Miracle-dz/UniFAR-FTUs huggingface dataset:
Wavelet Flow: Optical Flow Guided Wavelet Facial Image Fusion

Estimating the correspondence between the images using optical flow is the key component for image fusion, however, computing optical flow between a pair of facial images including backgrounds is challenging due to large differences in illumination, texture, color and background in the images. To improve optical flow results for image fusion, we propose a novel flow estimation method, wavelet flow, which can handle both the face and background in the input images. The key idea is that instead of computing flow directly between the input image pair, we estimate the image flow by incorporating multi‐scale image transfer and optical flow guided wavelet fusion. Multi‐scale image transfer helps to preserve the background and lighting detail of input, while optical flow guided wavelet fusion produces a series of intermediate images for further fusion quality optimizing. Our approach can significantly improve the performance of the optical flow algorithm and provide more natural fusion results for both faces and backgrounds in the images. We evaluate our method on a variety of datasets to show its high outperformance.
Idea Implementation and Recommendation in Open Innovation Platforms

Collaborative crowdsourcing communities help firms obtain ideas generated by the public sector at a lower cost compared to those generated in-house. However, the growth of these communities has led to a large influx of ideas. This has made it difficult for firm experts to identify and evaluate ideas and determine which ones to implement. We present a model to pre-evaluate ideas and to recommend those that are most likely to be implemented in the future. We use an elaboration likelihood model to integrate central and peripheral route processing by firm experts in idea implementation in relation to the content characteristics and popularity of the ideas. The empirical results based on 89,906 ideas submitted by 53,836 users to the new function discussion forum hosted by Xiaomi, one of the world’s largest electronics company, suggest that original, topic dispersion-focused, concrete, and negative ideas are more likely to be adopted and implemented. Ideas with lower number of views and ideas with higher number of comments or higher ratings are more likely to be implemented in the community. We implemented machine learning models for recommending ideas based on our empirical findings. Specifically, the results show that our proposed factors can provide an extra significant contribution in predicting and recommending idea implementation. We conclude by discussing the theoretical and practical implications.
Effect of Planning Period on MPC-based Navigation for a Biped Robot in a Crowd

We control a biped robot moving in a crowd with a Model Predictive Control (MPC) scheme that generates stable walking motions, with automatic footstep placement. Most walking strategies propose to re-plan the walking motion to adapt to changing environments only once at every footstep. This is because a footstep is planted on the ground, it usually stays there at a constant position until the next footstep is initiated, what naturally constrains the capacity for the robot to react and adapt its motion in between footsteps. The objective of this paper is to measure if re-planning the walking motion more often than once at every footstep can lead to an improvement in collision avoidance when navigating in a crowd. Our result is that re-planning twice (or more) during each footstep leads to a significant reduction of the number of collisions when walking in a crowd, but depends on the density of the crowd.
LoRaMoto: a LoRa-based communication system for coordinated response in an earthquake aftermath (excerpt)

Response of emergency units after natural disasters, such as earthquakes, has to be coordinated, fast and efficient in order to rescue and care for the victims, keeping all the population –and the units themselves– safe amidst the chaos. Outages in mobile networks, as well as fiber- or copper-based landline and Internet connections are to be expected in such situations, so alternative communication solutions must be considered. To contribute in this duty, we propose a communication system that uses the LoRaWAN architecture to allow citizens to report their status to emergency units and public authorities with simple messages and interaction mechanisms. To analyse the system performance and capabilities we model a district of Coquimbo, a harbour town in Chile, that houses approximately 28.000 people in 7.500 homes, and simulate it with a baseline configuration. We explore several modifications of the system in order to determine its characteristics and limitations, to better understand its scalability and portability to other environments, and to outline the remaining challenges to make the system attain to specific performance guarantees
Aerodynamic Design of Low-speed Model Compressor for Low-speed Model Testing

介绍了旨在改善高压压气机性能的低速模拟试验方法,及其在国内外的发展情况. 在参考国外成功经验的基础上,基于一定的相似准则,摸索出了一套低速模型压气机的设计方法. 为了验证该方法的可行性,针对某方案设计的高压压气机后面级设计了一台4级重复级低速模型压气机. 借助全三维黏性流场计算,证明该压气机模拟级进、出口各主要气动参数沿径向的分布,以及转子、静子叶片表面压力系数的分布与高压压气机的被模拟级是一致的. 从数.
Image Composition of Partially Occluded Objects

Image composition extracts the content of interest (COI) from a source image and blends it into a target image to generate a new image. In the majority of existing works, the COI is manually extracted and then overlaid on top of the target image. However, in practice, it is often necessary to deal with situations in which the COI is partially occluded by the target image content. In this regard, both tasks of extracting the COI and cropping its occluded part require intensive user interactions, which are laborious and seriously reduce the composition efficiency. This paper addresses the aforementioned challenges by proposing an efficient image composition method. First, we extract the semantic contents of the images by using state‐of‐the‐art deep learning methods. Therefore, the COI can be selected with clicks only, which can greatly reduce the demanded user interactions. Second, according to the user's operations (such as translation or scale) on the COI, we can effectively infer the occlusion relationships between the COI and the contents of the target image. Thus, the COI can be adaptively embedded into the target image without concern about cropping its occluded part. Therefore, the procedures of content extraction and occlusion handling can be significantly simplified, and work efficiency is remarkably improved. Experimental results show that compared to existing works, our method can reduce the number of user interactions to approximately one‐tenth and increase the speed of image composition by more than ten times.
A Dynamic Functional Split in 5G Radio Access Networks

The 3rd Generation Partnership Project (3GPP) proposes a centralized architecture for the 5G radio access network (RAN) in order to reduce costs and mitigate inter-cell interference, which helps to increase user data rates. However, the limited capacity of current fronthaul networks renders it impossible for many RANs to be fully centralized. Instead, the operators can opt for a partially centralized architecture, in which only some of the functions of the RAN's processing chain are centralized. Previous work has tackled the optimal selection of these functions in a static or semi- static manner. In this paper, we present a 5G RAN that is able to dynamically adapt the subset of centralized functions to maximize data rates at runtime. We analyze the dynamics of a dense 5G RAN to derive a maximum convergence time for the selection algorithms and show that a dynamic functional split significantly improves data rates with respect to statically centralized solutions.
Touch the Future with a Smart Touch

NFC – Near Field Communication – is a very short range wireless communication technology that supports the use of mobile handsets by touch-based interaction. This is an intuitive and user-friendly way of establishing connections and exchanging information between mobile handsets and other devices by just setting them side by side. The SmartTouch project (ITEA No 05024) has been the largest effort on piloting NFC technology in the European Union. The project started in 2006 and extended to the end of year 2008, examining in the role of NFC in City Life, Home, Wellness and Health, and its Technological Building Blocks, Security and Privacy, and Business Building Blocks. The extraordinary combination of partners in SmartTouch, including technology and service producers, researchers, and organizations setting up first pilots, has been pushing the NFC technology forward on many fronts. This book collects those developments by starting with NFC and its basics and continuing through genuine pilot cases towards an understanding of NFC enabled business areas. This book is a valuable source of information for those who want to study NFC as a technology for their business, as a technology primer, or learning from others experiences of NFC use.
ME-TLS: Middlebox-Enhanced TLS for Internet-of-Things Devices

In-network middleboxes are vital for the Internet-of-Things (IoT) system security, but the widely adopted transport layer security (TLS) protocol blinds application-level middleboxes due to the encryption of traffic data. To resolve this problem, many solutions have been proposed to date. Among them, SplitTLS is widely adopted in the industry by proxy manufacturers. It requires TLS client to install customized root certificates and incurs additional security flaws, e.g., disabling server authentication and using weak cipher suites. Another approach is to customize the TLS protocol where middleboxes are enabled via either performing handshake directly with TLS endpoints or receiving session key materials in an out-of-band manner. Overall, current solutions would either jeopardize the original TLS handshake procedure or incur additional overheads on the endpoints. In this article, we design a new middlebox-enhanced TLS (ME-TLS), which enables endpoints to introduce authenticated middleboxes into a TLS session while control middleboxes’ access permission and processing order of traffic data. Particularly, in our proposed ME-TLS, the handshake structure of TLS keeps unchanged and middleboxes work in a passive manner. That is, middleboxes in the ME-TLS could recover session key materials by monitoring handshake messages passively instead of interacting with endpoints; secondary secure channels for key transmission are also not needed in our ME-TLS. We implement our ME-TLS based on the TLS 1.3 protocol and evaluate its performances. The experimental results demonstrate that our proposal is practical and deployable for real-world IoT scenarios.
Formation of reputation in semantic fields

The semantic field of a certain community has many useful properties that can be used to promote the product. People belonging to the semantic form their own strategy of promoting goods and services, which subsequently consume all members of this society. The relevance of this article is to study the factors affecting the formation of reputation for goods and services within the semantic fields. The heterogeneity of the phenomena of semantic fields in modern institutions does not reduce the qualitative properties provided for the study of this kind. The object of the study is the relationship within the semantic field, the subject of the study are the processes of formation of reputation in the semantic fields.
Features and fluents : representation of knowledge about dynamical systems

Inert and inhabited dynamical systems Inference operations on scenario descriptions Underlying semantics for IDS worlds Elementary feature logic and meta-logical concepts Lexical-domain object-feature logic Temporal feature logic for discrete time domains Chronicle completion in k-IA Intended models for chronicles in k-IA Entailment methods for k-IA using DFL-1 Duration constraints Entailment methods for k-OA using occlusion Composite actions Upper applicability bounds and assessment of soundness Future directions Terms index Notation References to related work
A Study of Applied Passive TLS Analysis

While the Internet is moving towards more and more encryption of the network traffic, it is also a trend that is picked up by authors of malware. The traditional way of detecting malicious traffic or malicious behaviour on the network is to use a signature-based network intrusion detection system. A signature-based system relies on reading the network traffic in plaintext in order to detect patterns, which it is not able to do when the traffic is encrypted. One work-around for this problem is to use SSL/TLS inspection. Instead of breaking end users privacy by inspecting what they believe is encrypted communication, this thesis investigates the possibility of detecting malicious TLS encrypted network traffic passively. By taking a look at properties exchanged when the encrypted network communication channel is established, as well as the behaviour of the network traffic, the thesis uses these properties in a machine learning algorithm to classify network traffic as either benign or malicious. While the machine learning algorithm is easy to implement in a proof-of-concept, the lack of publicly, up-to-date datasets of benign, encrypted network traffic with TLS is almost non-existent. This leads to the creation of a TLS encrypted network traffic generator that creates a baseline for what is considered as benign TLS traffic. Malicious TLS traffic is collected from open sources, and run through a multilayer perceptron with backpropagation. Two experiments were carried out during this thesis; one leading to a correct classification rate of 83% using network behaviour. The other experiment looked at the ciphersuites found in the TLS handshake of the traffic, and had a correct classification rate of 80%.
Joint User Access Mode Selection and Content Popularity Prediction in Non-Orthogonal Multiple Access-Based F-RANs

Non-orthogonal multiple access (NOMA) is regarded as a promising technology for the next-generation wireless communication system. Introducing NOMA into the fog radio access networks (F-RANs) is able to provide simultaneous transmissions to multiple users and significantly enhance F-RAN performance. However, due to the increasing number of users and the constraint of caching storage capacity, there exists a tradeoff between NOMA transmission performance and fronthaul saving. In this paper, a hierarchical game framework is presented to solve the joint optimization problem of user access mode selection and content popularity prediction in NOMA based F-RANs. More specifically, the access mode selection problem is formulated as an evolutionary game. The proposals’ evolutionary payoff expressions are derived by stochastic geometry tool, and the cost functions are related to the fog access point (F-AP) content placement profile as well as the fronthaul constraint. Moreover, the problem of what contents the F-AP should cache is modeled as a content popularity prediction problem, and based on both local and global user request states, a machine learning algorithm is presented to solve it. Simulation results validate the accuracy of analytical results and demonstrate our proposed algorithms can further improve the performance of NOMA based F-RANs.
Toward Future Green Maritime Transportation: An Overview of Seaport Microgrids and All-Electric Ships

Nowadays, extensive electrification of maritime transportation, represented by the seaport microgrids and all-electric ships (AESs), has been viewed as a feasible route to enhance the overall system flexibility as well as to mitigate the resulted in growing environmental issues. However, with the trend of electrification, the connections between the seaport and ships are no longer limited in the logistic-side, but also expanded to the electric-side, which makes the future maritime transportation management as a complex transportation-power multi-microgrid coordination problem. In land-based applications, multi-microgrid coordination is a relatively mature technology and already brings enormous economic and environmental benefits, but there still exists some gaps before those land-based technologies being integrated into maritime applications. In this perspective, this overview study emphasizes the characteristic of seaport microgrid and AESs, then several emerging technical challenges and the future research prospects are raised after a comprehensive literature survey.
TSV-OCT: A Scalable Online Multiple-TSV Defects Localization for Real-Time 3-D-IC Systems

In order to detect and localize through-silicon-via (TSV) failures in both manufacturing and operating phases, most of the existing methods use a dedicated testing mechanism with long response time and prerequisite interruptions for online testing. This article presents an error correction code (ECC)-based method named “TSV on-communication test” (TSV-OCT) to detect and localize faults without halting the operation of TSV-based 3-D-IC systems. We first propose a statistical detector, a method to detect open and short defects in TSVs that work in parallel with data transactions. Second, we propose an isolation-and-check algorithm to enhance the localization ability of the method. Moreover, the Monte Carlo simulations show that the proposed statistical detector increases <inline-formula> <tex-math notation="LaTeX">$\times 2$ </tex-math></inline-formula> the number of detected faults when compared to conventional ECC-based techniques. With the help of isolation and check, TSV-OCT localizes the number of defects up to <inline-formula> <tex-math notation="LaTeX">$\times 4$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\times 5$ </tex-math></inline-formula> higher. In addition, the response time is kept below 65 000 cycles, which could be easily integrated into real-time applications. On the other hand, an implementation of TSV-OCT on a 3-D Network-on-Chip (NoC) router shows no performance degradation for testing while having a reasonable area overhead.
A 14-bit 4-MS/s VCO-Based SAR ADC With Deep Metastability Facilitated Mismatch Calibration

This article presents a 14-bit 4-MS/s voltage-controlled oscillator (VCO)-based successive approximation register (SAR) analog-to-digital converter (ADC), where the metastability of the VCO-based comparator is exploited for the background calibration of mismatch errors. A closed-form behavioral analysis of VCO-based comparators has been studied in the presence of noise, showing that the metastability is of unique characteristics as compared to voltage-domain comparators, and the metastability can be evaluated quantitatively by observing the number of oscillation cycles. Deep metastability (DM) indicates a condition where the signal and noise are sufficiently small as compared to mismatch errors, based on which an analog background calibration technique is proposed. A decision stabilizer is employed to deal with the limit-cycle oscillations (LCOs). Fabricated in a 40-nm CMOS technology, the ADC prototype exhibits peak signal-to-noise-and-distortion ratio (SNDR) of 78.7 dB and >93-dB spurious-free dynamic range (SFDR) across nine samples. At 2 and 4 MS/s, the ADC, including calibration logic, consumes only 94 and $157~\mu \text{W}$ from a 1.1-V supply, achieving a peak Schreier figure of merit (FoM) of and 177.7 dB, respectively.
Kadiminekak Kiwabigonem: Barriers and Facilitators to Fostering Community Involvement in a Prenatal Program in an

Background: Inequities in maternal child health exist for Aboriginal populations in Canada. Community involvement in health services programming can reduce health inequities, but barriers and facilitators to community involvement have been understudied. The primary aim of this study was to answer the question: What are the barriers and facilitators to fostering com
Discourse-Aware Hierarchical Attention Network for Extractive Single-Document Summarization

Discourse relations between sentences are often represented as a tree, and the tree structure provides important information for summarizers to create a short and coherent summary. However, current neural network-based summarizers treat the source document as just a sequence of sentences and ignore the tree-like discourse structure inherent in the document. To incorporate the information of a discourse tree structure into the neural network-based summarizers, we propose a discourse-aware neural extractive summarizer which can explicitly take into account the discourse dependency tree structure of the source document. Our discourse-aware summarizer can jointly learn the discourse structure and the salience score of a sentence by using novel hierarchical attention modules, which can be trained on automatically parsed discourse dependency trees. Experimental results showed that our model achieved competitive or better performances against state-of-the-art models in terms of ROUGE scores on the DailyMail dataset. We further conducted manual evaluations. The results showed that our approach also gained the coherence of the output summaries.
Research and Construction of Word Vector Extension Structure Based on Context

The CBOW (Continuous Bag-of-Words) model is a three-layer forward neural network that predicts the central word vector by the fixed-size window information. In fact, context has a very important role in understanding the meaning of words. However, the context information of the fixed window size is partial and it is not enough to represent the whole context. Due to the polysemy of Chinese words, the same word may have different semantics in different contexts, while the traditional CBOW method ignores the polysemy of words. Therefore, this paper proposes a context-based word vector extension structure for the above problems. The main contents are as follows: (1) Introducing the concept of context vector. The entire sentence in which the target word is located is represented by a vector. (2) Constructing the polysemy storage method of words, adding a contextual list to each word vector, so that the multiple semantics of the words can be effectively distinguished. (3) Based on the word vector extension structure of this paper, a new character vector generation model is proposed, and the effect of the new model and the traditional CBOW model in the news headline similarity sorting task is compared. The experimental results show that the new character vector generation model based on the extension structure of the word vector obtains better result.
Norm vanishment and its applications in constrained control - Part I: the L infinity case.

Low gain feedback has found several applications in constrained control, robust control and nonlinear control. Low gain feedback refers to a family of stabilizing state feedback gains that are parameterized in a scalar and go to zero as the scalar decreases to zero. Slow peaking, the peak value of the state of the closed-loop system increasing towards infinity as the time goes to infinity and the value of the low gain parameter decreases to zero, is a prominent phenomenon resulting from low gain feedback. The success of low gain feedback design lies in the fact that such slow peaking gets canceled when the state is multiplied by the feedback gain to obtain the control signal. In this paper, we generalize the existing low gain design methods by introducing the notion of L∞-vanishment and providing a full characterization of low gain feedback that achieves such an L∞-vanishment property. A time domain signal parameterized by a scalar e possesses the L∞-vanishment property if its L∞ norm approaches zero as e does. This notion of L∞-vanishment not only facilitates the description of low gain feedback and the resulting slow peaking phenomenon, but also allows us to establish a systematic approach to the design of low gain feedback. A salient feature of this new design approach is that it results in all possible low gain feedback laws, including those resulting from the existing design methods.