Reviewerly/exHarmony huggingface dataset:
Shuffled ImageNet Banks for Video Event Detection and Search

This article aims for the detection and search of events in videos, where video examples are either scarce or even absent during training. To enable such event detection and search, ImageNet concept banks have shown to be effective. Rather than employing the standard concept bank of 1,000 ImageNet classes, we leverage the full 21,841-class dataset. We identify two problems with using the full dataset: (i) there is an imbalance between the number of examples per concept, and (ii) not all concepts are equally relevant for events. In this article, we propose to balance large-scale image hierarchies for pre-training. We shuffle concepts based on bottom-up and top-down operations to overcome the problems of example imbalance and concept relevance. Using this strategy, we arrive at the shuffled ImageNet bank, a concept bank with an order of magnitude more concepts compared to standard ImageNet banks. Compared to standard ImageNet pre-training, our shuffles result in more discriminative representations to train event models from the limited video event examples. For event search, the broad range of concepts enable a closer match between textual queries of events and concept detections in videos. Experimentally, we show the benefit of the proposed bank for event detection and event search, with state-of-the-art performance for both tasks on the challenging TRECVID Multimedia Event Detection and Ad-Hoc Video Search benchmarks.
IDEBench: A Benchmark for Interactive Data Exploration

In recent years, many query processing techniques have been developed to better support interactive data exploration (IDE) of large structured datasets. To evaluate and compare database engines in terms of how well they support such workloads, experimenters have mostly used self-designed evaluation procedures rather than established benchmarks. In this paper we argue that this is due to the fact that the workloads and metrics of popular analytical benchmarks such as TPC-H or TPC-DS were designed for traditional performance reporting scenarios, and do not capture distinctive IDE characteristics. Guided by the findings of several user studies we present a new benchmark called IDEBench, designed to evaluate database engines based on common IDE workflows and metrics that matter to the end-user. We demonstrate the applicability of IDEBench through a number of experiments with five different database engines, and present and discuss our findings.
Winning Is Not Everything: Enhancing Game Development With Intelligent Agents

Recently, there have been several high-profile achievements of agents learning to play games against humans and beat them. In this article, we study the problem of training intelligent agents in service of game development. Unlike the agents built to “beat the game,” our agents aim to produce human-like behavior to help with game evaluation and balancing. We discuss two fundamental metrics based on which we measure the human-likeness of agents, namely skill and style, which are multifaceted concepts with practical implications outlined in this article. We report four case studies in which the style and skill requirements inform the choice of algorithms and metrics used to train agents; ranging from A* search to state-of-the-art deep reinforcement learning (RL). Furthermore, we, show that the learning potential of state-of-the-art deep RL models does not seamlessly transfer from the benchmark environments to target ones without heavily tuning their hyperparameters, leading to linear scaling of the engineering efforts, and computational cost with the number of target domains.
FamilyStories: Asynchronous Audio Storytelling for Family Members Across Time Zones

Family members who are separated across time zones can easily miss out on feeling connected. We designed and studied the usage of an asynchronous storytelling system, called FamilyStories, to explore the use of audio-based sharing. FamilyStories allows family members to share activities and experiences over distance in different time zones using three different devices that contain different contextual features. To evaluate the design, we conducted a five-week long field study with two family member pairs. Our results show the value of slow, flexible, and non-suggestive interfaces for asynchronous audio communication. We also found ephemerality helped in the sharing of 'instant' feelings, while large time zone differences could be 'synchronized' with time delayed messages. We raise these as design opportunities for asynchronous audio storytelling systems.
Creating Value with Artificial Intelligence: A Multi-stakeholder Perspective

This article provides a brief overview of artificial intelligence (AI), and its perceived value-creation potential as well as its perceived risks from a multi-stakeholder perspective. It also draws some guidelines for exploring a high-level AI business strategy by taking a multi-stakeholder perspective into consideration, i.e., shareholders, customers, employees, suppliers and society. A short survey among European business professionals reveals a perception that AI serves mainly financial purposes and is perceived predominantly as creating value for shareholders and customers, while employees and society are perceived as negatively impacted stakeholders.
Projection Boxes: On-the-fly Reconfigurable Visualization for Live Programming

Live programming is a regime in which the programming environment provides continual feedback, most often in the form of runtime values. In this paper, we present Projection Boxes, a novel visualization technique for displaying runtime values of programs. The key idea behind projection boxes is to start with a full semantics of the program, and then use projections to pick a subset of the semantics to display. By varying the projection used, projection boxes can encode both previously known visualization techniques, and also new ones. As such, projection boxes provide an expressive and configurable framework for displaying runtime information. Through a user study we demonstrate that (1) users find projection boxes and their configurability useful (2) users are not distracted by the always-on visualization (3) a key driving force behind the need for a configurable visualization for live programming lies with the wide variation in programmer preferences.
Beyond the Selected Completely at Random Assumption for Learning from Positive and Unlabeled Data

Most positive and unlabeled data is subject to selection biases. The labeled examples can, for example, be selected from the positive set because they are easier to obtain or more obviously positive. This paper investigates how learning can be enabled in this setting. We propose and theoretically analyze an empirical-risk-based method for incorporating the labeling mechanism. Additionally, we investigate under which assumptions learning is possible when the labeling mechanism is not fully understood and propose a practical method to enable this. Our empirical analysis supports the theoretical results and shows that taking into account the possibility of a selection bias, even when the labeling mechanism is unknown, improves the trained classifiers.
Solving ill-posed problems faster using fractional-order Hopfield neural network

The Hopfield neural network (HNN) is one of the most used neural network architectures, it has been used to solve ill-posed problems with great success. Some works in recent years have incorporated fractional-order derivative with respect to time into original Hopfield neural network model. The motivation for this proposal is that it will be included nonlocal features in the original model. However, these studies were restricted to answer questions about the existence, uniqueness and stability of the equilibrium point for the neural network equation system used. The question that was addressed here is how the fractional-order Hopfield neural network (FHNN) can be used to solve ill-posed problems in Physical-Chemistry research field and how they compare to conventional model. For this purpose, fractional-order derivative with respect to time was included on HNN equations, obtained from Lyapunov function defined by 2-norm of the residual function. Therefore, the FHNN model used here was different from that used in previous work. Three prototype problems for the examination of the FHNN model were presented and discussed. In all of them, the solution found by FHNN model was achieved in less time when compared to HNN model. In this work, the Mittag-Leffler stability of the modified FHNN model is demonstrated and used to discuss how fractional order affects the solution. This work shows how the improvement in Hopfield neural network model, using fractional derivative order, gained competitive advantages over integer order Hopfield neural networks.
The Adversarial Robustness of Sampling

Random sampling is a fundamental primitive in modern algorithms, statistics, and machine learning, used as a generic method to obtain a small yet "representative" subset of the data. In this work, we investigate the robustness of sampling against adaptive adversarial attacks in a streaming setting: An adversary sends a stream of elements from a universe U to a sampling algorithm (e.g., Bernoulli sampling or reservoir sampling), with the goal of making the sample "very unrepresentative" of the underlying data stream. The adversary is fully adaptive in the sense that it knows the exact content of the sample at any given point along the stream, and can choose which element to send next accordingly, in an online manner. Well-known results in the static setting indicate that if the full stream is chosen in advance (non-adaptively), then a random sample of size Ω(d/ε2) is an ε-approximation of the full data with good probability, where d is the VC-dimension of the underlying set system (U, R). Does this sample size suffice for robustness against an adaptive adversary? The simplistic answer is negative : We demonstrate a set system where a constant sample size (corresponding to a VC-dimension of 1) suffices in the static setting, yet an adaptive adversary can make the sample very unrepresentative, as long as the sample size is (strongly) sublinear in the stream length, using a simple and easy-to-implement attack. However, this attack is "theoretical only", requiring the set system size to (essentially) be exponential in the stream length. This is not a coincidence: We show that in order to make the sampling algorithm robust against adaptive adversaries, the modification required is solely to replace the VC-dimension term d in the sample size with the cardinality term log |R|. That is, the Bernoulli and reservoir sampling algorithms with sample size Ω(log |R|/ε2) output a representative sample of the stream with good probability, even in the presence of an adaptive adversary. This nearly matches the bound imposed by the attack.
Automated Microfossil Identification and Segmentation using a Deep Learning Approach

Computational analysis applicability to paleontological images ranges from the study of the evolution of animals, plants and microorganisms to the habitat simulation of living beings from a specific epoch. It can also be applied in several niches, e.g. oil exploration, where several factors can be analyzed in order to minimize costs related to oil extraction. One specific factor is the characterization of the environment to be explored. This analysis can occur in several ways: use of probes, samples extraction, correlation with logs of other drilling wells and so on. During the samples extraction phase, the Computed Tomography (CT) is of extreme importance, since it preserves the sample and makes it available for several analyses. Based on 3D images generated by CT, analyses and simulations can be performed, and processes currently performed manually and exhaustively, can be automated. In this work, we propose and validate a method for fully automated microfossil identification and segmentation. A pipeline is proposed that begins with scanning and ends with the microfossil segmentation process. For the microfossil segmentation, a Deep Learning approach was developed, which resulted in a high rate of correct microfossil segmentation (98% IOU). The validation was performed both through an automated quantitative analysis and visual inspection. The study was performed on a limited dataset, but the results provide evidence that our approach has potential to be generalized to other carbonatic rock substrates. To the extent of the authors' knowledge, this paper presents the first fully annotated MicroCT acquired microfossils dataset made publicly available.
Review of High-Quality Random Number Generators

Abstract This is a review of pseudorandom number generators (RNG’s) of the highest quality, suitable for use in the most demanding Monte Carlo calculations. All the RNG’s we recommend here are based on the Kolmogorov–Anosov theory of mixing in classical mechanical systems, which guarantees under certain conditions and in certain asymptotic limits, that points on the trajectories of these systems can be used to produce random number sequences of exceptional quality. We outline this theory of mixing and establish criteria for deciding which RNG’s are sufficiently good approximations to the ideal mathematical systems that guarantee highest quality. The well-known RANLUX (at highest luxury level) and its recent variant RANLUX++ are seen to meet our criteria, and some of the proposed versions of MIXMAX can be modified easily to meet the same criteria.
Bitcoin-Compatible Virtual Channels

Current permissionless cryptocurrencies such as Bitcoin suffer from a limited transaction rate and slow confirmation time, which hinders further adoption. Payment channels are one of the most promising solutions to address these problems, as they allow the parties of the channel to perform arbitrarily many payments in a peer-to-peer fashion while uploading only two transactions on the blockchain. This concept has been generalized into payment channel networks where a path of payment channels is used to settle the payment between two users that might not share a direct channel between them. However, this approach requires the active involvement of each user in the path, making the system less reliable (they might be offline), more expensive (they charge fees per payment), and slower (intermediaries need to be actively involved in the payment). To mitigate this issue, recent work has introduced the concept of virtual channels (IEEE S&P’19), which involve intermediaries only in the initial creation of a bridge between payer and payee, who can later on independently perform arbitrarily many off-chain transactions. Unfortunately, existing constructions are only available for Ethereum, as they rely on its account model and Turing-complete scripting language. The realization of virtual channels in other blockchain technologies with limited scripting capabilities, like Bitcoin, was so far considered an open challenge.In this work, we present the first virtual channel protocols that are built on the UTXO-model and require a scripting language supporting only a digital signature scheme and a timelock functionality, being thus backward compatible with virtually every cryptocurrency, including Bitcoin. We formalize the security properties of virtual channels as an ideal functionality in the Universal Composability framework and prove that our protocol constitutes a secure realization thereof. We have prototyped and evaluated our protocol on the Bitcoin blockchain, demonstrating its efficiency: for n sequential payments, they require an off-chain exchange of 9+2n transactions or a total of 3524+695n bytes, with no on-chain footprint in the optimistic case. This is a substantial improvement compared to routing payments in a payment channel network, which requires 8n transactions with a total of 3026n bytes to be exchanged.
RNN-Based online anomaly detection in nuclear reactors for highly imbalanced datasets with uncertainty

Accurate online condition monitoring and anomaly detection are crucial in nuclear applications to optimize economic performance and minimize safety risks. To achieve this goal, several major challenges exist which must be addressed. First, multi-sensor signals are often collected in the form of complex, multivariate time series. Second, relatively few anomaly records are available to train detection models. Lastly, the recorded data may contain uncertainties resulting from various sources, such as operator-induced variability and measurement error. In this paper, a recurrent neural network-based approach is proposed to tackle these issues by effectively utilizing historical data obtained during both normal and abnormal operations. Several advanced data preprocessing techniques are developed to improve the training process of the proposed neural network. The efficiency and sensitivity of the proposed method are evaluated on the multi-sensor signal measurements and operational reports obtained from a real case study. The results demonstrate much improved detection accuracy and practicality of the proposed method over conventional approaches.
CrossNet++: Cross-Scale Large-Parallax Warping for Reference-Based Super-Resolution

The ability of camera arrays to efficiently capture higher space-bandwidth product than single cameras has led to various multiscale and hybrid systems. These systems play vital roles in computational photography, including light field imaging, 360 VR camera, gigapixel videography, etc. One of the critical tasks in multiscale hybrid imaging is matching and fusing cross-resolution images from different cameras under perspective parallax. In this paper, we investigate the reference-based super-resolution (RefSR) problem associated with dual-camera or multi-camera systems. RefSR consists of super-resolving a low-resolution (LR) image given an external high-resolution (HR) reference image, where they suffer both a significant resolution gap ( 8×) and large parallax ( ∼ 10% pixel displacement). We present CrossNet++, an end-to-end network containing novel two-stage cross-scale warping modules, image encoder and fusion decoder. The stage I learns to narrow down the parallax distinctively with the strong guidance of landmarks and intensity distribution consensus. Then the stage II operates more fine-grained alignment and aggregation in feature domain to synthesize the final super-resolved image. To further address the large parallax, new hybrid loss functions comprising warping loss, landmark loss and super-resolution loss are proposed to regularize training and enable better convergence. CrossNet++ significantly outperforms the state-of-art on light field datasets as well as real dual-camera data. We further demonstrate the generalization of our framework by transferring it to video super-resolution and video denoising.
Alpha-cut representation used for defuzzification in rule-based systems

Alpha-cut representation of fuzzy sets has been used as a basis for fuzzy numbers ranking in some applications but rarely used for defuzzification of rule-based systems or fuzzy controllers. Moreover, such alpha-cut defuzzification (called ACD here) is not yet formally linked to the membership function (MF) or to the common MF-based defuzzification methods, namely the centroid. The ACD can be considered as a generalisation of the similar algorithms in fuzzy numbers to any fuzzy set. A close-form formula for ACD is developed that involves both MF and its derivative, which shows that ACD reflects both static and dynamic aspects of a fuzzy set. Moreover, formal links between ACD and some MF-based defuzzification methods are shown. Through two groups of experiments, the utility of the new method is compared with centroid defuzzification. Particularly, we examined how the ACD significantly outperforms the centroid for noisy time-series prediction. Finally, the computation complexity of ACD is shown to be about the same as the centroid method, for convex fuzzy sets. Our tests suggest that ACD can be considered as a viable alternative defuzzification method for fuzzy system designers.
A secure electronic medical record authorization system for smart device application in cloud computing environments

Abstract As cloud computing technology matures, along with an increased application of distributed networks, increasingly larger amounts of data are being stored in the cloud, and are thus available for pervasive application. At the same time, current independent medical record systems tend to be inefficient, and most previous studies in this field fail to meet the security requirements of anonymity and unlinkability. Some proposed schemes are even vulnerable to malicious impersonation attacks. The scheme proposed in this study, therefore, combines public and private clouds in order to more efficiently and securely preserve and manage electronic medical records (EMR). In this paper, a new secure EMR authorization system is proposed, which uses elliptic curve encryption and public-key encryption, providing a health care system with both public and private cloud environments with a message authentication mechanism, allowing the secure sharing of medical resources. The analysis shows that the proposed scheme prevents known attacks, such as replay attacks, man-in-the-middle attacks and impersonation attacks, and provides user anonymity, unlinkability, integrity, non-repudiation, forward and backward security.
Multimodal estimation and communication of latent semantic knowledge for robust execution of robot instructions

The goal of this article is to enable robots to perform robust task execution following human instructions in partially observable environments. A robot’s ability to interpret and execute commands is fundamentally tied to its semantic world knowledge. Commonly, robots use exteroceptive sensors, such as cameras or LiDAR, to detect entities in the workspace and infer their visual properties and spatial relationships. However, semantic world properties are often visually imperceptible. We posit the use of non-exteroceptive modalities including physical proprioception, factual descriptions, and domain knowledge as mechanisms for inferring semantic properties of objects. We introduce a probabilistic model that fuses linguistic knowledge with visual and haptic observations into a cumulative belief over latent world attributes to infer the meaning of instructions and execute the instructed tasks in a manner robust to erroneous, noisy, or contradictory evidence. In addition, we provide a method that allows the robot to communicate knowledge dissonance back to the human as a means of correcting errors in the operator’s world model. Finally, we propose an efficient framework that anticipates possible linguistic interactions and infers the associated groundings for the current world state, thereby bootstrapping both language understanding and generation. We present experiments on manipulators for tasks that require inference over partially observed semantic properties, and evaluate our framework’s ability to exploit expressed information and knowledge bases to facilitate convergence, and generate statements to correct declared facts that were observed to be inconsistent with the robot’s estimate of object properties.