Reviewerly/exHarmony huggingface dataset:
Unsupervised Object Keypoint Learning using Local Spatial Predictability

We propose PermaKey, a novel approach to representation learning based on object keypoints. It leverages the predictability of local image regions from spatial neighborhoods to identify salient regions that correspond to object parts, which are then converted to keypoints. Unlike prior approaches, it utilizes predictability to discover object keypoints, an intrinsic property of objects. This ensures that it does not overly bias keypoints to focus on characteristics that are not unique to objects, such as movement, shape, colour etc. We demonstrate the efficacy of PermaKey on Atari where it learns keypoints corresponding to the most salient object parts and is robust to certain visual distractors. Further, on downstream RL tasks in the Atari domain we demonstrate how agents equipped with our keypoints outperform those using competing alternatives, even on challenging environments with moving backgrounds or distractor objects.
Mining Text Outliers in Document Directories

Nowadays, it is common to classify collections of documents into (human-generated, domain-specific) directory structures, such as email or document folders. But documents may be classified wrongly, for a multitude of reasons. Then they are outlying w.r.t. the folder they end up in. Orthogonally to this, and more specifically, two kinds of errors can occur: (O) Out-of-distribution: the document does not belong to any existing folder in the directory; and (M) Misclassification: the document belongs to another folder. It is this specific combination of issues that we address in this article, i.e., we mine text outliers from massive document directories, considering both error types. We propose a new proximity-based algorithm, which we dub kj-Nearest Neighbours (kj-NN). Our algorithm detects text outliers by exploiting semantic similarities and introduces a self-supervision mechanism that estimates the relevance of the original labels. Our approach is efficient and robust to large proportions of outliers. kj-NN also promotes the interpretability of the results by proposing alternative label names and by finding the most similar documents for each outlier. Our real-world experiments demonstrate that our approach outperforms the competitors by a large margin.
Multi-stream Network for Human-object Interaction Detection

Detecting the interaction between humans and objects in images is a critical problem for obtaining a deeper understanding of the visual relationship in a scene and also a critical technology in many practical applications, such as augmented reality, video surveillance and information retrieval. Be that as it may, due to the fine-grained actions and objects in the real scene and the coexistence of multiple interactions in one scene, the problem is far from being solved. This paper differs from prior approaches, which focused only on the features of instances, by proposing a method that utilizes a four-stream CNNs network for human-object interaction (HOI) detection. More detailed visual features, spatial features and pose features from human-object pairs are extracted to solve the challenging task of detection in images. Specially, the core idea is that the region where people interact with objects contains important identifying cues for specific action classes, and the detailed cues can be fused to facilitate HOI recognition. Experiments on two large-scale HOI public benchmarks, V-COCO and HICO-DET, are carried out and the results show the effectiveness of the proposed method.
Design for Playfulness with Interactive Soft Materials

Designing for interactive play brings new dimensions for play experience blended with digital and physical characteristics. Starting from the insights of two interactive soft toy projects, the authors explore interactive soft materials in designing for playfulness through a material-driven design approach. This can bring a new dimension in designing interaction for ludic experiences and reveal specific characteristics of soft materials. By combining somaesthetic and embodied design methods, the studio aims at exploring what makes soft materials playful. Moreover, it addresses how technology can enhance the playfulness of these materials and what kind of playful interaction scenarios can be imagined with interactive soft materials by involving the whole-body in the design process. The learnings from the studio will help the authors build a framework which shows the potential and describes the characteristics of interactive soft materials to create new tangible, embedded and embodied interactions for play.
Combatting out-of-distribution errors using model-agnostic meta-learning for digital pathology

Clinical deployment of systems based on deep neural networks is hampered by sensitivity to domain shift, caused by e.g. new scanners or rare events, factors usually overcome by human supervision. We suggest a correct-thenpredict approach, where the user labels a few samples of the new data for each slide, which is used to update the network. This few-shot meta-learning method is based on Model-Agnostic Meta-Learning (MAML), with the goal of training to adapt quickly to new tasks. Here we adapt and apply the method to the histopathological setting by identifying a task as a whole-slide image with its corresponding classification problem. We evaluated the method on three datasets, while purposefully leaving out-of-distribution data out from the training data, such as whole-slide images from other centers, scanners or with different tumor classes. Our results show that MAML outperforms conventionally trained baseline networks on all our datasets in average accuracy per slide. Furthermore, MAML is useful as a robustness mechanism to out-of-distribution data. The model becomes less sensitive to difference between whole-slide images and is viable for clinical implementation when used with the correct-then-predict workflow. This offers a reduced need for data annotation when training networks, and a reduced risk of performance loss when domain shift data occurs after deployment.
Countering hate speech

Counterspeech refers to communication that responds to hate speech in order to reduce it and negate its harmful potential effects. This chapter defines hate speech and examines some of its negative impacts. In such scenarios, counterspeakers can play a calming role and try and prevent hateful rhetoric from turning into violence. This can involve countering hateful hashtags as they emerge following critical events or sending preventive ‘peace text’ messages promoting calm during key events or responding to false rumours. Before examining the concept of counterspeech more directly, it is important to understand that counterspeech is based on a set of assumptions. The chapter examines the audiences, goals, and tactics of counterspeech and then its messaging and effects. The importance of source credibility in persuasion is not unique to counterspeech and has been advocated as far back as at least Aristotle, who saw the credibility of the speaker to the audience as a central component of effective rhetoric.
Secure and Decentralized Crowdfunding Mechanism Based on Blockchain Technology

Crowdfunding is a mechanism of raising capital from investors for funding new business ventures. To facilitate crowdfunding, different crowdfunding platforms are available, such as Kickstarter and Indiegogo. Crowdfunding platforms provide a convenient way of raising funds for startups from investors. The major downside of the conventional crowdfunding platforms is that they require users to pay few percentages of the fundraised to the crowdfunding platforms as platform fees (e.g., Kickstarter charges 5% of the total fundraised as platform fee). In addition to platform fee, users are required to pay transaction fees to the payment processors. In this paper, we propose a secure and decentralized crowdfunding mechanism based on blockchain technology. The proposed crowdfunding mechanism eliminates the need of conventional crowdfunding platforms that charge entrepreneurs a sum of money as platform fees. The proposed mechanism allows entrepreneurs to make use of the total fundraised from investors and provides an immutable ledger of transactions between investors and entrepreneurs.
Greedy BIT* (GBIT*): Greedy Search Policy for Sampling-Based Optimal Planning with a Faster Initial Solution and Convergence

This paper presents Greedy Batch Informed Trees (GBIT*), a greedy version of Batch Informed Trees (BIT*) and Advanced Batch Informed Trees (ABIT*) with a greedy search policy inspired by RRT-Connect. BIT* and ABIT* use an edge queue ordered by the (inflated) potential path cost to find the best next edge to process. GBIT* builds on ABIT* by adding another preferential way, which is defined by the greedy search policy, to choose the next edge to process. Otherwise, it will follow ABIT*’s method. The greedy search policy guides the search moving forward greedily and towards the goal, which can make it faster to find the initial solution. An earlier initial solution can lead to a faster upper bound to define the informed set and start the convergence process earlier. The experiment results show that in different maps, GBIT* can find an initial solution faster than any other sampling-based asymptotically optimal planners, as well as RRT-Connect in most cases.
Ensemble Neural Networks with Random Weights for Classification Problems

To improve the prediction accuracy and stability of neural networks with random weights (NNRWs), we propose a novel ensemble NNRWs (E-NNRW) in this paper, which initializes its base learners by different distributions to improve their diversity. The final prediction results of the E-NNRW model are determined by these base learners through a voting mechanism, which minimizes the specific "blind zone" of a single learner, thus achieving higher prediction accuracy and better stability. Taking the random vector functional link network (RVFL), one of the most representative algorithms in NNRWs, as an example, we fully evaluate the performance of the proposed algorithm on nine benchmark classification problems. Extensive experimental results fully demonstrate the effectiveness of our method.
Green Computing in Cloud Computing

In this paper it is discussed how cloud computing harms the nature by emitting lots of harmful gases like carbon dioxide which harms the environment. There are lots of energy wasted and not used efficiently. Lots of processors and resources run for its entire lifetime and produces lots of heat. Therefore, here it is discussed how green computing in cloud computing can overcome this problems and save the nature by its eco-friendly way of cloud computing. At last, we will see the future scopes of green computing in cloud computing.
Robust Linearly Constrained Invariant Filtering for a Class of Mismatched Nonlinear Systems

Standard state estimation techniques require a perfect knowledge of the system's model, that is, process and measurement equations, inputs and the corresponding noise statistics. In practice this assumption does not hold and therefore robust filtering methods must be accounted for. A new approach to tackle a potential model mismatch via linear constraints within the Kalman filter, and its extended version (EKF), has been recently shown to be a very promising solution. This letter further explores robust linearly constrained filtering in the context of the Invariant EKF (InEKF). With respect to the EKF, the InEKF is a recent filtering technique which has been shown to better handle the particular structure of a class of problems through the use of Lie groups. In this contribution, a new robust linearly constrained InEKF is introduced, together with particular parametric mismatched models and mitigation strategies through linear constraints. Numerical results for an illustrative navigation example are provided to show the performance improvement and support the discussion.
A New Super Resolution Framework Based on Multi-Task Learning for Remote Sensing Images

Super-resolution (SR) algorithms based on deep learning have dominated in various tasks, including medical imaging, street view surveillance and face recognition. In the remote sensing field, most of the current SR methods utilize the low-resolution (LR) images that directly bicubic downsampled the high-resolution (HR) images as not only train set but also test set, thus achieving high PSNR/SSIM scores but showing performance drop in application because the degradation model in remote sensing images is subjected to Gaussian blur with unknown parameters. Inspired by multi-task learning strategy, we propose a multiple-blur-kernel super-resolution framework (MSF), in which a multiple-blur-kernel learning module (MLM) optimizes the parameters of the network transferable and sensitive for SR procedures with different blur kernels. Besides, to simultaneously exploit the prior of the large-scale remote sensing images and recurrent information in a single test image, a class-feature capture module (CCM) and an unsupervised learning module (ULM) are leveraged in our framework. Extensive experiments show that our framework outperforms the current state-of-the-art SR algorithms in remotely sensed imagery SR with unknown Gaussian blur kernel.
DILEMMA AND PATH SELECTION OF COLLEGE SPORTS RESOURCES INTO THE PUBLIC SERVICE SYSTEM OF NATIONAL FITNESS IN THE NEW ERA

ABSTRACT With the improvement of people's yearning for a healthy and beautiful life, national fitness has become a hot word in academic circles. Combining Data Envelopment Analysis (DEA) and the Tobit technology, this paper constructs the evaluation model of college sports resource efficiency based on the DEA Tobit model. Based on the establishment of the input-output index system of college sports resources, the model achieves the effect of accurate analysis on the allocation efficiency of university sports resources. Taking 10 universities in a city as experimental objects, the model is verified. The verification shows that the three efficiency indexes of STU University and IPE University in group M are all 1, which shows that the resource allocation is more reasonable; the comprehensive efficiency of Ju university is low, and the sports resources investment is excessive; in group n, except for the EU University, the efficiency of other colleges and universities is lower than the pass line. It can be concluded that there are problems such as excessive input of sports resources and low output rate in Colleges and universities of a city. Therefore, colleges and universities in a city should make full use of the existing resources, enhance their social sports guidance force, while improving the publicity of national fitness. This study has high reference significance for the path selection of national fitness integration in Colleges and Universities.
Overcoming the rate–distance limit of device-independent quantum key distribution: erratum

In this Erratum the funding and references sections of Opt. Lett. 46 , 1632 ( 2021 ) OPLEDP 0146-9592 10.1364/OL.417851 have been updated.
The Lesson Activities Map: A Domain-Specific Lesson Transcription Methodology

&lt;p style="text-align:justify"&gt;In educational research, audio-video recordings allow observing a lesson repeatedly. The collected data needs to be transcribed for analysis. Although methodologies for transcribing video-recorded lessons are established, there is lack of transcription methodologies for certain types of lessons, such as in arts education or the teaching to create new products. In our research project, we examine the teaching–learning of songs in class. Because of the absence of suitable transcription methodologies, we developed a new systematic approach. This paper presents the Lesson Activities Map (LAMap), which consists of symbols and icons representing graphically the constitutive elements of a domain-specific lesson. As a result, the LAMap provides a visualisation of the lesson content – in this context the song – and of how a teacher works on parts and the whole. The graphic representation supports the lesson analysis from different perspectives. The LAMap methodology and applications are valuable for transcribing other subject-specific lessons.&lt;/p&gt;
Artificial Intelligence and Unfair Competition – Unveiling an Underestimated Building Block of the AI Regulation Landscape

Abstract The article illustrates the underestimated role unfair competition law (UCL) can play as a building block of the regulatory landscape relating to artificial intelligence (AI). To this end, it examines to what extent overarching, prominent principles of AI regulation such as fairness, transparency, autonomy and innovation are reflected in paradigms of UCL, and on this basis evaluates how the latter can contribute to the realisation of the former. In this way, prominent problems raised by AI that are commonly discussed under different legal regimes are reconsidered from a UCL perspective, showing that this perspective may complement or even substitute traditional regulatory approaches. Finally, the article indicates how AI could inversely give an impulse to the doctrinal advancement of UCL as a still ambiguous and insufficiently understood body of law.
Implementation Of Data Mining In Determining Sales Pattern Of Snack Products Using Apriori Algorithm (Case Study: PT Siantar Top Tbk)

PT Siantar Top.Tbk is a company engaged in snack manufacturing which is located at Jl Raya Medan Tebing Tinggi, Ujung Serdang, Tanjung Morawa, Deli Serdang Regency, North Sumatra. In the sales transaction data processing process at PT Siantar Top Tbk, it has not been able to provide accurate information about the pattern or relationship of a set of items purchased by customers. So that the company has difficulty knowing every product that is sold, because the sales data is always increasing, but the company does not understand how to manage the sales data of these snack products. Because the snack product sales data is only archived and not managed by the company to get new results. The purpose of this research is to design and build a priori algorithm in determining sales patterns. This system is designed using UML and is built with the programming languages ​​PHP, HTML, CSS, Javascript and Mysql as the database. Then the determination of the sales pattern of snack products that are successful every month at PT Siantar Top Tbk using the Apriori algorithm. A priori algorithm is a data mining technique to find associative rules between a combination of items.
Adaptive exploitation of pre-trained deep convolutional neural networks for robust visual tracking

Due to the automatic feature extraction procedure via multi-layer nonlinear transformations, the deep learning-based visual trackers have recently achieved a great success in challenging scenarios for visual tracking purposes. Although many of those trackers utilize the feature maps from pre-trained convolutional neural networks (CNNs), the effects of selecting different models and exploiting various combinations of their feature maps are still not compared completely. To the best of our knowledge, all those methods use a fixed number of convolutional feature maps without considering the scene attributes (e.g., occlusion, deformation, and fast motion) that might occur during tracking. As a pre-requisition, this paper proposes adaptive discriminative correlation filters (DCF) based on the methods that can exploit CNN models with different topologies. First, the paper provides a comprehensive analysis of four commonly used CNN models to determine the best feature maps of each model. Second, with the aid of analysis results as attribute dictionaries, an adaptive exploitation of deep features is proposed to improve the accuracy and robustness of visual trackers regarding video characteristics. Third, the generalization of proposed method is validated on various tracking datasets as well as CNN models with similar architectures. Finally, extensive experimental results demonstrate the effectiveness of proposed adaptive method compared with the state-of-the-art visual tracking methods.
Determining a Return on Investment for Cybersecurity Technologies in Networked Critical Infrastructures

Much of modern life is dependent on networked critical infrastructure systems—many known to be susceptible to cyberattacks—such as the electrical grid, water purification, and transportation systems. The consequences of a successful cyberattack on these systems could be catastrophic. Appropriate levels and strategies for cybersecurity investment for networked critical infrastructures present a serious challenge that administering organizations, whether public or private, must overcome in order to provide resilient services. This challenge includes understanding the actual vulnerabilities of an organization's networked systems, as well as the cost of a successful cyberattack on those systems. On top of this, an organization's cybersecurity acquisition workforce must be able to discern reality from the marketing hype that is produced by cybersecurity sales forces. Many product offerings from industry promise to secure critical infrastructures, but there is no good method for determining which product (or combination of products) is most effective for a specific environment or scenario. This paper presents a return on cybersecurity investment (ROCI) model utilized, together with a previously-developed framework for evaluating cybersecurity technologies, by the resilient critical infrastructures through secure and efficient microgrids (ReCIst) capability. ReCIst uses this model to guide decision makers on how to best implement cybersecurity towards energy resiliency, from financial, security posture, and energy efficiency perspectives. Challenges and the current state of cyber investment modeling in this domain are presented along with technical details on ReCIst's ROCI model and future work.
Small Object Detection From Video and Classification Using Deep Learning

Object detection could be a technology associated with computer vision and image process that deals with crime squad work instances of semantics objects of a specific category in digital pictures and videos. Applications of object detection are in various fields of computer vision, together with image retrieval. In the existing work, SVM model has been applied to sight the article in CCTV videos, unlike many other classification or detection problems there is a strong real-time requirement for detecting. Hence, a trade-off between high accuracy and speed is inescapable. The main parameters that influence the performance are the length of the feature vector along with the algorithmic programme, and also, it is the best in two classifications. The proposed system introduces You Only Look Once (YOLO V2). YOLO V2 is highly efficient and quicker than the SVM. The simulated results set the threshold level to any confidence when compared to SVM.
Ball flight path determination in VR table tennis

This VR system simulates a table tennis game for adoption in training. The virtual environment allows a real player to play with a virtual AI opponent, with an update and display of the score they gained. Technical innovations are made in modelling of the multi-variant collision between the moving bat and ball with adjustable colliders, and determination of the ball flight path. We present the conceptualization of VR techniques, the detailed implementation of the system and comparative advantages.