Reviewerly/exHarmony huggingface dataset:
Illumination and Rotation Invariant Texture Representation for Face Recognition

This article presents a novel approach for illumination and rotation invariant texture representation for face recognition. A gradient transformation is used as illumination invariance property and a Galois Field for the rotation invariance property. The normalized cumulative histogram bin values of the Gradient Galois Field transformed image represent the illumination and rotation invariant texture features. These features are further used as face descriptors. Experimentations are performed on FERET and extended Cohn Kanade databases. The results show that the proposed method is better as compared to Rotation Invariant Local Binary Pattern, Log-polar transform and Sorted Local Gradient Pattern and is illumination and rotation invariant.
Toward a new way of minimizing the loss of information quality in the dynamic anonymization

In privacy preserving data publishing, the privacy models focuses only on single release of anonymzed data. The scenario of publishing is more complicated where the data is anonymized in different manners and at different times. Privacy Preserving Dynamic Data Publishing is the paradigm that addresses the dynamic data anonymization for various purposes. Improving the data utility and minimizing the loss of information quality after each release is the major issue in the dynamic anonymization context. The most operation used for the data anonymization is generalization. This operation is based on taxonomy called Value Generalization Hierarchies VGH which is responsible for the guidance of the anonymization. In this work, we aim to preserve the data utility based on the notion of ontology to quantitatively asses the quality of Value Generalization Hierarchies in terms of semantic.
Collaborative Filtering Algorithm Based on Rating Prediction and User Characteristics

Collaborative filtering directly predicts potential favorite items of user based on user's behavior records. It is one of the key technologies in personalized recommendation systems. The traditional similarity measurement method relies on user's rating data in the case of data sparseness, which causes a decrease in the recommendation quality of recommendation systems. To solve this problem, this paper proposes a collaborative filtering algorithm based on item rating prediction and user characteristics. The first step is to select the k nearest neighbor sets of the item using the KNN algorithm, and then calculate the similarity between the items using the improved similarity measurement method, and initially predict the user's rating on the unrated item to improve the sparsity problem. The second step considers the user characteristics when predicting the similarity between users according to the item ratings. Finally, the algorithm combining item-based rating prediction and user characteristics is adopted to make recommendations for the user. The experimental results on MovieLens and Douban datasets show that the proposed collaborative filtering algorithm based on rating prediction and user characteristics can effectively improve the quality of recommendation system compared with the traditional algorithm.
Security Architecture for Real IoT Systems

This chapter discusses security principles and the security engineer mindset to guide the security architecture process in making trade-offs and prioritizing decisions. Security architecture is similar but focuses on technological solutions to ensure that the goals or mission of the system are maintained even in the presence of an adversary attempting to subvert that mission. Security architecture leverages the work of system architecture by reusing its many different views of the system. The chapter presents some security analysis examples of Internet of Things (IoT) use cases that explore IoT unique security challenges and solutions. Good systems architecture creates different diagrams that communicate different perspectives of the system. Threat modeling, or threat analysis, is a technical exercise used to identify all the potential weaknesses in a system. Threat modeling does not attempt to correct the problems but instead only lists the potential problems that exist.
Targeted Optical Character Recognition: Classification Using Capsule Network

Optical Character Recognition (OCR) is a process of digitizing an image or document containing text in a machine-readable format. In this paper, we are focusing on targeting only the numeric part with a few special characters in the tables. Many firms dealing in financial information would want to parse data from scanned tables and in some cases, they do not focus on the row labels as they might not change a lot. Only focusing on numeric information may also provide language independence to such firms that deal with documents written in a variety of languages. They can have foreign language experts who can just read row labels and have the OCR extract the numeric data. This makes their collection processes fast. We developed a targeted OCR to save time by processing only important characters and it can also overcome erroneous predictions in case of under segmentation of characters. In this paper, we propose a novel approach which segments the document into blocks of text (each line or word into one block) and classifies each block as numeric or non-numeric using a binary CNN. The process of character level segmentation and classification using capsule networks is then applied only to the blocks which are classified as numeric by the binary CNN.
WEYE: An open-source software system for recording and analyzing of eye- and mouse- tracking data from webpages

In this paper, we present an open-source software WEYE, a system for investigation of browsing behaviors, including eye-and mouse- tracking data. This work is written in C# and is based on Google Chrome. It contains mainly two parts: Chrome extension and native application. Chrome extension obtains and sends webpage data to native application. Native application includes four modules that are record module, fixation module, analysis module and export module. Record module records eye and mouse movements of website visitors. Fixation module calculates gaze and mouse fixations. Analysis module is designed for visual and statistical data analysis. Export module exports the result from analysis module.
Deciding Quantifier-free Definability in Finite Algebraic Structures

This work deals with the definability problem by quantifier-free first-order formulas over a finite algebraic structure. We show the problem to be coNP-complete and present a decision algorithm based on a semantical characterization of definable relations as those preserved by isomorphisms of substructures. Our approach also includes the design of an algorithm that computes the isomorphism type of a tuple in a finite algebraic structure. Proofs of soundness and completeness of the algorithms are presented, as well as empirical tests assessing their performances.
Grasping Novel Objects by Semi-supervised Domain Adaptation

Learning-based robot arm grasping approach attracts increasing interests recently. The algorithm needs to accurately locate the grasping point and angle. Existing methods usually require large amount of training data from physical robotic trial or synthetic samples from simulation. The system can show promising result with pre-defined objects, but the performance may degrade for novel objects without annotation. Inspired by the fact that we can usually have a large set of pre-collected training data from external source, but only a small quantity of data for the target novel objects, we introduce a new deep adaptation learning approach that is able to transfer the grasping knowledge from the source domain with known objects to target domain with novel objects. Partially or totally not labeled target domain data can be employed in our method. A label propagation scheme is further utilized for domain transfer learning. Experiments on a Baxter robot demonstrate substantial grasp accuracy improvement with the proposed approach even the target objects are totally unlabeled.
Blackmailer or Consumer? A Character-level CNN Approach for Identifying Malicious Complaint Behaviors

Blackmailers are becoming a serious Ecommerce security problem in China. They use the threat of complaining in an extortion ploy which is disrupting normal business order and causing interference in law enforcement and justice. This paper proposed a character-level CNN approach, named DeepDetector, to identify malicious complaint behaviors. DeepDetector was a simple CNN with one layer of convolution on top of character vectors obtained from lookup table. The experimental results showed that the AUC and F1 score of DeepDetector were respectively 97.32% and 96.00%. Both higher than that of RNN and bag-of-words, bag-of-n-grams model using machine learning method like Random Forest or XGBoost as classifier. Based on the real-world dataset of “National 12358 price regulation platform of China”, the data-driven experimental results indicated the effectiveness and efficiency of DeepDetector. Finally, the model was implemented in the whole unlabeled dataset ranging from Apr 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> , 2015 to Apr 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> , 2019 to identify malicious complaint behaviors. We analyzed the behavioral patterns and gathering area of E-commerce blackmailers in detail. Our study is useful for market regulators to identify E-commerce blackmailers and allocate supervision resources.
Evolutionary Design of Deep Neural Networks

Artificial Intelligence is the science of combining technological and theoretical concepts from multiple disciplines aimed at creating similar behavior in machines to that found in humans. The present work investigates methods of improving the field of pattern classification by applying evolutionary algorithms to train deep neural networks architectures. A significant advantage of evolutionary algorithms over backpropagation is that the neuroevolutionary algorithms allows for training parts of the deep neural network. The framework developed for the present work can be adapted to the required memory and processing resources of the target hardware platform in different application scenarios.
Title Generation and Keyphrase Extraction from Persian Scientific Texts

Modern neural-based approaches, which usually rely on large volumes of training data, have presented magnificent progress in various fields of text processing. However, these approaches have not been studied adequately in low resource languages. In this paper we focus on title generation and keyphrase extraction in the Persian language. We build a large corpus of scientific Persian texts which enables us to train end-to-end neural models for generating titles and extracting keyphrases. We investigate the effect of input length on modeling Persian text in both tasks. Additionally, we compare subword-level processing with the word-level one and show that even a straightforward subword encoding method enhances results greatly on Persian as an agglutinative language. For keyphrase extraction we formulate the task in two different ways: training the model to output all keyphrases at once; training the model to output one keyphrase each time and then extract n-best keyphrases during decoding. The latter improves the performance greatly.
An Empirical Study of Ownership, Typestate, and Assets in the Obsidian Smart Contract Language.

Some blockchain programs (smart contracts) have included serious security vulnerabilities. Obsidian is a new typestate-oriented programming language that uses a strong type system to rule out some of these vulnerabilities. Although Obsidian was designed to promote usability to make it as easy as possible to write programs, strong type systems can cause a language to be difficult to use. In particular, ownership, typestate, and assets, which Obsidian uses to provide safety guarantees, have not seen broad adoption in popular languages and result in significant usability challenges. We performed an empirical study with 20 participants comparing Obsidian to Solidity, which is the language most commonly used for writing smart contracts today. We observed that most of the Obsidian participants were able to successfully complete most of the programming tasks we gave them. We also found that asset-related bugs, which Obsidian detects at compile time, were commonly accidentally inserted by the Solidity participants. We identified potential opportunities to improve the usability of typestate as well as to apply the usability benefits of Obsidian's ownership system to other languages.
Identifying and Training Data for Appraoching AI by Construction of Entropy and PSO

In the current field of AI, we use neural network and classification and clustering of data mining that those sciences train data for building model.  However, those training data method still need domain knowledge of humans to sort relative data.  So, we provide a construction to similar creature learning method. In physical space and in the dynamic environment the data seem chaos, but they do have regulars that we can find. Furthermore, our intelligence is dependent on our abilities of identifying the relation between outside environment and us, and we need to recognize the environmental data that this recognizing behavior or process is like learning system via  logic and sorting functions.  When each event  is happened that we need to identify what data is affected by them.  If we want to directly distinguish those relative data that we use entropy to analyse the dynamic data of environment with social function of particle swarm optimization(PSO) for achieving the learning method of creature.
Class Mapping between Different Versions of Android Applications without Source Code

In the software life cycle, maintenance and evolution are inevitable phases. Due to its complex structure and the significant amount of code, the evolving system is challenging to understand artificially. To better capture the system differences between difference versions, this paper proposes a method which divorced from the source code, based on given construction interface information and combined with dynamic analysis to obtain the mapping relationship between different versions of Android application. On the one hand, the preliminary similarity matrix is calculated in units of classes, and the run-time call sequence is dynamically obtained through log printing to establish the similarity between methods. On the other hand, the accuracy of class and method correspondence improved by an iterative algorithm. We evaluated our approach on several open source Android systems, and the experimental results show that our method can effectively detect the mapping relationships of different versions.
Innovative Path Planning Algorithm for an Autonomous Robot with Low Computational Cost

The processing of data maps in order to find the best solution to transverse between two points is a key task of an autonomous robot. The algorithm often used for this task must also define the computational power requirements of the embedded processor in the system to optimise the solution with the least number of steps. Low end processors can be used contrary to traditional computers. This paper presents a simplified path solving algorithm for known and unknown environments where the robot range is limited to the size of the map created within the computer internal memory. The map can be preloaded with obstacles expected to be encountered. Alternatively, the robot can be switched to discover mode, where information about its environment is gathered on-the-fly using a range sensor. Two path planning algorithms have been simulated in this study to analyse the most efficient execution to be implemented on the platform with a micro-controller with the A* algorithm showing the superior performance and lower computational cost in comparison with Djickstra.
Gyro: A Modular Scale-Out Layer for Single-Server DBMSs

Scaling out database management systems (DBMSs) requires distributed coordination, which can easily become a bottleneck. Recent work on speeding up distributed transactions has addressed this problem by proposing scale-out techniques that are deeply integrated with the concurrency control mechanism of the DBMS. This paper explores the design of modular coordination layers, which encapsulate all scale-out logic and can be applied to scale out any unmodified single-server DBMS. It proposes Gyro, a modular coordination layer that runs on top of a collection of single-server DBMS instances and interacts with them only through their client interface. Gyro distributes the load by ensuring that as many requests as possible are executed by only one DBMS instance. Our experiments show that modular distributed coordination is practically viable and that it can be much faster than traditional distributed transaction protocols using two-phase commit.
Studi Kebiasaan Sarapan Pagi dan Pengaruhnya Terhadap Status Anemia dan Prestasi Belajar Anak Sekolah Dasar di Wilayah Pesisir dan Non Pesisir

Breakfast is an activity of eating and drinking starting from waking up in the morning and fulfilling 15-30% of daily nutritional needs. Breakfast could increase the concentration of learning and it believed could improves children's learning achievement. Delaying breakfast could increase the risk of anemia in children. People in coastal and non-coastal areas have different food supplies according to the environment and natural products that exist and it might make the difference of provision of the breakfast because it was related to the provision of the food. The purpose of this study is to analyze the influence of breakfast habits on anemia status and learning achievement of elementary school students at coastal and non-coastal area. This study used cross sectional research design and the population of this study were all elementary school students at Kupang city. The determination of this research samples used Slovin formula with a sample size of 100 respondents. And for the statistical tests used analysis of Simple Loistic Regression Test and Whitney Mann test. The statistical test results showed that there was an influence between breakfast habits frequency (p = 0,000), energy contribution (p = 0,000) and protein (p = 0,020) on anemia status. Therefore research findings was shown an influence between the contribution of energy (p = 0.032) and protein (p = 0.004), learning achievement (p = 0.409) on student’s learning achievement. Otherwise, there was no influence between anemia status and learning achievement (p = 0.520). The results of the Mann Whitney test showed that there was no difference between breakfast habits, anemia status and learning achievement in coastal and non-coastal areas.
A Fast Particle Swarm Optimization Algorithm by Refining the Global Best Solution

A Fast Particle Swarm Optimization (FPSO) is proposed to improve the convergence response speed for some potential application scenarios such as the online or dynamical optimization environment which requires the fast convergence ability of an optimizer. Classical gradient-based optimization methods are good at finding the local optimal value of a convex region yet usually failure in searching the global optimal value of a multimodal problem. To further develop the characteristics of PSO with respect to the fast convergence and the global optimization, a pseudo-gradient method is proposed for calculating the approximate gradient at the location of the global best solution (gBest) of a swarm to refine the convergence accuracy of the gBest so as to accelerate the local convergence speed. The experimental results show that the performance of the proposed algorithm is significantly better than those of the five chosen competitive algorithms on a series of benchmark test functions with different characteristics. Furthermore, the sensitivity of the new introduced parameter in the proposed algorithm is empirically analyzed by a special experiment for recommending its best range of value.
LibGuides: Information Technology Help: VoiceThread

Resources to help Central Penn College students navigate the technology required in their courses.
Towards Automated Quality Assessment Methods in Algorithmic Music Composition

The current work in progress paper describes a proof of concept for an automatic fitness evaluator in an evolutionary music composition setting. The associated research project proposes a novel algorithmic music creation mechanism. It uses linear genetic programming to create short musical pieces statistically similar to real-world pieces from a corpus. We present two fully automatic quality assessment methods for music, both used as fitness functions in the genetic algorithm: one proposed in a previous research stage, as well as a novel one involving n-grams. Experiments are proposed and described for comparing these measurement mechanisms to each other as well as to other automated methods present in the literature.
Design of UAV Longitudinal Control law Based on Small Disturbance and Motion Characteristics

The motion equation of the aircraft is a nonlinear function of the motion parameters, so it is very difficult to solve it by the analytical method, and the design of the control law has great challenges. Therefore, the basic idea of the small disturbance method and the analysis method of the motion characteristics are used in the design of the control law of the UAV in this paper. Firstly, on the basis ofUAV's all state nonlinear mathematical model, small disturbance equations are constructed by decoupling grouping and linearization The longitudinal and lateral motion characteristics of UAV are analyzed from the angle of characteristic root and amplitude frequency curve. Finally, the attitude and trajectory control law of UAV is designed based on the gain preset theory. The simulation results show that the designed control law has good performance.
Improve the Detection of Clustered Outliers via Outlier Score Propagation

Most of the existing outlier detection methods in the literature often adopt the assumption that the outliers would locate in the low-density area of the input data space. However, such low-density assumption may not hold in the presence of clustered outliers, which usually demonstrate relatively high density. For this reason, the clustered and cluster-based outlier detection methods are proposed; however, these methods either make strong data distribution assumptions or rely on the high-quality clustering results which are often not available. To address this issue, we propose a graph-based outlier detection approach which is capable of significantly enhancing the performance of existing outlier detection methods, in the presence of clustered outliers. Experiments are conducted on both synthesized and real-world datasets to demonstrate the superiority of our proposed method. In particular, our proposed method outperforms the state-of-the-art clustered outlier detection methods by a large margin.
Practices of a Regional Revitalization Project Collaborated with a Local Government

This paper shows practices of a regional revitalization project utilizing bicycle power generation from 2016 to 2018. Tsuyama college has a multi-disciplinary exercise to develop high expertise and multi-disciplinary capability on third and fourth year in department of integrated science and technology. This project was one of themes of the exercise. Students in this project team developed a charging circuit for smartphones using a bicycle dynamo collaborated with a local government and improved the high expertise and the multi-disciplinary capability.