Miracle-dz/UniFAR-FTUs huggingface dataset:
To Verify the Correctness of IoT Sensor Data in Real-Time

Internet of things (IoT) systems are collecting an enormous amount of sensor data which in turn play a critical role in their decision-making process. However, the more sensor data are collected, the higher probability the faulty data occurs. The existence of faulty data may drive any system to make incorrect decisions and actions. It is clear that the faulty data should be detected and corrected as earlier as possible. In other words, the sensor data should be verified in the collecting steps. To best of our knowledge, the sensor data verification in the collecting process has not been well investigated. This research proposes a framework to address this issue right before the sensor data are stored. The verification makes use of a data forecasting technique to estimate the correct range of values for each received point of sensor data. The proposed system was implemented on a real test-bed on a Raspberry Pi 3. The experimental results show the proposed system can detect and correct faulty sensor data in less than 50ms. The accuracy of the proposed method is eighty five percent on average.
Optimized Train Path Selection Method for Daily Freight Train Scheduling

Train timetables are usually established far in advance of operations and are based on forecasted demands. However, due to changes in actual freight traffic, railways need to determine the actual operation of trains arising in daily operations through train path selection, i.e., selecting a portion of the timetable for these trains to execute. To improve current freight train scheduling in daily operations, this paper suggests taking into account the car flow transfer between consecutive trains and shipment delivery time requirements. A train path selection optimization model is developed to minimize the total travel time of freight trains while seeking minimum penalties for shipment delivery delays. A tabu search algorithm is designed to solve this problem. The effectiveness of the proposed model and algorithm is demonstrated by numerical experiments on instances built on real data from the Menghua railway, a rail freight corridor. The results show that, compared to current practice, this optimization method can achieve a reduction in total train travel time and ensure the punctual delivery of shipments.
High-Performance Nano-Sensing and Slow-Light Applications Based on Tunable Multiple Fano Resonances and EIT-Like Effects in Coupled Plasmonic Resonator System

A basic plasmonic system, consisting of a stub metal-insulator-metal (MIM) waveguide coupled with a ring resonator, is presented to realize Fano resonance and electromagnetically induced transparency-like (EIT-like) effect, which are numerically calculated by the finite element method (FEM). Meanwhile, the formation mechanism of Fano resonance is analyzed according to numerical simulations. Besides, the coupled mode theory (CMT) and the standing wave theory are used for explaining the Fano and EIT-like resonances phenomenon. Based on this system, an inner ring cavity is connected to the ring resonator by a slot and another ring cavity is later introduced under the stub resonator in order to constitute a new coupled plasmonic resonator system, providing quadruple Fano resonances and double EIT-like responses finally. In addition, the Fano and EIT-like resonances can be independently tuned by adjusting the structural parameters, which makes the design of highly integrated photonic circuits more flexible. The main contribution of this paper is that the proposed structure has a relatively good sensitivity of 1600 nm/RIU and an ultra-high FOM value of $1.2\times 10^{6}$ as a refractive index nanosensor. Moreover, it can serve as an all-optical switch with a high on/off extinction ratio of about 43 dB. Additionally, its maximum group delay time and group index are about 1.49 ps and 221, indicating that the proposed system has a pretty good slow light effect. Therefore, the proposed structures are believed to have significant applications in high-performance nanosensors, switches, slow light devices and nonlinear areas in highly integrated plasmonic devices.
Single Side Band Direct Detection for Data Center Interconnect and Digital Radio Over Fiber

Distributed data-centers have emerged as a key architecture for future optical networks to satisfy the ever-growing demand for high speed connectivity and cloud services. This network architecture relies on high speed optical connections with interface rates beyond 100 Gb/s over 80 km. The conventional low-cost IM/DD transmission scheme cannot meet this performance requirement while the full coherent detection solution is still too expensive. As a result, alternative self-coherent detection (SCD) schemes such as single-side-band (SSB) transmission have attracted a lot of attention recently for high speed data center interconnect (DCI) applications. This invited paper provides a thorough overview about the progress of SSB transmission over the last five years. In addition, it also discusses an emerging application of SSB transmission scheme for high capacity digital radio-over-fiber (RoF) systems.
Speech Emotion Recognition Using Speech Feature and Word Embedding

Emotion recognition can be performed automatically from many modalities. This paper presents a categorical speech emotion recognition using speech feature and word embedding. Text features can be combined with speech features to improve emotion recognition accuracy, and both features can be obtained from speech. Here, we use speech segments, by removing silences in an utterance, where the acoustic feature is extracted for speech-based emotion recognition. Word embedding is used as an input feature for text emotion recognition and a combination of both features is proposed for performance improvement purpose. Two unidirectional LSTM layers are used for text and fully connected layers are applied for acoustic emotion recognition. Both networks then are merged by fully connected networks in early fusion way to produce one of four predicted emotion categories. The result shows the combination of speech and text achieve higher accuracy i.e. 75.49% compared to speech only with 58.29% or text only emotion recognition with 68.01%. This result also outperforms the previously proposed methods by others using the same dataset on the same modalities.
Cross-Informant Agreement on Mental Health Outcomes in Children With Maltreatment Histories: A Systematic Review

Objective: This systematic review and meta-analysis examined cross-informant agreement concerning mental health outcomes for maltreated children. This study provides important information to individuals working with maltreated children (e.g., clinicians, child welfare practitioners) who must make critical decisions related to safety, mental health, and permanency. Method: We retrieved 29 studies that collected mental health data for maltreated children using standardized instruments. We were able to examine 4 informant pairs and 7 mental health outcomes. Results: Agreement across children’s mental health outcomes was highest for child–caregiver pairs (small to large, significant effect sizes), followed by child–teacher and caregiver–teacher (small to medium, significant effect sizes) and then caregiver–caregiver (small, nonsignificant effect sizes) pairs. Agreement generally was highest for overt behaviors. Conclusions: It is important to include children as informants, gather teacher and caregiver data when assessing child internalizing difficulties, and examine specific maltreatment outcomes (e.g., traumatic stress). Clinically, it is critical to understand why different informants perceive a child’s functioning in different ways for purposes of prognosis and treatment planning for the child and his or her family. It is also critical to triangulate data sources when assessing for maltreatment impact and to consider data points that both converge and diverge when making appropriate safety and treatment plans.
Differential Frequency-Reconfigurable Antenna Based on Dipoles for Sub-6 GHz 5G and WLAN Applications

A new differential frequency-reconfigurable antenna based on dipoles is presented in this letter. The basic structure of the proposed antenna consists of two pairs of vertical arms forming two dipoles, the feeding structure, mode switching structure, and p-i-n diodes. By switching “on” and “off” p-i-n diodes, the antenna can resonate at two states, and thus work centered at 3.5 and 5.5 GHz, respectively. Though the two states correspond to different resonant modes, the effective radiation parts are the same and the mode switching structure will not contribute to the far field for both states. This results in similar radiation patterns for both states. Besides, the proposed antenna is also simple in structure and wide in bandwidth. The measured −10 dB impedance bandwidths are 2.89–4.07 GHz (33%) and 5.1–6.19 GHz (19.8%) for the two states, respectively.
Network Intrusion Detection, Literature Review and Some Techniques Comparision

The massive usage of network-based communication technology to satisfy the user's requirements opens the way for enormous network attacks to be launched. An intrusion detection system (IDS) is the most effective solution for the detection of those attacks. In this paper, we discuss the IDS types; especially machine learning-based IDS. A large research area in IDS technologies is concerned with identifying known and unknown attacks. This paper provides the most common machine learning IDS systems. Also, we discuss the IDS requirements for perfect detection. Furthermore, a comparison between them in terms of detection accuracy and system performance is presented.
Traceable and Weighted Attribute-Based Encryption Scheme in the Cloud Environment

In order to solve the problem that the importance of the user’s attribute is seldom considered in the most of the existing attribute-based encryption schemes with traitor tracing, we designed a traceable and weighted attribute-based encryption scheme. In our constructed scheme, the private key of the user consisting of user’s identity information is applied to trace traitors. In addition, the idea of weighted attribute is introduced, and the attributes set is transformed into the segmentation set of weighted attributes through the attributes set segmentation algorithm. Via employing a linear secret sharing scheme, the constructed scheme offers fine-grained and nimble access control mechanism. Under the assumption of q-BDHE in the standard model, we prove that the designed scheme is able to reach security against chosen-plaintext attack. By comparing with other relevant schemes, it has significant improvement in the costs of communication and computation, and it is more suitable for the application of the mobile terminal in cloud computing.
Automatic Diagnosis of Olive Quick Decline Syndrome and Grapevine Yellows for the Agriculture Industry

The agriculture industry is facing a crisis and must consider AI to reduce operational costs. One such area is pest and disease scouting. Delays cause further infection and growers must act quickly if a disease is discovered. However, scouting often requires external consultants and lab tests, time consuming and costly. Deep learning has the potential to detect diseases automatically from leaf clipping images. We achieve good results on two diseases of interest to the industry: Olive Quick Decline Syndrome (OQDS) and Grapevine Yellows (GY). The current challenge is overcoming data requirements of deep learning, often addressed with crowd sourcing. However, it can be difficult to crowd-source data of control pathogens. We demonstrate that it is possible to use data augmentation in place of crowd sourcing. The system has positive predictive values of 98.1% and 99.9% for detecting OQDS and GY respectively. Additionally, we discuss the potential for diagnosis-as-a-service, and open-source our data for greatest benefit to the AI community.
Care and services at home for persons with dementia. Structure, process, and outcomes.

The overall aim of this thesis was to investigate formal care and services at home, regarding structure, process, and outcomes, for persons with dementia (aged 65+ years) at risk of nursing home admission. Availability and utilization of formal care and services at home for persons with dementia, from diagnosis to end-of-life stage, in eight European countries was described in study I. A mapping form was used to collect data. The results revealed that availability was reported to be higher than utilization, and indicated more similarities than differences among the countries involved. Dementia-specific care and services were sparsely available and even more sparsely utilized. Study II investigated formal care providers’ views on conditions for best practice in terms of collaboration and improvement needs in the chain of care from early to end-of-life stage for persons with dementia in Sweden. Three focus group interviews were conducted to collect data. Data were analysed using content analysis. The results indicated that best practice in terms of collaboration was achieved to a higher degree during the early stage compared with the moderate and end-of-life stages of the disease. Lack of best practice strategies during these stages made it difficult to meet the needs of persons with dementia and reduce burden on informal caregivers. A cross-sectional study design was used in studies III and IV. Questionnaire-based interviews were conducted with 177 persons with dementia and their informal caregivers. Data were analysed using descriptive and comparative statistics. Study III compared persons with dementia with different levels of cognitive impairment, regarding utilization of formal and informal care and services at home. The results showed that needs relating to ADLs and supervision appear to be met first and foremost by the informal caregivers, since the utilization of formal care and services was lower than utilization of informal care. Study IV described self-reported quality of life, different aspects of quality of care and the significance of quality of care for quality of life . The results revealed that pain significantly lowered quality of life in the dimensions behavioural competence and psychological wellbeing, compared with absence of pain. Satisfaction with received care seemed to have a positive effect on quality of life . The overall quality of life was perceived to be high even though one-third of the persons with dementia had daily pain and had had a weight loss of ≥4% during the preceding year. Altogether 23% of the persons with dementia had fallen during the last month and 40% of them had sustained an injury when falling. The thesis is inspired by Lawton's press-competence model, which provided a deeper understanding of the results and the context of the research. (Less)
A Cost-Effective Flip-Chip Interconnection for Applications from DC until 200 GHz

This paper presents design, fabrication, and characterization of a cost-effective flip-chip interconnection suitable for operation up to 200 GHz. The approach relies on gold balls produced with standard bonding tools, and welded on the pads of the interconnected chips by a thermo-compression process. Several simulation approaches have been used to optimize the interface, ranging from the stand-alone simulation of the gold balls up to the complete simulations of the test structures employed for the characterization of the proposed interface. The back-end-of-line of a commercially available 130 nm SiGe process was used to implement the test structures, and insertion-losses smaller than 0.5 dB at 100 GHz, and 1.5 dB at 200 GHz were demonstrated for the interface, in agreement with simulation results. The proposed approach reduces the insertion-loss by 1.5 dB at 150 GHz with respect to previous demonstrations making use of similar ground-signal-ground (GSG) pads interface, and fabrication procedure.
Domain Adversarial Training for Speech Enhancement

The performance of deep learning approaches to speech enhancement degrades significantly in face of mismatch between training and testing. In this paper, we propose a domain adversarial training technique for unsupervised domain transfer, that 1) overcomes domain mismatch, and 2) provides a solution to the scenario where we only have noisy speech data, and we don't have clean-noisy parallel data in the new domain. Specifically, our method includes two parts that are jointly trained, 1) an enhancement net to map noisy speech to clean speech by indirectly estimating a mask with a spectrum approximation loss, and 2) a domain predictor to distinguish between domains. As the proposed approach is able to adapt to a new domain only with noisy speech data in target domain, we call it an unsupervised learning technique. Experiments suggest that our approach delivers voice quality comparable with other supervised learning techniques that require clean-noisy parallel data.
Measuring Device Developed for Soft Materials and Deep-Sea Applications

This paper presents explanations of the development and preliminary experiments of a measuring device for examining the physical properties of soft materials, intended for deep-sea applications. In the field of robotics, soft materials have been used recently for robotic grippers to manipulate delicate and irregular-shaped objects. Soft grippers have also been developed for underwater exploration. One important benefit of soft materials is that they can interact with and manipulate various objects while deforming. However, those beneficial physical properties of soft materials might change with water pressure in the deep ocean. As described herein, we developed a small measuring device to investigate changes in the physical properties of soft materials under high water pressure. The final goal of our research is investigation of properties of flexible materials under water pressure of 10 MPa, equivalent to a water depth of 1000 m or more, we conducted preliminary experiments at 1.0 MPa as a first step in this research. Using the developed device, we measured the relation between the compressive displacement and elastic force of a soft material under pressure of 1.0 MPa.
Software Controlled Low Cost Thermoelectric Energy Harvester for Ultra-Low Power Wireless Sensor Nodes

General hardware architecture of an energy-harvested wireless sensor network node (EH-WSN) can be divided into power, sensing, computing and communication subsystems. Interrelation between these subsystems in combination with constrained energy supply makes design and implementation of EH-WSN a complex and challenging task. Separation of these subsystems into distinct hardware modules simplifies the design process and makes the architecture and software more generic, leading to more flexible solutions. From the other hand, tightly coupling these subsystems gives more room for optimizations at the price of increased complexity of the hardware and software. Additional engineering effort could be justified by a smaller, cheaper hardware, and more energy-efficient a wireless sensor node. The aim of this paper is to push further technical and economical boundaries related to EH-WSN by proposing a novel architecture which – by tightly coupling software and hardware of power, computing, and communication subsystems – allows the wireless sensor node to be powered by a thermoelectric generator working with about 1.5°C temperature difference while keeping the cost of all electronic components used to build such a node below 9 EUR (in volume).
Dementia Detection by Analyzing Spontaneous Mandarin Speech

Ahstract-The Chinese population has been aging rapidly resulting in the largest population of people with dementia. Unfortunately, current screening and diagnosis of dementia rely on the evidences from cognitive tests, which are usually expensive and time consuming. Therefore, this paper studies the methods of detecting dementia by analyzing the spontaneous speech produced by Mandarin speakers in a picture description task. First, a Mandarin speech dataset contains speech from both healthy controls and patients with mild cognitive impairment (MCI) or dementia is built. Then, three categories of features, including duration features, acoustic features and linguistic features, are extracted from speech recordings and are compared by building logistic regression classifiers for dementia detection. The best performance of identifying dementia from healthy controls is obtained by fusing all features and the accuracy is 81.9% in a 10-fold cross-validation. The importance of different features is further analyzed by experiments, which indicate that the difference of perplexities derived from language models is the most effective one.
Speech Loss Compensation by Generative Adversarial Networks

Speech loss, including frequency loss and packet loss, can lead to significant speech distortion in many Internet-based speech communication services. In this study, a generative adversarial networks (GANs) structure, which takes deep convolutional neural networks (CNN) as the generator and discriminator components, is adopted as a general framework for speech loss compensation. Network settings are modified for real-time communications. A set of experiments are conducted to evaluate the performance of the GANs-based framework for both bandwidth expansion (BWE) and packet loss concealment (PLC) at several simulated loss conditions. Experimental results demonstrate that the proposed system achieves better performance, with respective to 4 objective metrics, in both BWE and PLC compared to the baseline systems.
Node-Adaptive Multi-Graph Fusion Using Extreme Value Theory

This letter considers the problem of grouping data by their underlying categories with inputs from multiple sources, known as the multi-view clustering problem. One of the most fundamental challenges lies in how to benefit from the complementary information in the multi-view data, so that clustering on such data consistently achieves higher accuracy than clustering on each single-view component. In this letter, to tackle the multi-view clustering problem, we propose a novel approach to fuse multiple affinity graphs computed in each single view to a unified affinity graph, so that single-view affinity-based clustering methods can be accordingly applied on it. The edges in the unified affinity graph between a node and its neighbors are computed as weighted average over the corresponding edges from multiple single graphs, and the weights here are adaptive to each node, estimated using the Extreme Value Theory (EVT). Experiments on two challenging multi-view clustering tasks show that, combined with existing off-the-shelf single-view clustering algorithms, the proposed graph fusion method brings consistently performance gain compared with naive graph fusion baselines.