Reviewerly/exHarmony huggingface dataset:
Consistency of anchor-based spectral clustering

Abstract Anchor-based techniques reduce the computational complexity of spectral clustering algorithms. Although empirical tests have shown promising results, there is currently a lack of theoretical support for the anchoring approach. We define a specific anchor-based algorithm and show that it is amenable to rigorous analysis, as well as being effective in practice. We establish the theoretical consistency of the method in an asymptotic setting where data is sampled from an underlying continuous probability distribution. In particular, we provide sharp asymptotic conditions for the number of nearest neighbors in the algorithm, which ensure that the anchor-based method can recover with high probability disjoint clusters that are mutually separated by a positive distance. We illustrate the performance of the algorithm on synthetic data and explain how the theoretical convergence analysis can be used to inform the practical choice of parameter scalings. We also test the accuracy and efficiency of the algorithm on two large scale real data sets. We find that the algorithm offers clear advantages over standard spectral clustering. We also find that it is competitive with the state-of-the-art LSC method of Chen and Cai (Twenty-Fifth AAAI Conference on Artificial Intelligence, 2011), while having the added benefit of a consistency guarantee.
Analysis of the User Experience with a Multiperspective Tool for Explainable Machine Learning in Light of Interactive Principles

Machine Learning (ML) models have been widely used nowadays, as "magical black boxes", in many different domains and for distinct goals, but the way they generate their results is not fully understood yet, including by knowledgeable users. If users cannot interpret or trust the prediction generated by the model, they will not use it. Furthermore, the human role is often not (properly) considered for the developing of ML systems. In this article, we present a discussion about user centered development of Interactive Machine Learning systems. We ground our discussion on Explain-ML, a multi-perspective human-centered machine learning tool that assists users in the building, refinement and interpretability of ML models. To do so, we have conducted an analysis of the results of the evaulation of Explain-ML with potential users in light of the principles for Interactive ML systems design. Our results contribute to the understanding and consolidation of these principles. Moreover, the experience gained from the discussion about Explain-ML, its user centered development and its evaluation with users, based on these principles, is relevant to the research and development of Interactive ML Tools that involve ML explainability.
Reproducibility Companion Paper

Blind natural video quality assessment (BVQA), also known as no-reference video quality assessment, is a highly active research topic. In our recent contribution titled "Blind Natural Video Quality Prediction via Statistical Temporal Features and Deep Spatial Features" published in ACM Multimedia 2020, we proposed a two-level video quality model employing statistical temporal features and spatial features extracted by a deep convolutional neural network (CNN) for this purpose. At the time of publishing, the proposed model (CNN-TLVQM) achieved state-of-the-art results in BVQA. In this paper, we describe the process of reproducing the published results by using CNN-TLVQM on two publicly available natural video quality datasets.
A Blind Cloud/Shadow Removal Strategy for Multi-Temporal Remote Sensing Images

For multi-temporal remote sensing (RS) images, the distribution of surface materials is constant concerning time and the same material shows different spectral features at different times. Decomposing the image at each time into an abundance tensor and temporal features, there is a strong similarity between abundance tensors of all time. Based on this observation, we suggest a blind thick cloud/shadow removal model, which exploits the sparsity of the cloud component and the similarity between abundance tensors, achieving both cloud detection and multi-temporal information restoration. Moreover, a mask refinement strategy is designed to pursue the optimal cloud/shadow mask. We develop an efficient algorithm to solve the proposed model based on the augmented Lagrange multiplier method. The results of simulated experiments in different scenarios verify the superiority of the proposed method for thick cloud/shadow removal.
A Fast and Approximate Medial Axis Sampling Technique

One key aspect to planning safe motions for a robot requires maintaining a minimum distance from obstacles. The medial axis, or the set of all points equidistant to two or more obstacles, is useful for planning in this context. However, it is computationally intensive to compute this structure directly. As such, prior research proposed directly sampling this structure to avoid its explicit computation. In this paper, we revisit the computational cost of these sampling-based techniques for motion planning, and we propose a novel approximation algorithm that significantly reduces the necessary collision detection invocations in generating samples near the medial axis. At its core, our technique approximates the clearance of a configuration by reasoning about the clearance of nearby configurations. Ultimately, our approach reduces the computational burden of planning on or near the medial axis.
Research on the Relationship between Virtual Reality Content and Physiological Response

The development of VR (virtual reality) technology has greatly promoted immersion, but it may cause people to worry about the safety of humans. This study aims to determine the effect of virtual reality based on 9D-VR seat (9 Dimension VR Seat) on the physiological response of users. Recruit 20 volunteers to experience the VR roller coaster and VR big pendulum. Through analysis of heart rate data with visualization and statistical, it is found that there is a significant effect on heart rate when users experience virtual reality, and the excitement points of the VR content were related to the changes in heart rate. Further, we found that the difference between the first experience and the second experience for the same VR content, and the changes in heart rate have eased. These findings provide references for the analysis of users for VR and the design of content of the VR.
Traceability Analysis of Patterns Using Clustering Techniques

Currently, with the high rate of generation of new information, it is important the traceability of its evolution. This paper studies techniques that allow analyzing the evolution of the knowledge, starting with analyzing the capabilities of the techniques to identify the patterns that represent the common information in datasets. From the “patterns,” the evolution of their characteristics over time is analyzed. The paper considers the next techniques for the problem of tracking the traceability of the patterns: LDA (Latent Dirichlet allocation), Birch (Balanced Iterative Reducing and Clustering using Hierarchies), LAMDA (Learning Algorithm for Multivariate Data Analysis), and K-means. They are used both for the initial task of grouping the data, as well as, to analyze the characteristics of the patterns, and the relevance of them in the patterns through their evolution (traceability). This paper uses different types of data sources of educational contents, and with these datasets, the topological models to describe the “patterns” generated from the grouping of the analyzed data, and their dynamics (evolution over time), are Studied (traceability). For the evaluation, the paper considers three metrics: Calinski–Harabasz Index, Davies–Bouldin Index, and Silhouette Score.
Detection and location technology of substation personnel based on EfficientDet

Artificial intelligence technology has been widely used in the field of object detection and recognition. Limited by the complex working environment of power grid, the application of artificial intelligence in power grid system is just beginning. This paper focuses on a new neural network structure proposed by Google: EfficientDet. Based on the analysis of its characteristics, we applied the EfficientDet model to detect substation workers. The feasibility of applying EfficientDet model to substation personnel detection is verified. The advantages and disadvantages of EfficientDet model used in substation personnel detection and location are proved by experiments, and the possible improvement direction in the next step is discussed. The research results of this paper can also be applied to the environment of personnel location and detection in other power grid systems with a few changes.
Variational Circuits as Machine Learning Models

We explain how parametrised quantum circuits—quantum algorithms that are popular in near-term quantum computing—can be used as machine learning models, and review techniques to analyse and train such quantum models in a deep-learning fashion, including measures of expressivity and trainability, as well as parameter-shift rules.
ActiThings: Reminders for physical activity exercises in daily lives of inactive older adults

Physical activity is a key aspect of healthy ageing. However, following a strict workout regimen requires much motivation and is a considerable threshold for inactive persons. A relatively simple set of exercises that can easily be integrated in daily life and routine already provides good health results and is therefore suitable as a low-threshold start into physical activity for yet inactive persons. However, performing these exercises can easily be forgotten in daily life. We therefore developed a system that senses opportune moments for exercises and reminds persons to perform them. We conducted a comparative study in 19 households with and without technology over a timespan of 12 weeks, observing the use of the system, and asking about experiences with the technology. We identify challenges regarding, amongst other, the configurability of the system, the definition of opportune moments, design of reminders in daily lives, and the co-design of interventions.
A Model-Agnostic Algorithm for Bayes Error Determination in Binary Classification

This paper presents the intrinsic limit determination algorithm (ILD Algorithm), a novel technique to determine the best possible performance, measured in terms of the AUC (area under the ROC curve) and accuracy, that can be obtained from a specific dataset in a binary classification problem with categorical features regardless of the model used. This limit, namely, the Bayes error, is completely independent of any model used and describes an intrinsic property of the dataset. The ILD algorithm thus provides important information regarding the prediction limits of any binary classification algorithm when applied to the considered dataset. In this paper, the algorithm is described in detail, its entire mathematical framework is presented and the pseudocode is given to facilitate its implementation. Finally, an example with a real dataset is given.
Cross Lingual Video and Text Retrieval: A New Benchmark Dataset and Algorithm

Video retrieval using natural language queries requires learning semantically meaningful joint embeddings between the text and the audio-visual input. Often, such joint embeddings are learnt using pairwise (or triplet) contrastive loss objectives which cannot give enough attention to ‘difficult-to-retrieve’ samples during training. This problem is especially pronounced in data-scarce settings where the data is relatively small (10% of the large scale MSR-VTT) to cover the rather complex audio-visual embedding space. In this context, we propose to compensate for data scarcity by using domain knowledge to augment supervision. Specifically, in addition to the conventional three samples of a triplet (anchor, positive, and negative), we introduce a fourth term - a partial - to define a margin based partial-order loss. The partials are heuristically sampled such that they semantically lie in the overlap zone between the positives and the negatives, thereby resulting in broader embedding coverage. Our proposals consistently outperform the conventional max-margin and triplet losses and improve the state-of-the-art on MSR-VTT and DiDeMO datasets. To further evaluate our method in data-scarce and low-resource setting, we introduce Rudder - a multilingual video-text retrieval dataset that includes audio and textual captions in Marathi, Hindi, Tamil, Kannada, Malayalam and Telugu. We report benchmark results on Rudder while also observing significant gains using the proposed partial order loss, especially when the language specific retrieval models are jointly trained by availing the cross-lingual alignment across the language-specific datasets.1
Tagasaurus, a tool to assist manual image tagging and the creation of image collections

<h2>Abstract</h2> This paper presents Tagasaurus a tool to help manually annotate image sets and create annotated image collections. A key aspect of the workflow permitted by the interface is that it records independent emotional value associations, pictorial associations, and textual annotations. This version being showcased goes beyond previous approaches by allowing a user to create collections of images under an entity tag. This can help create a corpus of images under a specific label which is needed by machine learning models to train for classification of objects within the image scenes.
MM21 Pre-training for Video Understanding Challenge

The quality of video representation directly decides the performance of video related tasks, for both understanding and generation. In this paper, we propose single-modality pretrained feature fusion technique which is composed of reasonable multi-view feature extraction method and designed multi-modality feature fusion strategy. We conduct comprehensive ablation studies on MSR-VTT dataset to demonstrate the effectiveness of proposed method and it surpasses the state-of-the-art methods on both MSR-VTT and VATEX datasets. We further propose the multi-modality pretrained model finetuning technique and dataset augmentation scheme to improve the model's generalization capability. Based on these two proposed pretraining techniques and dataset augmentation scheme, we win the first place in the video captioning track of the MM21 pretraining for video understanding challenge.
Stochastic Markov gradient descent and training low-bit neural networks

The massive size of modern neural networks has motivated substantial recent interest in neural network quantization, especially low-bit quantization. We introduce Stochastic Markov Gradient Descent (SMGD), a discrete optimization method applicable to training quantized neural networks. The SMGD algorithm is designed for settings where memory is highly constrained during training. We provide theoretical guarantees of algorithm performance as well as encouraging numerical results.
Cyber-attacks on cameras in the IoT networks

The use of IoT devices has been a ubiquitous phenomenon nowadays, different functionalities are being designed, manufactured, and connected to the network so they can now interconnect and exchange data with each other. Despite the benefits, these devices are also subject to several security risks. This paper will present an overview that targeting some common cyber-attacks of internet-connected surveillance camera in IoT networks, comparing threats and available solutions to discuss the problems and future research directions.
Towards a Semi-Automated Data-Driven Requirements Prioritization Approach for Reducing Stakeholder Participation in SPL Development

Requirements prioritization (RP), part of Requirements engineering (RE), is an essential activity of Software Product-Lines (SPL) paradigm. Similar to standard systems, the identification and prioritization of the user needs are relevant to the software quality and challenging in SPL due to common requirements, increasing dependencies, and diversity of stakeholders involved. As prioritization process might become impractical when the number of derived products grows, recently there has been an exponential growth in the use of Artificial Intelligence (AI) techniques in different areas of RE. The present research aims to propose a semi-automatic multiple-criteria prioritization process for functional and non-functional requirements (FR/NFR) of software projects developed within the SPL paradigm for reducing stakeholder participation.
A progressive learning method on unknown protocol behaviors

Reverse analyzing of unknown protocol behaviors keeps being a tough nut in Protocol Reverse Engineering (PRE), which infers specifications of unknown protocols by observable information, especially when only transmitted messages are available. This paper proposes a novel protocol state machine model Stochastic Protocol finite-state Transducer (SPT) to describe the message interaction rules between communicating terminals in a probabilistic way attempting to simulate behavior rules of unknown protocols in certain implementation. Together with a state related field recognition and compensation method, a progressive SPT learning algorithm of unknown protocols named Sptia-PL, is designed and implemented to reconstruct the SPT of target protocol with the ability to predict succeeding behaviors. By updating the SPT progressively, the proposed method is able to learn continuously in linear time and remain the established model in optimal condition during the whole learning process. This strategy thoroughly avoids the state explosion problem existing in most state machine learning methods of PRE. Experiments on two open and three local collected datasets of FTP, SMTP and POP3 prove the rationality of SPT model and effectiveness of Sptia-PL algorithm by an average Accuracy over 0.94 and a Coverage close to 0.99. The small computing cost O(N) of this method and high confidence of results outperforms all the known state-of-the-art methods significantly.
Bidirectional Pathway Feature Pyramid Networks and Reverse Scale-Transfer Layer for Detecting Mult-Scale Ships

Multi-scale ship detection for remote sensing images is always a popular research field in civil and military application. In this paper, in order to solve the feature information single pathway flow in feature layer causes the lack of detailed information in the deep feature layer, we propose a bidirectional pathway feature pyramid networks (BP-FPN) method, which enables the deep feature layer to have strong semantic information as well as rich detailed information. At the same time, the Reverse Scale-transfer Layer down-sampling method is proposed to reduce the information loss of feature layer in the process of downsampling. It ensures that the feature layer maintain information during the down-sampling process. Experimental results on a dataset collected from Google Earth have quantitatively and qualitatively demonstrated the effectiveness of our approach
Analyzing MoLIC's Applicability to Model the interaction of Conversational Agents

Intelligent conversational agents, such as chatbots, are becoming increasingly popular in different domains of use. The use of this kind of technology offers challenges and opportunities for the Human-Computer Interaction (HCI) area, because it is based on natural language conversations, and it can present different degrees of intelligence and autonomy. One of these challenges is to investigate whether the existing design techniques are adequate to model this kind of agent. Semiotic Engineering relies on MoLIC, which is a tool used in the design phase to model interaction and communication between a system and its users. Therefore, a raised question is whether MoLIC would be able to model conversational agents. In this sense, the goal of this work is to present the emerging results of a research that seeks to analyze the MoLIC's applicability to model the interaction of conversational agents, through a case study on Chatbot ANA. The results show that MoLIC can model the conversation, according to some adaptations and that some points can be extended in the tool. The contributions of this study focus on (1) broadening the knowledge of the HCI community about the MoLIC's applicability to model this kind of technology and (2) directing the IHC community towards new initiatives that aim to adapt MoLIC to model conversational agents.
Homotopy-Driven Exploration of Human-made Spaces Using Signs

Robots deployed in airports, malls, and stadiums today require expert oversight, pre-provided maps, and infrastructure. These systems are engineered to their specific deployment spaces and rely heavily on geometric maps for motion planning. In this work we consider a robot with only local-sensing and present a navigation strategy that uses signs and homotopy classes to fuel planning. We prove that in the worst-case, this strategy still maintains the probabilistic completeness that sampling based motion planners provide. Furthermore, we experimentally show that this exploration strategy results in 100% goal completion in real airport floor plans, and demonstrate how the robot&#x2019;s sensing capabilities affects efficiency. We also show the effect of the environment variables, such as total number of obstacles, and density of obstacles, on navigation.
Mining Tag Relationships in CQA Sites

Community Question Answer (CQA) sites are very popular means for knowledge transfer in the form of questions and answers. They rely on tags to connect the askers with the answerers. Since each CQA site contains information about a wide range of topics, it is difficult for users to navigate through the set of available tags and select the best ones for their question annotation. At present, CQA sites present the tags to the users using simple orderings, such as order by popularity and lexical order. This paper proposes a novel unsupervised method to mine different types of relationships between tags and then create a forest of ontologies to representing those relationships. Extracting the tag relationships will help users to understand the tags meanings. Representing them in a forest of ontologies will help the users in better tag navigation, thereby providing the users a clear understanding of the tag usage for question annotation. Moreover, our method can also be combined with existing tag recommendation systems to improve them. We evaluate our tag relationship mining algorithms and tag ontology construction algorithm with the state-of-the-art baseline methods and the three popular knowledge bases, namely DBpedia, ConceptNet, and WebIsAGraph.
Collective Argumentation with Topological Restrictions

Collective argumentation studies how to reach a collective decision that is acceptable to the group in a debate. I introduce the concept of topological restriction to enrich collective argumentation. Topological restrictions are rational constraints assumed to be satisfied by individual agents. We assume that in a debate, for every pair of arguments that are being considered, every agent indicates whether the first one attacks the second, i.e., an agent’s argumentative stance is characterized as an argumentation framework, and only argumentation frameworks that satisfy topological restrictions are allowed. The topological constraints we consider in this paper include acyclicity, symmetry, as well as a newly defined topological property called t-self-defense. We show that when profiles of argumentation frameworks provided by agents satisfy topological restrictions, impossibility results during aggregation can be avoided. Furthermore, if a profile is topological-restricted with respect to t-self-defense, then the majority rule preserves admissibility during aggregation.