Reviewerly/exHarmony huggingface dataset:
Predicting Daily Activities Effectiveness Using Base-level and Meta level Classifiers

Collecting and analyzing Activities of Daily Living (ADL) could supplement elder care and long-term care services with very sensitive information about elder people and what they do during the day and what challenges they face. Providing care for elder people based on their ADL could let them live actively, independently and healthy. In this paper, we studied the effectiveness of base learners against ensemble methods for predicting ADL. The selected base learners are Naïve Bayes, Bayesian Network, Sequential Minimal Optimization, Decision Table and J48 while the selected ensemble learners are boosting, bagging, decorate and random forest. The dataset was gathered from a wearable accelerometer attached on the chest. The data used in this study is collected from fifteen participants conducting seven activities namely standing up, working at the computer, going up downstairs, standing, walking, walking and talking with someone and talking while standing, walking and going up downstairs. For base learners, J48 achieved the best results in terms of F-measure, precision and recall. Results also showed that Boosting using decision table as the base classifier achieved the best improvement over base classifier. In addition, Bagging was the only ensemble approach that improved the results using all classifiers as base learners. Moreover, Bagging was able to predict five activities out of seven more efficiently than the other approaches while the rotation forest approach was able to predict the remaining two activities more efficiently than the rest. The results also indicated that all approaches took a reasonable time to build the model except Decorate.
Fundamentals, Flavors, and Foibles of Adaptive Instructional Systems

Adaptive instructional interventions have traditionally been provided by a human tutor, mentor, or coach; but, with the development and increasing accessibility of digital technology, technology-based methods of creating adaptive instruction have become more and more prevalent. The challenge is to capture in technology that which makes individualized instruction so effective. This paper will discuss the fundamentals, flavors, and foibles of adaptive instructions systems (AIS). The section on fundamentals covers what all AIS have in common. The section on flavors addresses variations in how different AIS have implemented the fundamentals, and reviews different ways AIS have been described and classified. The final section on foibles discusses whether AIS have met the challenge of improving learning outcomes. There is a tendency among creators and marketers to assume that AIS—by definition—support better learning outcomes than non-adaptive technology-based instructional systems. In fact, the evidence for this is rather sparse. The section will discuss why this might be, and potential methods to increase AIS efficacy.
Password Enhancement Based on Semantic Transformation

With the continuous development of authentication approaches, password-based authentication is still the first choice for various online services today. The security of password-based authentication relies heavily on the strength of the passwords created by users. Password enhancement is a general way to increase the difficulty of cracking a password. An ideal password enhancement strategy should take into account both the usability (mainly the memorability) and the security of passwords. However, it has been found that the higher the password strength, the lower the usability of the password, and vice versa. In order to balance the usability and the security of the password, we propose a password enhancement method based on semantic transformation, which can effectively analyze the semantic structure of a given password. This enhances the password's strength through one or more password semantic transformations to make the password better protected against guessing attacks. Finally, we use publicly available real-world password data sets leaked in previous security incidents to conduct experiments. Our password enhancement strategy significantly reduces the proportion of guesses by a classic password guessing attack, which demonstrates the effectiveness of the method.
Robust 3D Human Pose Estimation Guided by Filtered Subsets of Body Keypoints

We propose a novel hybrid human 3D body pose estimation method that uses RGBD input. The method relies on a deep neural network to get an initial 2D body pose. Using depth information from the sensor, a set of 2D landmarks on the body are transformed in 3D. Then, a multiple hypothesis tracker uses the obtained 2D and 3D body landmarks to estimate the 3D body pose. In order to safeguard from observation errors, each human pose hypothesis considered by the tracker is constructed using a gradient descent optimization scheme that is applied to a subset of the body landmarks. Landmark selection is driven by a set of geometric constraints and temporal continuity criteria. The resulting 3D poses are evaluated by an objective function that calculates densely the discrepancy between the 3D structure of the rendered 3D human body model and the actual depth observed by the sensor. The quantitative experiments show the advantages of the proposed method over a baseline that directly uses all landmark observations for the optimization, as well as over other recent 3D human pose estimation approaches.
Multi-UAV Binocular Intersection With One-Shot Communication: Modeling and Algorithms

Binocular intersection is practically indispensable for stereo visual systems that extract 3D information from images of two distinct cameras. This paper proposes a multi-UAV binocular intersection approach where the two distinct cameras are fixed separately on multiple flying unmanned aerial vehicles (UAVs), rather than physically connected with each other as in the pre-existing works. Eventually, both modeling and algorithms are developed for the multi-UAV binocular intersection with extremely limited communication. As the formulated model states, once one flying vehicle detects the target and shares related information only in a one-shot manner, the other vehicles steer their pan-tilt units (PTUs) to reconstruct a spatial binocular intersection automatically. The binocular intersection algorithm is further improved from two aspects to maximize the probability of target capture. On one hand, the search scope is narrowed by developing an iterative algorithm with priori terrain height and variance. On the other hand, an approaching navigation law is designed to reduce view occlusion caused by terrain. Extensive simulations with different target speeds and flight altitudes are carried out to evaluate the effectiveness of the proposed method. The results show that the probability of binocular intersection is over 90% and is subject to little fluctuation against episodic parameter changes. Furthermore, the developed algorithms are verified localization-free and time-saving as well, and hereafter are potentially practicable for micro aerial swarm applications with extremely limited onboard computation and communication.
Variable Neighborhood Search-Based Symbiotic Organisms Search Algorithm for Energy-Efficient Scheduling of Virtual Machine in Cloud Data Center

The quest for energy-efficient virtual machine placement algorithms has attracted significant attention of researchers in the cloud computing platform. This paper applied a novel symbiotic organisms search (SOS) algorithm to minimize the number of active server by consolidation VMs on few servers for energy savings. SOS algorithm was inspired by symbiotic relationship exhibit by organisms in an ecosystem to boost their chances of survival. Essentially, SOS mimics mutualism, commensalism, and parasitism forms of relationship for traversing the search space. Hybridized with variable neighborhood search, the hybrid algorithm is termed SOS-VNS. SOS-VNS algorithm is efficient in minimizing energy consumption and improving resource utilization. The SOS-VNS algorithm is applied to various workload instances with varying number of VMs in a simulated IaaS cloud. The results obtained showed that SOS-VNS outperforms the heuristics and achieved reasonable energy savings while improving resource utilization.
A new image size reduction model for an efficient visual sensor network

Image size reduction for energy-efficient transmission without losing quality is critical in Visual Sensor Networks (VSNs). The proposed method finds overlapping regions using camera locations, which eliminate unfocussed regions from the input images. The sharpness for the overlapped regions is estimated to find the Dominant Overlapping Region (DOR). The proposed model partitions further the DOR into sub-DORs according to capacity of the cameras. To reduce noise effects from the sub-DOR, we propose to perform a Median operation, which results in a Compressed Significant Region (CSR). For non-DOR, we obtain Sobel edges, which reduces the size of the images down to ambinary form. The CSR and Sobel edges of the non-DORs are sent by a VSN. Experimental results and a comparative study with the state-of-the-art methods shows that the proposed model outperforms the existing methods in terms of quality, energy consumption and network lifetime.
COMEX: Identifying Mislabeled Human Behavioral Context Data Using Visual Analytics

Context-Aware (CA) systems that adapt their behavior based on their users' current context have broad applications in areas from healthcare to smart environments. Context Recognition (CR) is currently solved using machine and deep learning approaches that require realistic datasets with ground truth labels collected "in-the-wild" as users live their lives. In such studies, users periodically label their current context (current activity, body position, and location) while a mobile app continuously gathers sensor data. Unfortunately, users sometimes assign wrong or incomplete context labels, reducing the quality of the labeled dataset; and causing lower classification accuracy. We present COMEX, an interactive visual analytics tool that assists analysts in identifying instances of mislabeled context data to improve the quality of CA datasets. For this, we first provide a conceptual categorization of mislabeling error types. Thereafter we develop linked visualizations, augmented by anomaly scores indicating suspected labeling issues, which provide richer insights into the diverse characteristics of the target dataset. We validate our approach on an open source dataset that contains context information for 60 participants gathered over several days using smartphones. With the help of COMEX, participants of our case study identified numerous mislabelled instances in the dataset. We re-ran the classification task after excluding mislabelled data and saw improvements in classification accuracy.
Multi-Team: A Multi-attention, Multi-decoder Approach to Morphological Analysis.

This paper describes our submission to SIG-MORPHON 2019 Task 2: Morphological analysis and lemmatization in context.Our model is a multi-task sequence to sequence neural network, which jointly learns morphological tagging and lemmatization.On the encoding side, we exploit character-level as well as contextual information.We introduce a multi-attention decoder to selectively focus on different parts of character and word sequences.To further improve the model, we train on multiple datasets simultaneously and use external embeddings for initialization.Our final model reaches an average morphological tagging F1 score of 94.54 and a lemma accuracy of 93.91 on the test data, ranking respectively 3rd and 6th out of 13 teams in the SIG-MORPHON 2019 shared task.
Collaborative Filtering Recommendation Algorithm Classification and Comparative Study

With the rapid development of the information society, both the producers of teaching resources and students are facing enormous challenges. In this context, a recommendation system that can connect the producers and students of teaching resources to achieve a win-win situation is proposed. Collaborative filtering is currently the most successful recommendation technique. In this article the authors firstly introduced the collaborative filtering recommendation algorithm from the angle of user-based, project-based and model-based algorithms. Then they studied the current popular SVD and RBM recommendation algorithms mainly by comparing the experimental results of the basic project-based, SVD and RBM algorithms on the public data set movielens, and pointed out that the recommendation system will be a real-time, multi-modal trend in the age of educational informatization. The rapid development of education informatization has made MOOC, micro-classes and other teaching resources mainly relying on video resources to grow rapidly. The collaborative filtering recommendation algorithm expects to associate the most suitable high-quality resources with the most needed students.
Syntactical Carving of PNGs and Automated Generation of Reproducible Datasets

File carving is a technique to recover files from a storage medium without relying on a file system or other external metadata. As long as the files have been stored contiguously, most file formats are comparatively easy to carve. The moment files have been stored fragmented, the carving process becomes a highly complicated task—even when the fragmentation scenario is relatively simple—and most file carvers available today are not capable of restoring such files correctly. In this paper, we apply syntactical file carving, i.e. the process of utilizing the syntax of a file format to the maximum extent, to the PNG file format. By doing so, we show that the complexity of carving files even in very convoluted fragmentation scenarios can be significantly reduced. Furthermore, we provide a prototypical implementation of a syntactical PNG file carver. In our evaluation, the carver was able to restore 98% of the test files completely and correctly, while the remaining files were at least partially recovered. Since most of the publicly available file carving datasets do not contain PNG files, we created a custom dataset for our evaluation resembling the DFRWS forensic challenges from 2006 to 2007. To ease the creation of such datasets we implemented a dataset generation framework. Using our framework it is possible to create complex fragmentation scenarios with just a few lines of code and configuration. Through this, we hope to encourage the creation of publicly available datasets and to foster further research in the area of file carving.
SurgeryCuts: Embedding Additional Information in Maps without Occluding Features

Abstract Visualizing contextual information to a map often comes at the expense of overplotting issues. Especially for use cases with relevant map features in the immediate vicinity of an information to add, occlusion of the relevant map context should be avoided. We present SurgeryCuts, a map manipulation technique for the creation of additional canvas area for contextual visualizations on maps. SurgeryCuts is occlusion‐free and does not shift, zoom or alter the map viewport. Instead, relevant parts of the map can be cut apart. The affected area is controlledly distorted using a parameterizable warping function fading out the map distortion depending on the distance to the cut. We define extended metrics for our approach and compare to related approaches. As well, we demonstrate the applicability of our approach at the example of tangible use cases and a comparative user study.
Designing and Evaluating Technology for the Dependent Elderly in Their Homes

The ageing population and the increasing longevity of individuals is a challenging reality for healthcaretoday. Longevity often leads to increased dependence and the need for continued care, whichis often left to informal caregivers given the inability of the elderly care network to provide. The informal care provided to the dependent elderly occurs either at the caregiver's or the elderly person's home. Technology application in healthcare has been attracting the attention of engineers for a long time, especially in providing support for health recovery and maintaining therapy practices. Due to major advances in technology, particularly in movement capture optic systems and information extraction through digital image analysis, support systems are being created to monitorhow therapeutic plans are carried out, as well as to evaluate people's physical recovery or to assist healthcare professionals and informal caregiver to provide care for dependent elders. This paper reports on the accomplishments of a project with the general objective of using information and communication technologies to develop a prototype for a system focused on monitoring and assisting the execution of a therapeutic plan integrating physical mobilization and medication.
Toward Understanding Representation Methods in Visualization Recommendations through Scatterplot Construction Tasks

Abstract Most visualization recommendation systems predominantly rely on graphical previews to describe alternative visual encodings. However, since InfoVis novices are not familiar with visual representations (e.g., interpretation barriers [GTS10]), novices might have difficulty understanding and choosing recommended visual encodings. As an initial step toward understanding effective representation methods for visualization recommendations, we investigate the effectiveness of three representation methods (i.e., previews, animated transitions, and textual descriptions) under scatterplot construction tasks. Our results show how different representations individually and cooperatively help users understand and choose recommended visualizations, for example, by supporting their expect‐and‐confirm process. Based on our study results, we discuss design implications for visualization recommendation interfaces.
Heuristic Approach for Finding Threshold Value in Image Segmentation

Image segmentation is a major challenge in the field of image processing. Proper segmentation of medical image is difficult because of the size and position of the inhomogeneity in the tissues, blood flow and similar contrast to the wall. Thus, an automated approach is required to develop proper segmentation of the region of interest (ROI). This paper describes a Heuristic Image Segmentation Algorithm based on genetic algorithm optimization. OTSU algorithm is used for optimization. Threshold value of proposed method can be used for finding the ROI. A comparison has been made with other thresholding algorithms for supporting the better results.
Detection of Pulsars Using an Artificial Neural Network

The research paper demonstrates how to devise an optimal machine learning classifier for the detection of pulsars and then analyzes the performance of various classification models. Pulsars are classified as zero (not a pulsar) or one (pulsar) using logistic regression, decision tree, random forests, KNN classifier, and an artificial neural network. The performance has been analyzed based on five parameters: accuracy, recall, precision, specificity, and prevalence. For this, emission data from the HTRU2 dataset which was collected from the High Time Resolution Universe Survey is being used. Before using any classification algorithms, the patterns in the data were analyzed to understand the correlation between the different characteristics. The classifier proposed in this paper was found to give 98.01% accuracy.
Push and Pull

EOSIO is a blockchain platform utilizing a byzantine-fault-tolerance delegated-proof-of-stake (BFT-DPOS) consensus protocol to guarantee scalability. In EOSIO, a group of representatives called block producers (BPs) is elected by voting through a certain period, and is scheduled in an order to produce blocks. And new tokens are given to BPs every time they produce a block as a notion of reward. In this paper, we show that an attacker can disturb fairness of compensation policy by manipulating the production schedule of EOSIO. Such manipulation is possible through an attacker who has enough tokens to abuse the election process. In order to demonstrate the feasibility and the capability of our attack, we created a tool following EOSIO's voting and irreversible block generation procedure. Our simulation shows that an attacker can provide additional rewards or loss up to 3 BPs. Finally, we discuss the applicability of our attack against the real EOSIO mainnet, and at the same time, propose some countermeasures to prevent it.
A Human-in-The-Loop Context-Aware System Allowing the Application of Case-Based Reasoning for Asthma Management

Determining the asthma health status of a person is a relevant task in the application of context-awareness and case-based reasoning for asthma management. As there are no devices that can track the asthma health status of a person constantly, it is necessary to use a Human-in-The-Loop (HiTL) approach for creating a solution able to associate their health status with context-related data. This research work proposes a system that implements the Asthma Control Questionnaire (ACQ) for determining the asthma health status of a person. The system links this health status to context-related data the person is exposed, and creates the cases to be used by the CBR component of the system. The system is then evaluated by users from a usability perspective through the Health IT Usability Evaluation Model (Health-ITUEM).
Method of Deformed Stars for Multi-extremal Optimization. One- and Two-Dimensional Cases

This paper describes the task of optimizing a multi-extremal function, which in general can be given analytically, tabularly or algorithmically. The method of deformed stars is developed, which belongs to the class of evolutionary methods and allows to take into account the relief of the investigated function. Its advantages are the speed of convergence and result accuracy in comparison with other evolutionary methods. The obtained results of experiments allow us to conclude that the proposed method is applicable to solving problems of finding optimal (suboptimal) values, including non-differentiated functions.
On the computation of the Möbius transform

The Möbius transform is a crucial transformation into the Boolean world; it allows to change the Boolean representation between the True Table and Algebraic Normal Form. In this work, we introduce a new algebraic point of view of this transformation based on the polynomial form of Boolean functions. It appears that we can perform a new notion: the Möbius computation variable by variable and new computation properties. As a consequence, we propose new algorithms which can produce a huge speed up of the Möbius computation for sub-families of Boolean function. Furthermore we compute directly the Möbius transformation of some particular Boolean functions. Finally, we show that for some of them the Hamming weight is directly related to the algebraic degree of specific factors.
The Effect of Familiarity on Older Adults’ Engagement in Exergames

With entertaining game graphics and tasks, exergames can provide benefits for older adults to effectively exercise their physical and mental capabilities. However, it is sometimes difficult for exergames to engage older adults due to maladaptation. In this work, we suggest that the feeling of familiarity can positively influence older adult's adaptation to exergames. Based on this intuition, to help exercise older adult's upper limbs, we design a Ping Pong Exergame (PPE) infused with table tennis activities, which has been shown to be one of the most popular (thus familiar) sports among Singaporean older adults. A five-week study involving 44 Singaporean older adults shows that the participants who have higher levels of familiarity to table tennis exhibit higher motivation and ability in playing PPE, which indicate that familiarity can improve older adults' adaptation to exergames.