Reviewerly/exHarmony huggingface dataset:
Subspace reconstruction based correlation filter for object tracking

The correlation filter (CF) achieves excellent performance, showing high robustness to motion blur or illumination change by learning filters. However, tracking in challenging scenarios with occlusion or out-of-view is still not well resolved. In the scenario of occlusion, the background information is mixed into the image patch to learn the filter, which causes the filter to learn the background. To alleviate this problem, we improve CF trackers by proposing the subspace reconstruction based CF (SRBCF) tracker. In our method, the original image patch for learning filters is replaced by a reconstructed patch when the appearance of the object dramatically changes, such as occlusion or disappearance, so that the filter can learn from the object instead of the background. We construct the subspace with image patches of the searching window in previous frames. To track the changes in the subspace and mitigate the adverse effects of outliers on the subspace during the tracking process, we improve a dynamic L1-PCA method to construct and update the subspace with a slightly extra computational cost. Our method can be embedded in various correlation filter trackers, such as STAPLE and KCF. Extensive experiments on the OTB-100 dataset, UAV123, DTB70, and Temple Color Pure dataset (we removed 49 sequences repeated in OTB-100) validate the effectiveness of our method. The maximum AUC increase reaches 11.2% for the baseline method on DTB70.
Neural Network Computations with DOMINATION Functions

We study a new representation of neural networks based on DOMINATION functions. Specifically, we show that a threshold function can be computed by its variables connected via an unweighted bipartite graph to a universal gate computing a DOMINATION function. The DOMINATION function consists of fixed weights that are ascending powers of 2. We derive circuit-size upper and lower bounds for circuits with small weights that compute DOMINATION functions. Interestingly, the circuit-size bounds are dependent on the sparsity of the bipartite graph. In particular, functions with sparsity 1 (like the EQUALITY function) can be implemented by small-size constant-weight circuits.
Private and Secure Coded Computation in Straggler-Exploiting Distributed Matrix Multiplication

In this paper, we consider coded computation for matrix multiplication tasks in distributed computing, which can mitigate the effect of slow workers, called stragglers, by a coding approach. We assume that the stragglers' computation results can be leveraged at the master by assigning multiple sub-tasks to the workers. In this scenario, we propose a new coded computation scheme to preserve the data privacy and security from the non-colluding workers. We also prove that the data privacy and security constraints are satisfied in our scheme in an information-theoretic sense.
The Current State of The Art and Future of European Cyber Range Ecosystem

The Cyber Range Focus Group (CRFG) is established in the context of the four Horizon 2020 pilots contributing to the establishment of a European Cybersecurity Competence Network, with the purpose to horizontally coordinate the activities related to cyber ranges across the four pilots and contribute to the creation of a European ecosystem for cyber ranges, bringing together providers of both range infrastructure and range content. As the cyber domain is a non-deterministic complex one with a constantly evolving knowledge and threat landscape, simulation environments are emerging as the means to raise cyber resilience and dexterity. The EU cyber range ecosystem is constantly developing as the services become more affordable and accessible for more organizations and individuals through open-source technologies and federation of those resources. In contrast, a cyber range ecosystem-focused marketplace is foreseen to boost the market implementation, accessibility, and affordability.
Development and Evaluation of Prenatal Education for Environmental Health Behavior Using Cartoon Comics

This study aimed to develop and examine the effects of a prenatal program on environmental health behavior using cartoon comics among Korean pregnant women.This study used a non-equivalent control group pre-test/post-test design. The program used cartoon comics to explore environmental health behaviors during pregnancy. The program consisted of the following four components: environmental toxicants during pregnancy, avoiding particulate matter during pregnancy, environmental toxicants during baby care, and making a healthy environment for children. In total, 35 pregnant women participated in the study: 18 in the experimental group and 17 in the control group. Data collection and program adaptation were conducted between November 3, 2020 and January 19, 2021. The effect of the prenatal education program was evaluated by t-test and repeated measures ANOVA.Learning experience (t = - 2.35, p = .025), feasibility (t = - 2.46, p = .019), satisfaction (t = - 2.23, p = .032) were higher in the experimental group than in the control group in the first post-test. Feasibility (t = - 2.40, p = .022) was higher in the experimental group than in the control group in the second post-test. Repeated-measures ANOVA showed significant interactions between time and group in environmental susceptibility (F = 9.31, p < .001), self-efficacy (F = 3.60, p = .033), and community behavior (F = 5.41, p = .007).This study demonstrates the need for a prenatal education program to promote environmental health perceptions and behavior during pregnancy. We suggest a prenatal class adopting the creative cartoon comics to promote the maternal environmental health behaviors.
COSINE: Compressive Network Embedding on Large-Scale Information Networks

There is recently a surge in approaches that learn low-dimensional embeddings of nodes in networks. However, for large-scale real-world networks, it’s inefficient for existing approaches to store amounts of parameters in memory and update them edge by edge. With the knowledge that nodes having similar neighborhoods will be close to each other in the embedding space, we propose COSINE (COmpresSIve Network Embedding) algorithm, which reduces the memory footprint and accelerates the training process by parameter sharing among similar nodes. COSINE applies graph partitioning algorithms to networks and builds parameter sharing dependency of nodes based on the results of partitioning. In this way, COSINE injects prior knowledge about high-order structural information into models, which makes network embedding more efficient and effective. COSINE can be applied to any <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">embedding lookup</i> method and learn high-quality embeddings with limited memory and less training time. We conduct experiments on multi-label classification and link prediction, where baselines and our model have the same memory usage. Experimental results show that COSINE improves baselines by up to 23 percent on classification and 25 percent on link prediction. Moreover, the training time of all representation learning methods using COSINE decreases by 30 to 70 percent.
Equivalence of Non-Perfect Secret Sharing and Symmetric Private Information Retrieval with General Access Structure

We study the equivalence between non-perfect secret sharing (NSS) and symmetric private information retrieval (SPIR) with colluding and unresponsive servers. We prove the equivalence between NSS and SPIR in the following two senses. 1) Given any SPIR protocol, we can construct an NSS protocol. 2) Given any linear NSS protocol, we can construct a SPIR protocol. We prove the first relation even if the SPIR protocol has imperfect correctness and secrecy. From the first relation, we derive an upper bound of the SPIR capacity for general access structure. For the special case of n-server SPIR with r responsive and t colluding servers, this upper bound proves that the SPIR capacity is (r-t)/n. From the second relation, we prove that a SPIR protocol exists for any access structure.
History, Deployment and Service Models Towards the Evolution of Cloud Computing

The network concerned usually, though certainly not always, is the Internet. The sources provisioned cover a stable of services consisting of information, software program, storing, safety, etc. As an example, when we utilize a post including Gmail, enjoy a film on YouTube, shop at Amazon.com, or establishment files making use of DropBox, our team are utilizing cloud-based information (The Google Chrome Group, 2010). Within this phase, we examine the development of Cloud Computing from its own very early roots in mainframe-based computing to the present day and also clarify the different companies provided by Cloud Computing in today's company and also private computing situations. This paper provides an overview of the history, deployment and service models towards the evolution of cloud computing.
The Simplest Thing That Can Possibly Work: (Pseudo-)Relevance Feedback via Text Classification

Motivated by recent commentary that has questioned today's pursuit of ever-more complex models and mathematical formalisms in applied machine learning and whether meaningful empirical progress is actually being made, this paper tackles the decades-old problem of pseudo-relevance feedback with "the simplest thing that can possibly work". We present a technique based on training a document relevance classifier for each information need using pseudo-labels from an initial ranked list and then applying the classifier to rerank the retrieved documents. Experiments demonstrate significant improvements across a number of standard newswire collections, with initial rankings supplied by bag-of-words BM25 as well as from query expansion. Further evaluations in the TREC-COVID challenge using human relevance judgments verify the effectiveness and robustness of our proposed technique. While this simple idea draws elements from several well-known threads in the literature, to our knowledge this exact combination has not previously been proposed and rigorously evaluated.
Foiling zero-error attacks against coherent-one-way quantum key distribution

To protect practical quantum key distribution (QKD) against photon-number-splitting attacks, one could measure the coherence of the received signals. One prominent example that follows this approach is coherent-one-way (COW) QKD, which is commercially available. Surprisingly, however, it has been shown very recently that its secret key rate scales quadratically with the channel transmittance and thus this scheme is unsuitable for long-distance transmission. This result was derived by using a zero-error attack, which prevents the distribution of a secure key without introducing any error. Here we study various countermeasures to foil zero-error attacks against COW QKD. They require either monitoring some additional available detection statistics or increasing the number of quantum states emitted. We obtain asymptotic upper security bounds on the secret key rate that scale close to linearly with the channel transmittance, thus suggesting the effectiveness of the countermeasures to boost the performance of this protocol.
Quantum steering over an entangled network that is generated via Dipolar interaction

The possibility of steering a member by another one of a quantum network that is generated via Dipolar interaction is discussed. It is shown that, the steerability of direct connected members is much larger than that depicted for the indirect connected members. Although the steerability degree decreases as the interaction time increases, the regions in which the steering is possible increases. The violation and satisfaction of the steerability are displayed periodically as the interaction time increases. It is shown that, the steerability and its degree increase as the quantum correlation between the steerer and the steered member increases. Consequently, the strength of Dipolar interaction could be considered as a control parameter to maximize/minimize the steerability.
HAnS

When developers maintain or evolve software, they often need to know the locations of features. This proves challenging when the feature locations are not documented, when the code was written by different developers who may have left the organization, or when the developer's memory of the implementation has faded. Automated feature location techniques are hard to adopt in practice, especially since they boast too many false positives. To address these challenges, embedded feature annotations have been proposed to allow developers to trace features in code during development with minimal effort. However, tool support is needed for developers to effectively record and use these annotations. We propose HAnS as a tool to meet this need; it is implemented as an IntelliJ IDE plugin to support developers seamlessly record feature locations while they write their code. HAnS supports developers when mapping features to software assets, such as files and code fragments, with code completion and syntax highlighting. It also provides functionality to browse feature definitions and locations, as well as refactor features. A demo video is available at https://youtu.be/cx_-ZshHLgA.
A User-based Mid-air Hand Gesture Set for Spreadsheets

The spreadsheet is one of the most popular general user interfaces. Table manipulation, such as cell insertion and removal, is usually performed by selecting a menu command or using shortcut keys. However, the enormous number of shortcut keys makes them difficult to learn and remember. We sought to design a mid-air hand gesture-based interface for table manipulation in spreadsheet applications. As hand gestures and table manipulations are both spatial in nature, there should be a natural mapping between them. In this paper, we performed two user studies to derive a user-defined gesture set allowing 13 types of table manipulation. In the first study, we collected mid-air hand gestures devised by participants for manipulations implemented in existing spreadsheet software. In the second study, we asked other participants to vote for their most preferred gestures. We analyzed the characteristics of the collected gestures and votes, and created a final user-defined gesture set.
Graph-LSTM with Global Attribute for Scene Graph Generation

Abstract Lots of machine learning tasks require dealing with graph data, and among them, scene graph generation is a challenging one that calls for graph neural networks’ potential ability. In this paper, we present a definition of graph neural network (GNN) consists of node, edge and global attribute, as well as their corresponding update and aggregate functions. Based on this, we then propose a realization of GNN model called Graph-LSTM and use it in scene graph generation. The model first extracts the item features in the image as the initial states of the node-LSTM representing subject/object and edge-LSTM representing predicate. Two LSTMs update the states via LSTM’s timestep and aggregate information via message passing. Repeat the update-aggregate until convergence. Meanwhile, the tag feature, i.e., the generated probability distribution of image’s semantic concepts is sent to the LSTM through a semantic compositional network (SCN). The SCN-LSTM is trained in an ensemble style, and hence allows the tag feature to serve as the global attribute providing context information to all individuals. The LSTMs’ final states are input to inference modules and generate the triplet ( subject, predicate, object ) of the scene graph. Experimental results show that Graph-LSTM outperforms the Message Passing and the attention Graph Covolutional Network methods, proving the effectiveness of the proposed scheme.
Human Pose Estimation based on Attention Multi-resolution Network

Recently, multi-resolution neural networks, which combine features of different resolutions, have achieved good results in human pose estimation tasks. In this paper, we propose an attention-mechanism-based multi-resolution network, which adds an attention mechanism to the High-Resolution Network (HRNet) to enhance the feature representation of the network. It improves the ability of networks with different resolutions to extract key features from images, and causes the output to contain more effective multi-resolution representation information, so that the corresponding point positions of human joints can be estimated more accurately. Experiments on the MPII and COCO datasets, and verification on the MPII datasets, obtained an average accuracy of 90.3% under the [email protected] evaluation standard, and good results were also achieved on the COCO dataset (with an AP of 76.5). The experimental results show that our network model is effective in improving the accuracy of key point estimation in the human pose estimation task.
Ketchup GAN: A New Dataset for Realistic Synthesis of Letters on Food

This paper introduces a new dataset for the realistic synthesis of letters on food. Specifically, the "Ketchup GAN" dataset consists of real-world images of egg omelettes decorated with ketchup letters. Our dataset contains sufficient size and variety to train and evaluate deep learning-based generative models. In addition, we generate a synthetic ketchup-free set, which enables us to train paired-based generative adversarial networks (GAN). The ketchup GAN dataset comprises more than two thousand images of omelette dishes collected from Twitter. Automatically generated segmentation masks of egg and ketchup are also provided as part of the dataset. Thus, we can evaluate generative models based on segmentation inputs as well. With our dataset, two state-of-the-art GAN models (Pix2Pix and SPADE) are reviewed on photorealistic ketchup letter synthesis. We finally present an automatic application of omelette decoration with ketchup text input from users. The dataset and more details are publicly available at https://mm.cs.uec.ac.jp/omrice/.
Agile Principles in Automotive Software Development: Analysis of Potential Levers

Cars are becoming software-defined products and mobility is in a transition to become a digital network business. This leads to a high pressure on automotive software developers to deliver more and more complex software in ever faster innovation cycles. The classical development process of a car is a holistic and complex systems engineering approach which delivers a fully developed car (mechanics, electrics, electronics and software) after several years of development. This car will be produced (without major changes) for many years. The automotive industry mainly applies the V-model, a complex and much elaborated development process which systematically leads from a comprehensive set of requirements to a car which exactly fits the requirements. Very much different from this, agile development methodology starts with very fuzzy and uncertain requirements and drives the development via incremental improvements with close customer integration towards a product which might deviate a lot from the initial idea. For a number of reasons, this purely agile approach is not possible for automotive development. Therefore, ongoing research is conducted on integrating agile methods into the V-model – an approach which isn’t foreseen by the Agile Manifesto. Nevertheless, substantial progress is made and the automotive industry put big hopes into this approach. This contribution identifies four promising approaches for the integration of agile methods and principles into V-model. The approaches are analysed based on a literature review. A first set of hypotheses about the possible improvement and leverage for the automotive software development is developed. The hypotheses are tested in an early validation step with industry experts in order to support the design of experiments and surveys for further research.
Bee Intelligence-Guided Partitional Clustering for Outlier Detection

Parts of data with extreme or uncommon behavior are called outliers. Outlier research is a special area of interest. A variety of techniques have existed in literature. Clustering is one such method to separate outliers from data. However, the existence of outliers itself causes the clustering process complicated. Initialization, position, and the number of cluster centers are key factors for a clustering process. The existing clustering methods are suffering to deal with these issues. Optimization plays a vital role to improve cluster methods. This paper aims to set optimized initial cluster centers to improve clustering results and to separate outlier records through this clustering. Bee colony optimization is embedded into cluster initialization processes. Using this, phase-wise boosted subsets of data from a dataset are processed to optimize cluster centers. Here bee optimization is supposed to improve cluster center initializations. With improved cluster initializations, the partitional clustering process is followed. Outliers are identified through statistical analysis of clustering results. Two legendary datasets from the UCI machine learning repository i.e. Iris data set and Twitter Data set are used to test the proposed methods. The results are encouraging, and the proposed processes are better for clustering and outlier detection that provides finer separation of outliers from data.
Cognitive architectures for artificial intelligence ethics

Abstract As artificial intelligence (AI) thrives and propagates through modern life, a key question to ask is how to include humans in future AI? Despite human involvement at every stage of the production process from conception and design through to implementation, modern AI is still often criticized for its “black box” characteristics. Sometimes, we do not know what really goes on inside or how and why certain conclusions are met. Future AI will face many dilemmas and ethical issues unforeseen by their creators beyond those commonly discussed (e.g., trolley problems and variants of it) and to which solutions cannot be hard-coded and are often still up for debate. Given the sensitivity of such social and ethical dilemmas and the implications of these for human society at large, when and if our AI make the “wrong” choice we need to understand how they got there in order to make corrections and prevent recurrences. This is particularly true in situations where human livelihoods are at stake (e.g., health, well-being, finance, law) or when major individual or household decisions are taken. Doing so requires opening up the “black box” of AI; especially as they act, interact, and adapt in a human world and how they interact with other AI in this world. In this article, we argue for the application of cognitive architectures for ethical AI. In particular, for their potential contributions to AI transparency, explainability, and accountability. We need to understand how our AI get to the solutions they do, and we should seek to do this on a deeper level in terms of the machine-equivalents of motivations, attitudes, values, and so on. The path to future AI is long and winding but it could arrive faster than we think. In order to harness the positive potential outcomes of AI for humans and society (and avoid the negatives), we need to understand AI more fully in the first place and we expect this will simultaneously contribute towards greater understanding of their human counterparts also.