Reviewerly/exHarmony huggingface dataset:
Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis

Algorithm selection (AS) deals with the automatic selection of an algorithmfrom a fixed set of candidate algorithms most suitable for a specific instanceof an algorithmic problem class, where suitability often refers to analgorithm's runtime. Due to possibly extremely long runtimes of candidatealgorithms, training data for algorithm selection models is usually generatedunder time constraints in the sense that not all algorithms are run tocompletion on all instances. Thus, training data usually comprises censoredinformation, as the true runtime of algorithms timed out remains unknown.However, many standard AS approaches are not able to handle such information ina proper way. On the other side, survival analysis (SA) naturally supportscensored data and offers appropriate ways to use such data for learningdistributional models of algorithm runtime, as we demonstrate in this work. Weleverage such models as a basis of a sophisticated decision-theoretic approachto algorithm selection, which we dub Run2Survive. Moreover, taking advantage ofa framework of this kind, we advocate a risk-averse approach to algorithmselection, in which the avoidance of a timeout is given high priority. In anextensive experimental study with the standard benchmark ASlib, our approach isshown to be highly competitive and in many cases even superior tostate-of-the-art AS approaches.
Spherical Feature Transform for Deep Metric Learning

Data augmentation in feature space is effective to increase data diversity. Previous methods assume that different classes have the same covariance in their feature distributions. Thus, feature transform between different classes is performed via translation. However, this approach is no longer valid for recent deep metric learning scenarios, where feature normalization is widely adopted and all features lie on a hypersphere. This work proposes a novel spherical feature transform approach. It relaxes the assumption of identical covariance between classes to an assumption of similar covariances of different classes on a hypersphere. Consequently, the feature transform is performed by a rotation that respects the spherical data distributions. We provide a simple and effective training method, and in depth analysis on the relation between the two different transforms. Comprehensive experiments on various deep metric learning benchmarks and different baselines verify that our method achieves consistent performance improvement and state-of-the-art results.
Attention-based CNN-BiLSTM for Dialect Identification on Javanese Text

This study proposes a hybrid deep learning models called attention-based CNN-BiLSTM (ACBiL) for dialect identification on Javanese text. Our ACBiL model comprises of input layer, convolution layer, max pooling layer, batch normalization layer, bidirectional LSTM layer, attention layer, fully connected layer and softmax layer. In the attention layer, we applied a hierarchical attention networks using word and sentence level attention to observe the level of importance from the content. As comparison, we also experimented with other several classical machine learning and deep learning approaches. Among the classical machine learning, the Linear Regression with unigram achieved the best performance with average accuracy of 0.9647. In addition, our observation with the deep learning models outperformed the traditional machine learning models significantly. Our experiments showed that the ACBiL architecture achieved the best performance among the other deep learning methods with the accuracy of 0.9944.
Design and Implementation of Fuzzy Compensation Scheme for Temperature and Solar Irradiance Wireless Sensor Network (WSN) on Solar Photovoltaic (PV) System

Photovoltaic (PV) systems need measurements of incident solar irradiance and PV surface temperature for performance analysis and monitoring purposes. Ground-based network sensor measurement is preferred in many near real-time operations such as forecasting and photovoltaic (PV) performance evaluation on the ground. Hence, this study proposed a Fuzzy compensation scheme for temperature and solar irradiance wireless sensor network (WSN) measurement on stand-alone solar photovoltaic (PV) system to improve the sensor measurement. The WSN installation through an Internet of Things (IoT) platform for solar irradiance and PV surface temperature measurement was fabricated. The simulation for the solar irradiance Fuzzy Logic compensation (SIFLC) scheme and Temperature Fuzzy Logic compensation (TFLC) scheme was conducted using Matlab/Simulink. The simulation result identified that the scheme was used to compensate for the error temperature and solar irradiance sensor measurements over a variation temperature and solar irradiance range from 20 to 60 °C and from zero up to 2000 W/m2. The experimental results show that the Fuzzy Logic compensation scheme can reduce the sensor measurement error up to 17% and 20% for solar irradiance and PV temperature measurement.
Effective Learning of Tax Regulations using Different Chatbot Techniques

Teaching tax-related regulations have always been a challenge due to the inclusion of external variables that hinder the learning process, such as the high complexity of tax systems and legislation variability.Universities have sought different alternatives to support the teaching process both outside and inside the classroom, ranging from recreational activities to active learning.This article will show the experience resulting from using a chatbot to support learning in accounting students for the teaching of tax regulations related to the Chilean tax system, comparing two types of tools, on the one hand, a free conversation chatbot using natural language processing versus a rule-based chatbot driven by a decision tree.The experimentation process was carried out with 50 higher education students, divided into an experimental group and a control group, in two different courses.The results obtained demonstrated in both cases greater effectiveness of the use of the chatbot in learning the tax matter, both in the free conversation chatbot where the experimental group obtained a 15.7% improvement versus the control group that obtained a 1.05% improvement, as in the chatbot that applied decision tree where the experimental group obtained a 32% improvement versus the control group with 5.2%.Considering the complexity of the content in tax matters and the little innovation in the existing teaching subjects in the area and that the students improve learning using both chatbot tools compared to other learning techniques, is considered a relevant contribution to teaching innovation.
Real time face recognition of video surveillance system using haar cascade classifier

&lt;div&gt;This project investigates the use of face recognition for a surveillance system. The normal video surveillance system uses in closed-circuit television (CCTV) to record video for security purpose. It is used to identify the identity of a person through their appearances on the recorded video, manually. Today’s video surveillance camera system usually not occupied with a face recognition system. With some modification, a surveillance camera system can be used as face detection and recognition that can be done in real-time. The proposed system makes use of surveillance camera system that can identify the identity of a person automatically by using face recognition of Haar cascade classifier. The hardware used for this project were Raspberry Pi as a processor and Pi Camera as a camera module. The development of this project consist of three main phases which were data gathering, training recognizer, and face recognition process. All three phases have been executed using Python programming and OpenCV library, which have been performed in a Raspbian operation system. From the result, the proposed system successfully displays the output result of human face recognition, with facial angle within ±40°, in medium and normal light condition, and within a distance of 0.4 to 1.2 meter. Targeted image are allowed to wear face accessory as long as not covering the face structure. In conclusion, this system considered, can reduce the cost of manpower in order to identify the identity of a person in real time situation.&lt;/div&gt;
The Effect of Talking Chips Technique Toward the Improvement of Students’ Speaking Achievement at one of the Senior High Schools in Bandung

This study investigated how the Talking Chips Technique can help students to improve their speaking. The sample was only one class and total number of students was 36 students. This study used a time series design and three data collection, i.e. note taking, participant observation, and speaking test. The data from observation were analyzed by using thematic analysis and band score of speaking. This study was conducted by using multiple pre-test and post-test. Therefore, the findings of this study were based on pre- test, treatments and post- test. During the treatments, there were problems related to classroom management (noisy, low participation, laughing), fluency (pause, flow and speed) and accuracy (pronunciation, grammar and vocabulary). Those problems could be minimized. After post-test, there were still problems, but the problems were fewer than previous ones like only fluency and accuracy because students had showed the awareness and enthusiasm in speaking English. This study showed the improvement of students’ speaking achievement from treatment until posttest. It was proved by the mean score pretest and posttest. In pre-test, the mean score of fluency was 61.1 and 62.81 for accuracy. In post-test, the mean score of fluency was 71.451 and accuracy was 74.69. Then, t- test for fluency was 7.05 and t-test for accuracy was 8.31 with t critical or table was 2.031. It meant that H1 was accepted. There was significance improvement of students’ speaking in terms of fluency and accuracy. The findings suggest that the students still need further treatments to improve their speaking even though they have achieved the target.
Sentiment of the public: the role of social media in revealing important events

Purpose Social media is characterized by its volume, its speed of generation and its easy and open access; all this making it an important source of information that provides valuable insights. Content characteristics such as valence and emotions play an important role in the diffusion of information; in fact, emotions can shape virality of topics in social media. The purpose of this research is to fill the gap in event detection applied on online content by incorporating sentiment, more specifically strong sentiment, as main attribute in identifying relevant content. Design/methodology/approach The study proposes a methodology based on strong sentiment classification using machine learning and an advanced scoring technique. Findings The results show the following key findings: the proposed methodology is able to automatically capture trending topics and achieve better classification compared to state-of-the-art topic detection algorithms. In addition, the methodology is not context specific; it is able to successfully identify important events from various datasets within the context of politics, rallies, various news and real tragedies. Originality/value This study fills the gap of topic detection applied on online content by building on the assumption that important events trigger strong sentiment among the society. In addition, classic topic detection algorithms require tuning in terms of number of topics to search for. This methodology involves scoring the posts and, thus, does not require limiting the number topics; it also allows ordering the topics by relevance based on the value of the score. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-12-2019-0373
Design and Implementation of a Compressed Certificate Status Protocol

Trust in Secure Sockets Layer–based communications is traditionally provided by Certificate (or Certification) Authorities (CAs) in the form of signed certificates. Checking the validity of a certificate involves three steps: (i) checking its expiration date, (ii) verifying its signature, and (iii) ensuring that it is not revoked. Currently, such certificate revocation checks (i.e., step (iii) above) are done either via Certificate Revocation Lists (CRLs), or Online Certificate Status Protocol (OCSP) servers. Unfortunately, despite the existence of these revocation checks, sophisticated cyber-attackers can still trick web browsers to trust a revoked certificate, believing that it is still valid. Although frequently updated , nonced , and timestamped certificates can reduce the frequency and impact of such cyber-attacks, they add a huge burden to the CAs and OCSP servers. Indeed, CAs and/or OCSP servers need to timestamp and sign on a regular basis all the responses, for every certificate they have issued, resulting in a very high overhead. To mitigate this and provide a solution to the described cyber-attacks, we present CCSP : a new approach to provide timely information regarding the status of certificates, which capitalizes on a newly introduced notion called Signed Collections . In this article, we present in detail the notion of Signed Collections and the complete design, implementation, and evaluation of our approach. Performance evaluation shows that CCSP (i) reduces space requirements by more than an order of magnitude, (ii) lowers the number of signatures required by six orders of magnitude compared to OCSP-based methods, and (iii) adds only a few milliseconds of overhead in the overall user latency.
Lightweight FaceNet Based on MobileNet

Face recognition is a kind of biometric technology that recognizes identities through human faces.At first, the speed of machine recognition of human faces was slow and the accuracy was lower than manual recognition.With the rapid development of deep learning and the application of Convolutional Neural Network (CNN) in the field of face recognition, the accuracy of face recognition has greatly improved.FaceNet is a deep learning framework commonly used in face recognition in recent years.FaceNet uses the deep learning model GoogLeNet, which has a high accuracy in face recognition.However, its network structure is too large, which causes the FaceNet to run at a low speed.Therefore, to improve the running speed without affecting the recognition accuracy of FaceNet, this paper proposes a lightweight FaceNet model based on MobileNet.This article mainly does the following works: Based on the analysis of the low running speed of FaceNet and the principle of Mobi-leNet, a lightweight FaceNet model based on MobileNet is proposed.The model would reduce the overall calculation of the network by using deep separable convolutions.In this paper, the model is trained on the CASIA-WebFace and VGGFace2 datasets, and tested on the LFW dataset.Experimental results show that the model reduces the network parameters to a large extent while ensuring the accuracy and hence an increase in system computing speed.The model can also perform face recognition on a specific person in the video.
Long-distanceinfrared video pedestrian detection using deep learning and backgroundsubtraction

Abstract Infrared video-based pedestrian detectionplays an important role in surveillance and automatic driving. Compared with vision cameras, infrared cameras have the characteristics of all-day and all-weather availability. The current infrared video detection methods generally follow the traditional paradigm. However, when objectsare at a long distance, it is challenging for existing methods to achieve accurate detection. Meanwhile, deep learning-based methods have been widely used in visible light object detection. In this paper, we introduce an algorithm which combines deep learning and background subtraction methods to achieve infrared pedestrian detection. Firstly, background subtraction method is performed to provide the inter-frame information for the deep learning module. Secondly, the RefineDet equipped with an attention module is used to improve the detection accuracy for small pedestrian. Moreover, we develop a novel infrared video dataset which includes long-distance pedestrian for performance evaluation. Experiments demonstrated that our method can achieve a superior performance as compared to RefinDetand SSD methods.
Expert system for heart disease based on electrocardiogram data using certainty factor with multiple rule

&lt;span id="docs-internal-guid-b7163134-7fff-29de-9179-24e242f1239b"&gt;&lt;span&gt;Limited public health services in remote areas, where the lack of transportation infrastructure, facilities, communication facilities and minimal medical personnel, especially for areas with underdeveloped, foremost, and regular (3T) status. The limitation of medical personnel is one of the factors for the high mortality rate of heart disease. On the other hand, the development of information technology, especially in the field of computing, is very fast in the era of the industrial revolution 4.0, but not yet used optimally, especially in the health sector. This study aims to develop a system or software that can replace a doctor for the process of identifying heart defects based on an expert system. Expert system developed with the certainty factor with multiple rule approach. System testing is carried out from the results of the system validity with experts, so that the system test results produce a certainty factor value for a normal heart of 0.95 and an accuracy level of 95%, while the certainty factor (CF) value for an abnormal heart is 0.99 and produces an accuracy rate of 99%.&lt;/span&gt;&lt;/span&gt;&lt;br /&gt;&lt;div style="mso-element: comment-list;"&gt;&lt;div style="mso-element: comment;"&gt;&lt;div id="_com_1" class="msocomtxt"&gt;&lt;!--[if !supportAnnotations]--&gt;&lt;/div&gt;&lt;!--[endif]--&gt;&lt;/div&gt;&lt;/div&gt;
Telemetry Data-based Spacecraft Anomaly Detection Using Generative Adversarial Networks

The telemetry data of spacecraft is an ultra-high dimensional time series used to indicate on-orbit operation status, and anomaly detection can effectively ensure safety and reliability. Aiming at the characteristics and complex correlation of high dimensional telemetry data, this paper proposes a novel anomaly detection method based on Generative Adversarial Networks (GAN) for telemetry data anomaly detection. Instead of treating each variable independently, our proposed method captures the latent representation amongst multi-dimensional time series. For normal data, the GAN-based anomaly detection method can obtain a reconstructed time series similar to the original time series, learning the probability distribution model of normal data. For abnormal data, the reconstructed time series deviates greatly from the original time series. In the GAN framework, we use Long Short-Term Memory (LSTM) as the network structure of generator for time series reconstruction and discriminator for calculating the probability of being the real time series, which can learn the temporal features of telemetry data. We also propose a novel anomaly score called GDScore, which comprehensively considers the reconstruction error of the generator and the output of the discriminator. We conduct experiments with two telemetry datasets, which verifies that our proposed GAN-based anomaly detection method can effectively detect outliers.
Enhancing network modularity to mitigate catastrophic forgetting

Abstract Catastrophic forgetting occurs when learning algorithms change connections used to encode previously acquired skills to learn a new skill. Recently, a modular approach for neural networks was deemed necessary as learning problems grow in scale and complexity since it intuitively should reduce learning interference by separating functionality into physically distinct network modules. However, an algorithmic approach is difficult in practice since it involves expert design and trial and error. Kashtan et al. finds that evolution under an environment that changes in a modular fashion leads to the spontaneous evolution of a modular network structure. In this paper, we aim to solve the reverse problem of modularly varying goal (MVG) to obtain a highly modular structure that can mitigate catastrophic forgetting so that it can also apply to realistic data. First, we confirm that a configuration with a highly modular structure exists by applying an MVG against a realistic dataset and confirm that this neural network can mitigate catastrophic forgetting. Next, we solve the reverse problem, that is, we propose a method that can obtain a highly modular structure able to mitigate catastrophic forgetting. Since the MVG-obtained neural network can relatively maintain the intra-module elements while leaving the inter-module elements relatively variable, we propose a method to restrict the inter-module weight elements so that they can be relatively variable against the intra-module ones. From the results, the obtained neural network has a highly modular structure and can learn an unlearned goal faster than without this method.
PANAS-TDL: A Psychrometric Deep Learning Model for Characterizing Sentiments of Tourists Against the COVID-19 Pandemic on Twitter

One of the main sector that moves the economy of the countries worldwide is tourism and its associated services. Dynamics like globalization has led these countries to create tourism services with global standards, however, in the context of COVID-19 pandemic, these services have been affected as shown the social networks. This fact led to a change in the perception of tourists against a destination. In order to unify this change in an objective manner, we propose a Deep Learning model that integrates a PANAS scale (Positive and Negative Affect Scale) (PANAS-tDL), to characterize a tourist destination based on a series of potential factors (weather conditions, healthy, holidays, seasonality and economic factors) identified in comments obtained from a social network like Twitter. The results obtained by the PANAS-tDL model show its good performance evaluating the change of perception of tourists against four destinations affected by COVID-19, taking as reference the 11-sentiment scale defined by PANAS-t scale. Thanks to adaptation capacity, the model can be extended to evaluate the change in perception of tourists using different social networks and to evaluate different marketing strategies to promote a destination.
InstaHide's Sample Complexity When Mixing Two Private Images

Training neural networks usually require large numbers of sensitive training data, and how to protect the privacy of training data has thus become a critical topic in deep learning research. InstaHide is a state-of-the-art scheme to protect training data privacy with only minor effects on test accuracy, and its security has become a salient question. In this paper, we systematically study recent attacks on InstaHide and present a unified framework to understand and analyze these attacks. We find that existing attacks either do not have a provable guarantee or can only recover a single private image. On the current InstaHide challenge setup, where each InstaHide image is a mixture of two private images, we present a new algorithm to recover all the private images with a provable guarantee and optimal sample complexity. In addition, we also provide a computational hardness result on retrieving all InstaHide images. Our results demonstrate that InstaHide is not information-theoretically secure but computationally secure in the worst case, even when mixing two private images.