Reviewerly/exHarmony huggingface dataset:
An Energy-Efficient Fall Detection Method Based on FD-DNN for Elderly People

A fall detection module is an important component of community-based care for the elderly to reduce their health risk. It requires the accuracy of detections as well as maintains energy saving. In order to meet the above requirements, a sensing module-integrated energy-efficient sensor was developed which can sense and cache the data of human activity in sleep mode, and an interrupt-driven algorithm is proposed to transmit the data to a server integrated with ZigBee. Secondly, a deep neural network for fall detection (FD-DNN) running on the server is carefully designed to detect falls accurately. FD-DNN, which combines the convolutional neural networks (CNN) with long short-term memory (LSTM) algorithms, was tested on both with online and offline datasets. The experimental result shows that it takes advantage of CNN and LSTM, and achieved 99.17% fall detection accuracy, while its specificity and sensitivity are 99.94% and 94.09%, respectively. Meanwhile, it has the characteristics of low power consumption.
Continuous-Time Safe Learning with Temporal Logic Constraints in Adversarial Environments

This paper investigates a safe learning problem that satisfies linear temporal logic (LTL) constraints with persistent adversarial inputs, and quantified performance and robustness. Via a finite state automaton, the LTL specification is first decomposed to a sequence of several two point boundary value problems (TPBVP), each of which has an invariant safety zone. Then we employ a system transformation that guarantees state, and control safety with logarithmic barrier and hyperbolic-type functions as well as a worst-case adversarial input that wants to push the system outside the safety set. A safe learning method is used to solve the sub-problem, where the actors (approximators of the optimal control and the worst- case adversarial inputs) and the critic (approximator of the cost) are tuned to learn the optimal policies without violating any safety. Finally, by following a Lyapunov stability analysis we prove boundedness of the closed-loop system while simulation results are used to validate the effectiveness.
Semantically Tied Paired Cycle Consistency for Any-Shot Sketch-Based Image Retrieval

Abstract Low-shot sketch-based image retrieval is an emerging task in computer vision, allowing to retrieve natural images relevant to hand-drawn sketch queries that are rarely seen during the training phase. Related prior works either require aligned sketch-image pairs that are costly to obtain or inefficient memory fusion layer for mapping the visual information to a semantic space. In this paper, we address any-shot, i.e. zero-shot and few-shot, sketch-based image retrieval (SBIR) tasks, where we introduce the few-shot setting for SBIR. For solving these tasks, we propose a semantically aligned paired cycle-consistent generative adversarial network (SEM-PCYC) for any-shot SBIR, where each branch of the generative adversarial network maps the visual information from sketch and image to a common semantic space via adversarial training. Each of these branches maintains cycle consistency that only requires supervision at the category level, and avoids the need of aligned sketch-image pairs. A classification criteria on the generators’ outputs ensures the visual to semantic space mapping to be class-specific. Furthermore, we propose to combine textual and hierarchical side information via an auto-encoder that selects discriminating side information within a same end-to-end model. Our results demonstrate a significant boost in any-shot SBIR performance over the state-of-the-art on the extended version of the challenging Sketchy, TU-Berlin and QuickDraw datasets.
Trajectory control and image encryption using affine transformation of lorenz system

This paper presents a generalization of chaotic systems using two-dimensional affine transformations with six introduced parameters to achieve scaling, reflection, rotation, translation and/or shearing. Hence, the location of the strange attractor in space can be controlled without changing its chaotic dynamics. In addition, the embedded parameters enhance the randomness and sensitivity of the system and control its response. This approach overpasses performing the transformations as post-processing stages by applying them on the resulting time series. Trajectory control through dynamic parameters is demonstrated. Simulation results validate the proposed analysis for both the simplest and Lorenz chaotic systems. An image encryption scheme is implemented using transformed Lorenz system resulting in a more secure encryption scheme in comparison to Lorenz and other recent related works. The scheme exhibits good performance when assessed using the PRNG properties, encrypted image histogram and its uniformity through chi square test, pixel correlation, Mean Squared Error (MSE), entropy, Peak Signal-to-Noise Ratio (PSNR), the National Institute of Standards & Technology (NIST) test, key space, key sensitivity, resistance to differential, ciphertext-only, known plaintext, and chosen plaintext attacks, robustness against noise and computation time.
Deep Reinforcement Learning for MEC Streaming with Joint User Association and Resource Management

Mobile Edge Computing (MEC) is a promising technique in the 5G Era to improve the Quality of Experience (QoE) for online video streaming due to its ability to reduce the backhaul transmission by caching certain content. However, it still takes effort to address the user association and video quality selection problem under the limited resource of MEC to fully support the low-latency demand for live video streaming. We found the optimization problem to be a non-linear integer programming, which is impossible to obtain a globally optimal solution under polynomial time. In this paper, we first reformulate this problem as a Markov Decision Process (MDP) and develop a Deep Deterministic Policy Gradient (DDPG) based algorithm exploiting the supply-demand interpretation of the Lagrange dual problem. Simulation results show that our proposed approach achieves significant QoE improvement especially in the low wireless resource and high user number scenario compared to other baselines.
DEFace: Deep Efficient Face Network for Small Scale Variations

This study proposes a novel face detector called DEFace that focuses on the challenging tasks of face detection to cope with a small size that is under 12 pixels and occlusions due to a mask or human body parts. This study proposed the extended feature pyramid network (FPN) module to detect small faces by expanding the range of P layer, and the network by adding a receptive context module (RCM) after each predicted feature head from the top-down pathway in the FPN architecture to enhance the feature discriminability and the robustness. Based on the FPN principle, the combination between the low- and high-resolutions are beneficial for object detection with different object sizes. Furthermore, with assistance from the RCM, the proposed method can use a broad range of context information especially for small faces. To evaluate the performance of the proposed method, various public face datasets are used such as the WIDER Face dataset, the face detection dataset and benchmark (FDDB), and the masked faces (MAFA) dataset, which consist of challenging samples such as small face regions and occlusions by hair or other people. The results indicate that DEFace can detect the face region more accurately in comparison to the other state-of-the-art methods while maintaining the processing time.
A Method of Japanese Ancient Text Recognition by Deep Learning

Today, when the importance of the country culture is deeply rooted in the hearts of the people, the protection of ancient books and literature has received more and more attention. In this paper, a method to identify texts in ancient books by deep learning is proposed and ancient book “usonarubesh” is chosen as a dataset to test the performance of the model. In this experiment, the layout of the text is extracted into grayscale image through ARU-Net (a neural pixel labeling machine for historical document layout analysis). At the same time the original image which contains the texts is binarized, which the texts are filled with black, while the backgrounds are filled with white. Each area of text is judged by the density of black pixels and the layouts. The cut texts are then selected as the testing dataset for the trained model of deep learning CNN, AlexNet (the training dataset is ready). Finally, the experimental results are analyzed to draw conclusions and to decide the direction of future work.
Enhanced phase sensitivity with a nonconventional interferometer and nonlinear phase shifter

We propose a theoretical scheme of an enhanced phase sensitivity by introducing a nonlinear phase shifter to the nonconventional interferometer consisting of a balanced beam splitter (BBS) and an optical parameter amplifier (OPA), a modified nonlinear interferometer (MNI). Then we use coherent state and even coherent state as inputs and homodyne detection at one output port of the MNI for phase sensitivity, both without and with photon losses. We find that the nonlinear phase shifter can not only improve phase sensitivity, but also significantly resist the decoherence from photon losses. In comparison to both the BBS+OPA scheme with linear phase shifter and the traditional Mach–Zehnder interferometer with nonlinear one, the phase sensitivity of the MNI scheme shows the best performance. It is interesting that the nonlinear phase shifter can stimulate potential of the OPA, although there is no improvement in signal-to-noise ratio beyond standard quantum limit for the BBS+OPA scheme with a linear phase shifter.
Automated Proof of Bell–LaPadula Security Properties

Almost 50 years ago, D. E. Bell and L. LaPadula published the first formal model of a secure system, known today as the Bell–LaPadula (BLP) model. BLP is described as a state machine by means of first-order logic and set theory. The authors also formalize two state invariants known as security condition and *-property. Bell and LaPadula prove that all the state transitions preserve these invariants. In this paper we present a fully automated proof of the security condition and the *-property for all the model operations. The model and the proofs are coded in the $$\{log\}$$ tool. As far as we know this is the first time such proofs are automated. Besides, we show that the $$\{log\}$$ model is also an executable prototype. Therefore we are providing an automatically verified executable prototype of BLP.
Employing a deep convolutional neural network for human activity recognition based on binary ambient sensor data

Due to rising cost of social care, the number of older adults who prefer to live independently in their own home has increased. The independent lifestyle cannot be achieved if the elderly user suffers from mild cognitive impairment unless a suitable assistive environment is provided to monitor and recognise the daily activities. Different techniques are employed for gathering data representing the user's activities. Available systems with wearable sensors or camera devices are undesirable to many users due to privacy issues. This paper proposes the use of a Deep Convolutional Neural Network (DCNN) for human activity recognition using binary ambient sensors such as Passive Infrared (PIR) and door sensors. Each activity is represented as a binary string converted into a greyscale image. Uncorrelated features are selected and they are then used as inputs to an Adaptive Boosting (AdaBoost) and Fuzzy C-means (FCM) classifiers for recognising Activities of Daily Living (ADL). The performance of the proposed model is evaluated using a dataset representing the ADL for a single user. The achieved results using the extracted features from the greyscale image representing ADL with AdaBoost and FCM algorithms are 99.5% and 86.4%, respectively.
Comparative Analysis of Consensus Algorithms and Issues in Integration of Blockchain with IoT

In today's era of Big data and Machine Learning, IoT is playing a very crucial role in areas like social, economic, political, education, health care, etc., resulting in a drastic increase in data. Abundant amount of data, create security, privacy, trust issues in the era of the internet. The responsibility of IT is to ensure the privacy and security for huge incoming information and data due to the drastic evolution of the IoT in the coming years. The Blockchain has emerged as one of the major technologies that have the potential to transform the way of sharing the huge information while maintaining the trust. Building trust in the distributed and decentralized environment without the call for a trusted third party is a technological challenge for researchers. Due to the emergence of IoT, the huge and critical information is available over the internet. The trust over the information is reduced drastically, causing an increase in security and privacy concern day-by-day. Blockchain is one of the best emerging technology for ensuring privacy and security by using cryptographic algorithms and hashing. We will discuss the basics of blockchain technology, consensus algorithms, comparison of important consensus algorithms, integration of blockchain with IoT, integration issues and areas of application.
Fake Account Detection Using Machine Learning

Nowadays the usage of digital technology has been increasing exponentially. At the same time, the rate of malicious users has been increasing. Online social sites like Facebook and Twitter attract millions of people globally. This interest in online networking has opened to various issues including the risk of exposing false data by creating fake accounts resulting in the spread of malicious content. Fake accounts are a popular way to forward spam, commit fraud and abuse through an online social network. These problems need to be tackled in order to give the user a reliable online social network. In this paper, we are using different ML algorithms like Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF) and K-Nearest Neighbours (KNN). Along with these algorithms we have used two different normalization techniques such as Z-Score and Min-Max to improve accuracy. We have implemented it to detect fake Twitter accounts and bots. Our approach achieved high accuracy and true positive rate for Random Forest and KNN.
Image Polarity Detection on Resource-Constrained Devices

Image polarity detection opens new vistas in the area of pervasive computing. State-of-the-art frameworks for polarity detection often prove computationally demanding, as they rely on deep learning networks. Thus, one faces major issues when targeting their implementation on resource-constrained embedded devices. This article presents a design strategy for convolutional neural networks that can support image-polarity detection on edge devices. The outcomes of experimental sessions, involving standard benchmarks and a pair of commercial edge devices, confirm the approach suitability.
Monitoring Users’ Behavior: Anti-Immigration Speech Detection on Twitter

The proliferation of social media platforms changed the way people interact online. However, engagement with social media comes with a price, the users’ privacy. Breaches of users’ privacy, such as the Cambridge Analytica scandal, can reveal how the users’ data can be weaponized in political campaigns, which many times trigger hate speech and anti-immigration views. Hate speech detection is a challenging task due to the different sources of hate that can have an impact on the language used, as well as the lack of relevant annotated data. To tackle this, we collected and manually annotated an immigration-related dataset of publicly available Tweets in UK, US, and Canadian English. In an empirical study, we explored anti-immigration speech detection utilizing various language features (word n-grams, character n-grams) and measured their impact on a number of trained classifiers. Our work demonstrates that using word n-grams results in higher precision, recall, and f-score as compared to character n-grams. Finally, we discuss the implications of these results for future work on hate-speech detection and social media data analysis in general.
SMACS: Smart Contract Access Control Service

Although blockchain-based smart contracts promise a "trustless" way of enforcing agreements even with monetary consequences, they suffer from multiple security issues. Many of these issues could be mitigated via an effective access control system, however, its realization is challenging due to the properties of current blockchain platforms (like lack of privacy, costly on-chain resources, or latency). To address this problem, we propose the SMACS framework, where updatable and sophisticated Access Control Rules (ACRs) for smart contracts can be realized with low cost. SMACS shifts the burden of expensive ACRs validation and management operations to an off-chain infrastructure, while implementing on-chain only lightweight token-based access control. SMACS is flexible and in addition to simple access control lists can easily implement rules enhancing the runtime security of smart contracts. With dedicated ACRs backed by vulnerability-detection tools, SMACS can protect vulnerable contracts after deployment. We fully implement SMACS and evaluate it.
Camera calibration with global LBP-coded phase-shifting wedge grating arrays

Non-contact optical measurement methods have been widely used in the fields of three-dimensional (3D) machine vision and photogrammetry attributed to their advantages of high precision, high speed and low cost. The calibration results directly affect the accuracy of the vision measurement system. Most of the existing calibration methods with two-dimensional (2D) targets need all of the feature points to be captured by the camera, otherwise it would be difficult to distinguish them due to the usage of identical feature points. This paper presents an active calibration target that consists of phase-shifting wedge gratings (PWGs) with three different periods and globally coded on the basis of rotation invariant local binary pattern (LBP) coding. Feature points are the intersections of several straight lines fitted by the 0 and π phase points. The sub-pixel coordinates can be accurately obtained with the phase-shifting method and the windowed linear fitting. Each feature point has its corresponding label to determine its specific position, therefore the one-to-one correspondence between the image coordinates and world coordinates can be obtained even the images are partially captured, and this characteristic can expand the flexibility in both monocular and multi-camera calibrations. In addition, owing to the phase-shifting method, calibration can be accomplished when the camera is defocused. Simulations and experiments further prove the flexibility and robustness of the proposed method.
Segmentation based image compression of brain magnetic resonance images using visual saliency

The coding of medical images is of paramount importance due to an exponential increase in digital imaging data. The realization of these coding methods is highly challenging as the key fact is to maintain the visual quality by preserving the clinically critical information and also reducing the storage space at the same time. To address these issues, the current paper proposes an efficient non-uniform compression algorithm based on visual saliency that emulates the human visual system. This hybrid technique works in two phases. In the first phase, an automatic saliency-based Fuzzy C-Means clustering algorithm (sal_FCM) is designed for Region of Interest (ROI) detection and extraction. In the second phase, the encoding of ROI and the background are carried out using the SPIHT algorithm (ROI-SPIHT) at a high and low bit rate respectively. To curtail the computational complexity of the proposed ROI-SPIHT algorithm, a wavelet-based on a Lifting scheme is used. The empirical evaluation was carried out on the BRATS dataset where the results suggest that the proposed approach accurately identifies the ROI with comparatively better visual quality and compression ratio.
Structural and non-structural statistical methods: implications for delineating geochemical anomalies

Geochemical sampling media, be they rock chip, soil or stream sediment samples, routinely collected in mineral exploration, are key to detecting mineralisation-related geochemical dispersion patterns. Notwithstanding the diversity of methods applied to geochemical anomaly detection, they can be broadly divided into two major groups, namely structural and non-structural techniques. The former group covers those methods in which threshold values are assigned based on sample location while the sample locations are not required in the application of the non-structural techniques. In this study, rock chip samples from the Shadan porphyry copper–gold deposit are used to address the question as to how structural and non-structural methods can separate geochemical populations for the purpose of a deposit-scale study. It was revealed that the two structural techniques used in this study, concentration-area (C-A) fractal and U-spatial statistic methods, outperformed the non-structural techniques employed in this study.
Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.