Reviewerly/exHarmony huggingface dataset:
Parametric Conditions for a Monotone TSK Fuzzy Inference System to be an<i>n</i>-Ary Aggregation Function

Despite the popularity and practical importance of the fuzzy inference system (FIS), the use of an FIS model as an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> -ary aggregation function, which is characterized by both the monotonicity and boundary properties, is yet to be established. This is because research on ensuring that FIS models satisfy the monotonicity property, i.e., monotone FIS, is relatively new, not to mention the additional requirement of satisfying the boundary property. The aim of this article, therefore, is to establish the parametric conditions for the Takagi–Sugeno–Kang (TSK) FIS model to operate as an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> -ary aggregation function (hereafter denoted as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> -TSK-FIS) via the specifications of fuzzy membership functions and fuzzy rules. An absorption property with fuzzy rules interpretation is outlined, and the use of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> -TSK-FIS as a uninorm is explained. Exploiting the established parametric conditions, a framework for which an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> -TSK-FIS model can be constructed from data samples is formulated and analyzed, along with a number of remarks. Synthetic data sets and a benchmark example on education assessment are presented and discussed. To be best of the authors’ knowledge, this article serves as the first use of the TSK-FIS model as an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> -ary aggregation function.
Error Correctable Hand-Eye Calibration for Stripe-Laser Vision-Guided Robotics

The stripe-laser vision guidance technology has received more and more attention in industry and academia. The hand-eye calibration technology plays an important role in the applications of the stripe-laser vision-guided robotics. Most existing hand-eye calibration methods impose orthogonality constraints. However, the orthogonality in hand-eye relationship may not be true when the stripe-laser sensor has errors. This makes the final guiding error inevitable. In this article, an error correctable (EC) hand-eye calibration method based on the linear properties of the central projection for the stripe-laser vision sensor guidance system is proposed. In this method, the measurements of a vision sensor with errors can be corrected to the right tool center point coordinates. Experiments involving synthetic and real data confirm that the proposed hand-eye calibration method is robust to the vision sensor errors.
Minimizing Variability in Language Sampling Analysis

Variability is common in language sample analysis (LSA), arising from personal factors such as age or level of education, or from factors within the text such as its length and purpose. Variability can affect interpretation of results in clinical practice and research studies, as well as the ability to detect change in individuals over time. This article focuses on sample length and time-based variability in the LSA literature and how it has been addressed through a scoping review. We then propose a method for estimating the effect of this common source of variability to allow determination of reliable change in individuals over time. Although some sources of variability are acknowledged in the research literature and clinical evidence-based practice, there has been no consistent method to account for these. The proposed method we present offers a means to address text length and time-based variability and materials and examples to facilitate its implementation in future studies and practice.
A Novel Frontier-Based Exploration Algorithm for Mobile Robots

This paper aims to bring a novel approach to the exploration paradigm of mobile robots. Consequently, it uses the frontier-exploration method alongside a utility function in order to determine new goals to be achieved by the robot. The proposed approach was implemented using the Robot Operating System as the middleware, and makes use of several packages for, e.g., mapping and navigation. The algorithm implemented in this paper was motivated by the original work on frontier exploration developed by Yamauchi [1] as well as the more recent development, e.g., histogram-based frontier exploration. In addition, this paper aims to include additional parameters in order to enhance the decisions made by the exploration algorithm.
An Approach for Concept Drifting Streams: Early Dynamic Weighted Majority

Techniques for handling drifting concepts are very significant for many real time applications. In this paper, a novel methodology has proposed, Early Dynamic Weighted Majority, based on assigning weight to the classifiers in the ensemble. It dynamically updates the weight by increasing the weight when the local prediction of an expert is correct which is in contrast with a decreasing weight feature as in the earlier approaches. The system is a collection of experts that make a prediction based on weighted-majority voting. Empirical results prove that the proposed system is the best in managing drifting concepts in the underlying conceptual distribution.
Negative Latency Recognition Method for Fine-Grained Gestures Based on Terahertz Radar

Noncontact gesture recognition is gradually being applied to emerging applications, such as smart cars and smart phones. Negative latency gesture recognition (recognition before a gesture is finished) is desirable due to the instantaneous feedback. However, it is difficult for existing methods to achieve a high precision and negative latency gesture recognition. A fragment can provide too few features to directly identify all gestures well. By observing a large number of existing gesture sets and people's daily operating habits, we found that some high frequency used gestures are similar. To the best of our knowledge, it is the first time to redivide the gestures into two subsets according to their movement physical states. We divided the gestures with different shapes or motion states into a parent-class subset, and further divided each pair of parent-class gestures to obtain a child-class subset. In order to achieve a better tradeoff between the high-precision and negative latency, an approach of motion pattern and behavior intention (MPBI) is proposed. Taking full advantage of the characteristics of each subset, MPBI includes two models. First, pattern model coarsely classify the parent-class gestures by a convolutional network, and then intention model further classifies child-class gestures according to their opposite motion direction. MPBI is evaluated on a 340-GHz terahertz radar. With the advantage of its accurate ranging, intention model can recognize child-class gestures directly without training. MPBI is evaluated on 12 gestures and achieves a recognition accuracy of 94.13%, which only needs a 0.033-s gesture fragment as an input sample.
Cascaded Correlation Refinement for Robust Deep Tracking

Recent deep trackers have shown superior performance in visual tracking. In this article, we propose a cascaded correlation refinement approach to facilitate the robustness of deep tracking. The core idea is to address accurate target localization and reliable model update in a collaborative way. To this end, our approach cascades multiple stages of correlation refinement to progressively refine target localization. Thus, the localized object could be used to learn an accurate on-the-fly model for improving the reliability of model update. Meanwhile, we introduce an explicit measure to identify the tracking failure and then leverage a simple yet effective look-back scheme to adaptively incorporate the initial model and on-the-fly model to update the tracking model. As a result, the tracking model can be used to localize the target more accurately. Extensive experiments on OTB2013, OTB2015, VOT2016, VOT2018, UAV123, and GOT-10k demonstrate that the proposed tracker achieves the best robustness against the state of the arts.
Enhanced Forensic Process for Improving Mobile Cloud Traceability in Cloud-Based Mobile Applications

Recently, the raPID popularity of smart phones significantly impacts the day-to-day activities of humans due to the increased access to social media and instant messaging services. The misuse of social media services leads to different cybercrime activities. Both the mobile device and cloud are the most significant evidential sources for the social network application forensics. It is essential to establish a mobile cloud forensic process to deal with the crime activities performed by the intelligent mobile user. Also, improving the cloud event traceability is a challenging task in a massive cloud environment. This work presents a mobile cloud forensic process that incorporates the time synchronization and inter and intra-application analysis along with the traditional forensic procedure. Time synchronization is a prerequisite process that facilitates the investigator to perform the application forensics in the mobile cloud concisely. An inter and intra-application analysis process ensures the extraction of the forensic-rich evidence and enriches the performance of the cloud event traceability by utilizing the metadata of potential mobile evidence. The proposed forensic process maintains a chronological timeline of evidence before correlating the potential evidence of the mobile and cloud. Eventually, it submits the extracted evidence with a chain of custody information to validate the forensic investigation process. It significantly enhances the accuracy of the investigation and produces evidence by applying the step by step process in the Android WeChat social network application.
Kāvi: An Annotated Corpus of Punjabi Poetry with Emotion Detection Based on ‘Navrasa’

As the poetries are penned with numerous emotions and feelings, the computational linguistic analysis for emotion detection from poetry is a very challenging task. The main aim of this work is to detect emotions from Punjabi poetries based on different features present in Punjabi poetries. This is a pioneering approach in emotion detection from Punjabi poetries. ‘Kāvi’ Punjabi poetry corpus was manually annotated based on the Indian concept of ‘Navrasa’. In this corpus, 948 poetries were classified into 9 emotion states as presented in ‘Navrasa’, namely ‘karuna’, ‘shringar’, ‘hasya’, ‘raudra’, ‘veer’, ‘bhayanak’, ‘vibhata’, ‘adbhut’, and ‘shaanti’. This corpus was manually annotated and the Kappa Fleiss index was calculated for inter-annotator agreement. The different features (linguistic, poetic and statistical) were used for building the classifier and the two machine learning techniques, Naïve Bayes (NB) and Support Vector Machine (SVM) were experimented with these features. It was found that with an accuracy of 70.02%, SVM improved the overall accuracy of the emotion classification task. Further, Poetic features were found to outperform the linguistic features for the emotion detection task.
Text non-text classification based on area occupancy of equidistant pixels

Text detection and localization from text embedded natural images is still considered as a challenging problem in complex imagery environment. Foreground object segmentation followed by classification is a popular approach for this task. Component level object classification in clutter environment is therefore an important sub-problem. Appropriate extraction of foreground objects leads to effective classification that may certainly increase the performance of text detection. In this paper, a novel feature vector is developed based on area occupancy profile of equidistant pixels is reported for text/non-text classification. The generated feature descriptors are script invariant and much effective in practical scenario. This proposed feature set is evaluated on our dataset using five different pattern classifiers and experimental result shows that the said feature set yields more than 86% classification accuracy irrespective of scripts.
Aggregate in my way: Privacy-preserving data aggregation without trusted authority in ICN

Information-Centric Networking (ICN) is a novel future network architecture which in contrast to IP-based networks relies on content and its name. It separates the physical location of data from the discovery and forwarding process and solely relies on the content itself. For the Internet of Thing (IoT) networks, stripping the location information may provide privacy, but this does not translate to operational privacy. Attackers can infer user behavior patterns through operational privacy, by eavesdropping on meaningful information. The content name and designed signature in ICN can associate content with the identity of the provider, even if the IP address is hidden. Although several research efforts focus on the privacy protection of ICN, they do not consider the privacy implications of data aggregation. Most existing privacy-preserving data aggregation protocols are designed for special operations, such as sum, average, variance, max or min, and they cannot support arbitrary aggregation operations in the ciphertexts domain. Besides, the need for trusted authority (TA) restricts the use of existing protocols in the real world. In this paper, we propose a practical and privacy-preserving data aggregation scheme that can compute arbitrary aggregation functions without a TA. On one hand, our scheme can ensure users’ anonymity and privacy protection, while on the other, the scheme is efficient in enabling participants to join or leave the system dynamically. Security analysis shows that the proposed scheme can achieve the desired security properties, while experimental results demonstrate its effectiveness and efficiency.
Classification of Colposcopy Data Using GLCM-SVM on Cervical Cancer

cervical cancer is the second deadliest disease for women. To reduce the number of deaths caused by this disease, it is necessary that there is prevention by early detection of cancer. The method used to identify the presence of cervical cancer is to make visual observations that produce image data. However, a visual observation also has weaknesses, so it needs to be done computer-based observation to facilitate early detection. In this study, the computer-based observation method used is preprocessing, followed by a feature extraction process using the Gray Level Co-occurrence Matrix (GLCM) and Support Vector Machine (SVM) as a classification method. The best SVM classification results are using the polynomial kernel and GLCM feature extraction with an angle of 450. The accuracy rate obtained is 90%.
Hey Siri, tell me a story: Digital storytelling and AI authorship

Surveying narrative applications of artificial intelligence in film, games and interactive fiction, this article imagines the future of artificial intelligence (AI) authorship and explores trends that seek to replace human authors with algorithmically generated narrative. While experimental works that draw on text generation and natural language processing have a rich history, this article focuses on commercial applications of AI narrative and looks to future applications of this technology. Video games have incorporated AI and procedural generation for many years, but more recently, new applications of this technology have emerged in other media. Director Oscar Sharp and artist Ross Goodwin, for example, generated significant media buzz about two short films that they produced which were written by their AI screenwriter. It’s No Game (2017), in particular, offers an apt commentary on the possibility of replacing striking screenwriters with AI authors. Increasingly, AI agents and virtual assistants like Siri, Cortana, Alexa and Google Assistant are incorporated into our daily lives. As concerns about their eavesdropping circulate in news media, it is clear that these companions are learning a lot about us, which raises concerns about how our data might be employed in the future. This article explores current applications of AI for storytelling and future directions of this technology to offer insight into issues that have and will continue to arise as AI storytelling advances.
Exploring Disorder-Aware Attention for Clinical Event Extraction

Event extraction is one of the crucial tasks in biomedical text mining that aims to extract specific information concerning incidents embedded in the texts. In this article, we propose a deep learning framework that aims to identify the attributes (severity, course, temporal expression, and document creation time) associated with the medical concepts extracted from electronic medical records. The bi-directional long short-term memory network assisted by the attention mechanism is utilized to uncover the important aspects of the patient’s medical conditions. The attention mechanism specific to the medical disorder mention can focus on various parts of the sentence when different disorders are considered as input. The proposed methodology is evaluated on benchmark ShARe/CLEF eHealth Evaluation Lab 2014 shared task 2 datasets. In addition to the CLEF dataset, we also used the social media text, especially the medical blog posts. Experimental results of the proposed approach illustrate that our proposed approach achieves significant performance improvements over the state-of-the-art techniques and the highly competitive deep learning--based baseline methods.
Infrared image super-resolution via discriminative dictionary and deep residual network

Image super-resolution successfully overcomes the limitation of infrared imaging system and satisfies the increasing demand for high-resolution infrared images. In this paper, we propose a novel infrared image super-resolution method via discriminative dictionary and deep residual network, which is benefited from the advantages of both compressive sensing and deep learning for more accurate reconstruction results. A discriminative dictionary learning method is introduced to explicitly learn a shared sub-dictionary with a set of cluster-specific sub-dictionaries from the training dataset to better characterize the specific representation of each image. Besides, multiple constraints are integrated to preserve the intrinsic relationship and structure information among images in the learned dictionary. Moreover, to further compensate for the limitation of dictionary representation capacity, a deep residual network is constructed to capture more high-frequency details of infrared images. Both quantitative and qualitative evaluations demonstrate that the proposed method could achieve favorable performances against other state-of-the-art methods.
Mean Field Game for Equilibrium Analysis of Mining Computational Power in Blockchains

In a blockchain network, to mine new blocks like in cryptocurrencies or secure IoT networks, each node or player specifies the amount of computational power as its strategy by compromising between the cost and expected utility. Since the strategies of all players affect the expected utility of others through the probability of success, in this article, we first formulate the mining competition among the players in a blockchain network as a noncooperative game. The existence and uniqueness of the Nash equilibrium (NE) point of the game are proven. We consider a gradient learning strategy for the players while preserving their private information as a bounded rational learning model. Furthermore, the convergence of this learning strategy to the E-NE point of the game is studied analytically using the concept of the mean field (MF) game theory. While conventional analytical tools face problems in dealing with a large number of participants, which is a key feature in many IoT networks, deploying the MF game theory facilitates analyzing the behavior of a large population of players by encapsulating the network behavior in an MF term. As the number of players becomes larger, the accuracy of the MF method becomes greater. Moreover, in the MF approach, no information exchange among the agents is needed for optimal decision making and the privacy of the players is preserved. The minimal information exchange is also a proper motivation for using the MF approach in the IoT networks.
EMSim: A Microarchitecture-Level Simulation Tool for Modeling Electromagnetic Side-Channel Signals

Side-channel attacks have become a serious security concern for computing systems, especially for embedded devices, where the device is often located in, or in proximity to, a public place, and yet the system contains sensitive information. To design systems that are highly resilient to such attacks, an accurate and efficient design-stage quantitative analysis of side-channel leakage is needed. For many systems properties (e.g., performance, power, etc.), cycle-accurate simulation can provide such an efficient-yet-accurate design-stage estimate. Unfortunately, for an important class of side-channels, electromagnetic emanations, such a model does not exist, and there has not even been much quantitative evidence about what level of modeling detail (e.g., hardware, microarchitecture, etc.) would be needed for high accuracy. This paper presents EMSim, an approach that enables simulation of the electromagnetic (EM) side-channel signals cycle-by-cycle using a detailed micro-architectural model of the device. To evaluate EMSim, we compare its signals against actual EM signals emanated from real hardware (FPGA-based RISC-V processor), and find that they match very closely. To gain further insights, we also experimentally identify how the accuracy of the simulation degrades when key microarchitectural features (e.g., pipeline stall, cache-miss, etc.) and other hardware behaviors (e.g., data-dependent switching activity) are omitted from the simulation model. We further evaluate how robust the simulation-based results are, by comparing them to real signals collected in different conditions (manufacturing, distance, etc.). Finally, to show the applicability of EMSim, we demonstrate how it can be used to measure side-channel leakage through simulation at design-stage.
Framework for Error Detection &amp; its Localization in Sensor Data Stream for reliable big sensor data analytics using Apache Spark Streaming

Internet of Things (IoT) is one of the big sources of Big Sensor data in which collected sensor readings often polluted due to corruption and losses of the sensor readings. In Big Sensor data analytics, fast and efficient detection of error and its localization is a challenging research issue. Most of the existing solutions for the error detection and localization in sensor data use offline techniques for detecting errors. In this paper, we propose a novel framework for online error detection and its localization which uses an online scheme for detecting and localizing errors in sensor data using latest big data processing tools such as Apache Spark streaming. Performance evaluation of the proposed framework is done using two different datasets such as real-time air quality dataset of the Raipur city and Intel sensor dataset in terms of false positive.