Miracle-dz/UniFAR-FTUs huggingface dataset:
Decentralized Iot Edge Nanoservice Architecture for Future Gadget-Free Computing

In the envisioned ubiquitous world, services will follow users as they move across smart surroundings. Services are instantiated to users through the environment, appearing and disappearing as they move, which reduces the need for personal communication devices such as smartphones or tablets. To facilitate this development, service architectures need to support virtualized, on-demand service composition based on the hardware and software resources available at the current user location. The technical context for this type of user interaction with digital services through smart surroundings is called Internet of Everything (IoE). Today’s service architectures will be too inflexible in this highly decentralized and dynamic environment. Hence, in this article we propose a novel service model called nanoEdge, where nodes collaboratively provide needed functions for virtual services that need to be deployed locally due to performance, efficiency or reliability requirements, for example. The main contributions of this article are the nanoEdge conceptual model and its proof-of-concept (PoC) implementation to show that the model is feasible with regard to performance and resource-efficiency. The successful demonstration of PoC implementation exemplifies future IoE service scenarios with today’s hardware components.
Intelligent and Secure Content-Based Image Retrieval for Mobile Users

With the tremendous growth of smart mobile devices, the Content-Based Image Retrieval (CBIR) becomes popular and has great market potentials. Secure image retrieval has attracted considerable interests recently due to users’ security concerns. However, it still suffers from the challenges of relieving mobile devices of excessive computation burdens, such as data encryption, feature extraction, and image similarity scoring. In this paper, we propose and implement an IND-CPA secure CBIR framework that performs image retrieval on the cloud without the user’s constant interaction. A pre-trained deep CNN model, i.e., VGG-16, is used to extract the deep features of an image. The information about the neural network is strictly concealed by utilizing the lattice-based homomorphic scheme. We implement a real number computation mechanism and a divide-and-conquer CNN evaluation protocol to enable our framework to securely and efficiently evaluate the deep CNN with a large number of inputs. We further propose a secure image similarity scoring protocol, which enables the cloud servers to compare two images without knowing any information about their deep features. The comprehensive experimental results show that our framework is efficient and accurate.
Algorithms for Solving Misalignment Issues in Penalized PET/CT Reconstruction Using Anatomical Priors

In dual-modality systems, using anatomical priors has been shown to improve image quality and quantification in emission tomography. However, alignment between the functional and anatomical images is crucial. In this study, we propose two algorithms for solving misalignment issues. Both approaches are based on a recently published joint motion estimation and image reconstruction method. The first approach deforms the anatomical image to align it with the functional one while the second approach deforms both images to align them with the measured data. Our current implementation uses alternates between image reconstruction and alignment estimation. To evaluate the potential of these approaches, we have chosen Parallel Level Sets (PLS) as a representative anatomical penalty since it has shown promising results in literature, incorporating a spatially-variant penalty strength to achieve uniform local contrast and fast convergence rate. The performance evaluation was achieved by using simulated non-TOF data generated with an XCAT phantom in the thorax region. We used the attenuation image in the anatomical prior. The results demonstrated that both methods are able to estimate the misalignment and deform the anatomical image accordingly when a proper workflow for the alternating optimization is applied. However, the performance of the first approach depends highly on the workflow of the alternating process. In contrast, the second approach shows the ability to converge to the correct alignment faster than the first approach does, independent of the workflow. Our results indicate that it is possible to align functional and anatomical information, enabling the use of anatomical priors in practice.
Control of Ebola Epidemic System Based on Terminal Synergetic Controller Design

The Ebola virus disease (EVD) is the deadly epidemic diseases, which needs to be concerned about eradicating the spread of the disease. Studies about the policy or strategy to control the diseases have been conducted through different differential mathematical models as seen in several previous works. This paper focused on the study of applying feedback control scheme to determine the multiple control policies which are the combination of treatment and prevention actions for the EVD epidemic system. The synergetic control (SC) method can provide the chattering free characteristic for the control system. Moreover, terminal synergetic control (TSC) method can further improve the convergence rate of the control system. Thus, the TSC method was employed to define the multiple control polices for the EVD system in this study. Then, the stability of the control system was investigated. The control system was simulated to illustrate the performance of the designed terminal synergetic controller compared with the conventional synergetic controller. It was clear that using this control method, the EVD epidemic system could be regulated under the control policies without chattering, and the convergence rate of the control system was improved. Therefore, the determination the control policy for the Ebola epidemic system can be conducted alternatively and appropriately by the terminal synergetic controller design procedure.
Resting-State-Based Spatial Filtering for an fNIRS-Based Motor Imagery Brain-Computer Interface

Functional near-infrared spectroscopy (fNIRS) has attracted much attention in brain-computer interface (BCI) area due to its advantages of portability, robustness to electrical artifacts, etc. However, in practical applications, fNIRS-based BCI usually needs a labor-intensive and time-consuming training session (calibration procedure) to optimize the user-specific neural spatial and temporal patterns for further classification. Recently, studies revealed that neural spatial and temporal patterns extracted from user-specific resting-state brain signals were closely related to those of his/her task data. In this study, we proposed a resting-state independent component analysis (RSICA) based spatial filtering algorithm aiming at extracting individual task-related spatial and temporal brain patterns from the resting-state data. Specifically, independent component analysis (ICA) was applied to extract different independent components (ICs) from resting-state fNIRS data. The ICs with their spatial filter weights maximally lateralized over the sensorimotor regions were regarded as most relevant to motor imagery. These spatial filters were used to spatially filter the multi-channel motor imagery task data for feature extraction. Based on 8-minute resting-state data and a small training dataset (20 trials) from 10 participants, the proposed RSICA algorithm achieved an approximately 7% increase in left vs. right hand motor imagery classification accuracy, as compared to the conventional common spatial pattern (CSP)-based and shrinkage algorithms (69.8±12.1%, 63.3±10.3% and 63.4±11.8%, respectively). For acquiring a similar level of classification accuracy (i.e. 70%), the number of training data required could be reduced from 36 trials (CSP) to 22 trials (RSICA). As a relatively small training set is required to obtain a satisfactory performance, training burden is significantly reduced by RSICA, which might be useful for developing practical fNIRS-based motor imagery BCIs.
Search Personalization Based on Social-Network-Based Interestedness Measures

Personalized search methods try to tailor the search results to the user’s needs and preferences. A popular source of information for personalization is social networks of users. In this paper, we aim at proposing a social-network-based personalized information retrieval (PIR) method that: i) is holistic (not just considering some local neighborhood of the searcher); ii) is efficient in term of computational/storage cost; iii) its personalization component has a probabilistic basis. To the best of our knowledge, no such method exists. We propose an interestedness measure and several variations of it for quantifying the interestedness of each user in another user based on a social network of users. The general idea is to try to mimic a searcher’s behavior in the real world to estimate the interestedness. The measures are then exploited to personalize the retrieval results. We evaluate the resulting PIR methods and compare them in terms of retrieval effectiveness and computational/storage cost. We also compare them with some baseline methods. In summary, our analyses suggest that retrieval based on at least one of the measures performs well in terms of both retrieval effectiveness and computational/storage cost.
A New Paradigm for Non-contact Vitals Monitoring using Visible Light Sensing

Typical techniques for tracking vital signs require body contact and most of these techniques are intrusive in nature. Body-contact methods might irritate the patient’s skin and he/she might feel uncomfortable while sensors are touching his/her body. In this study, we present a new wireless (non-contact) method for monitoring human vital signs (breathing and heartbeat). We have demonstrated for the first time1 that vitals signs can be measured wirelessly through visible light signal reflected from a human subject, also referred to as visible light sensing (VLS). In this method, the breathing and heartbeat rates are measured without any body-contact device, using only a simple photodetector and a light source (e.g., LED). The light signal reflected from human subject is modulated by the physical motions during breathing and heartbeats. Signal processing tools such as filtering and Fourier transform are used to convert these small variations in the received light signal power to vitals data.We implemented the VLS-based non-contact vital signs monitoring system by using an off-the-shelf light source, a photodetector and a signal acquisition and processing unit. We observed more than 94% of accuracy as compared to a contact-based FDA (The Food and Drug Administration) approved devices. Additional evaluations are planned to assess the performance of the developed vitals monitoring system, e.g., different subjects, environments, etc. Non-contact vitals monitoring system can be used in various areas and scenarios such as medical facilities, residential homes, security and human-computer-interaction (HCI) applications.
Context-Aware Implicit Authentication of Smartphone Users Based on Multi-Sensor Behavior

Implicit authentication is a new research direction to enhance the privacy protection of smartphones. However, implicit authentication has low robustness due to its vulnerability to environment. Motivated by this, this paper proposes a context-aware implicit authentication, which is a scheme to improve the robustness of authentication by introducing context awareness module. In the scheme, multi-sensor data (including accelerometer, gyroscope, magnetometer, timestamp, pressure, touch size) is first captured in a fine-grained manner to characterize one’s touch action. Then, based on various sensors data, gesture features and touch features are extracted by employing both statistical method and distance measurement method. Particularly, one’s body posture when touch action happens can be used as context. In each context, we present a weighted sum fusion rule to fuse the results of different features obtained by One-Class SVM (OC-SVM). We have collected 10000+ sensor data from 87 participants for experimental evaluation. The results show that the authentication in an unrestricted environment can achieve a best equal error rate (EER) of 0.0071%, which is more than one percent lower than the non-context-aware authentication. The proposed method can effectively improve the reliability and practicability of implicit authentication.
Linear Active Disturbance Rejection Control for Nonaffine Strict-Feedback Nonlinear Systems

This paper presents a linear active disturbance rejection controller (LADRC) for nonaffine nonlinear systems with strict-feedback form. The proposed LADRC results in a closed-loop system with a three-time-scale structure, in which a reduced-order linear extended state observer estimates unmeasured states and total disturbance in the fastest time scale and dynamic inversion based on sector conditions is used to deal with nonaffine inputs in the intermediate time scale while the system dynamics evolves in the slowest time scale. The singular perturbation method is used to analyze the stability and performance of the system. The effectiveness of the proposed LADRC is demonstrated through simulation studies and experimental validation on a linear motor servo system with nonaffine uncertainties.
Quadratic stabilization of switched uncertain linear systems: a convex combination approach

We consider quadratic stabilization for a class of switched systems which are composed of a finite set of continuous-time linear subsystems with norm bounded uncertainties. Under the assumption that there is no single quadratically stable subsystem, if a convex combination of subsystems is quadratically stable, then we propose a state-dependent switching law, based on the convex combination of subsystems, such that the entire switched linear system is quadratically stable. When the state information is not available, we extend the discussion to designing an output-dependent switching law by constructing a robust Luenberger observer for each subsystem.
VCAM: Variation Compensation through Activation Matching for Analog Binarized Neural Networks

We propose an energy-efficient analog implementation of binarized neural network with a novel technique called VCAM, variation compensation through activation matching. The architecture consists of 1T1R ReRAM arrays and differential amplifiers for implementing synapses and neurons, respectively. To restore classification test accuracy degraded by process variation, we adjust the biases of the neurons to match their average output activations with those of ideal neurons. Experimental results show that the proposed approach recovers the accuracy to 98.55% on MNIST and 89.63% on CIFAR-10 even in the presence of 50% threshold voltage and 15% resistance variations at 3-sigma point. This result corresponds to the accuracy degradation of only 0.05% and 1.35%, respectively, compared to the ideal case.
A Fast Hybrid Model for Large–Scale Zero–Shot Image Recognition Based on Knowledge Graphs

Zero–shot learning aims to recognize unseen categories by learning an embedding space between data samples and semantic representations. For the large–scale datasets with thousands of categories, embedding vectors of category labels are often used for semantic representation since it is difficult to define the semantic attributes of categories manually. Facing the problem of underutilization of prior knowledge during the construction of embedding vectors, this paper first constructs a novel knowledge graph as the supplement to the basic WordNet graph, and then proposes a fast hybrid model ARGCN–DKG, which means Attention based Residual Graph Convolutional Network on Different types of Knowledge Graphs. By introducing residual mechanism and attention mechanism, and integrating different knowledge graphs, the accuracy of knowledge transfer between different categories can be improved. Our model only use 2-layer GCN, the pretrained image features and category semantic features, so the training process could be done in minitues on single GPU, which could be one of the fastest training models for large-scale image recognition. Experiment results demonstrate that ARGCN–DKG model could get better results for large–scale datasets than the state–of–the–art model.
Revocable Cloud-Assisted Attribute-Based Signcryption in Personal Health System

PHR System is a favorable platform for personal health information exchange. In order to ensure that the personal information is not falsified and leaked by malicious users, we use the attribute-based signcryption technology to provide secure and reliable data protection. At the same time, in order to prevent users from accessing the data in the system by collusion of attributes, we proposes a revocable cloud-assisted attribute-based signcryption scheme which using the broadcast encryption technology and key segmentation technology realize user revocation function. Moreover, the proposed scheme is proven to be confidentiality and unforgeability under chosen plaintext attack in the random oracle model. And the experimental evaluation indicates that the proposed scheme is practical and feasible.
Uncertainty calculation for AMN impedance contribution using the Monte Carlo Method

The probability density function (PDF) of the error is derived for several frequencies, taking different source impedances of the EUT into consideration. The assumption of a triangular distribution cannot be confirmed. It is shown that the coverage interval is not always correlated to the minima and maxima of the PDF, as it is for the worst case analysis. The phase requirement for the AMN impedance is questionable, since the probability for the circumstance where a problem really occurs is neglectable. Further efforts must be put in the improvement of the measurement model, since the results heavily depend on the assumption of the EUT source impedance.
ShotgunWSD 2.0: An Improved Algorithm for Global Word Sense Disambiguation

ShotgunWSD is a recent unsupervised and knowledge-based algorithm for global word sense disambiguation (WSD). The algorithm is inspired by the Shotgun sequencing technique, which is a broadly-used whole genome sequencing approach. ShotgunWSD performs WSD at the document level based on three phases. The first phase consists of applying a brute-force WSD algorithm on short context windows selected from the document, in order to generate a short list of likely sense configurations for each window. The second phase consists of assembling the local sense configurations into longer composite configurations by prefix and suffix matching. In the third phase, the resulting configurations are ranked by their length, and the sense of each word is chosen based on a majority voting scheme that considers only the top configurations in which the respective word appears. In this paper, we present an improved version (2.0) of ShotgunWSD which is based on a different approach for computing the relatedness score between two word senses, a step that stays at the core of building better local sense configurations. For each sense, we collect all the words from the corresponding WordNet synset, gloss and related synsets, into a sense bag. We embed the collected words from all the sense bags in the entire document into a vector space using a common word embedding framework. The word vectors are then clustered using k-means to form clusters of semantically related words. At this stage, we consider that clusters with fewer samples (with respect to a given threshold) represent outliers and we eliminate these clusters altogether. Words from the eliminated clusters are also removed from each and every sense bag. Finally, we compute the median of all the remaining word embeddings in a given sense bag to obtain a sense embedding for the corresponding word sense. We compare the improved ShotgunWSD algorithm (version 2.0) with its previous version (1.0) as well as several state-of-the-art unsupervised WSD algorithms on six benchmarks: SemEval 2007, Senseval-2, Senseval-3, SemEval 2013, SemEval 2015, and overall (unified). We demonstrate that ShotgunWSD 2.0 yields better performance than ShotgunWSD 1.0 and some other recent unsupervised or knowledge-based approaches. We also performed paired McNemar’s significance tests, showing that the improvements of ShotgunWSD 2.0 over ShotgunWSD 1.0 are in most cases statistically significant, with a confidence interval of 0.01.
Authentication of geographical growth origin of black pepper (piper nigrum l.) based on volatile organic compounds profile: A case study for Malaysia and India black peppers

Black pepper is one of the common spices used in foods as a favoring agent. Their utilization is diversified into other applications such as pepper spray for defense, medicine for healthcare and pest control in the agriculture sector. In order to meet the industry demand, various countries with a suitable tropical climate have grown black pepper as one of their export commodities. Indirectly, reputation and quality of the black pepper are associated with the growth origin as the soil quality, handling processes, and growing environment will influence the final grade of the black pepper. Geographical origin has become one of the parameters in determining the price of the pepper. In view of this, there is a pressing demand to have a standard to identify the growth origin of black pepper to avoid counterfeit issues with the intention to scope for higher market price. This paper reports an attempt to determine the growth origin of black pepper (Piper nigrum L.) based on the volatile organic compounds (VOCs) profile recorded using high resolution gas chromatography mass spectrometry system. In this work, black peppers from two different countries of origin; namely from Malaysia and India were selected as a case study. A total of 252 of black pepper samples from these two origins were extracted for non-polar compounds using a standard method and the VOCs profiles were then subjected to Principal Component Analysis (PCA) and Fold Change Analysis. PCA cluster plots showed that Indian and Malaysian black pepper can be confidently discriminated by means of unique non-polar compounds present in the pepper samples.
Joint Beamforming-Power-Bandwidth Allocation in Terahertz NOMA Networks

A downlink Terahertz Non-Orthogonal Multiple Access (THz-NOMA) system with M beams is proposed in this paper, where each beam serves four users as a user cluster. Furthermore, two NOMA groups are grouped in each cluster. The Beamforming-Power-Bandwidth (B-P-B) problem is formulated aiming to maximize the network throughput while satisfying the QoS requirement of each user. Based on the THz-NOMA architecture and user distribution information, the beamforming design is first performed at base station (BS), in terms of the beam direction and the beamwidth. Then the B-P-B problem is evolved to the Power-Bandwidth (P-B) resource allocation problem, which is of importance to exploit the full benefit of THz-NOMA system. The mixed integer P-B problem can be solved with the support of decomposition theory, which allows sub-band assignment to be conducted in each cluster, while allowing power allocation to be carried out in the cell. The Long-User-Central-Window (LUCW) peculiarity of THz is captured by the Hungarian algorithm to adaptively assign sub-band between NOMA groups of a user cluster. According to the LUCW principle, the central sub-band of a THz window is assigned to the long NOMA group, while the side sub-band unavailable at long NOMA group is allocated to the short NOMA group. Then an iterative algorithm is proposed to acquire the optimal power allocation. Simulation results show an improved sum rate performance of THz-NOMA, 928.4 Gbps can be achieved, compared with the OMA system. Furthermore, the effects of sub-band assignment coefficient and NOMA grouping scheme are investigated. The results indicate that the sub-band assignment coefficient is preferred to be set between 0.47 and 0.7. Moreover, the favored NOMA grouping scheme requires a larger distance difference.