Reviewerly/exHarmony huggingface dataset:
A Fast Algorithm for Identifying Density-Based Clustering Structures Using a Constraint Graph

OPTICS is a state-of-the-art algorithm for visualizing density-based clustering structures of multi-dimensional datasets. However, OPTICS requires iterative distance computations for all objects and is thus computed in O ( n 2 ) time, making it unsuitable for massive datasets. In this paper, we propose constrained OPTICS (C-OPTICS) to quickly create density-based clustering structures that are identical to those by OPTICS. C-OPTICS uses a bi-directional graph structure, which we refer to as the constraint graph, to reduce unnecessary distance computations of OPTICS. Thus, C-OPTICS achieves a good running time to create density-based clustering structures. Through experimental evaluations with synthetic and real datasets, C-OPTICS significantly improves the running time in comparison to existing algorithms, such as OPTICS, DeLi-Clu, and Speedy OPTICS (SOPTICS), and guarantees the quality of the density-based clustering structures.
Extracting Clothing Features for Blind People Using Image Processing and Machine Learning Techniques: First Insights

Vision is one of the senses that dominates the life of humans. It allows them to know and have the perception of the world around them, while giving meaning for objects, concepts and ideas, and tastes. How to dress and the style we prefer for different occasions is part of one’s identity. Blind people do not have this sense, and dressing can be a difficult and stressful task. With the advance of technology it is important to minimize all the limitations of a blind person in the management of garments. Not knowing the colors, the type of pattern, or even the state of the garments make this a daily challenge in which nowadays resources are not the best. Thus, the approach of this project is to address this issue of extracting the basic characteristics and conditions of the garment (in good conditions, dirty or wrinkly) in order to help the blind.
ToriLLE: Learning Environment for Hand-to-Hand Combat

We present Toribash Learning Environment (To-riLLE), a learning environment for machine learning agents based on the video game Toribash. Toribash is a MuJoCo-like environment of two humanoid characters fighting each other hand-to-hand, controlled by changing actuation modes of the joints. Competitive nature of Toribash as well its focused domain provide a platform for evaluating self-play methods, and evaluating machine learning agents against human players. In this paper we describe the environment with ToriLLE's capabilities and limitations, and experimentally show its applicability as a learning environment with baseline and human experiments. The source code of the environment and conducted experiments can be found at https://github.com/Miffyli/ToriLLE.
Term Discrimination Value for Cross-Language Information Retrieval

Term discrimination value is among the three basic heuristics exploited, directly or indirectly, in almost all ranking models for ad-hoc Information Retrieval (IR). Query term discrimination in monolingual IR is usually estimated based on document or collection frequency of terms. In the query translation approach for CLIR, the discrimination value of a query term needs to be estimated based on document or collection frequencies of its translations, which is more challenging. We show that the existing estimation models do not correctly estimate and adequately reflect the difference between the discrimination power of query terms, which hurts retrieval performance. We then propose a new model to estimate discrimination values of query terms for CLIR and empirically demonstrate its impact in improving the CLIR performance.
A comparative review of Urdu stemmers: Approaches and challenges

With the advent of globalization epoch, the Internet-based resources for Urdu are increasing in depth and breadth at a higher pace than ever and thus require a mechanism for computational processing of Urdu text. Information retrieval (IR) systems have now become the major tool for seeking varied information on the web. It uses variant forms of the word transformed through stemmer. Broadly speaking, current Urdu stemmers can be categorized into two major categories: linguistic-based stemmers and statistical stemmers. In this paper, the authors explain the applications where stemming is used as a first step and highlight the challenges in Urdu text stemming. This is the first comparative study of the state-of-the-art Urdu stemmers, based on various distinct features such as used approach, main idea, limitations, the rules or affixes, data set, evaluation criteria and claimed accuracy. A comparative analysis, among state-of-the-art Urdu stemmers, is performed by using the standard data set. Finally, we outline the relevant research gaps in the literature and suggest recommendations for future research on Urdu text stemming.
A Method of Speaker Recognition for Small-scale Speakers Based on One-versus-rest and Neural Network

There are a large number of application for the speaker recognition with a small size of speakers. A characteristic for this type of application is that the number of speakers is often changed slightly. To adapt this characteristic, we apply one-versus-rest(OvR) and neural network to implement speaker recognition. The task is first split into a series of binary classification problems by OvR strategy. The binary classification problems are then solved by neural network and the recognition result is obtained by the simple majority voting. When a few speakers are added to the set of speakers, we can only train the binary classifiers for the newly added speakers. The experiment on a dataset with small-scale speakers shows that both the recognition accuracy and the time cost is satisfactory, which indicates that the method is effective and feasible.
Asymptotic Properties of Recursive Particle Maximum Likelihood Estimation

Using stochastic gradient search and the optimal filter derivative, it is possible to perform recursive (i.e., online) maximum likelihood estimation in a non-linear state-space model. As the optimal filter and its derivative are analytically intractable for such a model, they need to be approximated numerically. In [17], a recursive maximum likelihood algorithm based on a particle approximation to the optimal filter derivative has been proposed and studied through numerical simulations. Here, this algorithm and its asymptotic behavior are analyzed theoretically.
Abnormal Usage Sequence Detection for Identification of User Needs via Recurrent Neural Network Semantic Variational Autoencoder

In this paper, we propose an advanced method to detect abnormal usage patterns for identifying the fine-grained levels of user needs. Most previous studies investigated user need identification based on users textual reviews. Thus, they focused only on the explicit needs of the product levels, and not on the implied needs of the fine-grained levels. Although in a few recent studies the authors attempted to identify user needs based on abnormality detection, they considered only limited elements of the usage sequence, such as touching buttons, and did not consider the important elements, such as the dragging interaction and the pop-up and notification components. Thus, in this study, we considered all the elements of the usage sequence to identify abnormal usage sequences for recognizing user needs at the fine-grained level. Moreover, we utilized the recurrent neural network semantic variational autoencoder (RNN-SVAE) architecture, which is a state-of-the-art architecture for sentence embedding, to represent the usage sequences effectively. In detail, we calculate the vector representation of the entire usage sequence utilizing the RNN-SVAE architecture based on heterogeneous embedding to apply the abnormality detection method for determining abnormal sequences corresponding to user needs. The experimental results verify that our proposed method extracts meaningful abnormal usage patterns that previous approaches do not identify. Additionally, our proposed method shows a higher correlation of the coefficient score between the abnormality score and the importance score of the extracted sequences than do previous approaches.
Relativism: shedding the stigma

Frank Fischer’s (2019) contribution is an excellent account of social constructivism for students of public policy. He argues persuasively that neither postmodernism nor social constructivism is re...
Automatic Proofreading in Chinese: Detect and Correct Spelling Errors in Character-Level with Deep Neural Networks

Rapid increase of the scale of text carries huge costs for manual proofreading. In comparison, automatic proofreading shows great advantages on time and human resource, drawing more researchers into it. In this paper, we propose two attention based deep neural network models combined with confusion sets to detect and correct possible Chinese spelling errors in character-level. Our proposed approaches first model the context of Chinese character embedding using Long Short-Term Memory (LSTM) networks, then score the probabilities of candidates from its confusion set through attention mechanism, choosing the highest one as the prediction answer. Also, we define a new methodology for obtaining (preceding text, following text, candidates, target) quads and provides a supervised dataset for training and testing (Our data has been released to the public in https://github.com/ccit-proofread .). Performance evaluation indicates that our models achieve the state-of-the-art performance and outperform a set of baselines.
MORe++: k-Means Based Outlier Removal on High-Dimensional Data

MORe++ is a k-Means based Outlier Removal method working on high dimensional data. It is simple, efficient and scalable. The core idea is to find local outliers by examining the points of different k-Means clusters separately. Like that, one-dimensional projections of the data become meaningful and allow to find one-dimensional outliers easily, which else would be hidden by points of other clusters. MORe++ does not need any additional input parameters than the number of clusters k used for k-Means, and delivers an intuitively accessible degree of outlierness. In extensive experiments it performed well compared to k-Means-- and ORC.
Multi-Label Classification for Automatic Human Blastocyst Grading with Severely Imbalanced Data

Quality scores assigned to blastocyst inner cell mass (ICM), trophectoderm (TE), and zona pellucida (ZP) are critical markers for predicting implantation potential of a human blastocyst in IVF treatment. Deep Convolutional Neural Networks (CNNs) have shown success in various image classification tasks, including classification of blastocysts into two quality categories. However, the problem of multi-label multi-class classification for blastocyst grading remains unsolved. In this paper, a single CNN is proposed to automatically classify human microscopic blastocyst images in three grading labels, each with multiple quality classes. Additionally, network training with severely imbalanced data (i.e. minority class representing <5% of total samples) is addressed using parameter sharing and stratified sampling schemes. Network parameters associated with each output label are trained jointly, achieving accuracy of 73.9% for ICM grading, 67.3% for TE grading, and 81.8% for ZP grading. This represents accuracy improvement of 3.1%, 18.3%, and 18.2% for ICM, TE, and ZP grading, respectively, over three baseline models.
Conclusion: Technology Integration in Chemistry Education and Research: What Did We Learn and What Can We Expect Going Forward?

Chemical education has experienced a shift with advances in technology. Advances in interactive communications technology and development of networked sysftems are reflected in the growing use of technology for teaching and learning. These modern tools have invigorated interest in the field while simultaneously expanding the goals and possibilities of connected learning in the 21st century. The use of virtual classrooms and social media and the presence of online databases have made it possible to connect various stakeholders in chemistry education with vast amounts of knowledge and information and enhanced their abilities to engage in assessment of learning in real time. The chapter presents a brief overview of some insights gained from the chapters presented in this book and also the opportunities and challenges that lie ahead for chemistry education and research with respect to the use of technology and its integration in research and teaching. The chapter also stresses the need for research-based evidence to study the effectiveness of technology for teaching and learning.
Discovering Attractive Segments in the User Generated Video Streams

With the rapid development of digital equipment and the continuous upgrading of online media, a growing number of people are willing to post videos on the web to share their daily lives [1, 2]. Generally, not all video segments are popular with audiences, some of which may be boring. In recent years, crowd-sourced time-sync video comments have emerged worldwide, supporting further research on temporal video labelling. In this paper, we propose a novel framework to achieve the following goal: Predicting which segment in a newly generated video stream will be popular among the audiences. At last, experimental results on real-world data demonstrate the effectiveness of the proposed framework and justify the idea of predicting the popularities of segments in a video exploiting crowd-sourced time-sync comments as a bridge to analyse videos.
Architectures for Intelligent Assessment on Complex Information System Skills

In the information society, the enterprises have to deploy complex information system. Therefore, talents with complex information system skills are increasingly favored by enterprises. In enterprise practice, complex information system is defined as Enterprise Information System. Based on the research of literature, this paper studies the three technical architectures for intelligent assessment on complex information system skills. It lays a foundation for the development of the complex information system skills assessment system which can better meet the actual needs.
ConcurDB: Concurrent Query Authentication for Outsourced Databases

Clients of outsourced databases need Query Authentication (QA) guaranteeing the integrity and authenticity of query results returned by potentially compromised providers. Prior work provides QA assurances for a limited class of queries by deploying several software-based cryptographic constructs. The constructs are often designed assuming read-only or infrequently updated databases. For dynamic datasets, the data owner is required to perform all updates on behalf of clients. Hence, for concurrent updates by multiple clients, such as for OLTP workloads, existing QA solutions are inefficient. We present ConcurDB, a concurrent QA scheme that enables simultaneous updates by multiple clients. To realize concurrent QA, we have designed several new mechanisms. First, we identify and use an important relationship between QA and memory checking to decouple query execution and verification. We allow clients to execute transactions concurrently and perform verifications in parallel using an offline memory checking based protocol. Then, to extend QA to a multi-client scenario, we design new protocols that enable clients to securely exchange a small set of authentication data even when using the untrusted provider as a communication hub. Finally, we overcome provider-side replay attacks. Using ConcurDB, we provide and evaluate concurrent QA for the full TPC-C benchmark. For updates, ConcurDB shows a 4x performance increase over existing solutions.
Security-Related Stress: A Perspective on Information Security Risk Management

In this study, the enactment of information security risk management by novice practitioners is studied by applying an analytical lens of security-related stress. Two organisations were targeted in the study using a case study approach to obtain data about their practices. The study identifies stressors and stress inhibitors in the ISRM process and the supporting ISRM tools and discusses the implications for practitioners. For example, a mismatch between security standards and how they are interpreted in practice has been identified. This mismatch was further found to be strengthened by the design of the used ISRM tools. Those design shortcomings hamper agility since they may enforce a specific workflow or may restrict documentation. The study concludes that security-related stress can provide additional insight into security-novice practitioners' ISRM challenges.
Gaussian Graphical Model Selection from Size Constrained Measurements

In this paper, we introduce the problem of learning graphical models from size constrained measurements. This is inspired by a wide range of problems where one is unable to measure all the variables involved simultaneously. We propose notions of data requirement for this setting and then begin by considering an extreme case where one is allowed to only measure pairs of variables. For this setting we propose a simple algorithm and provide guarantees on its behavior. We then generalize to the case where one is allowed to measure up to r variables simultaneously, and draw connections to the field of combinatorial designs. Finally, we propose an interactive version of the proposed algorithm that is guaranteed to have significantly better data requirement on a wide range of realistic settings.
Learning to Select Mates in Evolving Non-playable Characters

Procedural content generation (PCG) is an active area of research with the potential to significantly reduce game development costs as well as create game experiences meaningfully personalized to each player. Evolutionary methods are a promising method of generating content procedurally. In particular asynchronous evolution of AI agents in an artificial life (A-life) setting is notably similar to the online evolution of non-playable characters in a video game. In this paper, we are concerned with improving the efficiency of evolution via more effective mate selection. In the spirit of PCG, we genetically encode each agent’s preference for mating partners and thereby allowing the mate-selection process to evolve. We evaluate this approach in a simple predator-prey A-life environment and demonstrate that the ability to evolve a per-agent mate-selection preference function indeed significantly increases the extinction time of the population. Additionally, an inspection of the evolved preference function parameters shows that agents evolve to favor mates who have survival traits.
Digital Culture and Information

In the fall of 2018, the Washington and Lee University Library launched an interdisciplinary minor called Digital Culture and Information (DCI) that aims to fill a curricular gap between how information exists and how it is used. Built from a successful campus-wide digital humanities initiative, DCI challenges students to become more highly literate consumers and creators of information through coursework focusing on data analysis, website development, and the impact of electronic communication on society. This poster presents the summation of an exploratory summer sabbatical project that conducted a content analysis on similar programs worldwide. Interviews with several program leaders and coordinators followed offering an in-depth look at their programs' history, successes, and issues. The research looks at how our minor differs from others including the uniqueness of it being housed in the library and how our program can grow.
Interworking technology of neural network and data among deep learning frameworks

Based on the growing demand for neural network technologies, various neural network inference engines are being developed. However, each inference engine has its own neural network storage format. There is a growing demand for standardization to solve this problem. This study presents interworking techniques for ensuring the compatibility of neural networks and data among the various deep learning frameworks. The proposed technique standardizes the graphic expression grammar and learning data storage format using the Neural Network Exchange Format (NNEF) of Khronos. The proposed converter includes a lexical, syntax, and parser. This NNEF parser converts neural network information into a parsing tree and quantizes data. To validate the proposed system, we verified that MNIST is immediately executed by importing AlexNet's neural network and learned data. Therefore, this study contributes an efficient design technique for a converter that can execute a neural network and learned data in various frameworks regardless of the storage format of each framework.
Towards Procedurally Generated Languages for Non-playable Characters in Video Games

Non-playable characters (NPCs) enhance a player's immersion in a video game. Communications among NPCs create atmosphere and, in some games, are a core element of the gameplay. Yet, the majority of games manually script inter-NPC communications creating only an illusion of such exchanges. Doing so is laborious and results in fixed interactions which may not respond to the player's actions or changes in the environment. In this paper we propose procedural content generation (PCG) for emergent inter-NPC languages. Indeed, recent research demonstrated that deep neural networks can be trained to develop an artificial language to communicate with each other. The work used a fixed, handcrafted network architecture identical for both the sending and receiving agents. We extend the work by using neuroevolution for both the sender and the receiver. In doing so we evolve both the architecture and weights of the agents and show that they successfully develop novel languages to communicate among themselves.
An Adequate While-Language for Hybrid Computation

Hybrid computation harbours discrete and continuous dynamics in the form of an entangled mixture, inherently present in various natural phenomena and in applications ranging from control theory to microbiology. The emergent behaviours bear signs of both computational and physical processes, and thus present difficulties not only in their analysis, but also in describing them adequately in a structural, well-founded way.
Enforcing Secure Coding Rules for the C Programming Language Using the Eclipse Development Environment

Creating secure software is challenging, but necessary due to the prevalence of large data breaches that have occurred for organizations such as Equifax, Uber, and U.S. Securities and Exchange Commission. Many static analysis tools are available that can identify vulnerable code, however many are proprietary, do not disclose their rule set or do not integrate with development environments. One open source tool that integrates well with the Eclipse development environment is the Secure Coding Assistant that was developed at California State University, Sacramento (CSUS), which is featured by early error detection. The tool provides support for secure coding rules for the Java programming language that were developed at the CERT division of the Software Engineering Institute at Carnegie Mellon University. The tool also provides error correction and contract programming support. To provide secure coding assistance in C programming, we further extend the tool to support the C programming language by semi-automating a subset of the CERT secure coding rules for C. The tool detects rule violations for the C programming language in the Eclipse development environment and provides feedback to aid and educate developers in secure coding practices. The tool is open source to the community and maintained at GitHub ( http://benw408701.github.io/SecureCodingAssistant/ ).