Reviewerly/exHarmony huggingface dataset:
Language, modality, and mobile media use experiences: Social responses to smartphone cues in a task-oriented context

With people’s growing use of virtual agents and voice assistants on smartphones, researchers have pointed out that mobile phones are not only acting as intermediaries that connect users from different places, but also communication terminals that present different combinations of social cues. This study applies the Computers are Social Actors paradigm in human-phone interaction and postulates that compared to non-anthropomorphic language and text cues, anthropomorphic language and vocal cues will have more effects on users’ social responses to smartphones. This study also explores the role of individual differences in users’ social responses to smartphones. Based on a lab experiment using a between-subjects factorial design, the study suggests that although anthropomorphic language and voice-based information did not have main effects on users’ social responses, people’s mobile media usage and intensive phone use interacted with these cues in their social responses to the smartphones. In addition, this study implies that it is the combination of social cues, individual differences, and communication contexts that contributes to people’s social reactions to the smartphones. The findings of the study can inform user interface design and precipitate further discussion about the ethical issues in human-phone interaction.
Fully Automatic Stroke Symptom Detection Method Based on Facial Features and Moving Hand Differences

In this article, we suggest a new approach that automatically analyzes the degree of left and right symmetry of the cheek wrinkle lines and the movement of both arms in order to detect the symptom of the early stroke. To achieve that was created a special model, using Active appearance model (AAM) algorithm that detected cheek wrinkle line by training face dataset. Hand moving part our method was detected combining motion detection algorithm and skin model of the patient. Experimental results show that our method correctly detects stroke warning signs and symptoms to save patients life without wasting time.
A deep model method for recognizing activities of workers on offshore drilling platform by multistage convolutional pose machine

The growing diversity of image scenes brings a great challenge to human activity recognition in practice. Traditional activity recognition methods cannot satisfy the demand of precise action recognition in complex scenes. In this work, we build a training set of worker's activities on offshore drilling platform by collecting data from offshore drilling monitor, and then an improved multi-level convolutional pose machine (MCPM) method is proposed and trained to recognize activities of workers on the platforms. In human object detection, a multi-rule region proposal marker algorithm is developed to separate the seawater area, and the ducts of similar personnel is pre-discriminated by support vector machine. We use the characteristics of the human body key-points not affected by complex background noise to assist the detection of the human target. As results, it shown that our method performs better than Faster-RCNN, MobileNet-SSD and SSD algorithms in detecting human target on the offshore drilling platform, and achieves well accuracy in recognizing many key activities. To our best acknowledge, it is the first attempt of using deep model to recognize worker's activities on offshore drilling platform.
MELA: A Visual Analytics Tool for Studying Multifidelity HPC System Logs

To maintain a robust and reliable supercomputing hardware system there is a critical need to understand various system events, including failures occurring in the system. Toward this goal, we analyze various system logs such as error logs, job logs and environment logs from Argonne Leadership Computing Facility's (ALCF) Theta Cray XC40 supercomputer. This log data incorporates multiple subsystem and component measurements at various fidelity levels and temporal resolutions - a very diverse and massive dataset. To effectively identify various patterns that characterize system behavior and faults over time, we have developed a visual analytics tool, MELA, to better identify patterns and glean insights from these log data.
Activities of Daily Living and Environment Recognition Using Mobile Devices: A Comparative Study

The recognition of Activities of Daily Living (ADL) using the sensors available in off-the-shelf mobile devices with high accuracy is significant for the development of their framework. Previously, a framework that comprehends data acquisition, data processing, data cleaning, feature extraction, data fusion, and data classification was proposed. However, the results may be improved with the implementation of other methods. Similar to the initial proposal of the framework, this paper proposes the recognition of eight ADL, e.g., walking, running, standing, going upstairs, going downstairs, driving, sleeping, and watching television, and nine environments, e.g., bar, hall, kitchen, library, street, bedroom, living room, gym, and classroom, but using the Instance Based k-nearest neighbour (IBk) and AdaBoost methods as well. The primary purpose of this paper is to find the best machine learning method for ADL and environment recognition. The results obtained show that IBk and AdaBoost reported better results, with complex data than the deep neural network methods.
Mask-RCNN Based People Detection Using A Top-View Fisheye Camera

People detection is a core problem in many applications, such as those related to visual surveillance and behavior analysis. In recent years, we have seen dramatic improvements of people detection techniques based on deep neural networks. However, most of the techniques are developed for projective cameras where people mostly appear upright in the images. On the other hand, images taken with top-view fisheye cameras, another major modality used in visual surveillance, have received little attention. In this paper, we present a study on extending the well-known Mask-RCNN people detection algorithm to such images.
Some pedagogical observations on using augmented reality in a vocational practicum

Abstract The use of augmented reality (AR) in vocational education and training can still be considered as being in its infancy, although interest in it is growing in response both to its potential for teaching and its adoption as a practical tool in various industry sectors. One example of early‐stage AR use is illustrated from a practicum for training apprentices in the chemical industry; this involves the use of various AR objects for learning a simple production operation. Although this application had been conceived of purely in terms of substitution for existing instructional methods, it also gave learners greater control of the learning process and created opportunities for collaborative learning. Pedagogically, AR can be considered as a mildly disruptive technology that favours learner managed learning, a factor that is aligned with its ability to support localised decision‐making in industry. Practitioner Notes What is already known about this topic Augmented reality (AR) tends to favour exploratory, learner managed and increasingly, collaborative learning. Evaluations of AR for vocational learning are on balance positive, with improved understanding, fewer errors and faster task completion or improved learning performance all reported. AR is still in the early stages of adoption in vocational education and training (VET), with trials and small‐scale applications dominating. What this paper adds Introducing AR even for simple learning tasks affects the pedagogical approaches that are used. Effects include greater learner control of the learning process and encouragement of reflection‐in‐action. Implications for practice and/or policy AR is a mildly disruptive technology that needs to be considered in terms of its pedagogical implications as well as its effectiveness as a learning tool. The directions favoured by AR in education and training appear consistent with changing skill and learning demands in industry.
Personalized Activity Recognition with Deep Triplet Embeddings

A significant challenge for a supervised learning approach to inertial human activity recognition is the heterogeneity of data generated by individual users, resulting in very poor performance for some subjects. We present an approach to personalized activity recognition based on deep feature representation derived from a convolutional neural network (CNN). We experiment with both categorical cross-entropy loss and triplet loss for training, and describe a novel loss function based on subject triplets. We evaluate these methods on three publicly available inertial human activity recognition datasets (MHEALTH, WISDM, and SPAR) comparing classification accuracy, out-of-distribution activity detection, and generalization to new activity classes. The proposed triplet algorithm achieved an average 96.7% classification accuracy across tested datasets versus the 87.5% achieved by the baseline CNN algorithm. We demonstrate that personalized algorithms, and, in particular, the proposed novel triplet loss algorithms, are more robust to inter-subject variability and thus exhibit better performance on classification and out-of-distribution detection tasks.
Trace Element Distributions in the Zn-Pb (Mississippi Valley-Type) and Cu-Ag (Kupferschiefer) Sediment-Hosted Deposits in Poland

We applied geochemical (ICP-MS, WD-XRF, GFAAS, and AMA 254) and mineralogical (EPMA) studies of 137 samples to ore mineralization from Middle-Triassic sediment-hosted Zn-Pb (Mississippi Valley-type MVT) and Lower Zechstein sediment-hosted stratiform (SSC) Cu-Ag (Kupferschiefer-type) deposits in Poland. They contain a number of trace elements which are not recovered during the ore processing. Only Cu, Ag, Pb, Ni, Re, Se, Au, and PGE are extracted from Cu-Ag deposits while Zn and Pb are the only elements produced from Zn-Pb deposits. Zn-Pb deposits contain Cd, Ag, Ga, and Ba in slightly elevated concentrations and have potential to be mineral resources. This applies to a lesser extent to other trace elements (Bi, As, Hf, Tl, Sb, Se, and Re). However, only Cd and Ag show high enrichment factors indicative of potential for recovery. The bulk-rock analyses reveal strong correlations between Zn and Cd and Se, As and Mo, and weaker correlations between Ag and Cd, as well as Ga and Zn. Electron microprobe analyses of sphalerite revealed high concentrations of Cd (≤2.6 wt%) and Ag (≤3300 ppm). Zn-Pb deposits have fairly significant estimated resources of Ga and Sc (&gt;1000 tons) and Cd (&gt;10,000 tons). The Cu-Ag deposits have element signatures characterized by high values of Co, V, Ni, and Mo and much lower of Bi, As, Cd, Hg, Mo, Sb, and Tl. Bulk-rock analyses show strong correlations between Se and V; As and Co; Bi and Re; and weaker correlations between, for example, Cu and Mo; V, Ni, Ag and Mo; and Ni, V, and Co and Ni. The EPMA determinations reveal strong enrichments of Ag in Cu sulfides (geerite ≤ 10.1 wt %, chalcocite ≤ 6.28 wt %, bornite ≤ 3.29 wt %, djurleite ≤ 9080 ppm, yarrowite ≤ 6614 ppm, and digenite ≤ 3545 ppm). Silver minerals and alloys, as well as the native Ag and Au, were recorded in the Cu-Ag ores. Large resources of Co, V, and Ni (&gt;100,000 tons) and Sc and Mo (&gt;10,000 tons) are notable in Cu-Ag deposits. A number of trace elements, classified as critical for the economy of the European Union, including Ga and Ba (to a lesser extent Hf, Nb, and Sc) in Zn-Pb deposits, and Co and V in the Cu-Ag deposits, may eventually be recovered in the future from the studied deposits if proper ore-processing circuits and increasing demand are favorable.
Code Completion for Programming Education based on Recurrent Neural Network

In solving programming problems, it is difficult for beginners to create program code from scratch. One way to navigate this difficulty is to provide a function of automatic code completion. In this work, we propose a method to predict the next word following a given incomplete program that has two key constituents, prediction of within-vocabulary words and prediction of identifiers. In terms of predicting within-vocabulary words, a neural language model based on a Long Short-Term Memory (LSTM) network is proposed. Regarding the prediction of identifiers, a model based on a pointer network is proposed. Additionally, a model for switching between these two models is proposed. For evaluation of the proposed method, source code accumulated in an online judge system is used. The results of the experiment demonstrate that the proposed method can predict both the next within-vocabulary word and the next identifier to a high degree of accuracy.
Data-level information enhancement: Motion-patch-based Siamese Convolutional Neural Networks for human activity recognition in videos

Data augmentation is critical for deep learning-based human activity recognition (HAR) systems. However, conventional data augmentation methods, such as random-cropping, may generate bad samples that are unrelated to a particular activity (e.g. the background patches without saliency motion information). As a result, the random-cropping based data augmentation may affect negatively the overall performance of HAR systems. Humans, in turn, tend to pay more attention to motion information when recognizing activities. In this work, we attempt to enhance the motion information in HAR systems and mitigate the influence of bad samples through a Siamese architecture, termed as Motion-patch-based Siamese Convolutional Neural Network (MSCNN). The term motion patch is defined as a specific square region that includes critical motion information in the video. We propose a simple yet effective method for selecting those regions. To evaluate the proposed MSCNN, we conduct a number of experiments on the popular datasets UCF-101 and HMDB-51. The mathematical model and experimental results show that the proposed architecture is capable of enhancing the motion information and achieves comparable performance.
A 4900-$\mu$ m<sup>2</sup> 839-Mb/s Side-Channel Attack- Resistant AES-128 in 14-nm CMOS With Heterogeneous Sboxes, Linear Masked MixColumns, and Dual-Rail Key Addition

Cryptographic circuits such as advanced encryption standard (AES) are vulnerable to correlation power analysis (CPA) side-channel attacks (SCAs), where an adversary monitors chip supply current signatures or electromagnetic (EM) emissions to decipher the value of embedded keys. This article describes an all-digital, fully synthesizable SCA-resistant 16-b serial AES-128 hardware accelerator fabricated in 14-nm CMOS, occupying 4900 μm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Randomized byte-order shuffling through heterogeneous Sboxes, linear masked MixColumns, and dual-rail AddRoundKey circuits enable: 1) 9.2× lower correlation between current signatures and hamming distance (HD)/hamming weight (HW) power models compared to an unprotected AES implemented in 14-nm CMOS; 2) 2.3× attenuation of a correlation ratio for correct key guesses; 3) 839-Mb/s encryption throughput with 11-mW total power consumption measured at 750 mV, 25 °C; 4) peak energy efficiency of 390 Gbps/W measured at an energy optimal point of 290 mV, 25 °C, representing an overhead of 23% over the unprotected AES engine; 5) <; 1% performance impact compared to unprotected AES; 6) >1200× improvement in minimum-traces-to-disclosure (MTD) over an unprotected AES accelerator, with no successful CPA attacks observed after 12M encryptions; and 7) >1100× improvement in test vector leakage assessment (TVLA ) metric in power and EM time- and frequency-domain analyses.
Traceability Link Recovery between Requirements and Models using an Evolutionary Algorithm Guided by a Learning to Rank Algorithm: Train control and management case

Traceability Link Recovery (TLR) has been a topic of interest for many years within the software engineering community. In recent years, TLR has been attracting more attention, becoming the subject of both fundamental and applied research. However, there still exists a large gap between the actual needs of industry on one hand and the solutions published through academic research on the other. In this work, we propose a novel approach, named Evolutionary Learning to Rank for Traceability Link Recovery (TLR-ELtoR). TLR-ELtoR recovers traceability links between a requirement and a model through the combination of evolutionary computation and machine learning techniques, generating as a result a ranking of model fragments that can realize the requirement. TLR-ELtoR was evaluated in a real-world case study in the railway domain, comparing its outcomes with five TLR approaches (Information Retrieval, Linguistic Rule-based, Feedforward Neural Network, Recurrent Neural Network, and Learning to Rank). The results show that TLR-ELtoR achieved the best results for most performance indicators, providing a mean precision value of 59.91%, a recall value of 78.95%, a combined F-measure of 62.50%, and a MCC value of 0.64. The statistical analysis of the results assesses the magnitude of the improvement, and the discussion presents why TLR-ELtoR achieves better results than the baselines.
Mapping a lithologically complex terrain using Sentinel-2A data: a case study of Suriyan area, southwestern Iran

The Suriyan area, southwestern Iran, possesses a variety of sedimentary, volcanic, and metamorphic rocks. This paper investigates the potential of Sentinel-2A multispectral data for lithological mapping of this geologically complex terrain using the principal component analysis (PCA), band ratio, and spectral information divergence (SID) methods. Spectral features of lithological units were analysed by surveying the laboratory spectra of hand samples, the resampled spectra of rock types to spectra from Sentinel-2A wavelength ranges, and the spectra extracted directly from applied imagery. The absorption features dominantly attributed to Fe2+, Fe3+, and Al-OH electronic and vibrational modes were observable in Sentinel-2A bands; however, the main absorption feature of Mg-OH and C-O near 2.33 µm is not collected in a distinct band by this satellite. Data processing results revealed that PCA was more successful in discriminating vegetation, greenschist, and shale, whereas it could not differentiate various types of limestone. The proposed Band Ratios, (4+6)/(7+8), (2 + 11 + 6)/(3 + 4), 12/8, and 2/5, enhanced sandstone, shale, basaltic dyke, greenschist, mica schist, meta-tuff, and various types of limestone. However, the SID algorithm discriminated few lithological units including greenschist, mica schist, shale, and Fe-bearing limestone. Considering output results, it was suggested that proposed band ratios as well as the PCA method were more successful in discriminating various lithological units, while the SID algorithm was advantageous in enhancing the metamorphic units.Field observations and microscopic studies verified the occurrences of rock units and their spatial compatibility with differentiated lithologies. It was concluded that Sentinel-2A data were successful in enhancement and discrimination of various rock types in a lithologically complex terrain.
Cryptocurrency Pump and Dump Schemes: Quantification and Detection

In the past years, cryptocurrencies have received a lot of attention in popular media. Having attracted significant speculation, prices have soared in 2017, fell in 2018 and are generally known to be very volatile. However, some of the price changes have been due to organized manipulation. Traditionally known in the world of penny stocks and made illegal in most countries, pump and dump schemes are frequent in cryptocurrencies, and mostly unregulated. In this paper, we perform quantification and detection of pump and dump schemes that are coordinated through Telegram chats and executed on Binance - one of the most popular cryptocurrency exchanges. We detail how pumps are organized on Telegram, and quantify the properties of 149 confirmed events with respect to market capitalization, trading volume, price impact and profitability. Based on this ground truth, and regular trading intervals obtained from twitter timestamps, we optimize a binary classifier in order to be able to detect additional suspicious trading activity. Our results indicate that pump and dump schemes occur frequently in cryptocurrencies with market capitalizations below $50 million, that scheme operators often organize their actions across multiple channels, that such activity tends to lead to inflated prices over longer time periods and machine learning can help to identify activity that is similar to known pump and dump schemes.
A unique key sharing protocol among three users using non-commutative group for electronic health record system

AbstractAbstractWe know that the importance, need and utility of EHR (Electronic Health Record) system is increasing rapidly in the modern world. The developed countries like USA, Canada has adopted this system years ago but still they are facing security issues in that system. The situation is very poor for developing countries like India where data breaching news are very common in EHR system. One can imagine the situation that Indian government is accepting data breaching cases with associated authentication problems and helpless to develop secure EHR system. EHR information is very sensitive and communication over insecure wireless communication channels increases the vulnerability. Here we are going to propose an authenticated key agreement protocol among three users (doctor, hospital and patient) using conjugacy search problem (CSP) and braid decomposition problem (BDP). We discuss the required security analysis of the protocol and prove that the proposed protocol passes the security parameters assuming that the BDP and CSP are hard in braid groups.Subject Classification: Network SecurityKeywords: AuthenticationBraid Decomposition Problem (BDP)Conjugacy Search Problem (CSP)Electronic Health Record (EHR)Key Agreement
Efficient Probabilistic Collision Detection for Non-Gaussian Noise Distributions

We present an efficient algorithm to compute tight upper bounds of collision probability between two objects with positional uncertainties, whose error distributions are represented with non-Gaussian forms. Our approach can handle noisy datasets from depth sensors, whose distributions may correspond to Truncated Gaussian, Weighted Samples, or Truncated Gaussian Mixture Model. We derive tight probability bounds for convex shapes and extend them to non-convex shapes using hierarchical representations. We highlight the benefits of our approach over prior probabilistic collision detection algorithms in terms of tighter bounds (10x) and improved running time (3x). Moreover, we use our tight bounds to design an efficient and accurate motion planning algorithm for a 7-DOF robot arm operating in tight scenarios with sensor and motion uncertainties.