Miracle-dz/UniFAR-FTUs huggingface dataset:
A Structural Hole-Based Approach to Control Public Opinion

Structural hole spanners play an important role in information diffusion. Compared with opinion leader, structural hole spanners have better location in social network to expand the scope of information diffusion. In the past, researchers focused on the evolution rules and the opinion dynamics environments to monitor public opinion and even manage public opinion to the place they want. In this paper, we propose a novel structural-hole-based approach to control public opinion in social networks (shorten for SHCPO approach). We discuss influences of opinion evolution with ordinary agents and structural hole spanners via using our improving Friedkin-Johnsen (FJ) model. We analysis evolution tendency of public opinion, which is the final consensus of public opinion via FJ model, with ordinary agents in a community and with structural hole spanners in joint communities. We conclude three kinds of connections between structural hole spanners and ordinary agents in joint communities. They are structural hole spanners connecting (1) two opinion leaders; (2) two ordinary agents; (3) one opinion leader and one ordinary agents. The three connections will lead to different conditions for opinion evolution. And then, we remove the connections if public opinion tendencies are negative in joint communities and its end-communities. It prevents public opinion of the community and one of its end-communities. It guides the public opinion tendencies of joint communities going towards positive. The experiment result shows that SHCPO approach has certain extent effects.
Food Calorie and Nutrition Analysis System based on Mask R-CNN

Over the past few decades, obesity has become a serious problem. Obesity is associated with many of the leading causes of death, such as chronic diseases including diabetes, heart disease, stroke, and cancer. The most effective way to prevent obesity is through food intake control, which involves understanding food ingestion, including the nutrients and calories of each meal. To assist with this issue, this study develops a food calorie and nutrition system that can analyze the composition of a food based on a provided image. Further, we introduce a newly collected dataset, Ville Cafe, for food recognition. This dataset contains 16 categories with 35,842 images, including salad, fruit, toast, egg, sausage, chicken cutlet, bacon, French toast, omelet, hash browns, pancake, ham, patty, sandwich, French fries, and hamburger. The system is based on a Mask Region-based Convolutional Neural Network (R-CNN) with a union postprocessing, which modifies the extracted bounding boxes and masks, without the non-maximum suppression (NMS), to provide a better result in both analytics and visualization. The recognition accuracy for the combination of Ville Cafe and the Food-256 Datasets was 99.86%, and the intersection over union (IoU) was 97.17%. The food weight estimation experiment included eight classes: salad, fruit, toast, sausage, bacon, ham, patty, and French fries. These classes respectively had 40, 40, 44, 40, 41, 49, 26, and 40 data points, adding up to 320 data points for the linear regression model. In the experimental results, the average absolute error was 8.22, and the average relative error was 0.13.
30.5 A 0.5V BLE Transceiver with a 1.9mW RX Achieving −96.4dBm Sensitivity and 4.1dB Adjacent Channel Rejection at 1MHz Offset in 22nm FDSOI

Towards the emerging AI era, IoT is becoming more important. Current IoT wireless nodes require replacement or recharging of the battery. This is impractical when a massive number of nodes are connected to the network. Ultra-low-voltage wireless connectivity that can operate with power supplied from energy harvesters, for example 0.5V from solar cells, is necessary [1]. BLE is a good candidate, but a high blocker resilience in the 2.4GHz ISM band is required even though the BLE specifications are not strict compared to other standards. It is a challenge to design RX circuits at low voltage without sacrificing performance, especially blocker resilience and sensitivity.
Cooperative awareness using roadside unit networks in mixed traffic

Vehicle-to-vehicle (V2V) messaging is an indispensable component of connected autonomous vehicle systems. Although V2V standards have been specified by the European Union, United States, and Japan, the deployment phase represents mixed traffic in which connected and legacy vehicles coexist. To enhance cooperative awareness in this mixed traffic, we assessed the special roadside unit that we developed in our previous work that generates required V2V messages on behalf of sensed target vehicles. In this paper, we extend our earlier work to propose a system called Grid Proxy Cooperative Awareness Message to broaden the cooperative awareness message dissemination area by connecting infrastructure using high-speed roadside networks. To minimize delay in message delivery, we designed the proposed system to use edge computing. The proposed scheme delivers cooperative messages to a wider area with a low delay and a high packet delivery ratio by prioritizing packets by their respective safety contributions. Our simulation results indicate that the proposed scheme efficiently delivers messages in heavy road traffic conditions modeled on real maps of Tokyo and Paris.
A Survey of Some Interdisciplinary Methods and Tools to Measure Learners' Emotions in Intelligent Tutoring Systems

Emotion measurement in Educational technology is an important topic which amalgamates the computer-based education (specifically the intelligent tutoring systems ITS) and the emotion measurement. This will have a positive effect for gradually improving the performance of the educational process. It will be fruitful for the educational environment to adopt the measuring emotions techniques, emotion intelligence, and emotion recognition. This is useful for collecting information about students' emotions and directs the educational process to better performance. Adopting such technologies will face some challenges. Some of these challenges include data acquisition from the physiological and psychological tools, handling of different data types, manipulation of data, decision to recover the detected problem, cost of both the physiological and psychological tools,. …etc. The researchers are interested in this field of studies to adapt instruction to develop the responding of a student's affective state. So, these studies will take student gradually to the best performance in his educational material.
29.1 A 0.42THz 9.2dBm 64-Pixel Source-Array SoC with Spatial Modulation Diversity for Computational Terahertz Imaging

Computational THz Imaging (CTI) is the process of indirectly forming images using algorithms that rely on a significant amount of computing. Unlike focal-plane arrays (FPAs), a single-pixel camera (SPC) uses spatially patterned illumination in conjunction with CTI to measure the entire image using only one receiver [1]. CTI can overcome existing SNR limitations in THz imaging systems as it relaxes the implementation issues of high-power sources and low-noise FPAs (Fig. 29.1.1). However, measurements must be taken in series rather than all at once, which calls for fast THz system-on-a-chip (SoC) implementations in advanced silicon technologies.
A Novel Adaptive Modulation Algorithm For Backscatter Communication in IoT

Nowadays, backscatter modulation gets more and more concern in IoT field, mostly because of its ultra-low power character. However, it also faces serious problems. This prominently manifests that the traditional backscatter modulation lacks enough flexibility and ability to adapt to the environment, especially the varying channel condition. For this reason, we design a novel adaptive modulation algorithm for backscatter communication. The main idea is that transmitter could flexibly select modulation algorithm from a set before sending data, based on its perception of the physical channel. We also simulate our algorithm and analyze its performance. We find that: (1) While the emitted power of RF Transmitter is very low, our algorithm has smaller BER than traditional ASK-based backscatter modulation. (2) While the emitted power of RF Transmitter increases to high value, our algorithm has faster bit rate than traditional ASK-based backscatter modulation.
A Community Based Routing Algorithm for Mobile Opportunistic Networks

Opportunistic networking is a kind of delay-tolerant networking in which wireless mobile nodes communicate with each other without the support network infrastructure. Opportunistic networking uses locally available wireless technologies such as Bluetooth for pair-wise data forwarding hoping that the data will ultimately reach the destination. Intermittent connectivity and long delays in data delivery are inherent properties of this kind of opportunistic networking. The existence of communities among larger groups of people presents us with a use case for opportunistic networking where the content of interest could be shared opportunistically among the members of communities. In such communities, members are not just attached to a single community but are usually connected to several communities based on their interests. Forwarding and routing algorithms that route content of interest among these communities should focus on the interests of the members of communities and other inherent properties of opportunistic networking. Therefore a more efficient routing algorithm that can work well with the inherent properties in such a community set up is needed. In this work we propose, design and develop a community-based routing algorithm, named SWift routing algorithm (SW algorithm), that can be used to route messages among the community members in an opportunistic manner. Our simulation-based experimental results show that our proposed algorithm outperforms three well-known algorithms in the field under varying network conditions.
Power and Frequency Selection optimization in Anti-Jamming Communication: A Deep Reinforcement Learning Approach

This paper studies the problem of anti-jamming in selecting communication frequency and power by using deep reinforcement learning (DRL) algorithm. In existing works, the anti-jamming methods are considered in frequency domain or power domain only. With the development of communication devices, more and more communication devices have the ability to switch communication frequency and adjust power simultaneously. To be specifically, the anti-jamming problem can be considered in both frequency and power domains. In this paper, we formulate the anti-jamming problem as a Markov decision process (MDP). And we use DRL algorithm to cope with the problem. The results of simulation show that the proposed algorithm achieves high throughput and with lower power.
Orthogonal Non-Negative Matrix Factorization using Ridge Term for Classifying Expressed Gene

This research provides the development of Non-Negative Matrix Factorization method for classifying gene expression data. Furthermore, we also compare the NMF method with Uni-Orthogonal NMF for classifying the data. We use two schemas of datasets which use complete dataset and incomplete dataset. The incomplete dataset contains missing values which used to prove that the methods can handle the missing values problem on the classification problem. Sparse regularization in dimensionality reduction using ridge term (Lz-Norm) is applied in this study to see the effect of sparse regularization in the incomplete dataset. In this paper, we propose an approach to classify the diseases from gene expression data using the combination of matrix factorization methods and support vector machine (SVM). The experimental results show that adding ridge term in Y-orthogonal NMF make accuracy higher in training data with incomplete data. Y-orthogonal NMF using ridge term is the best method for classifying expressed genes with incomplete data than the other compared methods.
Silicon Wafer Map Defect Classification Using Deep Convolutional Neural Network With Data Augmentation

Wafer map defect classification is one of the most important process for semiconductor manufacturing. Wafer map defects are also cause of die failures. So, we propose a better method for classifying defects of wafer map. We propose deep convolutional neural network architecture and our proposed architecture is the improved version of previous researcher’s architecture. We also solve the data imbalance problem by applying data augmentation technique before training of data. The test accuracy of our research is 99.29% which is the best performance so far.
30.2 NB-IoT and GNSS All-in-One System-on-Chip Integrating RF Transceiver, 23dBm CMOS Power Amplifier, Power Management Unit and Clock Management System for Low-Cost Solution

Due to extensive application prospects and huge market potential of narrowband internet-of-things (NB-IoT), there is growing demand for low-power and low-cost system-on-chips (SoCs) and a massive number of wireless devices. For a low-cost solution, the most challenging blocks such as an RF transceiver, an RF power amplifier (RF-PA), and a high-power switching converter have to be integrated to eliminate the need for expensive multichip components. To support power scenarios of NB-IoT, low-power circuits and highly efficient power regulators are needed simultaneously [1]. This work aims for an all-in-one system to integrate all necessary blocks. An RF transceiver is integrated to support multiband cellular IoT and the global-navigation satellite system (GNSS) with a CMOS RF power amplifier. A power-management system (PMIP) including bucks, a boost, and low-dropout regulators (LDOs) is integrated to support coin-cell or AAA battery supply and operate over a wide-input supply voltage from 2.5V to 5V. In addition, a clock-management system is embedded with a digitally controlled crystal oscillator (DCXO), a relaxation oscillator (RCO), and temperature-sensor units (TSUs) [2].
Poster: A Vehicular Participant Recruiting Strategy for Improving Sensing Quality in Vehicular Crowdsensing

In vehicular crowdsensing (VCS) applications, vehicular participants should be carefully selected to meet the limited operation budget while providing sufficient quality of sensing. In this paper, we propose a matching based vehicular participant recruiting (MVP) strategy. The MVP strategy in-centivizes detouring vehicles to the target region, which allows the maximization of the sensing quality under the given VCS operation budget. The preliminary simulation results based on real traces demonstrate that MVP outperforms the existing vehicular crowdsensing strategy.
Selectivity and Autoscaling as Complementary Defenses for DDoS Protection to Cloud Services

Distributed Denial-of-Service (DDoS) is becoming an even more complex problem with the migration of these services and applications to shared and centralized cloud infrastructures. Application layer Denial-of-Service attacks (ADDoS) is an special type of DDoS attacks, and the main problem in mitigating these attacks is because attacker requests are similar to legitimate clients. This paper proposes to use the scalability feature of cloud infrastructure as a defense from high-rate DDoS attacks, and selectivity defense to mitigate low-rate ADDoS attacks. Experiments are conducted in an OpenStack cloud environment to show that the combined use of selectivity and autoscaling can be used as a defense against low- and high-rate DDoS attacks.
System Delay Minimization for NOMA-Based Cognitive Mobile Edge Computing

In this paper, we first employ non-orthogonal multiple access (NOMA) in a cognitive radio (CR) based mobile edge computing (MEC) network to reduce the system delay. In particular, each secondary user has two computation tasks which can be offloaded to the MEC servers via NOMA, while the transmissions of different secondary users are orthogonal by exploiting different spectrum bands. Then, we investigate the system delay minimization problem by jointly optimizing the offloading policy, the transmit power for offloading, the computing capabilities for local secondary users and MEC servers. Since the formulated problem is non-convex and difficult to be solved straightforwardly, we first analyze its characteristics and decompose it into four sub-problems, which can be solved independently. After that, we propose a low-complexity algorithm to obtain the sub-optimal solution. Simulation results prove the superiority of the proposed scheme in terms of the system delay compared with the equal allocation scheme. Our proposed scheme can achieve a sub-optimal solution which is very close to the optimal solution with low complexity compared with the Branch and Bound method.
Image Super-Resolution based on Multi-Convolution Neural Network

In recent years, convolutional neural network method has been widely and successfully applied in the field of image super-resolution. With the development of CNN structure, the reconstruction algorithm based on CNN has also been developed. However, in these reconstruction models, the scale of convolution operator is single. This will greatly limit the model’s learning ability about input image and impact the reconstruction effect. In order to improve the accuracy of the convolution network to the input image, a reconstruction method based on multi-scale convolution operator is proposed. In this method, multi-scale convolution operators are set up in each layer network to calculate multi-scale features of the input image. Experiments show that this method can effectively improve the accuracy of the reconstructed image in detail.
23.8 A 41μW 16MS/s 99.2dB-SFDR Capacitively Degenerated Dynamic Amplifier with Nonlinear-Slope-Factor Compensation

The amplifiers used to improve the noise performance in analog front-ends and ADCs must have sufficiently low noise and high linearity to achieve overall system performance targets. Achieving the target noise level requires a certain amount of power, but nonlinearity can be improved by analog or digital techniques. Residue amplifiers used in pipelined ADCs have been improved to dissipate low power, preserving their linearity. Traditionally, closed-loop amplifiers with high open loop-gain are used for the residue amplifiers [1], but they require static current, thus degrading power efficiency. To improve the power efficiency of the residue amplifier, dynamic amplifiers have been investigated [2], [4], which allow using only the required bandwidth, thus minimizing the power consumption for a given noise requirement. However, a dynamic amplifier requires digital calibration to compensate for the nonlinearity, increasing the design complexity and limiting the robustness to PVT variations [2], [3].
Formulation and Evaluation of Polyherbal Shampoo

Shampooing is the most common form of hair treatment. Shampoos are primarily been products aimed at cleansing the hair and scalp. In the present scenario, it seems improbable that herbal shampoo, although better in performance and safer than the synthetic ones, will be popular with the consumers. A more radical approach in popularizing herbal shampoo would be to change the consumer expectations from a shampoo, with emphasis on safety and efficacy. We have evaluated and compared the herbal shampoo, which was formulated in previous study, with marketed shampoo. The findings of this investigation reveal that synthetic preservatives have sometimes been the cause of adverse effects among consumers. However, the aesthetic attributes, such as lather and clarity, of the laboratory shampoo are not comparable with the marketed shampoos. The foam volume was on a par. Although the retail products were not fare so well in the tests conducted by us, they enjoy market popularity, especially if they foam well. This is mainly due to the false notion among consumers that ‘a shampoo that foams well, works well’, and no real effort on the part of manufacturers to counter this fallacy.
34.5 Human-Body-Coupled Power-Delivery and Ambient-Energy-Harvesting ICs for a Full-Body-Area Power Sustainability

As the number of body area electronics increases, the power source is becoming a major bottleneck. Using a battery for each node is bulky and inconvenient due to the need of frequent battery replacement/recharging. Conventional harvesting methods are largely placement confined and thus unable to sustain multiple nodes of heterogeneous placements simultaneously [1], [2]. For simultaneous power delivery to multiple nodes on body, [3] utilizes the near-field inductive link for power delivery to sensor nodes placed on the eTextiles shirt and thus exhibits placement constraints, whereas the far-field RF transfer based [4] suffers from antenna pattern distortion and body shadowing effect (RF energy blocked by human body), leaving sensor nodes under such effect practically uncovered due to the 20~40 dB more channel degradation [5]. Such placement-induced nonidealities, along with distance/environment induced impedance variations and voltage/power degradations, challenges the powering system design for wider body coverage. This paper presents the full-body coverage “body-coupled power delivery” and the placement-independent “body-coupled ambient energy harvesting” ICs, achieved by the Bulk Adaptation Rectifier (BAR) and the Detuned Impedance Booster (DIB), with the power sustainability supported by the Dual Mode Buck-Boost Converter (DM-BBC).
Real-Time Recognition of Facial Expressions Using Facial Electromyograms Recorded Around the Eyes for Social Virtual Reality Applications

Recent developments of social virtual reality (VR) services using avatars have increased the need for facial expression recognition (FER) technology. FER systems are generally implemented using optical cameras; however, the performance of these systems can be limited when users are wearing head-mounted displays (HMDs) as users’ faces are largely covered by the HMDs. Facial electromyograms (fEMGs) that can be recorded around users’ eyes can be potentially used for implementing FER systems for VR applications. However, this technology lacks practicality owing to the need for large-scale training datasets; furthermore, it is hampered by a relatively low performance. In this study, we proposed an fEMG-based FER system based on the Riemannian manifold-based approach to reduce the number of training datasets needed and enhance FER performance. Our experiments with 42 participants showed an average classification accuracy as high as 85.01% for recognizing 11 facial expressions with only a single training dataset for each expression. We further developed an online FER system that could animate a virtual avatar’s expression reflecting a user’s facial expression in real time, thus demonstrating that our FER system can be potentially used for practical interactive VR applications, such as social VR networks, smart education, and virtual training.
A Database-Assisted Spectrum Sharing for Satellite System and Terrestrial Cellular Network

A database-assisted spectrum sharing is proposed for satellite system and terrestrial cellular network in millimeter wave (mmWave) scenario. Due to the under utilization of mmWave spectrum resources in the satellite system and mmWave operation in the upcoming fifth generation systems, we are motivated to develop a spectrum sharing scheme to utilize spatial-temporal spectrum sharing opportunity with the premise that the satellite links are sufficiently protected. In the proposed scheme, with the information of coverage area broadcasted by the low earth orbit (LEO) and medium earth orbit (MEO) satellites and shared by earth stations, the cellular base stations adjust transmit power in accordance with the outage requirement to the earth station deployed at the worst case position. Simulation results demonstrate the superior performance of the proposed scheme over the coexistence scenario of satellite system and terrestrial cellular network, in terms of outage probability and throughput.