Reviewerly/exHarmony huggingface dataset:
Instance search based on weakly supervised feature learning

Instance search has been conventionally addressed as an image retrieval issue. In the existing solutions, traditional hand-crafted features and global deep features have been widely adopted. Unfortunately, since the features are not directly derived from the exact area of an instance in an image, satisfactory performance from most of them is undesirable. In this paper, a compact instance level feature representation is proposed. The scheme basically consists of two convolutional neural network (CNN) pipelines. One is designed for localizing potential instances from an image, while another is trained to learn object-aware weights to produce distinctive features. The sensitivity to the unknown categories, the distinctiveness to different instances, and most importantly, the capability of localizing an instance in an image are all carefully considered in the feature design. Moreover, both pipelines only require image level annotations, which makes the framework feasible for large-scale image collections with variety of instances. To the best of our knowledge, this is the first piece of work that builds the instance level representation based on weakly supervised object detection.
Hilbert-Space Reduced-Rank Methods For Deep Gaussian Processes

A deep Gaussian process is a hierarchy of Gaussian processes where the process at each level is Gaussian given the process on the next level. In this paper, we recast special deep Gaussian processes as solutions of stochastic partial differential equations (SPDEs). Each of these SPDEs has parameters which are functions of the solutions to other SPDEs. To avoid solving SPDEs explicitly, we transform the SPDEs to finite-dimensional objects by truncating the underlying Hilbert space expansion. We then use a Markov chain Monte Carlo technique designed for function spaces to sample its posterior distribution. For a one-dimensional signal example, we show that the regression can offer discontinuity detection and smoothness constraints, which are competing with each other.
Syntax and Stack Overflow: A Methodology for Extracting a Corpus of Syntax Errors and Fixes

One problem when studying how to find and fix syntax errors is how to get natural and representative examples of syntax errors. Most syntax error datasets are not free, open, and public, or they are extracted from novice programmers and do not represent syntax errors that the general population of developers would make. Programmers of all skill levels post questions and answers to Stack Overflow which may contain snippets of source code along with corresponding text and tags. Many snippets do not parse, thus they are ripe for forming a corpus of syntax errors and corrections. Our primary contribution is an approach for extracting natural syntax errors and their corresponding human made fixes to help syntax error research. A Python abstract syntax tree parser is used to determine preliminary errors and corrections on code blocks extracted from the SOTorrent data set. We further analyzed our code by executing the corrections in a Python interpreter. We applied our methodology to produce a public data set of 62,965 Python Stack Overflow code snippets with corresponding tags, errors, and stack traces. We found that errors made by Stack Overflow users do not match errors made by student developers or random mutations, implying there is a serious representativeness risk within the field. Finally we share our dataset openly so that future researchers can re-use and extend our syntax errors and fixes.
Formal Description of Use Cases for Industry 4.0 Maintenance Processes Using Blockchain Technology

Maintenance processes in Industry 4.0 applications try to achieve a high degree of quality to reduce the downtime of machinery. The monitoring of executed maintenance activities is challenging as in complex production setups, multiple stakeholders are involved. So, full transparency of the different activities and of the state of the machine can only be supported, if these stakeholders trust each other. Therefore, distributed ledger technologies, like Blockchain, can be promising candidates for supporting such applications. The goal of this paper is a formal description of business and technical interactions between non-trustful stakeholders in the context of Industry 4.0 maintenance processes using distributed ledger technologies. It also covers the integration of smart contracts for automated triggering of activities.
Smart Triggering of the Barometer in a Fall Detector Using a Semi-Permeable Membrane

The inclusion of a barometer in a wearable fall detector has been shown to improve the detection accuracy by measuring the altitude change associated with a fall event. However, the barometer is a power-hungry sensor, and the sensing power of barometer can be the dominant power consumption source in a wearable fall detector. In this study, we propose a triggering method that reduces barometer power consumption and prolongs the battery life. This approach utilizes a hermetically sealed and waterproof enclosure, with a small inlet covered by a semi-permeable membrane (SPM) to delay the time at which equilibrium between the internal and external pressures is reached, allowing the barometer to be woken from an idle low-power mode and capture the rising air pressure caused by the decrease in altitude during the fall. Two alternative signal processing methods were applied to the pressure waveform to detect the rising pressure pattern, a differential moving average filter (DMAF) and a Kalman filter (KF). The proposed fall detector was evaluated with data collected from a laboratory-based trial and a free-living trial, in which the barometric pressure data, recorded in open-air, were passed through a mathematical model of the leaky enclosure and the SPM assembly. The results show that the proposed fall detector with a 3.7 V, 140 mAh lithium-polymer battery provides a long battery life (DMAF 447 days, KF 444 days) while not compromising the sensitivity (DMAF 91.8%, KF 91.9%), specificity (DMAF 95.2% and KF 95.5%), or the false alarm rate (DMAF 0.035 alarms/hour and KF 0.064 alarms/hour).
Modeling and Prediction of Time-Series-A Case Study with Forex Data

Time series data reveals dynamic behavior of systems. A few real life examples are traffic flow, amount of rainfall, usage of electricity, share values, Forex rate etc.. Depending on the complexity of the system dynamics, algorithms differ to model the time series data accurately, so that the created model can be used for interpolation and more commonly extrapolation or prediction. For example, AR model performs well in stationary time series, but for non-stationary, it cannot capture the non-linear dynamics. In this research, we use Forex rate data, and experimented with various algorithms to capture the dynamics of the data. The success of the model is evaluated by accuracy in prediction. In our experiments, we applied two state-of-the-art models -Support Vector Regression (SVR) and Recurrent Neural Network (RNN). The target of the experiment is the prediction of longer future by recursion (feeding back predicted value to input for the next step prediction). The result shows that RNN with proper Long Short-Term Memory (LSTM) has better performance in predicting longer future.
Using Google TensorFlow Machine Learning Library for speech recognition

Abstract This work is devoted to the development and study of the procedural processing of speech signals using recurrent neural networks. Our method for speech recognition was a connectionist temporal classification based on networks of long short-term memory. The main goal of this work was to study the specifics of the Russian language, to develop methods and algorithms for converting oral speech into text based on artificial neural networks. The final recognition coefficient is about 62%.
Massive-scale image retrieval based on deep visual feature representation

This paper proposes an image retrieval algorithm towards massive-scale multimedia data. In order to be consistent with human visual system, we first design a color attention function to describe the important of different image patches. Subsequently, we combine color and texture to construct candidate regions, which will be fed into a deep neural network (DNN) for deep representation extraction. Then, we design a similarity function to calculate the distance among different images, where top-ranking images are considered as the required images. Experimental results show the effectiveness and robustness of our proposed method.
New Lower Bounds for Permutation Codes Using Linear Block Codes

In this paper we prove new lower bounds for the maximal size of permutation codes by connecting the theory of permutation codes with the theory of linear block codes. More specifically, using the columns of a parity check matrix of an [n,k,d] <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> linear block code, we are able to prove the existence of a permutation code in the symmetric group of degree n, having minimum distance at least d and large cardinality. With our technique, we obtain new lower bounds for permutation codes that enhance the ones in the literature and provide asymptotic improvements in certain regimes of length and distance of the permutation code.
Automated Characterization of Software Vulnerabilities

Preventing vulnerability exploits is a critical software maintenance task, and software engineers often rely on Common Vulnerability and Exposure (CVEs) reports for information about vulnerable systems and libraries. These reports include descriptions, disclosure sources, and manually-populated vulnerability characteristics such as root cause from the NIST Vulnerability Description Ontology (VDO). This information needs to be complete and accurate so stakeholders of affected products can prevent and react to exploits of the reported vulnerabilities. In this study, we demonstrate that VDO characteristics can be automatically detected from the textual descriptions included in CVE reports. We evaluated the performance of 6 classification algorithms with a dataset of 365 vulnerability descriptions, each mapped to 1 of 19 characteristics from the VDO. This work demonstrates that it is feasible to train classification techniques to accurately characterize vulnerabilities from their descriptions. All 6 classifiers evaluated produced accurate results, and the Support Vector Machine classifier was the best-performing individual classifier. Automating the vulnerability characterization process is a step towards ensuring stakeholders have the necessary data to effectively maintain their systems.
Image encryption based on highly sensitive chaotic system

In the recent past, various algorithms for image encryption is introduced based on chaotic systems. Some of them appeared weak in some points, such as resisting some attacks with low sensitivity to change of plain-images. In this article, a new two-dimensional (2D) hyperchaotic system is proposed, which is derived from three maps, namely, Henon, logistic, iterative chaotic map with infinite collapse (ICMIC) maps. This system shows high sensitivity to the initial condition, complicated dynamics, and hyperchaotic properties. However, it is adopted in the design of a new image encryption algorithm. The encryption process is mainly depending on changing of eigenvalues and eigenvectors of the original image utilizing a function to change the intensity of pixels. The plain-image is divided into blocks to generate four different keys (multi-key) based on the proposed hyperchaotic system, which helps to increase the complexity and reduce the computation time. Eventually, to show the efficiency of the encryption image, some analysis tests are performed, such as; NPCR, UACI, image histogram, pixels correlation, and Shannon entropy.
Development of e-learning content with H5P and iSpring features

Abstract E-learning is currently a fast-developing learning method. Interactive multimedia technology, which combines computer technology, video, and audio, is needed to support this e-learning system. The objective of this study is to develop e-learning content based on interactive multimedia to support the effectiveness of a learning system. SDLC ( System Development Life Cycle ) method with the waterfall model was used in this study. The output of this study is developed e-learning with additional content that utilizes interactive multimedia features, H5P, and iSpring. Through this system, the teacher can create content improvement in the learning model. The system provides various learning sources and activities to support a more interactive teaching and learning process. This e-learning can ease students in understanding the materials and motivates them in learning through this multimedia learning media.
Enhancing the effectiveness of cybercrime prevention through policy monitoring

This article examines the feasibility of designing and implementing a cybercrime prevention monitoring approach to enhance the quality of knowledge about policies that aim to reduce the prevalence and impact of online harms. Despite very significant investments made by governments over the past decade to improve the cybersecurity of publicly and privately-operated computer systems, there is very limited systematic knowledge about what cybercrime prevention policies have been adopted in various parts of the world and even less knowledge about their effectiveness in reducing the exposure of individuals and organizations to cybercrime. Borrowing from the policy monitoring (or policy surveillance) methodology that was developed in a broad range of fields such as public health and education, this article argues that such an approach would be critical in advancing our understanding of what is being done to control cybercrime, what works, what doesn’t, and what is promising. This article provides an overview of the principles and benefits of the policy monitoring approach, reviews the main features of a sample of 18 policy monitoring platforms, assesses a dozen cybersecurity policy rating initiatives—concluding that very few of them include cybercrime in their framework, then provides a template for the creation of a dedicated cybercrime prevention monitoring tool that would benefit academics, policy-makers and practitioners.
Survey on Text-Based Sentiment Analysis of Bengali Language

Digital contents in Bengali text are increasing with the advancements of World Wide Web (WWW) to express the sentiment of content originators from mixed perspectives. These high voluminous unstructured web contents can be utilized to build smarter tools to assist people through Natural Language Processing (NLP). Though Bengali NLP tools are still inadequate due to its inherent complexities, research on Sentiment Analysis in Bengali is flourishing as a challenging area and is getting researcher’s attention at a rapid pace. To do research in this area, researchers spend a lot of their time to investigate previously published works to understand the advancements for further enhancement; one of the most stressful and challenging parts of research. In this regard, we are going to demonstrate domain specific available pre-processing steps along with existing research methodologies, respective datasets and evaluation metrics of notable works related to sentiment analysis of Bengali language as our research finding. Moreover, we also incorporate a series of directions for future researchers to augment their research in this exciting domain.
RMAS Architecture for Autonomic Computing in Cyber-Physical Systems

Autonomic computing initiative aimed to develop computer systems capable of self-management in order to overcome the rapidly growing complexity of computing systems management. Similar complexity affects the management and control of the systems in the industry of the future that currently relies on the advancements in cyber-physical systems frameworks. With this position paper, the RMAS architecture is checked against the major properties of autonomic systems. RMAS is proposed as a methodological and technological platform to reduce the barriers that complexity poses to further growth in intelligent automation and control.
Bayesian 3D ConvNets for Action Recognition from Few Examples

A core challenge in action recognition from videos is obtaining sufficient training examples to train deep networks. This holds especially for action tasks from non-standard sensors such as infra-red cameras. In this work, we investigate Bayesian 3D ConvNets for action recognition when training examples are scarce. This work connects 3D ConvNets, a state-of-the-art approach for action recognition, with Bayesian networks, which have shown to be effective regularizers for deep networks. We do so by extending Bayes by Backprop to 3D ConvNets. Experimental evaluation on three small-scale action datasets from both RGB and infra-red sensors shows that Bayesian 3D ConvNets have a better test generalizing than standard 3D ConvNets. We find that, the more scarce the number of training examples per action, the better our Bayesian 3D ConvNets performs, highlighting the potential of Bayesian learning in the video domain.
Statistically Robust Evaluation of Stream-Based Recommender Systems

Online incremental models for recommendation are nowadays pervasive in both the industry and the academia. However, there is not yet a standard evaluation methodology for the algorithms that maintain such models. Moreover, online evaluation methodologies available in the literature generally fall short on the statistical validation of results, since this validation is not trivially applicable to stream-based algorithms. We propose a <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -fold validation framework for the pairwise comparison of recommendation algorithms that learn from user feedback streams, using prequential evaluation. Our proposal enables continuous statistical testing on adaptive-size sliding windows over the outcome of the prequential process, allowing practitioners and researchers to make decisions in real time based on solid statistical evidence. We present a set of experiments to gain insights on the sensitivity and robustness of two statistical tests-McNemar's and Wilcoxon signed rank-in a streaming data environment. Our results show that besides allowing a real-time, fine-grained online assessment, the online versions of the statistical tests are at least as robust as the batch versions, and definitely more robust than a simple prequential single-fold approach.
Complexity results for probabilistic answer set programming

We analyze the computational complexity of probabilistic logic programming with constraints, disjunctive heads, and aggregates such as sum and max. We consider propositional programs and relational programs with bounded-arity predicates, and look at cautious reasoning (i.e., computing the smallest probability of an atom over all probability models), cautious explanation (i.e., finding an interpretation that maximizes the lower probability of evidence) and cautious maximum-a-posteriori (i.e., finding a partial interpretation for a set of atoms that maximizes their lower probability conditional on evidence) under Lukasiewicz's credal semantics.
Detecting and Mapping Slag Heaps at Ancient Copper Production Sites in Oman

This study presents a new approach for detection and mapping of ancient slag heaps using 16-band multispectral satellite imagery. Understanding the distribution of slag (a byproduct of metal production) is of great importance for understanding how metallurgy shaped long-term economic and political change across the ancient Near East. This study presents results of slag mapping in Oman using WorldView-3 (WV3) satellite imagery. A semi-automated target detection routine using a mixed tuned matched filtering (MTMF) algorithm with scene-derived spectral signatures was applied to 16-band WV3 imagery. Associated field mapping at two copper production sites indicates that WorldView-3 satellite data can differentiate slag and background materials with a relatively high (&gt;90%) overall accuracy. Although this method shows promise for future initiatives to discover and map slag deposits, difficulties in dark object spectral differentiation and underestimation of total slag coverage substantially limit its use. Resulting lower estimations of combined user’s (61%) and producer’s (45%) accuracies contextualize these limitations for slag specific classification. Accordingly, we describe potential approaches to address these challenges in future studies. As sites of ancient metallurgy in Oman are often located in areas of modern exploration and mining, detection and mapping of ancient slag heaps via satellite imagery can be helpful for discovery and monitoring of vulnerable cultural heritage sites.
Deep learning for magnitude prediction in earthquake early warning

Fast and accurate magnitude prediction is the key to the success of earthquake early warning (EEW). However, it is difficult to significantly improve the performance of magnitude prediction by empirically defined characteristic parameters. In this study, we have proposed a new approach (EEWNet) based on deep learning to predict magnitude for EEW. The initial few seconds of P-wave recorded by a single station without any preprocessing is used as the input to EEWNet, and the maximum displacement for the whole record is predicted and by which the magnitude is calculated. A large number of borehole underground strong motion records are used to train, validate and test the proposed EEWNet, and the predicted results are compared against those by empirical peak displacement Pd method. The comparison demonstrates that EEWNet produces better and quicker results than those by Pd, and EEWNet can predict magnitude between 4.0 and 5.9 as early as the first 0.5 s P-wave arrives. EEWNet is therefore expected to significantly enhance the accuracy and speed of magnitude estimation in practical regional EEW systems.
An Augmented Reality Approach to Delivering a Connected Digital Forensics Training Experience

Teaching digital forensics can be regarded as being rather challenging due to the multi-faceted nature of a typical investigation, leading to the call for providing digital forensics students with a context that is clearer and more encompassing in representing the investigative process. This paper describes how an online gaming engine can be leveraged to deliver students with such a learning experience. We report on the experiences reported by our students who completed the exercise and provide suggestions going forward on how the system can potentially be extended to enhance the overall realization of a full case that encompasses the typical resolution of a case or incident.
Learning along a Channel: the Expectation part of Expectation-Maximisation

This paper first investigates a form of frequentist learning that is often called Maximal Likelihood Estimation (MLE). It is redescribed as a natural transformation from multisets to distributions that commutes with marginalisation and disintegration. It forms the basis for the next, main topic: learning of hidden states, which is reformulated as learning along a channel. This topic requires a fundamental look at what data is and what its validity is in a particular state. The paper distinguishes two forms, denoted as ‘M’ for ‘multiple states’ and ‘C’ for ‘copied states’. It is shown that M and C forms exist for validity of data, for learning from data, and for learning along a channel. This M/C distinction allows us to capture two completely different examples from the literature which both claim to be instances of Expectation-Maximisation.