Reviewerly/exHarmony huggingface dataset:
DOA Estimation of LFM Signal Based on Single-Source Time-Frequency Points Selection Algorithm by Using the Hough Transform

Direction of arrival (DOA) estimation performance may degrade substantially when linear frequency modulation (LFM) signals are spectrally-overlapped in time-frequency (TF) domain.In order to solve this problem, the single-source TF points selection algorithm based on Wigner-Ville distribution (WVD) and Hough transform is studied in this paper.Firstly, the signal intersections in TF domain can be solved based on the Hough transform.Secondly, by removing multiple-source TF points at intersections according to the empirical threshold value which is calculated by using the statistical experiment method, we can get single-source TF points set.Then, based on the Euclidean distance operator, single-source TF points set belonging to each signal can be obtained according to the property that TF points of the same signal have the same eigenvector.Finally, the averaged spatial TF distribution matrix is constructed and DOA estimation is realized based on the multiple signal classification (MUSIC) algorithm.In this way, the proposed algorithm can resolve the TF non-disjoint LFM signals because it can automatically select single-source TF points set of each signal.Simulation results illustrate that the proposed algorithm possesses higher angular resolution and has pretty good DOA estimation precision compared with existing algorithms.
Seismologists Search for the Indian Ocean’s “Missing Mass”

An Indian scientific team set out to sea last year to find clues to the source of an unusually weak gravitational potential in the middle of the Indian Ocean.
An N-of-1 Evaluation Framework for Behaviour Change Applications

Mobile behaviour change applications should be evaluated for their effectiveness in promoting the intended behavior changes. In this paper we argue that the 'gold standard' form of effectiveness evaluation, the randomised controlled trial, has shortcomings when applied to mobile applications. We propose that N-of-1 (also known as single case design) based approaches have advantages. There is currently a lack of guidance for researchers and developers on how to take this approach. We present a framework encompassing three phases and two related checklists for performing N-of-1 evaluations. We also present our analysis of using this framework in the development and deployment of an app that encourages people to walk more. Our key findings are that there are challenges in designing engaging apps that automate N-of-1 procedures, and that there are challenges in collecting sufficient data of good quality. Further research should address these challenges.
Item Clustering as An Input for Skin Care Product Recommended System using Content Based Filtering

A lot of skin care products sold in the market shows us that skin care is an important part of lifestyle for both women and men. Considering that everyone has different skin profle, a recommendation system is required to help and give a personalised suggestion products based on the user's preferences. Recommendation system provides suggestions that effciently narrowing down the amount of informations so users will directed to the items which is most suitable for their skin. The method used in this paper is Content Based Filtering with K-means clustering for suggestion product calculation. The result is the system that can recommend skin care products for the users based on products they like, thus the users would fnd products those possibly suitable for their skin.
Violation Detection in Heterogeneous Events Streams

Violation detection at production facilities is a vital task, especially for critical infrastructures. Nowadays, many monitoring sensors of different types are used for the event collection. These sensors often provide a data in different format making thus complex analysis of such data more difficult. This paper suggests an approach for such data analysis, its distinctive feature is grouping events and application of an individual classification method to each group. The suggested approach is demonstrated by its application on real data within continuous time period.
Translation of UML Models for Self-adaptive Systems into Event-B Specifications

Engineering self-adaptive systems using a standard or a guide is a challenging task. In this context, we propose using a set of MAPE (Monitor-Analyze-Plan-Execute) patterns for modeling self-adaptive systems. Ensuring that there is no ambiguity, incompleteness and misunderstanding of self-adaptive systems that instantiate MAPE patterns is very crucial. For this purpose, we propose to specify self-adaptive systems instantiating MAPE patterns using the Event-B formal method to provide a precise reference for designer to carry out an effective verification. To this end, we propose to generate Event-B specifications from conceptual modeling of the self-adaptive systems. In this paper, we propose a set of structural transformation rules to transform each element in the model to its corresponding concept in the Event-B method.
Ontology model for tourism information in Banyumas

Banyumas has tourism potential to be promoted. An interesting issue that arises is how to store tourism information in Banyumas more semantically and make the information can be shared and reusable. This research proposed an ontology model in the tourism domain in Banyumas. Ontology implementation uses protégé software. Ontology models are developed into ontology forms with class, object property, and data property. Ontology testing uses four questions that are transformed into SPARQL. The results of the research and testing that have been carried out show that the ontology model can store and provide tourism information in Banyumas more semantically.
Improving Human Text Comprehension through Semi-

Sebastian Gehrmann, Steven Layne, Franck Dernoncourt. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Reflection Symmetry Detection by Embedding Symmetry in a Graph

Reflection symmetry is ubiquitous in nature and plays an important role in object detection and recognition tasks. Most of the existing methods for symmetry detection extract and describe each keypoint using a descriptor and a mirrored descriptor. Two keypoints are said to be mirror symmetric key-points if the original descriptor of one keypoint and the mirrored descriptor of the other keypoint are similar. However, these methods suffer from the following issue. The background pixels around the mirror symmetric pixels lying on the boundary of an object can be different. Therefore, their descriptors can be different. However, the boundary of a symmetric object is a major component of global reflection symmetry. We exploit the estimated boundary of the object and describe a boundary pixel using only the estimated normal of the boundary segment around the pixel. We embed the symmetry axes in a graph as cliques to robustly detect the symmetry axes. We show that this approach achieves state-of-the-art results in a standard dataset.
Cybersecurity and Digital Forensics – Course Development in a Higher Education Institution

Individuals and companies have a feeling of insecurity in the Internet, as every day a reasonable amount of attacks take place against users’ privacy and confidentiality. The use of digital equipment in illicit and unlawful activities has increasing. Attorneys, criminal polices, layers and courts staff have to deal with crimes committed with digital “weapons”, whose evidences have to be examined and reported by applying digital forensics methods. Digital forensics is a recent and fast-growing area of study which needs more graduated professionals. This fact has leveraged higher education institutions to develop courses and curricula to accommodate digital forensics topics and skills in their curricular offers. This paper aims to present the development of a cybersecurity and digital forensics master course in Polytechnic of Leiria, a public higher education institution in Portugal. The authors depict the roadmap and the general milestones that lead to the development of the course. The strengths and opportunities are identified and the major students’ outcomes are pointed out. The way taken and the decisions made are also approached, with a view to understanding the performance obtained so far.
Feature Clustering to Improve Fall Detection: A Preliminary Study

In this study, the fall detection method is carried out as stated on [1, 11]; a simple finite state machine is used to process acceleration data in sliding windows and whenever a fall-like event is found, features are extracted from this data. Using some clustering and classification algorithms described here, the event is classified as FALL or NOT_FALL. This research evaluates the performance of different proposed clustering and classification methods. It makes use of a new dataset, with data gathered by a wearable device placed on the wrist and used by several members of the research team and an emergency rescue training manikin under different fall scenarios to simulate the falls. A 10-fold cross-validation is also made to evaluate these methods on unseen data.
Convolutional Neural Network Approach for Extraction and Recognition of Digits from Bank Cheque Images

Recognition of handwritten character and digits is one of the major challenges in the computer vision system. One of the major application areas of character and number recognition is the financial sector, which deals with enormous amount of document data. In this paper, we have proposed to provide an automation system for bank cheque processing. The proposed system takes the cheque images as input and extracts the digits from account number, date and amount field, respectively. Initially, cheque images are preprocessed, and then digits are segmented using simple connected component analysis. Extracted digit images are normalized and given as input to convolutional neural network (CNN) classifier. Classifier is trained using large data set: MNIST and built-in MATLAB digit data set along with our data set. Total samples of 91,000 images are used for training and testing. Post-processing is done to construct the whole numbers for account number, date and amount. Recognition rate achieved for 50 cheque images is 95.59%.
Visual Comparison of Trace Files in Vampir

Comparing data is a key activity of performance analysis. It is required to relate performance results before and after optimizations, while porting to new hardware, and when using new programming models and libraries. While comparing profiles is straightforward, relating detailed trace data remains challenging. This work introduces the Comparison View. This new view extends the trace visualizer Vampir to enable comparative visual performance analysis. It displays multiple traces in one synchronized view and adds a range of alignment techniques to aid visual inspection. We demonstrate the Comparison View’s value in three real-world performance analysis scenarios.
A comparison of Hong Kong primary four students’ Chinese and English reading attainment in 2016

This study examines Hong Kong primary four (P4) students' Chinese and English reading test performance in a bilingual reading proficiency study (BR) conducted in 2016, the fifth round in a series of studies monitoring the bilingual reading competence of primary school students in Hong Kong since 2004. It also compares students' bilingual reading performance in 2016 with that in the 2013 round of testing. A total of 3,592 P4 students from 38 schools in Hong Kong participated in the study. Significant improvements were found both in the students' Chinese and English reading test performance, with marked increases in the overall English reading test scores and in performance on literary and informational texts. Such improvements may be due to the implementation of the English Language Curriculum, support from the Hong Kong Language Committee and new medium of instruction (MOI) arrangements in Hong Kong primary schools. The students' performance was significantly worse on Chinese literary texts overall and on the first three levels of reading comprehension processes in BR 2016. This may be a consequence of the increasing use of digital devices among young students and of low achievers' poor test performance. Future studies need to examine whether there are causal relationships between the variables mentioned above and the bilingual reading test performance of P4 students in Hong Kong.
CNN-based Visual/Auditory Feature Fusion Method with Frame Selection for Classifying Video Events

In recent years, personal videos have been shared online due to the popular uses of portable devices, such as smartphones and action cameras.A recent report [1] predicted that 80% of the Internet traffic will be video content by the year 2021.Several studies have been conducted on the detection of main video events to manage a large scale of videos.These studies show fairly good performance in certain genres.However, the methods used in previous studies have difficulty in detecting events of personal video.This is because the characteristics and genres of personal videos vary widely.In a research, we found that adding a dataset with the right perspective in the study improved performance.It has also been shown that performance improves depending on how you extract keyframes from the video.we selected frame segments that can represent video considering the characteristics of this personal video.In each frame segment, object, location, food and audio features were extracted, and representative vectors were generated through a CNN-based recurrent model and a fusion module.The proposed method showed mAP 78.4% performance through experiments using LSVC[2] data.
Shaft diameter measurement using digital image composition at two different object distances

A new machine visionmeasuring scheme was presented to improve the measurement accuracies of machine vision systems with an ordinary industrial lens and thus to address the extensive requirements for accurately measuring the diameter of shafts. First, method for measuring diameters based on two images was derived on the basis of a pinhole imaging principle, similar triangles and tangent property theorems. Then, a machine visionmeasuring system was introduced. Finally, experiments were performed on a transmission screw, and the results indicate that the method has achieved a higher measurement accuracy, with systematic errors of approximately 2 μm and uncertainties of 4.9 μm.
Graphing Data and Visual Analysis

As previously described, single-case design has several advantages in the evaluation of evidence-based practices and for the evaluation of the effects of interventions in applied settings. Following collection of data, data are typically graphed in order to determine the effects of an intervention on student behavior. However, recent research has determined that the manner in which graphs are constructed is likely to impact the decisions that visual analysts make regarding the effect of an intervention. As such, it is important that graphs be constructed in a manner that minimizes potential for error. This chapter describes quality indicators for graphs, and discusses analysis- and aesthetic-altering elements of graphs. In particular, the chapter describes two analysis-altering elements that must be considered when constructing graphs: scaling of the <italic>y</italic>-axis and the data points per <italic>x</italic>- to <italic>y</italic>-ratio (DPPXYR). Finally, the chapter describes how to conduct visual analysis. Six elements are discussed: changes in level, trend, and variability, consistency across similar conditions, overlap across adjacent phases, and immediacy of intervention effects.
Foreground target extraction method based on neighbourhood pixel intensity correction

The automatic processing and prediction of monitoring video information have attracted great attention in many fields, so how to extract the foreground and target information effectively and quickly is very important. In this paper, the surveillance video sequence is taken as the processing object, and a novel foreground extraction method is proposed for the dynamic background problem. The method is divided into two steps. Firstly, in order to solve the problem of the foreground and background moving, a foreground detection method based on neighbourhood pixel intensity correction is proposed. Then, the grey projection method is used to eliminate the video jitter caused by camera shake to achieve foreground extraction. The experimental results show that the proposed method has a wide application range and can extract the foreground target clearly and accurately when both the foreground and the background are moving and video jitter.
Cybercrime in Ghana and the Reaction of the Law

Since the advent of the internet, the world has become a global community.This is because through the worldwide web links information is being passed on and received almost instantaneously or in real time.People from different locations are able to communicate with one another by exploring social media platforms to advance their course.However, the increasingly use of the internet has fueled the emergence of cybercrime in Ghana and concerns have been raised by people in the position of trust about whether our current laws are robust to deal with the menace.The main thrust of the study was therefore to examine how the law enforcement agencies, especially the police and the judiciary are reacting to the cybercrime peril in the country.A total of nine ( 9) respondents took part in the study.Structured interview guide was employed as data collection instrument.In the data analysis, each of the in-depth interviews was transcribed as soon as the information is gathered and developed them into codes.The study revealed that the Ghana Police Service efforts to thwart the menace are being limited by factors such as computer crime investigators, ultramodern equipment, and cooperation from the Internet Service Providers to adduce electronic evidence.In addition, the Electronic Transaction Act of 2008, (Act 772) needs to be reviewed to accommodate the new challenges that have emerged in the cyber ecosystem.It was concluded from the discussions that a progressive capacity building programmes should be organized for law enforcement agencies to enable them acquire ICT skills to deal with cyber laws.
Recurrent-Convolution Approach to DeepFake Detection - State-Of-Art Results on FaceForensics++

The spread of misinformation through synthetically generated yet realistic images and videos has become a significant problem, calling for robust manipulation detection methods. Despite the predominant effort of detecting face manipulation in still images, less attention has been paid to the identification of tampered faces in videos by taking advantage of the temporal information present in the stream. Recurrent convolutional models are a class of deep learning models which have proven effective at exploiting the temporal information from image streams across domains. We thereby distill the best strategy for combining variations in these models along with domain specific face preprocessing techniques through extensive experimentation to obtain state-of-the-art performance on publicly available video-based facial manipulation benchmarks. Specifically, we attempt to detect Deepfake, Face2Face and FaceSwap tampered faces in video streams. Evaluation is performed on the recently introduced FaceForensics++ dataset, improving the previous state-of-the-art by up to 4.55% in accuracy.
Live Demonstration: Real-Time Implementation of Proto-Object Based Visual Saliency Model

We will demonstrate a real-time implementation of a protoobject based neuromorphic visual saliency model [1] on an embedded processing board. Visual saliency models are difficult to implement in hardware for real-time applications due to their computational complexity. The conventional implementation is not optimal because of the requirement of a large number of convolution operations for filtering on several feature channels across multiple image pyramids. Our current implementation considers the dynamic temporal motion change by convoluting along time efficiently by parallelly processing them. We have implemented the model on an NVIDIA Jetson TX1 board (Fig. 1), which has NVIDIA Maxwell GPU with 256 NVIDIA CUDA Cores, hosted on an Ubuntu environment. The board has a 5 MP fixed focus MIPI CSI camera through which the frames are fetched using a Quad-core ARM Cortex-A57 MPCore Processor with 4 GB LPDDR4 Memory. The camera module fetches the frames to the application for processing, and the result is then displayed through the HDMI port. The application is written in Tensorflow, Cuda, and Python and uses several Python libraries. For further analysis, the user can also save the output onto a file.
Rotation Invariant Householder Parameterization for Bayesian PCA

We consider probabilistic PCA and related factor models from a Bayesian perspective. These models are in general not identifiable as the likelihood has a rotational symmetry. This gives rise to complicated posterior distributions with continuous subspaces of equal density and thus hinders efficiency of inference as well as interpretation of obtained parameters. In particular, posterior averages over factor loadings become meaningless and only model predictions are unambiguous. Here, we propose a parameterization based on Householder transformations, which remove the rotational symmetry of the posterior. Furthermore, by relying on results from random matrix theory, we establish the parameter distribution which leaves the model unchanged compared to the original rotationally symmetric formulation. In particular, we avoid the need to compute the Jacobian determinant of the parameter transformation. This allows us to efficiently implement probabilistic PCA in a rotation invariant fashion in any state of the art toolbox. Here, we implemented our model in the probabilistic programming language Stan and illustrate it on several examples.
Individual, Team, Organization, and Market: Four Lenses of Productivity

When we think about productivity in software development, it’s reasonable to start with a basic concept of work per unit of effort. The more work a developer accomplishes with their efforts, the better. But when researchers have investigated how developers think about productivity, some surprising nuance surfaces about what software engineering “work” actually is and at what level this work should be considered. In particular, there are four lenses through which one can reason about productivity and each of these has different implications for what actions one might take to increase productivity in a company.
The application of restructuring of knowledge in civil engineering

In this paper, it is shown how knowledge theories and knowledge acquisition techniques are integrated by contextualisation to lead to the drawing of concept maps that can be used in civil engineering design, and to analyse and record specific experience. The concept maps form part of concept-based ontologies that are analysed to identify problems and constraints. Solutions to these problems and constraints create new knowledge and can be reported and linked to the world-wide-web. This linkage is made possible by utilising the Top-Level-Ontologies or Upper-Level-Ontologies to link to existing or new ontologies on the world-wide-web. The logic base acts as a procedure to lead and integrate all the above-mentioned aspects into three modules. These modules of the logic base are described and simple examples are given of how the logic base functions. The logic base is a technique to bring knowledge closer to the practising engineer, and facilitates thinking processes that will greatly assist in systematising knowledge, the analysis thereof and making it accessible on the word-wide-web.