Reviewerly/exHarmony huggingface dataset:
Enhancing Physical Activity of Walking and Dual-Tasking among Older Adults Using Personalized Navigation Practice

Older adults experience a decrease in physical ability which may lead to psychological issues, poor quality of life and even death. Therefore, it is very important for them to maintain their physical prowess by encouraging the performance of physical activity. Previous research has focused primarily on physical activity as a single task without consideration of numerous additional coinciding events that almost always co-occur. The most common concomitant, secondary task (i.e., dual-task) is related to the cognitive requirements associated with daily activity pursuits. Thus, any procedure designed to promote greater physical activity should include complex tasks. In addition, the older population is diverse, and therefore, physical activity should be personalized according to individual capabilities and skills. We intend to combine navigation as a secondary task to walking, as a task "instructor", guiding the users through their personalized routes and also provide an added value of doing physical activity outside the home.
Scalable clustering of segmented trajectories within a continuous time framework: application to maritime traffic data

In the context of the surveillance of the maritime traffic, a major challenge is the automatic identification of traffic flows from a set of observed trajectories, in order to derive good management measures or to detect abnormal or illegal behaviours for example. In this paper, we propose a new modelling framework to cluster sequences of a large amount of trajectories recorded at potentially irregular frequencies. The model is specified within a continuous time framework, being robust to irregular sampling in records and accounting for possible heterogeneous movement patterns within a single trajectory. It partitions a trajectory into sub-trajectories, or movement modes, allowing a clustering of both individuals’ movement patterns and trajectories. The clustering is performed using non parametric Bayesian methods, namely the hierarchical Dirichlet process, and considers a stochastic variational inference to estimate the model’s parameters, hence providing a scalable method in an easy-to-distribute framework. Performance is assessed on both simulated data and on our motivational large trajectory dataset from the automatic identification system, used to monitor the world maritime traffic: the clusters represent significant, atomic motion-patterns, making the model informative for stakeholders.
Lower Bias, Higher Density Abusive Language Datasets: A Recipe

Datasets to train models for abusive language detection are at the same time necessary and still scarce. One the reasons for their limited availability is the cost of their creation. It is not only that manual annotation is expensive, it is also the case that the phenomenon is sparse, causing human annotators having to go through a large number of irrelevant examples in order to obtain some significant data. Strategies used until now to increase density of abusive language and obtain more meaningful data overall, include data filtering on the basis of pre-selected keywords and hate-rich sources of data. We suggest a recipe that at the same time can provide meaningful data with possibly higher density of abusive language and also reduce top-down biases imposed by corpus creators in the selection of the data to annotate. More specifically, we exploit the controversy channel on Reddit to obtain keywords that are used to filter a Twitter dataset. While the method needs further validation and refinement, our preliminary experiments show a higher density of abusive tweets in the filtered vs unfiltered dataset, and a more meaningful topic distribution after filtering.
On the List Recoverability of Randomly Punctured Codes.

We show that a random puncturing of a code with good distance is list recoverable beyond the Johnson bound. In particular, this implies that there are Reed-Solomon codes that are list recoverable beyond the Johnson bound. It was previously known that there are Reed-Solomon codes that do not have this property. As an immediate corollary to our main theorem, we obtain better degree bounds on unbalanced expanders that come from Reed-Solomon codes.
Free Theorems Simply, via Dinaturality

Free theorems are a popular tool in reasoning about parametrically polymorphic code. They are also of instructive use in teaching. Their derivation, though, can be tedious, as it involves unfolding a lot of definitions, then hoping to be able to simplify the resulting logical formula to something nice and short. Even in a mechanised generator it is not easy to get the right heuristics in place to achieve good outcomes. Dinaturality is a categorical abstraction that captures many instances of free theorems. Arguably, its origins are more conceptually involved to explain, though, and generating useful statements from it also has its pitfalls. We present a simple approach for obtaining dinaturality-related free theorems from the standard formulation of relational parametricity in a rather direct way. It is conceptually appealing and easy to control and implement, as the provided Haskell code shows.
CONTEXTUAL CONTRADICTIONS TO THE SCALING-UP OF MOBILE HEALTH IN ARMED-CONFLICT SETTINGS OF NORTH-EASTERN NIGERIA: A CASE STUDY OF ALMANACH

Developing countries are undergoing an unprecedented growth on the adoption of ICT's.Mobile technologies, digital platforms and services for health care are gaining acceptance and use for detection and response for disease outbreaks, support of the healthcare value chains to facilitate improvements of public health care delivery and services.This study through a case study examines how m-Health platforms and services produce positive results in the context of Sub-Saharan Africa, specifically investigating the challenges of m-Health adoption and use in the Boko Haram settings in Adamawa State, North Eastern Part of Nigeria.Our study also explores the challenges that impede the successful scale-up of m-Health intervention in armed conflict settings of Adamawa State in the North Eastern region of the Federal Republic of Nigeria.We examined the use of ALMANACH in five local government areas (LGAs) namely; Yola south, Girei, Mubi-South, Mubi-North, and Maiha located in the armed-conflict (Boko Haram) settings of Adamawa State, Nigeria.Nineteen PHCs where ALMANACH has been deployed, primarily conducted in children clinics.A m-Health: "Algorithm for the Management of Acute Childhood Illnesses" (ALMANACH) was introduced as a clinical decision support tool into the child health system to aid the management of common acute illnesses in children between 2 months and 5 years.The ALMANACH is an electronic version of the integrated management of childhood illnesses (IMCI) running on mobile tablets.The lens of activity theory has guided the study to unearth the emergence of some developments in ALMANACH adoption and use in our study setting.Activity theory (AT) was used as the analytical lens because it is central for understanding challenges in adoption and use which can present in the form of contradictions.Thus, our findings suggest several challenges in the form of contradictions that can impede the successful scale-up of ALMANACH in peculiar settings of Boko Haram.Hence, a key contribution to the literature from our study is uncovering that contradictions are critical to achieving mHealth scale-up in our study setting.
TRAINING AND GAMING SYSTEM FOR DEVELOPMENT OF LOGIC WITH INTELLIGENT INTERFACE

In today's education, there are problems associated with the development of attention in preschool and primary school children. It is difficult for children to focus on a given task, especially if it is not very interesting and monotonous, their attention is attracted by everything new and bright. For this reason, the gamification of educational processes is one of the most popular modern trends in the development of information systems. Using the game, children in an interesting and simple way can develop the skills of logical thinking, solving certain problems of logic-mathematical games, such as sudoku. Most of the current informational training and gaming systems have a standard interface and are age-specific. The authors propose a modern training and gaming system with an intellectual interface, which will increase the efficiency of teaching children of preschool and primary school age the basics of logical thinking by adapting to each specific child. The article provides a comparative analysis of existing systems for the development of logic, the basic requirements for such a system, the analysis of methods of gesture recognition and the algorithm of work of the intellectual interface based on the use of neural networks, namely the convolutional neural network, which is the logical development of ideas of such architectures neural networks such as cognitron and neocognitron. After that, a conceptual model of training and gaming system was developed, which consists of a game subsystem that interacts with the intellectual interface with the help of a database and a knowledge base. The authors also developed a general scheme of the game subsystem. The innovative training-gaming system offered by the authors not only allows to use the intelligent interface but also adapts to each individual player, depending on the previous results of the game, which leads to an increase in the quality and effectiveness of teaching children of preschool and primary school age. Learning through play is an interesting and simple learning that leads not only to the development of logic but also to the development of sustained attention in children.
Sensitivity, Specificity, Generalizability, and Reusability Aspirations for Machine Learning (ML) Models in mHealth

Mobile sensor big data collected from smartphones, smartwatches, fitness trackers, and other wearables can be mined for signatures (called mHealth biomarkers) of subtle changes in daily behaviors (e.g., mobility, gait, sleep, etc.) and/or physiology (e.g., heart function, breathing, sweating, etc.). Clinical adoption of these mHealth biomarkers can lead to potent temporally-precise interventions, enabling patients to initiate and sustain the healthy lifestyle choices and treatment regimes that are necessary to prevent and/or successfully manage the growing burden of multiple chronic conditions.
Taura Whiri a Whanaungatanga Manaakitanga me Kotahitanga kia awhi te Tamaiti 1

The title states the rope is plaiting whanaungatanga (relationship), manaakitanga (hospitality), and (unity) to embrace a tamaiti (child). Maui Tikitiki a Taranga was stillborn according to his mother, so she wrapped him in a topknot using her hair and tossed him into Tangaroa (guardian of the sea). Maui survived the ordeal, where Tangaroa rocked him, the jellyfish and seaweed covered him, and the birds folded their wings around him. Tama-nui-ke-ti-Rangi a koroua (old man), could see the commotion and ran to see what was happening. The koroua rescued Maui Tikitiki a Taranga and saved him. This chapter will look at Maui and his ordeals and how they can resonate with troubled tamariki (children) attending Kura Kaupapa Māori (a school with Māori language and where Māori principles are taught). Māori principles such as whanaungatanga, manaakitanga, and kotahitanga will be braided telling the stories of Maui, his dispositions, and how his dispositions reverberate with tamariki in Kura Kaupapa Māori.
İnşaat Mühendisliğinde Derin Öğrenme Algoritmalarının Değerlendirilmesi ve Uygulanması

In this study, the applicability of deep learning algorithms in the field of civil engineering has been investigated.Firstly, the information that is about deep learning algorithms has been given.Additionally, deep learning applications, which are made, in subjects such as classification, estimation, and interpretation in the field of civil engineering, have been examined.The applications are elaborated according to civil engineering's sub-branches that transportation, geotechnical and construction.The contributions of the realized applications' in view of success rates to civil engineering that were analyzed.As a result of the study, it is foreseen that in the studies where the number of data is high, high performance will be achieved in the use of deep algorithms.
A Study on use of Big Data in Cloud Computing Environment

The purpose of this paper is to present definition, characteristics, and classification of big data along with environment for carrying net analytics in clouds for Big Data application. Big data and Cloud computing are the hot issues in current vast Information Technology. Effective management and analysis of large-scale data present an interesting but critical challenge. Now a days handling of big data is one of the main problem and this can be sorted with the help of cloud computing.  The connection between the big data, cloud computing, big data storage systems, and Hadoop technology are also presented. This paper provides a complete review of the big data state of the art, characteristics, management and research challenging aspects.
Deep Graph Generators.

Learning distributions of graphs can be used for automatic drug discovery, molecular design, complex network analysis and much more. We present an improved framework for learning generative models of graphs based on the idea of deep state machines. To learn state transition decisions we use a set of graph and node embedding techniques as memory of the state machine. Our analysis is based on learning the distribution of random graph generators for which we provide statistical tests to determine which properties can be learned and how well the original distribution of graphs is represented. We show that the design of the state machine favors specific distributions. Models of graphs of size up to 150 vertices are learned. Code and parameters are publicly available to reproduce our results.
Gazing at Pac-Man: Lessons learned from a Eye-Tracking Study Focusing on Game Difficulty

This paper investigates the players’ gaze behavior in different game difficulty settings to explore potential use cases of gaze-informed design interventions for future research activities. A comparative study was set up where subjects played the game Pac-Man in three difficulty settings while their gaze behavior was recorded via an eye-tracking device. Several measures were employed, such as the current position of the players’ gaze, the current position of the Pac-Man character, and the currently attended game object. While some game aspects did not show any significant results (e.g., the distance between Pac-Man and the gaze point), the time spent looking at one of Pac-Man’s enemies revealed a highly significant effect for the difficulty level. With the findings, we aim at informing designers and researchers regarding the pitfalls of using gaze as an analysis tool in the field of challenge in games. Furthermore, the insights of our efforts provide the basis for our future research activities that will use the obtained data to provide player guidance and support players in challenging game situations through visually augmenting objects located in the peripheral visual field.
A Framework for Handling Internet of Things Data with Confidentiality and Blockchain Support.

The Internet of Things (IoT) has emerged as a disruptive technology in recent time. Due to a sharp rise in the deployment of IoT devices across the globe, the total amount of generated data reaching record highs every day. There is no denying that IoT devices with the data that they produce enhancing our day to day lives by bringing in innovative solutions. However, there is a darker side of this technology as it enables device manufacturers and service providers to retain this data that they can later use for several purposes, including targeted marketing. Unfortunately, not many people are aware of this data misuse by service providers. To address this issue, we propose a framework for the confidential handling of IoT data with Blockchain-based verification. We used a combination of Solid and Blockchain to accomplish this goal. Our experimental results show that the proposed approach is promising and has the potential to bring a considerable change in the IoT industry.
Approximating General Kernels by Extended Fuzzy Measures: Application to Filtering

Convolution kernels are essential tools in signal processing: they are used to filter noisy signal, interpolate discrete signals, $$\ldots $$ . However, in a given application, it is often hard to select an optimal shape of the kernel. This is why, in practice, it may be useful to possess efficient tools to perform a robustness analysis, talking the form in our case of an imprecise convolution. When convolution kernels are positive, their formal equivalence with probability distributions allows one to use imprecise probability theory to achieve such an imprecise convolution. However, many kernels can have negative values, in which case the previous equivalence does not hold anymore. Yet, we show mathematically in this paper that, while the formal equivalence is lost, the computational tools used to describe sets of probabilities by intervals on the singletons still retain their key properties when used to approximate sets of (possibly) non-positive kernels. We then illustrate their use on a single application that consists of filtering a human electrocardiogram signal by using a low-pass filter whose order is imprecisely known. We show, in this experiment, that the proposed approach leads to tighter bounds than previously proposed approaches.
CubifAE-3D: Monocular Camera Space Cubification on Autonomous Vehicles for Auto-Encoder based 3D Object Detection

We introduce a method for 3D object detection using a single monocular image. Depth data is used to pre-train an RGB-to-Depth Auto-Encoder (AE). The embedding learnt from this AE is then used to train a 3D Object Detector (3DOD) CNN which is used to regress the parameters of 3D object poses after the encoder from the AE generates a latent embedding from the RGB image. We show that we can pre-train the AE using paired RGB and depth images from simulation data once and subsequently only train the 3DOD network using real data, comprising of RGB images and 3D object pose labels (without the requirement of dense depth). Our 3DOD network utilizes a particular $cubification$ of 3D space around the camera, where each cuboid is tasked with predicting N object poses, along with their class and confidence values. The AE pre-training and this method of dividing the 3D space around the camera into cuboids give our method its name - CubifAE-3D. We demonstrate results for monocular 3D object detection on the Virtual KITTI 2, KITTI, and nuScenes datasets for Autnomous Vehicle (AV) perception.
Haze image restoration based on physical optics model using raspberry pi B+V1.2

Dehazing is still a challenging problem for the outdoor images. The occurrence of haze, fog, mist etc., reduce the visibility of captured images in the outdoor environment. There are several dehazing techniques proposed to resolve the haze in the outdoor images but the existing techniques suffers from the computational time complexity. For a real-time application time complexity plays a major role. As a part of real-time application haze removal is done using the raspberry pi b+v1.2. The hazy image is restored by taking dark channel with patch size of 11 × 11 to avoid the oversaturation of recovered scene radiance and halo effect. Getting the image size and channels, the image is flattened to get the top bright points. Then the image is padded at the ends with a square of side of the window to get the minimum comparision at the ends. Because the atmospheric light to be taken from the foreground as the background has almost same pixel values distributed. The intensity is found, from that maximum among these pixels are found and considered for atmospheric light. The transmission map is found, hazy image is restored. And the performance of modified approach is compared with paper perception oriented haze image restoration based on physical optics model. Hence, to give a better dehazing images with reduced computational time using raspberry pi b+v1.2.
Research on Intelligent Substation Wireless Sensor Network System Architecture Based on Internet of Things Technology

This paper first describes the main business of intelligent substation, and analyzes different application requirements of these business to technologies such as Internet of Things, wireless sensor network and electromagnetic compatibility technology. Then, the characteristics of several technologies based on Internet of Things in intelligent substation are studied respectively. The characteristics and hazards of electromagnetic disturbance and electromagnetic radiation generated during the operation of electrical equipment in substation are studied. This paper analyzes the safety risk of electromagnetic environment in intelligent substation, and the electromagnetic radiation environment modeling, given several radiation electromagnetic interference protection technologies. Finally, the wireless sensor network construction scheme of intelligent substation based on Internet of things is studied, and several typical application modes and scheme design of wireless sensor network are analyzed.
FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis

Balancing performance and safety is crucial to deploying autonomous vehicles in multi-agent environments. In particular, autonomous racing is a domain that penalizes safe but conservative policies, highlighting the need for robust, adaptive strategies. Current approaches either make simplifying assumptions about other agents or lack robust mechanisms for online adaptation. This work makes algorithmic contributions to both challenges. First, to generate a realistic, diverse set of opponents, we develop a novel method for self-play based on replica-exchange Markov chain Monte Carlo. Second, we propose a distributionally robust bandit optimization procedure that adaptively adjusts risk aversion relative to uncertainty in beliefs about opponents' behaviors. We rigorously quantify the tradeoffs in performance and robustness when approximating these computations in real-time motion-planning, and we demonstrate our methods experimentally on autonomous vehicles that achieve scaled speeds comparable to Formula One racecars.
Generalized Mean Estimation in Monte-Carlo Tree Search

We consider Monte-Carlo Tree Search (MCTS) applied to Markov Decision Processes (MDPs) and Partially Observable MDPs (POMDPs), and the well-known Upper Confidence bound for Trees (UCT) algorithm. In UCT, a tree with nodes (states) and edges (actions) is incrementally built by the expansion of nodes, and the values of nodes are updated through a backup strategy based on the average value of child nodes. However, it has been shown that with enough samples the maximum operator yields more accurate node value estimates than averaging. Instead of settling for one of these value estimates, we go a step further proposing a novel backup strategy which uses the power mean operator, which computes a value between the average and maximum value. We call our new approach Power-UCT, and argue how the use of the power mean operator helps to speed up the learning in MCTS. We theoretically analyze our method providing guarantees of convergence to the optimum. Finally, we empirically demonstrate the effectiveness of our method in well-known MDP and POMDP benchmarks, showing significant improvement in performance and convergence speed w.r.t. state of the art algorithms.