Reviewerly/exHarmony huggingface dataset:
An Improved Data Anonymization Algorithm for Incomplete Medical Dataset Publishing

To protect sensitive information of patients and prevent privacy leakage, it is necessary to deal with data anonymously in medical dataset publishing. Most of the existing anonymity protection technologies discard the records with missing data, and it will cause large differences in characteristics in data anonymization, resulting in severe information loss. To solve this problem, we propose a novel data anonymization algorithm for incomplete medical dataset based on L-diversity algorithm (DAIMDL) in this work. In the premise of preserving records with missing data, DAIMDL clusters data on the basis of the improved k-member algorithm, and uses the information entropy generated by data generalization to calculate the distance in clustering stage. Then, the data groups obtained by clustering are generalized. The experimental results show that it can protect the sensitive attributes of patients better, reduce the information loss during the anonymization process of missing data, and improve the availability of the dataset.
Characterising tree-like Frege proofs for QBF

We examine the tree-like versions of QBF Frege and extended Frege systems. While in the propositional setting, tree-like and dag-like Frege are equivalent, we show that this is not the case for QBF Frege, where tree-like systems are exponentially weaker. This applies to the version of QBF Frege where the universal reduction rule substitutes universal variables by 0/1 constants. To show lower bounds for tree-like QBF Frege we devise a general technique that provides lower bounds for all tree-like QBF systems of the form P+∀red, where P is a propositional system. The lower bound is based on the semantic measure of strategy size corresponding to the size of countermodels for false QBFs. We also obtain a full characterisation of hardness for tree-like QBF Frege. Lower bounds for this system either arise from a lower bound to propositional Frege, from a circuit lower bound, or from a lower bound to strategy size.
A New Clustering Validity Index based on K-means Algorithm

Although cluster analysis has got great achievements, there are many questions in it. In this paper, the question on determining optimal number of clusters in cluster analysis is studied mainly. KMS (K-means Silhouette) for determining optimal number of clusters in K-means clustering algorithm are proposed. KMS (K-means Silhouette) algorithm improves the way of setting initial clustering centers in K-means clustering algorithm, and uses Silhouette validity index to determine optimal number of clusters. The experimental results on artificial datasets indicate the effectiveness of the proposed algorithms.
AI Powered Book Recommendation System

The purpose of this study is to demonstrate the development of book recommendation systems through the usage of Artificial Intelligence (AI). Models of recommender systems displayed in the study are popularity-based, correlation-based (otherwise known as collaborative filtering), and content-based. Another type implemented in this study is the Lexile recommender, which suggests books based on similar Lexile levels. For the dataset, the researcher used a sample of books and user information from the public site Goodreads. The target audience for these books in the sample was high school students. As prior research shows that there is no single best way to create reading lists for students, the results of this study would encourage both leisure and educational reading to these students and allow readers to be able to create reading lists of their own that are personalized to their preferences. The researcher used Python libraries to implement these recommender structures. The results of this study showed that creating recommendation systems through AI was successful, but there is still much room for improvement in the complexity of these structures.
Data Mining Approach for Identifying Suitable Sport for Beginners

Anthropometric measurements are generally used to determine and predict achievement in different sports. An athlete's anthropometric and physical characteristics may perform important preconditions for successful participation in any given sport. Further, anthropometric profiles indicate whether the player would be suitable for the competition at the highest level in a specific sport. Recently, more researches have been carried out on Sport Data mining. In this study, we propose an approach to identify the most suitable sport for beginners using data mining and anthropometric profiles. We propose clustering base approach. We apply a spatial clustering technique called the Spherical Associated Keyword Space which is projected clustering result from a three-dimensional sphere to a two dimensional (2D) spherical surface for 2D visualization. Empirical study of our approach has proved the effectiveness of clustering results.
Development of Multi-View Video Browsing Interface Specialized for Developmental Child Training

Efforts to support the skills of developmental child training/education workers are implemented using visual information captured by nursery teachers, therapists, and children's intervention work. This paper proposes a highly operable multi-view video browsing interface utilizing a multi-touch input method, which is suitable for users who are unfamiliar with handling devices that require complicated operations. We conduct evolutional questionnaires and request surveys at an actual developmental child training site and propose an interface based on the results. The proposed interface improves the operability and visibility by two approaches. First, we differentiate between the operation methods to reduce the operation errors that occur during image browsing. Second, we introduce image processing to promote the understanding of the presented image. In experimental evaluations, we investigate the video presentation of scenes that users can easily understand and verify the operability improvement of the proposed interface by a comparative experiment using our pilot system.
Linking gemology and spectral geology: a case study of elbaites from Seridó Pegmatite Province, Northeastern Brazil

reflectance spectroscopy is a fast tool for mineral identification and classification. We conducted spectral characterization of elbaite samples (n = 30) from Seridó Pegmatite Province, Borborema Province, NE Brazil, using a FieldSpec portable instrument and geochemical data from electron probe microanalysis. Visual interpretation of reflectance spectra reveals the absorption features in the visible to near infrared (350-1,200 nm) range related to the electronic transition processes of iron (e.g., ~720, ~1,180 nm), copper (e.g., ~694, ~922 nm) and manganese (e.g., ~400, ~520 nm) ions. In general, electron probe microanalysis data show correlation with the absorption features related to chromophore elements (e.g., Cu, Fe, Mn), but the complex crystal structure of the tourmalines also accounts for the generation of absorption features. Vibrational processes in the short-wave infrared (1,200-2,500 nm) spectral range appear associated with Mn-OH, OH, Al-OH, H2O and B-OH bonds. A statistical approach, using principal component analysis, was chosen for distinguishing tourmalines of different composition. We discriminated tourmalines characterized by green, blue, blue-green, red/purple colors, as well as the colorless ones. This included the Paraíba tourmaline, a high value variety of elbaite that is the focus of exploration carried out in Seridó Pegmatite Province.
Start-End Time Detection in Baseball Videos for Automatic Pitching Trajectory Analysis

A baseball pitching trajectory analysis system is used to train pitchers and provide baseball content for viewers. Previous systems needed a system manager because the trajectory was detected manually, thus system automation is essential for popularizing baseball pitching trajectory analysis systems, and detecting the pitching start and end times comprise the key point toward achieving this. In this paper, we define the pitching start and end times according to the situations in baseball games and accurately detect these points in time based on a physical model. Experiments on image data captured at a professional baseball game indicate that the proposed method could detect the start and end times of a pitch as accurately as a human can.
Why simple is best

Public displays play an important role in information dissemination - market reports highlight the increasing number of displays deployed. Due to the often prominent placement of public displays in the physical environment, displays can play an important role in the dissemination of trusted content, particularly during emergency situations. In order to leverage displays in emergency situations however, appropriate content creation and dissemination technology is key to allow display and space owners to efficiently distribute important information and target affected user groups. In this paper, we present our lessons learned from designing and developing an emergency alerts system in the context of a large public display testbed. We provide insights into two design probes and feedback captured through focus groups with stakeholders of the display network. Based on the feedback, we provide insights into requirements captured and provide a discussion on lessons and design considerations.
Modelling dynamics of marathons – A mixture model approach

In this paper, statistical properties of marathon dynamics are studied. We find that changes of velocity in marathons follow an unconventional mechanism; in which the log-change of velocity is highly dependent on current velocity with a complex relationship. The conditional distributions of log-change of velocity exhibit patterns of varying means, variances, and skewnesses; as such, the overall velocity distributions are also found to have departed from Gaussian. We illustrate the mechanism with a finite mixture of generalized linear regressions with varying weights and skew normal errors; and we show that the completion time distribution can be approximated by skewed distributions.
Face Attribute Editing using AttGAN and Guide Mask

Recently, face attribute editing researches using GAN have shown excellent performance. AttGAN, one of the most recent studies, has suggested a way to change only the desired attributes. However, unnatural distortion may occur in the background area when changing attributes such as hair color or baldhead. In this paper, we propose a method to remove the background distortion and make the result more natural by using a mask made by human parsing algorithm and a mask made by hand. Experiments with CelebA datasets show that this method can reduce distortion caused by existing methods.
NeuralTaint: A Key Segment Marking Tool Based on Neural Network

Dynamic taint analysis techniques are a popular dynamic software analysis method. Marking a key segment of program function by dynamic taint analysis is an important part of software vulnerability research. Key segment marking usually related to the control flow taint analysis, however, several specific program structure may cause failure in key segment marking due to the control flow dependence, and overtainting and undertainting problem. In this paper, we proposed a novel method to mark a key segment accurately and efficiently with deep learning technology. Firstly, we fit the program function execution into a continuous function by the convoluntional network, and then mark the key segment roughly through derivative information of fitted nerual network. Finally, we mark the key segment of specific program function completely and accurately by filtering and diffusion algorithm. We developed the key segment marking tool NeuralTaint on this principle. We design an experiment to select the specific neural network structure of NeuralTaint. Our extensive evaluations demonstrate that NeuralTaint significantly outperforms the two state-of-the-art traditional dynamic taint analysis tool on seven popular real-world programs.
The order estimation for hidden Markov models

The hidden Markov model has been successfully applied to many fields. In this paper, we provide a novel method to estimate the order of finite state stationary hidden Markov models. Our method relies on the fact that return times of a fixed observation are identical distribution if starting points correspond to the unique hidden state. We obtain the order estimator by clustering all return times of different starting points, and prove that the estimator is strong consistent. The results of numerical experiments show that the proposed method has a better performance compared to the previous, its accuracy is greatly improved, and its computational complexity is significantly reduced. Finally, we give the application of our method to a real-life data set.
Automatic Event Identification From Tectonic Earthquakes with Modified Akaike Information Criterion (mAIC)

It is difficult to identify P- and S-waves arrival time from the recorded seismograms with low Signal – to – Noise Ratio (SNR) because the onset is not always clearly observed. However, the arrival time of P and S-waves are important for the advanced analysis, such as for seismic event localization and seismic velocity tomography. In this paper, we employ Akaike Information Criterion (AIC) with the new characteristic function (CF) so-called mAIC, that is proposed by Hendriana et al. (2018) to detect P- and S - waves onset accurately and automatically from the denoised seismic signal of tectonic earthquake events in Sunda Strait. The mAIC was proposed to overcome the window–dependence of AIC function from continuous seismogram by computing AIC in a translatable window. The results show that mAIC successfully determine the first onsite of P- and S-wave for the denoised seismogram data.
An Image Processing Framework for Automatic Tracking of Wave Fronts and Estimation of Wave Front Velocity for a Gas Experiment

This work presents an image processing framework designed to automatically track the wave front in a sequence of images from a high speed film. A watershed algorithm is used for segmentation and contouring, while an active contour model is used for controlling the flexibility and the rigidity of the contour. The velocity of the wave front is calculated by estimating the displacement of the front in two frames, divided by the time difference between the frames. The calculated velocity is compared with the sensor measurements. Further, the calculated velocities can be used to estimate thermodynamic properties like the Mach number, the pressure and the temperature across the wave. With the purposed framework, these properties can be estimated along the entire wave front. Otherwise, these thermodynamic properties are limited to either theoretical values or to sparse measurements from sensors. The experiment is done by using a shock tube and the film is captured by a high speed camera using the shadowgraph system.
Effect of chosen features on performance of privacy preserving image retrieval systems

A huge amount of personal multimedia data is generated daily and stored on cloud servers. This data needs to be secure from curious servers, while also being searchable by them. The visual features used in a content-based image retrieval (CBIR) system are encrypted to ensure privacy of the cloud users. This paper aims to investigate several features for use in building a privacy-preserving CBIR system. These features range from simple and fast statistical bit plane distribution features to the more sophisticated local features such as Speeded-Up and Robust Features (SURF). Moreover, both series and parallel aggregation approaches of combining several features are studied to improve retrieval efficiency. Feature vectors are encrypted using distance-preserving techniques. The accuracy of the retrieval system is examined under different combinations of encrypted visual features. The results are insightful offering interesting trade-offs between security, computational efficiency and accuracy.
A Face Recognition System Using Off-the-Shelf Feature Extractors and an Ad-Hoc Classifier

Face recognition systems areMarsi, Stefano of great interest in manyDe Bortoli, Luca applications. We present someGuzzi, Francesco results from a comparison on different classification methods using an open source tool that works withBhattacharya, Jhilik Convolutional Neural NetworksCicala, Francesco to extract facial features. This work focuses on theCarrato, Sergio performance obtainable from a multi-class classifier, trainedCanziani, Alfredo with a reduced number images, to identify a person between aRamponi, Giovanni group of known and unknown subjects . The overall system has been implemented in an Odroid XU-4 Platform.
Understanding the Mobile Game App Activity

The ratings and reviews found on online stores leave a powerful impact on new users. The idea of the project focuses on identifying clues from ratings, reviews and other related factors to ensure a secure future of the application. This project collects raw data such as ratings, customer reviews and price of top game applications on Google Play store, and performing analysis based on category and applications to draw results.
Using Emulab for Deep Learning Performance Comparisons among Network Topologies

Emulab is a versatile research framework proposed and implemented by Utah University, instantly providing a dedicated cluster system using real systems and switches upon a user request. As machine learning has been used in most areas, it is also natural to try to use Emulab for machine learning. However, there has been no study on how to configure Emulab nodes to run machine learning. In particular, our research focuses on comparing the performance of TensorFlow, one of the most widely used tools in deep learning field, to find out which network topology has the best performance. In our experiments, the star topology cluster running the data parallelism model of TensorFlow showed the best performance. In addition, to our best knowledge, this study is the first research to investigate to figure out the relationship between cluster interconnects and deep learning performance by using Emulab.
Photography and Exploration of Tourist Locations Based on Optimal Foraging Theory

Animals search for food in their environment with a decision strategy which keeps them fit. Optimal foraging theory models this foraging behavior to determine the optimal decision strategy followed by animals. This theory has been successfully applied for humans as they search for information and is termed as information foraging. When people visit a tourist location, they follow a similar strategy to move from one spot to another, and collect information by capturing photographs. This behavior has similarities with the foraging behavior of animals which has been widely studied by the researchers. In this paper, we propose to employ optimal foraging theory to help tourists explore a location and capture photographs in an optimal way. We determine a decision strategy for tourist which provides a list of interesting spots to visit in a tourist location along with corresponding stay time. Finally, we solve an optimization problem to find a path through these spots which can be followed by tourists. The experimental results on a public dataset demonstrate the effectiveness of the proposed method. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code available at https://github.com/vyzuer/foraging_theory.
Automatic detection and correction of discourse marker errors made by Spanish native speakers in Portuguese academic writing

Discourse markers are words and expressions (such as: firstly, then, for example, because, as a result, likewise, in comparison, in contrast) that explicitly state the relational structure of the information in the text, i.e. signalling a sequential relationship between the current message and the previous discourse. Using these markers improves the cohesion and coherence of texts, facilitating reading comprehension. Although often included in tools that support the rhetoric structuring of texts, discourse markers have hardly been explored in writing support tools for learners of a second language. However, learners of a second language, including those at advanced levels, have trouble producing these lexical items, frequently replacing them with items from their native language or with literal translations of items in their own language, which often do not result in proper lexical items in the second language. In addition, students learn a single marker per function and use it repeatedly, producing monotonous texts. With the aim of contributing to reducing these difficulties, this paper presents a lexicon that will be used to support the task of automatically detecting and correcting discourse marker errors. Several heuristics have been evaluated to generate different types of errors. Automatic translation methods were used to semi-automatically compile the lexicon used in these heuristics. Similarity measures were also combined with these heuristics to correct discourse marker errors. The evaluated methods proved to be suitable for the task of identifying some types of discourse marker errors and can potentially identify many others, as long as new lexical inputs are incorporated into them.
Investigating the effectiveness of outfitting nuclear power plant with automatic seismic trip system based on the early detection of earthquake

Safety analysis indicates the significant effect of seismic induced accidents in overall risk of nuclear power plants located in those quake-prone regions of faults vicinity. In other words, since earthquake impacts the reliability of safety functions, seismic induced Core Damage Frequency is of considerable importance in total CDF of NPPs. One of the main concerns in seismic safety is the ability of plant safety systems to immediately shutdown the reactor and reach a safe shutdown state, upon earthquakes of predefined accelerations. Albeit, consensus on whether to implement an automatic trip signal upon earthquake of predefined acceleration, or not, is a controversial issue, with ambivalent interpretations of nuclear safety and electric power dispatching bodies. This article intends to perform a so-called risk-informed-decision-making study to decide on provisioning of Reactor Protection System of an, with automatic digitized seismic trip signal, in addition to the existing trip signals. In this regard, the two most severe accident sequences of Loss of Offsite Power and Loss of Coolant Accident are chosen to investigate and compare the advantageous and disadvantageous of early trip of the reactor on non-destructive primary wave before arrival of the potentially damaging secondary wave. Both deterministic behavior, from MELCOR model of typical PWR plant, and the probabilistic measures, from Risk Spectrum model of Level 1 Probabilistic Safety Assessment, are taken into consideration to analyze the accident progression for defined sensitivity cases on peak ground acceleration (pga) level of actuating the seismic trip setpoints. The results are useful to reach an agreement for pros and cons of outfitting NPPs with Automatic Seismic Trip System. Based on the results, an optimized actuation level of pga for seismic trip setpoint can be determined from the risk point of view. Investigating the late and early effects of the proposed change on the response of the plant will be useful in accident management decision making.