Reviewerly/exHarmony huggingface dataset:
Using collective curation to pay data forward in the life cycle

Drawing from a study of archaeological excavation teams, four collective curation opportunities are proposed to identify and resolve differences in data and documentation practices that arise in team-based research. To create more integrated, well-documented data, the opportunities attend to integrating people rather than technology. The actions people take as data move through the life cycle become the focal point of change.
Hypothesis Testing for Class-Conditional Label Noise

In this paper we aim to provide machine learning practitioners with tools to answer the question: have the labels in a dataset been corrupted? In order to simplify the problem, we assume the practitioner already has preconceptions on possible distortions that may have affected the labels, which allow us to pose the task as the design of hypothesis tests. As a first approach, we focus on scenarios where a given dataset of instance-label pairs has been corrupted with class-conditional label noise, as opposed to uniform label noise, with the former biasing learning, while the latter – under mild conditions – does not. While previous works explore the direct estimation of the noise rates, this is known to be hard in practice and does not offer a real understanding of how trustworthy the estimates are. These methods typically require anchor points – examples whose true posterior is either 0 or 1. Differently, in this paper we assume we have access to a set of anchor points whose true posterior is approximately 1/2. The proposed hypothesis tests are built upon the asymptotic properties of Maximum Likelihood Estimators for Logistic Regression models. We establish the main properties of the tests, including a theoretical and empirical analysis of the dependence of the power on the test on the training sample size, the number of anchor points, the difference of the noise rates and the use of relaxed anchors.
Hash-Enabled Garbling and the Insecurity of Free-Hashing Garbled Circuits

Hashing garbled circuits is an important, albeit expensive, bandwidth- saving technique that can be an order-of-magnitude slower than generating garbled circuits. In a recent work, Fan et al. (EURO- CRYPT, 2017) proposed a method to produce GC-hashes with- out any calls to expensive collision-resistant hash functions. They showed experimentally that the overhead of hashing GCs can be eliminated almost entirely.
Faster R-CNN for Detecting Regions in Human-Annotated Micrograph Images

Object detection is a computer vision problem that involves classifying objects and identifying their location in a picture, video, or webcam feed. Applying deep learning techniques to this problem has led to huge contributions and attracted much research in this field. In this work, we use Faster RCNN to detect objects in micrograph images that have been manually annotated. We considered a Faster RCNN model because of its speed and its high accuracy of detection. The objective of this work is to detect objects in medical images and to evaluate the performance of Faster RCNN for medical images. We also compared the detecting highly overlapped objects and non-overlapping objects. Our experiment included 65 micrograph images with approximately 1000 objects, with findings that demonstrate that Faster RCNN obtained highly accurate results with mPA 76% for non overlapping objects.
Video training as a means of developing artistic skills of elementary school students

The subject of this research is video training as a means of developing artistic skills of elementary school students. The relevance of the selected topic is substantiated by the rapidly changing world and implementation of new technologies in education system. Analysis of the forms of video transmission and distance learning allows determining the means aimed at the development of artistic skills of elementary school students. The author formulates the goal and subject of research that focus on searching for the new teaching techniques for developing artistic skills of elementary school students. The conclusion is made that the process of video training prompts the development of aesthetic, cognitive and activity skills of the students. It is indicated that the use of information technologies in the modern system of education is becoming increasingly relevant. The novelty of this research lies in implementation of video training in working with elementary school students as a means of developing their artistic skills. The variety of video learning techniques help to develop different angles of visual activity in elementary school students. Such new approach to learning effectively develops artistic skills of elementary school students. Video training allows you to do the task or repeat the material at any convenient time.
Modeling Text Data Over Time - Example on Job Postings

Modelling multilingual text data over time is a challenging task. This PhD is focused on semantic representation of domain specific short to mid length time stamped textual data. The proposed method is evaluated on the example of job postings, where we are modeling demand on IT jobs. More specifically, we addresses the following three problems: unifying the representation of multilingual text data; clustering similar textual data; using the proposed semantic representation to model and predict future demand of jobs. This works starts with a problem statement, followed by a description of the proposed approach and methodology and is concluded with an overview of the first results and summary of the ongoing research.
Development of a gaming application for a customized eight-processor device with a tangible interface

We consider some functions used in modern gadgets, in particular, the split screen, and the possibilities to employ gyroscope and accelerometer using an example of a customized gaming device, Wowcube. This device is a smart toy with tangible interface and is an analogue of a portable console with its potential going beyond launching applications only. The design of a firmware complex of a customized eight-processor gadget with a tangible interface is described. The structure of a data batch exchanged by the units of the complex to ensure synchronization is presented. The method of exchanging messages and the message lifetime are described. The commands used to exchange messages between the unit and the launched application are described. A list of the commands used by the platform and applications is determined. In order to determine the topology of the cube unit network, an example of operation of the message exchange system between the platform and an application, as well as between the units, is considered. An application for the customized computer complex has been developed using an SDK. The developed application takes into account the specific features and limitations of the device. The process of interaction of the device elements is described using this application as an example. Operation of the main menu of the device is presented. Means of the user’s interaction with the tangible interface are described.
Secured Cloud Based Health Care Framework with Blockchain

Abstract PHRs are a new patient-centric model of health information exchange that is often outsourced and processed by third parties such as cloud providers. Third-party servers and unauthorised parties can have access to personal health information, causing widespread privacy concerns. Encrypting PHRs before outsourcing is a promising way to keep patients in charge of their PHRs. However, issues like privacy risks, scalability in key management, versatile access, and successful user revocation have remained the most important roadblocks to achieving fine-grained, cryptographically enforced data access control. We use attribute based encryption (ABE) techniques to encrypt each patient’s PHR file in order to achieve fine-grained and scalable data access control for PHRs. We concentrate on the multiple data owner situation, and separate users in the PHR scheme into multiple protection domains, which significantly reduces key management complexity for owners and users. Using multi-authority ABE, a high standard of patient privacy is maintained at the same time. Our framework also allows for dynamic changes to access policies or file attributes, as well as powerful emergency break-glass control and on-demand user/attribute revocation. The security, scalability, and efficiency of the system are demonstrated via comprehensive analytical and experimental results.
Sentiment Analysis for Thai dramas on Twitter

Since most consumers are interested in watching TV series and using online media such as Twitter to exchange opinions about them, there are a lot of comments are found and the consumers must take more time for reading and understanding the overall messages of the other consumers' views. Therefore, this research has studied about word grouping, classification of the sentiments of the text about Thai TV series called Thai dramas or Lakorn.  The objective is to analyze the opinion messages expressed as like, dislike and neutral comments and the scope of this research is collecting texts about the dramas in Thai language, but it does not cover slang, misspellings, and dialects.  Moreover, the implemented web application for analyzing opinions about Thai dramas on Twitter is developed to help analyzing and summarizing the preferences for Thai dramas.  All words of collected Thai messages will be looked up in the vocabulary list created for the Thai dramas.  Then, the word vectors of messages are generated for training a learning model using the Naive Bayes approach. After that, the model will classify the comments about the dramas, whether most consumers like or not like the drama. The developed system is expected to be a tool that will be able to make decision watching the dramas easier and this will be beneficial to the drama producers to facilitate planning the production of the dramas in the future.Keywords: Sentiment analysis, Thai dramas, Naive Bayes
A Differentially Private Hybrid Approach to Traffic Monitoring

Abstract In recent years, privacy research has been gaining ground in vehicular communication technologies. Collecting data from connected vehicles presents a range of opportunities for industry and government to perform data analytics. Although many researchers have explored some privacy solutions for vehicular communications, the conditions to deploy them are still maturing, especially when it comes to privacy for sensitive data aggregation analysis. In this work, we propose a hybrid solution combining the original differential privacy framework with an instance-based additive noise technique. The results show that for typical instances we obtain a significant reduction in outliers. As far as we know, our paper is the first detailed experimental evaluation of differentially private techniques applied to traffic monitoring. The validation of the proposed solution was performed through extensive simulations in typical traffic scenarios using real data.
A Hardware Accelerator Implementation of Multilayer Perceptron

In recent years, there has been a surge in the use of machine learning models for various important applications in a wide variety of fields. Neural networks play a huge role in this area. The hardware acceleration of neural network models has been a recent area of active research. In hardware acceleration-based implementation of computing tasks, the throughput of the system is increased by decreasing latency. This work focuses on developing a digital system implementation of the most common structure for a feedforward neural network (FFNN) known as a multilayer perceptron (MLP). In pursuit of this aim, the proposed architecture is designed by considering the performance, accuracy, and resource usage and also the remarkable benefits of the design to accelerate the feedforward neural network computation.
Peer-to-Peer Application Threat Investigation

Understanding the layers of an application leads to a better threat investigation outcome as well as helping with developing proper controls with optimized cost. Starting with the blockchain as the peer-to-peer application we will analyze the peer-to-peer networks and how they provide the underlay for blockchain. We’ll have a look at the layers of the peer-to-peer application starting from the network layer, communication flows and communication ports through analyzing packet captures collected from both client side and network taps, up to the blockchain layer and client-side processes where we start to have a look at imported functions, memory and CPU utilization. We aim to have a structured approach for threat investigation for peer-to-peer applications.
Two-step iterative algorithm immune to tilt shifts for phase extraction

A two-step iterative algorithm (TSIA) immune to tilt shifts is proposed for phase extraction in phase-shifting interferometry (PSI). The TSIA constructs a model of the least-squares iteration of the phase distribution and the tilt shifts based on parametric decoupling. Finally, the phase distribution is extracted via the least-squares method. The experimental results show that the PTI has high accuracy and fast iterative convergence speed for the conditions of the large amplitude of tilt shifts, closed fringes, nonuniform background and modulation.
Automated Facial Recognition Entry Management System Using Image Processing Technique

The Entry administration is able to be an enormous saddle on the Staff if it is done by hand like note or record. To determination this dilemma, elegant and auto entry administration scheme is being utilized. Other than endorsement is a significant concern in this coordination. The Automated admission administration organization is in general complete through the facilitate of biometrics. Face educational is introverted of the biometric technique to get well this life form. Being a most significant facet of biometric confirmation, facial identification is mortal used a great deal in numerous such function, similar to videotape keep an look at on and CCTV recording organization, an communication sandwiched between central processing unit & human and admittance systems in attendance inside and system refuge. After that to make use of this programme, the dilemma of substitute and student earthly obvious near flush yet they are not in the soft tissue near can easily be resolve. If the student is entered the college gate but they aren’t attending the class also identified. The leading attainment ladders used in this kind of harmonization are tolerated detection and recognize the detect features. This paper propose a replica for implement an automatic Facial credit plus admission organization scheme for student of a school entry by creation use of countenance gratitude method, by means of Eigen countenance principles, facial discovery and indoctrination, and countenance corresponding means. Succeeding to these, the bonds of predictable face encompass to be reasonable by compare with the file contain student countenance. This representation will be a winning method to run the student’s admission time and information the books of student.
Mammogram Classification Using Rotation-Invariant Local Frequency Features

Breast cancer accounts for the highest mortality rate among women in the world. Mammograms play a prominent role in detecting abnormalities in the breast. Computer-aided diagnosis systems help the radiologist in detection abnormalities in less time. This work deals with the extraction of features from ROIs to reduce false positives in computer-aided diagnosis systems. In this paper, the rotational invariant local frequency technique is implemented using three methods for the extraction of features from mammogram region of interest (ROIs). Features obtained from ROIs are given to SVM for further classification of ROIs into normal-abnormal using SVM classifier via 10 fold cross-validation method. The proposed methods are validated using ROIs obtained from Image Retrieval in Medical Applications (IRMA) database for feature extraction
An Instrument to Define the Product Scope at Preselling Time

At project pre-selling time, customers and software providers collaborate to define the product required by the former; and based on it, the latter can generate a project proposal and a bid. This collaborative activity, that we have named product scoping, is not properly supported by the current tools and techniques, mainly due to the constraints imposed by the preselling phase; e.g., the activity has to be done quickly, accurately, and involving few resources. This article presents an instrument that supports the product scoping in bespoke projects, dealing with the main constraints involved in the pre-selling phase. The instrument is a canvas, and its design was particularly conceived to support the product scoping of data processing projects; however, the canvas can be adapted to address other project types. The instrument has been evaluated with real customers and providers, and the obtained results show that it helps conduct the product scoping process in a cost-effective way.
Aplikasi Marketplace Pertanian Dan Peternakan (Maritani) Berbasis Android

The Maritani application is an android-based agricultural and livestock marketplace application specifically designed to bring together sellers and buyers on one digitalized platform. The maritime marketplace application aims to facilitate sellers and buyers in buying and selling agricultural and livestock needs as well as food needs online. In its business process, the maritime marketplace application uses the B2C (Business to Customer) business model. Where business activities are carried out by producers to consumers using electronic media directly. The development of the Android-based Maritani marketplace is built with the Flutter framework and uses the Dart programming language. At the time of making the Maritani marketplace application for system development using the waterfall method, the development was started with an analysis of system requirements, then carried out the application display design (Graphic User Interface) implementation stage of the application implementation with the Dart programming language. The result of this research is a market application that can have a positive impact on users, both sellers and buyers. In the Maritani application which has a greater opportunity from the community to share knowledge with other sellers, sellers do not need to be afraid of losing consumers, features and regulations of the online market which greatly affect to increase the credibility and safety of the seller's shop, sellers are not required to have large capital
Study on Preparation of Saturated Unstructured Loess and Its Strength and Deformation Characteristics

Abstract In the study of geotechnical damage mechanics, the weighted average of the mechanical deformation states of structurally undamaged and structurally completely damaged soils is used to describe the actual mechanical deformation properties of soils. In the past, when the saturated remolded loess was used to simulate the mechanical deformation of loess, the loess still had some initial structural strength. A method for the preparation of saturated unstructured loess is presented. The strength deformation characteristics of saturated remolded loess were compared by lateral compression test and triaxial shear test. The method of preparing saturated unstructured loess is proved to be reasonable.
Monitoring the Treatments and Interventions to Intensive Care Patients: A Study for Multidimensional Evaluation of Changes in Patient Test Results with Principal Component Analysis

Medical doctors of today are challenged with increasingly large volumes of high-dimensional, heterogeneous and unstructured data from various sources that pose significant challenges for manual analysis. However, this unstructured data is mainly vital for decision making but there exists shortage of intelligent tools to extract the hidden knowledge. In view of these facts, application of machine learning methods in healthcare is a growing phenomenon. This paper explores machine learning approaches for interpreting large quantities of continuously acquired, multivariate patient based laboratory data, in intensive care unit (ICU) settings. The research hypothesizes that principal component analysis (PCA) can be able to capture the changes in the outcomes of applying medical interventions. We adopted PCA as a main method, to observe and capture the daily changes for intensive care unit patients. The approach will be able to inform the physicians, which laboratory tests are exhibiting variances after an intervention, and their associated epiphenomenon. This can be used as a clue to make decisions on which treatment or diagnosis to apply further. Experimental analysis results indicate that PCA was able to capture patient progression in terms of variances and permutation tests for the validity and stability of the model exhibits an acceptable significance level with a p-value of 0.001. Results showed that the approach provides promising results for interpreting large quantities of patient data for establishing a cause-effect relationship from medical interventions and be used as an early warning system. The study retrospectively demonstrated the capability of PCA to monitor and provide an alert to the clinicians about the patient’s changing conditions, thereby providing opportunities for timely interventions. If coupled with other machine learning models, the approach can also be able to support clinical decision making and enable an effective patient tailored care for better health outcomes.
Management Information System for the Philippine Coast Guard K9

Used police dogs as partner to solve crimes that would otherwise be difficult for an average police officer. Dogs, known as a man's best friend, have fulfilled this role remarkably because of their noses that are highly sensitive in detecting smells, even going so far as detecting objects under running water. Unfortunately, not all dogs are suited to be a police dog, also known as K9s. This research aims to help the Philippine Coast Guard K9 (PCGK9) in managing their K9 assets efficiently and effectively throughout the K9s entire life cycle. The main objective of this project is to design, develop, and implement a Management Information System specifically created for the needs of PCGK9. The system developed known as K9MIS mainly focused on monitoring the K9 assets of the organization and their performance. It also includes the management of inventory assets as well as transactional processes. And lastly, the system generates different reports that consist of the consolidated data, which will then be used for managerial decisions.
An Extensionally Equivalence-ensured Language for Task Parallel Processing with Backtracking-based Load Balancing

Backtracking-based load balancing is a promising method for task parallel processing with work stealing. Tascell is a framework for developing applications with backtracking-based load balancing. Users are responsible for ensuring the consistent behavior of Tascell programs when backtracking is triggered in the Tascell runtimes. Nevertheless, the operational semantics for Tascell programs have not been formally studied. Moreover, no extensional equivalence between Tascell programs is provided. In this paper, we formally specify operational semantics for Tascell programs and define extensional equivalence between Tascell programs using the Church-Rosser modulo equivalence notion in abstract rewriting theory. We propose left invertibility and well-formedness properties for Tascell programs, which ensure extensional equivalence between sequential and concurrent behaviors of Tascell programs. We also propose a domain-specific language based on reversible computation, which allows only symmetric pre/post-processing to update states. Tascell programs written in our language have left invertibility and well-formedness properties by construction. Finally, we confirm that Tascell programs to solve typical search problems such as pentomino puzzles, N-queens, and traveling salesman problems can be written in our language.