Reviewerly/exHarmony huggingface dataset:
MIR_MAD: An Efficient and On-line Approach for Anomaly Detection in Dynamic Data Stream

Anomaly detection in a dynamic data stream is a challenging task. The endless bound and high arriving rate of data prohibits anomaly detection models to store all observations in memory for processing. In addition, the dynamically moving properties of the data stream exhibit concept drift. While recent studies focus on feature extraction for anomaly detection, majority of them assume data stream are static ignoring the possibility of concept drift occurring. Anomaly detection models must operate efficiently in order to deal with high volume and velocity data, that is to have low complexity and to learn incrementally from each arriving observation. Incremental learning allows the model to adapt to concept drift. In cases where drifting rate is higher than adaptation rate, the capability to detect concept drift and retraining a new model is much preferable to minimize the performance losses. In this paper, we propose MIR_MAD, an approach based on multiple incremental robust Mahalanobis estimators that is efficient, learns incrementally and has the capability to detect concept drift. MIR_MAD is fast, can be initialized with small amount of data, and is able to estimate the drift location on the data stream. Our empirical results show that MIR_MAD achieves state-of-the-art performance and is significantly faster. We also performed a case study to show that detecting concept drift is critical to minimize the reduction in model's performance.
Eye Movement Classification with Temporal Convolutional Networks

Recently, deep learning approaches have been proposed to detect eye movements such as fixations, saccades, and smooth pursuits from eye tracking data. These are end-to-end methods that have shown to surpass traditional ones, requiring no ad hoc parameters. In this work we propose the use of temporal convolutional networks (TCNs) for automated eye movement classification and investigate the influence of feature space, scale, and context window sizes on the classification results. We evaluated the performance of TCNs against a state-of-the-art 1D-CNN-BLSTM model using GazeCom, a public available dataset. Our results show that TCNs can outperform the 1D-CNN-BLSTM, achieving an F-score of 94.2% for fixations, 89.9% for saccades, and 73.7% for smooth pursuits on sample level, and 89.6%, 94.3%, and 60.2% on event level. We also state the advantages of TCNs over sequential networks for this problem, and how these scores can be further improved by feature space extension.
Use of technologies by nurses in the management of primary health care

ABSTRACT Objective To analyze the management actions of nurses in Primary Health Care services from the perspective of technologies. Method Descriptive-exploratory study with a quantitative approach, carried out from February to April 2019. Through the snowball technique, the sample consisted of 42 nurses from Primary Health Care, using an electronic tool for data collection. The data were analyzed using descriptive statistics techniques. Results 54.8% of nurses use technologies through SISREG and PEC electronic systems, care training related to permanent health education, online and face-to-face courses, in addition to care technologies such as telehealth and telemedicine. Conclusion Management actions focused on technology showed restricted use. Nurses need to empower themselves with these organizational devices, with the aim of improving the provision and quality of care for users.
The ethics of experimental research employing intrusive technologies in tourism: A collaborative ethnography perspective

This study probes the ethics of intrusive technologies for experimental research in tourism, through the lens of collaborative ethnography. Amidst the increasing uptake of technology to assess participant responses, the role of ethics in an experimental setting has received scant attention in tourism and hospitality. While intrusive technologies such as eye tracking, skin sensors and neuroscience headgear become more ubiquitous, the ethical boundaries of using such equipment are increasingly blurred and inconsistently approved. Seeking convergence of ethics concerning intrusive technologies is complicated when framing political spaces, target audiences and management of data obtained. Rather than view the role of intrusive technologies as a dichotomous outcome of ethical or unethical approaches, this paper argues that ethics needs to be contextually embedded with increased collaboration and co-creation in the application preparation and approval process.
Opening Research Commissioning To Civic Participation: Creating A Community Panel To Review The Social Impact of HCI Research Proposals

In this paper we report insights from the design and delivery of a process that invited distinct groups of citizens to co-develop and apply social impact assessment criteria for the purpose of reviewing research proposals on HCI, social justice and digital technologies. We describe our process, designed to create dialogic spaces that foster critical engagements with technologies and social issues, cooperation and peer-support. In our findings we explore how people defined and contextualised social impact in lived experiences, negotiated and legitimised their role as reviewers, and articulated the value of HCI research for social justice. We reflect on the significance of involving citizens in the commissioning of research that addresses inequalities and social justice in technology design and draw implications for HCI researchers concerned with ethical dimensions of technology. The work contributes to HCI and civic engagement's traditions to develop effective participatory methods and collaborative processes to produce digital technologies that support social justice.
Robust transmission of medical records using dual watermarking and optimization algorithm

Abstract Nowadays, security of medical records is an important issue for tele-health system. Motivated by importance of such critical issue, we propose a robust watermarking method in the DWT-SVD-optimization domain. To solve the security issue, traditional watermarking schemes uses manual scaling factor to manage balance between imperceptibility, robustness and capacity. However, selection of manual scaling factor loses their optimize trade-off between these important parameters of watermarking. The suggested scheme encourages protection of medical data via techniques of dual watermarking and optimization scheme. In our scheme, the data owner imperceptibly embeds the dual watermarks in the medical cover image for extra level of security. Here, appropriate optimization schemes are used to find the scaling factor for embedding purpose. Further, the performance of this scheme is examined and compared. Moreover, the patient text data is coded via hamming code before inserting in to the cover so that bit error rate can be avoided or eliminated, if any. We show that the suggested scheme not only offers the high imperceptible but also robust for various attacks. Compared with existing schemes, our work offers more robustness while imperceptible and good capacity at the same time.
Face2Face Manipulation Detection Based on Histogram of Oriented Gradients

Nowadays with rapid advances in computer vision and deep learning, it is possible to create highly realistic synthetic faces in digital videos. This situation increases anxiety and suspicion in the video content. It can be a big challenge for humans and machines to differentiate between real and fake faces in a video, especially when the video is compressed or has low resolution. In this paper, an efficient method is proposed for detecting Face2Face manipulations. With the analysis of manipulated videos, we found that there are some visual artifacts that can be exploited to detect fake faces. The proposed method utilizes the histogram of oriented gradients for feature extraction that can be effective in exposing Face2Face manipulations. Experimental results clarify that our method effectively detects the manipulated faces with a high-performance accuracy under various compression quality levels.
Latent factor recommendation models for integrating explicit and implicit preferences in a multi-step decision-making process

Recommendation models are vital to the success of recommender systems. The latent factor model is one of the outstanding collaborative recommendation models. It faces many difficulties due to the lack of quality and quantity of preferences observed from users. An effective approach is to use both types of user preferences, which are implicit and explicit, in the latent factor models. In this paper, we aim to propose two different latent factor models, namely SC1 and SC2, for integrating these two types of user preferences in a multi-step decision-making process, instead of just a single step as in previous studies. SC1 works well for experienced users, who have a lot of field knowledge, while SC2 is suitable for users with less domain knowledge, who do not exactly know what they want in the system.
Do Not Perturb Me: A Secure Byzantine-Robust Mechanism for Machine Learning in IoT

With the development of Internet of Things (IoTs) and big data, collaborative machine learning has achieved many impressive successes in IoT to improve the system performance and provide more diversified services for people. Although one of the motivations for collaborative learning is privacy preservation, the adversary can still launch an inference attack through task nodes' shared information. What's worse, a few task nodes may perform as a Byzantine attacker to compromise the entire system. Many Byzantine-robust mechanisms have been proposed, but they relied on outsourcing the calculation on two non-colluding servers which were not realistic in practice or had privacy issues in one-server architecture. In this paper, we design a novel mechanism for secure Byzantine-robust collaborative machine learning, namely Omega, to allow IoT devices to achieve the collaborative model training without exposing their local data to the others. Specifically, we construct a single-server architecture to achieve the private aggregation of parameter gradients, which protects task nodes' local data even n-1 of n nodes colluded. A new secure Byzantine-robust protocol is also designed to resist the Byzantine attack and this protocol can be extended to any distance-based robust rule. Furthermore, we prove that Omega can ensure task nodes' privacy preservation. Finally, we conduct an experiment to evaluate Omega over real-world dataset and empirical results demonstrate that Omega can efficiently achieve the collaborative machine learning.
Fuzzy-Based Pseudo Segmentation Approach for Handwritten Word Recognition Using a Sequence to Sequence Model with Attention

Sequence to sequence models have shown significant progress in the field of handwriting recognition. The recent trend has been that the input to these models is fed from a convolutional neural network (CNN) that acts as a generic feature extractor for the handwritten text images. The input to the CNN is usually either a sequence of patches extracted from the text image or the output of a segmentation algorithm applied on the text image to break it up into individual characters. However, patching is unable to convey proper information about character boundaries in the image, and the segmentation-based approach often suffers from over and under segmentation. To this end, we propose a fuzzy-based pseudo segmentation approach for handwritten word recognition using a sequence to sequence model. We use a fuzzy triangular function that generates column wise weights based on the distance of the nearest data pixel from the top of the text image. Thus the probable segmentation regions are assigned higher weights than the other regions. These weights are superimposed on the original text image and this modified input is fed patch wise to the CNN. The features extracted are then encoded as a first part of a sequence to sequence model and then decoded to obtain the sequence of characters in the input. An attention mechanism is used to ensure that the decoder focuses on the appropriate section of the features outputted by the encoder while generating each character. Our method is tested upon the IAM word database after the words in it undergo skew and slant correction. The architecture of the model is optimized based on exhaustive numerical simulations on the IAM database and it shows promising results.
A Trustworthy Blockchain based framework for Impregnable IoV in Edge Computing

The concept behind the Internet of Things (IoT) is taking everything and connecting to the internet so that all devices would be able to send and receive data online. Internet of Vehicles (IoV) is a key component of smart city which is an outcome of IoT. Nowadays the concept of IoT has plaid an important role in our daily life in different sectors like healthcare, agriculture, smart home, wearable, green computing, smart city applications, etc. The emerging IoV is facing a lack of rigor in data processing, limitation of anonymity, privacy, scalability, security challenges. Due to vulnerability IoV devices must face malicious hackers. Nowadays with the help of blockchain (BC) technology energy system become more intelligent, eco-friendly, transparent, energy efficient. This paper highlights two major challenges i.e. scalability and security issues. The flavor of edge computing (EC) considered here to deal with the scalability issue. A BC is a public, shared database that records transactions between two parties that confirms owners through cryptography. After a transaction is validated and cryptographically verified generates “block” on the BC and transactions are ordered chronologically and cannot be altered. Implementing BC and smart contracts technologies will bring security features for IoV. It plays a role to implement the rules and policies to govern the IoV information and transactions and keep them into the BC to secure the data and for future uses.
Facilitating corals in an early Silurian deep‐water assemblage

Abstract Corals are powerful ecosystem engineers and can form reef communities with extraordinary biodiversity through time. Understanding the processes underlying the spatial distribution of corals allows us to identify the key biological and physical processes that structure coral communities and how these processes and interactions have evolved. However, few spatial ecology studies have been conducted on coral assemblages in the fossil record. Here we use spatial point process analysis ( SPPA ) to investigate the ecological interactions of an in situ tabulate and rugose coral community (n = 199), preserved under volcanic ash in the Silurian of Ireland. SPPA is able to identify many different sorts of interactions including dispersal limitation and competition within and between taxa. Our SPPA found that the spatial distribution of rugose corals were best modelled by Thomas clusters ( p d = 0.834), indicating a single dispersal episode and that the tabulate corals were best modelled by double Thomas clusters ( p d = 0.820), indicating two dispersal episodes. Further, the bivariate distribution was best modelled by linked double clusters ( p d = 0.970), giving significant evidence of facilitation between the tabulate and rugose populations, and identifying the facilitators in this community to be the tabulate corals. This interaction could be an important ecological driver for enabling the aggregation of sessile organisms over long temporal periods and facilitation may help to explain trends in reef diversity and abundance during the Ordovician biodiversification and in the early Silurian.
Efficient Multi-Authority Attribute-based Signcryption with Constant-Size Ciphertext

Recently, efficient fine-grained access mechanism has been studied as a main concern in cloud storage area for several years. Attribute-based signcryption (ABSC) which is logical combination of attribute-based encryption(ABE) and attribute-based signature(ABS), can provide confidentiality, authenticity for sensitive data and anonymous authentication. At the same time it is more efficient than previous “encrypt-then-sign” and “sign-then-encrypt” patterns. However, most of the existing ABSC schemes fail to serve for real scenario of multiple authorities and have heavy communication overhead and computing overhead. Hence, we construct a novel ABSC scheme realizing multi-authority access control and constant-size ciphertext that does not depend on the number of attributes or authorities. Furthermore, our scheme provides public verifiability of the ciphertext and privacy protection for the signcryptor. Specially, it is proven to be secure in the standard model, including ciphertext indistinguishability under adaptive chosen ciphertext attacks and existential unforgeability under adaptive chosen message attack.
Enhanced Anisotropic Diffusion-based CNN-LSTM Architecture for Video Face Liveness Detection

While considerable research has been recently done in improving the accuracy of face liveness detection, the best current approaches use a two-step process of first applying non-linear anisotropic diffusion to the incoming image and then using a deep network for final liveness decision. We develop a novel deep architecture for face liveness detection on video frames that uses the diffusion of images followed by a deep Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) to classify the video sequence as real or fake. Even though the use of CNN followed by LSTM is not new, combining it with diffusion (that has proven to be the best approach for single image liveness detection) is novel. Performance evaluation of our architecture on the REPLAY-ATTACK dataset yields competitive results with 98.64% accuracy and an HTER of 3.63%.
Integrated eye tracking on Magic Leap One during augmented reality medical simulation: a technical report

Augmented reality (AR) has been studied as a clinical teaching tool, however eye-tracking capabilities integrated within an AR medical simulator have limited research. The recently developed Chariot Augmented Reality Medical (CHARM) simulator integrates real-time communication into a portable medical simulator. The purpose of this project was to refine the gaze-tracking capabilities of the CHARM simulator on the Magic Leap One (ML1). Adults aged 18 years and older were recruited using convenience sampling. Participants were provided with an ML1 headset that projected a hologram of a patient, bed and monitor. They were instructed via audio recording to gaze at variables in this scenario. The participant gaze targets from the ML1 output were compared with the specified gaze points from the audio recording. A priori investigators planned to iterative modifications of the eye-tracking software until a capture rate of 80% was achieved. Two consecutive participants with a capture rate less than 80% triggered software modifications and the project concluded after three consecutive participants' capture rates were greater than 80%. Thirteen participants were included in the study. Eye-tracking concordance was less than 80% reliable in the first 10 participants. The investigators hypothesised that the eye movement detection threshold was too sensitive, thus the algorithm was adjusted to reduce noise. The project concluded after the final three participants' gaze capture rates were 80%, 80% and 80.1%, respectively. This report suggests that eye-tracking technology can be reliably used with the ML1 enabled with CHARM simulator software.
User Profiling Using Smartphone Network Traffic Analysis

The recent decade has witnessed phenomenal growth in communication technology. Development of user friendly software platforms, such as Facebook, WhatsApp etc. have facilitated ease of communication and thereby people have started freely sharing messages and multimedia over the Internet. Further, there is a shift in trends with services being accessed from smartphones over personal computers. To protect the security and privacy of the smartphone users, most of the applications use encryption that encapsulates communications over the Internet. However, research has shown that the statistical information present in a traffic can be used to identify the application, and further, the activity performed by the user inside that application. In this paper, we extend the scope of analysis by proposing a learning framework to leverage application and activity data to profile smartphone users in terms of their gender, profession age group etc. This will greatly help the authoritative agencies to conduct their investigations related to national security and other purposes.
Anytime Kinodynamic Motion Planning using Region-Guided Search

Many kinodynamic motion planners have been developed that guarantee probabilistic completeness and asymptotic optimality for systems for which steering functions are available. Recently, some planners have been developed that achieve these properties of completeness and optimality without requiring a steering function. However, these planners have not taken strong advantage of heuristic guidance to speed their search. This paper introduces Region Informed Optimal Trees (RIOT), a sampling-based, asymptotically optimal motion planner for systems without steering functions. RIOT's search is guided by a low-dimensional abstraction of the state space that is updated during planning for better guidance. Simulation results suggest RIOT is adaptable, scalable, and more effective on difficult problems than previous work.
Hyperbolic-valued Hopfield neural networks in hybrid mode

A complex-valued Hopfield neural network (CHNN) has been applied as a multistate neural associative memory. Although synchronous mode accelerates the recall process, a CHNN with a projection rule may be trapped at a cycle of length two. A hyperbolic-valued Hopfield neural network (HHNN) with a conventional projection rule converges to a fixed point in synchronous mode. A noise robust projection rule improves the noise tolerance of HHNN, though it is not able to be applied to an HHNN in synchronous mode. In this work, we proposed hybrid mode, that is, asynchronous mode after synchronous mode. The conventional projection rule is employed in synchronous mode, and the noise robust projection rule is employed in asynchronous mode. Thus, the HHNN in hybrid mode converges in both modes. The HHNN in hybrid mode is expected to provide better noise tolerance than an HHNN in synchronous mode and faster recall than an HHNN in asynchronous mode. Computer simulations imply that our expectations are achieved.