Reviewerly/exHarmony huggingface dataset:
Performance Comparison of Machine Learning Algorithms for Imbalanced Class Classification in Hydraulic System

Several studies have been conducted over the past decades on fault detection for data-based system monitoring and have also been applied in various industry groups. However, there are various factors that make it difficult to apply in reality, even though algorithms for detecting various faults have been studied. The class imbalance problem is considered one of the most serious problems with fault detection. In many areas of classification, the data collected is largely imbalanced. Thus, in order to actually apply data-based system monitoring, there is a need for a method that can effectively solve such class imbalances. Various methodologies have been proposed to address this class imbalance problem, and good results have been recorded in several studies. In this paper, the overfitting problem caused by class imbalance is verified through experiment, and we find out which methodology is effective in solving class imbalance among several proposed methodologies for hydraulic system.
Denoising Internet Delay Measurements using Weak Supervision

To understand the delay characteristics of the Internet, a myriad of measurement tools and techniques are proposed by the researchers in academia and industry. Datasets from such measurement tools are curated to facilitate analyses at a later time. Despite the benefits of these tools and datasets, the systematic interpretation of measurements in the face of measurement noise. Unfortunately, state-of-the-art denoising techniques are labor-intensive and ineffective. To tackle this problem, we develop NoMoNoise, an open-source framework for denoising latency measurements by leveraging the recent advancements in weak-supervised learning. NoMoNoise can generate measurement noise labels that could be integrated into the inference and control logic to remove and/or repair noisy measurements in an automated and rapid fashion. We evaluate the efficacy of NoMoNoise in a lab-based setting and a real-world setting by applying it on CAIDA's Ark dataset and show that NoMoNoise can remove noisy measurements effectively with high accuracy.
Study of Subjective Data Integrity for Image Quality Data Sets with Consumer Camera Content

We need data sets of images and subjective scores to develop robust no reference (or blind) visual quality metrics for consumer applications. These applications have many uncontrolled variables because the camera creates the original media and the impairment simultaneously. We do not fully understand how this impacts the integrity of our subjective data. We put forward two new data sets of images from consumer cameras. The first data set, CCRIQ2, uses a strict experiment design, more suitable for camera performance evaluation. The second data set, VIME1, uses a loose experiment design that resembles the behavior of consumer photographers. We gather subjective scores through a subjective experiment with 24 participants using the Absolute Category Rating method. We make these two new data sets available royalty-free on the Consumer Digital Video Library. We also present their integrity analysis (proposing one new approach) and explore the possibility of combining CCRIQ2 with its legacy counterpart. We conclude that the loose experiment design yields unreliable data, despite adhering to international recommendations. This suggests that the classical subjective study design may not be suitable for studies using consumer content. Finally, we show that Hoßfeld–Schatz–Egger α failed to detect important differences between the two data sets.
Improving Resource Efficiency of Deep Activity Recognition via Redundancy Reduction

Compression methods for deep learning have been recently used to port deep neural networks into resource-constrained devices - such as digital gloves and smartwatches - for human activity recognition (HAR). While the results have been in favor of utilizing compressed models, we envision that the current paradigm of long and fixed-size overlapping sliding windows that permeate the literature of HAR contributes negatively toward the goal of more resource-efficient systems, as it induces redundancies in memory and computation. In this work, we provide a different perspective by demonstrating that memory footprint, computational expense, and possibly energy consumption can be dramatically spared by modifying the architecture of the neural networks and their training. It is achieved by enabling non-overlapping short sliding windows and skipping fine-grained features in favor of rough ones on certain occasions, thus reducing the demand for more powerful hardware. Compared with the state-of-the-art, our method is able to achieve comparable performance far more efficiently in terms of resource use.
Attribute-Based Convergent Encryption Key Management for Secure Deduplication in Cloud

Data deduplication is a methodology for eliminating duplicate copies of data by keeping the single copy of data with proper access policies, instead of maintaining duplicates of the same data. It reduces cloud storage slot and communication bandwidth. Secure deduplication is a challenge in multi-user cloud setup, where each user uses their key to secure their data, resulting in different enciphered data for the same unencrypted data. Convergent Encryption (CE) addresses this issue by fixing the single hash value of the file as the key for enciphering the content of the file. The challenge now is to store the key in the cloud by multiple users. The existing key management system that uses an encoding scheme suffers in the realistic environment. To overcome this issue, in the proposed work ABCEKM (Attribute-Based Convergent Encryption Key Management), the file key is enciphered by specifying access policies for the attributes of the file. The proposed approach is efficient and reliable for cloud storage.
Evaluation of Novelty Detection Methods in On-Body Smartphone Localization Problem

On-body device position-awareness plays an important role relative to providing smartphone-based services with high levels of usability and quality. Existing on-body device localization methods deal with a fixed number of positions. In contrast, we propose a framework to discover new positions and add them as recognition targets during use. A preliminary experiment into new position detection is presented in this paper.
Mobile Marker-based Augmented Reality Coloring Sheets Development for Dengue Awareness

Many of the tropical and subtropical regions of the world are facing significant socioeconomic concerns. In relation to this they face the spread of dengue fever. From the populations of Southeast Asia and Western Pacific regions, Asia remains disproportionately affected by 75% of the dengue infection. Thus, it has become a major challenge to the public health because of the worldwide spread and the dramatic increase in dengue cases particularly in the Asian region. Even though, action has been taken by the government and community, to combat the diseases through media campaign and other activities, the lack of knowledge such as understanding the life cycle and breeding of a mosquito may result in less effective mosquito control measures. Taking the advantages of augmented reality (AR) technology using marker-based will be able to facilitate the visualization and integration of information by displaying it directly to the user's view. This study aims to edutain school aged children about the mosquito breeding. The total participants involved in this survey were 86 standard 6 students from public schools. The results show that the scores for all scales in UEQ tools represent a positive evaluation describing a pragmatic quality aspect (efficiency, perspicuity, dependability) with a value of 1.26, hedonic quality (stimulation and novelty) with a value of 1.50 and attractiveness with a value of 1.57. Alpha coefficients are 1.570 for attractiveness, 1.302 for perspicuity, 1.279 for efficiency, 1.186 for dependability, 1.750 for stimulation and 1.244 for novelty. The value of attractiveness and stimulation give the highest scale in the results. This showed that users have found that the overall impression of the application is attractive, exciting and motivate them to use it. This suggested that the use of AR application can assist students understanding because it provides a dynamic visualization to support learning. Thus, able to create awareness in the early prevention of dengue control.
Speech encryption based on the synchronization of fractional-order chaotic maps

This work presents a new method of encrypting and decrypting speech based on a chaotic key generator. The proposed scheme takes advantage of the best features of chaotic systems. In the proposed method, the input speech signal is converted into an image which is ciphered by an encryption function using a chaotic key matrix generated from a fractional-order chaotic map. Based on a deadbeat observer, the exact synchronization of system used is established, and the decryption is performed. Different analysis are applied for analyzing the effectiveness of the encryption system. The obtained results confirm that the proposed system offers a higher level of security against various attacks and holds a strong key generation mechanism for satisfactory speech communication.
Underwater Object Detection and Tracking

Underwater imagery is a powerful tool for hydrographic inspection including the bathymetry and aquatic possibilities over the extent of the swath. This paper describes a flexible technique for detecting a specific object from the clump of objects, using the reference imaging and target imaging. It facilitates to track the path followed by the object in the streaming video. The proposed methodology operates as an optimal interface between the ideal and the empirical situations, whether it is for image processing or video processing. The objectives accomplished through this paper are the summation as, irrespective of the resolution and clarity of image or video frame, the algorithm reliance and achieves the target. To this end, a reliable measure and analysis of data on certain conditions have been done which show the higher precision of the approach, with the detected and predicted locations, as compared to the traditional techniques which often fail at certain points.
Practical and Bilateral Privacy-preserving Federated Learning

Federated learning, as an emerging distributed training model of neuralnetworks without collecting raw data, has attracted widespread attention.However, almost all existing researches of federated learning only considerprotecting the privacy of clients, but not preventing model iterates and finalmodel parameters from leaking to untrusted clients and external attackers. Inthis paper, we present the first bilateral privacy-preserving federatedlearning scheme, which protects not only the raw training data of clients, butalso model iterates during the training phase as well as final modelparameters. Specifically, we present an efficient privacy-preserving techniqueto mask or encrypt the global model, which not only allows clients to trainover the noisy global model, but also ensures only the server can obtain theexact updated model. Detailed security analysis shows that clients can accessneither model iterates nor the final global model; meanwhile, the server cannotobtain raw training data of clients from additional information used forrecovering the exact updated model. Finally, extensive experiments demonstratethe proposed scheme has comparable model accuracy with traditional federatedlearning without bringing much extra communication overhead.
A Particle Swarm Optimization K-Means Algorithm for Mongolian Elements Clustering

Text clustering is an important research area of clustering technique. Clustering analysis groups the text according to similar characteristics, so the text in the same clusters have the greatest similarity, while the text in different clusters have the greatest dissimilarity. In this paper, we proposed a hybrid clustering technique called PSOKM that combined particle swarm optimization algorithm with K-Means. Numerical experiments show that our proposed algorithm outperforms than existing others.
Enhancing Claims Handling Processes with Insurance Based Language Models

Insurance companies manage a large number of claims on a daily basis as new claims are reported and existing claims are serviced. A key component for servicing a claim is the ability for Claims personnel to enter in raw text, aka claims notes. Claims notes contain invaluable information often beyond that of structured data, capturing this information in a machine learning setting offers remarkable benefits to many downstream tasks in a Claims department. The ability to leverage claims notes enables an insurance company not only to make data-driven and insightful decisions while handling claims, but to create value through working more efficiently and serve their customers more effectively. To best leverage the information contained claims notes, we develop insurance-based language models (IBLMs) by further pre-training existing general domain language models (ULMFiT and BERT) on a large number of claim notes with enhanced vocabulary. Furthermore, we tested these IBLMs against three downstream binary classification tasks: (1) identification of auto claims with attorney retention, (2) bodily injury prediction, and (3) auto claims fraud investigation detection. We train different classifiers based on claims notes available on day 1 and through day 10 from when the claim was reported. We found that IBLMs show a significant improvement over the traditional classification approaches. Further, we provide practical insight into how an insurance company might use these models through the analysis of volume (capacity) thresholds.
A Unified Endpointer Using Multitask and Multidomain Training

In speech recognition systems, we generally differentiate the role of endpointers between long-form speech and voice queries, where they are responsible for speech detection and query endpoint detection respectively. Detection of speech is useful for segmentation and pre-filtering in long-form speech processing. On the other hand, query endpoint detection predicts when to stop listening and send audio received so far for actions. It thus determines system latency and is an essential component for interactive voice systems. For both tasks, endpointer needs to be robust in challenging environments, including noisy conditions, reverberant environments and environments with background speech, and it has to generalize well to different domains with different speaking styles and rhythms. This work investigates building a unified endpointer by folding the separate speech detection and query endpoint detection tasks into a single neural network model through multitask learning. A categorical domain representation is further incorporated into the model to encourage learning domain specific information. The final unified model achieves around 100 ms (18% relatively) latency improvement for near-field voice queries and 150 ms (21% relatively) for far-field voice queries over simply pooling all the data together and 7% relative frame error rate reduction for long-form speech compared to a standalone speech detection model. The proposed approach also shows good robustness to noisy environments and yields 180 ms latency improvement on voice queries from an unseen domain.
JPEG Encryption with File Format Preservation and File Size Reduction

We present a novel format preserving JPEG encryption scheme, which also losslessly reduces the files size by compressing the quantized DC coefficients. The main idea behind this paper is to introduce additional lossless compression as a part of format preserving encryption to reduce the redundancy as well as encryption. The result reduces the encrypted file size up to 3.2%.
Acceleration of Path Planning Computation Based on Evolutionary Artificial Potential Field for Non-static Environments

In this work, a mobile robot path-planning algorithm based on the evolutionary artificial potential field (EAPF) for non-static environments is presented. With the aim to accelerate the path planning computation, the EAPF algorithm is implemented employing novel parallel computing architectures. The EAPF algorithm is capable of deriving optimal potential field functions using evolutionary computation to generate accurate and efficient paths to drive a mobile robot from the start point to the goal point without colliding with obstacles in static and non-static environments. The algorithm allows parallel implementation to accelerate the computation to obtain better results in a reasonable runtime. Comparative performance analysis in terms of path length and computation time is provided. The experiments were specifically designed to show the effectiveness and the efficiency of the mobile robot path-planning algorithm based on the EAPF in a sequential implementation on CPU, a parallel implementation on CPU, and a parallel implementation on GPU.
Collection development practices in academic libraries in Tanzania

The aim of this study was to assess the collection development practices in academic libraries in Tanzania. Specifically, the study examined the collection development practices and factors that influence these practices. The study involved four academic libraries and employed both qualitative and quantitative methods for data collection and analysis. Primary data were collected through informant interviews, observations and questionnaires methods. The study involved 44 library staff who filled in self-administered questionnaires. The study discloses that collection development practices and particularly evaluation and weeding or deselection are rarely conducted at these academic libraries. The findings further reveal that collection development practices in academic libraries in Tanzania are constrained by inadequate funding, lack of skills for hybrid collection management and lack of collection development policy catering for both print- and electronic-based information resources. On the basis of the findings, the study recommends that academic libraries should establish a comprehensive collection development policy and introduce professional development programmes to library staff for efficient management of hybrid collections.
Vehicle-Mounted with Tracked Robotic System Based on the Kinect

In the research of robot controlling, existing methods are mainly traditional human-computer interactive control methods such as mobile APP or handle. These traditional methods, however, rely on external devices to control the robot, which is inefficient to perform more complex functions. Although the Kinect has more benefits than other sensors in motion recognition, there is still no proper research on motion and gesture recognition of a tracked vehicle robot. Therefore, this paper proposes a research method of vehicle robot system based on Kinect dynamic gesture recognition of depth information. By using the Kinect sensor, we can recognize and obtain information such as human bones and gestures, and finally, realize accurate control of vehicle-mounted robots with multiple gestures.
Enhanced Bootstrapping Algorithm for Automatic Annotation of Tweets

Annotations are critical in various text mining tasks such as opinion mining, sentiment analysis, word sense disambiguation. Supervised learning algorithms start with the training of the classifier and require manually annotated datasets. However, manual annotations are often subjective, biased, onerous, and burdensome to develop; therefore, there is a need for automatic annotation. Automatic annotators automatically annotate the data for creating the training set for the supervised classifier, but lack subjectivity and ignore semantics of underlying textual structures. The objective of this research is to develop scalable and semantically rich automatic annotation system while incorporating domain dependent characteristics of the annotation process. The authors devised an enhanced bootstrapping algorithm for the automatic annotation of Tweets and employed distributional semantic models (LSA and Word2Vec) to augment the novel Bootstrapping algorithm and tested the proposed algorithm on the 12,000 crowd-sourced annotated Tweets and achieved a 68.56% accuracy which is higher than the baseline accuracy.
The Interval Weighted Average and Its Importance to Type-2 Fuzzy Sets and Systems

Type-reduction (TR) is a widely used operation for internal and general type-2 fuzzy sets and systems. It maps a general type-2 fuzzy set (GT2 FS) into a type-1 fuzzy set (T1 FS), and an interval type-2 (IT2) FS into a T1 interval fuzzy number. This is chapter is about the interval weighted average (IWA) and demonstrates that it is the underlying basic operation for all kinds of TR, and also explains how the IWA can be used and computed for centroid TR and centre-of-sets TR for both IT2 and GT2 FSs and systems.
Semi-Supervised Training and Data Augmentation for Adaptation of Automatic Broadcast News Captioning Systems

In this paper we present a comprehensive study on building and adapting deep neural network based speech recognition systems for automatic closed captioning. We develop the proposed systems by first building base automatic speech recognition (ASR) systems that are not specific to any particular show or station. These models are trained on nearly 6000 hours of broadcast news data using conventional hybrid and more recent attention based end-to-end acoustic models. We then employ various adaptation and data augmentation strategies to further improve the trained base models. We use 535 hours of data from two independent BN sources to study how the base models can be customized. We observe up to 32% relative improvement using the proposed techniques on test sets related to, but independent of the adaptation data. At these low word error rates (WERs), we believe the customized BN ASR systems can be used effectively for automatic closed captioning.
Online Takagi-Sugeno Fuzzy Identification of a Quadcopter Using Experimental Input-Output Data

This paper presents a sequential learning machine based on the Takagi-Sugeno (TS) fuzzy inference system to model the dynamics of a MIMO nonlinear quadcopter using experimental data. Unlike conventional TS-fuzzy systems, all the antecedent and consequent parameters of our proposed TS-fuzzy model are updated using the gradient descent-based back-propagation algorithm. After extensive numerical simulations, the accuracy of the proposed model is validated and compared with the Fuzzy C-Means clustering (FCM) algorithm and also with the ARMAX linear model identification technique. This paper leverages the advantages of model-free systems, which can incorporate various uncertainties such as noise, wind gusts, etc. The learning capability using back-propagation method is also suitable to represent the nonlinear dynamics of our quadcopter.
Simultaneous measurement of displacement, pitch, and yaw angles of a cavity output mirror based on phase measuring deflectometry

Accurate and high dynamic range measurement of the three attitude variations (i.e., displacement, pitch, yaw angles) has been widely researched based on various approaches to meet different application requirements. However, none of the existing methods can detect these three attitude variations at the same time. This paper proposes a method based on a classical phase measuring deflectometry (PMD) system and a double iteration algorithm to simultaneously measure the three variations of the output mirror in a laser resonator. The relationship between the attitude variations and the lateral displacement parameters (LDP) of the imaged structural fringes is derived in the PMD system. Through the established relationship, the attitude variations could be calculated once the LDP are detected. Furthermore, a two-step measuring scheme combining the four-step shifting phase and the color-coded fringe is proposed to effectively detect the LDP. Finally, an experiment is conducted to verify the feasibility of the proposed method.