Reviewerly/exHarmony huggingface dataset:
POTUS: Predictive Online Tuple Scheduling for Data Stream Processing Systems

Most online service providers deploy their own data stream processing systems in the cloud to conduct large-scale and real-time data analytics. However, such systems, e.g., Apache Heron, often adopt naive scheduling schemes to distribute data streams (in the units of tuples) among processing instances, which may result in workload imbalance and system disruption. Hence, there still exists a mismatch between the temporal variations of data streams and such inflexible scheduling scheme designs. Besides, the fundamental limits of benefits of predictive scheduling to data stream processing systems remain unexplored. In this article, we focus on the problem of tuple scheduling with predictive service in Apache Heron. With a careful choice in the granularity of system modeling and decision making, we formulate the problem as a stochastic network optimization problem and propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">POTUS</i> , an online predictive scheduling scheme that aims to minimize the response time of data stream processing by steering data streams in a distributed fashion. Theoretical analysis and simulation results show that POTUS achieves an ultra-low response time with a stability guarantee. Moreover, POTUS only requires mild-value of future information to effectively reduce the response time, even with mis-prediction.
Comparison of Sensor-Based Datasets for Human Activity Recognition in Wearable IoT

Human activity recognition (HAR) has made a significant impact in a variety of applications, not least of which includes wearable Internet of Things (IoT). Considerable research is currently underway in the field of machine learning, deep learning and neural networks, an objective of which is to improve the efficiency of relevant algorithms, as well as to increase the truthfulness of the recognition model. For purposes of analysis and consistency, much of the published research uses either self-collected sensor-based databases or publicly available databases for potential research objectives. There exist diverse open source sensor-based time-series datasets that are applied to the testing of algorithm efficiency, which is then used for human activity detection. Essential to algorithm performance testing are datasets that consist of balanced data and labels. Such datasets vary in size, labeling, accuracy, number of activities performed by each user, type of sensor data included, and various other elements. In this paper, we evaluate two popular datasets by comparing selected factors; we also demonstrate our experimental results. We implement minimal data processing by extracting five basic features - mean, standard deviation, absolute mean error, mean squared error, and histogram plots – and perform experiments on both datasets to study their outcomes. We compare the UCI HAR and WISDM (Wireless Sensor Data Mining) datasets, based on their accuracy, precision, recall, and F1-score on nine different machine learning algorithms. Both datasets contain tri-axial sensor data. Experimental results show that the UCI HAR dataset is the more promising of the two. Additionally, we show that with basic data processing, simple activity recognition is possible with current state-of-the-art algorithms. Specifically, as we increase the type of sensor data, the accuracy of simple human activity detection is improved. We conclude with a discussion of results, as well as study limitations, challenges, and future research.
ASAYAR: A Dataset for Arabic-Latin Scene Text Localization in Highway Traffic Panels

The extraction of text information from traffic panels is one of the challenging problems in computer vision. Although the past decade has seen a promising shift and important progress in object detection, few works and a limited number of datasets focus specifically on extracting text from traffic signs. To address the lack of data for text detection in traffic panels, especially those with Arabic scripts, this paper introduces a new multilingual and multipurpose dataset named ASAYAR. It consists of three sub-datasets: Arabic-Latin scene text localization, traffic sign detection, and directional symbol detection. The dataset contains 1763 images collected on different Moroccan highways, and annotated manually, using 16 object categories. The fully annotated ASAYAR images contains more than 20000 bounding box objects. The paper also investigates the usability of the dataset, by evaluating the performance of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">state-of-the-art</i> algorithms for object and text detection. Experimental results show good detection scores, demonstrating the potential contribution of ASAYAR in the development of methods for text extraction from traffic panels.
Filter Pruning and Re-Initialization via Latent Space Clustering

Filter pruning is prevalent for pruning-based model compression.Most filter pruning methods have two main issues: 1) the pruned network capability depends on that of source pretrained models, and 2) they do not consider that filter weights follow a normal distribution.To address these issues, we propose a new pruning method employing both weight re-initialization and latent space clustering.For latent space clustering, we define filters and their feature maps as vertices and edges to be a graph, transformed into a latent space by graph convolution, alleviating to prune zero-near weight filters only.In addition, a part of filters is re-initialized with a constraint for enhancing filter diversity, and thus the pruned model is less dependent on the source network.This approach provides more robust accuracy even when pruned from the pretrained model with low accuracy.Extensive experimental results show our method decreases 56.6% and 84.6% of FLOPs and parameters of VGG16 with negligible loss of accuracy on CIFAR100, which is the state-of-the art performance.Furthermore, our method presents outperforming or comparable pruning results against state-of-the-art models on multiple datasets.
Calibration of In-Plane Center Alignment Errors in the Installation of a Circular Slide with Machine-Vision Sensor and a Reflective Marker

This paper describes a method for calibrating in-plane center alignment error (IPCA) that occurs when installing the circular motion slide (CMS). In this study, by combini ng the moving carriage of the CMS and the planar PKM (parallel kinematic mechanism) with the machine tool, the small workspace of the PKM is expanded, and the workpiece is placed on the table with the CMS installed is processed through the machine tool. However, to rigidly mount the CMS on the table, the preload between the guide and the support bearings must be adjusted with the eccentric bearing, and in this process, the IPCA occurs. After installing a reflective marker on the PKM, the PKM is slowly rotated along with the ring guide in the way of stop-and-go without the PKM's own motion. Then, using a machine vision camera installed at the top of the CMS, the IPCA, which is the difference between the actual center position and the nominal center position of the CMS with respect to the camera, can be successfully calibrated through the circular fitting process. Consequently, it was confirmed that the IPCA of 0.37 mm can be successfully identified with the proposed method.
Supervised Deep Learning for MIMO Precoding

In this paper, we aim to design an end-to-end deep learning architecture for a broadcast MIMO system with precoding at the transmitter. The objective is to transmit interference-free data streams to multiple users over a wireless channel. We propose end-to-end learning of communication systems modelled as a Deep autoencoder network with a novel cost function to achieve this goal. This architecture enables optimization of the transmitter and receiver network weights jointly over a wireless channel. We also introduce a way to precode the transmitter embeddings before transmission. An end-to-end training of the autoencoder framework of transmitter-receiver pairs is employed while training the proposed transmit-precoded MIMO system model. Several numerical evaluations over Rayleigh block-fading (RBF) channels with slow fading are presented to prove this approach. Specific training methods are suggested to improve performance over RBF channels in this paper.
JCMathLib: Wrapper Cryptographic Library for Transparent and Certifiable JavaCard Applets

The JavaCard multi-application platform is now deployed to over twenty billion smartcards, used in various applications ranging from banking payments and authentication tokens to SIM cards and electronic documents. In most of those use cases, access to various cryptographic primitives is required. The standard JavaCard API provides a basic level of access to such functionality (e.g., RSA encryption) but does not expose low-level cryptographic primitives (e.g., elliptic curve operations) and essential data types (e.g., Integers). Developers can access such features only through proprietary, manufacturer-specific APIs. Unfortunately, such APIs significantly reduce the interoperability and certification transparency of the software produced as they require non-disclosure agreements (NDA) that prohibit public sharing of the applet's source code.We introduce JCMathLib, an open library that provides an intermediate layer realizing essential data types and low-level cryptographic primitives from high-level operations. To achieve this, we introduce a series of optimization techniques for resource-constrained platforms that make optimal use of the underlying hardware, while having a small memory footprint. To the best of our knowledge, it is the first generic library for low-level cryptographic operations in JavaCards that does not rely on a proprietary API.Without any disclosure limitations, JCMathLib has the potential to increase transparency by enabling open code sharing, release of research prototypes, and public code audits. Moreover, JCMathLib can help resolve the conflict between strict open-source licenses such as GPL and proprietary APIs available only under an NDA. This is of particular importance due to the introduction of JavaCard API v3.1, which targets specifically IoT devices, where open-source development might be more common than in the relatively closed world of government-issued electronic documents.
AIR: A Light-Weight Yet High-Performance Dataflow Engine based on Asynchronous Iterative Routing

Distributed Stream Processing Engines (DSPEs) are currently among the most emerging topics in data management, with applications ranging from real-time event monitoring to processing complex dataflow programs and big data analytics. In this paper, we describe the architecture of our AIR engine, which is designed from scratch in C++ using the Message Passing Interface (MPI), pthreads for multithreading, and is directly deployed on top of a common HPC workload manager such as SLURM. AIR implements a light-weight, dynamic sharding protocol (referred to as "Asynchronous Iterative Routing"), which facilitates a direct and asynchronous communication among all worker nodes and thereby completely avoids any additional communication overhead with a dedicated master node. With its unique design, AIR fills the gap between the prevalent scale-out (but Java-based) architectures like Apache Spark and Flink, on one hand, and recent scale-up (and C++ based) prototypes such as StreamBox and PiCo, on the other hand. Our experiments over various benchmark settings confirm that AIR performs as good as the best scale-up SPEs on a single-node setup, while it outperforms existing scale-out DSPEs in terms of processing latency and sustainable throughput by a factor of up to 15 in a distributed setting.
Preprocessing Improves CNN and LSTM in Aspect-Based Sentiment Analysis for Vietnamese

The deep learning method has achieved particularly good results in many application fields, such as computer vision, image processing, voice recognition, and signal processing. Recently, this method has also been used in the field of natural language processing and has achieved impressive results. In this field, the problem of categorizing subjective opinions which is an individual’s thinking or judgment of a product or an event or a cultural and social issue. Subjective opinions have received attention from many producers and businesses who are interested in exploiting the opinions of the community and scientists. This paper experiments with the deep learning model convolution neural network (CNN), long short-term memory (LSTM), and the boxed model of CNN and LSTM. Training data sets comprise reviews of cars in Vietnamese. Cars are objects with a significant number of specifications that are provided in user reviews. The Vietnamese opinion set is preprocessed according to the method of aspect analysis based on an ontology of semantic and sentimental approaches. A Vietnamese corpus experiment with CNN, LSTM, and CNN + LSTM models are used to evaluate the effectiveness of the data preprocessing method that was used in this paper. To assess the validity of the test models with the Vietnamese opinion set, the paper also tests the sentiment classification with the English Sentence Collection Stanford Sentiment Treebank (SST).
Entropy-Driven Adaptive Filtering for High-Accuracy and Resource-Efficient FPGA-Based Neural Network Systems

Binarized neural networks are well suited for FPGA accelerators since their fine-grained architecture allows the creation of custom operators to support low-precision arithmetic operations, and the reduction in memory requirements means that all the network parameters can be stored in internal memory. Although good progress has been made to improve the accuracy of binarized networks, it can be significantly lower than networks where weights and activations have multi-bit precision. In this paper, we address this issue by adaptively choosing the number of frames used during inference, exploiting the high frame rates that binarized neural networks can achieve. We present a novel entropy-based adaptive filtering technique that improves accuracy by varying the system’s processing rate based on the entropy present in the neural network output. We focus on using real data captured with a standard camera rather than using standard datasets that do not realistically represent the artifacts in video stream content. The overall design has been prototyped on the Avnet Zedboard, which achieved 70.4% accuracy with a full processing pipeline from video capture to final classification output, which is 1.9 times better compared to the base static frame rate system. The main feature of the system is that while the classification rate averages a constant 30 fps, the real processing rate is dynamic and varies between 30 and 142 fps, adapting to the complexity of the data. The dynamic processing rate results in better efficiency that simply working at full frame rate while delivering high accuracy.
Family’s health: Opportunities for non-collocated intergenerational families collaboration on healthy living

When a family is engaged in healthy living practices together, it enhances the quality of life for all individuals. However, when members in families are separated over distance, the everyday encouragement and support may shift and obstacles arise within the family. In this study, we investigate non-collocated family members' practices of healthy living, their perspectives on their family's healthy living activities, and what obstacles exist regarding collaboration on their family health. We conducted an interview study with 26 independently living participants representing "elderly parents" and "adult children" in a family dynamic. We present members' practices and strategies for sustainable healthy living activities. We also explore members' creative use of technology for health promotion and describe existing obstacles that prevent families to effectively collaborate in healthy living. Based on our findings, we suggest design implications to support family members living apart on their efforts to cultivate health within their families.
Survey on Data Integrity and Verification for Cloud Storage

Nowadays people store much of their information in Cloud, which is a vast network of remote servers, each with a unique function. Cloud storage provides reliable storage space for the users where the data can be stored in pay-as-you-go pricing model. Cloud storage data is monitored, backed up and remotely maintained. The Cloud Storage provides the benefits of low-cost storage, easy accessibility of data and disaster recovery of the data but faces challenges on confidentiality, data security and integrity of the data. Data integrity and verification checks whether the data stored in the cloud is unaltered as the data may be altered by cloud service providers or by malicious attackers. This paper gives a brief introduction about different techniques used for data integrity and verification for cloud storage and classification of them based on the security level provisioning used in the techniques.
Cross‐disciplinary data practices in earth system science: Aligning services with reuse and reproducibility priorities

Abstract As a data intensive field that unites researchers from many disciplines, Earth System Science (ESS) is an ideal site for examining evolving cross‐disciplinary data practices. This paper reports on results from a survey examining data sharing, data reuse, and research reproducibility practices of ESS researchers, aimed at informing improvements in data services for interdisciplinary sciences. Data reuse was found to be very high for new and comparative analyses but very limited for reproducing research. Data sharing was also strong, mostly through supplements to published papers, with moderate use of open access repositories. At the same time, there was interesting variability in both data sharing and reuse among ESS disciplines. The most pronounced challenges to reuse and reproducibility stem from limited documentation on how data are collected and managed, practices that are poorly supported by institutions, funders, and publishers. A more refined approach to “reproducibility” is needed that aligns with priorities and practices within the research community. Just as importantly, advances in data service models for ESS and other interdisciplinary fields need to account for the diverse and distributed system of repositories and build a workforce with deeper knowledge of the complex data and methods that drive integrative systems science.
Good for the Many or Best for the Few?

Applications in a range of domains, including route planning and well-being, offer advice based on the social information available in prior users' aggregated activity. When designing these applications, is it better to offer: a) advice that if strictly adhered to is more likely to result in an individual successfully achieving their goal, even if fewer users will choose to adopt it? or b) advice that is likely to be adopted by a larger number of users, but which is sub-optimal with regard to any particular individual achieving their goal? We identify this dilemma, characterized as Goal-Directed vs. Adoption-Directed advice, and investigate the design questions it raises through an online experiment undertaken in four advice domains (financial investment, making healthier lifestyle choices, route planning, training for a 5k run), with three user types, and across two levels of uncertainty. We report findings that suggest a preference for advice favoring individual goal attainment over higher user adoption rates, albeit with significant variation across advice domains; and discuss their design implications.
Retrievability based Document Selection for Relevance Feedback with Automatically Generated Query Variants

To mitigate the problem of over-dependence of a pseudo-relevance feedback algorithm on the top-M document set, we make use of a set of equivalence classes of queries rather than one single query. These query equivalents are automatically constructed either from a) a knowledge base of prior distributions of terms with respect to the given query terms, or b) iteratively generated from a relevance model of term distributions in the absence of such priors. These query variants are then used to estimate the retrievability of each document with the hypothesis that documents that are more likely to be retrieved at top-ranks for a larger number of these query variants are more likely to be effective for relevance feedback. Results of our experiments show that our proposed method is able to achieve substantially better precision at top-ranks (e.g. higher [email protected] and [email protected] values) for ad-hoc IR and points-of-interest (POI) recommendation tasks.
A Taxonomy of Metrics for Software Fault Prediction

Researchers in the field of Software Fault Prediction (SFP) make use of software metrics to build predictive models, for example, by means of machine learning and statistical techniques. The number of metrics used for SFP has increased dramatically in the last few decades. Therefore, a taxonomy of metrics for SFP could be useful to standardize the lexicon, to simplify the communication among researchers/practitioners, and to organize and classify such metrics. In this research, we built a taxonomy of metrics for SFP with the aim of making it as comprehensive as possible. We exploited and extended two Systematic Literature Reviews (SLRs) to collect and classify a total of 512 metrics for SFP and then to build our taxonomy. We also provide information on the metrics in this taxonomy in terms of: acronym(s), extended name, description, granularity of the prediction, category, and research papers in which they were used. To allow the taxonomy to be constantly updated over time, we provide external contributors the possibility to ask for changes via pull-requests on GitHub.
Minard Revisited – Exploring Augmented Reality in Information Design

This study intends to test and confirm the interest and viability of incorporating augmented reality (AR) technologies in cultural mediation driven by information design, focusing on narrative representation. It is specifically intended to explore semantic relations between reality and virtuality in augmented narratives, i.e. expanded narratives through the multimodality enhanced by the use of interactive processes based in augmented reality systems.
A Novel Time-Aware Hybrid Recommendation Scheme Combining User Feedback and Collaborative Filtering

Nowadays, recommender systems are used widely in various fields to solve the problem of information overload. Collaborative filtering and content-based models are representative solutions in recommender systems; however, the content-based model has some shortcomings, such as single kind of recommendation results and lack of effective perception of user preferences, while for the collaborative filtering model, there is a cold start problem, and such a model is greatly affected by its adopted clustering algorithm. To address these issues, a hybrid recommendation scheme is proposed in this paper, which is based on both collaborative filtering and content-based. In this scheme, we propose the concept of time impact factor, and a time-aware user preference model is built based on it. Also, user feedback on recommendation items is utilized to improve the accuracy of our proposed recommendation model. Finally, the proposed hybrid model combines the results of content recommendation and collaborative filtering based on the logistic regression algorithm.