Reviewerly/exHarmony huggingface dataset:
A Novel On-Wrist Fall Detection System Using Supervised Dictionary Learning Technique

Wrist-based fall detection system provides a very comfortable and multi-modal healthcare solution, especially for elderly risking falls. However, the wrist location presents a very challenging and unstable spot to distinguish falls among other daily activities. In this paper, we propose a Supervised Dictionary Learning approach for wrist-based fall detection. Three Dictionary learning algorithms for classification are invoked in this study, namely SRC, FDDL, and LRSDL. To extract the best descriptive representation of the signal data we followed different preprocessing scenarios based on accelerometer, gyroscope, and magnetometer. A considerable overall performance was obtained by the SRC algorithms reaching respectively 99.8%, 100%, and 96.6% of accuracy, sensitivity, and specificity using raw data provided by a triaxial accelerometer, accordingly overthrowing previously proposed methods for wrist placement.
Hybrid Text Feature Modeling for Disease Group Prediction Using Unstructured Physician Notes

Existing Clinical Decision Support Systems (CDSSs) largely depend on the availability of structured patient data and Electronic Health Records (EHRs) to aid caregivers. However, in case of hospitals in developing countries, structured patient data formats are not widely adopted, where medical professionals still rely on clinical notes in the form of unstructured text. Such unstructured clinical notes recorded by medical personnel can also be a potential source of rich patient-specific information which can be leveraged to build CDSSs, even for hospitals in developing countries. If such unstructured clinical text can be used, the manual and time-consuming process of EHR generation will no longer be required, with huge person-hours and cost savings. In this article, we propose a generic ICD9 disease group prediction CDSS built on unstructured physician notes modeled using hybrid word embeddings. These word embeddings are used to train a deep neural network for effectively predicting ICD9 disease groups. Experimental evaluation showed that the proposed approach outperformed the state-of-the-art disease group prediction model built on structured EHRs by 15% in terms of AUROC and 40% in terms of AUPRC, thus proving our hypothesis and eliminating dependency on availability of structured patient data.
Understanding REST APIs

This chapter throws light on building representational state transfer (REST)-based services. Since we’ll be learning RESTful Web Services through our chosen language, Node.js, and using an awesome framework like Hapi, it’s first necessary to introduce ourselves to REST APIs and then understand how Hapi makes the process easy. Along the ride, we’ll learn about HTTP verbs, resource handling, stateless constraints, and tying it all into representational state transfer.
A principled approach for generating adversarial images under non-smooth dissimilarity metrics

Deep neural networks perform well on real world data but are prone to adversarial perturbations: small changes in the input easily lead to misclassification. In this work, we propose an attack methodology not only for cases where the perturbations are measured by $\ell_p$ norms, but in fact any adversarial dissimilarity metric with a closed proximal form. This includes, but is not limited to, $\ell_1, \ell_2$, and $\ell_\infty$ perturbations; the $\ell_0$ counting norm (i.e. true sparseness); and the total variation seminorm, which is a (non-$\ell_p$) convolutional dissimilarity measuring local pixel changes. Our approach is a natural extension of a recent adversarial attack method, and eliminates the differentiability requirement of the metric. We demonstrate our algorithm, ProxLogBarrier, on the MNIST, CIFAR10, and ImageNet-1k datasets. We consider undefended and defended models, and show that our algorithm easily transfers to various datasets. We observe that ProxLogBarrier outperforms a host of modern adversarial attacks specialized for the $\ell_0$ case. Moreover, by altering images in the total variation seminorm, we shed light on a new class of perturbations that exploit neighboring pixel information.
The Application of Mobile Learning Technologies at Malaysian Universities Through Mind Mapping Apps for Augmenting Writing Performance

21 st century learning focuses on the flow of information, media, and technology.In Malaysia, many university students face problems in English writing.Thus, students should be exposed to the technology training in innovative ways to produce students with a dynamic in this ever-changing world.Recently, the transformation and the evolution of mobile have created a huge impact on mobile users, as it is the current trend.Due to this matter, university students are now experiencing innovative learning development through mobile application and this can certainly improve their learning performance.The purpose of the study is to examine the application of mobile learning technologies through mind mapping applications for augmenting writing performance at Malaysian universities.The study was based on three different research theories -Flower and Hayes Writing Process Model, Radiant Thinking Theory, and Unified Theory of Acceptance and Use of Technology (UTAUT).The results of the study show that the students had positive responses towards English writing skills background, mobile technologies application background and mind mapping applications background.The proposed conceptual framework, Mobile-assisted Mind Mapping Technique Model (MMMTM) supports the need for Malaysian university students to augment their writing performance.It is hoped that this study will benefit the policymakers, tertiary educators and university students in teaching and learning specifically in writing courses.
The Evaluation of Ontologies for Quality, Suitability for Reuse, and the Significant Role of Social Factors

A frequent challenge faced by ontologists and knowledge engineers is the choice of the correct or most appropriate ontology for reuse. Despite the importance of ontology evaluation and selection and the widespread research on these topics, there are still many unanswered questions and challenges. Most of the evaluation metrics and frameworks in the literature are mainly based on a limited set of internal characteristics of ontologies, e.g., their content and structure, which ignore how the community uses and evaluates them. This paper used a survey questionnaire to investigate the notion of quality and reusability in ontology engineering, and to explore and identify the set of metrics that can affect the process of ontology evaluation and selection for reuse. Responses from 157 ontologists and knowledge engineers were collected, and their analysis suggests that the process of ontology evaluation and selection for reuse, not only depends on different internal characteristics of ontologies, but that it also depends on different metadata, and social and community related metrics. Findings of this research can contribute to facilitating and improving the process of selecting an ontology for reuse.
An improved bioinspired cognitive map-building system based on episodic memory recognition

Before the cognitive map is generated through the fire of the rodent hippocampal spatial cells, mammals can obtain the outside knowledge through the visual information, which comes from the eyeball to the brain. The information is encoded and transferred to the two regions of the brain based on the fact of biophysiological research, which are known as “what” loop and “where” loop. In this article, we simulate an episodic memory recognition unit consisting of the integration of two-loop information, which is applied to building the accurate bioinspired spatial cognitive map of real environments. We employ the visual bag of word algorithm based on oriented Feature from Accelerated Segment Test and rotated Binary Robust Independent Elementary Features feature to build the “what” loop and the hippocampal spatial cells cognitive model, which comes from the front-end visual information input system to build the “where” loop. At the same time, the environmental cognitive map is a topological map containing information about place cell competition firing rate, oriented Feature from Accelerated Segment Test and rotated Binary Robust Independent Elementary Features feature descriptor, similarity of image retrieval, and relative location of cognitive map nodes. The simulation experiments and physical experiments in a mobile robot platform have been done to verify the environmental adaptability and robustness of the algorithm. This proposing algorithm would provide a foundation for further research on bioinspired navigation of robots.
Depth-Dependent High Distortion Lens Calibration

Accurate correction of high distorted images is a very complex problem. Several lens distortion models exist that are adjusted using different techniques. Usually, regardless of the chosen model, a unique distortion model is adjusted to undistort images and the camera-calibration template distance is not considered. Several authors have presented the depth dependency of lens distortion but none of them have treated it with highly distorted images. This paper presents an analysis of the distortion depth dependency in strongly distorted images. The division model that is able to represent high distortion with only one parameter is modified to represent a depth-dependent high distortion lens model. The proposed calibration method obtains more accurate results when compared to existing calibration methods.
3D Eye Model-Based Gaze Tracking System with a Consumer Depth Camera

Most existing gaze tracking systems are high-cost, intrusive and difficult to calibrate, and some rely on the infrared illuminant. However, such systems may not work outdoor and meet real-time requirements. This paper proposes a non-intrusive system based on the 3D eyeball model, which does not need the exact infrared illuminant and complicated calibration process and allows the natural movement of the head. In the proposed system, Kinect is used to track the iris center and face model of the person, and the 3D information is easy to obtain. At the same time, point cloud registration algorithm is applied based on feature points in the face model sequence to obtain accurate head pose estimation results. In this paper, a personal calibration process is also proposed to obtain the gaze model parameters for different users, such as the eyeball center and angle kappa. The proposed method has good adaptability to the change of illuminant and head movement. In the actual operating environment, the system speed reaches 30 fps, which can meet the requirements of real-time control.
Teaching Automated Theorem Proving by Example: PyRes 1.2

PyRes is a complete theorem prover for classical first-order logic. It is not designed for high performance, but to clearly demonstrate the core concepts of a saturating theorem prover. The system is written in extensively commented Python, explaining data structures, algorithms, and many of the underlying theoretical concepts. The prover implements binary resolution with factoring and optional negative literal selection. Equality is handled by adding the basic axioms of equality. PyRes uses the given-clause algorithm, optionally controlled by weight- and age evaluations for clause selection. The prover can read TPTP CNF/FOF input files and produces TPTP/TSTP proof objects. Evaluation shows, as expected, mediocre performance compared to modern high-performance systems, with relatively better performance for problems without equality. However, the implementation seems to be sound and complete.
Use Mouse Ballistic Movement for User Authentication Based on Hilbert-Huang Transform

In order to explore the frequency domain characteristics of mouse operation for user authentication. This paper collected experimental data on mouse ballistic movements of 10 participants on the AML website. Hilbert-Huang transform was used to extract the frequency-domain information of 9 features such as speed and acceleration during mouse movement, and formed a frequency-domain feature matrix. The Bagged-tree algorithm was used to build an authentication model. The method proposed in this paper obtained Precision = 90.25%, Recall = 88.20%. The results show that there are differences in the frequency domain information when different users operate the mouse to complete the same task, which can be used for user authentication.
Automatic Detection and Counting of Malaria Parasite-Infected Blood Cells

In this paper, we present a technique for automatic detection and counting of Plasmodium vivax-infected red blood cells by means of a convolutional neural network and a feature- based counting process. Current approaches for object detection or counting often rely on prior knowledge of certain salient features of the to-be-identified objects or require time-consuming pre-processing. For this reason, many detection problems, for example infected cell counting, remain a manual task for trained professionals, leading to potentially high amounts of time between infection and diagnosis and treatment, which, in turn, can have lethal consequences. Using the BBBC041 data set, we annotated the ground truth (GT) of infected cells with circles in each image and then trained a convolutional neural network to predict these GTs from previously unseen cell images. Subsequently, the algorithm computes the number of cells using Canny edge detection and circular Hough Transform.
Development and Effectiveness Analysis of Dropout Prevention Programs for Local College Students

The purpose of this study is to develop an optimal program to prevent dropouts of local university students. The program was designed to integrate prior research into students of major professors and non-adaptive students to conduct study-related activities outside of class for more than four weeks. After the program activity, students personally prepared an activity report for each hour, and at the end of all activities, the professor completed and submitted a result report. To sum up the results, it was said that many students were able to have practical experiences related to their majors outside of the classroom with their professors and colleagues, and they were able to find information and direction about their majors and knowledge. Through this process, the reflection of the program and program operation, problems, and improvement measures were analyzed. This will promote the appropriateness and internalization of program operation in the future. Based on the actual learning and life experiences of local university students, this study is expected to suggest a lot as a basic data for preparing solutions to prevent dropouts.
ADDITIVE DIGITAL TECHNOLOGIES FOR FORMING METASUBJECT LEARNING OUTCOMES

According the requirements of the new Federal State Educational Standards of General Education, the article examines the experience of generating metasubject learning outcomes at informatics lessons by the example of performing by technical school students the project and research work to study 3D modeling, 3D printing and additive technologies. The stages of the project are described in detail: study of the theory of the additive process of layer-by-layer model creation; development in the SolidWorks system of the graphic part of the work on creating a prototype; printing of a prototype on a 3D printer; making calculations to justify the benefits of using a 3D prototype; checking the performance of the finished product, the possibility of its use in laboratory and educational purposes; developing recommendations for working with a 3D printer.
The group action on the finite projective planes of orders 53, 61, 64

The goal of this research is to partition the projective plane PG(2, q) over Galois Field of order q where q = 53, 61, 64. Classify the group action cyclic groups on PG(2, q), when the number of points and lines is equal to q2 + q + 1 which is not a prime have been described. Study the properties of the subsets (orbits, points, lines, planes, triangles) geometrically and algebraically. Also establish the projective codes and arcs with different sizes and degrees by using the program of GAP.
Paranoia and Control—A Narrative About the Social Factory

The rise of paranoiac control behaviour is analysed as supported by the advance of IT control systems. These systems materialise a language that increasingly dominates the use of live language (the social factory) thus undermining peoples development to social beings. Focus is not on the ability to predict and control human behaviour but on the nature of control through the IT language as a hidden digital script, which contrary to live language is void of meaning and value. The decoupling of values and meaning necessitates applying principles of emotive ‚ethics‘, i.e. subjective emotive reactions (the wide spread call for likings without documented reasons), to achieve social control. This results in ruling by establishing ‚emotive axioms‘ that are paranoiac because they define the ‚truth‘ and are used to legitimate the course of action – typically some form of which hunt. The tension between the logical positivism and the early Wittgensteins digital philosophy on language and the philosophy of ordinary language especially of the late Wittgenstien is used as the frame of the analysis.
Object Authentication in the Context of the Internet of Things: A Survey

Internet of Things (IoT) is considered as the future of the Internet that connects billions of objects all together. Trusted communication between these objects is a crucial requirement for the wide deployment of IoT services. Consequently, effective authentication procedures should be applied between the communicating objects. This paper provides a comprehensive survey of object authentication in the IoT. The survey aims to direct future researchers in the field of IoT object authentication by delving into the details of authentication schemes and going through different comparisons. Comparisons are based on various criteria which include authentication process characteristics, the underlying architecture, key generation and distribution techniques, supporting IoT challenges, security analysis, and performance evaluation. Additionally, this survey highlights the main issues and challenges of IoT objects authentication and recommends future research directions.
3D Travel Comparison Study between Multi-Touch and GamePad

This study explored interactions between users and a 3D virtual world using a multi-touch display and sought to identify the advantages of this form of interaction. An experiment was conducted with 28 subjects, who were classified into two groups: experienced gamers and casual gamers. The experiment involved a pseudo-universe, where users were required to find five objects using the multi-touch display, and five objects using a game controller (GamePad). Overall, multi-touch was slightly faster than the GamePad in the search for objects. Experienced users performed significantly better when using the GamePad, compared to casual gamers. When casual gamers were analyzed separately, their performance was better (at a statistically significant level) with the multi-touch display, compared to the GamePad. Finally, users had a significantly higher error rate when switching from the GamePad to the keyboard, than when switching from the multi-touch to the keyboard.
Suspicious Activity Detection and Tracking through Unmanned Aerial Vehicle Using Deep Learning Techniques

This paper focuses on crime detection that is extensively used in real world visual monitoring applications like video surveillance.Crime prevention is widely implemented in some countries, through police force and in many cases, private policing methods such as private security and home defense.Implementation of such private and public security could not completely eradicate such awful crimes.Therefore, there's a requirement for credible and effective surveillance in order to diminish the brutal and skeevy crimes.The reason for occurrence of many offences is late communication and unestablishment of authentic security or surveillance system .It's tragic that more than 20000 people lost their lives in Hit-and-run cases in India alone.Many notorious criminals who deserve dreadful punishment for their recklessness fly the coop due to the lack of evidence.Many other instances can be drawn out where there's a requirement for an organized system of surveillance like ATM's, shopping malls, etc.To stop such awful offences there's a need for genuine security system.The idea of an Intelligent Unmanned Aerial Vehicle (UAV) proposed in this paper, is inspired from various sources [1], that is capable of monitoring its surroundings constantly for suspicious or illegal activities.Whenever it encounters a suspicious activity, it automatically captures and processes the scene, sends out an alert to the administrator and waits for his/her command.The main intention is to constantly monitor the surroundings using a bird shaped surveillance device, with very minimal human intervention.
Аналіз та моделювання методів вимірювання зміни швидкості психологічної реакції людини на рухомі об’єкти для використання у розробці arcade-подібної гри

The methods of sensorimotor reaction, method of reaction of choice, method of reaction of distinction, Shtenberg’s method and method of «mental rotation» for determination of the most effective use in software were considered outages during researches. On the basis of this analysis a programmatic interface is worked out for arcade-like game. The changes of speed reaction of a user on movable objects and possibility of their correlation were analysed. The modeling changes of speed of psychological of a user gives an opportunity to use this software product for the improvement of studies, training of sportsmen.
On the Hardness of Massively Parallel Computation

We investigate whether there are inherent limits of parallelization in the (randomized) massively parallel computation (MPC) model by comparing it with the (sequential) RAM model. As our main result, we show the existence of hard functions that are essentially not parallelizable in the MPC model. Based on the widely-used random oracle methodology in cryptography with a cryptographic hash function h:{0,1}n → {0,1}n computable in time th, we show that there exists a function that can be computed in time O(T · th) and space S by a RAM algorithm, but any MPC algorithm with local memory size s < S/c for some c>1 requires at least ~Ω(T) rounds to compute the function, even in the average case, for a wide range of parameters n ≤ S ≤ T ≤ 2n1/4. Our result is almost optimal in the sense that by taking T to be much larger than th,e.g., T to be sub-exponential in th, to compute the function, the round complexity of any MPC algorithm with small local memory size is asymptotically the same (up to a polylogarithmic factor) as the time complexity of the RAM algorithm. Our result is obtained by adapting the so-called compression argument from the data structure lower bounds and cryptography literature to the context of massively parallel computation.