Reviewerly/exHarmony huggingface dataset:
Designing Eco-Feedback Systems for Communities: Interrogating a Techno-solutionist Vision for Sustainable Communal Energy

The notion of establishing energy communities where householders collectively participate in using renewable energy is highly topical. In this paper, we interrogate a design vision of sustainable communal energy developed through a techno-solutionist narrative. The vision is exemplified in a recommendation-based mobile app, the Community Energy Planner app, that provides individual and collective energy feedback on renewable energy actions. To obtain insights into how householders understand and experience embedding such recommended communal energy feedback into domestic energy-consuming practices, we deployed the app with six households for one month. Through a qualitative study, we report on householders' experiences living with the app on an individual and at a community level. Our findings are presented in four themes, revealing that recommended feedback on individual and collective energy actions is challenging to align with the messiness of domestic life. Finally, we discuss alternative design visions for sustainable communal energy. The main contribution of this paper is twofold: 1) a field deployment study of a techno-solutionist narrative of communal energy facilitated through an app and, 2) a discussion on alternatives to this design vision.
Evaluating Hand-tracking Interaction for Performing Motor-tasks in VR Learning Environments

The past few years has witnessed a promising surge in immersive media technologies, however, a widespread adoption is still some years away. Recently, virtual reality (VR) head-mounted displays with inside-out tracking and hand-tracking are becoming popular in various fields. Representing users' hands and fingers in VR is crucial for many tasks. This is because hand-tracking enables a more natural, direct, interaction with a virtual environment (VE) which, compared to traditional handheld controllers, improves the VR immersive experience. This paper investigates user-perceived quality of experience for a reach-pick-place task inside VR. It presents a performance assessment methodology based on in-game analytics to evaluate user experience for different VR interaction types. We ran a controlled experiment (N=33) comparing two commodities: controller vs. hand-tracking. Results indicate a significant difference between the two data sets. The performance analytics show lower play-durations and trigger frequencies for the handheld controller compared to hand-tracking. The perceived mental workload for the tasks was also evaluated using Rated Scale Mental Effort (RSME). The reported scores were higher when using hand-tracking. The results help our understanding of the two interaction modalities in terms of their viability for naturalistic experiences akin to real-world scenarios.
Generalizable Multi-Camera 3D Pedestrian Detection

We present a multi-camera 3D pedestrian detection method that does not need to train using data from the target scene. We estimate pedestrian location on the ground plane using a novel heuristic based on human body poses and person’s bounding boxes from an off-the-shelf monocular detector. We then project these locations onto the world ground plane and fuse them with a new formulation of a clique cover problem. We also propose an optional step for exploiting pedestrian appearance during fusion by using a domain-generalizable person re-identification model. We evaluated the proposed approach on the challenging WILDTRACK dataset. It obtained a MODA of 0.569 and an F-score of 0.78, superior to state-of-the-art generalizable detection techniques.
CRFace: Confidence Ranker for Model-Agnostic Face Detection Refinement

Face detection is a fundamental problem for many down-stream face applications, and there is a rising demand for faster, more accurate yet support for higher resolution face detectors. Recent smartphones can record a video in 8K resolution, but many of the existing face detectors still fail due to the anchor size and training data. We analyze the failure cases and observe a large number of correct predicted boxes with incorrect confidences. To calibrate these confidences, we propose a confidence ranking network with a pairwise ranking loss to rerank the predicted confidences locally within the same image. Our confidence ranker is model-agnostic, so we can augment the data by choosing the pairs from multiple face detectors during the training, and generalize to a wide range of face detectors during the testing. On WiderFace, we achieve the highest AP on the single-scale, and our AP is competitive with the previous multi-scale methods while being significantly faster. On 8K resolution, our method solves the GPU memory issue and allows us to indirectly train on 8K. We collect 8K resolution test set to show the improvement, and we will release our test set as a new benchmark for future research.
Linear support vector regression with linear constraints

This paper studies the addition of linear constraints to the Support Vector Regression when the kernel is linear. Adding those constraints into the problem allows to add prior knowledge on the estimator obtained, such as finding positive vector, probability vector or monotone data. We prove that the related optimization problem stays a semi-definite quadratic problem. We also propose a generalization of the Sequential Minimal Optimization algorithm for solving the optimization problem with linear constraints and prove its convergence. We show that an efficient generalization of this iterative algorithm with closed-form updates can be used to obtain the solution of the underlying optimization problem. Then, practical performances of this estimator are shown on simulated and real datasets with different settings: non negative regression, regression onto the simplex for biomedical data and isotonic regression for weather forecast. These experiments show the usefulness of this estimator in comparison to more classical approaches.
Review: Development and Technical Design of Tangible User Interfaces in Wide-Field Areas of Application

A tangible user interface or TUI connects physical objects and digital interfaces. It is more interactive and interesting for users than a classic graphic user interface. This article presents a descriptive overview of TUI’s real-world applications sorted into ten main application areas—teaching of traditional subjects, medicine and psychology, programming, database development, music and arts, modeling of 3D objects, modeling in architecture, literature and storytelling, adjustable TUI solutions, and commercial TUI smart toys. The paper focuses on TUI’s technical solutions and a description of technical constructions that influences the applicability of TUIs in the real world. Based on the review, the technical concept was divided into two main approaches: the sensory technical concept and technology based on a computer vision algorithm. The sensory technical concept is processed to use wireless technology, sensors, and feedback possibilities in TUI applications. The image processing approach is processed to a marker and markerless approach for object recognition, the use of cameras, and the use of computer vision platforms for TUI applications.
Cascaded Multiscale Structure With Self-Smoothing Atrous Convolution for Semantic Segmentation

Convolutional neural networks (CNNs) have attracted great attention in the semantic segmentation of very-high-resolution (VHR) images of urban areas. However, large-scale variation of objects in the urban areas often makes it difficult to achieve good segmentation accuracy. Atrous convolution and atrous spatial pyramid pooling composed of atrous convolution can alleviate this problem by exploring multiscale contextual information. Unfortunately, atrous convolution causes gridding artifacts, where actual receptive fields are separated unit sets and fail to cover all the receptive fields. To address this problem, in this article, we first propose a self-smoothing atrous convolution (SS-AConv) that intrinsically improves atrous convolution, unlike existing methods. SS-AConv enhances sampling rates with low computational costs by adding several key parameters in the spatial dimension of its filter. Then, in the backbone, the Xception network, all max pooling operations are replaced with SS-AConvs for a large and effective receptive field. Moreover, to extract diverse features at multiple scales, we propose the SS-AConv cascaded multiscale structure (SCMS) by integrating SS-AConvs with different rates and the residual correction scheme (RCS) into a cascaded spatial pyramid. Finally, to extract diverse features at dense multiple scales, the SS-AConv convolutional network (SS-ACNet) is constructed by integrating SCMS into both the encoder and decoder layers of the modified Xception network. Extensive experimental results show that SS-ACNet outperforms some state-of-the-art methods on four open challenge datasets: the ISPRS Vaihingen and Potsdam, Cityscapes, and PASCAL VOC 2012.
Fair near neighbor search via sampling

Similarity search is a fundamental algorithmic primitive, widely used in many computer science disciplines. Given a set of points S and a radius parameter r &gt; 0, the rnear neighbor (r-NN) problem asks for a data structure that, given any query point q, returns a point p within distance at most r from q. In this paper, we study the r-NN problem in the light of individual fairness and providing equal opportunities: all points that are within distance r from the query should have the same probability to be returned. In the low-dimensional case, this problem was first studied by Hu, Qiao, and Tao (PODS 2014). Locality sensitive hashing (LSH), the theoretically strongest approach to similarity search in high dimensions, does not provide such a fairness guarantee.
Accelerating Triangle Counting on GPU

Triangle counting is an important problem in graph mining, which has achieved great performance improvement on GPU in recent years. Instead of proposing a new GPU triangle counting algorithm, in this paper, we propose a novel lightweight graph preprocessing method to boost many state-of-the-art GPU triangle counting algorithms without changing their implementations and data structures. Specifically, we find common computing patterns in existing algorithms, and abstract two analytic models to measure how workload imbalance and diversity in these computing patterns affect performance exactly. Then, due to the NP-hardness of the model optimization, we propose approximate solutions by determining edge directions to balance workloads and reordering vertices to maximize the degree of parallelism within GPU blocks. Finally, extensive experiments confirm the significant performance improvement and high usability of our approach.
Clustering-driven Deep Adversarial Hashing for scalable unsupervised cross-modal retrieval

With the advent of the big data era, multimedia data is growing rapidly, and its data modalities is also becoming diversified. Therefore, the demand for the speed and accuracy of cross-modal information retrieval is increasing. Hashing-based cross-modal retrieval technology attracts widespread attention, it encodes multimedia data into a common binary hash space, thereby effectively measuring the correlation between samples from different modalities. In this paper, we propose a novel end-to-end deep cross-modal retrieval framework, namely Clustering-driven Deep Adversarial Hashing (CDAH), which has three main characteristics. Firstly, CDAH learns discriminative clusters recursively through a soft clustering model. It attempts to generate modal-invariant representations in a common space by obfuscating the modality classifier, which tries to distinguish different modalities according to the generated representations. Secondly, in order to minimize the modal gap between feature representations from different modalities with the same semantic label, and to maximize the distance between images and texts with different labels, CDAH constructs a fused-semantics matrix to integrate the original domain information from different modalities, serving as self-supervised information to refine the binary codes. Finally, CDAH skillfully uses a scaled tanh function to adaptively learn the binary codes, which will gradually converge to the original tricky binary coding problem. We conduct comprehensive experiments on four popular datasets, and the experimental results demonstrate the superiority of our model against the state-of-the-art methods.
A transport monitoring system for cultural relics protection based on blockchain and internet of things

Cultural relic transport is quite an essential area of work for realizing cooperation among museums. It involves key functional areas such as object distribution and knowledge sharing. At the same time, the transportation process of cultural relics is dynamic and requires special prevention mechanisms to avoid risks. A real-time management system for the transport of cultural relics using Internet of Things terminals and blockchain technology is provided in this study. In the system, real-time sensing of physical and environmental changes has been achieved, such as those in speed, temperature, relative humidity, GPS location, triaxial acceleration and video images, for effective data feedback and dynamic remote management. Furthermore, a sample application of the system is detailed as it was used to record the changes during the transport of cultural relics from Changsha Museum to the Museum of Nanyue King of the Western Han dynasty. It is shown that no damage happened to the cultural relics and the transport was relatively safe. Effective management of the vehicle, the drivers, and the museum staff were successfully achieved. Overall, it is indicated that the system can be beneficial for tracking the transportation process and preventing untoward incidents during the process.
AsymmNet: Towards ultralight convolution neural networks using asymmetrical bottlenecks

Deep convolutional neural networks (CNN) have achieved astonishing results in a large variety of applications. However, using these models on mobile or embedded devices is difficult due to the limited memory and computation resources. Recently, the inverted residual block becomes the dominating solution for the architecture design of compact CNNs. In this work, we comprehensively investigated the existing design concepts, rethink the functional characteristics of two pointwise convolutions in the inverted residuals. We propose a novel design, called asymmetrical bottlenecks. Precisely, we adjust the first pointwise convolution dimension, enrich the information flow by feature reuse, and migrate saved computations to the second pointwise convolution. Doing so we can further improve the accuracy without increasing the computation overhead. The asymmetrical bottlenecks can be adopted as a dropin replacement for the existing CNN blocks. We can thus create AsymmNet by easily stack those blocks according to proper depth and width conditions. Extensive experiments demonstrate that our proposed block design is more beneficial than the original inverted residual bottlenecks for mobile networks, especially useful for those ultralight CNNs within the regime of <220M MAdds. Code is available at https://github.com/Spark001/AsymmNet
Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture Search

Weight sharing has become a de facto standard in neural architecture search because it enables the search to be done on commodity hardware. However, recent works have empirically shown a ranking disorder between the performance of stand-alone architectures and that of the corresponding shared-weight networks. This violates the main assumption of weight-sharing NAS algorithms, thus limiting their effectiveness. We tackle this issue by proposing a regularization term that aims to maximize the correlation between the performance rankings of the shared-weight network and that of the standalone architectures using a small set of landmark architectures. We incorporate our regularization term into three different NAS algorithms and show that it consistently improves performance across algorithms, search-spaces, and tasks.
Handling data heterogeneity with generative replay in collaborative learning for medical imaging

Collaborative learning, which enables collaborative and decentralized training of deep neural networks at multiple institutions in a privacy-preserving manner, is rapidly emerging as a valuable technique in healthcare applications. However, its distributed nature often leads to significant heterogeneity in data distributions across institutions. In this paper, we present a novel generative replay strategy to address the challenge of data heterogeneity in collaborative learning methods. Different from traditional methods that directly aggregating the model parameters, we leverage generative adversarial learning to aggregate the knowledge from all the local institutions. Specifically, instead of directly training a model for task performance, we develop a novel dual model architecture: a primary model learns the desired task, and an auxiliary "generative replay model" allows aggregating knowledge from the heterogenous clients. The auxiliary model is then broadcasted to the central sever, to regulate the training of primary model with an unbiased target distribution. Experimental results demonstrate the capability of the proposed method in handling heterogeneous data across institutions. On highly heterogeneous data partitions, our model achieves ∼4.88% improvement in the prediction accuracy on a diabetic retinopathy classification dataset, and ∼49.8% reduction of mean absolution value on a Bone Age prediction dataset, respectively, compared to the state-of-the art collaborative learning methods.
A comprehensive study on pre-pruning and post-pruning methods of decision tree classification algorithm

The decision tree is the most effective classification method. However, the results of the decision tree can show errors due to overfitting or if the data is too noisy. This may cause the tree to be too big with unnecessary nodes and branches. To handle the error rate pruning is done in the decision tree. Using pruning, the scope of the tree is cut short only keeping the necessary nodes and branches. Pruning is possible in two manners, that is, pre-pruning where the pruning of the tree is done while the tree is in the process of construction and post pruning is done after the tree is fully constructed, the size is reduced to the expected tree. In this paper, several techniques of both pre-pruning and post-pruning are described to get an overall better understanding of with method to use based on the type of data. The performance of the pruning methods based on various datasets are measured.
A Multi-Party Contract Signing Solution Based on Blockchain

Fair digital signature of contracts and agreements is an essential process in any electronic commerce scenario, and therefore also in data marketplaces, where the relationships and agreements among the different parties (consumers and providers) are more dynamic. In multi-party contract signing, N parties wish to sign a contract in a such a way that either all signatories obtain evidence of the signing or none obtains conflicting evidence regarding the honest signatories; the exchange must be fair. In this paper, we propose a blockchain-based multi-party contract signing protocol. This solution does not require the existence or potential intervention of a trusted third party (TTP), thus avoiding the difficulty of N signatories agreeing upon a TTP. In addition, this proposal meets the necessary requirements: fairness, timeliness, non-repudiation of origin, and non-repudiation of receipt. Furthermore, confidentiality can be easily achieved. To minimize costs associated with the use of blockchain, it should be invoked in the case of exception (analogous to optimistic solutions with a TTP) and by only one of the N signatories. However, when the use of blockchain is required, we show that its cost is within perfectly manageable margins.
A Quantum Interpretation of Bunched Logic &amp; Quantum Separation Logic

We propose a model of the substructural logic of Bunched Implications (BI) that is suitable for reasoning about quantum states. In our model, the separating conjunction of BI describes separable quantum states. We develop a program logic where pre- and post-conditions are BI formulas describing quantum states—the program logic can be seen as a counterpart of separation logic for imperative quantum programs. We exercise the logic for proving the security of quantum one-time pad and secret sharing, and we show how the program logic can be used to discover a flaw in Google Cirq’s tutorial on the Variational Quantum Algorithm (VQA).
PrettyTags: An Open-Source Tool for Easy and Customizable Textual MultiLevel Semantic Annotations

Labeled data are required for feeding machine learning algorithms and training effectively performing models. Handcrafted annotations of data, made by human experts, require much effort and this task is made heavier when some comfortable tools, for making annotations over the objects, are not available or easily accessible. Furthermore, annotations should be provided in machine-readable formats, to be ready to use in machine learning tasks. In this work, we introduce PrettyTags, an easy-to-use and customizable tool for making text spans annotations, that will be released as an open-source web application. We present a detailed overview of the main features offered by PrettyTags and we also discuss the possibility to link entities annotations in the textual documents to an ontology-based system, for enriching entities semantic representations.
An Analysis of Privacy Issues and Solutions in Public Blockchain (Bitcoin)

Blockchain is a new class of technology in computer science that combines cryptography and distributed computing. It provides a digitally timestamped database where the network of computers can communicate and collaborate without trusting each other. Blockchain can be used in an enormous number of applications in industries like supply chain management, identity verification, voting system, Real estate, and health care system, etc. Bitcoin is the first application of public blockchain, is used to exchange cryptocurrency by rolling out the involvement of a centralized third-party system or single consortium like government, bank, organization. etc. One of the major issues of blockchain is user privacy leaks. This paper focused to detail the underlying concepts of blockchain, identify the user privacy leaks in various phases of blockchain concerning bitcoin application, and finally discussed the available solutions like deterministic key generation, mixing services, ring signature, zero-knowledge proof, and quantum-resistant algorithms.