Miracle-dz/UniFAR-FTUs huggingface dataset:
Hand Gesture Recognition Based on Active Ultrasonic Sensing of Smartphone: A Survey

With the rapid development of Internet of Things, hand gesture recognition has drawn wide attention in the field of ubiquitous computing because it provides us with simple and natural human-computer interaction mode. Among these various implementations, hand gesture recognition using ultrasonic signals of smartphone has become a hot research topic due to its various advantages. In this paper, we consider the smartphone as an active sonar sensing system to identify hand movements. Specifically, the speakers emit ultrasonic signal and the microphone on the same phone receives the changed echo affected by hand movements. This paper investigates the state-of-the-art hand gesture applications and presents a comprehensive survey on the characteristics of studies using the active sonar sensing system. Firstly, we review the existing research of hand gesture recognition based on acoustic signals. After that, we introduce the characteristics of ultrasonic signal and describe the fundamental principle of hand gesture recognition. Then, we focus on the typical methods used in these studies and present a detailed analysis on signal generation, feature extraction, preprocessing, and recognition methods. Next, we investigate the state-of-the-art ultrasonic-based applications of hand gesture recognition using smartphone and analyze them in detail from dynamic gesture recognition and hand tracking. Afterwards, we make a discussion about these systems from signal acquisition, signal processing, and performance evaluation to obtain some insight into development of the ultrasonic hand gesture recognition system. Finally, we conclude by discussing the challenges, insight, and open issues involved in hand gesture recognition based on ultrasonic signal of the smartphone.
Preventing Activity Hijacking Attacks in Android APP

In recent years, the hijacking vulnerabilities of Android components have been widely discussed, and hijacked Android components have been used to disclose personal information or private data to attackers. Such attacks redirect the Android component's original workflow to malicious code, or even execute malware. We propose an Activity Hijacking Attacks (AHA) scheme that examines the original activity workflow to keep track of every activity in the Android framework. This enables AHA to detect a malicious app that attempts to open an activity in the foreground or whose layout is similar to a login page, i.e., with a text field for account names and passwords. When such activities are detected, our scheme notifies users to be cautious of keying in their credentials for the new activity. Experimental results show that using AHA can prevent attacks designed to steal personal information. Furthermore, our proposed scheme can be easily patched into the existing Android system and has a negligible overhead.
Stock Price Prediction Through the Sentimental Analysis of News Articles

As people's interest in forecasting stock prices has been recently increased, research on stock price analysis through big data and artificial intelligence has been actively conducted. In this paper, we performed sentimental analysis by building and analyzing a sentimental dictionary with news articles. Through the sentimental dictionary, we can obtain the positive index of news articles for each date. By analyzing the correlation value between the positive index value and the stock return value, we can confirm the utility and possibility of the sentimental analysis in stock market.
OneFile: A Wait-Free Persistent Transactional Memory

A persistent transactional memory (PTM) library provides an easy-to-use interface to programmers for using byte-addressable non-volatile memory (NVM). Previously proposed PTMs have, so far, been blocking. We present OneFile, the first wait-free PTM with integrated wait-free memory reclamation. We have designed and implemented two variants of the OneFile, one with lock-free progress and the other with bounded wait-free progress. We additionally present software transactional memory (STM) implementations of the lock-free and wait-free algorithms targeting volatile memory. Each of our PTMs and STMs is implemented as a single C++ file with ~1,000 lines of code, making them versatile to use. Equipped with these PTMs and STMs, non-expert developers can design and implement their own lock-free and wait-free data structures on NVM, thus making lock-free programming accessible to common software developers.
An Interleaved Boost Converter with Parallel Input and Output Series for Renewable energy system

A novel non-isolated interleaved boost converter is proposed with high step-up conversion ratio, which integrates two coupled inductors and voltage multiplier cells. At the input, the primary sides of the double coupled inductors are connected in parallel, and the two blocking capacitors are separately embedded into every phase. So the converter inhibits current ripples of input, and reduced voltage stress for the semiconductor devices; At the output, the secondary sides of the two coupled inductors are connected in series to achieve the purpose of much higher voltage gain and lower voltage stress on the semiconductor devices. In addition, the leakage inductance energy of two coupled inductors can be absorbed and recycled to the output, and the problem of reverse-recovery for diodes can be effectively suppressed. The voltage balance of the output capacitors and input current sharing by two interleaved phases are realized through the double closed-loop control of voltage and current. Finally, a 400W laboratory prototype is built to verify the correctness of theoretical analysis.
Multi-Turn Response Selection for Chatbots With Hierarchical Aggregation Network of Multi-Representation

Matching an appropriate response with its multi-turn context is a crucial challenge in retrieval-based chatbots. Current studies construct multiple representations of context and response to facilitate response selection, but they use these representations in isolation and ignore the relationships among representations. To address these problems, we propose a hierarchical aggregation network of multi-representation (HAMR) to leverage abundant representations sufficiently and enhance valuable information. First, we employ bidirectional recurrent neural networks (BiRNN) to extract syntactic and semantic representations of sentences and use a self-aggregation mechanism to combine these representations. Second, we design a matching aggregation mechanism for fusing different matching information between each utterance in context and response, which is generated by an attention mechanism. By considering the candidate response as the real part of the context, we try to integrate all of them in chronological order and then accumulate the vectors to calculate the final matching degree. An extensive empirical study on two multi-turn response selection data sets indicates that our proposed model achieves a new state-of-the-art result.
Improvement of Haar Feature Based Face Detection in OpenCV Incorporating Human Skin Color Characteristic

detection method detects facial features and ignores anything else, such as building, trees and also bodies. However, many face detection method fail to detect correct faces from images. David, Kriegman, and Ahuja presented a survey of face detection and presented the trends of researches in face detection [4]. In the survey authors categorized and evaluated different face detection algorithms. Some limitations of those algorithms were also discussed in a brief. A common problem of the existing methods is that they treat non facial area as a facial area. The popular Haar like feature based face detection [9, 12] also suffers from the same problem.
In-Situ Transmission Electron Microscopy Coupled with Resonant Microcantilever for Comprehensive Evaluating Sulfurization Performance of Zinc Oxide Nanowires

This paper reports a new technique with in-situ transmission electron microscopy (in-situ TEM) and resonant microcantilever to comprehensively evaluate sulfurization performance of ZnO nanowires. Herein, in-situ TEM is used to real-time observe the sulfurization process of ZnO nanowires under SO2-contained atmosphere. Based on temperature-varying micro-gravimetric method, thermodynamic interaction between ZnO nanowires and SO2 molecules is quantitatively evaluated by resonant microcantilever. By exposing the ZnO nanowires sample to SO2-contained atmosphere, a thick shell layer of ZnSO3 can be formed onto the surface of ZnO nanowires and a novel core-shell nanowire structure of ZnO@ZnSO3 is obtained finally. According to our comprehensive evaluation results, the ZnO nanowires sample with 100 nm diameter exhibits high reactive to SO2 molecules and is suitable for SO2 capture and storage.
Short-Term Traffic Flow Prediction Based On Deep Learning Network

Short-term traffic flow prediction is of importance for traffic control and guidance, and it also plays a crucial role in the development of the management and maintenance of the cross-sea great bridge. Therefore, this paper proposed a short-term traffic flow prediction method based on the data of operating private cars and minibuses on the bridge of Chang Tai expressway and a variety of LSTM network. The main work includes: cleaning the abnormal data of the original data and calculating the traffic time series in the period of five minutes, then fill in the missing data with the average of history traffic flow data. Further, the prediction model based on the LSTM algorithm is used to forecast the traffic flow of cars operating on the highway. Finally, the prediction model is tested in four different traffic conditions and the results indicate that the prediction model achieves high accuracy and generalizes well.
ile Radar Fonksiyon Sınıﬂandırma Towards Cognitive Sensing: Radar Function Classiﬁcation using Multitask Learning

Özetçe —Elektronik Harp (EH) sistemleri için ortamda yayın yapan bir radarın tespiti ve ilgili radarın fonksiyonunun belirlen-mesi, sistemin en önemli görevlerinden biridir. Bu çalı¸smada, EH sistemleri tarafından ölçülen radar parametreleri kullanılarak radarların Elektronik Kar¸sı Tedbir (EKT) kullanım konseptine uygun olarak fonksiyonlarının belirlenmesi hedeﬂenmi¸stir. Çok Görevli Ö˘grenme ve Tek Görevli Ö˘grenme sinir a˘gları probleme uyarlanmı¸stır. Sınıﬂandırıcı öncesinde a¸sırı örnekleme, aralık de˘gerleri için nicemleme ve sınıf de˘gerleri için gruplama ön
A survey of different storage methods for NGN mobile networks

Following the impressive progress of the current technologies and the computerization of various transactions and devices recently, and the appearance of 5G the creation of data in its various forms has become increasingly important. Hence, the usefulness of studying the different stockpiling methods. Researchers have already focused on several stockpiling techniques to provide unlimited storage capabilities. This paper highlights the various storage methods, with the definition and architecture. Also, it examines the methods based on three criteria which are: the storage capacity, response time andsecurity.
Data Fused Motor Fault Identification Based on Adversarial Auto-Encoder

In this paper, a fault identification algorithm for motors is proposed. The Adversarial Auto-Encoder is adopted as the main structure of deep neural network, extracting the latent vectors of input signals. The latent vectors obey the designed prior distribution instead of random distribution in the traditional Auto-Encoder. The structure of neural network is improved to be non-fully-connected for data fusion, which improves the stability and reliability of fault identification. To solve the diverge training problem and accelerate the training processing, a cyclic training method with variable learning rate is proposed for the non-fully-connected network. Finally, the algorithm is testified by open source data of bearing fault motor whose current and vibration signal are fused for fault identification. The proposed algorithm has the ability to determine the fault type with accuracy reaching 85%, 10% much higher than the traditional machine learning models.
TSV-Free Vertical Interconnection Technology Using Au-Si Eutectic Bonding for MEMS Wafer-Level Packaging

Developments of advanced Vertical Interconnection Technologies have been indispensable parts of achieving MEMS three-dimensional (3D) integration. This paper presents a TSV-free Vertical Interconnection Technology using Au-Si eutectic bonding applied on MEMS wafer-level packaging (WLP), which simplifies processes and promotes all-Si fabrication abilities in MEMS 3D WLP. Specifically, by forming Au-Si ohmic contacts in Au-Si bonding, it is cost-efficacious to employ low-resistivity Si columns (rather than conventional metal TSV structures) in Cap wafers as vertically electrical pathways while accomplishing sealing functions. Based on testing results of signal interfacing and hermetic packaging for MEMS devices, the proposed technology possesses extensive application prospects.
A High-Sensitivity Microfluidic Chip Calorimeter for Biochemical Reaction Monitoring Applications

Miniaturized calorimeter can monitor biochemical procedures by just detecting heat change during the reaction with minimum reagent consumption and short inspection time. This paper presents a high-sensitivity chip calorimeter fabricated by micromachining process. The device is composed of a polyimide-based flexible Bi-Sb thermopile thermal sensor and polydimethylsiloxane (PDMS) microfluidic chambers. With the novel design of the thermopile, the device has a high heat power sensitivity of 5.3V/W and a thermal response time about 0.93s which is good enough for real time reaction heat monitoring. With three identical 1μL PDMS chambers, the calorimeter can detect the heat of biochemical process with very little sample consumption. The chip calorimeter has prospects to realize high-throughput biochemical process monitoring and analysis in biological, chemical, pharmaceutical and medical applications.
A Framework for Industrial Symbiosis Systems for Agent-Based Simulation

The industrial symbiosis (IS) practice refers to replacing production inputs with wastes generated by other production processes, thus helping to close the loop of manufacturing processes. Companies adopting IS can reduce the amounts of wastes disposed of in landfills and primary inputs used in production processes, enhancing their production efficiency and reducing their production cost. The Agent-based modeling (ABM) approach has been recently adopted to advance the knowledge in the IS field, in particular to explore the extent to which the establishment of IS relationships can be affected by policy measures, benefit-sharing contracts, information-sharing tools, or social factors. However, the agentbased models proposed in the literature are highly different in terms of agents considered, their attributes, possible actions, and decisional rules. In particular, these models might result far from the real world, in terms of how the agents are modeled, as well as on the actions that they can undertake. The aim of this paper is to provide a general framework on how to model IS systems with ABM in a comprehensive way. The paper provides a literature review on IS aimed at identifying the potential agents in IS, their attributes, and their potential actions, shaped by technical, economic, and regulatory issues affecting the behavior of agents in terms of strategic management. Based on the results of the literature review, a general framework is proposed and discussed. This paper is expected to advance the knowledge on ABM for IS by providing useful suggestions to modelers and academicians.
Power Minimization of Full Duplex Communication System Based on Interference Exploit

In this paper, we investigate the power saving resource allocation algorithm design for multiuser full-duplex (FD) multi-input multi-output (MIMO) wireless communication system. Different from the conventional approaches which regards the interference as a harmful factor to mitigate it, we propose to use the channel state information (CSI) of FD radio base station (BS) and the downlink data symbol knowledge to constructively exploit multiuser interference. We formulate a multi-objective optimization problem (MOOP) via modified weighted Tchebycheff method to study the joint minimization of two conflicting system design objectives, i.e., total downlink transmit power minimization and total uplink transmit power minimization, while guaranteeing the quality of service (QoS) of all users. In the proposed MOOP, in order to adapt to the constructive interference (CI) of general phase shift keying (PSK) modulation signals, we adjust the signal to interference plus noise ratio (SINR) constraints for downlink users. The simulation results show that compared with the existing algorithms, the proposed algorithm can achieve more power savings.
Deanonymization and Linkability of Cryptocurrency Transactions Based on Network Analysis

Bitcoin, introduced in 2008 and launched in 2009, is the first digital currency to solve the double spending problem without relying on a trusted third party. Bitcoin provides a way to transact without any trusted intermediary, but its privacy guarantees are questionable. Despite the fact that Bitcoin addresses are not linked to any identity, multiple deanonymization attacks have been proposed. Alternative cryptocurrencies such as Dash, Monero, and Zcash aim to provide stronger privacy by using sophisticated cryptographic techniques to obfuscate transaction data. Previous work in cryptocurrency privacy mostly focused on applying data mining algorithms to the transaction graph extracted from the blockchain. We focus on a less well researched vector for privacy attacks: network analysis. We argue that timings of transaction messages leak information about their origin, which can be exploited by a well connected adversarial node. For the first time, network level attacks on Bitcoin and the three major privacy-focused cryptocurrencies have been examined. We describe the message propagation mechanics and privacy guarantees in Bitcoin, Dash, Monero, and Zcash. We propose a novel technique for linking transactions based on transaction propagation analysis. We also unpack address advertisement messages (ADDR), which under certain assumptions may help in linking transaction clusters to IP addresses of nodes. We implement and evaluate our method, deanonymizing our own transactions in Bitcoin and Zcash with a high level of accuracy. We also show that our technique is applicable to Dash and Monero. We estimate the cost of a full-scale attack on the Bitcoin mainnet at hundreds of US dollars, feasible even for a low budget adversary.
Research on the Algorithm of Elliptical Interpolation for Equal-Step Length

Elliptical interpolation algorithm is the key technology of numerical control machining of elliptical contour. This paper starts from the parameter equation of ellipse. It uses the idea that the chord length approximately equal to the arc length, and uses the basic principle of the interpolation algorithm which gradually approaches the elliptical interpolation point. The using of the elliptical parameter equation to ensure that the interpolation point is always located on the elliptical outline. Through the above methods, the equal-step length elliptical interpolation algorithm is obtained. The algorithm is simple to calculate. It need not to discuss quadrants. The interpolation accuracy is high, and it can ensure the stability of feed speed during processing.
Towards protected VNFs for multi-operator service delivery

Value-added 5G verticals are foreseen to be delivered as a service chain over multiple network operators with extensive outsourcing of Virtual Network Functions (VNFs). In this short paper we introduce the initial design of SafeLib, a software middlebox platform based on Intel SGX, which protects user traffic, VNF code, policy input and state in such scenarios, while also retaining high performance. Augmenting the smart integration of existing hardware and software building blocks with new secure elements, the SafeLib architecture shows considerable promise in a carrier-grade service context.
Decentralized Resource Allocation for Multicast D2D Communications Using Stochastic Geometry

In this paper, we discuss on the device-to-device (D2D) communication to fulfill the exponentially growing demands of the high data rate services. Multicast D2D communication is envisioned as a promising solution due to its advantages of high data rates and high spectrum efficiency. However, integrating D2D communication severely degrades the quality of service (QoS) of the network. To improve the QoS, we formulate a self-organizing resource allocation scheme using a stochastic algorithm for multicast D2D communication. Using the proposed algorithm, each uplink cellular resource can be simultaneously reused by the active D2D users in a network. Theoretical analysis and simulation results validate the proposed scheme.
Traveling-Wave Series-Fed Patch Array Antenna Using Novel Reflection-Canceling Elements for Flexible Beam

The methodology of designing traveling-wave series-fed patch array antennas (TSPAAs) based on reflection-canceling elements for flexible radiation pattern has been proposed. By introducing a quarter-wave transformer as the reflection-canceling structure, the cascaded patch antenna with tapered width can be used as the reflection-canceling element for a nonuniformly spaced TSPAA. Because of its simple structure, the parameters of the transformer can be determined directly by the corresponding equivalent circuit. To achieve a flexible radiation pattern, the array aperture distribution can be controlled by changing the element width (for amplitude distribution) and the element spacing (for phase distribution). In this method, the initial array is synthesized through a genetic algorithm approach, and a simulation-based iterative process considering the accurate mutual coupling is developed to optimize the initial array. To evaluate the proposed method, two different types of 14-element linear arrays operating at ISM 24GHz are implemented for a low sidelobe pattern and shaped-beam pattern, respectively. The two arrays are both simulated, fabricated and measured, the measured results show reasonable agreement with the simulation results, which verifies the effectiveness and correctness of the proposed method in this paper.
First-Principle Electronic Properties of Monoclinic (AlxInyGa1-x-y)2O3 Alloys

First-Principle DFT calculations are carried out to investigate the electronic properties of β-(Al<inf>x</inf>In<inf>y</inf>Ga<inf>1-x-y</inf>)<inf>2</inf>O<inf>3</inf> alloys with x ranging from 0~12.5% and y ranging from 0~ 18.75%. The electronic properties of monoclinic gallium oxide alloy with added aluminium and indium atoms are explored. The incorporation of both aluminium and indium results in the reduction of energy bandgap of β-(Al<inf>x</inf>In<inf>y</inf>Ga<inf>1-x-y</inf>)<inf>2</inf>O<inf>3</inf> alloys. Additionally, indium of higher content than aluminium in the quaternary alloy promotes the indirect bandgap transferring to direct bandgap. This work provides new findings on the flexible band property modification of monoclinic gallium oxide-based material and indicates their potential in deep ultraviolet photodetector.