Reviewerly/exHarmony huggingface dataset:
Usability of Voice-based Intelligent Personal Assistants

Recently the emergence of speech-based Intelligent Personal Assistants (sIPA) has opened a new modality of user interaction with machines. However, till date research in human computer interaction (HCI) has largely focused on graphical user interfaces (GUIs), and not fully facilitate the design of the sIPA's. Therefore, usability of sIPA's is an important area that current research should focus on keeping in mind their commercialization aspect. System usability scale (SUS) has been used by HCI researchers for a long time for measuring the usability aspect and the end-user experiences (UX) in a GUI environment. However, keeping in mind the fundamental differences between a speech-based and a GUI environment, the applicability and performance of SUS in this new context is unknown. In this study a subjective experiment is performed with 61 subjects across some popular commercially available smart-speakers (sIPA's) to investigate into their current state of art usability aspect along with checking if SUS is still a suitable usability evaluation scale in this new scenario. Results suggest that SUS might not be the best scale to use while evaluating the usability of these highly interactive speech-based systems and future research must focus on developing new scales for the current scenario.
Article Use in Russian and Spanish Learner Writing at CEFR B1 and B2 Levels: Effects of Proficiency, Native Language, and Specificity

This paper investigates article use in learner corpora in order to (i) examine how both the native language and proficiency level influence the patterns of article use, misuse, and omission and (ii) examine whether patterns of the overuse of the with specific indefinites found in experimental studies are also present in learner corpora. To this end, 200 texts from the Cambridge Learner Corpus are examined: 100 texts from Spanish-speaking learners of English and 100 texts from Russian and Ukrainian-speaking learners of English. Each subcorpus has an even distribution between learners at CEFR B1 and B2 proficiency levels. The results confirm that the learners' native language affects article use: speakers of Spanish, an article language, show an increase in correct article use with proficiency; in contrast, speakers of Russian, an article-less language, continue to make article errors. On the other hand, the particular patterns of article misuse found in prior experimental work are not found in learner corpora: while Russian-speaking learners do overuse the with indefinites in the corpora, this error type is not tied to specificity. This null finding could be due in part to the relatively low number of errors of the overuse in the learner corpus.
Filter Pruning via Probabilistic Model-based Optimization for Accelerating Deep Convolutional Neural Networks

Accelerating Deep Convolutional Neural Networks(CNNs) has recently received ever-increasing research focus. Among various approaches proposed in the literature, filter pruning has been regarded as a promising solution, which is due to its advantage in significant speedup and memory reduction of both network model and intermediate feature maps. Previous works utilized "smaller-norm-less-important" criterion to prune filters with smaller lp-norm values by pruning and retraining alternately. However, they ignore the effects of $feedback: most current approaches that prune filters only consider the statistics of the filters (e.g., prune filter with small lp-norm values), without considering the performance of the pruned model as an important feedback signal in the next iteration of filter pruning. To solve the problem of non-feedback, we propose a novel filter pruning method, namely Filter Pruning via Probabilistic Model-based Optimization (FPPMO). FPPMO solves the problem of non-feedback by pruning filters in a probabilistic manner. We introduce a pruning probability for each filter, and pruning is guided by sampling from the pruning probability distribution. An optimization method is proposed to update the pruning probability based on the performance of the pruned model in the pruning process. When applied to two image classification benchmarks, the effectiveness of our FPPMO is validated. Notably, on CIFAR-10, our FPPMO reduces more than 57% FLOPs on ResNet-110 with even 0.08% relative accuracy improvement. Moreover, on ILSVRC-2012, our FPPMO reduces more than 50% FLOPs on ResNet-101 without top-5 accuracy drop. Which proving that our FPPMO outperforms the state-of-the-art filter pruning method.
Face Recognition System for Prevention of Car Theft with Haar Cascade and Local Binary Pattern Histogram using Raspberry Pi

In this era, the occurrence of vehicle theft has become a fairly frequent problem, especially in big cities like Jakarta and Surabaya. Although the technology for car security is very sophisticated (e.g. keyless system), but there are many cases that thieves still can break into the system. Once a car was stolen, the whereabouts of the car was unknown and the thief was on the loose. The goal of this research is to overcome this problem. As a device, this research works on Raspberry Pi 3 that connected with the Raspberry Pi Camera. Using the OpenCV library, with the Haar Cascade method for face detection, and Local Binary Pattern Histogram for face recognition. The device must be connected to the internet in order to send the information using a Telegram message. The research results show the success of the device system in face-recognizing between the car owner and car thief with optimal conditions in the morning until the afternoon with the light intensity around 660 to 1000 lux, and best recognizing distance at 50 cm. The success rate for obtaining the car’s location for the outdoor condition is 100%. Even if there is a slope or an error data, it can be tolerated because the difference was not too high, about 0.1-1.0 m.
Two‐level pruning based ensemble with abstained learners for concept drift in data streams

Abstract Mining data streams for predictive analysis is one of the most interesting topics in machine learning. With the drifting data distributions, it becomes important to build adaptive systems which are dynamic and accurate. Although ensembles are powerful in improving accuracy of incremental learning, it is crucial to maintain a set of best suitable learners in the ensemble while considering the diversity between them. By adding diversity‐based pruning to the traditional accuracy‐based pruning, this paper proposes a novel concept drift handling approach named Two‐Level Pruning based Ensemble with Abstained Learners (TLP‐EnAbLe). In this approach, deferred similarity based pruning delays the removal of under performing similar learners until it is assured that they are no longer fit for prediction. The proposed scheme retains diverse learners that are well suited for current concept. Two‐level abstaining monitors performance of learners and chooses the best set of competent learners for participating in decision making. This is an enhancement to traditional majority voting system which dynamically chooses high performing learners and abstains the ones which are not suitable for prediction. In our experiments, it has been demonstrated that TLP‐EnAbLe handles concept drift more effectively than other state‐of‐the‐art algorithms on nineteen artificially drifting and ten real‐world datasets. Further, statistical tests conducted on various drift patterns which include gradual, abrupt, recurring and their combinations prove efficiency of the proposed approach.
Assessment of Daily Routine Uniformity in a Smart Home Environment Using Hierarchical Clustering

The gradual decline in routine patterns is a major symptom of early-stage dementia, therefore an unobtrusive real-life assessment of the elder's routine can potentially be of significant clinical importance. This article focuses on the assessment of changes in a person's daily routine using longitudinal data recorded from a network of nonintrusive motion sensors in a smart home environment. In this article, we propose to identify repeating patterns in a person's daily routine over the span of multiple days using hierarchical clustering algorithms, which provide an effective way to mitigate noise artifacts and confounding factors that contribute to the momentary variability of the sensor data. We have evaluated our proposed algorithm on both synthetic and real-world data recorded in the span of 50-100 days from four elderly adults. Our results indicate that the proposed hierarchical clustering approach can more reliably capture the gradual change in the degree of routineness compared to baseline approaches that measure the similarity between two consecutive days or capture variations in the occurrence of recognized activities.
SDHF: Spotting DeepFakes with Hierarchical Features

DeepFake videos are widely distributed on social media platforms, which has seriously affected the authenticity of digital media content, calling for robust DeepFake detection methods. Although numerous detection methods are formulated as frame-based binary classification, less attention has been paid to aggregate the features over individual frames to get a video-based judgement. We observed that for the detection of DeepFake videos, three different level forgery features from frame, clip and video can complement each other. We also found that discrete, large interval sampling strategy is more suitable for DeepFake detection, which can sample more complex video scenes, including multiple subjects, diverse facial expressions and head poses. In this work, we propose a hierarchical framework, using 2D convolutional neural networks for frame-level features extraction followed by a 1D convolutional aggregator to extract clip-level and video-level features, which can comprehensively exploit three different levels of features to make decisions. Evaluation was performed on four datasets, including DFDC, Celeb-DF, FaceForensics++ and UADFV, which provides competitive results compared to other methods. Experimental results of cross-test demonstrate that our hierarchical framework has excellent generalization performance in the face of unknown datasets.
Requirements of API Documentation: A Case Study into Computer Vision Services

Using cloud-based computer vision services is gaining traction, where developers access AI-powered components through familiar RESTful APIs, not needing to orchestrate large training and inference infrastructures or curate/label training datasets. However, while these APIs seem familiar to use, their non-deterministic run-time behaviour and evolution is not adequately communicated to developers. Therefore, improving these services' API documentation is paramount-more extensive documentation facilitates the development process of intelligent software. In a prior study, we extracted 34 API documentation artefacts from 21 seminal works, devising a taxonomy of five key requirements to produce quality API documentation. We extend this study in two ways. Firstly, by surveying 104 developers of varying experience to understand what API documentation artefacts are of most value to practitioners. Secondly, identifying which of these highly-valued artefacts are or are not well-documented through a case study in the emerging computer vision service domain. We identify: (i) several gaps in the software engineering literature, where aspects of API documentation understanding is/is not extensively investigated; and (ii) where industry vendors (in contrast) document artefacts to better serve their end-developers. We provide a set of recommendations to enhance intelligent software documentation for both vendors and the wider research community.
History and Evolution of Single Pass Connected Component Analysis

The techniques for single pass connected component analysis have undergone significant changes from their initial development to current state-of-the-art algorithms. This review traces the evolution of the algorithms, and explores the linkages and development of ideas introduced by various researchers. Three significant developments are: the recycling of labels to enable processing with resources proportional to the image width; reduction of overheads associated with label merging; and processing of multiple pixels in parallel. These are of particular interest to those developing high speed and low latency image processing and machine vision systems.
A log –barrier approach for airborne gamma-ray spectrometry inversion

The standard processing of airborne gamma-ray spectrometry (AGRS) data provides useful preliminary information to interpretation in several contexts, such as environmental studies, geological mapping, and analysis of mineral deposits. For optimal results, the acquisition conditions should be nearly constant, and the flight height should be uniform. However, abrupt changes in flight height (often originated from mountainous terrain) are common and lead to spurious variations and incorrect anomaly interpretation. Moreover, the commonly used corrections applied to radiometric data do not consider the effective sampling area of a survey, especially the overlap between successive fields of view. As a consequence, the concentrations estimates of potassium (K), uranium (eU), and thorium (eTh) are not sharp. A solution to deal with this problem is to compute the concentration from the AGRS data through an inversion algorithm. Inversion of AGRS data has shown to be an effective approach to suppress the significant overlap between successive fields of view. We introduce a logarithmic barrier approach for the radiometric inversion to avoid spurious negative values in the radioelement concentration models. We choose calibration ranges from Brazil and Canada to test the proposed methodology. We produced concentration models for K, eU and eTh and compared the standard approach model with the recovered model from both ranges. The predicted data from inversion of both calibration ranges are mostly consistent with the survey data, not over smoothing the data or fitting noise. The predicted data from inversion showed more consistency with the observed data than the one predicted from the standard procedure.
Development of databases of intelligent expert systems for automatic control of product quality indicators

Abstract Currently existing methods of product quality assessment are far from perfect, since this assessment is carried out in the laboratories of enterprises. Successful solution of this problem is possible when implementing intelligent expert systems for monitoring and forecasting in the flow of product quality indicators using intelligent technologies in the production process. One of the most important components of the expert system is the database. The article deals with the methodological basis for creating databases for building an intelligent expert system for monitoring and forecasting product quality indicators in the production process. Distinctive features of databases are highlighted. The method of database development is investigated and proposed. The main stages of database development are investigated and analyzed. An integrated conceptual model of the domain database of the integrated expert system has been developed. Developed: a tree of database goals, the composition of the main elements of the database system of the intellectual expert system of product quality control. The main phases and stages of designing the database and expert system have been worked out. VAD data flow diagrams are presented. The description of the domain semantics is developed in the form of a system of functional and multi-valued dependencies between attributes of the conceptual model of the domain database. The database architecture has been developed. The developed specialized database of the integrated expert system for monitoring food quality indicators is a single information environment that provides information for automatic control of product quality indicators.
Accelerator Design and Performance Modeling for Homomorphic Encrypted CNN Inference

The rapid advent of cloud computing has brought with it concerns on data security and privacy. Fully Homomorphic Encryption (FHE) is a technique for enabling data security that allows arbitrary computations to be performed directly on encrypted data. In particular, FHE can be used with convolutional neural networks (CNN) to perform inference as a service on homomorphic encrypted input data. However, the high computational demands of FHE inference require a careful understanding of the tradeoffs between various parameters such as security level, hardware resources and performance. In this paper, we propose a parameterized accelerator for homomorphic encrypted CNN inference. We first develop parallel algorithms to implement CNN operations via FHE primitives. We then develop a parameterized model to evaluate the performance of our CNN design. The model accepts inputs in terms of available hardware resources and security parameters and outputs performance estimates. As an illustration, for a typical image classification task on CIFAR-10 dataset with a seven-layer CNN model, we show that a batch of 4K encrypted images can be classified within 1 second on a device operating at 2 GHz clock rate with 16K MACs, 64 MB on-chip memory and 256 GB/s external memory bandwidth.
Implementation of Textrank Algorithm in Product Review Summarization

Internet technology led to the emergence of Web 2.0 which increase the number of User Generated Content (UGC) on the network. Online product review is a form of UGC. The case study in this research is a review of handphone products. The large number of reviews will take long time to read and compare between existing product reviews, so we need a technique that online product review can be read quickly without losing of its important information. The technique that can be used is the text summarizing technique. Text summarization techniques produce simplified versions of texts. In general, text summarization can be divided into two types, namely extractive and abstractive summaries. This research used extractive summaries. One important component in the process of obtaining an extractive summary is sentence extraction. In this study, the algorithm used for sentence extraction is TextRank. The purpose of this study was to determine the performance of the TextRank algorithm with handphone product reviews data by implementing it in different data conditions based on the presence or absence of a stopword and typo. These data conditions are used to formulate test scenarios. Testing is done by calculating the Rouge-1 value which compares the summary of system and experts. Expert who involved in this study are 2. Expert 1 is a person with expertise in Indonesian and Expert 2 is someone who has the knowledge and understanding of mobile phones with various types and characteristics. From the test results obtained, Expert 1 gets the best results for scenario 2 where data conditions are there is typo and no stopword with an average value of Rouge-1 of 42.29% and Expert 2 gets the best results for scenario 3 where data conditions are no typo and there is stopword with an average value of Rouge-1 is 46.71%. The results shows that the TextRank algorithm is not able to produce a good summary for handphone product review dataset.
Knowledge Distillation Framework for Action Recognition in Still Images

Still image action recognition is generally challenging, despite the considerable progress of convolutional neural networks in image classification. The absence of motion cues in still images is a chief challenge in human action recognition from images. Currently, most efficient methods train a deep CNN network directly on action recognition images. However, these methods have many parameters and high computational costs. Furthermore, most available methods have extracted auxiliary data such as human body gestures, involved objects, and the appearance of body parts from images. These methods employ bounding boxes as auxiliary input in the training and testing phases. However, this solution is inefficient because bounding boxes are not always available. We propose a knowledge distillation technique for action recognition in still images. This technique distills the knowledge of the cumbersome model into a small model. This framework also eliminates the requirement of using bounding boxes during training and testing networks. The best mean Average Precision (mAP) of our method is 92.11% on Stanford40 dataset.
Sensitivity-Aware Bit Allocation for Intermediate Deep Feature Compression

In this paper, we focus on compressing and transmitting deep intermediate features to support the prosperous applications at the cloud side efficiently, and propose a sensitivity-aware bit allocation algorithm for the deep intermediate feature compression. Considering that different channels' contributions to the final inference result of the deep learning model might differ a lot, we design a channel-wise bit allocation mechanism to maintain the accuracy while trying to reduce the bit-rate cost. The algorithm consists of two passes. In the first pass, only one channel is exposed to compression degradation while other channels are kept as the original ones in order to test this channel's sensitivity to the compression degradation. This process will be repeated until all channels' sensitivity is obtained. Then, in the second pass, bits allocated to each channel will be automatically decided according to the sensitivity obtained in the first pass to make sure that the channel with higher sensitivity can be allocated with more bits to maintain accuracy as much as possible. With the well-designed algorithm, our method surpasses state-of-the-art compression tools with on average 6.4% BD-rate saving.
Model Expert System for Diagnosis of Covid-19 Using Naïve Bayes Classifier

Abstract This paper offers an expert system model for COVID-19 diagnosis as an effort to overcome the spread of COVID-19 in Indonesia. The expert system model was built using the Naive Bayes Classifier method. Model development is carried out with preliminary research stages, data collection, analysis, model design, implementation, and testing. The data used to build and test the model comes from the health department and the acceleration of the Covid- 19 countermeasure group in Indonesia. The model was developed with a unified modeling language and a prototyping approach. Tests show that the developed COVID-19 diagnosis system expert model can diagnose COVID-19 based on the symptoms inputted by the user into the system. The application of the model produced in this study helps assist doctors in diagnosing COVID-19.
A Profit-maximizing Mechanism for Query-based Data Trading with Personalized Differential Privacy

Abstract Data trading has attracted increasing attention over the years as a cost-effective business paradigm, probably producing a tremendous amount of economic value. However, the study of query-based trading in the user data market is still in the initial stage. To design a practical user data trading mechanism, we have to consider three major challenges: privacy concern, compensation cost minimization and revenue maximization in a Bayesian environment. By jointly considering these challenges, we propose a profit-maximizing mechanism for user data trading with personalized differential privacy, called READ, which comprised two components, READ-COST for cost minimization and READ-REV for revenue maximization. Especially, READ adopts personalized differential privacy to satisfy each data owner’s diverse privacy preferences. READ-COST greedily selects the most cost-effective data owner to achieve the sub-optimal data query cost. Given this query cost, READ-REV calculates the maximum expected revenue in a Bayesian setting. Through rigorous theoretical analysis and real-data based experiments, we demonstrate that READ achieves all desired properties and approaches the optimal profit.