Reviewerly/exHarmony huggingface dataset:
Convolutional Neural Network Based Models for Improving Super-Resolution Imaging

Many fields, such as remote sensing, medical imaging, and biological detection, pose a technical challenge for achieving super-resolution imaging.Convolutional neural networks (CNNs) are considered one of the potential solutions to realize the super-resolution.In this paper three-layer, CNN-based models are proposed to reconstruct the super-resolution images using four optimization algorithms, i.e., stochastic gradient descent, adaptive gradient (AdaGrad), root mean square prop (RMSprop), and adaptive moment estimation (ADAM).Among these four optimizations, ADAM is considered to have the best performance.To further verify the impact of the number of convolution layers on performance, a selection of CNN-based models with four convolutional layers is then proposed, each of which is named with the convolution parameters.All the four-layer models are optimized with ADAM, and the experimental results indicate that the 9-3-3-5 model achieves the best performance in the super-resolution reconstruction task.
RP-Net: A 3D Convolutional Neural Network for Brain Segmentation From Magnetic Resonance Imaging

Quantitative analysis of brain volume is quite significant for the diagnosis of brain diseases.Accurate segmentation of essential brain tissues from 3D medical images is fundamental to quantitative brain analysis.Since manual segmentation is extremely tedious and time-consuming, there is a growing demand for automated segmentation.In this paper, we propose a 3D convolutional neural network including recursive residual blocks and a pyramid pooling module (RP-Net) for segmenting brain from 3D magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF).RP-Net is an U-Net like the network that consists of a downsampling path and an upsampling path.Each path consists of four stages with a recursive residual block.All layers in RP-Net are implemented in a 3D manner.The pyramid pooling module is applied before a voxel-wise classification layer for obtaining both local and global context information.The RP-Net has been evaluated on WM, GM, and CSF segmentation with CANDI, IBSR18, and IBSR20 dataset.The experiments show that the RP-Net achieved mean dice similarity coefficients of 90.7% on CANDI, 90.49% on IBSR18 and 84.96% on IBSR20.The results demonstrate that our proposed method has achieved a significant improvement in segmentation accuracy compared to other reported approaches.
Hard Drive of Hearing: Disks that Eavesdrop with a Synthesized Microphone

Security conscious individuals may take considerable measures to disable sensors in order to protect their privacy. However, they often overlook the cyberphysical attack surface exposed by devices that were never designed to be sensors in the first place. Our research demonstrates that the mechanical components in magnetic hard disk drives behave as microphones with sufficient precision to extract and parse human speech. These unintentional microphones sense speech with high enough fidelity for the Shazam service to recognize a song recorded through the hard drive. This proof of concept attack sheds light on the possibility of invasion of privacy even in absence of traditional sensors. We also present defense mechanisms, such as the use of ultrasonic aliasing, that can mitigate acoustic eavesdropping by synthesized microphones in hard disk drives.
HawkEye - Deploying a Design Fiction Probe

This paper explores how a design fiction can be designed to be used as a pragmatic user-centred design method to generate insights on future technology use. We built HawkEye, a design fiction probe that embodies a future fiction of dementia care. To learn how participants respond to the probe, we employed it with eight participants for three weeks in their own homes as well as evaluating it with six HCI experts in sessions of 1.5h. In addition to presenting the probe in detail, we share insights into the process of building it and discuss the utility of design fiction as a tool to elicit empathetic and rich discussions about potential outcomes of future technologies.
Target-aware convolutional neural network for target-level sentiment analysis

Target-level sentiment analysis (TLSA) is a classification task to extract sentiments from targets in text. In this paper, we propose target-dependent convolutional neural network (TCNN) tailored to the task of TLSA. The TCNN leverages the distance information between the target word and its neighboring words to learn the importance of each word to the target. Experimental results show that the TCNN achieves state-of-the-art performance on both single- and multi-target datasets. Qualitative evaluations were conducted to demonstrate the limitations of previous TLSA methods and also to verify that distance information is crucial for TLSA. Furthermore, by exploiting a convolutional neural network (CNN), the TCNN trains six times faster per epoch than other baselines based on recurrent neural networks.
Socio-linguistic innovations in education: productive implementation of intercultural communication

Socio-linguistic innovations in education as effective managerial tool provide resolving wide range of cross-cultural communication problems, including the process of social and psychological adaptation of international students; however, methodological component of this notion is not completely identified. We performed innovative productive method in international students' education and their socio-cultural adaptation. Our strategic target was to define the productive method effectiveness in the sphere of intercultural communication of international students in St. Petersburg Polytechnic University. We observed the level of communicative competence formation including linguistic, sociolinguistic, discursive, socio-cultural and social skills of international students which are highly important for their social and psychological comfort. We tested the students and identified that the level of their communicative skills was not enough for successful intercultural communication in a new socio-cultural environment (average point of 43% from 100% in Russian and 55% in English). Productive method implementation provided the communicative competence increase to the average point of 70% in Russian and to the average point of 71% in English. In conclusion, this study confirms the effectiveness of the productive approach as a methodological system developing student's communicative competence which allows designing a flexible socially-oriented educational environment including elements of psychological support and adaptation.
What do home robots want? The ambivalent power of cuteness in robotic relationships

A wave of social, domestic robots is poised to enter our homes. Robots such as Jibo, Kuri and Olly are networked with other ‘smart’ devices and use cameras and voice control to provide companionship, care and household management. These robots are proposed as members of the family, and as such must encourage intimacy and trust with their human caregivers. In this article, we explore the nexus between the cute aesthetic of home robots and the kinds of affective relationships this aesthetic enables with the human user. Our argument is that the cuteness of home robots creates a highly ambivalent relationship of power between (human) subject and (robotic/digital) object, whereby the manifestation of consciousness and the production of lasting emotional bonds require home robots to exceed the affective and semiotic limitations, even as their cute appearance may encourage the production of intimacy. By exceeding the borders established by their own design, home robots are able to manifest as conscious beings, a manifestation which both destabilizes the power differential between user and robot and, paradoxically, points to the possibility of their own replacement. To explore these ideas, we discuss three soon-to-be-released social robots: Mayfield Robotics’ Kuri, Emotech’s Olly and Jibo Inc.’s Jibo. Each promises a unique personality that will integrate them as a member of the family.
Vehicle speed measurement model for video-based systems

Advanced analysis of road traffic data is an essential component of today’s intelligent transportation systems. This paper presents a video-based vehicle speed measurement system based on a proposed mathematical model using a movement pattern vector as an input variable. The system uses the intrusion line technique to measure the movement pattern vector with low computational complexity. Further, the mathematical model introduced to generate the pdf (probability density function) of a vehicle’s speed that improves the speed estimate. As a result, the presented model provides a reliable framework with which to optically measure the speeds of passing vehicles with high accuracy. As a proof of concept, the proposed method was tested on a busy highway under realistic circumstances. The results were validated by a GPS (Global Positioning System)-equipped car and the traffic regulations at the measurement site. The experimental results are promising, with an average error of 1.77 % in challenging scenarios.
A secure large-scale instant payment system based on blockchain

Off-chain payments are an important technique for improving the scalability of blockchain-based cryptocurrencies. However, since tokens locked in off-chain channels cannot circulate from one channel to another, current off-chain payment systems have a limitation in their capacities. In this work, we present a secure large-scale instant payment (SLIP) system to improve the capacity of blockchain systems. The SLIP system uses an aggregate signature scheme to connect some of the off-chain channels such that tokens locked in these channels can circulate from one channel to the next. Therefore, the amount of circulated tokens in these channels is the sum of all locked-in tokens instead of the minimum locked-in tokens. In other words, it improves the negotiability of tokens locked in these connected off-chains channels. Besides, as payers can aggregate the signatures incrementally, payees only need to verify a line of transactions once in each transaction while nodes (miners) who maintain the blockchain system only need to verify all transactions once for all, which makes the SLIP system very efficient. We prove that if the underlying blockchain system is secure, and aggregate signature satisfies aggregate chosen-key security, then the SLIP system is secure. Finally, analyses show that the capacity of the SLIP system is much larger than the Lightning Network.
Creating sentiment lexicon for sentiment analysis in Urdu: The case of a resource‐poor language

Abstract The sentiment analysis (SA) applications are becoming popular among the individuals and organizations for gathering and analysing user's sentiments about products, services, policies, and current affairs. Due to the availability of a wide range of English lexical resources, such as part‐of‐speech taggers, parsers, and polarity lexicons, development of sophisticated SA applications for the English language has attracted many researchers. Although there have been efforts for creating polarity lexicons in non‐English languages such as Urdu, they suffer from many deficiencies, such as lack of publically available sentiment lexicons with a proper scoring mechanism of opinion words and modifiers. In this work, we present a word‐level translation scheme for creating a first comprehensive Urdu polarity resource: “Urdu Lexicon” using a merger of existing resources: list of English opinion words, SentiWordNet, English–Urdu bilingual dictionary, and a collection of Urdu modifiers. We assign two polarity scores, positive and negative, to each Urdu opinion word. Moreover, modifiers are collected, classified, and tagged with proper polarity scores. We also perform an extrinsic evaluation in terms of subjectivity detection and sentiment classification, and the evaluation results show that the polarity scores assigned by this technique are more accurate than the baseline methods.
The Impact of VR Application on Student’s Competency Development: A Comparative Study of Regular and VR Engineering Classes with Similar Competency Scopes

This research focuses on the Fourth Industrial Revolution and the Korean National Competency Standards. The aim of this research is to perform a comparative study of two university engineering courses to present the difference between the effect of virtual reality (VR)-based teaching and traditional teaching on learning robotics. To achieve this goal, the authors used the expert method, focus group interviews, and a comparative study. There were three hypotheses and four research questions on the relationship between teaching method and competency development. As a case study for this research, the authors chose Korea University of Technology and Education and designed a survey to assess students’ competencies. The participants were second- and third-grade university students. Quantitative data were collected from the interview forms developed by the researcher; qualitative data were analyzed using the expert method and a comparative study. A significant difference was observed between competency development in the VR class and that in a regular class with a similar syllabus. Furthermore, it was noted that different teaching methods affect competency development in different ways; VR tools develop some competencies, but not all of them. Authors found that the VR tool requires an appropriate teaching method and affects the student’s ability to develop competencies.
SNIPER: Few-shot Learning for Anomaly Detection to Minimize False-negative Rate with Ensured True-positive Rate

In anomaly detection systems, overlooking anomalies may result in serious incidents. Thus, when a system overlooks an anomaly, we need to update the system to never overlook the observed type of anomalies twice. There are roughly two possible approaches to solve this problem; re-training the whole system using all training data, or cascading a new specific detector for the overlooked anomaly. The first approach is the most effective solution; however, a huge computational cost and an amount of anomalous training data are required to re-train the system when it consists of a deep-learning-based anomaly detector. We focused on the latter approach and propose a training method for a cascaded specific anomaly detector using few-shot (just 1 to 3) samples. To suppress the false-negative rate of the overlooked anomaly, the proposed method works to decrease the false-positive rate under the constraint of true-positive rate equaling 1. Experimental results show that the proposed method outperformed conventional cross-entropy-based few-shot learning methods.
ANID-SEoKELM: Adaptive network intrusion detection based on selective ensemble of kernel ELMs with random features

This paper presents an adaptive network intrusion detection (ANID) method based on the selective ensemble of kernel extreme learning machines (KELMs) with random features (termed ANID-SEoKELM), aiming at identifying various unauthorized uses, misuses and abuses of computer systems in real time. To generate a lightweight intrusion detector, multiple KELMs are learned independently based on the Bagging strategy with sparse random feature representation (SRFR), to reduce noise and redundant or irrelevant information in network connection instances and ensure the diversity of base learners for the effective ensemble of base learners. A marginal distance minimization (MDM)-based selective ensemble (MDMbSE) method is introduced to generate the ultimate intrusion detector. To ensure the adaptability of the intrusion detector, an incremental learning-based detection-model updating procedure is also derived. Extensive validation and comparative experiments on the benchmark KDD99 dataset and a hybrid heterogeneous network simulation platform mixed with wireless networks and Ethernet networks demonstrate that the ANID-SEoKELM is able to adapt to the dynamically changing network environments hence it can achieve higher detection accuracies stably and efficiently than classic single learner-based intrusion detection methods and representative ensemble-based intrusion detection methods.
Adversarial Examples for Improving End-to-end Attention-based Small-footprint Keyword Spotting

In this paper, we explore the use of adversarial examples for improving a neural network based keyword spotting (KWS) system. Specially, in our system, an effective and small-footprint attention-based neural network model is used. Adversarial example is defined as a misclassified example by a model, but it is only slightly skewed from the original correctly-classified one. In the KWS task, it is a natural idea to regard the false alarmed or false rejected queries as some kind of adversarial examples. In our work, given a well-trained attention-based KWS model, we first generate adversarial examples using the fast gradient sign method (FGSM) and find that these examples can dramatically degrade the KWS performance. Using these adversarial examples as augmented data to retrain the KWS model, we finally achieve 45.6% relative and false reject rate (FRR) reduction at 1.0 false alarm rate (FAR) per hour on a collected dataset from a smart speaker.
Blockchain for Modern Digital Forensics: The Chain-of-Custody as a Distributed Ledger

Blockchain technology can be incorporated into new systems to facilitate modern Digital Forensics and Incident Response (DFIR). For example, it is widely acknowledged that the Internet-of-Things (IoT) has introduced complexity to the cyberspace, however, incident responders should also realise the advantages presented by these new “Digital Witnesses” (DW) to support their investigation. Logs generated by IoT devices can help in the process of event reconstruction, but their integrity -and therefore admissibility- can be achieved only if a Chain-of-Custody (CoC) is maintained within the wider context of an on-going digital investigation. Likewise, the transition to electronic documentation improves data availability, legibility, the utility of notes, and therefore enhances the communication between stakeholders. However, without a proof of validity, these data could be falsified. For example, in an application area such as eHealth, there is a requirement to maintain various existing (and new) rules and regulations concerning authorship, auditing, and the integrity of medical records. Lacking data control could lead to system abuse, fraud and severe compromise of service quality. These concerns can be resolved by implementing an online CoC. In this paper, we discuss the value and means of utilising Blockchain in modern systems to support DFIR. we demonstrate the value of Blockchain to improve the implementation of Digital Forensic Models and discuss why law enforcement and incident responders need to understand Blockchain technology. Furthermore, the admissibility of a Digital Evidence to a Court of Law requires chronological documentation. Hence, we discuss how the CoC can be sustained based on a distributed ledger. Finally, we provide a practical scenario related to eHealth to demonstrate the value of this approach to introduce forensic readiness to computer systems and enable better Police interventions.
A Deep Dive Into Deep Learning Techniques for Solving Spoken Language Identification Problems

Automatic language identification has always been a challenging issue and an important research area in speech signal processing. It is the process of identifying a language from a random spoken utterance. This era is dominated by artificial intelligence and specifically, deep learning techniques. Prominent among the deep learning techniques are feed-forward deep neural network, convolutional neural network, long short term memory-recurrent neural network, etc. The various types of deep neural network techniques that were recently introduced have overshadowed conventional methods such as Gaussian mixture model, hidden Markov model, etc. These techniques showed significant improvement in recognition performance over various parameters. It is the ability of the deep neural network’s techniques to perform complex correlation among speech signal features, which enhances its performance over traditional approaches. This chapter provides in-depth concepts of various deep learning techniques for spoken language identification. It also explores and analyzes several works for speech recognition. Advantages and limitations of each of the techniques are reviewed. A summary of future scope for spoken language identification is also reviewed.
Boosting for Mineral Prospectivity Modeling: A New GIS Toolbox

With an increasing demand for raw materials, predictive models that support successful mineral exploration targeting are of great importance. We evaluated different machine learning techniques with an emphasis on boosting algorithms and implemented them in an ArcGIS toolbox. Performance was tested on an exploration dataset from the Iberian Pyrite Belt (IPB) with respect to accuracy, performance, stability, and robustness. Boosting algorithms are ensemble methods used in supervised learning for regression and classification. They combine weak classifiers, i.e., classifiers that perform slightly better than random guessing to obtain robust classifiers. Each time a weak learner is added; the learning set is reweighted to give more importance to misclassified samples. Our test area, the IPB, is one of the oldest mining districts in the world and hosts giant volcanic-hosted massive sulfide (VMS) deposits. The spatial density of ore deposits, as well as the size and tonnage, makes the area unique, and due to the high data availability and number of known deposits, well-suited for testing machine learning algorithms. We combined several geophysical datasets, as well as layers derived from geological maps as predictors of the presence or absence of VMS deposits. Boosting algorithms such as BrownBoost and Adaboost were tested and compared to Logistic Regression (LR), Random Forests (RF) and Support Vector machines (SVM) in several experiments. We found performance results relatively similar, especially to BrownBoost, which slightly outperformed LR and SVM with respective accuracies of 0.96 compared to 0.89 and 0.93. Data augmentation by perturbing deposit location led to a 7% improvement in results. Variations in the split ratio of training and test data led to a reduction in the accuracy of the prediction result with relative stability occurring at a critical point at around 26 training samples out of 130 total samples. When lower numbers of training data were introduced accuracy dropped significantly. In comparison with other machine learning methods, Adaboost is user-friendly due to relatively short training and prediction times, the low likelihood of overfitting and the reduced number of hyperparameters for optimization. Boosting algorithms gave high predictive accuracies, making them a potential data-driven alternative for regional scale and/or brownfields mineral exploration.