Reviewerly/exHarmony huggingface dataset:
Robust Cubature Kalman Filter for SINS/GPS Integrated Navigation Systems With Unknown Noise Statistics

to the vehicle's severe maneuver and abnormal measurements of GPS in practical applications, the statistic of process noise in SINS/GPS integrated navigation system may be unknown and the measurement noise may not follow the Gaussian distribution, which results in a deteriorated performance for the conventional cubature Kalman filter. To address this issue, we propose in this paper a new robust cubature Kalman filter based on the adaptive information entropy theory. In the proposed filter, the process uncertainty and non-Gaussian measurement noise are simultaneously suppressed based on a new constructed cost function using the maximum correntropy and residual orthogonal principle based weighted least squares technology, which is independent of noise distribution and more insensitive to the non-Gaussian noise. Moreover, a multiple-channel adaptive strategy for the better process uncertainty suppression is given. Furthermore, some improvements are proposed to avoid the numerical problem and implement the proposed robust filter effectively. Extensive simulation and car-mounted experiment demonstrate that the proposed filter can achieve higher estimation accuracy and better robustness as compared with the related state-of-the-art methods.
Urdu Documents Clustering with Unsupervised and Semi-Supervised Probabilistic Topic Modeling

Document clustering is to group documents according to certain semantic features. Topic model has a richer semantic structure and considerable potential for helping users to know document corpora. Unfortunately, this potential is stymied on text documents which have overlapping nature, due to their purely unsupervised nature. To solve this problem, some semi-supervised models have been proposed for English language. However, no such work is available for poor resource language Urdu. Therefore, document clustering has become a challenging task in Urdu language, which has its own morphology, syntax and semantics. In this study, we proposed a semi-supervised framework for Urdu documents clustering to deal with the Urdu morphology challenges. The proposed model is a combination of pre-processing techniques, seeded-LDA model and Gibbs sampling, we named it seeded-Urdu Latent Dirichlet Allocation (seeded-ULDA). We apply the proposed model and other methods to Urdu news datasets for categorizing. For the datasets, two conditions are considered for document clustering, one is “Dataset without overlapping” in which all classes have distinct nature. The other is “Dataset with overlapping” in which the categories are overlapping and the classes are connected to each other. The aim of this study is threefold: it first shows that unsupervised models (Latent Dirichlet Allocation (LDA), Non-negative matrix factorization (NMF) and K-means) are giving satisfying results on the dataset without overlapping. Second, it shows that these unsupervised models are not performing well on the dataset with overlapping, because, on this dataset, these algorithms find some topics that are neither entirely meaningful nor effective in extrinsic tasks. Third, our proposed semi-supervised model Seeded-ULDA performs well on both datasets because this model is straightforward and effective to instruct topic models to find topics of specific interest. It is shown in this paper that the semi-supervised model, Seeded-ULDA, provides significant results as compared to unsupervised algorithms.
Application of Unmanned Aerial Vehicles in Smart Cities using Computer Vision Techniques

Unmanned aerial vehicle (UAV) systems are being widely used as mobile edge devices to provide mobile edge computing services to the ground terminals. This work presents a typical application of intersection monitoring in smart cities through UAVs equipped with cameras. A deep visual tracking system is developed by utilizing off-the-shelf YOLOv3 along with the Discriminative correlation filter for road user detection and tracking respectively. To deal with the camera movement, which naturally happens in UVA systems, an optical flow method is used to boost the tracking system. The optical method collects additional motion cues to help the tracking system for camera motion compensation as well as vehicles speed measurements. The experimental results show the success of the system for tracking vehicles through UAVs and providing critical traffic measurements for smart cities such as vehicle flow, headway, speed profile and online speed-based activity analysis.
The potential use of Bayesian Networks to support committee decisions in programmatic assessment

Abstract Context The benefits of programmatic assessment are well‐established. Evidence from multiple assessment formats is accumulated and triangulated to inform progression committee decisions. Committees are consistently challenged to ensure consistency and fairness in programmatic deliberations. Traditional statistical and psychometric techniques are not well‐suited to aggregating different assessment formats accumulated over time. Some of the strengths of programmatic assessment are also vulnerabilities viewed through this lens. While emphasis is often placed on data richness and considered input of qualified experts, committees reasonably wish for practical, defensible solutions to these challenges. Methods We draw upon on existing literature regarding Bayesian Networks (BN), noting their utility and application in educational systems. We provide illustrative examples of how they could potentially be used in contexts that embed programmatic principles. We show a simple BN for a knowledge domain before presenting a full‐scale ‘proof of concept’ BN to support committee decisions. We zoom in on one ‘node’ to demonstrate the capacity of incorporating disparate evidence throughout the network. Conclusions Bayesian Networks offer an approach that is theoretically well‐supported for programmatic assessment. They can aid committees in managing evidence accumulation, help them make inferences under conditions of uncertainty, and buttress decisions by adding a layer of defensibility to the process. They are a pragmatic tool adding value to the programmatic space by applying a complementary statistical framework. We see four major benefits of BNs in programmatic assessment: BNs allow for visual capturing of evidentiary arguments by committees during decision‐making; ‘recommendations’ from probabilistic pathways can be used by committees to confirm their qualitative judgments; BNs can ensure precedents are maintained and consistency occurs over time; and the imperative to capture data richness is maintained without resorting to questionable methodological strategies such as adding qualitatively different things together. Further research into their feasibility and robustness in practice is warranted.
Understanding the Impact of Dynamic Channel Pruning on Conditionally Parameterized Convolutions

Recent trends have shown that deep learning models have become larger and more accurate at an increased computational cost, making them difficult to deploy for latency-constrained applications. Conditional execution methods address this increase in cost while preserving the accuracy of the model by only performing the most important computation on important features. In this paper, we analyze a recent method, Feature Boosting and Suppression (FBS), which dynamically assesses which channels contain the most important input-dependent features and prune the others based on a runtime threshold gating mechanism. FBS is able to dynamically prune convolution filters with little loss in accuracy at conservative pruning rates. However, at aggressive pruning rates FBS suffers from heavy accuracy loss in a similar way to aggressive static pruning, due to a low number of active filters and correspondingly narrower effective network per inference. Conditionally parameterized convolutions (CondConv) is another work in the conditional execution domain that increases performance at small computation cost through the generation of input-dependent expert filters. We discover that substituting standard convolutional filters with input-specific filters, as described in CondConv, we enable FBS to address this accuracy loss because CondConv expert filters are able to bolster the narrower network's reduced capacity and capture distinct features from various classes in a single composite filter as needed, making aggressive pruning appealing again. We test our FBS-pruned CondConv model on CIFAR-10 with a custom 9-layer CNN and demonstrate that we can achieve up to 47.2% savings in computational costs at iso-accuracy and 1.01% improvement in accuracy at iso-computational costs over the state-of-art FBS technique.
Discrete Haze Level Dehazing Network

In contrast to traditional dehazing methods, deep learning based single image dehazing (SID) algorithms have achieved better performances by creating a mapping function from haze to haze-free images. Usually, the images taken from the natural scenes have different haze levels, but deep SID algorithms only process the hazy images as one group. It makes the deep SID algorithms difficult to deal with the image set with some images having specific haze density. In this paper, a Discrete Haze Level Dehazing network (DHL-Dehaze), a very effective method to dehaze multiple different haze level images, is proposed. The proposed approach considers a single image dehazing problem as a multi-domain image-to-image translation, instead of grouping all hazy images into the same domain. DHL-Dehaze provides computational derivation to describe the role of different haze levels for image translation. To verify the proposed approach, we synthesize two largescale datasets with multiple haze level images based on the NYU-Depth and DIML/CVL datasets. The experiments show that DHL-Dehaze can obtain excellent quantitative and qualitative dehazing results, especially when the haze concentration is high.
Playful User-Generated Treatment

Overcoming a range of challenges that traditional therapy faces, VRET yields great potential for the treatment of phobias such as acrophobia, the fear of heights. We investigate this potential and present playful user-generated treatment (PUT), a novel game-based approach for VRET. Based on a requirement analysis consisting of a literature review and semi-structured interviews with professional therapists, we designed and implemented the PUT concept as a two-step VR game design. To validate our approach, we conducted two studies. (1) In a study with 31 non-acrophobic subjects, we investigated the effect of content creation on player experience, motivation and height perception, and (2) in an online survey, we collected feedback from professional therapists. Both studies reveal that the PUT approach is well applicable. In particular, the analysis of the user study shows that the design phase leads to increased interest and enjoyment without notably influencing affective measures during the exposure session. Our work can help guiding researchers and practitioners at the intersection of game design and exposure therapy.
A Mobile Cloud Collaboration Fall Detection System Based on Ensemble Learning

Falls are one of the major causes of accidental or unintentional injury death worldwide. Therefore, this paper proposes a reliable fall detection algorithm and a mobile cloud collaboration system for fall detection. The algorithm is an ensemble learning method based on decision tree, named Fall-detection Ensemble Decision Tree (FEDT). The mobile cloud collaboration system is composed of three stages: 1) mobile stage: a light-weighted threshold method is used to filter out activities of daily livings (ADLs), 2) collaboration stage: TCP protocol is used to transmit data to cloud and meanwhile features are extracted in the cloud, 3) cloud stage: the model trained by FEDT is deployed to give the final detection result with the extracted features. Experiments show that the proposed FEDT outperforms the others' over 1-3% both on sensitivity and specificity and has superior robustness on different devices.
Threshold Password-Hardened Encryption Services

Password-hardened encryption (PHE) was introduced by Lai et al. at USENIX 2018 and immediately productized by VirgilSecurity. PHE is a password-based key derivation protocol that involves an oblivious external crypto service for key derivation. The security of PHE protects against offline brute-force attacks, even when the attacker is given the entire database. Furthermore, the crypto service neither learns the derived key nor the password. PHE supports key-rotation meaning that both the server and crypto service can update their keys without involving the user. While PHE significantly strengthens data security, it introduces a single point of failure because key-derivation always requires access to the crypto service. In this work, we address this issue and simultaneously increase security by introducing threshold password-hardened encryption. Our formalization of this primitive revealed shortcomings of the original PHE definition that we also address in this work. Following the spirit of prior works, we give a simple and efficient construction using lightweight tools only. We also implement our construction and evaluate its efficiency. Our experiments confirm the practical efficiency of our scheme and show that it is more efficient than common memory-hard functions, such as scrypt. From a practical perspective this means that threshold PHE can be used as an alternative to scrypt for password protection and key-derivation, offering better security in terms of offline brute force attacks.
Multi-resolution Cascaded Network with Depth-similar Residual Module for Real-time Semantic Segmentation on RGB-D Images

Multi-class indoor semantic segmentation using deep fully convolutional neural networks on RGB images has been widely used in scene parsing and human-computer interaction. Due to the wide application of depth information sensors, we can get more understanding of geographic location information from the depth information channel, but it also leads to high computational cost and memory usage. In this paper, we present a real-time deep neural network for semantic segmentation tasks on RGB-D images. First, we use an intuitive and efficient convolution operation to approximate the depth information to the pixel operation without adding additional parameters, which can be easily integrated into the deep convolutional neural network. Then, we use a multi-resolution branching structure and train the network with appropriate label guidance as the loss function to obtain a high-quality performance of semantic segmentation. The proposed approach demonstrates real-time inference on datasets NYUv2 and SUN RGB-D with a good balance of accuracy and speed on a single GPU card.
Exploring Occupation Differences in Reactions to COVID-19 Pandemic on Twitter

Coronavirus disease 2019 (COVID-19) pandemic-related information are flooded on social media, and analyzing this information from an occupational perspective can help us to understand the social implications of this unprecedented disruption. In this study, using a COVID-19-related dataset collected with the Twitter IDs, we conduct topic and sentiment analysis from the perspective of occupation, by leveraging Latent Dirichlet Allocation (LDA) topic modeling and Valence Aware Dictionary and sEntiment Reasoning (VADER) model, respectively. The experimental results indicate that there are significant topic preference differences between Twitter users with different occupations. However, occupation-linked affective differences are only partly demonstrated in our study; Twitter users with different income levels have nothing to do with sentiment expression on covid-19-related topics.
Gesture Recognition based on BP Neural Network and Data Glove

Various devices for hand data collection have been proposed for gesture recognition. In this paper, a method is proposed for gesture recognition based on data glove and BP neural network. This data glove can collect data of the hand and forearm, it gives a comprehensive description of all aspects of arm movement, and BP neural network algorithm is used to process and classify the collected data and compress the data. Experiments show that hand data can be classified effectively by BP neural network.
Hybrid domain adaptation with deep network architecture for end-to-end cross-domain human activity recognition

Machine learning-based human activity recognition (HAR) as the means of human–computer interaction is important to empower the existing systems in the industry such as manufacturing and logistics to be more autonomous. However, it is often difficult to build HAR models due to the limitation of annotated samples. Domain adaptation has emerged to address such limitation (or absence) of labeled samples in the domain of interest (i.e., target domain) by using abundant amount of labeled samples in the other domain (i.e., source domain). Domain adaptation aims to solve learning problems where a source domain and a target domain are different but still related. With the use of homogeneous feature space, the existing approaches on homogeneous domain adaption is prohibitive for the real-life scenario, in which two different domains could be possibly of heterogeneous feature space. Although heterogeneous domain adaptation approaches exist, it still requires additional information (i.e., instance correspondence) which is difficult to satisfy when we deal with sensor data. Hybrid domain adaptation, a special case of heterogeneous domain adaptation where common feature between domain exists, is more realistic as the feature commonality is easier to satisfy. However, the existing approach operates using common features in the original feature space, which in fact, may still have distribution difference. In addition, the existing one requires hand-crafted feature extractions for more informative descriptors to classify human activities. In this work, we propose a deep architecture for hybrid domain adaptation, to enable end-to-end learning for human activity classification. The architecture is tested on sensor-based human activity recognition dataset. The experimental result shows that our approach yields better result compared to the existing human activity recognition approaches, both the deep and shallow approaches.
A Common Notation and Tool Support for Embedded Feature Annotations

Features are typically used to describe the functionalities of software systems. They help understanding systems as well as planning their evolution and managing systems. Especially agile methods foster their use. However, to use features, their locations need to be known. When not documented, they are easily forgotten and then need to be recovered, which is costly. While automated feature-location techniques exist, they are not usable in practice given their inaccuracies. We take a different route and advocate to record locations early using a lightweight annotation system, where feature information is embedded in software assets. However, given the potential design space of annotations, a unified notation and tool support is needed. Extending our prior work, we present a unified, concise notation for embedded annotations, which we implemented in FAXE, a library for parsing and retrieving such annotations, use-able in third-party tooling. We demonstrate its use, especially for an advanced use case of feature-oriented isolated development by automating partial commits.
Computer-Based Education in the Course “Digital Electronics” Teaching the Topic “Adders-Subtractors”

The paper presents different computer-based tools for teaching the topic “Adders-Subtractors” in four courses for students-bachelors of five specialties in the University of Ruse. These courses introduce the students to the main issues of digital electronics. The aim of these courses is connecting the functional capabilities of the digital components with their microelectronic base and their applications in constructing pulse and digital devices. For the purposes of the courses, students use different software tools for studying the topic. The paper presents a way of presenting the topic of arithmetic circuits to the students making the classes more attractive using active learning.
Design of English Translation Computer Intelligent Scoring System Based on Natural Language Processing

Abstract With the development and maturity of automatic scoring technology of English composition, the computer-aided composition marking system based on automatic scoring technology has begun to enter colleges and universities to assist English writing teaching. However, there are still many problems in the current scoring system, such as long running time and large deviation of scoring results, which requires us to design an English translation computer intelligent scoring system based on natural language processing. Through the system, we can reduce the workload of manual scoring, which will improve the efficiency of scoring. Therefore, we need to construct the structure of English translation scoring system, including translation data collection module, information feature extraction module, analysis model construction module and result feedback scoring module. By building a language model, the system can translate the probability distribution of specific sentences or word sequences. As an adaptive learning model, BP neural network has more advantages in dealing with the relationship between complex variables. Through BP network model, the system can extract the feature information of translation translation and translation training set. Through fitting calculation, the system will realize the intelligent scoring of English translation.
Squeezed Polynomial Codes: Communication-Efficient Coded Computation in Straggler-Exploiting Distributed Matrix Multiplication

In a distributed computing environment, there may exist slow processing workers, which are known as “stragglers”, and they can slow down the whole computing process. In this article, we consider coded computation for matrix multiplication tasks in distributed computing, which can mitigate the effect of stragglers by a coding approach. We propose a new communication-efficient coded computation scheme, namely squeezed polynomial codes, for a straggler-exploiting scenario where multiple sub-tasks are assigned to the workers to partially leverage the computation capability of stragglers. The key idea of squeezed polynomial codes is to overlap the encoded matrices in assigning multiple sub-tasks with appropriate polynomial functions in order to reduce the task-allocation communication load from a master to its workers. We compare squeezed polynomial codes with the existing schemes for distributed matrix multiplication in a communication load perspective. Consequently, we show that squeezed polynomial codes can efficiently reduce the communication load while ensuring the optimal recovery condition at a master to obtain final product.
Cloud Computing Virtualization: A Comprehensive Survey

Cloud computing technology nowadays is a leading IT technology and introduces the next generation of distributed systems with a great acceptance and impact in the businesses. It influences the businesses on structural and operational processes with potential to reduce total costs and increase revenues. This technology is mainly service oriented and focuses on cost reduction, like hardware reduction, reduction on maintenance and total cost of ownership, and with different pricing models such as pay-per-use. Virtualization is the foundation process and technology in Cloud computing, as it hides the complexity of underlying hardware and software resources. It is the objective of virtualization to achieve an improvement in data security aspects, to provide flexible operations and data transfer, elimination (masking) of system failures and to ensure economic benefits. The aim of this research is through a systematic literature review of the existing work to provide a comprehensive understanding of the role and significance of virtualization within the domain of Cloud Computing.