Reviewerly/exHarmony huggingface dataset:
Minimax Active Learning Via Minimal Model Capacity

Active learning is a form of machine learning which combines supervised learning and feedback to minimize the training set size, subject to low generalization errors. Since direct optimization of the generalization error is difficult, many heuristics have been developed which lack a firm theoretical foundation. In this paper, a new information theoretic criterion is proposed based on a minimax log-loss regret formulation of the active learning problem. In the first part of this paper, a Redundancy Capacity theorem for active learning is derived along with an optimal learner. Building on this, a new active learning criterion is proposed which naturally induces an exploration - exploitation trade-off in feature selection. In the second part, the linear separator hypotheses class with additive label noise is considered and a low complexity algorithm is proposed which optimizes the active learning criterion from the first part. This greedy algorithm is based on the Posterior Matching scheme for communication with feedback and is shown that for BSC and BEC label noise, the proposed information theoretic criterion decays at an exponential rate.
Task Scheduling and Resource Allocation Based on Ant-Colony Optimization and Deep Reinforcement Learning

Cloud computing has become a significant aspect of today’s rapidly growing technology, accessing as it does a large number of servers, given users’ constant need to access their data efficiently and quickly. Cloud computing providers can flexibly place a user’s task into an appropriate virtual machine and allocate the resource to the tasks for proper execution. However, user tasks can take a long time to complete the execution when the required resources are not available on the server. To overcome this problem, we propose a task scheduling and resource allocation model based on Hybrid Ant Colony Optimization and Deep Reinforcement Learning. In this article, our goal is to minimize the overall task completion time and improve the utilization of idle resources. The task scheduling was performed by constructing a Binary In-order Traversal Tree using weighted values. We then introduced a Deep Reinforcement Learning (DRL) algorithm to reduce space complexity by splitting resources into state space and action space. A state space will contain idle resources, which are used in task allocation. Then the scheduled task will search the resources based on Ant Colony Optimization. When it finds an optimal resource, it will allocate it to the task, and the server will put the allocated resources into action space. If the VM is overloaded, migration is performed. We simulated the proposed algorithm using CloudSim and evaluated the performance in terms of task completion time and resource utilization. Our proposed work evaluation shows mitigation of the above-described problems and illustrates the reduction of waiting time and improvement in idle resource utilization.
A cooperative detection method for tracking a non-cooperative space target

This paper investigates the cooperative detection problem for tracking a non-cooperative space target. A cooperative detection and tracking method is proposed to obtained the position and velocity of the target by fusing measurements from an observer and an interceptor. First, the relative motion between the observer, interceptor and target are modeled. Then, the cooperative detection and tracking method is proposed, which contains a binocular detection mode and a fractionated detection mode for different phases of the interception and capture process. The simulation results show the effectiveness and advantages of the proposed method.
Convolutional Neural Network for Object Detection System for Blind People

Blind or visually impaired people are usually unaware of the danger that they are facing in their daily life. They may face many challenges when performing their daily activity even in familiar environments. This work proposed a smart object detection system based on Convolutional Neural Network (CNN) to provide a smart and safe living for visually impaired people. To reduce the complexity load, region proposals from the edge maps of each image were produced using edge box algorithm. Then, the proposals passed through a fine-tuned CaffeNet model. The object scene was captured by the webcam in real time and the feature of the image was extracted. After that, audio-based detector was generated on the detected object to notify the visually impaired people about the identified object. The result was evaluated by using mean average precision (mAP) and frame-per-second and it was found that the Single Shot MultiBox Detector (SSD) reduces the complexity and achieves higher accuracy as well as faster speed in object detection compared to Fast R-CNN.
Machine Learning Based User Interface Generation

Every product development starts with idea conceptualization and design. The ideators and designers first choice is sketching the idea to conceptualize the product. After a few iterations of product design, mockups/prototypes are passed to developers who have the job of converting these designs into a functional product by grasping the design concepts and using their coding skills. Utilizing the advancements in deep learning and computer vision technologies, gap between designers and developers is bridged by simplifying the product development process. This will also help people with little or no knowledge of web development to bring their design ideas into reality. The service will consist of two stages firstly accepting images of web mockups including hand-drawn sketches and identifying the various HTML components in the sketch using Convolutional Neural Networks and tag them appropriately, secondly, the identified components will be converted to structured JSON files which will be converted to code based on HTML5, Flexbox and Bootstrap.
Offline Signature Verification using Clustering Technique

Signature verification is one of the most basic and heavily used biometric which finds its application in many fields in the day to day life. However, it is also very complex and difficult to correctly classify a signature as genuine or forged because of the discrepancies associated with a signature and due to the skill and precision with which a forgery of the genuine signature is done. The proposed method is based on extraction and analysis of the features of the handwritten signature from the scanned images using Agglomerative Hierarchical Clustering technique. After pre-processing of the images and formation of clusters, based upon the error threshold, which is calculated using the intra-cluster distances, the signatures are classified as either genuine or forged. The proposed method is reliable, quick and does not require large datasets since it is based on an unsupervised approach and has shown promising results while dealing with skilled forgeries.
Adversarial Example Generation

Deep Neural Networks have achieved remarkable success in computer vision, natural language processing, and audio tasks.
Design and Application of Games in College English Classroom Teaching

College English teaching games are particularly interesting and interactive, which can stimulate college students' motivation and drive for English learning effectively.In the meantime, this kind of activity solidifies students' self-confidence to learn English and dispels psychological stress and anxiety as well, so as to optimize the efficiency of English learning.This paper summarizes the basic principles, which should be followed, and offers the specific methods and items we should pay attention to in view of the game design and application in college English classroom teaching.The thesis is based on the relevant research achievements of the predecessors, and several examples are listed according to the practice experience, such as words solitaire, sentences renewing and reciting, class discussions, debates, role playing, etc.The practice indicates that his kind of classes is being longed for and unforgettable if we can both successfully cultivate the students' capability in cross-culture communication and influence their learning and life.
Development of Solar-Panel Monitoring Method Using Unmanned Aerial Vehicle and Thermal Infrared Sensor

Abstract Interest in solar energy, which is clean energy, is growing due to continuously high oil prices because of the unstable situation in the Middle East and the explosion of the Fukushima nuclear-power plant in Japan. South Korea has made various efforts to develop renewable energy, and facilities using solar energy are rapidly growing. Periodic inspection of solar panels is very important for the effective management of photovoltaic (PV) modules. In this study, a method to monitor and diagnose faulty solar modules through an Unmanned Aerial Vehicle (UAV)-based thermal infrared camera and GIS spatial analysis is discussed. First, the image of the PV module was taken by installing an RGB camera and thermal infrared camera on the rotor blade of the UAV. The images were generated by using Pix4D SW, and the normal operation of the PV module was diagnosed using the orthoimage. In particular, thermal infrared cameras are very sensitive to reaction speed, unlike optical sensors, so it is very important to find appropriate monitoring methods, such as shooting speed. Therefore, we suggest a way to utilize an optical sensor and thermal infrared sensor together through various attempts. In the images, GIS spatial analysis was used to extract abnormal heat or faulty modules. Using the Virtual Reference Service (VRS) survey method, UAV images could be 3D-mapped with a very precise temperature, within 2 cm in x, y, and z direction errors. The rapid development of PV powergeneration facilities and of monitoring technologies is a very important factor for improving energy efficiency. The result of this study is providing a stable solar-panel-monitoring technology by comparing features of ground observation and UAV-based thermal infrared sensor observation. The developed monitoring technology was able to accurately analyze the failure point and temperature of the PV module.
Quantifying the auto-oscillation complexity following water spraying with interest for phonation

Human voiced sound production or phonation is the result of a fluid-structure instability in the larynx leading to vocal folds auto-oscillation. In this paper, the effect of surface hydration following water spraying (0 up to 5 ml) on an ongoing auto-oscillation is studied experimentally using different mechanical deformable vocal folds replicas. The complexity of the oscillation is quantified on the upstream pressure by a phase space recurrence and complexity analysis. It is shown that: (1) the ratio of the degree of determinism to the recurrence rate of the phase space states γ and (2) estimated correlation dimension D_{2} are suitable parameters to grasp the effect of hydration on the oscillation pattern. The oscillation regime after hydration can either remain deterministic or approach a chaotic regime depending on initial conditions prior to water spraying, such as elasticity, glottal aperture, as well as oscillation complexity.
Weakly-supervised Deep Anomaly Detection with Pairwise Relation Learning.

Anomaly detection is typically posited as an unsupervised learning task in the literature due to the prohibitive cost and difficulty to obtain large-scale labeled anomaly data, but this ignores the fact that a very small number (e.g.,, a few dozens) of labeled anomalies can often be made available with small/trivial cost in many real-world anomaly detection applications. To leverage such labeled anomaly data, we study an important anomaly detection problem termed weakly-supervised anomaly detection, in which, in addition to a large amount of unlabeled data, a limited number of labeled anomalies are available during modeling. Learning with the small labeled anomaly data enables anomaly-informed modeling, which helps identify anomalies of interest and address the notorious high false positives in unsupervised anomaly detection. However, the problem is especially challenging, since (i) the limited amount of labeled anomaly data often, if not always, cannot cover all types of anomalies and (ii) the unlabeled data is often dominated by normal instances but has anomaly contamination. We address the problem by formulating it as a pairwise relation prediction task. Particularly, our approach defines a two-stream ordinal regression neural network to learn the relation of randomly sampled instance pairs, i.e., whether the instance pair contains two labeled anomalies, one labeled anomaly, or just unlabeled data instances. The resulting model effectively leverages both the labeled and unlabeled data to substantially augment the training data and learn well-generalized representations of normality and abnormality. Comprehensive empirical results on 40 real-world datasets show that our approach (i) significantly outperforms four state-of-the-art methods in detecting both of the known and previously unseen anomalies and (ii) is substantially more data-efficient.
Efficient 3D Face Recognition in Uncontrolled Environment

Face recognition in an uncontrolled environment is challenging as body movement and pose variation can result in missing facial features. In this paper, we tackle this problem by fusing multiple RGB-D images with varying poses. In particular, we develop an efficient pose fusion algorithm that frontalizes the faces and combines the multiple inputs. We then introduce a new 3D registration method based on the unified coordinate system (UCS) to compensate for pose and scale variations and normalize the probe and gallery face. To perform 3D face recognition, we train a Support Vector Machine (SVM) with both 2D color and 3D geometric features. Experimental results on a RGB-D dataset show that our method can achieve a high recognition rate and is robust in the presence of pose and expression variations.
Random forest age estimation model based on length of left hand bone for Asian population

In forensic anthropology, age estimation is used to ease the process of identifying the age of a living being or the body of a deceased person. Nonetheless, the specialty of the estimation models is solely suitable to a specific people. Commonly, the models are inter and intra-observer variability as the qualitative set of data is being used which results the estimation of age to rely on forensic experts. This study proposes an age estimation model by using length of bone in left hand of Asian subjects range from newborn up to 18-year-old. One soft computing model, which is Random Forest (RF) is used to develop the estimation model and the results are compared with Artificial Neural Network (ANN) and Support Vector Machine (SVM), developed in the previous case studies. The performance measurement used in this study and the previous case study are R-square and Mean Square Error (MSE) value. Based on the results produced, the RF model shows comparable results with the ANN and SVM model. For male subjects, the performance of the RF model is better than ANN, however less ideal than SVM model. As for female subjects, the RF model overperfoms both ANN and SVM model. Overall, the RF model is the most suitable model in estimating age for female subjects compared to ANN and SVM model, however for male subjects, RF model is the second best model compared to the both models. Yet, the application of this model is restricted only to experimental purpose or forensic practice.
A Coarse-to-fine Deep Convolutional Neural Network Framework for Frame Duplication Detection and Localization in Forged Videos

Videos can be manipulated by duplicating a sequence of consecutive frames with the goal of concealing or imitating a specific content in the same video. In this paper, we propose a novel coarse-to-fine framework based on deep Convolutional Neural Networks to automatically detect and localize such frame duplication. First, an I3D network finds coarse-level matches between candidate duplicated frame sequences and the corresponding selected original frame sequences. Then a Siamese network based on ResNet architecture identifies fine-level correspondences between an individual duplicated frame and the corresponding selected frame. We also propose a robust statistical approach to compute a video-level score indicating the likelihood of manipulation or forgery. Additionally, for providing manipulation localization information we develop an inconsistency detector based on the I3D network to distinguish the duplicated frames from the selected original frames. Quantified evaluation on two challenging video forgery datasets clearly demonstrates that this approach performs significantly better than four recent state-of-the-art methods.
The Power of Graph Convolutional Networks to Distinguish Random Graph Models

Graph convolutional networks (GCNs) are a widely used method for graph representation learning. To elucidate the capabilities and limitations of GCNs, we investigate their power, as a function of their number of layers, to distinguish between different random graph models (corresponding to different class-conditional distributions in a classification problem) on the basis of the embeddings of their sample graphs. In particular, the graph models that we consider arise from graphons, which are the most general possible parameterizations of infinite exchangeable graph models and which are the central objects of study in the theory of dense graph limits. We give a precise characterization of the set of pairs of graphons that are indistinguishable by a GCN with nonlinear activation functions coming from a certain broad class if its depth is at least logarithmic in the size of the sample graph. This characterization is in terms of a degree profile closeness property. Outside this class, a very simple GCN architecture suffices for distinguishability. We then exhibit a concrete, infinite class of graphons arising from stochastic block models that are well-separated in terms of cut distance and are indistinguishable by a GCN. These results theoretically match empirical observations of several prior works on GCNs. To prove our results, we exploit a connection to random walks on graphs.
Image Aesthetic Assessment Based on Perception Consistency

Automatically assessing the aesthetic quality of images that is consistent with humans is a challenging task. Previous works based on Convolution Neural Network (CNN) lacks of perception consistency in two aspects. First, they mainly extract features from the entire image without distinguishing between the foreground and background. Second, they classify images with highly-compressed semantic feature. In this paper, we proposed a visual perception network (VP-Net) to support perception consistency learning. It was designed as a double-subnet network which can learn from subject region feature and multi-level features. In addition, a subject region search algorithm was proposed to find out a composed of multiple subject regions. Experimental results on a large scale aesthetic dataset (AVA) have demonstrated the superiority of our approach.
Metadata Revisited: Updating Metadata Profiles and Practices in a Vendor-Hosted Repository

Implemented as a way to host open-access journals, the University of Nevada Las Vegas (UNLV) Libraries institutional repository (IR) expanded into collecting other researcher-created materials, a process that did not always include clear metadata and descriptive guidelines. Series-specific settings, unclear field definitions, and other varying practices created an inconsistent bibliographic database, however, and the unclear field definitions and lack of thorough internal documentation pointed to issues that would need to be addressed if the Libraries wanted to reliably share its IR metadata with its discovery layer and external harvesters and aggregators. To resolve this problem, UNLV undertook a metadata review intended to reconcile the fields used and provide recommendations on vocabularies and standards for capturing metadata. Through a collaborative, iterative process, the Metadata Review Team suggested and implemented changes to the IR’s metadata structures, in consultation with vendor support, resulting in improved descriptive policies for IR resources.
Towards a New Data Replication Management in Cloud Systems

Applications produce huge volumes of data that are distributed on remote and heterogeneous sites. This generates problems related to access and sharing data. As a result, managing data in large-scale environments is a real challenge. In this context, large-scale data management systems often use data replication, a well-known technique that treats generated problems by storing multiple copies of data, called replicas, across multiple nodes. Most of the replication strategies in these environments are difficult to adapt to cloud environments. They aim to achieve the best performance of the system without meeting the important objectives of the cloud provider. This article proposes a new dynamic replication strategy. The proposed algorithm significantly improves provider gain without neglecting customer satisfaction.
Predicting Degree Completion through Data Mining

Universities and colleges continuously strive to increase student retention and degree completion.The U.S. Department of Education has set the goal of preparing a society with individuals capable to "understand, explore and engage with the world" specific skills that can be achieved through STEM majors.Currently, considerable student data are collected and there is a latent opportunity to make the available information useful for determining the factors that influence retention and completion rates.Analyzing student data with those aims is vital for intentional student advising.To this end, this research presents the application of decision trees to predict degree completion within three years for STEM community college students.Decision trees also enable the identification of the factors that impact program completion using non-parametric models by classifying data using decision rules from the patterns learned.The model was developed using data on 283 students with 14 variables.The variables included age, gender, degree, and college GPA, among others.The results offer important insight into how to develop a more efficient and responsive system to support students.
Querying and Searching Heterogeneous Knowledge Graphs in Real-time Linked Dataspaces

As the volume and variety of data sources within a dataspace grow, it becomes a semantically heterogeneous and distributed environment; this presents a significant challenge to querying the dataspace. Approaches used for querying siloed databases fail within large dataspaces because users do not have an a priori understanding of all the available datasets. This chapter investigates the main challenges in constructing query and search services for knowledge graphs within a linked dataspace. Search and query services within a linked dataspace do not follow a one-size-fits-all approach and utilise a range of different techniques to support different characteristics of data sources and user needs.