Reviewerly/exHarmony huggingface dataset:
Unsupervised Cross-domain Image Classification by Distance Metric Guided Feature Alignment

Learning deep neural networks that are generalizable across different domains remains a challenge due to the problem of domain shift. Unsupervised domain adaptation is a promising avenue which transfers knowledge from a source domain to a target domain without using any labels in the target domain. Contemporary techniques focus on extracting domain-invariant features using domain adversarial training. However, these techniques neglect to learn discriminative class boundaries in the latent representation space on a target domain and yield limited adaptation performance. To address this problem, we propose distance metric guided feature alignment (MetFA) to extract discriminative as well as domain-invariant features on both source and target domains. The proposed MetFA method explicitly and directly learns the latent representation without using domain adversarial training. Our model integrates class distribution alignment to transfer semantic knowledge from a source domain to a target domain. We evaluate the proposed method on fetal ultrasound datasets for cross-device image classification. Experimental results demonstrate that the proposed method outperforms the state-of-the-art and enables model generalization.
Local Intrinsic Dimensionality III: Density and Similarity

In artificial intelligence, machine learning, and other areas in which statistical estimation and modeling is common, distributions are typically assumed to admit a representation in terms of a probability density function (pdf). However, in many situations, such as mixture modeling and subspace methods, the distributions in question are not always describable in terms of a single pdf. In this paper, we present a theoretical foundation for the modeling of density ratios in terms of the local intrinsic dimensionality (LID) model, in a way that avoids the use of traditional probability density functions. These formulations provide greater flexibility when modeling data under the assumption of local variation in intrinsic dimensionality, in that no explicit dependence on a fixed-dimensional data representation is required.
CollegeBot: A Conversational AI Approach to Help Students Navigate College

In an organization as big as a university that has many distinct departments and administrative bodies, it becomes almost impossible to easily obtain information online or by other means. Assistance over the phone or in-person is often limited to office hours and the information online is scattered through numerous (often nested) web pages, often independently administered and maintained by each sub-division. In this work, we present CollegeBot, a conversational AI agent that uses natural language processing and machine learning to assist visitors of a university's web site in easily locating information related to their queries. We discuss how we create the knowledge base by collecting and appropriately preprocessing information that is used to train the conversational agent for answering domain-specific questions. We have evaluated two different algorithms for training the conversational model for the chatbot, namely a semantic similarity model and a deep learning one leveraging Sequence-to-Sequence learning model. The proposed system is able to capture the user's intent and switch context appropriately. It also leverages the open source AIML chatbot ALICE to answer any generic (non domain-specific) questions. We present a proof-of-concept prototype for San Jose State University, to demonstrate how such an approach can be easily adopted by other academic institutions as well.
Enhancing Factorization Machines With Generalized Metric Learning

Factorization Machines (FMs) are effective in incorporating side information to overcome the cold-start and data sparsity problems in recommender systems. Traditional FMs adopt the inner product to model the second-order interactions between different attributes, which are represented via feature vectors. The problem is that the inner product violates the triangle inequality property of feature vectors. As a result, it cannot well capture fine-grained attribute interactions, resulting in sub-optimal performance. Recently, the euclidean distance is exploited in FMs to replace the inner product and has delivered better performance. However, previous FM methods including the ones equipped with the euclidean distance all focus on the attribute-level interaction modeling, ignoring the critical intrinsic feature correlations inside attributes. Thereby, they fail to model the complex and rich interactions exhibited in the real-world data. To tackle this problem, in this paper, we propose a FM framework equipped with generalized metric learning techniques to better capture these feature correlations. In particular, based on this framework, we present a Mahalanobis distance and a deep neural network (DNN) methods, which can effectively model the linear and non-linear correlations between features, respectively. Besides, we design an efficient approach for simplifying the model functions. Experiments on several benchmark datasets demonstrate that our proposed framework outperforms several state-of-the-art baselines by a large margin. Moreover, we collect a new large-scale dataset on second-hand trading to justify the effectiveness of our method over cold-start and data sparsity problems in recommender systems.
Fractional Gradient Descent Optimizer for Linear Classifier Support Vector Machine

Supervised learning is one of the activities in data mining that aims to classify or predict data. One of the powerful supervised learning algorithms is the Support Vector Machine which is included in the linear classifier. In data prediction activities, efforts are needed to improve the accuracy of predictions by optimizing parameters in the classification algorithm. In this study, the proposed Fractional Gradient Descent as an unconstraint optimization algorithm on objective functions in the SVM classifier. With Fractional Gradient Descent as an optimizer classification model in training data activities to progress the exactness of prediction models. Fractional Gradient Descent optimizes the SVM classification model using fractional values so that it has small steps with a small learning rate in the process of reaching global minimums, and achieving convergence with lower iterations. With a learning rate of 0.0001 SVM Classifier with fractional gradient descent have error rate = 0.273083, at learning rate 0.001 with error rate = 0.273070, and at learning rate 0.01 with error rate = 0.273134. The results of the SVM Classifier with stochastic gradient descent optimization reach the convergence point at iteration 350. With fractional gradient descent optimization, it reaches a convergence point of 50 iterations smaller than the SVM Classifier with stochastic gradient descent.
Design Framework and Intelligent In-Vehicle Information System for Sensor-Cloud Platform and Applications

The sensor-cloud system (SCS) integrates sensors, sensor networks, and the cloud for managing sensors, collecting data, and decision-making. Smart transportation based on the sensor-cloud approach is constantly improving. The sensor cloud has promoted the industrialization of the Internet of Vehicles and has also brought it to a new research stage. SCS permits users to utilize its platform as a service, and providers offer several environments to users for the development of applications, which enhances the user driving experience. However, many users have concerns with the sensor-cloud platform (SCP) and the environment. Furthermore, to save sensor resources, we propose a novel design model and method for in-vehicle information systems (IVIS) in the framework of SCS, which can optimize the SCP user experience. Our contribution has three main aspects: first, we extract the new features presented by the IVIS under the sensor-cloud environment; second, we establish a mapping between the IVIS and user needs and innovatively propose the SCP-oriented experience element-level scheme; finally, an IVIS design method based on the experience element is proposed. Furthermore, this research establishes a mapping between user experience elements and intelligent IVIS design features in the SCS environment and proposes an innovative IVIS model.
Experimental method and evaluation for interlaminar shear properties of randomly oriented strand thermoplastic composites based on modified double-notch specimen and two dimensional digital image correlation

A new interlaminar shear test method has been proposed based on the double-notch compression test method, in which the test specimen shape with a modified overlap length of the notches is proposed, and the interlaminar shear strain is measured by two-dimensional digital image correlation (2D-DIC) analysis. In order to obtain the interlaminar shear properties in the elastic and non-linear regions of randomly oriented strand thermoplastic composites, we adopted a 2D-DIC analysis system using a bi-telecentric lens, which can accurately measure the strain in a small field of view, and a test system including an interlaminar shear test jig, which can apply a compressive load to the end face of the specimen at high perpendicularity. It is necessary to provide an overlap of the double-notch of 1 mm or more in order to simultaneously obtain the strain in the elastic region and the non-linear region from the actual test results. When numerical analysis by finite element method (FEM) was carried out on four types of test specimen with different shapes used in this research, by using the interlaminar shear property data obtained from actual tests on the optimum test specimen, there was a high level of agreement with the 2D-DIC analysis results for the shear stress – strain diagrams and the interlaminar shear strain distribution. Therefore, the benefits of the test specimen shape and dimensions in the new test method and the validity of the interlaminar shear property values that can be obtained using this method have been confirmed.
Characterizing Task-Relevant Information in Natural Language Software Artifacts

To complete a software development task, a software developer often consults artifacts which mostly consist of natural language text, such as API documentation, bug reports, and Q&A forums. Not all information within these artifacts is relevant to a developer's current task, forcing them to filter through large amounts of irrelevant information, a frustrating and time-consuming activity. Since failing to locate relevant information may lead developers to incorrect or incomplete solutions, many approaches attempt to automatically extract relevant information from natural language artifacts. However, existing approaches are able to identify relevant text only for certain types of tasks and artifacts. To explore how these limitations could be relaxed, we conducted a controlled experiment in which we asked 20 software developers to examine 20 natural language artifacts consisting of 1,874 sentences and highlight the text they considered relevant to six software development tasks. Although the 2,463 distinct highlights participants created indicate variability in the perceived relevance of the text, the information considered key to completing the tasks was consistent. We observe consistency in the text using frame semantics, an approach that captures the key meaning of sentences, suggesting that frame semantics can be used in the future to automatically identify task-relevant information in natural language artifacts.
Planning and Assessing Patron Experience and Needs for an Academic Library Website

This article describes a research study performed at Northeastern State University during the spring semester of 2020. The authors detail the initial phase of a website redesign process informed by responses gathered from an online survey of website design and functionality. Results from the patron experience survey were used to gauge respondents' level of satisfaction with the library website, as well as to inform potential redesign strategies. Data will be used to plan and assess the next phase of the university’s website design project. The purpose of this paper is to provide a framework for implementing and “going live” with a new library website. The methods presented are within the context of library planning and assessment, and may serve to inform future migration efforts for web design in an academic library setting.
IPGAN: Generating Informative Item Pairs by Adversarial Sampling

Negative sampling plays an important role in ranking-based recommender models. However, most existing sampling methods cannot generate informative item pairs with positive and negative instances due to two limitations: 1) they merely treat observed items as positive instances, ignoring the existence of potential positive items (i.e., nonobserved items users may prefer) and the probability of observed but noisy items and 2) they fail to capture the relationship between positive and negative items during negative sampling, which may cause the unexpected selection of potential positive items. In this article, we introduce a dynamic sampling strategy to search informative item pairs. Specifically, we first sample a positive instance from all the items by leveraging the overall features of user's observed items. Then, we strategically select a negative instance by considering its correlation with the sampled positive one. Formally, we propose an item pair generative adversarial network named IPGAN, where our sampling strategy is realized in two generative models for positive and negative instances, respectively. In addition, IPGAN can also ensure that the sampled item pairs are informative relative to the ground truth by a discriminative model. What is more, we propose a batch-training approach to further enhance both user and item modeling by alleviating the special bias (noise) from different users. This approach can also significantly accelerate the process of model training compared with classical GAN method for recommendation. Experimental results on three real data sets show that our approach outperforms other state-of-the-art approaches in terms of recommendation accuracy.
Incremental Type Migration Using Type Algebra

When a new set of types is introduced into a large software system, the task of retrofitting that system to take advantage of those types is generally cost-prohibitive. Existing automated type migration systems assume a one-to-one correspondence between old types and new types, making them unable to do partial migrations to more complex type sets. By using compiler-based migration tooling and an algebraically modeled type set, it is possible to use a small amount of manually seeded type information to automatically propagate the new types through a large monolithic C++ codebase. We demonstrate this technique by automatically generating 20k+ individual changes across a large industrial codebase of millions of lines of C++.
Voice-Indistinguishability -- Protecting Voiceprint with Differential Privacy under an Untrusted Server

With the rising adoption of advanced voice-based technology together with increasing consumer demand for smart devices, voice-controlled "virtual assistants" such as Apple's Siri and Google Assistant have been integrated into people's daily lives. However, privacy and security concerns may hinder the development of such voice-based applications since speech data contain the speaker's biometric identifier, i.e., voiceprint (as analogous to fingerprint). To alleviate privacy concerns in speech data collection, we propose a fast speech data de-identification system that allows a user to share her speech data with formal privacy guarantee to an untrusted server. Our open-sourced system can be easily integrated into other speech processing systems for collecting users' voice data in a privacy-preserving way. Experiments on public datasets verify the effectiveness and efficiency of the proposed system.
Human Age and Gender Estimation using Facial Image Processing

The increasing demand of smart security systems has enhanced the demand for the proper identification and verification of a person. In this context, accurate estimation of age as well as proper identification of gender is highly significant. Therefore, in this work, we have implemented two separate methods with satisfactory runtime and efficiency to estimate both human age and gender using facial images. Our image processing based method involves comparison of some features extracted from the post-processed facial images of people of various age ranges followed by some edge-detection procedures, creation of binary masks and evaluation of wrinkle densities. Afterwards, thresholds were set via Naïve Bayes Classification to estimate classes. For the assessment purpose, we developed a database namely BUET facial database, which consists of images of both male and female of diverse ages. For the developed database, our proposed algorithm exhibits 76.3% accuracy in the age group classification while it shows 86.6% accuracy in the gender classification. Apart from BUET facial database, our developed algorithm has also been tested in three other databases and compared its performance with the reported literature for these databases. The mean absolute error is almost below 5.0 for this work, whereas, others exceed 5.0 in most of the cases. Moreover, the proposed algorithm exhibits reasonably good accuracy under different lighting conditions of images as well. Our study would provide further insight into the choice of appropriate features for the efficient and accurate estimation of the age and the gender of a person.
Configurable Graph Reasoning for Visual Relationship Detection

Visual commonsense knowledge has received growing attention in the reasoning of long-tailed visual relationships biased in terms of object and relation labels. Most current methods typically collect and utilize external knowledge for visual relationships by following the fixed reasoning path of {subject, object → predicate} to facilitate the recognition of infrequent relationships. However, the knowledge incorporation for such fixed multidependent path suffers from the data set biased and exponentially grown combinations of object and relation labels and ignores the semantic gap between commonsense knowledge and real scenes. To alleviate this, we propose configurable graph reasoning (CGR) to decompose the reasoning path of visual relationships and the incorporation of external knowledge, achieving configurable knowledge selection and personalized graph reasoning for each relation type in each image. Given a commonsense knowledge graph, CGR learns to match and retrieve knowledge for different subpaths and selectively compose the knowledge routed path. CGR adaptively configures the reasoning path based on the knowledge graph, bridges the semantic gap between the commonsense knowledge, and the real-world scenes and achieves better knowledge generalization. Extensive experiments show that CGR consistently outperforms previous state-of-the-art methods on several popular benchmarks and works well with different knowledge graphs. Detailed analyses demonstrated that CGR learned explainable and compelling configurations of reasoning paths.
Fuzzing to Estimate Gas Costs of Ethereum Contracts

This paper studies how a simple approach based on fuzzing testing can help authors of Solidity contracts to accurately estimate the gas cost of services specified in a contract. Our fuzzer creates a private blockchain and randomly generates transactions. Such an environment is meant to simulate large scale behavior that may be seen in a public blockchain. Our fuzzer handles Ethereum starting and target endpoints in a transaction to accommodate requirements expressed in financial contracts. By comparing the gas computation made by the Ethereum Solidity compiler and the actual consumption during our fuzzing, we are able to find discrepancies between predicted and real gas consumption. Our findings are beneficial to transaction authors to correctly predict the computing resources of Ethereum miners.
Shortcut-to-Adiabaticity-Like Techniques for Parameter Estimation in Quantum Metrology

Quantum metrology makes use of quantum mechanics to improve precision measurements and measurement sensitivities. It is usually formulated for time-independent Hamiltonians, but time-dependent Hamiltonians may offer advantages, such as a T4 time dependence of the Fisher information which cannot be reached with a time-independent Hamiltonian. In Optimal adaptive control for quantum metrology with time-dependent Hamiltonians (Nature Communications 8, 2017), Shengshi Pang and Andrew N. Jordan put forward a Shortcut-to-adiabaticity (STA)-like method, specifically an approach formally similar to the “counterdiabatic approach”, adding a control term to the original Hamiltonian to reach the upper bound of the Fisher information. We revisit this work from the point of view of STA to set the relations and differences between STA-like methods in metrology and ordinary STA. This analysis paves the way for the application of other STA-like techniques in parameter estimation. In particular we explore the use of physical unitary transformations to propose alternative time-dependent Hamiltonians which may be easier to implement in the laboratory.
Comparison of Pearson correlation coefficient and distance correlation in Correlation Power Analysis on Digital Multiplier

Correlation power analysis (CPA) is a side-channel attack (SCA) which exploits the information leaked through the power supply current and voltage, or the electromagnetic emissions of the attacked digital system. It uses statistical analysis of a large number of power supply measurements to retrieve the secrets of the digital system. Correlation power analysis uses a number of hypothetical secret keys which are correlated to the measurements of the attacked system. Usually correlation power analysis uses the Pearson correlation coefficient, but the intermediary values and the power supply measurements can have a nonlinear relationship. The paper investigates the application of the distance correlation in the correlation power analysis and compares it to the Pearson correlation coefficient. The comparison is based on a side-channel attack on a multiplication operation of an input message and a secret key. The results of the comparison show that the distance correlation achieves a higher prominence of the correct secret key than the Pearson correlation coefficient.