Reviewerly/exHarmony huggingface dataset:
Cooperative Localization and Multitarget Tracking in Agent Networks with the Sum-Product Algorithm

This paper addresses the problem of multitarget tracking using a network of sensing agents with unknown positions. Agents have to both localize themselves in the sensor network and, at the same time, perform multitarget tracking in the presence of clutter and miss detection. These two problems are jointly resolved using a holistic and centralized approach where graph theory is used to describe the statistical relationships among agent states, target states, and observations. A scalable message passing scheme, based on the sum-product algorithm, enables to efficiently approximate the marginal posterior distributions of both agent and target states. The proposed method is general enough to accommodate a full multistatic network configuration, with multiple transmitters and receivers. Numerical simulations show superior performance of the proposed joint approach with respect to the case in which cooperative self-localization and multitarget tracking are performed separately, as the former manages to extract valuable information from targets. Lastly, data acquired in 2018 by the NATO Science and Technology Organization (STO) Centre for Maritime Research and Experimentation (CMRE) through a network of autonomous underwater vehicles demonstrates the effectiveness of the approach in a practical application.
Public library Twitter use during the early period of the COVID-19 lockdown in the United States

Purpose The purpose of this study is to explore the Twitter use of public libraries during the early period of lockdown due to the COVID-19 pandemic to evaluate the focus of Twitter communication. Design/methodology/approach A total of 57 active, public library Twitter accounts were used for data collection and analysis. The tweets examined were a combination of original tweets ( n = 1,465) and retweets ( n = 516) posted from other Twitter accounts on the public libraries’ Twitter feeds. A content analysis scheme was developed to analyse topical aspects of the tweets. Findings The most frequent tweets were about library events, programmes and activities. However, there was a relatively low focus on sharing community information and addressing information related to the pandemic. The study suggests that public libraries could use Twitter to provide library resources and services to their patrons, whilst also acting as a virtual community centre safely keeping patrons engaged in the face of a global pandemic. By doing so, Twitter could be used as an integral part of promoting the mission of public libraries. Research limitations/implications The study examined a limited number of public libraries’ Twitter posts. Whilst the study carried out a random sampling of 10% of public libraries from the five states that had the highest COVID-19 cases in the month of April 2020, the study only examined tweets of 57 public libraries being active in posting. Thus, the findings of the study are not for generalizing. Practical implications The content scheme includes content types regarding library services and community information. The content category scheme is general to reflect themes of content during a normal time and any emergency. Thus, this framework could be helpful for the content development of public libraries in planning social media use. Originality/value The study used a new content analysis framework to examine both original tweets and retweets for information sharing of library services and community information. The approach of content analysis is distinctive to examine libraries’ communication trends on social media not only in normal times but also in times of crisis as well. The study also incorporated additional measures to assess Twitter practices including hashtags.
LocalGLMnet: interpretable deep learning for tabular data

Deep learning models have gained great popularity in statistical modeling because they lead to very competitive regression models, often outperforming classical statistical models such as generalized linear models.The disadvantage of deep learning models is that their solutions are difficult to interpret and explain, and variable selection is not easily possible because deep learning models solve feature engineering and variable selection internally in a nontransparent way.Inspired by the appealing structure of generalized linear models, we propose a new network architecture that shares similar features as generalized linear models but provides superior predictive power benefiting from the art of representation learning.This new architecture allows for variable selection of tabular data and for interpretation of the calibrated deep learning model, in fact, our approach provides an additive decomposition that can be related to other model interpretability techniques.
MP-VR: An MPTCP-Based Adaptive Streaming Framework for 360-degree Virtual Reality Videos

360-degree virtual reality videos greatly improve the video experience by providing users with a more immersive and interactive environment than standard streaming video. However, 360-degree videos suffer from bandwidth limits. Existing bandwidth-efficient solutions mainly focus on spatially cutting 360-degree video into tiles, and only provide video content in the Field-of-View (FoV) of users with high quality to reduce bandwidth consumption. Although existing tile-based schemes can reduce the bandwidth consumption, the bandwidth and transmission delay provided by a single-path TCP may still not meet the high requirements of 360-degree videos. Multipath TCP (MPTCP) allows a TCP connection to operate across multiple paths simultaneously and becomes highly attractive to support the mobile devices with various radio interfaces to aggregate multipath bandwidth and improve the throughput. In this paper, by taking the advantage of MPTCP, we propose an MPTCP-based adaptive streaming framework for 360-degree Virtual Reality videos, named MP-VR. MP-VR dynamically selects the appropriate tile bitrate according to the bandwidth and transmission delay of different subflows. Then it schedules the video segments to subflows to improve QoE of users. We conduct experiments on a testbed in our lab and simulations on NS-3. Evaluation results show that MP-VR outperforms existing tile-based strategies when network fluctuations or errors in FoV predictions occur.
Cross-Domain Speech Recognition with Unsupervised Character-Level Distribution Matching

End-to-end automatic speech recognition (ASR) can achieve promising performance with large-scale training data. However, it is known that domain mismatch between training and testing data often leads to a degradation of recognition accuracy. In this work, we focus on the unsupervised domain adaptation for ASR and propose CMatch, a Character-level distribution matching method to perform fine-grained adaptation between each character in two domains. First, to obtain labels for the features belonging to each character, we achieve frame-level label assignment using the Connectionist Temporal Classification (CTC) pseudo labels. Then, we match the character-level distributions using Maximum Mean Discrepancy. We train our algorithm using the self-training technique. Experiments on the Libri-Adapt dataset show that our proposed approach achieves 14.39% and 16.50% relative Word Error Rate (WER) reduction on both cross-device and cross-environment ASR. We also comprehensively analyze the different strategies for frame-level label assignment and Transformer adaptations.
Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document Summarization.

We consider the problem of automatically generating a narrative biomedical evidence summary from multiple trial reports. We evaluate modern neural models for abstractive summarization of relevant article abstracts from systematic reviews previously conducted by members of the Cochrane collaboration, using the authors conclusions section of the review abstract as our target. We enlist medical professionals to evaluate generated summaries, and we find that summarization systems yield consistently fluent and relevant synopses, but these often contain factual inaccuracies. We propose new approaches that capitalize on domain-specific models to inform summarization, e.g., by explicitly demarcating snippets of inputs that convey key findings, and emphasizing the reports of large and high-quality trials. We find that these strategies modestly improve the factual accuracy of generated summaries. Finally, we propose a new method for automatically evaluating the factuality of generated narrative evidence syntheses using models that infer the directionality of reported findings.
UAV Path Planning Based on an Improved Ant Colony Algorithm

In order to improve operational efficiency and survival probability, the optimal path of an UAV should be designed before the UAV performs a mission. The basic ant colony algorithm is easy to fall into the local optimum, and slow convergence speed. This paper improves the basic algorithm from two aspects: guidance factor and node pheromone update. The improved algorithm proposed in this paper can overcome the problems of the basic ant colony algorithm. The simulation results show that the improved ant colony algorithm is feasible for UAV path planning and the flight path optimization ability of UAV is improved.
Task-Oriented Dialogue System as Natural Language Generation

In this paper, we propose to formulate the task-oriented dialogue system as the purely natural language generation task, so as to fully leverage the large-scale pre-trained models like GPT-2 and simplify complicated delexicalization prepossessing. However, directly applying this method heavily suffers from the dialogue entity inconsistency caused by the removal of delexicalized tokens, as well as the catastrophic forgetting problem of the pre-trained model during fine-tuning, leading to unsatisfactory performance. To alleviate these problems, we design a novel GPT-Adapter-CopyNet network, which incorporates the lightweight adapter and CopyNet modules into GPT-2 to achieve better performance on transfer learning and dialogue entity generation. Experimental results conducted on the DSTC8 Track 1 benchmark and MultiWOZ dataset demonstrate that our proposed approach significantly outperforms baseline models with a remarkable performance on automatic and human evaluations.
Towards retrieval-based conversational recommendation

Conversational recommender systems have attracted immense attention recently. The most recent approaches rely on neural models trained on recorded dialogs between humans, implementing an end-to-end learning process. These systems are commonly designed to generate responses given the user's utterances in natural language. One main challenge is that these generated responses both have to be appropriate for the given dialog context and must be grammatically and semantically correct. An alternative to such generation-based approaches is to retrieve responses from pre-recorded dialog data and to adapt them if needed. Such retrieval-based approaches were successfully explored in the context of general conversational systems, but have received limited attention in recent years for CRS. In this work, we re-assess the potential of such approaches and design and evaluate a novel technique for response retrieval and ranking. A user study (N=90) revealed that the responses by our system were on average of higher quality than those of two recent generation-based systems. We furthermore found that the quality ranking of the two generation-based approaches is not aligned with the results from the literature, which points to open methodological questions. Overall, our research underlines that retrieval-based approaches should be considered an alternative or complement to language generation approaches.
Managing systems evolving in space and time

Software companies need to provide a large set of features satisfying functional and non-functional requirements of diverse customers, thereby leading to variability in space. Feature location techniques have been proposed to support software maintenance and evolution in space. However, so far only one feature location technique also analyses the evolution in time of system variants, which is required for feature enhancements and bug fixing. Specifically, existing tools for managing a set of systems over time do not offer proper support for keeping track of feature revisions, updating existing variants, and creating new product configurations based on feature revisions. This paper presents four challenges concerning such capabilities for feature (revision) location and composition of new product configurations based on feature/s (revisions). We also provide a benchmark containing a ground truth and support for computing metrics. We hope that this will motivate researchers to provide and evaluate tool-supported approaches aiming at managing systems evolving in space and time. Further, we do not limit the evaluation of techniques to only this benchmark: we introduce and provide instructions on how to use a benchmark extractor for generating ground truth data for other systems. We expect that the feature (revision) location techniques maximize information retrieval in terms of precision, recall, and F-score, while keeping execution time and memory consumption low.
Multiple histogram based adaptive pairwise prediction-error modification for efficient reversible image watermarking

The prediction-error expansion (PEE) based reversible data hiding (RDH) methods mainly contain two steps: prediction-error histogram (PEH) generation and PEH modification. Multiple histograms modification (MHM) and pairwise PEE are two efficient methods used in PEH modification, which can greatly reduce the distortion to the cover image under given payloads. However, these two effective methods are not used together for their conflict on causality and large computational complexity. To address the above two problems, in this paper, a group of non-causal predictors with a specially designed complexity metric are proposed so that the conflict on causality is resolved. Then, the original optimization problem to determine the parameters of MHM and pairwise PEE is decomposed into two easier ones, which are addressed by a step-by-step optimization. Several efficient constraints are used to narrow the search range for the optimal parameters while minimizing the performance degradation. As a result, the proposed method can obtain high quality on the embedded images, that is, the average PSNR of the 8 standard 512 × 512 sized grayscale images from the USC-SIPI image database can reach 60.43 dB for the payload of 10,000 bits, while the running time for the proposed algorithm is acceptable. Experimental results show that the proposed method outperforms many kinds of RDH schemes, and achieves an obvious increment in fidelity over a series of state-of-the-art RDH schemes.
Anomaly Detection With Representative Neighbors

Identifying anomalies from data has attracted increasing attention in recent years due to its broad range of potential applications. Although many efforts have been made for anomaly detection, how to effectively handle high-dimensional data and how to exactly explore neighborhood information, a fundamental issue in anomaly detection, have not yet received sufficient concerns. To circumvent these challenges, in this article, we propose an effective anomaly detection method with representative neighbors for high-dimensional data. Specifically, it projects the high-dimensional data into a low-dimensional space via a sparse operation and explores representative neighbors with a self-representation learning technique. The neighborhood information is then transformed into similarity relations, making the data converge or disperse. Eventually, anomalies are discriminated by a tailored graph clustering technique, which can effectively reveal structural information of the data. Extensive experiments were conducted on ten public real-world datasets with 11 popular anomaly detection algorithms. The results show that the proposed method has encouraging and promising performance compared to the state-of-the-art anomaly detection algorithms.
Custom IMU-Based Wearable System for Robust 2.4 GHz Wireless Human Body Parts Orientation Tracking and 3D Movement Visualization on an Avatar

Recent studies confirm the applicability of Inertial Measurement Unit (IMU)-based systems for human motion analysis. Notwithstanding, high-end IMU-based commercial solutions are yet too expensive and complex to democratize their use among a wide range of potential users. Less featured entry-level commercial solutions are being introduced in the market, trying to fill this gap, but still present some limitations that need to be overcome. At the same time, there is a growing number of scientific papers using not commercial, but custom do-it-yourself IMU-based systems in medical and sports applications. Even though these solutions can help to popularize the use of this technology, they have more limited features and the description on how to design and build them from scratch is yet too scarce in the literature. The aim of this work is two-fold: (1) Proving the feasibility of building an affordable custom solution aimed at simultaneous multiple body parts orientation tracking; while providing a detailed bottom-up description of the required hardware, tools, and mathematical operations to estimate and represent 3D movement in real-time. (2) Showing how the introduction of a custom 2.4 GHz communication protocol including a channel hopping strategy can address some of the current communication limitations of entry-level commercial solutions. The proposed system can be used for wireless real-time human body parts orientation tracking with up to 10 custom sensors, at least at 50 Hz. In addition, it provides a more reliable motion data acquisition in Bluetooth and Wi-Fi crowded environments, where the use of entry-level commercial solutions might be unfeasible. This system can be used as a groundwork for developing affordable human motion analysis solutions that do not require an accurate kinematic analysis.
An ensemble deep learning for automatic prediction of papillary thyroid carcinoma using fine needle aspiration cytology

Accurately cytopathological diagnosis of Papillary Thyroid Carcinoma (PTC) is of importance for reducing costs and increasing efficiency of treatments. In this paper, we pursue that goal by introducing artificial intelligence (AI) for automatic classification of malignant PTC cell clusters from Fine Needle Aspiration Cytology (FNAC) processed by ThinPrep. High-resolution cytological images obtained with a 40 × objective lens digital camera attached to an Olympus microscope were segmented into fragments and then divided into training, validation, and testing subsets. Fragments are non-overlapped patches containing only regions-of-interest that cover informative tissue structures for making proper diagnoses. Deep learning CNN models were pre-trained and fine-tuned on large-scale ImageNet domain before they were re-trained on cytology fragments. Moreover, we proposed a method to compute certainty of the patient-level prediction that undoubtedly provides additional evidence for reliability and confidence of the prediction. Results showed that the best classification performance on digital FNAC images achieved using DenseNet161, obtaining a mean accuracy of 0.9556 (p < 0.01), a mean sensitivity of 0.9734, and a mean specificity of 0.9405 on yet-to-be-seen test-set. Ensemble learning findings suggested combinations of AdaBoost classifier with multiple CNN models boosted predictive performances, up to 0.9971 accuracy. Moreover, stain normalization introduced by Reinhard increased the predictive ability, outperforming histogram specification, and Macenko methods. Presented findings demonstrate deep learning can integrate into computer-aided diagnosis systems to support cytopathologists in accurate diagnosis of PTC.
Automated Data analytics approach for examining the background economy of Cybercrime

In-spite of fast growth of cyber threats, there is need of the methodologies to assist the information systems to deal with the cyber security. In supplement to that, Crime-as-a-service (Caas) model is developed to reinforces the background information occurring in the cybercrime. The motivation of the work is to scrutinize the problems occurring in the cybercrime using the data analysis method for designing the information system. In order to accomplish, First the framework is created for examining the cybercrime activities. Second step, the definition for CaaS need to be created. Third step, classification model is used for classifying the various activities. For the evaluation of the proposed techniques, tested with the dataset collected form the online hacking community is used. Research gap is resolved by developing the effective information system for handling the various problem in cybercrime and also provide the practical perceptions for both the private and public sectors to record the attacks occurring in the cybercrime.
Dense prediction of label noise for learning building extraction from aerial drone imagery

Label noise is a commonly encountered problem in learning building extraction tasks; its presence can reduce performance and increase learning complexity. This is especially true for cases where high resolution aerial drone imagery is used, as the labels may not perfectly correspond/align with the actual objects in the imagery. In general machine learning and computer vision context, labels refer to the associated class of data, and in remote sensing-based building extraction refer to pixel-level classes. Dense label noise in building extraction tasks has rarely been formalized and assessed. We formulate a taxonomy of label noise models for building extraction tasks, which incorporates both pixel-wise and dense models. While learning dense prediction under label noise, the differences between the ground truth clean label and observed noisy label can be encoded by error matrices indicating locations and type of noisy pixel-level labels. In this work, we explicitly learn to approximate error matrices for improving building extraction performance; essentially, learning dense prediction of label noise as a subtask of a larger building extraction task. We propose two new model frameworks for learning building extraction under dense real-world label noise, and consequently two new network architectures, which approximate the error matrices as intermediate predictions. The first model learns the general error matrix as an intermediate step and the second model learns the false positive and false-negative error matrices independently, as intermediate steps. Approximating intermediate error matrices can generate label noise saliency maps, for identifying labels having higher chances of being mis-labelled. We have used ultra-high-resolution aerial images, noisy observed labels from OpenStreetMap, and clean labels obtained after careful annotation by the authors. When compared to the baseline model trained and tested using clean labels, our intermediate false positive-false negative error matrix model provides Intersection-Over-Union gain of 2.74% and F1-score gain of 1.75% on the independent test set. Furthermore, our proposed models provide much higher recall than currently used deep learning models for building extraction, while providing comparable precision. We show that intermediate false positive-false negative error matrix approximation can improve performance under label noise.