model with a multi-layer perceptron (MLP) to estimate a value of the node which presents its importance over the network. The node value is further used to regularize the edge flow learning, so that for each node the balance of flowing-in and flowing-out reach the node value. We simultaneously minimize the reconstruction error of neighborhood LSTM for each node, the approximation error of node value, and the consistency loss between node value and its conjunctive edge flow values. Experiments show the advantage of the proposed algorithm over benchmark datasets.
An Improved Artificial Bee Colony Algorithm and Its Application in Machine Learning

Abstract In order to effectively overcome the problems and shortcomings of the basic improved artificial bee colony algorithm in solving the optimization accuracy of complex standard test functions, the low accuracy of the solution function and the large blindness of the local search strategy, a more basic improved artificial bee colony is proposed. Parameter optimization algorithm. The above artificial bee colony algorithm introduces the conjugate gradient method with strong local optimization search strategy performance in the follow bee stage of the basic improved artificial bee colony algorithm to change the optimization search strategy, and replaces the blind search strategy with a local deterministic optimization search strategy. The sexual optimization search reduces the randomness, enhances the local certainty of the food source of the follower bee and the optimization search ability, and ensures that each update of the follower bee’s food source will quickly be substantially improved. This improved artificial bee colony algorithm is widely used in parameter optimization of traditional dengue virus propagation model. The simulation results of the improved standard test function on the problem show that the improved artificial bee colony parameter optimization algorithm has higher optimization and solution function accuracy during simulation than the basic improved artificial bee colony parameter optimization algorithm. The obtained standard test the parameter output corresponding to the dengue virus model parameter output is in good agreement with the actual situation when the data is simulated.
Exploring the Need and Design for Situated Video Analytics

Visual video analytics research, stemming from data captured by surveillance cameras, have mainly focused on traditional computing paradigms, despite emerging platforms including mobile devices. We investigate the potential for situated video analytics, which involves the inspection of video data in the actual environment where the video was captured [14]. Our ultimate goal is to explore the means to visually explore video data effectively, in situated contexts. We first investigate the performance of visual analytic tasks in situated vs. non-situated settings. We find that participants largely benefit from environmental cues for many analytic tasks. We then pose the question of how best to represent situated video data. To answer this, in a design session we explore end-users’ views on how to capture such data. Through the process of sketching, participants leveraged being situated, and explored how being in-situ influenced the participants’ integration of their designs. Based on these two elements, our paper proposes the need to develop novel spatial analytic user interfaces to support situated video analysis.
CasualVRVideos: VR videos from casual stationary videos

Thanks to the ubiquity of devices capable of recording and playing back video, the amount of video files is growing at a rapid rate. Most of us have now video recordings of major events in our lives. However, until today, these videos are captured mainly in 2D and are mostly used for screen-based video replay. Currently there is no way for watching them in more immersive environments such as on a VR headset. They are simply not optimized for playback in stereoscopic displays or even tracked Virtual Reality devices.
Modelling the yield of 8.64 kW PV panels installed on a rooftop of a building in the Kingdom of Bahrain

Purpose This paper allows more accurate estimation of the economy in investing in PV electrification for buildings, especially for Gulf Cooperation Council Countries (GCCC) where they have nearly similar climate and building structure. The actual solar electricity yield from this building is used to make empirical modelling. Design/methodology/approach The accurate automated daily-recorded solar electricity from 8.64 kW solar PV on a rooftop of Sadeem Building at Awali, Bahrain, was modelled to polynomial equations of order of 6. The effect of the tilt ( β ) and azimuth ( Ψ ) angle of PV panels for smart and sustainable buildings is studied. Findings The correlation of each set of polynomial equation ( R 2 ) is listed and had reached a highest value of 0.9792 (for order of 6) with lowest value of 0.1853 (for order of 1). The model may be also applied to the GCCC. The results show that each kW of PV will have a solar electricity yield, on average, of 4.1 kWh. It also shows that the tilt angle has little influence on the solar electricity yield (less than 10%) when the tilt angle changed from 26° to 0° or from 26° to 50°. The influence of the azimuth angle is found to be more than 50% in changing Ψ from 90° to 180°. Research limitations/implications The model may not be restricted to Bahrain but applies – to a certain extent – to GCCC (six countries) and to other countries having buildings with similar roof design and at latitude close to the latitude of Bahrain. Practical implications The model enables developers and investors to estimate, with high accuracy, the solar electricity provided from a building if PV panels are to be installed on its rooftop (or facade) at different tilt ( β ) and azimuth ( Ψ ) angle for smart and sustainable buildings. Social implications Empirically finding out how much each kW of solar PV integrated to the building will produce solar energy electricity (in kWh), that is, 1 kW of PV yield, on average, 4.1 kWh. Originality/value Establishing empirical models to evaluate the outcome of each installed kW of PV panels. Each 1 kW installation of PV panels is 4.0 kWh/day, on average. This is less than what commercial companies claim for this region, that is, 1 kW produces 5.5 kWh/ day – which affects the estimated economic outcome of PV projects.
Sensorless Collision Detection Method for Robots with Uncertain Dynamics Based on Fuzzy Logics

Collison detection in human-robot collaboration plays an important role in avoiding the injuries to both humans and environment caused by robots. The traditional methods usually require additional sensors, which increase cost and the systematic complexity. And most sensorless methods assume that the robot dynamic model is accurate. The estimation of torque bounds based on fuzzy logics is proposed as an approach to detect collision in a sensorless robot with dynamic uncertainty. The dynamic model of the industrial robot is built and its parameters are identified firstly. Then the uncertainty of the dynamic model is analyzed and bounds of the actual torques are estimated online. The performance of the proposed collision detection method is evaluated using a 6-DOF industrial manipulator. The results show that collision can be reliably detected only with proprioceptive sensors.
An Improved Collaborative Filtering Recommendation Algorithm Based on Differential Privacy

In this paper, differential privacy protection method is applied to matrix factorization method that used to solve the recommendation problem. For centralized recommendation scenarios, a collaborative filtering recommendation model based on matrix factorization is established, and a matrix factorization mechanism satisfying ε-differential privacy is proposed. Firstly, the potential characteristic matrix of users and projects is constructed. Secondly, noise is added to the matrix by the method of target disturbance, which satisfies the differential privacy constraint, then the noise matrix factorization model is obtained. The parameters of the model are obtained by the stochastic gradient descent algorithm. Finally, the differential privacy matrix factorization model is used for score prediction. The effectiveness of the algorithm is evaluated on the public datasets including Movielens and Netflix. The experimental results show that compared with the existing typical recommendation methods, the new matrix factorization method with privacy protection can recommend within a certain range of recommendation accuracy loss while protecting the users' privacy information.
A Novel Smartphone Identity Authentication Mechanism

Identity authentication plays an important role in maintaining social security. With the rapid growth of population, an important issue is put in front of people. How to identify people more accurately and faster? This paper presents a novel behavioral biometric authentication mechanism for smartphones, named PickupAuth. PickupAuth relies on a combined movement, which is the user picks up the phone to his chest and clicks on the touch screen. PickupAuth uses the built-in smartphone sensors to collect the force of the click screen, the acceleration data, the magnetic field data, and the rotation rate. A new proposed feature subset selection algorithm based on the correlation and weight values and Random Forest classification algorithm are used to analyze the sensor data. Experiments on 110 identity users show that the accuracy reaches 98.44% in sitting posture and 97.46% in standing posture. Besides, PickupAuth is more efficient than state of the art considering the training data acquisition time and the training time.
User-Controlled, Auditable, Cross-Jurisdiction Sharing of Healthcare Data Mediated by a Public Blockchain

We tackle the problem of sharing eHealth data across different jurisdictions. As a general rule, and due to the sensitive nature of the information, different national regulations impose severe limits on what can be exchanged, even in case of emergencies. Furthermore, different systems in different jurisdictions do not communicate. We propose BRUE as a scheme that allows eHealth data to be securely exchanged, with the data subject always in the position of mediation. We combine several technologies, namely, Blockchain, OAuth and User-Managed Access, and concept Receipts, to achieve our aim.
Canny Edge and Hough Circle Transformation for Detecting Computer Answer Sheets

The use of the computer answer sheet media as a medium for writing answers has now become a necessity, this is because the computer answer sheet media is considered to be very easy and fast in the correction process. Some research and implementation applied in solving cases of correction computer answer sheet with various methods, but the use of inappropriate methods will affect the results that are less than the maximum in detecting. Some use the detection of circles which are not precise so that it has the potential to detect answers that should not be detected with clearly. This study propose Canny and Hough circle transformation method for enhanced by calculating the distance between answers to increase accuracy by 95.75%. This can be used as a basic method in making detection devices.
Multi-Perspective, Simultaneous Embedding

We describe MPSE: a Multi-Perspective Simultaneous Embedding method for visualizing high-dimensional data, based on multiple pairwise distances between the data points. Specifically, MPSE computes positions for the points in 3D and provides different views into the data by means of 2D projections (planes) that preserve each of the given distance matrices. We consider two versions of the problem: fixed projections and variable projections. MPSE with fixed projections takes as input a set of pairwise distance matrices defined on the data points, along with the same number of projections and embeds the points in 3D so that the pairwise distances are preserved in the given projections. MPSE with variable projections takes as input a set of pairwise distance matrices and embeds the points in 3D while also computing the appropriate projections that preserve the pairwise distances. The proposed approach can be useful in multiple scenarios: from creating simultaneous embedding of multiple graphs on the same set of vertices, to reconstructing a 3D object from multiple 2D snapshots, to analyzing data from multiple points of view. We provide a functional prototype of MPSE that is based on an adaptive and stochastic generalization of multi-dimensional scaling to multiple distances and multiple variable projections. We provide an extensive quantitative evaluation with datasets of different sizes and using different number of projections, as well as several examples that illustrate the quality of the resulting solutions.
Flexibility measurement model of multi-agent systems

Flexibility is considered as one of the key objectives of agent-based technology. Despite this, we still lack a fundamental understanding of just what “flexibility in multi-agent system (MAS)” really is. Two main questions must be asked. First, how do agents and MAS achieve a high degree of flexibility? Second, what makes one agent or one MAS more flexible than others agents or others MASs? This paper addresses the answer to these two questions by proposing an ontology of the flexibility property and a mathematical measurement model for this property. The proposed ontology gives a comprehensive view of the flexibility by decomposing it on several characteristics and presents several techniques for implementing each characteristic. In addition, it relates these characteristics to MAS components. The proposed model presents a set of metrics for measuring the different characteristics of the flexibility property. The proposed metrics have been applied to JADE applications using a tool developed for this purpose.
THED: A Wrist-Worn Thermal Display to Perceive Spatial Thermal Sensations in Virtual Reality

This paper presents THED, a wearable thermal display to perceive spatial thermal sensations within a virtual reality (VR) environment. THED consists of a wrist-worn thermal stimulation module and a control module utilizing BluetoothTM communication to connect with the VR environment. To demonstrate THED, we have developed a VR environment showing a virtual campfire in a snowy climate where participants were able to experience the virtual campfire in different predetermined distances. We have conducted a user experiment to 1) determine the distance based perception of spatial thermal sensations in a VR setting (VR only), 2) determine the differences of thermal stimuli on participants’ wrists (thermal only), and 3) evaluate the effects of combined thermal stimuli towards their expected spatial thermal stimuli (VR + thermal). Our primary aim of this study is to learn how humans spatially perceive thermal sensations on their hands (utilizing only one hand vs. both hands) when given a wrist-worn thermal source coupled with a virtual reality scenario. Our findings show that different thermal stimuli utilized by THED were able to provide thermal sensations in virtual reality that closely mirrored participants’ expected thermal sensations in respective real world scenarios.