Reviewerly/exHarmony huggingface dataset:
A MUSIC Spectrum Combining Approach for ENF-based Video Timestamping

Electric Network Frequency (ENF) fluctuates over time around the nominal value because of power load changes. Such fluctuations have a subtle influence on the artificial light emitted by sources that are supplied by the electric grid. Captured by camera sensors, this information can be used in digital forensic investigations to timestamp, geolocate or authenticate video recordings. However, extracting an ENF signal of sufficient quality from digital videos is still challenging, especially in presence of rolling shutter mechanisms and scene movements that interfere with the ENF extraction. In this paper, we present a novel method for the extraction of ENF signals in video recordings by exploiting parametric and non-parametric spectral estimation, and combining them to limit ENF distortions and increase frequency resolution. The experimental results obtained evaluating our technique using a publicly available dataset demonstrate its effectiveness in timestamping video recordings with a high level of accuracy.
Expert System in Periodontal Diseases Diagnosis Using the Certainty Factor Method

Abstract Dental and oral health is sometimes not a priority for some people, even though dental and oral diseases have a serious impact on health in general, because teeth and mouth are a place for germs and bacteria to enter, so they are likely to interfere with the health of other body organs. Periodontal disease is a gum disease caused by bacteria that damage the tissue supporting the teeth and cause tooth loss. Periodontal disease is divided into 2, namely gingivitis and periodontitis. An expert system is a system that tries to implement human knowledge on a computer so that computers can solve problems as experts do. The certainty factor method is used for calculations because the certainty factor method can solve the uncertainty of a problem by measuring a person’s belief. The result of this research is an expert system that can produce the name of periodontal disease and the level of certainty for the user’s disease. This application has 100% accuracy based on test results on 20 medical record data, it is obtained that the 20 data match with the results of the system diagnosis.
Instrument Response Removal and the 2020 MLg 3.1 Marlboro, New Jersey, Earthquake

Abstract To better understand earthquakes as a hazard and to better understand the interior structure of the Earth, we often want to measure the physical displacement, velocity, or acceleration at locations on the Earth’s surface. To this end, a routine step in an observational seismology workflow is the removal of the instrument response, required to convert the digital counts recorded by a seismometer to physical displacement, velocity, or acceleration. The conceptual framework, which we briefly review for students and researchers of seismology, is that of the seismometer as a linear time-invariant system, which records a convolution of ground motion via a transfer function that gain scales and phase shifts the incoming signal. In practice, numerous software packages are widely used to undo this convolution via deconvolution of the instrument’s transfer function. Here, to allow the reader to understand this process, we start by taking a step back to fully explore the choices made during this routine step and the reasons for making them. In addition, we introduce open-source routines in Python and MATLAB as part of our rflexa package, which identically reproduce the results of the Seismic Analysis Code, a ubiquitous and trusted reference. The entire workflow is illustrated on data recorded by several instruments on Princeton University campus in Princeton, New Jersey, of the 9 September 2020 magnitude 3.1 earthquake in Marlboro, New Jersey.
Matching Point Sets with Quantum Circuit Learning

In this work, we propose a parameterised quantum circuit learning approach to point set matching problem. In contrast to previous annealing-based methods, we propose a quantum circuit-based frame-work whose parameters are optimised via descending the gradients w.r.t a kernel-based loss function. We formulate the shape matching problem into a distribution learning task; that is, to learn the distribution of the optimal transformation parameters. We show that this framework is able to find multiple optimal solutions for symmetric shapes and is more accurate, scalable and robust than the previous annealing-based method.
Single-Pass On-Line Event Detection in Twitter Streams

Intensive information is emerged in social media every second. Many breaking news often appear first in social media, much earlier than they appear in traditional news media. Through the technology of event detection on social media data streams, scatter information can be gathered together to inform us the popular events discussing online. An event is often modeled as a cluster of documents which discuss the same subject. Traditional event detection methods perform poorly on social media because of their huge amount of data and irregular expressions. In this paper, we propose a simple yet efficient event detection method towards social media. An event is represented by a sequence of keywords extracted from social media. We use a single-pass incremental clustering method with a trained encoder mapping documents and events into the same semantic space, which is helpful for the similarity calculation between them. We consider the similarity calculation between a tweet and an event as a matching process and construct a relevance matching dataset with tweet-event pairs. We finetune BERT (Bidirectional Encoder Representations from Transformers) model in the matching dataset to get an appropriate semantic encoder. Keywords are dynamically changed to represent an event for capturing the development of the event. Our proposed method achieves 0.86 on NMI (Normed Mutual Information), 0.69 on ARI (Adjusted Rand Index) and 0.70 on F1-score on a public twitter dataset, which shows the superiority of our method compared with baseline methods.
Blockchain-Enabled Privacy-Preserving Location Sharing Scheme for LBSNs

The rise of Internet of Things (IoT) technology promotes the rapid development of location services industry. The idea of smart connectivity also provides a new direction for Location-Based Social Networks (LBSNs). However, due to limited calculate ability and internal storage space of IoT devices, historical location data of users is generally stored in the central server, which is likely to cause the disclosure of users’ private data. In this paper, we propose a Blockchain-enabled Privacy-Preserving Location Sharing (B-PPLS) scheme, which is a new framework that not only protects user location privacy but also provides effective location sharing services for users. For B-PPLS, location data owners can share the location area instead of location coordinates to Requesters, in order to realize the location privacy preserving. Also, the Merkle hash tree is utilized to divide the location area, so as to realize the multilevel privacy preserving. Furthermore, four algorithms are proposed to achieve the four stages of initialization, location record, location sharing, and location verification, respectively. Finally, we analyze the security of the proposed B-PPLS scheme and compare the performance with other related location privacy-preserving schemes by experimental evaluation.
Design and Implementation of Game Technologies in the Bachelor Program «Personnel Management» Using the ADDIE Model

The issues of the need to take into account the requirements of professional standards in the higher education system are highlighted and considered in order to harmonize the relationship between the labor market and the sphere of vocational education. The characteristics of the work program of the discipline are analyzed in the context of updating the Federal State Educational Standard of Higher Education in connection with changes in the organizational and content component of higher education in Russia. The main attention is paid to the problem of using active teaching methods that contribute to the formation and development of practice-oriented competencies of students. Based on the study of materials on various approaches to ensuring the effectiveness of practice-oriented learning, the significant role of business games has been established. Consideration of a business game as a simulation model of a real professional and life situation is proposed. The functions and structure of the business game are highlighted. The specificity of pedagogical design as a systematic approach to the formation of a curriculum is considered, as well as the ADDIE model, which is used as a universal approach to the development of an educational resource. The main results of using the ADDIE model in the development and implementation of a business game in the discipline of the variable block of the curriculum of the bachelor's educational program "Personnel Management" are presented.
Detection of Pulsars by Classical Machine Learning Algorithms

This paper compares the classical machine learning algorithms to classify a pulsar (rapidly spinning neutron star) in the HTRU-2 dataset [6]. This dataset was gathered during the survey, called the High Time Resolution Universe (South), and is an imbalanced dataset consisting of more than 17 thousand records. Being highly imbalanced, several methodologies have been implemented (such as sampling, resampling, and feature selection) to overcome the imbalance between target classes (binary in this case) to achieve higher accuracies. The results of the proposed work show that by applying different methodologies, it is possible to overcome the class imbalance of the dataset, which in turn helps the classical machine learning algorithms to obtain promising accuracies.
Prototyping Things: Reflecting on Unreported Objects of Design Research for IoT

Prototypes and other ‘things’ have had many uses in HCI research—to help understand a problem, as a stepping stone towards a solution, or as a final outcome of a research process. However, within the messy context of a research through design project, many of these roles do not form part of the final research narratives, restricting the ability of other researchers to learn from this practice. In this paper we revisit prototypes used in three different design research projects, conducted over a period when the Internet of Things emerged into everyday life, exploring complex hidden relationships between the internet, people and physical objects. We aim to explore the unreported roles that prototypes played in these projects, including brokering relationships with participants and deconstructing opaque technologies. We reflect on how these roles align with existing understandings of prototypes in HCI, with particular attention to how these roles can contribute to design around IoT.
Challenges of Sentiment Analysis - A Survey

Sentiment Analysis (SA) is a field that studies the polarity of text that conveys people's feelings or opinions about a topic. People use social media sites to openly express themselves. This paper gives an overview of recent developments in the field. Despite the fact that research in this area is still ongoing and rising by the day, it still faces a number of obstacles. Researchers also consider finding a research issue to be a challenging challenge. The key contribution of this paper is to discuss some of the most pressing issues in SA and to present a holistic image of these issues based on the contributions of other scholars, which will assist upcoming scientists in this field in identifying a research problem to which they can contribute their own contribution.
Expectation-Maximization Attention Cross Residual Network for Single Image Super-resolution

The depth of deep convolution neural network and self-attention mechanism is widely used for the single image super-resolution (SISR) task. Nevertheless, we observed that the deeper network was more hard to train and the self-attention mechanism is computationally consuming. Residual learning has been widely recognized as a common approach to improve network performance for deep learning, but most existing methods did not make the best of the learning ability of deep CNN, thus hindering the ability of representative CNN. In order to tackle these problems, we introduce a deep learning network namely expectation-maximization attention cross residual network (EACRN) to tackle the super-resolution task. Particularly, we propose a cross residual in cross residual (CRICR) structure that makes up very deep networks consisting of multiple cross residual groups (CRG) with global residual skip connections. Every cross residual group (CRG) consists of some cross residual blocks with cross short skip connections. At the same time, CRICR allows network focused on capturing high-frequency patterns by connecting rich low-frequency patterns to be bypassed and several short skip connections. In addition, we introduce various convolution kernel size so that adaptive capture the image pattern in different scales, which make these features get the more efficacious image information through interacting with each other. The introduced Expectation-Maximization Attention (EMA) module is robust to the variance of input and is also friendly in memory and computation. Extensive experiments demonstrate our EACRN obtains superior performance and visual effect relative to the most advanced algorithm.
Digital Elevation Model resolution: Effects on the chronometry-oriented morphological analysis of monogenetic scoria cones in the Sierra Chichinautzin, central Mexico

Morphological characterization of monogenetic cones provides essential information on different genetic, evolutionary, and hazard-related features of dispersive volcanism. Specifically, it may be used to measure the degree of erosional shape alteration of scoria cones, and from there, to estimate the age of individual volcanoes in extensive volcanic fields and the hazard implications of the cones' age distribution. Here we compare diverse morphometric parameters, some resulting from the elevation profiles analyses, and a novel one obtained from a shape characterization of elevation contours using Elliptical Fourier Descriptors, of 19 radiometrically-dated monogenetic scoria cones in the Sierra Chichinautzin Volcanic Field in Central Mexico. The parameters were calculated from two independent Digital Elevation Models with different resolutions: the 12 m resolution TanDEM-X SAR database, and a 5 m resolution airborne LIDAR DEM. From the analysis of the available information on the volcanic cones in the age range 1.6 to 222 ka, we conclude that the age-relevant morphometric parameters calculated from the TanDEM-X satellite DEM render alike values and perform equally well as the LIDAR airborne DEM for the morpho-chronological characterization of scoria cones, expanding the role of digital satellite elevation data to the study of detailed features of distributed volcanism at a global level. • Equivalence of morphometric parameters characterizing volcanic cones obtained from 5m and 12m resolution DEMs. • Assessment of various resolution-dependent, age-related morphometric parameters of monogenetic scoria cones. • Terrain-inclination corrections for the morphometric parameters of monogenetic volcanic cones.
Securing Cloud Native Applications Using Blockchain

Cloud computing technologies have dominated more that a decade. Wider adoption of cloud based processing has given rise to different architectural patterns. Cloud native applications have emerged out ensuring application agility and scalability. But they have their own challenges in terms of security. In the last few years, blockchain technology has found its applicability in non-cryptocurrency areas as well. This paper illustrates how blockchain can be used to address security challenges for the cloud native applications. This work focuses challenges and possible blockchain based solution in the areas of network security, identity management, authentication, container security and audit log for forensics.
The Modified-Half-Normal distribution: Properties and an efficient sampling scheme

We introduce a new family of probability distributions that we call the Modified-Half-Normal distributions. It is supported on the positive part of the real line with its probability density proportional to the function x↦xα−1 exp (−βx2+γx)I(x>0),α>0,β>0,γ∈R. We explore a number of its properties including showing the fact that the normalizing constant and moments of the distribution can be represented in terms of the Fox-Wright function. We demonstrate its relevance by showing its connection to a number of Bayesian statistical methods appearing from multiple areas of research such as Bayesian Binary regression, Analysis of Directional data, and Bayesian graphical model. The availability of its efficient sampling scheme is important to the success of such Bayesian procedures. Therefore, a major focus of this article is the development of methods for generating random samples from the Modified-Half-Normal distribution. To ensure efficiency, we prove that the constructed accept reject algorithms are “uniformly efficient” with high acceptance probability irrespective of the choice of the parameter specifications. Finally, we provide basic inference procedure for its parameters when analyzing data assuming the Modified-Half-Normal probability model.
An Evaluation of Task and Information Driven Approaches for Radar Resource Allocation

This paper describes an evaluation of different methods of radar resource allocation. The evaluation operates in a cognitive fully adaptive radar framework, specialized to concurrent tracking and classification of multiple airborne targets using a single airborne radar platform. The framework is based on the perception-action cycle of cognition and includes a perceptual processor that performs multiple radar system tasks and an executive processor that allocates system resources to the tasks to decide the next transmission of the radar on a dwell-by-dwell basis. We compare allocation algorithms using task-based and information-based strategies. Our main contribution is the illustration of the allocation algorithms in a MATLAB-based testbed and a comparison of the performance and the sensor task selections made by each.
On Improving Deep Learning Trace Analysis with System Call Arguments

Kernel traces are sequences of low-level events comprising a name and multiple arguments, including a timestamp, a process id, and a return value, depending on the event. Their analysis helps uncover intrusions, identify bugs, and find latency causes. However, their effectiveness is hindered by omitting the event arguments. To remedy this limitation, we introduce a general approach to learning a representation of the event names along with their arguments using both embedding and encoding. The proposed method is readily applicable to most neural networks and is task-agnostic. The benefit is quantified by conducting an ablation study on three groups of arguments: call-related, process-related, and time-related. Experiments were conducted on a novel web request dataset and validated on a second dataset collected on pre-production servers by Ciena, our partnering company. By leveraging additional information, we were able to increase the performance of two widely-used neural networks, an LSTM and a Transformer, by up to 11.3% on two unsupervised language modelling tasks. Such tasks may be used to detect anomalies, pre-train neural networks to improve their performance, and extract a contextual representation of the events.
Is large improvement in efficiency of impulsive noise removal in color images still possible?

The substantial improvement in the efficiency of switching filters, intended for the removal of impulsive noise within color images is described. Numerous noisy pixel detection and replacement techniques are evaluated, where the filtering performance for color images and subsequent results are assessed using statistical reasoning. Denoising efficiency for the applied detection and interpolation techniques are assessed when the location of corrupted pixels are identified by noisy pixel detection algorithms and also in the scenario when they are already known. The results show that improvement in objective quality measures can be achieved by using more robust detection techniques, combined with novel methods of corrupted pixel restoration. A significant increase in the image denoising performance is achieved for both pixel detection and interpolation, surpassing current filtering methods especially via the application of a convolutional network. The interpolation techniques used in the image inpainting methods also significantly increased the efficiency of impulsive noise removal.
Exploring Generative Reflection by Agency of Visual Practice

This study explores generative dimensions of reflection, shifting the focus of inquiry from tracking data to generating insights by visual practice. Two authors conducted autoethnographic research through visual exercises and analyzed structural elements and outcomes of the practice. Five generative reflection stages were developed by thematic analysis of written reflection notes: 1) notice and focus, 2) translate and frame, 3) make with materials, 4) interpret, and 5) continue and connect. Themes and styles of resulting images were interpreted through compositional analysis. Based on the study findings, we discuss some types of agency found by visual practice in generating visual insights: 1) making the invisible visible, 2) de/re-constructing with visual elements, 3) filling with compositional integrity, and 4) re-styling and re-mixing. We conclude with implications of visual inquiry as a means of noticing and investigating agency in design research and creative practice for reflection.
Policy Gradient Importance Sampling for Bayesian Inference

In this paper, we propose a novel adaptive importance sampling (AIS) algorithm for probabilistic inference. The sampler learns a proposal distribution adaptation strategy by framing AIS as a reinforcement learning problem. Under this structure, the proposal distribution of the sampler is treated as an agent whose state is controlled using a parameterized policy. At each iteration of the algorithm, the agent earns a reward that is related to its contribution to the variance of the AIS estimator of the normalization constant of the target distribution. Policy gradient methods are employed to learn a locally optimal policy that maximizes the expected value of the sum of all rewards. Numerical simulations on two different examples demonstrate promising results for the future application of the proposed method to complex Bayesian models.
Appropriation of Digital Tracking Tools in an Online Weight Loss Community: Individual and Shared Experiences

Online health communities provide a space where people seek out and provide support for weight loss activities, including tracking. Our study examined the experiences of members of an online community (r/loseit on Reddit.com) who posted about using digital tracking tools for weight loss. A targeted search garnered 379 public posts, which were analyzed using Thematic Analysis. Four themes reflected members’ individual and shared experiences: Tracking as gaining insight, Tracking as a vehicle of control, Confronting challenges in sustaining tracking and Teaching and learning the skills of tracking. We highlight complex socio-technical processes that members developed around tracking tools and discuss how knowledge of these appropriations can be applied to designing future user-centered tracking tools to support weight loss. We discuss how the social context of an online health community can shape both the usage of tracking tools and self-regulatory processes for health behaviour change.