Reviewerly/exHarmony huggingface dataset:
Automated Development of Custom Fall Detectors: Position, Model and Rate Impact in Performance

The past years have witnessed a boost in fall detection-related research works, disclosing an extensive number of methodologies built upon similar principles but addressing particular use-cases. These use-cases frequently motivate algorithm fine-tuning, making the modelling stage a time and effort consuming process. This work contributes towards understanding the impact of several of the most frequent requirements for wearable-based fall detection solutions in their performance (usage positions, learning model, rate). We introduce a new machine learning pipeline, trained with a proprietary dataset, with a customisable modelling stage which enabled the assessment of performance over each combination of custom parameters. Finally, we benchmark a model deployed by our framework using the UMAFall dataset, achieving state-of-the-art results with an F1-score of 84.6% for the classification of the entire dataset, which included an unseen usage position (ankle), considering a sampling rate of 10 Hz and a Random Forest classifier.
Scalable and responsive information for industrial maintenance work

This paper describes the process and results of bringing responsive and scalable technical documentation to smart glasses to support industrial maintenance. Development and testing was done in four development cycles to discover how maintenance information can be delivered to smart glasses to support maintenance technicians. Test case was elevator maintenance, and several user tests were performed in a real or realistic environment by real maintenance experts. The concept of using smart glasses to view technical information during a maintenance task was received very well by the test users. This study confirms that DITA XML is a good candidate for the creation of technical information content for smart glasses, but information design is needed to ensure the scalability and usability of the information.
Real-Time Human Detection and Tracking Using Two Sequential Frames for Advanced Driver Assistance System

Real-time human detecting and tracking is an important task in Advanced Driver Assistance System (ADAS) especialy in providing an information about situation in front of vehicle. Deep Convolutional Neural Networks (CNN) is one algorithm that is widely applied to classify and detect objects. CNN has shown an impressive performance. However, the high computation of Deep CNN makes the algorithm difficult to be applied to the real ADAS system. Since 2014, the One-stage Detector approach such as SSD and YOLO began to be applied on devices with low computation. In this experiment, we present a real-time system for the detection and the tracking of humans (pedestrians, cyclists, and riders) for the ADAS system implemented in Raspberry Pi 3 Model B Plus. The object detection approach in this study applies the SSD framework, and the tracking human movements approach is done by calculating the movement of midpoint coordinates from bounding box objects from two sequenced frames. The result shows the realtime human detection and tracking on Raspberry Pi 3 B devices with input frame with a height 300 and a width 300 runs at 0.8 FPS with 77.6 percent processor consumption and 70.3 percent memory. Therefore, the use of Raspberry Pi 3 B Plus for human detection and tracking in ADAS systems is not suitable for the vehicle speeds above 50 Km per hour when runs at 0.8 FPS. Then the tracking system based on the coordinate movement of the midpoint bounding box has a problem when there is a bounding box overlapping or slicing each other.
Nonlinear Polynomial Graph Filter for Anomalous IoT Sensor Detection and Localization

Detecting the existence of anomaly and localizing the faulty sensors in the Internet-of-Things (IoT) systems are extremely critical, since the incorrect data could lead to catastrophic consequences in many vertical industry applications. The difficulties of such problems come from deriving an explicit error function for each sensor in IoT, and the data continuity in the temporal domain would also be seriously challenged. To overcome these difficulties, the irregular spatial information of the IoT sensors is utilized by constructing an adjacency matrix using the distances among different sensors, and the nonlinear polynomial graph filter (NPGF) is employed to characterize the relationships among the collected sensor data. The NPGF provides a more accurate model for reconstructing the sensor data by taking the data nonlinear relationships into account. The error functions at each sensor for newly detection data are theoretically derived, and it is demonstrated that the error at the anomalous sensor performs differently from that of the normal sensors if the adjacency matrix is designed appropriately. The proposed NPGF-based algorithm is illustrated and validated with a real-world data set for temperature monitoring. The simulation results demonstrate the superior performance of our scheme in both anomaly detection and faulty sensor localization when compared with existing algorithms, such as the graph frequency algorithm and oversampling PCA (OS-PCA) method, especially for the case of small sensor data deviations.
A Template Based Approach for Training NMT for Low-Resource Uralic Languages - A Pilot with Finnish

In this paper, we present a novel way of building parallel data by filling abstract morphosyntactic structures for endangered low-resource languages that exhibit a rich productive morphology. We use Finnish to pilot our approach by limiting the resources we feed into the machine translation model to the level of the resources available for Erzya. We also present a way of automatically mapping abstract morphosyntactic structures of two languages to produce a parallelized set of templates
Joint Stacked Hourglass Network and Salient Region Attention Refinement for Robust Face Alignment

Facial landmark detection aims to locate keypoints for facial images, which typically suffer from variations caused by arbitrary pose, diverse facial expressions, and partial occlusion. In this article, we propose a coarse-to-fine framework that joins a stacked hourglass network and salient region attention refinement for robust face alignment. To achieve this goal, we first present a multi-scale region learning module to analyze the structure information at a different facial region and extract a strong discriminative deep feature. Then we employ a stacked hourglass network for heatmap regression and initial facial landmarks prediction. Specifically, the stacked hourglass network introduces an improved Inception-ResNet unit as a basic building block, which can effectively improve the receptive field and learn contextual feature representations. Meanwhile, a novel loss function takes into account global weights and local weights to make the heatmap regression more accurate. Different from existing heatmap regression models, we present a salient region attention refinement module to extract a precise feature based on the heatmap regression, and utilize the filtered feature for landmarks refinement to achieve accurate prediction. Extensive experimental results of several challenging datasets (including 300 Faces in the Wild, Caltech Occluded Faces in the Wild, and Annotated Facial Landmarks Faces in the Wild) confirm that our approach can achieve more competitive performance than the most advanced algorithms.
Surface topography evolution of woven thermoplastic composites under deformation

The evolution of architectural distortion of woven composites can significantly influence the deformation behaviour but is challenging to measure experimentally. In this paper, two techniques are proposed to examine the out-of-plane topography of composites under deformation measured using stereo Digital Image Correlation (DIC). The primary method relies on fitting a surface to the data to compensate for global sample distortion and reveal local topography while the supplementary method uses local curvatures calculated for each data point on the surface. These methods have been applied to thermoplastic composites with different weave architectures, matrix and fibre materials. Topography results show good agreement in magnitude and form with mesoscale Finite Element Analysis (FEA). The methods improve existing experimental techniques to examine local effects with multiple applications regarding deformation, defect detection and failure analysis of composites.
My Gadget Just Cares for Me - How NINA Can Prove Security Against Combined Attacks

Differential Power Analysis and Differential Fault Analysis threaten the security of even the most trustworthy cryptographic primitives. It is important we protect their implementation such that no sensitive information is leaked using side channels and it withstands injected faults or combined physical attacks. In this work, we propose security notions tailored against advanced physical attacks consisting of both faults and probes on circuit wires. We then transform the security notions to composable security notions. The motivation for this research includes the ease of verification time; the creation of secure components; and the isolation of primitives in larger protocols such as modes of operations. We dub our notion NINA, which forms the link between the established Non-Interference (NI) property and our composable active security property, Non-Accumulation (NA). To illustrate the NINA property, we use it to prove the security of two multiplication gadgets: an error checking duplication gadget and an error correcting duplication gadget. The NINA proofs for error detecting gadgets capture the effect of Statistical Ineffective Fault Analysis (SIFA), an attack vector which threatens most current masked implementations. Additionally, we study error correcting techniques. We show that error correcting gadgets can attain the Independent NINA property. A stronger property which captures a clear separation between the effect of faults and probes. Thus, we show that clever error correcting gadgets improve on error detecting ones by achieving significant higher levels of combined security along with guaranteed output delivery.
Robust model reference tracking control for interval type‐2 fuzzy stochastic systems

This study considers the uncertainty and disturbance estimator-based control problem for interval type-2 fuzzy stochastic systems with disturbances. The proposed control approach accurately estimates system unknown delays, uncertainties, non-linearities and external disturbances by introducing an appropriate filter. Precisely, the filter's bandwidth is one of the most important parameters in designing and tuning the controller to deal with unknown delays, uncertainties, non-linearities and disturbances. By using the Lyapunov–Krasovskii stability theorem and the linear matrix inequality approach, the required stability conditions and the control gain matrices for the system under consideration are obtained. Further, the effectiveness of the proposed control law is verified using two numerical examples.
Anomaly Detection at the IoT Edge using Deep Learning

Irregularities in the use of electrical energy could result in failure of power grids and blackouts. Anomaly detection is required not only for ensuring grid safety, but also to prevent illegal hacking. Long-term data are recorded for such anomaly detection. However, due to the nonlinear characteristics of the time series data, correlation regression becomes difficult when using non-deep learning techniques. To address this issue, we use Long-Short Term Memory (LSTM) recurrency techniques. We experiment with two datasets from two different smart meters, which are sampled once every 30 seconds for one month. A total of 120,000 data samples were used for training and 40,000 data samples for testing. From the experiment results, the testing accuracy, True-Positive Rate, and False Positive Rate were 0.92, 0.81, and 0.50, respectively. Further, to demonstrate that the LSTM model can actually be designed at the network edge, we implemented the model and the trained weights on a Raspberry Pi platform. The inference time for each sample was 935 μs, which is short enough for realizing edge-based anomaly detection.
Active voice authentication

Active authentication refers to a new mode of identity verification in which biometric indicators are continuously tested to provide real-time or near real-time monitoring of an authorized access to a service or use of a device. This is in contrast to the conventional authentication systems where a single test in form of a verification token such as a password is performed. In active voice authentication, voice is the biometric modality. This paper describes an ensemble of techniques that make reliable speaker verification possible using unconventionally short voice test signals. These techniques include model adaptation and minimum verification error training that are tailored for the extremely short training and testing requirements. A database of 25 speakers is recorded for developing this system. In our off-line evaluation on this dataset, the system achieves an average windowed-based equal error rates of 3-4% depending on the model configuration, which is remarkable considering that only 1 second of voice data is used to make every single authentication decision. On the NIST SRE 2001 Dataset, the system provides a 3.88% absolute gain over i-vector when the duration of test segment is 1 second. A real-time demonstration system has been implemented on Microsoft Surface Pro.
Study of the virtual reality education and digitalization in China

Abstract Human society has entered the era of information digitization. The rise and spread of electronic technology, digital media, the Internet, blockchain, Augmented Reality/Virtual and Reality, etc. affect all aspects of human society. At the end of the 20th century, the development and achievements of information technology constituted an important background for the existence of education, and promoted the development of online teaching, network examination, and other aspects, which made a big difference. However, digital technology has not been applied in primary education and secondary education. The existing teaching method is still taught in the form of textbooks and manual drawings. This research focuses on the application of digital technology in primary, secondary and advanced teaching in the digital age. This study conducts research and analysis on local schools and students through observation, experimental and comparative research methods, and compares the differences between ordinary teaching and digital teaching.Taking virtual reality technology as an example, breaking the impression that traditional teaching gives people can effectively implement diversified learning, cultivate students’ practical ability and innovative ability, also can improve their independent learning ability.
A Bi-Directional LSTM Model with Attention for Malicious URL Detection

Malicious URLs have become an important attack vector used by attackers to perpetrate cybercrimes, how to effectively detect malicious URLs is an important and urgent problem to be solved. Due to current feature based malicious URLs detection models need manual feature engineering, and deep learning based models have their limit on processing long sequences, which reduces the detection performance. We proposed an attentional based BiLSTM model AB-BiLSTM for the Malicious URLs detection in this paper. Firstly, the URLs were preprocessed and converted into word vectors by using pre-trained Word2Vec, then BiLSTM combined with an attention mechanism was trained to extract URL sequences features and classify them. The model was tested on collected dataset, the experimental results show that our proposed model can achieve the accuracy of 98.06%, the precision rate of 96.05, the recall rate of 95.79% and the F1 Score of 95.92%, which achieved better performance than other comparison traditional machine learning based and deep learning based models.
Optimization model for Sub-feature selection in data mining

This paper proposes sub-feature selection based classification in data mining. Many existing feature selection methods are used to analyze traditional feature values as well as class values. But all feature's values don't support to distinguish the unique class from existing datasets, because these selected features are irrelevant towards traditional class. Thus it needs to analyze the feature's tiny data such as sub-feature data which help to find distinguish class. So this paper has considered identifying the new class from usual class. It selects needed sub-features from existing feature's data using optimization model. In this paper, it proposed Lagrangian functional method of optimization model for sub-feature selection. This method finds appropriate sub-feature data based on optimal evaluation value. It demonstrates the experiment using different dataset to get sub-feature data for novel class.
Feature reduction based on semantic similarity for graph classification

Classification and recognition of graph data are crucial problems in many fields, such as bioinformatics, chemoinformatics and data mining. In graph kernel-based classification methods, the similarity among substructures is not fully considered; in addition, poorly discriminative substructures will affect the graph classification accuracy. To improve the graph classification accuracy, we propose a feature reduction algorithm based on semantic similarity for graph classification in this paper. In the algorithm, we first learn vector representations of subtree patterns using neural language models and then merge semantically similar subtree patterns into a new feature. We then provide a new feature discrimination score to select highly discriminative features. Comprehensive experiments on real datasets demonstrate that the proposed algorithm achieves a significant improvement in classification accuracy over compared graph classification methods.
Pbsx: A practical private boolean search using Intel SGX

Searchable encryption enables private keyword search over encrypted data held on an untrusted cloud. However, existing searchable encryption schemes promise search efficiency at the expense of additional privacy leakage or expensive storage overhead. Intel SGX is a promising tool to allow efficient and private keyword search. However, since the size of enclave created by SGX is limited, frequent sensitive-page swaps between the enclave and the untrusted memory cause severe performance degradation for large datasets. Moreover, SGX does not protect against access pattern leakages which are vulnerable to statistical inference attacks. To overcome the above problems, we present Pbsx, an efficient private boolean searchable encryption scheme using Intel SGX. Pbsx firstly analyzes and chooses the suitable index data structures (e.g. bitmap and bloom filter tree) for private boolean search in the context of Intel SGX. Pbsx then combines these index data structures with oblivious random access machine (ORAM) schemes to design oblivious index data structures (e.g. oblivious bitmap and oblivious bloom filter tree), such that it could provide storage efficiency without suffering from access pattern leakages. Since the direct composition of the index data structures and the ORAM schemes is extremely costly for search operations, we design auxiliary data structures (e.g. map table and id index tree) to mitigate the efficiency barrier. Based on these oblivious index data structures, we propose oblivious search algorithms which can achieve a worst-case sub-linear search time. We implement Pbsx and evaluate the performance of our constructions with various metrics. Comparing with previous schemes, the results indicate that Pbsx can achieve a 5~12x speed-up for the million-level dataset and a 13~51x speed-up for the small dataset. In addition, Pbsx also achieves less access pattern leakages.
Blockchain and Structural Relationship Based Access Control for IoT: A Smart City Use Case

Smart city is one of the major Internet of Things (IoT) applications and has become an emerging paradigm with the recent advancements of IoT devices and sensors. But the heterogenous nature of a smart city IoT environment makes it vulnerable to many privacy and security concerns and introduces significant challenges for access control of IoT resources especially where access needs to be provided to third parties and external organizations. This paper proposes a new structural relationships-based access control (SRBAC) model that can be used to delegate resource access rights to users in a large scale IoT scenario like smart city while keeping the resource owner in full control. The proposed architecture uses smart contracts and public blockchain for managing access control for external users and a local off-block chain storage for managing access control for organization’s internal users and enforcing fine-grained access control for the resources.
Accurate and Flexible Calibration Method for a Class of Visual Sensor Networks

Accurate and flexible calibration is a prerequisite for visual sensor networks to retrieve metric information from image data. This paper presents the design and implementation of an accurate and flexible calibration method for a class of visual sensor networks intended for 3D measurement and tracking in large volumes. The proposed method employs a generic camera model that is applicable for wide-angle lens cameras as well as for conventional cameras. It does not require all the employed cameras to share a common field of view, and only pairwise overlap is needed. In the calibration process, the poses between stereo cameras are first initialized using essential matrix decomposition and then optimized by the Levenberg-Marquardt algorithm. A weighted vision graph is proposed to select optimal transformation paths among cameras by using Dijkstra's shortest path algorithms for multi-camera calibration. Then, the global coordinates are constructed using a four-marker calibration triangle. Finally, a Unity3D-based virtual platform, in which the total number and configurations of cameras, as well as the environment scene, can be arbitrarily edited, is designed to test the proposed calibration algorithms. Extensive experiments based on synthetic and real data are performed to demonstrate the effectiveness of the proposed multi-camera calibration method. Experimental results show that the multi-camera calibration method is accurate and easy-to-implement in the presence of noise.
Metastatic Breast Cancer Recognition in Histopathology Images Using Convolutional Neural Network with Attention Mechanism

Lymph node tissue pathological analysis is one of the common methods for doctors to evaluate the types and stage of breast cancer. Using deep learning methods to detect breast cancer metastases has great research value. We proposes a model to automatically classify breast cancer metastases, using a convolutional neural network with attention mechanism. A Convolutional Block Attention Module is used in our network. We validate our model on PCam dataset, and obtain an AUC score of 0.976. These results demonstrate that using computer vision method has a great performance in pathological diagnoses.