Reviewerly/exHarmony huggingface dataset:
Communication-Efficient Distributed SGD with Error-Feedback, Revisited

We show that the convergence proof of a recent algorithm called dist-EF-SGD for distributed stochastic gradient descent with communication efficiency using error-feedback of Zheng et al. (NeurIPS 2019) is problematic mathematically. Concretely, the original error bound for arbitrary sequences of learning rate is unfortunately incorrect, leading to an invalidated upper bound in the convergence theorem for the algorithm. As evidences, we explicitly provide several counter-examples, for both convex and non-convex cases, to show the incorrectness of the error bound. We fix the issue by providing a new error bound and its corresponding proof, leading to a new convergence theorem for the dist-EF-SGD algorithm, and therefore recovering its mathematical analysis.
PS-RCNN: Detecting Secondary Human Instances in a Crowd via Primary Object Suppression

Detecting human bodies in highly crowded scenes is a challenging problem. Two main reasons result in such a problem: 1). weak visual cues of heavily occluded instances can hardly provide sufficient information for accurate detection; 2). heavily occluded instances are easier to be suppressed by Non-Maximum-Suppression (NMS). To address these two issues, we introduce a variant of two-stage detectors called PS-RCNN. PS-RCNN first detects slightly/none occluded objects by an R-CNN module (referred as P-RCNN), and then suppress the detected instances by human-shaped masks so that the features of heavily occluded instances can stand out. After that, PS-RCNN utilizes another R-CNN module specialized in heavily occluded human detection (referred as S-RCNN) to detect the rest missed objects by P-RCNN. Final results are the ensemble of the outputs from these two R-CNNs. Moreover, we introduce a High Resolution RoI Align (HRRA) module to retain as much of fine-grained features of visible parts of the heavily occluded humans as possible. Our PS-RCNN significantly improves recall and AP by 4.49% and 2.92% respectively on CrowdHuman, compared to the baseline. Similar improvements on Widerperson are also achieved by the PS-RCNN.
Diffusion-based Deep Active Learning

The remarkable performance of deep neural networks depends on the availability of massive labeled data. To alleviate the load of data annotation, active deep learning aims to select a minimal set of training points to be labelled which yields maximal model accuracy. Most existing approaches implement either an `exploration'-type selection criterion, which aims at exploring the joint distribution of data and labels, or a `refinement'-type criterion which aims at localizing the detected decision boundaries. We propose a versatile and efficient criterion that automatically switches from exploration to refinement when the distribution has been sufficiently mapped. Our criterion relies on a process of diffusing the existing label information over a graph constructed from the hidden representation of the data set as provided by the neural network. This graph representation captures the intrinsic geometry of the approximated labeling function. The diffusion-based criterion is shown to be advantageous as it outperforms existing criteria for deep active learning.
Incorporating Relational Background Knowledge into Reinforcement Learning via Differentiable Inductive Logic Programming

Relational Reinforcement Learning (RRL) can offers various desirable features. Most importantly, it allows for incorporating expert knowledge into the learning, and hence leading to much faster learning and better generalization compared to the standard deep reinforcement learning. However, most of the existing RRL approaches are either incapable of incorporating expert background knowledge (e.g., in the form of explicit predicate language) or are not able to learn directly from non-relational data such as image. In this paper, we propose a novel deep RRL based on a differentiable Inductive Logic Programming (ILP) that can effectively learn relational information from image and present the state of the environment as first order logic predicates. Additionally, it can take the expert background knowledge and incorporate it into the learning problem using appropriate predicates. The differentiable ILP allows an end to end optimization of the entire framework for learning the policy in RRL. We show the efficacy of this novel RRL framework using environments such as BoxWorld, GridWorld as well as relational reasoning for the Sort-of-CLEVR dataset.
Python and OpenCV in Automation of Live Surveillance

There have been many uses of image recognition and feature detection in recent days. With the growth in the popularity of Python and simplification of automation allows us to bring the live surveillance in the domain. Nowadays, the situation, with the sectors like banking, needs high amount of security due to their increasing importance. Especially on remote location where services like ATM are provided, the security factor becomes main concern. By automation of surveillance, there will be an efficient way to reduce stress on security.
Spatial-Temporal Fusion Convolutional Neural Network for Compressed Video Enhancement in HEVC

Convolutional neural network has witnessed remarkable progress in compressed video quality enhancement in high efficiency video coding (HEVC) standard. But most existing methods focus on single frame quality enhancement where copious temporal and spatial information is neglected. In this paper, we propose a spatial-temporal fusion convolutional neural network (STEF-CNN) to employ spatial and temporal information to improve the performance of in-loop filter in HEVC. Specifically, the STEF-CNN adopts a pre-denoising network which in advance processes the compressed videos frame by frame. The pre-denoising operation alleviates the impact of noise and blocking artifacts. Then the denoised frames are sent to temporal-spatial fusion module which picks out valuable temporal and spatial information. The fused frames are eventually fed to quality enhancement network which is based on residual learning and dense network. The STEF-CNN is capable of capturing abundant information from consecutive neighboring frames. Extensive experimental results demonstrate the effectiveness of the proposed method. The STEF-CNN achieves 11.53% BD-BR reduction in all-intra (AI) configuration and 10.20% BD-BR reduction in random-access (RA) configuration.
Reservoir Computing with Planar Nanomagnet Arrays

Reservoir computing is an emerging methodology for neuromorphic computing that is especially well-suited for hardware implementations in size, weight, and power (SWaP) constrained environments. This work proposes a novel hardware implementation of a reservoir computer using a planar nanomagnet array. A small nanomagnet reservoir is demonstrated via micromagnetic simulations to be able to identify simple waveforms with 100% accuracy. Planar nanomagnet reservoirs are a promising new solution to the growing need for dedicated neuromorphic hardware.
NMS by Representative Region: Towards Crowded Pedestrian Detection by Proposal Pairing

Although significant progress has been made in pedestrian detection recently, pedestrian detection in crowded scenes is still challenging. The heavy occlusion between pedestrians imposes great challenges to the standard Non-Maximum Suppression (NMS). A relative low threshold of intersection over union (IoU) leads to missing highly overlapped pedestrians, while a higher one brings in plenty of false positives. To avoid such a dilemma, this paper proposes a novel Representative Region NMS approach leveraging the less occluded visible parts, effectively removing the redundant boxes without bringing in many false positives. To acquire the visible parts, a novel Paired-Box Model (PBM) is proposed to simultaneously predict the full and visible boxes of a pedestrian. The full and visible boxes constitute a pair serving as the sample unit of the model, thus guaranteeing a strong correspondence between the two boxes throughout the detection pipeline. Moreover, convenient feature integration of the two boxes is allowed for the better performance on both full and visible pedestrian detection tasks. Experiments on the challenging CrowdHuman and CityPersons benchmarks sufficiently validate the effectiveness of the proposed approach on pedestrian detection in the crowded situation.
Computer-Supported Exploration of a Categorical Axiomatization of Modeloids

A modeloid, a certain set of partial bijections, emerges from the idea to abstract from a structure to the set of its partial automorphisms. It comes with an operation, called the derivative, which is inspired by Ehrenfeucht-Fraïssé games. In this paper we develop a generalization of a modeloid first to an inverse semigroup and then to an inverse category using an axiomatic approach to category theory. We then show that this formulation enables a purely algebraic view on Ehrenfeucht-Fraïssé games.
Ruins of the smart city: a visual intervention

The visual imaginary of the future city is increasingly dichotomized between visions of hyper-technological digital urbanism and the city in a state of ruin, without people, overtaken by nature. These alternating imaginaries key into concerns over urban futures, as questions of sustainability and rising inequality come to bear on urban life. Such binary imaginaries produce volumes of visual material, lauding and critiquing philosophies of newness, endless progress and the city without decline. This article uses an inventive visual methodology to ask how these imaginaries become situated in the everyday ecologies of living. This methodology focuses on several so-called ‘brownfield’ sites in Salford, UK, and the ‘smart’ Oxford Road Corridor in neighbouring Manchester, to playfully and visually map the entanglement of digital urban ecologies through the themes of wilderness, play and compost. These three themes relate to the pleasure of urban wilderness described by Rose Macaulay, reflecting on London’s wild ruins after the Second World War; the playful contrast between smart urbanism and urban wastelands, understood through interdisciplinary visual methods; and Haraway’s notion of compost as the fertile ground of collaboration that marks a material–semiotic entanglement between place, people and nature. The authors investigate how these frameworks reflect the diversity of urban ecology (animals, plants and humans) and might provide an alternative vision of how the city could be, a vision built from how the city currently is.
LoGAN: Latent Graph Co-Attention Network for Weakly-Supervised Video Moment Retrieval

The goal of weakly-supervised video moment retrieval is to localize the video segment most relevant to the given natural language query without access to temporal annotations during training. Prior strongly- and weakly-supervised approaches often leverage co-attention mechanisms to learn visual-semantic representations for localization. However, while such approaches tend to focus on identifying relationships between elements of the video and language modalities, there is less emphasis on modeling relational context between video frames given the semantic context of the query. Consequently, the above-mentioned visual-semantic representations, built upon local frame features, do not contain much contextual information. To address this limitation, we propose a Latent Graph Co-Attention Network (LoGAN) that exploits fine-grained frame-by-word interactions to reason about correspondences between all possible pairs of frames, given the semantic context of the query. Comprehensive experiments across two datasets, DiDeMo and Charades-Sta, demonstrate the effectiveness of our proposed latent co-attention model where it outperforms current state-of-the-art (SOTA) weakly-supervised approaches by a significant margin. Notably, it even achieves a 11% improvement to Recall@1 accuracy over strongly-supervised SOTA methods on DiDeMo.
Anomaly Detection in Multicore Embedded Systems

In this paper, we present an Anomaly Detection implementation with the usage of Artificial Neural Network (ANN) for Multicore Embedded Systems. The detector is built over a sophisticated Real-Time Multicore scheduling framework that allowed capturing high-quality run-time data for the Machine Learning (ML) process and provided the necessary infrastructure for the ANN to be embedded. To conceive the detector we first defined the system's sane behaviour through a set of performance counters, providing the necessary information to define an anomaly. After describing the ML process and the ANN embedding details, we evaluate the results of the detection adding a different task to the execution and showing the embedded detector was able to successfully classify over 95% of the execution, never misinterpreting an anomaly as a sane task, with no interference on application execution time, once the anomaly detector runs on core 0, which is reserved for system management and control operations. Also, the maximum delay to detect that the running task is an anomaly was equal to 1 sampling of the performance monitoring counters (configured with captures spaced by 10ms, or 100 captures per second). We conclude the experiments showing the effectiveness of our runtime ANN anomaly detector by actuating on the suspension of the tasks classified as an anomaly, maintaining a sane execution by mitigating anomalies.
Modern Approaches to Detect and Classify Comment Toxicity Using Neural Networks

The growth of popularity of online platforms which allow users to communicate with each other, share opinions about various events, and leave comments boosted the development of natural language processing algorithms. Tens of millions of messages per day are published by users of a particular social network need to be analyzed in real time for moderation in order to prevent the spread of various illegal or offensive information, threats and other types of toxic comments. Of course, such a large amount of information can be processed quite quickly only automatically. that is why there is a need to and a way to teach computers to “understand” a text written by humans. It is a non-trivial task even if the word “understand” here means only “to classify”. the rapid evolution of machine learning technologies has led to ubiquitous implementation of new algorithms. A lot of tasks, which for many years were considered almost impossible to solve, are now quite successfully solved using deep learning technologies. this article considers algorithms built using deep learning technologies and neural networks which can successfully solve the problem of detection and classification of toxic comments. In addition, the article presents the results of the developed algorithms, as well as the results of the ensemble of all considered algorithms on a large training set collected and tagged by Google and Jigsaw.
Semantic Private Information Retrieval

We investigate the problem of semantic private information retrieval (semantic PIR). In semantic PIR, a user retrieves a message out of $K$ independent messages stored in $N$ replicated and non-colluding databases without revealing the identity of the desired message to any individual database. The messages come with \emph{different semantics}, i.e., the messages are allowed to have \emph{non-uniform a priori probabilities} denoted by $(p_i>0,\: i \in [K])$, which are a proxy for their respective popularity of retrieval, and \emph{arbitrary message sizes} $(L_i,\: i \in [K])$. This is a generalization of the classical private information retrieval (PIR) problem, where messages are assumed to have equal a priori probabilities and equal message sizes. We derive the semantic PIR capacity for general $K$, $N$. The results show that the semantic PIR capacity depends on the number of databases $N$, the number of messages $K$, the a priori probability distribution of messages $p_i$, and the message sizes $L_i$. We present two achievable semantic PIR schemes: The first one is a deterministic scheme which is based on message asymmetry. This scheme employs non-uniform subpacketization. The second scheme is probabilistic and is based on choosing one query set out of multiple options at random to retrieve the required message without the need for exponential subpacketization. We derive necessary and sufficient conditions for the semantic PIR capacity to exceed the classical PIR capacity with equal priors and sizes. Our results show that the semantic PIR capacity can be larger than the classical PIR capacity when longer messages have higher popularities. However, when messages are equal-length, the non-uniform priors cannot be exploited to improve the retrieval rate over the classical PIR capacity.
Leveraging Fine-Grained Access Control in Blockchain-Based Healthcare System

Due to the development of health care industry and digitization of medical data, recent years have experienced major changes in storage of electronic health record on cloud environment, making data exchange feasible between patient and healthcare provider. However, this new shift comes with the risk of security and privacy concerns of patient and data. The exchange of healthcare data among patients and healthcare provider with high level of security is a challenging task. In this paper, we proposed a novel system with the combination of blockchain and interplanetry file system for data sharing and uploading. We design a fine-grained access control mechanism using smart contract which provide a different level of access rights to the users in the network. We implement our system on Ethereum private blockchain and use proof of authority for consensus mechanism.
Methods and tools for teaching parallel and distributed computing in universities: a systematic review of the literature

As computer hardware becomes more and more parallel, there is a need for software engineers who are experienced in developing parallel programs, not only by “parallelizing” sequential designs. Teach students a parallelism in elementary courses in computer science this is a very important step towards building the competencies of future software engineers. We have conducted research on “teaching parallel and distributed computing” and “parallel programming” publications in the Scopus database, published in English between 2008 and 2019. After quality assessment, 26 articles were included in the analysis. As a result, the main tool for teaching parallel and distributed computing is a lab course with a C++ programming language and MPI library.
Formal approach to model complex adaptive computing systems

Abstract Complex adaptive systems provide a significant number of concepts such as reaction, interaction, adaptation, and evolution. In general, these concepts are modelled employing different techniques which give an inexplicit vision on the system. Therefore, all concepts must be carefully modelled using the same approach to avoid contradiction and guarantee system homogeneity and correctness. However, developing a computing system that includes all these concepts using the same approach is not an easy task and requires a perfect understanding of the system’s behaviour. In this paper, we contribute as stepwise towards proposing an approach to model the most important concepts of complex adaptive systems while ensuring homogeneity and the correctness of models. For this aim, we present five standard agent-based models formalizing agent properties, reaction, interaction, adaptation, and evolution. These models are adapted to all cases of complex adaptive systems since they include an abstract description of these concepts. To implement our approach formally, we choose the Event-B method due to the strong assurance of bugs’ absence that it guarantees. Besides, it supports horizontal and vertical refinement which facilitates the specification process. Furthermore, the approach of this paper addresses the very abstract level of modelling which expand the use of this approach to other formal methods and tools.
Bifurcation-Based Quantum Annealing with Nested Spins

We study a bifurcation mechanism of quantum annealing. Using spins with quantum number S=1, we construct a simple model to make a bifurcation. The qutrit can be composed by nesting two qubits. We numerically solve the Schroedinger equation to confirm that the bifurcation-based quantum annealing (BQA) works well and the ground state can be found efficiently. The result is compared with that by the standard quantum annealing (QA) using qubits. We find that the performance of the BQA is comparable to the standard QA, or gives better results in some cases.
Semantically Multi-modal Image Synthesis

In this paper, we focus on semantically multi-modal image synthesis (SMIS) task, namely, generating multi-modal images at the semantic level. Previous work seeks to use multiple class-specific generators, constraining its usage in datasets with a small number of classes. We instead propose a novel Group Decreasing Network (GroupDNet) that leverages group convolutions in the generator and progressively decreases the group numbers of the convolutions in the decoder. Consequently, GroupDNet is armed with much more controllability on translating semantic labels to natural images and has plausible high-quality yields for datasets with many classes. Experiments on several challenging datasets demonstrate the superiority of GroupDNet on performing the SMIS task. We also show that GroupDNet is capable of performing a wide range of interesting synthesis applications. Codes and models are available at: https://github.com/Seanseattle/SMIS.
Simplified Parsing Expression Derivatives

This paper presents a new derivative parsing algorithm for parsing expression grammars; this new algorithm is both simpler and faster than the existing parsing expression derivative algorithm presented by Moss [12]. This new algorithm improves on the worst-case space and runtime bounds of the previous algorithm by a linear factor, as well as decreasing runtime by about half in practice.
Black-Box Constructions of Bounded-Concurrent Secure Computation

We construct a general purpose secure multiparty computation protocol which remains secure under (a-priori) bounded-concurrent composition and makes only black-box use of cryptographic primitives. Prior to our work, constructions of such protocols required non-black-box usage of cryptographic primitives; alternatively, black-box constructions could only be achieved for super-polynomial simulation based notions of security which offer incomparable security guarantees. Our protocol has a constant number of rounds and relies on standard polynomial-hardness assumptions, namely, the existence of semi-honest oblivious transfers and collision-resistant hash functions. Previously, such protocols were not known even under sub-exponential assumptions.
Usability Requirements Extraction Method from Software Document

Extracting the usability requirements are crucial during requirements review and requirements validation for different purposes. Usability requirements are hard to be determined until the real user has experienced the software. It is even more challenging when these usability requirements are documented in natural language, which has an inconsistent and unrestricted structure. Automated requirements extraction has been widely studied to facilitate the process of requirements checking. Nevertheless, the accuracy of requirements extraction method still can be improved. Thus, this paper has presented the usability keywords repository that followed the ISO 9126 and ISO 25010 usability category and has gone through the expert validation process. The usability requirement extraction method is moreover enhanced with extra procedures in conforming the usability requirement statement. First, each statement in the requirement document is checked if there is a keyword usability, whereby the keywords used must match with the usability keyword repository. In order to ensure that the selected statement is a usability requirement, the corresponding usability keyword position should be after the fixed auxiliary verbs. The performance of the improved usability requirements extraction method is then evaluated using precision, recall and accuracy.