Reviewerly/exHarmony huggingface dataset:
Bias-Constrained <i>H</i>₂ Optimal Finite Impulse Response Filtering for Object Tracking Under Disturbances and Data Errors

The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> finite impulse response (FIR) filtering approach allows for optimal object tracking under harsh industrial conditions. In this brief, we propose a bias-constrained <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> optimal unbiased FIR ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -OUFIR) filter for linear discrete time-invariant systems under bounded disturbances, data errors, and initial errors. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -OUFIR filter is derived using the backward Euler method by minimizing the squared Frobenius norm of the weighted transfer function. A bias-constrained suboptimal <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> FIR filtering algorithm using a linear matrix inequality (LMI) is also designed. Based on experimental examples of global positioning system (GPS)-based vehicle tracking and video human tracking, it is shown that the batch <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -OUFIR filter operating on short horizons with full error matrices is able to outperform the Kalman, optimal finite impulse response (OFIR), and unbiased finite impulse response (UFIR) filters.
Graph Sampling with Fast Random Walker on HBM-enabled FPGA Accelerators

Graph neural networks (GNNs) have gained increasing popularity among researchers recently and have been employed in many applications. Training GNNs introduce a crucial stage called graph sampling. One of the most important sampling algorithms is Random Walk. However, Random Walk and many of its variants share and suffer from the same performance problem caused by random and fragmented memory access patterns, leading to significant system performance degradation. In this work, we present an efficient graph sampling engine on modern FPGAs integrated with in-package high bandwidth memory (HBM), which brings data closer and faster to the core logic. The hardware walker design is modular and easily scalable for massive parallelism, to fully utilize the available HBM channels. Our design also provides the flexibility to support Random Walk and two of its variants on both homogeneous and heterogeneous graphs. On real-world graph datasets, we achieve a 1.39 × -3.74 × speedup with a 2.42 × -6.69 × higher energy efficiency over highly optimized parallel baselines on a Xeon CPU. We also implement these algorithms on a NVIDIA Tesla VIOO GPU and achieve comparable dynamic energy consumption.
Addressing Extraction and Generation Separately: Keyphrase Prediction With Pre-Trained Language Models

Keyphrase prediction is a crucial task that can effectively provide underlying support for numerous downstream Natural Language Processing (NLP) tasks, e.g., information retrieval and document summarization. Existing keyphrase prediction approaches mostly focus on either extractive or generative methods. Extractive methods directly extract keyphrases that present in the document, while they cannot obtain absent keyphrases. Generative methods are designed to generate both present and absent keyphrases. However, the absent keyphrases are generated at the cost of hurting the performance of the present keyphrase prediction. The generation of present keyphrases mainly relies on the copying mechanism, ignoring the interdependence of the overall decisions. In contrast, the extractive model that directly extracts a text span from the document is more suitable for predicting the present keyphrase. Therefore, it is necessary to coordinate the extractive and generative patterns to obtain accurate and comprehensive keyphrases. Specifically, we divide the keyphrase prediction into two subtasks, i.e., present keyphrase extraction (PKE) and absent keyphrase generation (AKG), and propose a joint inference framework to exploit their respective advantages fully. For PKE, we treat it as a sequence labeling problem and apply a BERT-based sentence selector to select salient sentences that contain present keyphrases. For AKG, we introduce a Transformer-based architecture equipped with a gated fusion attention module, which fully integrates the present keyphrase knowledge learned from PKE by the fine-tuned BERT. The experimental results demonstrate that our approach can achieve state-of-the-art performance on all benchmark datasets.
NF-iSAM: Incremental Smoothing and Mapping via Normalizing Flows

This paper presents a novel non-Gaussian inference algorithm, Normalizing Flow iSAM (NF-iSAM), for solving SLAM problems with non-Gaussian factors and/or non-linear measurement models. NF-iSAM exploits the expressive power of neural networks, and trains normalizing flows to draw samples from the joint posterior of non-Gaussian factor graphs. By leveraging the Bayes tree, NF-iSAM is able to exploit the sparsity structure of SLAM, thus enabling efficient incremental updates similar to iSAM2, albeit in the more challenging non- Gaussian setting. We demonstrate the performance of NF-iSAM and compare it against the state-of-the-art algorithms such as iSAM2 (Gaussian) and mm-iSAM (non-Gaussian) in synthetic and real range-only SLAM datasets.
Reinforced iLQR: A Sample-Efficient Robot Locomotion Learning

Robot locomotion is a major challenge in robotics. Model-based approaches are vulnerable to model errors, and incur high computation overhead resulted from long control horizon. Model-free approaches are trained with a large number of training samples, which are expensive to obtain. In this paper, we develop a hybrid control and learning framework, called Reinforced iLQR (RiLQR), which combines the advantages of model-based iLQR control with model-free RL policy learning to simultaneously achieve high sample efficiency, low computation overhead, and high robustness against model errors in robot locomotion. Through extensive evaluation on the Mujoco platform, we demonstrate that RiLQR outperforms the state-of-the-art model-based and model-free baselines by big margins in a set of tasks with different complexities.
Cyberbullying Classification based on Social Network Analysis

With the popularity of social media platforms, such as Facebook, Twitter, and Instagram, people widely share their opinions and comments over the Internet. The extensive use of social media has also caused a lot of problems. A representative problem is Cyberbullying, which is a serious social problem, mostly among teenagers. Cyberbullying occurs when a social media user posts aggressive words or phrases to harass other users, and that can lead to negatively affects on their mental and social well-being. Additionally, it may ruin the reputation of that media. We are considering the problem of detecting posts that are aggressive. Moreover, we try to detect Cyberbullies. In this research, we study Cyberbullying as a classification problem by combining text mining techniques, and the graph structure of the social network relationships that are based on a dataset from Twitter. We enhance a given dataset to a new one that has more information for each tweet (post). We manage to improve the classification accuracy by considering the additional social network features that we get from the user’s follower list and retweet information.
ConAn: A Usable Tool for Multimodal Conversation Analysis

Multimodal analysis of group behavior is a key task in human-computer interaction, and in the social and behavioral sciences, but is often limited to more easily controllable laboratory settings or requires elaborate multi-sensor setups and time-consuming manual data annotation. We present ConAn – a usable tool to explore and automatically analyze non-verbal behavior of multiple persons during natural group conversations. In contrast to traditional multi-sensor setups, our tool only requires a single 360° camera and uses state-of-the-art computer vision methods to automatically extract behavioral indicators, such as gaze direction, facial expressions, and speaking activity. As such, our tool allows for easy and fast deployment and supports researchers in understanding individual behavior, group interaction dynamics, and in quantifying user-object interactions. We illustrate the benefits of ConAn on three sample use cases: conversation analysis, assessment of collaboration quality, and impact of technology on audience behavior. Taken together, ConAn represents an important step towards democratizing automatic conversation analysis in HCI and beyond.
Idyll Studio: A Structured Editor for Authoring Interactive &amp; Data-Driven Articles

Interactive articles are an effective medium of communication in education, journalism, and scientific publishing, yet are created using complex general-purpose programming tools. We present Idyll Studio, a structured editor for authoring and publishing interactive and data-driven articles. We extend the Idyll framework to support reflective documents, which can inspect and modify their underlying program at runtime, and show how this functionality can be used to reify the constituent parts of a reactive document model—components, text, state, and styles—in an expressive, interoperable, and easy-to-learn graphical interface. In a study with 18 diverse participants, all could perform basic editing and composition, use datasets and variables, and specify relationships between components. Most could choreograph interactive visualizations and dynamic text, although some struggled with advanced uses requiring unstructured code editing. Our findings suggest Idyll Studio lowers the threshold for non-experts to create interactive articles and allows experts to rapidly specify a wide range of article designs.
Collision Avoidance in Tightly-Constrained Environments without Coordination: a Hierarchical Control Approach

We present a hierarchical control approach for maneuvering an autonomous vehicle (AV) in tightly-constrained environments where other moving AVs and/or human driven vehicles are present. A two-level hierarchy is proposed: a high-level data-driven strategy predictor and a lower-level model-based feedback controller. The strategy predictor maps an encoding of a dynamic environment to a set of high-level strategies via a neural network. Depending on the selected strategy, a set of time-varying hyperplanes in the AV’s position space is generated online and the corresponding halfspace constraints are included in a lower-level model-based receding horizon controller. These strategy-dependent constraints drive the vehicle towards areas where it is likely to remain feasible. Moreover, the predicted strategy also informs switching between a discrete set of policies, which allows for more conservative behavior when prediction confidence is low. We demonstrate the effectiveness of the proposed data-driven hierarchical control framework in a two-car collision avoidance scenario through simulations and experiments on a 1/10 scale autonomous car platform where the strategy-guided approach outperforms a model predictive control baseline in both cases.
Cascade Cross-modal Attention Network for Video Actor and Action Segmentation from a Sentence

In this paper, we address the problem that selectively segments the actor and its action in the video clip given the sentence description. The main challenge is to match the local semantic features of the video with the heterogeneous textual features. A widely used language processing method in previous works is to leverage bi-LSTM and self-attention, which fixed the attention of the sentence and neglected the personality of the video, leading the attention of the sentence mismatch the most discriminative feature of the video. The proposed algorithm in this paper allows the sentence to learn the most discriminative features of the video, remarkably improving the accuracy of matching and segmentation. Specifically, we propose a cascade cross-modal attention to leverage two perspectives visual features to attend language from coarse to fine to generate the discriminative vision-aware language features. Moreover, equipping our framework with a contrastive learning method and a designed hard negative mining strategy benefits our proposed network from identifying the positive sample from numbers of negatives, and further improving the performance. To demonstrate the effectiveness of our approach, we conduct experiments on two datasets: A2D Sentences and J-HMDB Sentences. Experimental results show that our method significantly improves the performance over recent state-of-the-art methods.
Knowledge transfer in project-based organisations: A dynamic granular cognitive maps approach

Motivation is crucial for enhancing the effectiveness of knowledge transfer. This study aims to investigate how motivation for knowledge transfer and organisational context influence the effectiveness of knowledge transfer in project-based organisations. We further identify the key factors of motivation in an organisation context. We applied Dynamic Granular Cognitive Maps (DGCMs) to reveal the influencing mechanism of motivation and organisation context. Results show that three factors – knowledge transfer involvement, knowledge transfer satisfaction, and knowledge psychological ownership – are global controlled variables that reflect knowledge transfer performance. The motivation factors balanced reciprocity, avoiding punishment, organisational affective commitment, and achievement motivation are more important than others for knowledge transfer. Moreover, organisation context has a serious impact on knowledge transfer performance. Based on these results, a series of strategies are recommended to improve knowledge transfer in project-based organisations. This study offers a new approach to establishing crucial organisational relationships based on empirical evidence.
Search-Based Online Trajectory Planning for Car-like Robots in Highly Dynamic Environments

This paper presents a search-based partial motion planner for generating feasible trajectories of car-like robots in highly dynamic environments. The planner searches for smooth, safe, and near-time-optimal trajectories by exploring a state graph built on motion primitives. To enable fast online planning, we propose an efficient path searching algorithm based on the aggregation and pruning of motion primitives. We then propose a fast collision checking algorithm that takes into account the motions of moving obstacles. The algorithm linearizes relative motions between the robot and obstacles, and then checks collisions by calculating a point-line distance. Benefiting from the fast searching and collision checking algorithms, the planner can effectively explore the state-time space to generate near-time-optimal solutions. Experiments show that the proposed method can generate feasible trajectories within milliseconds while maintaining a higher success rate than up-to-date methods, which significantly demonstrates its advantages.
Information Privacy in Decentralized Applications

Numerous reviews of decentralized applications using Blockchain have been published in recent years. However, these have predominantly focused on how applications map to the characteristics of underlying protocols and discuss privacy only in passing. This chapter takes a more focused perspective by analyzing recently proposed prominent decentralized applications, categorizing them according to information privacy requirements. Three dimensions emerge as key differentiators: accessibility requirements (i.e., whether an application needs to be publicly accessible), hierarchicality requirements (i.e., the notion of different types of participants with fundamentally different permissions), and requirements on joint verifiability of transaction data (i.e., which subset of an application’s data model needs to be verifiable by others). Grouping applications along these dimensions, it becomes clear that use cases with closed accessibility requirements are most dominant in the market today. These use cases can establish information privacy through governance, deliberate data modeling, and role-based access management.
Evaluation of Adoption of Blockchain Technology for Supply Chain Management: A Case of Indian MSME

Industry 4.0 is the information-intensive transformation that has taken place in manufacturing and its related activities. Blockchain Technology (BCT) is one such prominent component of the Industry 4.0 concept, which enables the workforce and end-users to access digital information at real-time, based on its data abstraction features. The aim of this paper is to analyze the determinants that influence the BCT adoption in supply chain management in the context of MSME firms in India. We consider the case of MSMEs, in developing economies such as India as MSMEs play a crucial role in enhancing the growth and development of such economies. The determinants to evaluate the implementation of BCT is based on the integrated conceptual model including features of Technology Adoption Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and Technology-Organization-Environment (TOE) frameworks. Sixteen major determinants were selected by integrating the opinions of industry experts and from the literature. Since there are many conflicting criteria, the current paper utilizes the Multi-criteria Decision Making (MCDM) method, Fuzzy-Analytical Hierarchy Process (F-AHP) to prioritize these determinants so as to aid the decision-makers in the adoption of BCT in their supply chain. The present study integrates fuzzy theory with AHP method to incorporate the subjectivity of real-life decision making. The results of this study will aid various stakeholders, such as industry practitioners, policymakers and regulatory bodies to get a better understanding of benefits of BCT in the supply chain and provide good service to consumers such that the organization can stay ahead of the competition.
Arbitrary-Oriented SAR Ship Detection Via Frequency Learning

A growing number of researchers begin to use the arbitrary-oriented detector to detect ships. Because in the remote sensing dataset, ships are shown to be narrow and dense. Most of the deep learning methods used in synthetic aperture radar (SAR) ship detection are the same as or variants of those of optical remote sensing. However, due to the relatively low resolution and signal-to-noise ratio, the amplitude information in SAR image becomes more contaminated than that in optical image. Therefore, it is very difficult to train the arbitrary-oriented target detection task based on SAR dataset. In order to solve this problem, we attempt to further extract the frequency domain features of the SAR image by introducing a frequency attention module. The discrete cosine transform (DCT) to converts SAR image from spatial domain to frequency domain. Experimental results on HRSID datasets show that the proposed method can significantly improve the performance of arbitrary-oriented SAR ship detection.
No Face-Touch: Exploiting Wearable Devices and Machine Learning for Gesture Detection

Avoiding face-touches has been one of the most common medical recommendations since the beginning of the COVID-19 pandemic. This work aims at providing people with help in contrasting this widespread, yet noxious habit. The solution we present exploits wearable devices to detect hand motions ending up into a face-touch and promptly notify the user exploiting haptic feedback. To this aim, we propose a recurrent neural network taking as input temporal sequences of accelerometer data acquired by a smartwatch worn by the user. The trained RNN (NFT_RNN) achieves good generalization capabilities to data coming from different users, besides a lower false detections rate with respect to a rule-based detection algorithm. The suggested solution is ready-to-use and large-scale deployable, being portable on smartwatches, fitness bands and DIY devices.
UP-tree &amp; UP-Mine: A fast method based on upper bound for frequent pattern mining from uncertain data

In recent years, frequent pattern mining from uncertain data has been actively researched in data mining. There are numerous exact and upper bound-based approaches for uncertain frequent pattern mining. Exact-based algorithms may produce a large data structure and need time-consuming calculations and upper bound-based algorithms may produce many false positives. As a result, these algorithms demand much time and memory. There have been efforts to resolve the problem of upper bound-based algorithms, however, all of these methods only try to tighten the upper bound of expected support for long patterns. This is while pruning infrequent short patterns has a greater impact on reducing the false positives. To overcome these drawbacks, in this paper an efficient method based on upper bound is proposed for mining uncertain frequent patterns. The proposed method uses a new Tightened upper bound to expected support of patterns (Tup) which has a significant effect on reducing the number of false positives by tightening the upper bound of expected support and early pruning of infrequent 2-itemsets and their supersets. Comprehensive experimental results show that the proposed method reduces memory consumption in most cases and dramatically improves the performance of exact and upper bound-based methods in terms of runtime and scalability for dense and sparse uncertain data.
On-chain is not enough: Ensuring pre-data on the chain credibility for blockchain-based source-tracing systems

The blockchain provides a reliable and scalable method for enabling source-tracing functionality in large-scale Internet of Things (IoT) systems. Traditional blockchain-based source tracing applications are generally based on the hypothesis that the raw data collected by each IoT node are credible and consistent, which however may not always be the truth. As no mechanism ensures the reliability of the original data collected from the IoT devices, these data may be accidently screwed up or maliciously tampered with before they are uploaded on-chain. To address this issue, we propose the Multi-dimensional Certificates of Origin (MCO) method to filter out the potentially incredible data-till all the data uploaded to the chain are credible. To achieve this, we devise the Multi-dimensional Information Cross-Verification (MICV) and Multi-source Data Matching Calculation (MDMC) methods. MICV verifies whether a to-be-uploaded datum is consistent or credible, and MDMC determines which data should be discarded and which data should be kept to retain the most likely credible/untampered ones in the circumstance when data inconsistency appears. Large-scale experiments show that our scheme ensures on the credibility of data and off the chain with an affordable overhead.