Reviewerly/exHarmony huggingface dataset:
Activity Classification Using Smartphone Gyroscope and Accelerometer Data

Activities, such as walking and sitting, are commonly used in biomedical settings either as an outcome or covariate of interest. Researchers have traditionally relied on surveys to quantify activity levels of subjects in both research and clinical settings, but surveys are not objective in nature and have many known limitations, such as recall bias. Smartphones provide an opportunity for unobtrusive objective measurement of various activities in naturalistic settings, but their data tends to be noisy and needs to be analyzed with care. We explored the potential of smartphone accelerometer and gyroscope data to distinguish between five different types of activity: walking, sitting, standing, ascending stairs, and descending stairs. We conducted a study in which four participants followed a study protocol and performed a sequence of various activities with one phone in their front pocket and another phone in their back pocket. The subjects were filmed throughout, and the obtained footage was annotated to establish ground truth activity. We applied the so-called movelet method to classify their activity. Our results demonstrate the promise of smartphones for activity detection in naturalistic settings, but they also highlight common challenges in this field of research.
PENERAPAN ALGORITMA APRIORI DALAM MENENTUKAN POLA PEMBELIAN KONSUMEN DI KAFE HIDDEN TOAST AND FLOAT

Progress in Information Technology encourages culinary businesses to innovate, one of them is a computerized system, online-based sales and several interesting features that can increase consumer interest and increase sales to be the most frequently used innovation today. The cafe "Hidden Toast and Float" is a cafe in the City of Kediri. To increase sales from the cafe, a system is needed to facilitate the owner in recording sales and increasing the number of sales by providing automatic menu recommendations to customers. Based on the problem, in this thesis a website-based sales system and sales system will be created that is accompanied by the application of a priori algorithm to determine the purchasing patterns of customers and automatic menu recommendations from the system for customers. The test results of this thesis are two website-based systems with admin systems used to process existing data on the database and customer websites that are used for online purchases, as well as the application of a priori algorithms with the results of testing sample data and real data that produce menu combination recommendations. most often purchased based on all transaction data, namely Dark Choco Jam and Cappucino with a support value of 15% and a confidence value of 45%.
Investigating the Effect of Blended Learning Activity in Teaching Reading at Second Grade Senior High School

Blended learning has been growing in popularity as it has proved to be an effective approach for accommodating an increasingly diverse student population while adding value to the learning environment through the incorporation of online teaching resources. This study aims to investigate the effects of The use of blended learning techniques in teaching reading at the second-grade students of Man 2 Model Mataram In Academic Year 2017/2018. The purpose of the study is to know the effect of using Blended Learning technique in teaching reading at the second-grade student of Man 2 Model Mataram. The research method was quasi-experimental research with nonequivalent control group design. The population of the research was of the conducted of two classes as the sample; Experimental group (XI IS 2) and control group ( XI IS 1 ). XI IS 2 was taught by using Blended learning technique of reading while XI IS 1 was taught without Blended learning technique of reading. The researcher gave reading the test to gather the data. The formula that used to analyze the data was t-test. It is supposed to know the significant difference between students’ score in the experimental group and control group. Based on the computation of the t-test formula, it was found that the mean score of the experimental group was 14,21 and the mean score of the control group was 8,42 after analyzing the data,
Everyone is a Cartoonist: Selfie Cartoonization with Attentive Adversarial Networks

Selfie and cartoon are two popular artistic forms that are widely presented in our daily life. Despite the great progress in image translation/stylization, few techniques focus specifically on selfie cartoonization, since cartoon images usually contain artistic abstraction (e.g., large smoothing areas) and exaggeration (e.g., large/delicate eyebrows). In this paper, we address this problem by proposing a selfie cartoonization Generative Adversarial Network (scGAN), which mainly uses an attentive adversarial network (AAN) to emphasize specific facial regions and ignore low-level details. More specifically, we first design a cycle-like architecture to enable training with unpaired data. Then we design three losses from different aspects. A total variation loss is used to highlight important edges and contents in cartoon portraits. An attentive cycle loss is added to lay more emphasis on delicate facial areas such as eyes. In addition, a perceptual loss is included to eliminate artifacts and improve robustness of our method. Experimental results show that our method is capable of generating different cartoon styles and outperforms a number of state-of-the-art methods.
Computational studies of flow in flue pipes

Direct numerical solutions of the Navier-Stokes (NS) equations have been used to study the air flow in a recorder. When the recorder is driven with a DC flow into the windway, we observe the familiar oscillating flow out of the window adjacent to the labium. The associated flow at the open end is also studied. For a pipe with a square cross-section, we find the flow at the open end to be small but nonzero, consistent with recent studies using speckle pattern interferometry by Coyle and Moore. In addition, we have analyzed the NS results for the air density so as to compare directly with the interferometry experiments. We also describe simulations for circular pipes. [Work supported by NSF Grant PHY1513273.]
Bridging the Domain Gap for Ground-to-Aerial Image Matching

The visual entities in cross-view images exhibit drastic domain changes due to the difference in viewpoints each set of images is captured from. Existing state-of-the-art methods address the problem by learning view-invariant descriptors for the images. We propose a novel method for solving this task by exploiting the generative powers of conditional GANs to synthesize an aerial representation of a ground level panorama and use it to minimize the domain gap between the two views. The synthesized image being from the same view as the target image helps the network to preserve important cues in aerial images following our Joint Feature Learning approach. Our Feature Fusion method combines the complementary features from a synthesized aerial image with the corresponding ground features to obtain a robust query representation. In addition, multi-scale feature aggregation preserves image representations at different feature scales useful for solving this complex task. Experimental results show that our proposed approach performs significantly better than the state-of-the-art methods on the challenging CVUSA dataset in terms of top-1 and top-1% retrieval accuracies. Furthermore, to evaluate the generalization of our method on urban landscapes, we collected a new cross-view localization dataset with geo-reference information.
Pixelation is NOT Done in Videos Yet

This paper introduces an algorithm to protect the privacy of individuals in streaming video data by blurring faces such that face cannot be reliably recognized. This thwarts any possible face recognition, but because all facial details are obscured, the result is of limited use. We propose a new clustering algorithm to create raw trajectories for detected faces. Associating faces across frames to form trajectories, it auto-generates cluster number and discovers new clusters through deep feature and position aggregated affinities. We introduce a Gaussian Process to refine the raw trajectories. We conducted an online experiment with 47 participants to evaluate the effectiveness of face blurring compared to the original photo (as-is), and users' experience (satisfaction, information sufficiency, enjoyment, social presence, and filter likeability)
Peek-a-boo, I Can See You, Forger: Influences of Human Demographics, Brand Familiarity and Security Backgrounds on Homograph Recognition.

Homograph attack is a way that attackers deceive victims about which domain they are communicating with by exploiting the fact that many characters look alike. The attack becomes serious and is raising broad attention when recently many brand domains have been attacked such as Apple Inc., Adobe Inc., Lloyds Bank, etc. We first design a survey of human demographics, brand familiarity, and security backgrounds and apply it to 2,067 participants. We build a regression model to study which actors affect participants' ability in recognizing homograph domains. We then find that participants exhibit different ability for different kinds of homographs. For instance, female participants tend to be able to recognize homographs while male participants tend to be able to recognize non-homographs. Furthermore, 16.59% of participants can recognize homographs whose visual similarity with the target brand domains is under 99.9%; however, when the similarity increases to 99.9%, the number of participants who can recognize homographs drops down significantly to merely 0.19%; and for the homographs with 100% of visual similarity, there is no way for the participants to recognize. We also find that people working or educated in computer science or computer engineering are the ones who tend to exhibit the best ability to recognize all kinds of homographs and non-homographs. Surprisingly to us, brand familiarity does not influcence the ability in either homographs or non-homographs. Stated differently, people who frequently use the brand domains but do not have enough knowledge are still easy to fall in vulnerabilities.
Self Training Autonomous Driving Agent

Intrinsically, driving is a Markov Decision Process which suits well the reinforcement learning paradigm. In this paper, we propose a novel agent which learns to drive a vehicle without any human assistance. We use the concept of reinforcement learning and evolutionary strategies to train our agent in a 2D simulation environment. Our model's architecture goes beyond the World Model's by introducing difference images in the auto encoder. This novel involvement of difference images in the auto-encoder gives better representation of the latent space with respect to the motion of vehicle and helps an autonomous agent to learn more efficiently how to drive a vehicle. Results show that our method requires fewer (96% less) total agents, (87.5% less) agents per generations, (70% less) generations and (90% less) rollouts than the original architecture while achieving the same accuracy of the original.
NO-REFERENCE IMAGE QUALITY ASSESSMENT BASED ON DISTORTION SPECIFIC AND NATURAL SCENE STATISTICS BASED PARAMETERS: A HYBRID APPROACH

In web applications, for efficient use of bandwidth and storage requirement, images are compressed with lossy techniques. Quality of images degrades due to the compression artifacts. Transmission channels also contribute in degradation by adding noise. Image quality assessment plays a vital role to address this issue. In majority of cases, reference image remains unavailable for the assessment. Thus, No-Reference quality assessment techniques are widely used. In this paper, machine learning based hybrid approach for NR quality assessment is proposed. Blockiness based parameters, other statistical parameters and NSS based features are provided as input to the feed forward neural network. The back propagation training algorithm predicts a quality score. This score is correlated with differential mean opinion score (DMOS). Here, input can be images of any type from best to worst quality, and the approach used exploits the nonlinearity in the behaviors of parameters. It has been noticed that the predicted score correlates well with the DMOS with 93% accuracy. The above parameters are also used as input to Support Vector Machine for classification. The observed accuracy of this classifier is 89%.
Shape Recognition in High-level Image Representations: Data Preparation and Framework of Recognition Method

The automatic shape recognition is an important task in various image processing applications, including medical problems. Choosing the right image representation is key to the recognition process. In the paper, we focused on high-level image representation (using line segments), thanks to which the amount of data necessary for processing in subsequent stages is significantly reduced. We present the framework of recognition method with the use of graph grammars.
A Linear-logical Reconstruction of Intuitionistic Modal Logic S4

We propose a linear to reformulate intuitionistic modal logic S4 (IS4) in terms of linear logic, establishing an S4-version of Girard translation from IS4 to it. While the Girard translation from intuitionistic logic to linear logic is well-known, its extension to modal logic is non-trivial since a naive combination of the S4 modality and the exponential modality causes an undesirable interaction between the two modalities. To solve the problem, we introduce an extension of intuitionistic multiplicative exponential linear logic with a modality combining the S4 modality and the exponential modality, and show that it admits a sound translation from IS4. Through the Curry-Howard correspondence we further obtain a Geometry of Interaction Machine semantics of the modal lambda-calculus by Pfenning and Davies for staged computation.
Monitoring Usage of Open Access Long-Form Content

Presentation for NISO Virtual Conference Long Form Content: Ebooks, Print Volumes and the Concerns of Those Who Use Both. The Andrew W. Mellon Foundation recently funded a study of the landscape of usage data for open-access scholarly monographs and an investigation of the viability of creating a data trust for sharing of usage data among stakeholders in the publishing ecosystem. In spring 2019, the Book Industry Study Group (BISG) will publish a final white paper that takes into account feedback from the community during a consultation period. This presentation, part of a NISO virtual conference (https://www.niso.org/events/2019/03/long-form-content-ebooks-print-volumes-and-concerns-those-who-use-both ), provides a summary of the main findings and proposals of the forthcoming white paper.
Differential Logical Relations, Part I: The Simply-Typed Case (Long Version)

We introduce a new form of logical relation which, in the spirit of metric relations, allows us to assign each pair of programs a quantity measuring their distance, rather than a boolean value standing for their being equivalent. The novelty of differential logical relations consists in measuring the distance between terms not (necessarily) by a numerical value, but by a mathematical object which somehow reflects the interactive complexity, i.e. the type, of the compared terms. We exemplify this concept in the simply-typed lambda-calculus, and show a form of soundness theorem. We also see how ordinary logical relations and metric relations can be seen as instances of differential logical relations. Finally, we show that differential logical relations can be organised in a cartesian closed category, contrarily to metric relations, which are well-known not to have such a structure, but only that of a monoidal closed category.
The Application of Historical Materials Teaching Method in High School History Class

In the new curriculum reform, historical materials teaching method is becoming more and more important in middle school history teaching. This article is the analysis of materials in two aspects. This paper analyzes the practical significance of historical materials teaching, and points out the problems that should be paid attention to in the teaching of historical materials. Through the analysis of some teaching cases, it can provide practical experience for the better use of historical materials in the later period.
Cashless Societies and the Rise of the Independent Cryptocurrencies: How Governments Can Use Privacy Laws to Compete with Independent Cryptocurrencies

Many individuals (including governments) envision living in a future world where physical currency is a thing of the past.Many countries have made great strides in their efforts to go cashless.At the same time, there is increasing awareness among citizens of the decreasing amount of privacy in their lives.The potential hazards cashless societies pose to financial privacy may incentivize citizens to hold some of their money in independent cryptocurrencies.This article argues that in order for governments in cashless societies to keep firm control over their money supply, they should enact stronger privacy law protections for its citizens in order to decrease the real or perceived loss of (financial) privacy.This paper compares the privacy laws that exist today in both the United States and the European Union and suggests combining elements of both legal systems in order create a more privacy-friendly legal framework that can enable governments to complete against independent cryptocurrencies.
Community Partnership Program (PKM) Research Statistics for Teachers in NU Plus Vocational School, Sidoarjo

The NU PLUS vocational school (SMK) located in Sidoarjo Regency as a forum for nation generation, always applies various methods and development of learning media to answer the demands of the era to produce high-quality human resources, the problem facing this school is that the ability of teachers is still lacking in analyzing research data using statistical tests. Therefore, it is necessary to increase human resources concerning research through data analysis training. One way to help teach teachers in conducting research is to provide training in research data analysis using SPSS software. SPSS software is a software that can help process research data making it easier for researchers to analyze the data. This training aims to solve the problems faced by Sidoarjo Vocational High School teachers as partners, by providing training and mentoring methods of data analysis which include: 1) Methods of data analysis using descriptive statistics; 2) Methods of Quantitative data analysis using parametric statistics; 3) Qualitative Data Analysis Method using non-parametric statistics; 4) Research Methods, Classroom Action Research. The training design above is expected to improve some of the abilities and skills of the Sidoarjo Vocational High School teachers, including the following: 1) Processing research data using descriptive statistics; 2) Quantitative data processing using parametric statistics; 3) Processing of qualitative data using non-parametric statistics; 4) Mastering Research methods with Quantitative and Qualitative Approaches; 5) Able to carry out Classroom Action Research. The expected target of the community service activities in Sidoarjo Plus Vocational School includes: 1) Increased understanding, ability, and skills in analyzing data using descriptive statistics; 2) Increasing understanding, ability and skills in analyzing quantitative data by utilizing various kinds of parametric statistical tests that are tailored to the problem; 3) Improve understanding, ability and skills in analyzing qualitative data by utilizing various kinds of non-parametric statistical tests that are tailored to the problem; 4) Training manual; 5) Certificates and posters; 6) Scientific Publication in the Journal
Research on the Construction of Safety Guarantee System for Police Technical and Tactical Training in Public Security Colleges

The security system for police technical and tactical training in public security colleges in China is mainly composed of seven security guarantee systems: management organization, safety education, laws and regulations, training control, site equipment, safety supervision and emergency plan, scientifically and rationally constructing a security system in relevant public security colleges. This paper has carried out a comprehensive research on Hebei Vocational College of Public Security Police, and provided corresponding safety theory and guarantee for the security protection of the college during the police training period.
The Least Significant Two-bit Substitution Algorithm for Image Steganography

Steganography hides various data within different file types, which ensures secure communication. In recent years, the science of steganography has gained importance, due to the increase of large data on the internet and the safe transmission of these data. The main objective is to hide a large amount of data into the cover image in secure and incomprehensible manner. In this article, the two-bit least significant substitution (LSB) method is used to hide data on the colored cover images. In experimental studies, two-bit LSB substitution algorithm was performed in the form of LSB R2G2, LSB R2B2 and LSB G2B2 methods. In the classical LSB substitution method, the data are hidden in sequence, while the data are hidden by the shuffling algorithm at the proposed study. In this way, the security of hidden data is provided.
Development of E-Learning Based Poodle (Portable Moodle) for Computer and Basic Network Subject at Vocational High School

Abstract: The purpose of this study was to (1) develop E-Learning based Poodle (Portable Moodle) for Computer and Basic Network Subject (2) produce E-Learning based Poodle (Portable Moodle) on Computer and Basic Network Subject which is valid, practical and effective. In designing learning media, researchers using ADDIE development model. The resulting product is a developed media-based learning E-Learning Poodle (Portable Moodle) equipped with a guide book. This research conducted at SMK Negeri 1 Pangkep in Class X (Ten). The test subjects in this study consist of three students on one to one trial, Five students at the small group trial, twenty students on field trials, and a teacher and two observers in field trials. Instruments used in collecting the data are observational documents, questionnaire, documentation, and test. The results of this study, namely; (1) the process of learning development Poodle-based E-Learning refers to the ADDIE models that analyze, design, development, implementation, and evaluation; and (2) the product produced in the form of E-Learning based Poodle which is a valid, practical, and effective media of learning Computer and Basic Network Subject in SMK Negeri 1 Pangkep. The collected data were analyzed using descriptive techniques. Results showed that the products produced in the form of E-Learning based Poodle are valid, practical, and effective to be used as a medium of learning in computer and basic network subject in SMK Negeri 1 Pangkep.
Formare e gestire collezioni con i discovery tools

Discovery tools are specialized portals for bibliographic research widely used in libraries with heterogeneous collections of electronic and digital resources. The feature distinguishing them from software for federated search among online resources is their central index, that is a database to which libraries have access thanks to the contract stipulated with the discovery tool software companies. Between the two parts composing a discovery tool - the central index and the search interface - librarians have paid great attention to the second during the selection and parametrization of these research systems. The fact that discovery tools are becoming the only access portal to library collections, especially in the academic field, should shift the attention to their core tool: the central index, that is, a large collection not containing documents but metadata redirecting the user to the original databases containing information and publications.