Reviewerly/exHarmony huggingface dataset:
Accelerating Deep Learning Inference with Cross-Layer Data Reuse on GPUs

Accelerating the deep learning inference is very important for real-time applications. In this paper, we propose a novel method to fuse the layers of convolutional neural networks (CNNs) on Graphics Processing Units (GPUs), which applies data reuse analysis and access optimization in different levels of the memory hierarchy. To achieve the balance between computation and memory access, we explore the fusion opportunities in the CNN computation graph and propose three fusion modes of convolutional neural networks: straight, merge and split. Then, an approach for generating efficient fused code is designed, which goes deeper in multi-level memory usage for cross-layer data reuse. The effectiveness of our method is evaluated with the network layers from state-of-the-art CNNs on two different GPU platforms, NVIDIA TITAN Xp and Tesla P4. The experiments show that the average speedup is 2.02 $$\times $$ on representative structures of CNNs, and 1.57 $$\times $$ on end-to-end inference of SqueezeNet.
Camera Orientation Optimization in Stereo Vision Systems for Low Measurement Error

Stereo vision (SV) is widely used for noncontact three-dimensional industrial measurement. In SV systems, camera orientation (the angles formed by the two cameras optical axes across the baseline) is among the factors that influence the measurement accuracy. Presently, there is a great divergence regarding the optimal value of camera orientation, i.e., existing theoretical analyses suggest that the optimal angle lies in 30 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> -50 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> ; however, a camera orientation angle between 60 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> -80 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> is typically used in practical SV systems and commercial products. Unlike previous models that treated system parameters as uncorrelated, the proposed model introduces covariance to model the correlation between camera orientation and the accuracy of detecting corresponding points. The proposed model proves the optimal camera orientation angle lies in 60 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> -80 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> , which was verified experimentally via measurements with a circle center tracker (CCT), fringe projection profilometry (FPP), and stereo digital image correlation (DIC).
Customized 2D Barcode Sensing for Anti-Counterfeiting Application in Smart IoT with Fast Encoding and Information Hiding

With the development of commodity economy, the emergence of fake and shoddy products has seriously harmed the interests of consumers and enterprises. To tackle this challenge, customized 2D barcode is proposed to satisfy the requirements of the enterprise anti-counterfeiting certification. Based on information hiding technology, the proposed approach can solve these challenging problems and provide a low-cost, difficult to forge, and easy to identify solution, while achieving the function of conventional 2D barcodes. By weighting between the perceptual quality and decoding robustness in sensing recognition, the customized 2D barcode can maintain a better aesthetic appearance for anti-counterfeiting and achieve fast encoding. A new picture-embedding scheme was designed to consider 2D barcode, within a unit image block as a basic encoding unit, where the 2D barcode finder patterns were embedded after encoding. Experimental results demonstrated that the proposed customized barcode could provide better encoding characteristics, while maintaining better decoding robustness than several state-of-the-art methods. Additionally, as a closed source 2D barcode that could be visually anti-counterfeit, the customized 2D barcode could effectively prevent counterfeiting that replicate physical labels. Benefitting from the high-security, high information capacity, and low-cost, the proposed customized 2D barcode with sensing recognition scheme provide a highly practical, valuable in terms of marketing, and anti-counterfeiting traceable solution for future smart IoT applications.
Development modes analysis of renewable energy power generation in North Africa

North African countries generally have strategic demands for energy transformation and sustainable development. Renewable energy development is important to achieve this goal. Considering three typical types of renewable energies— wind, photovoltaic (PV), and concentrating solar power (CSP)—an optimal planning model is established to minimize construction costs and power curtailment losses. The levelized cost of electricity is used as an index for assessing economic feasibility. In this study, wind and PV, wind / PV / CSP, and transnational interconnection modes are designed for Morocco, Egypt, and Tunisia. The installed capacities of renewable energy power generation are planned through the time sequence production simulation method for each country. The results show that renewable energy combined with power generation, including the CSP mode, can improve reliability of the power supply and reduce the power curtailment rate. The transnational interconnection mode can help realize mutual benefits of renewable energy power, while the apportionment of electricity prices and trading mechanisms are very important and are related to economic feasibility; thus, this mode is important for the future development of renewable energy in North Africa.
ECS‐CP‐ABE: A lightweight elliptic curve signcryption scheme based on ciphertext‐policy attribute‐based encryption to secure downlink multicast communication in edge envisioned advanced metering infrastructure networks

Abstract As an integral part of the smart grid, advanced metering infrastructure (AMI) implements two‐way communication to enable the smart grid achieving the desired performance improvements over the legacy grid. Integrating communication in addition to the inherited weakness of the power grid paves the way to countless number of security threats to target its communication networks, therefore ensuring secure data exchange is a key concern for AMI networks. In this regard, many schemes are proposed in the literature to secure this two‐way communication, however, very little effort has been focused on multicast transmissions carried out between the control center and a group of smart meters. In order to empower efficient, authentic, and confidential data exchange, this article proposes a new signcryption scheme for multicast downlink communication in edge envisioned AMI networks. The proposed scheme is constructed based on ciphertext‐policy attribute‐based encryption to attain secure fine‐grained access control for the multirecipient communication between the utility control center and a group of smart meters. The novelty of our scheme lies behind the fact that it is an elliptic curve cryptosystem that is pairing free, in opposite to most ABE schemes. Instead of the complex bilinear pairing operations, we exploited faster elliptic curve point multiplication operations to construct the proposed signcryption model. we demonstrate the strength of our protocol in defending against passive, collusion, reply, data modification, and signature forgery attacks. Simulation results prove the efficiency of our protocol in terms of messages size and computation complexity when compared with other schemes.
NLP for the Greek Language: A Brief Survey

There is a plethora of methods, tools and resources for processing text in the English language, however this is not the case for other languages, like Greek. Due to the increasing interest in NLP, and since there is a noteworthy number of works related to the processing of the Greek language, in this paper we survey the work related to the processing of Greek language. In particular, we list and briefly discuss related works, resources and tools, categorized according to various processing layers and contexts. This survey can be useful for researchers and students interested in NLP tasks, Information Retrieval and Knowledge Management for the Greek language.
Fast and effective distribution-key recommendation for amazon redshift

How should we split data among the nodes of a distributed data warehouse in order to boost performance for a forecasted workload? In this paper, we study the effect of different data partitioning schemes on the overall network cost of pairwise joins. We describe a generally-applicable data distribution framework initially designed for Amazon Redshift, a fully-managed petabyte-scale data warehouse in the cloud. To formalize the problem, we first introduce the Join Multi-Graph , a concise graph-theoretic representation of the workload history of a cluster. We then formulate the "Distribution-Key Recommendation" problem - a novel combinatorial problem on the Join Multi-Graph - and relate it to problems studied in other subfields of computer science. Our theoretical analysis proves that "Distribution-Key Recommendation" is NP-complete and is hard to approximate efficiently. Thus, we propose BaW, a hybrid approach that combines heuristic and exact algorithms to find a good data distribution scheme. Our extensive experimental evaluation on real and synthetic data showcases the efficacy of our method into recommending optimal (or close to optimal) distribution keys, which improve the cluster performance by reducing network cost up to 32x in some real workloads.
Surveillance Camera using IoT and Raspberry Pi

Security remains as one of the most important aspects of smart cities. At present, due to the increased population and urbanization there is a huge demand for smart security system. But, existing CCTV and IP cameras are inefficient interms of image quality and these systems require large amount of storage space. Hence, this paper proposed a surveillance camera by employing Raspberry Pi and IoT. It will make use of raspicam camera for capturing the images. The proposed surveillance system requires less storage space as it records the video and captures the images only when the motion is detected in the monitored area. Motion detection can be done by using PIR sensor. The implementation of the proposed surveillance camera is simple, where the captured data is encrypted at the transmitter side and decrypted at the receiver side. Hence, the proposed system provides a secured data transmission. The live feeding is deployed by using Raspicam Remote App. The proposed model can be easily configured, where it allows us to test the image filters of the camera. Hence, the proposed surveillance camera using raspberry pi and IoT appears to be more superior to the conventional IP cameras.
Neural Denoising for Path Tracing of Medical Volumetric Data

In this paper, we transfer machine learning techniques previously applied to denoising surface-only Monte Carlo renderings to path-traced visualizations of medical volumetric data. In the domain of medical imaging, path-traced videos turned out to be an efficient means to visualize and understand internal structures, in particular for less experienced viewers such as students or patients. However, the computational demands for the rendering of high-quality path-traced videos are very high due to the large number of samples necessary for each pixel. To accelerate the process, we present a learning-based technique for denoising path-traced videos of volumetric data by increasing the sample count per pixel; both through spatial (integrating neighboring samples) and temporal filtering (reusing samples over time). Our approach uses a set of additional features and a loss function both specifically designed for the volumetric case. Furthermore, we present a novel network architecture tailored for our purpose, and introduce reprojection of samples to improve temporal stability and reuse samples over frames. As a result, we achieve good image quality even from severely undersampled input images, as visible in the teaser image.
CLICK-ID: A novel dataset for Indonesian clickbait headlines

News analysis is a popular task in Natural Language Processing (NLP). In particular, the problem of clickbait in news analysis has gained attention in recent years [1, 2]. However, the majority of the tasks has been focused on English news, in which there is already a rich representative resource. For other languages, such as Indonesian, there is still a lack of resource for clickbait tasks. Therefore, we introduce the CLICK-ID dataset of Indonesian news headlines extracted from 12 Indonesian online news publishers. It is comprised of 15,000 annotated headlines with clickbait and non-clickbait labels. Using the CLICK-ID dataset, we then developed an Indonesian clickbait classification model achieving favourable performance. We believe that this corpus will be useful for replicable experiments in clickbait detection or other experiments in NLP areas.
Interplaying Cassandra NoSQL Consistency and Performance: A Benchmarking Approach

This experience report analyses performance of the Cassandra NoSQL database and studies the fundamental trade-off between data consistency and delays in distributed data storages. The primary focus is on investigating the interplay between the Cassandra performance (response time) and its consistency settings. The paper reports the results of the read and write performance benchmarking for a replicated Cassandra cluster, deployed in the Amazon EC2 Cloud. We present quantitative results showing how different consistency settings affect the Cassandra performance under different workloads. One of our main findings is that it is possible to minimize Cassandra delays and still guarantee the strong data consistency by optimal coordination of consistency settings for both read and write requests. Our experiments show that (i) strong consistency costs up to 25% of performance and (ii) the best setting for strong consistency depends on the ratio of read and write operations. Finally, we generalize our experience by proposing a benchmarking-based methodology for run-time optimization of consistency settings to achieve the maximum Cassandra performance and still guarantee the strong data consistency under mixed workloads.
A Note on the Reproducibility of Chaos Simulation

An evergreen scientific feature is the ability for scientific works to be reproduced. Since chaotic systems are so hard to understand analytically, numerical simulations assume a key role in their investigation. Such simulations have been considered as reproducible in many works. However, few studies have focused on the effects of the finite precision of computers on the simulation reproducibility of chaotic systems; moreover, code sharing and details on how to reproduce simulation results are not present in many investigations. In this work, a case study of reproducibility is presented in the simulation of a chaotic jerk circuit, using the software LTspice. We also employ the OSF platform to share the project associated with this paper. Tests performed with LTspice XVII on four different computers show the difficulties of simulation reproducibility by this software. We compare these results with experimental data using a normalised root mean square error in order to identify the computer with the highest prediction horizon. We also calculate the entropy of the signals to check differences among computer simulations and the practical experiment. The methodology developed is efficient in identifying the computer with better performance, which allows applying it to other cases in the literature. This investigation is fully described and available on the OSF platform.
WSODPB: Weakly supervised object detection with PCSNet and box regression module

Weakly supervised object detection (WSOD) task uses only image-level annotations to train object detection task. WSOD does not require time-consuming instance-level annotations, which attracts more and more attention. Previous weakly supervised object detection methods iteratively update detectors and pseudo-labels or use rules-based methods, which could not generate complete and accurate proposals. We utilize the features extracted by the convolutional layers to optimize the proposals generated by rules-based methods, and solve the above problem through combining the two different features. Then, a box regression module is added to the weakly supervised object detection network, which supervised by a proposal completeness scoring network (PCSNet). The box regression module modifies proposals to obtain higher intersection-over-Union (IoU) with ground truth. PCSNet scores the proposal output from the box regression network and utilizes the score to improve the box regression module. In addition, we take advantage of the random proposal scoring (RPS) algorithm for generating more accurate pseudo labels to train the box regression module. The results show that our methods have made significant improvements on PASCAL VOC 2007 and 2012 datasets.
HFER: Promoting Explainability in Fuzzy Systems via Hierarchical Fuzzy Exception Rules

When developing a Machine Learning model, the consideration of explainability as an additional design driver can improve its deployment into any application context. Given an audience, an explainable Artificial Intelligence system is one that produces details or reasons to make it's functioning clear or easy to understand. Among different paradigms that inherently support these capabilities, Fuzzy Rule Based Systems are a very accountable solution. The main issue when dealing with fuzzy systems is to select an appropriate granularity to represent (fuzzify) the input data. A low value may cause the generation of too generalist rules, causing a hinder on predictive performance, whereas a high value may lead to both overfitting and/or very complex solutions. To overcome this situation, we propose a novel hierarchical fuzzy classification system based on fuzzy exception rules. To do so, low granularity rules are first generated and their confidence is examined. For those cases in which the fuzzy confidence is below a quality threshold, new higher granularity rules are created to cover the "instances in conflict" for the general rule, which is still kept in the rule base. Experimental results show the achievement of a compact and interpretable final rule base while maintaining or improving the predictive performance in comparison with the baseline fuzzy rule based classification and hierarchical systems.
Automatic extraction of subordinate clauses and its application in second language acquisition research

Abstract Clause subordination is an important linguistic phenomenon that is relevant to research in psycholinguistics, cognitive and behavioral sciences, language acquisition, and computational information retrieval. The paper presents a comprehensive tool called AutoSubClause, which is specifically designed for extracting subordinate clause (SC) information from natural English production. Using dependency parsing, AutoSubClause is able to extract not only information characterizing the three main types of SCs—complement, adverbial, and relative clauses—but also information regarding the internal structure of different clause types and their semantic and structural relations with elements of the main clause. Robustness testing of the system and its underlying dependency parser Stanford CoreNLP showed satisfactory results. To demonstrate the usefulness of AutoSubClause, we used it to analyze a large-scale learner corpus and investigate the effects of first language (L1) on the acquisition of subordination in second language (L2) English. Our analysis shows that learners from an L1 that is typologically different from the L2 in clause subordination tend to have different developmental trajectories from those whose L1 is typologically similar to the L2. Furthermore, the developmental patterns for different types of SCs also vary. This finding suggests the need to approach clausal subordination as a multi-componential construct rather than a unitary one, as is the case in most previous research. Finally, we demonstrate how NLP technology can support research questions that rely on linguistic analysis across various disciplines and help gain new insights with the increasing opportunities for up-scaled analysis.
A Performance Analysis of Pre-trained Neural Network and Design of CNN for Sports Video Classification

Video content analysis and video classification are some of the very tricky problems among computer vision researchers. Especially in sports videos, shared common actions appear in different sports categories like jumping is a common action for both diving and high jump. These criteria need a tool or model to classify the video with an elevated rate of accuracy. Convolution neural network is an essential tool for deep learning. This is especially suited for image recognition and classification. Alexnet, googlenet and mobilenet are the examples for previously trained network. Feature extraction, classification, and transfer learning are the three primary functions of pre-trained networks. The main purpose of video classification in sports is to support sportspersons to find the video of their own interest in training and improve the performance. This paper analyzes the performance of the pre-trained network and the result is compared with a deep convolution network with customized layers on the same dataset. The experimental results are shown with graph and table format with the measure of accuracy and performance.
Immersive learning: Applications of virtual reality for undergraduate education

Virtual reality technology has applications across many academic disciplines and has the potential to contribute to student centered, experiential learning opportunities. Some academic libraries have begun to offer access to this technology as part of their mission to provide broad access to information and learning resources. The virtual reality studio in the Taylor Family Digital Library, the central library at the University of Calgary, was introduced in the summer of 2016, five years after the building first opened. Since the launch of the virtual reality studio, it has experienced a diverse range of uses encompassing teaching, learning, and research across many disciplines. Due to its success, VR offerings have been expanded to include a semi-mobile VR cart, a VR development room, and loanable VR equipment. This article will describe the configuration, access policies, and outreach activities around the library's VR services and spaces, and discuss in more detail how they have been used in undergraduate classes including kinesiology, dance, literatures, and art history.
PbO-CCSAT: Boosting Local Search for Satisfiability Using Programming by Optimisation

Propositional satisfiability (SAT) is a prominent problem in artificial intelligence with many important applications. Stochastic local search (SLS) is a well-known approach for solving SAT and known to achieve excellent performance on randomly generated, satisfiable instances. However, SLS solvers for SAT are usually ineffective in solving application instances. Here, we propose a highly configurable SLS solver dubbed PbO-CCSAT, which leverages a powerful technique known as configuration checking (CC) in combination with the automatic algorithm design paradigm of programming by optimisation (PbO). Our PbO-CCSAT solver exposes a large number of design choices, which are automatically configured to optimise the performance for specific classes of SAT instances. We present extensive empirical results showing that our PbO-CCSAT solver significantly outperforms state-of-the-art SLS solvers on SAT instances from many applications, and further show that PbO-CCSAT is complementary to state-of-the-art complete solvers.