Reviewerly/exHarmony huggingface dataset:
Automatic Detection of Dynamic and Static Activities of the Older Adults Using a Wearable Sensor and Support Vector Machines

Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant C and the kernel function parameter γ—are investigated. The changes associated with adding white-noise and pink-noise on these two parameters along with adding different sources of movement variations (i.e., localized muscle fatigue and mixed activities) are further discussed. The results indicate that the SVM algorithm is capable of keeping high overall accuracy by adjusting the two parameters for dynamic as well as static activities, and may be applied as a tool for automatically identifying dynamic and static activities of daily life in the older adults.
Interpretable Dictionary Learning Using Information Theory

We propose a novel supervised dictionary learning framework, which is based on discriminative power maximization and the classic Lempel-Ziv-Welch (LZW) source coding algorithm that can handle variable-length inputs. In the first stage, the input data is serialized and a dictionary is generated by the LZW algorithm. In the second stage, the dictionary is updated by discarding or selecting a certain amount of atoms in order to increase discriminative power measured by information bottleneck as well as linear similarity metrics. This dictionary learning framework is then analyzed using information bottleneck principles. Experiments on real life datasets from information security and social networking illustrate the effectiveness and accuracy of the proposed algorithms, especially for variable length data. This novel framework also identifies interpretable dictionary atoms, which provide insights to decision processes of the learning system.
Evaluation and Assessment of Students in College-School-Enterprise Cooperation Based on Entropy Bayesian Method

This paper adopts the entropy weighting approach for calculating the weights of teaching attitude, teaching content, basic teaching skill, and teaching effect in student assessment of school-enterprise cooperation (SEC) majors and combines with the Bayesian approach for calculating the a posteriori probability of student assessment ratings of eight teachers of SEC majors in a college to reflect the student assessment ratings of different teachers. The outcomes exhibit that, in comparison to the traditional cumulative integration approach, the entropy-weighted Bayesian approach can better reflect the actual teacher-student rating. Finally, this paper summarizes the shortcomings of the entropy-weighted Bayesian approach in the calculation of the a posteriori probability and suggests the necessity of further refining the scores in data collection to solve this problem.
A Tale of Three Priors

The histogram-probabilistic multihypothesis tracker (H-PMHT) is an efficient, track-before-detect algorithm that can operate directly on detection surfaces such as data from a focal-plane array (FPA). It provides good performance for low computational cost but is sensitive to sensor parameter changes, e.g. number of channels, gain, normalization, etc. This is because H-PMHT interprets the measured data as a histogram of point measurements; this sensor model is analogous to an FPA counting the number of photons that fall within each of its pixels. Typically this photon count is enormous, which can overwhelm the prior distribution describing target motion. The H-PMHT algorithm addresses this issue by applying a target's prior distribution to every measurement. For a linear, Gaussian target, this results in a data-dependent scaling term on both the measurement and the process noise covariance matrices. However, if the size or gain of an FPA is changed, the photon count changes significantly, and these scaling terms can cause unpredictable performance. One solution to this problem is to change the H-PMHT so that a target's prior distribution is only applied to photons from the same target. This article describes two different approaches to constructing target prior distributions that satisfy this constraint. The benefits of adopting either of these new target prior distributions are demonstrated using simulation.
A Patten Matcher for English Idioms on Web IndeX

Web Index (WIX in short) is a system that achieves joining information resources on the Web. WIX replaces keywords in Web documents hyperlinks to other web pages based on a WIX file that a user chose. WIX file is a kind of a dictionary that have a set of WIX entries (keyword and target URL). Using WIX, users can join any Web contents and arbitrary dictionaries. In conventional WIX, matching and linking are executed only for fixed character strings between the keyword set and the input text. However, when a user wants to search for phrases like idioms, this matching system is not sufficient because of the declension of words, change of the verb tense, and so on. Therefore, we propose a phrasal pattern matching mechanism on WIX. This helps users easily find idiom expressions in the text on the web and get more information.
Data streams are time series: challenging assumptions

The increasingly relevance of data streams in the context of machine learning and artificial intelligence has motivated this paper which discusses and draws necessary relationships between the concepts of data streams and time series in attempt to build on theoretical foundations to support online learning in such scenarios. We unify the concepts of data streams and time series by assessing their definitions in the literature and discuss the major implications of this claim on the way that data streams research and practice is carried out, showing that many common assumptions are incorrect or unnecessary. We analyzed six data sources typically used in benchmark data-stream classification and found that none of those meet the requirements and assumptions qualifying them for online learning.
Assessing the provision of public-facing cybersecurity guidance for end-users

The increased use of digital technologies and services brings with it a similarly increasing requirement for their end-users to have the awareness and ability to protect the security and privacy of their devices and data. However, this raises the related questions of what they need to know and from where they may obtain related guidance. The discussion begins by considering the knowledge and skills that could usefully characterize a cybersecurity literate user. The paper then proceeds to examine the provision of end-user guidance from 17 public-facing sources, spanning national sources, operating system provides, training providers and popular online services. The findings illustrate that, while some topics are commonly covered, none of them are found to be uniformly addressed across all sources. As such, the level of awareness that users may have, and the level of good practice that they may follow, is likely to be highly dependent upon where they have looked for guidance. This points toward the need for a consistent and more harmonized approach if we wish to ensure that a common baseline of cybersecurity literacy can be achieved across an increasingly digital society.
Advanced Image Segmentation Technique using Improved K Means Clustering Algorithm with Pixel Potential

Image segmentation is the method of partitioning, or segmenting, different parts of the image in such a way that all segments are disjoint and each has similar elements. This process has wide applications in the field of medicine and photography industry. There are many ways in which image segmentation can be performed, from which K-Means clustering algorithm is well renowned due to its simplicity and effectiveness to perform the task. In this paper, an improved variant of K-Means Clustering algorithm is presented. The algorithm rests on applying partial contrast stretching, eliminating randomness in choosing the initial cluster centres for K-means algorithm, and removing the unwanted noise from median filters to obtain a high-quality image output.
Blockchain and Decentralised Autonomous Organisations (DAOs): The Evolution of Companies?

Blockchain’s potential uses are wider than simply cryptocurrencies. Blockchain can and is being used to create Decentralised Autonomous Organisations (DAOs). DAOs represent a radical rethink of how organisations, such as companies, can be structured and run, including changes in ownership, governance, decision-making and profit distribution. DAOs not only lower transaction costs dramatically — and transaction costs are the very reason for a firm’s existence — through their use of smart contracts they can prevent laws and other rules being broken. This article shows that business structures have evolved over the centuries and DAOs are simply another evolution. DAOs require a rethink of the law, including the granting of legal personality to DAOs as well as granting limited liability to DAO token holders.
Fly Slower, Deliver Faster: the Multiple Flying Sidekicks Traveling Salesman Problem with Variable Drone Speeds

This research considers unmanned aerial vehicles (UAVs) that may travel at varying speeds in a last-mile delivery system involving a single truck and a fleet of UAVs. In existing truck-and-UAV delivery models that assume constant or unlimited UAV endurance, the natural conclusion is that operating UAVs at higher speeds will either decrease, or have no adverse effect on, total delivery times. However, in reality, UAV power consumption is a nonlinear function of both speed and parcel weight; flying at high speeds can dramatically reduce flight ranges, thus limiting the effectiveness of these logistics systems. This paper addresses the tradeoffs between speed and range in a new variant of the combined truck/UAV delivery problem in which UAV speeds are treated as decision variables. A three-phased algorithm is provided that dynamically adjusts UAV speeds to achieve superior performance, with the goal of minimizing the total delivery time (or makespan). Results indicate that significant time savings can be achieved by operating UAVs at variable speeds. Furthermore, under certain conditions, optimizing UAV speeds also results in shorter truck travel distances, reduced UAV energy consumption per trip, and less UAV loitering while waiting to rendezvous with the truck.
Extreme Learning Machine Based Model Improved with Adaptive Activation Functions

This paper proposes an approach combining an Extreme Learning Machine based model with adaptive activation functions for regression problems. Research related to machine learning seeks to solve a given problem in the best possible way, generating increasingly accurate generalist predictive models. On the other hand, these models become more complex, requiring massive computational power to train and make inferences. The use of adaptive activation functions aims at increasing the models’ predictive capacity allowing generating better models without increasing their complexity. We evaluated the models’ performance in a benchmark of ten regression problems. The results showed that Extreme Learning Machine with adaptive activation functions, in general, outperforms standard functions with the same architecture.
Verification of performance of Multi-Parental Real-Valued Crossover in Interactive Genetic Algorithm

In interactive evolutionary computation, it is important to reduce user fatigue and to enhance the search performance in a few generations, since fatigue has an impact on users' evaluation process. These issues also have been found in in interactive genetic algorithm (IGA). In this study, we adopt multi-parental unimodal normal distribution crossover (UNDX- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> ) to solve these problems in IGA. UNDX- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> generates many offspring with considering the distribution of selected parents. The next population can be generated by applying UNDX- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> once with more than two parent solutions. Through this mechanism, the evaluation process becomes simple manner where users only select their preferable solutions. In addition, UNDX- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> can generate offspring that inherit their parents' traits, which enhance the search quality. In this paper, we evaluate the effectiveness of UNDX- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> through the numerical experiment, by using a color combination problem in Web design.
Image Animations on Driving Videos with DeepFakes and Detecting DeepFakes Generated Animations

The concept of image animation is to create a video or animation such that an object from an image is animated as per the motion of driving video. We plan to analyze with minor modifications of an existing framework which does this without any information beforehand about the object which is to be animated. To achieve that, we train our dataset on a set of images and videos for the objects of same category, for example (face, body, street views) etc. Some recent applications of neural networks (CNN) have proved to form realistic human heads. Realistic talking heads can be created by training the dataset of large number of images and videos. A source image of a person can be animated on target poses of a person (driving video), by keeping the appearance and body of the person. However, on the parallel side, there are advancements in the development of systems which are capable of detecting DeepFakes generated videos and animations as it is a crucial security concern. We did experiments on Image Animation to achieve talking heads, Image generations with conditional generative adversarial networks for DeepFakes Generations and the results were realistic. Moreover, we implemented a DeepFake Detector XceptionNet with minor modifications which achieved 95% accuracy on detecting DeepFakes. At last, we implemented a newly introduced technique in which the DeepFake generation is perturbed through which it can easily fool the deepfake detector. XceptionNet was able to achieve less than 30% accuracy on detecting DeepFakes generations when they were perturbed.
Global Cognition and Local Perception Network for Blind Image Deblurring

Nowadays, people are more and more used to taking pictures with handheld devices. However, the photos taken by handheld devices are easy to blur. Recent state-of-the-art methods for image deblurring often restore image through multiple stages, which leads to excessive network parameters and slow running speed, and makes it difficult to apply to mobile devices. While one-stage image deblurring methods have fewer parameters and faster running speed, the performance of their models is not so satisfactory. To solve these problems, we propose a lightweight one-stage image deblurring network called Global Cognition and Local Perception Network (GCLPN) in this paper. We design the Global Cognition Module (GCM) to obtain global feature descriptors and present the Local Perception Module (LPM) to help image reconstruction in local regions. Furthermore, we introduce a gradient loss that focuses on subtle image texture information during network training. Experimental results illustrate that our GCLPN surpasses the state-of-the-art methods in standard metrics of PSNR and SSIM with the least number of parameters (about 1.7M) and real-time running speed (about 0.37 s per \(720 \times 1280\) image).
Ultra-short-term PV Power Forecasting Based on Cloud Image Modeling

Abstract Due to the impact of many local sudden changes in photovoltaic (PV) output power, its ultra-short-term forecasting is facing great challenges. In a time scale of 1 to 2 minutes, the generation, movement, and ablation of clouds in the sky are the main factors that affect the output power. Therefore, a prediction method based on cloud images is proposed for ultra-short-term prediction of PV output power. First, apply image processing technology to extract image features that affect the changes in PV output power, including cloud coverage rate, direct sunlight rate, and image brightness; second, calculate the extraterrestrial irradiation and air mass at each time point; finally, use the above features as input factors, and use PV output power output data, and a long short-term memory network (LSTM) is used to construct a PV power prediction model to achieve ultra-short-term forecasting of PV output power.
Free Speech in India

This work intends to share a sense of free speech regulation in India. It is divided into three parts: Part I considers the underlying socio-legal basis of the free speech laws and norms in India, Part II explores what free speech regulation comprises from the points of view of social norm, positive law, and normative law, and, finally, Part III looks at how free speech restrictions are actually implemented in the country.
A Survey on Various Cryptography Techniques for Image Encryption and Decryption

In today’s world, the use of digital applications and multimedia is a necessity. And, for the communication of multimedia data, security is also needed. So, to secure multimedia data such as videos and images, the various cryptographic techniques can be used. These cryptographic techniques include encryption and decryption techniques. Hence, over a medium of the channel, while transferring these multimedia data encryption techniques are used that convert the original image into ciphertext which is not readable to the unauthorized user and then communication is done successfully. In past years, many encryption techniques are proposed and discovered for the protection of confidential and important data. In this paper, various encryption techniques have been surveyed and discussed in detail.
A Methodological Framework for Validating ZKP Authentication Process

The Internet of Things (IoT) is completely transforming the way network-connected devices are made. Manufacturers and intelligent transportation systems are using thousands of IoT devices and machine-to-machine communication to drive industrial automation. Existing access control schemes for IoT authentication fail to support user anonymity. They rely on the surrendering of the device/user authentication parameters to the trusted server, which hence can be utilized by the IoT infrastructure to track users' behavioral patterns. Furthermore, existing access control mechanisms lack the support of run-time integrity assessment capabilities that are used to verify the authenticity of an authentication process during execution. This paper presents a parametrized crypto-based privacy-preserving authentication protocol that support anonymity, it is based on Zero Knowledge Proof (ZKP). Without the loss of anonymity, a methodological framework for bootstrapping a parametrized authentication process's integrity is introduced herein. We show that run-time integrity assessment of an authentication process running on an IoT device can be achieved through the utilization of the IoT device's physical characteristics, specifically energy consumption and computation time. Behavioral patterns based on the device's power/energy consumption for the ZKP-based protocol were captured and recorded during this effort. In addition, fine-grained behavioral patterns that capture the authentication protocol's processing time were collected and analyzed. To validate the proposed scheme, it was fully implemented and deployed on an IoT testbed. We have tested the performance of the proposed scheme in terms of power consumption and computation time.
Viewport Rendering Algorithm with a Curved Surface for a Wide FOV in 360° Images

Omnidirectional visual content has attracted the attention of customers in a variety of applications because it provides viewers with a realistic experience. Among the techniques that are used to construct omnidirectional media, a viewport rendering technique is one of the most important modules. That is because users can perceptually evaluate the quality of the omnidirectional service based on the quality of the viewport picture. To increase the perceptual quality of an omnidirectional service, we propose an efficient algorithm to render the viewport. In this study, we analyzed the distortions in the viewport picture to be the result of sampling. This is because the image data for the unit sphere in the omnidirectional visual system are non-uniformly sampled to provide the viewport picture in the conventional algorithms. We propose an advanced algorithm to construct the viewport picture, in which the viewport plane is constructed using a curved surface, whereas conventional methods use flat square surfaces. The curved surface makes the sampling interval more equally spaced than conventional techniques. The simulation results show that the proposed technique outperforms the conventional algorithms with respect to the objective quality based on the criteria of straightness, conformality, and subjective perceptual quality.
Web GIS based assessment using SAW methods to identify high risk areas of tuberculosis transmission and incidence in Bandar Lampung City

Abstract Tuberculosis (TB) is still a major problem in the public health sector, even in 2016 Indonesia was included in the five countries with the highest TB case burden in the world. In 2017, the number of TB cases in Lampung Province was 7,040 cases and one of the obstacles faced was the tendency for TB sufferers to be difficult to open so that many cases were not detected. Based on these facts, a Geographical Information System (GIS) is proposed which can process data into maps to see high risk areas where TB disease was found in Bandar Lampung. It will make it easier for health agencies to make policies in handling or preventing TB disease in Bandar Lampung. In this study, a decision-making system with Simple Additive Weighting (SAW) method was also proposed to produce a map of the area with the level of TB risk factors. The criteria used to summarize the weights are TB cases, population density, mortality, healthy and clean behavior, and health facilities. The result of data processing is presented in a color polygon map that has 4 levels of risk factors, namely very high, high, medium, and low.
KLASTERISASI MEDIA PEMBELAJARAN DARING DI ERA PANDEMI COVID-19 MENGGUNAKAN METODE AGGLOMERATIVE

This study aims to determine the use of learning media clusters from more effective and efficient in terms of user assessments, both students, and teachers in the midst of the Coronavirus Disease 2019 (Covid-19) pandemic. The method used in this research is the agglomerative algorithm method hierarchial clustering to classify online learning media according to user ratings. This method will calculate the grouping or clustering with several agglomerative methods, namely the single linkage method, average linkage, and complete the environment. Each of these methods can perform distance calculations Euclidean distance, which will later determine the appropriate online learning media cluster user ratings. The data used is user assessment data (students, students and teachers). The result of clusterization for learning media that is most widely used is google classroom which is included in the learning application cluster, while for the internet, users choose internet data quota and for learning devices users use smartphones more. Keywords : Agglomerative , Klasterisasi , Media Pembelajaran Daring.