Reviewerly/exHarmony huggingface dataset:
The Use of Machine Translation System for Component Development of Adaptive Computer System for Individual Testing of Students’ Knowledge

The article is devoted to the research of the modern machine translation systems as well as different aspects of their using while development and in the course of functioning of adaptive computer system for individual testing of students’ knowledge. The contrastive analysis of computer-aided translation and automated translation is reported, computer-based translation in its three main modern sorts: based on rules, statistical and hybrid is estimated in the context of technological opportunities of application by both linguistics specialists and in the field of component development of learning management systems. In the article the approaches to the problem of adaptation of computer system for individual testing of students in preparation of bachelors of engineering degrees are stated. The peculiarities of the use of machine translation systems for training course development, structuring of test material and in the process of realizing of adaptive algorithm for individual testing of students’ educational progress are discussed.
Coarse-to-Fine Activity Annotation and Recognition Algorithm for Solitary Older Adults

Older adults want to remain independent with dignity for as long as possible, especially the solitary older adults. Activity recognition plays an essential role in elderly care and rehabilitation by detecting activity shifts among the elderly population. Despite over a decade of research and development in activity recognition, accurate and reliable systems for older adults in use are few. We propose an automatic data collecting and labeling system by addressing the annotation issue, and a novel coarse-to-fine activities of daily living(ADLs) recognition algorithm for older adults, by combining supervised and unsupervised machine learning methods. The automatic data collecting and labeling system targets at the annotation issue caused by the diversity of ADLs in free-living situations. Multiple sensors fusion strategy is employed to interpret and annotate the ADLs. Leveraging supervised and unsupervised machine learning methods, we can discover and recognize ambulatory and trivial ADLS for older adults. The performance of the automatic data collecting and labeling system is double-checked in a four days long test. With the reliable ground truth, we evaluate the coarse-to-fine ADLs recognition algorithm. The performance of our algorithm is promising, the recognition accuracy is larger than 91%.
Face Tells Detailed Expression: Generating Comprehensive Facial Expression Sentence Through Facial Action Units

Human facial expression plays the key role in the understanding of the social behavior. Many deep learning approaches present facial emotion recognition and automatic image captioning considering human sentiments. However, most current deep learning models for facial expression analysis do not contain comprehensive, detailed information of a single face. In this paper, we newly introduce a text-based facial expression description using several essential components describing comprehensive facial expression: gender, facial action units, and corresponding intensities. Then, we propose comprehensive facial expression sentence generating model along with facial expression recognition model for a single facial image to verify the effectiveness of our text-based dataset. Experimental results show that the proposed two models are supporting each other improving their performances: the text-based facial expression description provides comprehensive semantic information to the facial emotion recognition model. Also, the visual information from the emotion recognition model guides the facial expression sentence generation to produce a proper sentence describing comprehensive description. The text-based dataset is available at https://github.com/joannahong/Text-based-dataset-with-comprehensive-facial-expression-sentence .
Remote Attestation as a Service for IoT

Remote attestation is a two-party security protocol that aims to detect the presence of malware in a remote untrusted IoT device. In order to perform the attestation, an IoT device typically has to stop the regular operation and perform expensive computations that will consume the battery life of the device. In this paper, we use cloud/fog computing to attest an IoT device in an efficient way. We propose Remote Attestation as a Service (RAaS) which allows even a low-end IoT device to securely offload the attestation process to the cloud. We argue that RAaS allows the clone of the device, securely created in the cloud, to perform the most expensive attestation computations. Our proposed approach could reduce the number of attestation operations running on the real IoT device, saving energy consumption, and reducing the downtime of the usual operation of an IoT device during the execution of remote attestation.
A Blockchain-Based Traffic Conditions and Driving Behaviors Warning Scheme in the Internet of Vehicles

With the economic development, the number of cars is increasing, and the traffic accidents and congestion problems that follow will not be underestimated. The concept of the Internet of Vehicles is becoming popular, and demand for intelligent traffic is growing. In this paper, the warning scheme we proposed aims to solve the traffic problems. Using intelligent terminals, it is faster and more convenient to obtain driving behaviors and road condition information. The application of blockchain technology can spread information to other vehicles for sharing without third-party certification. Group signature-based authentication protocol guarantees privacy and security while ensuring identity traceability. In experiments and simulations, the recognition accuracy of driving behavior can reach up to 94.90%. The use of blockchain provides secure, distributed, and autonomous features for the solution. Compared with the traditional signature method, the group signature-based authentication time varies less with the increase of the number of vehicles, and the communication time is more stable.
Evaluation of Intra-Coding Based Image Compression

Considering modern cameras, increasing image resolutions and thousands of images uploaded to sharing platforms there is still reason to have a deeper look into image compression. Especially lossy image compression is always a trade-off between file-size and image quality, where high quality is usually preferred for storage. Beside classical image compression, e.g. JPEG, there is also ongoing development to use video codecs to compress images. We analyze four different video codecs, namely AV1, H.264, H.265 and VP9, in comparison with JPEG. Our evaluation considers classical image quality metrics, e.g. PSNR, and also a modern subjective quality metric, i.e. Netflix's VMAF. We are able to show that modern video codecs can outperform classical JPEG compression both in terms of quality and file-size. For this we used 1133 uncompressed images and applied different encoding settings and estimated image quality.
Kd-Tree Based Efficient Ensemble Classification Algorithm for Imbalanced Learning

Ensemble learning combined with resampling is an effective approach for solving the imbalanced classification problems. But it is not suitable for large-scale imbalanced data due to its slower training speed and larger computation. In this paper, an efficient ensemble classification method based on kd-tree for imbalanced data is proposed. First, a kd-tree is constructed from the training data, which will be used for searching quickly k-nearest neighbors of each instance. Then, the under-sampling is repeatedly adopted till the difference of the intra-class coherence between different classes is zero. In the process of under-sampling, the constructed kd-tree is used to get the intra-class coherence. Based on this balanced dataset, ensemble learning is performed. The experimental results show the effectiveness of the proposed the method, especially for large-scale imbalanced data.
Precambrian basement rocks of Wadi-Khuda-Shut area, South Eastern Desert of Egypt: Geology and remote sensing analysis

Wadi Khuda-Wadi Shut area in the South Eastern Desert (SED) of Egypt is mainly made up of gneisses, ophiolitic serpentinites and intermediate schistose metavolcanics. These metamorphic rock units are intruded by syn-late-tectonic diorite, granodiorite, ultramafic-mafic, and monzogranite intrusions. The gneisses comprise biotite hornblende gneiss, hornblende gneiss and migmatites. They are of granodiorite and diorite composition. The ophiolitic serpentinite with talc carbonate rocks were thrusted over the metavolcanics with NW-SE thrust contact. The late tectonic ultramafic-mafic rock units are represented in the study area by Dahanib pluton, which is consisted of layered peridotite-dunite associated with olivine gabrro, normal gabbro and gabbro-norite as well as rare hornblende gabbro. These varieties are related to the layered intrusions rather than Alaskan-types, and formed by magmatic differentiation. The granodiorite and monzogranite represent the youngest basement rock units in the study area. The principal components and band ratios of ASTER and Landsat-8 data were successfully used for the first time in lithological discrimination and geological mapping of the study area. This study concluded that, the interpreted data of ASTER and Landsat-8 combined with field study and petrographic investigation clearly discriminated the exposed rock units. Accordingly, a detailed geological map emphasizing the lithological units in Khuda-Shut area has been presented.
Exploring Cognitive States: Temporal Methods for Detecting and Characterizing Physiological Fingerprints

Cognitive state detection and its relationship to observable physiologically telemetry has been utilized for many human-machine and human-cybernetic applications. This paper aims at understanding and addressing if there are unique psychophysiological patterns over time, a ''physiological temporal fingerprint'', that is associated with specific cognitive states. This preliminary work involves commercial airline pilots completing experimental benchmark task inductions of three cognitive states: 1) Channelized Attention (CA); 2) High Workload (HW); and 3) Low Workload (LW). We approach this objective by modeling these "fingerprints" through the use of Hidden Markov Models and Entropy analysis to evaluate if the transitions over time are complex or rhythmic/predictable by nature. Our results indicate that cognitive states do have unique complexity of physiological sequences that are statistically different from other cognitive states. More specifically, CA has a significantly higher temporal psychophysiological complexity than HW and LW in EEG and ECG telemetry signals. With regards to respiration telemetry, CA has a lower temporal psychophysiological complexity than HW and LW. Through our preliminary work, addressing this unique underpinning can inform whether these underlying dynamics can be utilized to understand how humans transition between cognitive states and for improved detection of cognitive states.
Regularized UNet for Automated Pancreas Segmentation

Leading other traditional methods by a large margin, convolutional neural networks (CNNs) become the first choice for dense classification problems such as semantic segmentation. However, when given few training images, CNNs often could not deal with details well. Coarse edges and isolated points often appear in the segmentation results provided by CNNs. Since medical datasets usually contain dozens to hundreds training samples, the segmentation results need further refinement. In this paper, we implement regularized UNet (RUNet) with multi-step primal-dual block which is an end-to-end framework to regularize the segmentation results. The proposed framework could produce smooth edges and eliminate isolated points. Comparing to other post-processing methods, our method needs little extra computation thus is effective and efficient.
Listen!: Audio-based Smart IoT Device Pairing Protocol

Context-based zero-interaction has become the trend for smart IoT device pairing. In this paper, we propose a secure and usable mechanism to authenticate devices co-located in smart home scenario, and build a secure communication channel between legitimate devices by utilizing on-board microphones to capture a common audio context. After receiving randomly generated sound signals, smart IoT device uses the time intervals between salient sound signals to derive audio fingerprint which can be matched among co-present devices and then be used to bootstrap trust of the devices. The protocol is based on the idea that devices co-located within a physical security boundary (e.g., single family house) can hear similar sounds, and the devices outside would miss parts of sound signals due to the attenuation when sounds pass through the wall. To accelerate the generation rate of audio fingerprint, an extra sound source is introduced. We implement our protocol on Android devices, and the experiment results show that the protocol can distinguish the malicious devices outside from the legitimate devices located inside a security boundary and can quickly establish a strong secret-key between legitimate devices.
Fast Marching-Based Path Generating Algorithm in Anisotropic Environment With Perturbations

Fast Marching is a widely used method in path planning, especially continuity is demanded due to kinodynamic constraints of automatic vehicles. However, its application in real environment with obstacles and perturbations requires simplifications to be made, resulting in non-optimal or even unfeasible path. We introduce a risk function ζ to describe the effect of obstacles to the safety of a generated path. Combined with the physical law based description of external flow perturbation, we formulate the path planning problem in realistic environment as a wave front propagation in terms of an extended Eikonal equation. Inspired by the underlying mechanism of the numerical recipe of Fast Marching algorithm, we transform the equation into its canonical form based on the physical rules behind. This enables solutions of the equation without any simplification nor introduction of additional complexity. Comparing to the classical fast marching based method in anisotropic environment, numerical simulations show that the proposed method is more efficient and robust to external flow perturbations, especially in the case of weak or strong external flow environment.
Line-Detection Based on the Sum of Gradient Angle Differences

This paper presents a method to detect line pixels based on the sum of gradient angle differences (SGAD). The gradient angle differences are calculated by comparing the four pairs of gradients arising from eight neighboring pixels. In addition, a method to classify line pixels into ridges and valleys is proposed. Furthermore, a simple line model is defined for simulation experiments. Experiments are conducted with simulation images generated using the simple line model for three line-detection methods: second-derivatives (SD)-based method, extremity-count (EC)-based method, and proposed method. The results of the simulation experiments show that the proposed method produces more accurate line-detection results than the other methods in terms of the root mean square error when the line width is relatively large. In addition, the experiments conducted with natural images show that the SD- and EC-based methods suffer from bifurcation, fragmentation, and missing pixels. By contrast, for the original and the noise-contaminated versions of the natural images, the proposed SGAD-based line-detection method is affected by such problems to a considerably smaller extent than the other two methods.
Deep Palette-Based Color Decomposition for Image Recoloring with Aesthetic Suggestion

Color edition is an important issue in image processing and graphic design. This paper presents a deep color decomposition based framework for image recoloring, allowing users to achieve professional color edition through simple interactive operations. Different from existing methods that perform palette generation and color decomposition separately, our method directly generates the recolored images by a light-weight CNN. We first formulate the generation of color palette as an unsupervised clustering problem, and employ a fully point-wise CNN to extract the most representative colors from the input image. Particularly, a pixel scrambling strategy is adopted to map the continuous image color to a compact discrete palette space, facilitating the CNN focus on color-relevant features. Then, we devise a deep color decomposition network to obtain the projected weights of input image on the basis colors of the generated palette space, and leverage them for image recoloring in a user-interacted manner. In addition, a novel aesthetic constraint derived from color harmony theory is proposed to augment the color reconstruction from user-specified colors, resulting in an aesthetically pleasing visual effect. Qualitative comparisons with existing methods demonstrate the effectiveness of our proposed method.
Accountable Data Sharing Scheme Based on Blockchain and SGX

Nowadays, data has been more intensive and collaborative than in the past. In this paper, we propose a new accountable data sharing scheme based on blockchain and SGX, in which data subscription undeniable. By utilizing blockchain, the records of users data sharing acts are tamper-resistant, and our scheme no longer needs a trusted third party. SGX ensures the confidentiality of sharing data.
Release Planning in a Hybrid Project Environment

Context: Even regulated domains like the automotive domain increasingly adopt agile software development. However, traditional sequential processes are still in use and have to coexist with the new development approaches. Collaboration between agile and hybrid projects within complex traditional product development processes is challenging, especially regarding the creation and synchronization of a qualification phase plan. Objective: The aim of this study is to motivate research related to the combined use of agile and traditional paradigms in release planning in the automotive domain and to report challenges from industry. Method: We introduce and motivate the research topic and discuss related work based on the results of a small literature study. Further, an online survey with 56 respondents from an automotive Original Equipment Manufacturer was conducted. Results: There is a clear research gap regarding release planning for combined agile and traditional projects. The state-of-the-practice survey identified challenges, such as a lack of transparency regarding the status quo of related projects. Conclusions: The research gap as well as the challenges from industry should motivate further research on this topic, in order to improve release planning processes in this specific context.
Logical Layout Analysis using Deep Learning

Logical layout analysis plays an important part in document understanding. It can become a challenging task due to varying formats and layouts. Researchers have proposed different ways to solve this problem, mostly using visual information in some way and a complex pipeline. In this paper, we present a simple technique for labelling the logical structures in document images. We use visual and textual features from the document images to label zones. We utilize Recurrent Neural Networks, specifically 2 layers of LSTM, which input the text from the zone that we want to classify as sequences of words and the normalized position of each word with respect to the page width and height. Comparisons are made by comparing the image under test with the known layouts and labels are assigned to zones accordingly. The labels are abstract, title, author names, and affiliation; however, the text also contains very important information for the task at hand. The presented approach achieved an overall accuracy of 96.21% on publicly available MARG dataset.
Rethinking the Test Collection Methodology for Personal Self-tracking Data

While vast volumes of personal data are being gathered daily by individuals, the MMM community has not really been tackling the challenge of developing novel retrieval algorithms for this data, due to the challenges of getting access to the data in the first place. While initial efforts have taken place on a small scale, it is our conjecture that a new evaluation paradigm is required in order to make progress in analysing, modeling and retrieving from personal data archives. In this position paper, we propose a new model of Evaluation-as-a-Service that re-imagines the test collection methodology for personal multimedia data in order to address the many challenges of releasing test collections of personal multimedia data.
Reading Meter Numbers in the Wild

In this work we introduce a pipeline to detect and recognize various utility meter numbers in the wild. The system leverages on deep neural networks for detection and recognition. In the detection phase, we employ a fully Convolutional Neural Network to perform a pixel-wise classification, while the recognition phase employs another deep neural network to predict the length and individual digits in a meter. We qualitatively showed that the proposed approach is robust against severe perspective distortions, different lighting conditions and blurred images. Furthermore, it is capable of detecting small scale digits. Our approach is suitable for billing companies aiming to increase efficiency, lowering the time consumed by manual checks performed in the billing process. Finally, we release the dataset used in this work to benchmark the task.
Holding Virtual Objects Using a Tablet for Tangible 3D Sketching in VR

We present an idea that uses a tracked graphics tablet attached a virtual or physical object to grasp in VR for direct 3D sketching on virtual objects, giving users the feeling of holding a physical object and drawing on it. We developed a prototype system and evaluated the effect of different tablet manipulation modes on user performance and experience when drawing strokes on virtual objects in VR. Results suggested that having a physical handle or object to grasp enhanced the participants' sense of realism, compared to conditions where there was no tablet or a simple tablet interface.
Tunable asymmetric Einstein–Podolsky–Rosen steering of microwave photons in superconducting circuits

We present an alternative scheme for generating the asymmetric steering of microwave photons via using a superconducting circuit system, where a single <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi mathvariant="normal">Δ<!-- Δ --></mml:mi> </mml:math> -type three-level fluxoninum qubit interacts dispersively with three superconducting resonators. The nondegenerate parametric down-conversion occurs among three microwave modes by adiabatically eliminating the atomic variables of the artificial atom, which is responsible for the existence of quantum correlation. Furthermore, the asymmetric steering is easily established with the help of coherent driving of the resonators, and its directionality can be controlled by adjusting the driving strengths to two modes among three modes without additional noise. The scheme we present is based on general quantum operations under conditions of decoherence and nonideal coupling efficiency, and the asymmetric steering of microwave photons is a useful resource for the construction of long-distance quantum communication networks in solid-state systems.