bluuebunny/crossref_metadata_2025 huggingface dataset:
Dependency Graph-based High-level Synthesis for Maximum Instruction Parallelism
Authors: Gu Zhenghua , Wan Wenqing , Xie Jundong , Wu Chang
Published: September 2021

Performance optimization is an important goal for High-level Synthesis (HLS). Existing HLS scheduling algorithms are all based on Control and Data Flow Graph (CDFG) and will schedule basic blocks in sequential order. Our study shows that the sequential scheduling order of basic blocks is a big limiting factor for achievable circuit performance. In this article, we propose a Dependency Graph (DG) with two important properties for scheduling. First, DG is a directed acyclic graph. Thus, no loop breaking heuristic is needed for scheduling. Second, DG can be used to identify the exact instruction parallelism. Our experiment shows that DG can lead to 76% instruction parallelism increase over CDFG. Based on DG, we propose a bottom-up scheduling algorithm to achieve much higher instruction parallelism than existing algorithms. Hierarchical state transition graph with guard conditions is proposed for efficient implementation of such high parallelism scheduling. Our experimental results show that our DG-based HLS algorithm can outperform the CDFG-based LegUp and the state-of-the-art industrial tool Vivado HLS by 2.88× and 1.29× on circuit latency, respectively.
PANOLA: A Personal Assistant for Supporting Users in Preserving Privacy
Authors: Ulusoy Onuralp , Yolum Pinar
Published: September 2021

Privacy is the right of individuals to keep personal information to themselves. When individuals use online systems, they should be given the right to decide what information they would like to share and what to keep private. When a piece of information pertains only to a single individual, preserving privacy is possible by providing the right access options to the user. However, when a piece of information pertains to multiple individuals, such as a picture of a group of friends or a collaboratively edited document, deciding how to share this information and with whom is challenging. The problem becomes more difficult when the individuals who are affected by the information have different, possibly conflicting privacy constraints. Resolving this problem requires a mechanism that takes into account the relevant individuals’ concerns to decide on the privacy configuration of information. Because these decisions need to be made frequently (i.e., per each piece of shared content), the mechanism should be automated. This article presents a personal assistant to help end-users with managing the privacy of their content. When some content that belongs to multiple users is about to be shared, the personal assistants of the users employ an auction-based privacy mechanism to regulate the privacy of the content. To do so, each personal assistant learns the preferences of its user over time and produces bids accordingly. Our proposed personal assistant is capable of assisting users with different personas and thus ensures that people benefit from it as they need it. Our evaluations over multiagent simulations with online social network content show that our proposed personal assistant enables privacy-respecting content sharing.
Context-aware Adaptive Surgery
Authors: Wang Hongli , Guo Bin , Liu Jiaqi , Liu Sicong , Wu Yungang , Yu Zhiwen
Published: September 2021

Deep Neural Networks (DNNs) have made massive progress in many fields and deploying DNNs on end devices has become an emerging trend to make intelligence closer to users. However, it is challenging to deploy large-scale and computation-intensive DNNs on resource-constrained end devices due to their small size and lightweight. To this end, model partition, which aims to partition DNNs into multiple parts to realize the collaborative computing of multiple devices, has received extensive research attention. To find the optimal partition, most existing approaches need to run from scratch under given resource constraints. However, they ignore that resources of devices (e.g., storage, battery power), and performance requirements (e.g., inference latency), are often continuously changing, making the optimal partition solution change constantly during processing. Therefore, it is very important to reduce the tuning latency of model partition to realize the real-time adaption under the changing processing context. To address these problems, we propose the Context-aware Adaptive Surgery (CAS) framework to actively perceive the changing processing context, and adaptively find the appropriate partition solution in real-time. Specifically, we construct the partition state graph to comprehensively model different partition solutions of DNNs by import context resources. Then "the neighbor effect" is proposed, which provides the heuristic rule for the search process. When the processing context changes, CAS adopts the runtime search algorithm, Graph-based Adaptive DNN Surgery (GADS), to quickly find the appropriate partition that satisfies resource constraints under the guidance of the neighbor effect. The experimental results show that CAS realizes adaptively rapid tuning of the model partition solutions in 10ms scale even for large DNNs (2.25x to 221.7x search time improvement than the state-of-the-art researches), and the total inference latency still keeps the same level with baselines.
Captivates
Authors: Chwalek Patrick , Ramsay David , Paradiso Joseph A.
Published: September 2021

We present Captivates, an open-source smartglasses system designed for long-term, in-the-wild psychophysiological monitoring at scale. Captivates integrate many underutilized physiological sensors in a streamlined package, including temple and nose temperature measurement, blink detection, head motion tracking, activity classification, 3D localization, and head pose estimation. Captivates were designed with an emphasis on: (1) manufacturing and scalability, so we can easily support large scale user studies for ourselves and offer the platform as a generalized tool for ambulatory psychophysiology research; (2) robustness and battery life, so long-term studies result in trustworthy data individual's entire day in natural environments without supervision or recharge; and (3) aesthetics and comfort, so people can wear them in their normal daily contexts without self-consciousness or changes in behavior.

Captivates are intended to enable large scale data collection without altering user behavior. We validate that our sensors capture useful data robustly for a small set of beta testers. We also show that our additional effort on aesthetics was imperative to meet our goals; namely, earlier versions of our prototype make people uncomfortable to interact naturally in public, and our additional design and miniaturization effort has made a significant impact in preserving natural behavior.

There is tremendous promise in translating psychophysiological laboratory techniques into real-world insight. Captivates serve as an open-source bridge to this end. Paired with an accurate underlying model, Captivates will be able to quantify the long-term psychological impact of our design decisions and provide real-time feedback for technologists interested in actuating a cognitively adaptive, user-aligned future.
ALWAES
Authors: Jiang Dongzhe , Ding Yi , Zhang Hao , Liu Yunhuai , He Tian , Yang Yu , Zhang Desheng
Published: September 2021

For an online delivery platform, accurate physical locations of merchants are essential for delivery scheduling. It is challenging to maintain tens of thousands of merchant locations accurately because of potential errors introduced by merchants for profits (e.g., potential fraud). In practice, a platform periodically sends a dedicated crew to survey limited locations due to high workforce costs, leaving many potential location errors. In this paper, we design and implement ALWAES, a system that automatically identifies and corrects location errors based on fundamental tradeoffs of five measurement strategies from manual, physical, and virtual data collection infrastructures for online delivery platforms. ALWAES explores delivery data already collected by platform infrastructures to measure the travel time of couriers between merchants and verify all merchants' locations by cross-validation automatically. We explore tradeoffs between performance and cost of different measurement approaches. By comparing with the manually-collected ground truth, the experimental results show that ALWAES outperforms three other baselines by 32.2%, 41.8%, and 47.2%, respectively. More importantly, ALWAES saves 3,846 hours of the delivery time of 35,005 orders in a month and finds new erroneous locations that initially were not in the ground truth but are verified by our field study later, accounting for 3% of all merchants with erroneous locations.
ISACS
Authors: Falcão João Diogo , Ruiz Carlos , Bannis Adeola , Noh Hae Young , Zhang Pei
Published: September 2021

90% of retail sales occur in physical stores. In these physical stores 40% of shoppers leave the store based on the wait time. Autonomous stores can remove customer waiting time by providing a receipt without the need for scanning the items. Prior approaches use computer vision only, combine computer vision with weight sensors, or combine computer vision with sensors and human product recognition. These approaches, in general, suffer from low accuracy, up to hour long delays for receipt generation, or do not scale to store level deployments due to computation requirements and real-world multiple shopper scenarios.

We present ISACS, which combines a physical store model (e.g. customers, shelves, and item interactions), multi-human 3D pose estimation, and live inventory monitoring to provide an accurate matching of multiple people to multiple products. ISACS utilizes only shelf weight sensors and does not require visual inventory monitoring which drastically reduces the computational requirements and thus is scalable to a store-level deployment. In addition, ISACS generates an instant receipt by not requiring human intervention during receipt generation. To fully evaluate the ISACS, we deployed and evaluated our approach in an operating convenience store covering 800 square feet with 1653 distinct products, and more than 20,000 items. Over the course of 13 months of operation, ISACS achieved a receipt daily accuracy of up to 96.4%. Which translates to a 3.5x reduction in error compared to self-checkout stations.
Model-based Head Orientation Estimation for Smart Devices
Authors: Yang Qiang , Zheng Yuanqing
Published: September 2021

Voice interaction is friendly and convenient for users. Smart devices such as Amazon Echo allow users to interact with them by voice commands and become increasingly popular in our daily life. In recent years, research works focus on using the microphone array built in smart devices to localize the user's position, which adds additional context information to voice commands. In contrast, few works explore the user's head orientation, which also contains useful context information. For example, when a user says, "turn on the light", the head orientation could infer which light the user is referring to. Existing model-based works require a large number of microphone arrays to form an array network, while machine learning-based approaches need laborious data collection and training workload. The high deployment/usage cost of these methods is unfriendly to users. In this paper, we propose HOE, a model-based system that enables Head Orientation Estimation for smart devices with only two microphone arrays, which requires a lower training overhead than previous approaches. HOE first estimates the user's head orientation candidates by measuring the voice energy radiation pattern. Then, the voice frequency radiation pattern is leveraged to obtain the final result. Real-world experiments are conducted, and the results show that HOE can achieve a median estimation error of 23 degrees. To the best of our knowledge, HOE is the first model-based attempt to estimate the head orientation by only two microphone arrays without the arduous data training overhead.
Approaching the Real-World
Authors: Kwon Hyeokhyen , Wang Bingyao , Abowd Gregory D. , Plötz Thomas
Published: September 2021

Recently, IMUTube introduced a paradigm change for bootstrapping human activity recognition (HAR) systems for wearables. The key idea is to utilize videos of activities to support training activity recognizers based on inertial measurement units (IMUs). This system retrieves video from public repositories and subsequently generates virtual IMU data from this. The ultimate vision for such a system is to make large amounts of weakly labeled videos accessible for model training in HAR and, as such, to overcome one of the most pressing issues in the field: the lack of significant amounts of labeled sample data. In this paper we present the first in-detail exploration of IMUTube in a realistic assessment scenario: the analysis of free-weight gym exercises. We make significant progress towards a flexible, fully-functional IMUTube system by extending it such that it can handle a range of artifacts that are common in unrestricted online videos, including various forms of video noise, non-human poses, body part occlusions, and extreme camera and human motion. By overcoming these real-world challenges, we are able to generate high-quality virtual IMU data, which allows us to employ IMUTube for practical analysis tasks. We show that HAR systems trained by incorporating virtual sensor data generated by IMUTube significantly outperform baseline models trained only with real IMU data. In doing so we demonstrate the practical utility of IMUTube and the progress made towards the final vision of the new bootstrapping paradigm.
How Low Can You Go?
Authors: Rinta-Homi Mikko , Motlagh Naser Hossein , Zuniga Agustin , Flores Huber , Nurmi Petteri
Published: September 2021

We contribute by systematically analysing the performance trade-offs, costs (privacy loss and deployment cost) and limits of low-resolution thermal array sensors for occupancy detection. First, to assess performance limits, we manipulate the frame rate and resolution of images to establish the lowest possible values where reliable occupancy information can be captured. We also assess the effect of different viewing angles on the performance. We analyse performance using two datasets, an open-source dataset of thermal array sensor measurements (TIDOS) and a proprietary dataset that is used to validate the generality of the findings and to study the effect of different viewing angles. Our results show that even cameras with a 4 × 2 resolution - significantly lower than what has been used in previous research - can support reliable detection, as long as the frame rate is at least 4 frames per second. The lowest tested resolution, 2 × 2, can also offer reliable detection rates but requires higher frame rates (at least 16 frames per second) and careful adjustment of the camera viewing angle. We also show that the performance is sensitive to the viewing angle of the sensor, suggesting that the camera's field-of-view needs to be carefully adjusted to maximize the performance of low-resolution cameras. Second, in terms of costs, using a camera with only 4 × 2 resolution reveals very few insights about the occupants' identity or behaviour, and thus helps to preserve their privacy. Besides privacy, lowering the resolution and frame rate decreases manufacturing and operating costs and helps to make the solution easier to adopt. Based on our results, we derive guidelines on how to choose sensor resolution in real-world deployments by carrying out a small-scale trade-off analysis that considers two representative buildings as potential deployment areas and compares the cost, privacy and accuracy trade-offs of different resolutions.
WR-Hand
Authors: Liu Yang , Lin Chengdong , Li Zhenjiang
Published: September 2021

This paper presents WR-Hand, a wearable-based system tracking 3D hand pose of 14 hand skeleton points over time using Electromyography (EMG) and gyroscope sensor data from commercial armband. This system provides a significant leap in wearable sensing and enables new application potentials in medical care, human-computer interaction, etc. A challenge is the armband EMG sensors inevitably collect mixed EMG signals from multiple forearm muscles because of the fixed sensor positions on the device, while prior bio-medical models for hand pose tracking are built on isolated EMG signal inputs from isolated forearm spots for different muscles. In this paper, we leverage the recent success of neural networks to enhance the existing bio-medical model using the armband's EMG data and visualize our design to understand why our solution is effective. Moreover, we propose solutions to place the constructed hand pose reliably in a global coordinate system, and address two practical issues by providing a general plug-and-play version for new users without training and compensating for the position difference in how users wear their armbands. We implement a prototype using different commercial armbands, which is lightweight to execute on user's phone in real-time. Extensive evaluation shows the efficacy of the WR-Hand design.
DualRing
Authors: Liang Chen , Yu Chun , Qin Yue , Wang Yuntao , Shi Yuanchun
Published: September 2021

We present DualRing, a novel ring-form input device that can capture the state and movement of the user's hand and fingers. With two IMU rings attached to the user's thumb and index finger, DualRing can sense not only the absolute hand gesture relative to the ground but also the relative pose and movement among hand segments. To enable natural thumb-to-finger interaction, we develop a high-frequency AC circuit for on-body contact detection. Based on the sensing information of DualRing, we outline the interaction space and divide it into three sub-spaces: within-hand interaction, hand-to-surface interaction, and hand-to-object interaction. By analyzing the accuracy and performance of our system, we demonstrate the informational advantage of DualRing in sensing comprehensive hand gestures compared with single-ring-based solutions. Through the user study, we discovered the interaction space enabled by DualRing is favored by users for its usability, efficiency, and novelty.
RF-ray
Authors: Ding Han , Zhai Linwei , Zhao Cui , Hou Songjiang , Wang Ge , Xi Wei , Zhao Jizhong , Gong Yihong
Published: September 2021

This paper presents a non-invasive design, namely RF-ray, to recognize the shape and material of an object simultaneously. RF-ray puts the object approximate to an RFID tag array, and explores the propagation effect as well as coupling effect between RFIDs and the object for sensing. In contrast to prior proposals, RF-ray is capable to recognize unseen objects, including unseen shape-material pairs and unseen materials within a certain container. To make it real, RF-ray introduces a sensing capability enhancement module and leverages a two-branch neural network for shape profiling and material identification respectively. Furthermore, we incorporate a Zero-Shot Learning based embedding module that incorporates the well-learned linguistic features to generalize RF-ray to recognize unseen materials. We build a prototype of RF-ray using commodity RFID devices. Comprehensive real-world experiments demonstrate our system can achieve high object recognition performance.
Anti-Spoofing Voice Commands
Authors: Zhao Cui , Li Zhenjiang , Ding Han , Xi Wei , Wang Ge , Zhao Jizhong
Published: September 2021

This paper presents an anti-spoofing design to verify whether a voice command is spoken by one live legal user, which supplements existing speech recognition systems and could enable new application potentials when many crucial voice commands need a higher-standard verification in applications. In the literature, verifying the liveness and legality of the command's speaker has been studied separately. However, to accept a voice command from a live legal user, prior solutions cannot be combined directly due to two reasons. First, previous methods have introduced various sensing channels for the liveness detection, while the safety of a sensing channel itself cannot be guaranteed. Second, a direct combination is also vulnerable when an attacker plays a recorded voice command from the legal user and mimics this user to speak the command simultaneously. In this paper, we introduce an anti-spoofing sensing channel to fulfill the design. More importantly, our design provides a generic interface to form the sensing channel, which is compatible to a variety of widely-used signals, including RFID, Wi-Fi and acoustic signals. This offers a flexibility to balance the system cost and verification requirement. We develop a prototype system with three versions by using these sensing signals. We conduct extensive experiments in six different real-world environments under a variety of settings to examine the effectiveness of our design.
Duco
Authors: Cheng Tingyu , Li Bu , Zhang Yang , Li Yunzhi , Ramey Charles , Jung Eui Min , Cui Yepu , Swaminathan Sai Ganesh , Do Youngwook , Tentzeris Manos , Abowd Gregory D. , Oh HyunJoo
Published: September 2021

Human environments are filled with large open spaces that are separated by structures like walls, facades, glass windows, etc. Most often, these structures are largely passive offering little to no interactivity. In this paper, we present Duco, a large-scale electronics fabrication robot that enables room-scale & building-scale circuitry to add interactivity to vertical everyday surfaces. Duco negates the need for any human intervention by leveraging a hanging robotic system that automatically sketches multi-layered circuity to enable novel large-scale interfaces. The key idea behind Duco is that it achieves single-layer or multi-layer circuit fabrication on 2D surfaces as well as 2D cutouts that can be assembled into 3D objects by loading various functional inks (e.g., conductive, dielectric, or cleaning) to the wall-hanging drawing robot, as well as employing an optional laser cutting head as a cutting tool. Our technical evaluation shows that Duco's mechanical system works reliably on various surface materials with a wide range of roughness and surface morphologies. The system achieves superior mechanical tolerances (0.1mm XY axis resolution and 1mm smallest feature size). We demonstrate our system with five application examples, including an interactive piano, an IoT coffee maker controller, an FM energy-harvester printed on a large glass window, a human-scale touch sensor and a 3D interactive lamp.
ULoc
Authors: Zhao Minghui , Chang Tyler , Arun Aditya , Ayyalasomayajula Roshan , Zhang Chi , Bharadia Dinesh
Published: September 2021

A myriad of IoT applications, ranging from tracking assets in hospitals, logistics, and construction industries to indoor tracking in large indoor spaces, demand centimeter-accurate localization that is robust to blockages from hands, furniture, or other occlusions in the environment. With this need, in the recent past, Ultra Wide Band (UWB) based localization and tracking has become popular. Its popularity is driven by its proposed high bandwidth and protocol specifically designed for localization of specialized "tags". This high bandwidth of UWB provides a fine resolution of the time-of-travel of the signal that can be translated to the location of the tag with centimeter-grade accuracy in a controlled environment. Unfortunately, we find that high latency and high-power consumption of these time-of-travel methods are the major culprits which prevent such a system from deploying multiple tags in the environment. Thus, we developed ULoc, a scalable, low-power, and cm-accurate UWB localization and tracking system. In ULoc, we custom build a multi-antenna UWB anchor that enables azimuth and polar angle of arrival (henceforth shortened to '3D-AoA') measurements, with just the reception of a single packet from the tag. By combining multiple UWB anchors, ULoc can localize the tag in 3D space. The single-packet location estimation reduces the latency of the entire system by at least 3×, as compared with state of art multi-packet UWB localization protocols, making UWB based localization scalable. ULoc's design also reduces the power consumption per location estimate at the tag by 9×, as compared to state-of-art time-of-travel algorithms. We further develop a novel 3D-AoA based 3D localization that shows a stationary localization accuracy of 3.6 cm which is 1.8× better than the state-of-the-art two-way ranging (TWR) systems. We further developed a temporal tracking system that achieves a tracking accuracy of 10 cm in mobile conditions which is 4.3× better than the state-of-the-art TWR systems.
Mechanical Behavior of Frozen Porous Sandstone under Uniaxial Compression
Authors: Wang Hong-Ying , Zhang Qiang
Published: September 2021

The influence of low temperature on longitudinal wave velocity, uniaxial compression strength, tensile strength, peak strain, secant modulus, and acoustic emission characteristics of yellow sandstones was studied. The results show that the secant modulus increases with decreasing temperature when the axial strain is less than 0.6%, and a contrary influence performs for the subsequent stage due to the fracture of the pore ice. With the decrease in temperature, the uniaxial compression strength first increases and then remains at a relatively constant value of 34.44 MPa at about -40°C while the temperature ranges from -40°C to -70°C. The tensile strength shows an approximate linear increment as the temperature. The peak strain gradually increases with temperature in a three-stage piecewise linear form, and the increasing rate gradually decreases with the decreasing temperature. The phase transformation from liquid water at a temperature of 20°C to solid ice at a temperature of -3°C significantly increases the longitudinal wave velocity from 1.55 km/s to 3.36 km/s. When the temperature is lower than -10°C, the longitudinal wave velocity approximately increases linearly at a rate of

2.67
×

10

−
3

 
km
/
s
·

°

C

−
1

with decreasing temperature.
Limit Drainage of Separated Tunnel Based on Ecological Groundwater Table: A Case Study
Authors: Yu Jianyou , Ge Shensheng , Hou Lingchao , Zhang Yiteng , Liu Yuzhu
Published: September 2021

After the excavation of the tunnel in water‐rich regions, the groundwater is continuously discharged into the tunnel. Excessive discharge will cause the groundwater table to drop, which will destroy the normal growth of vegetation and ecological balance. In order to protect the ecological environment, the key is to develop an effective method to determine the tunnel drainage. In this paper, a method for calculating the limit drainage of the tunnel is proposed based on area‐well method and the concept of ecological groundwater table in ecology and agronomy. Some conclusions are drawn as follows. (1) When the ecological groundwater table is constant, with the increase of groundwater discharge, the drainage influence range of the tunnel decreases, the rainfall supplement quantity Wt within the drainage influence range decreases, and the total groundwater discharge quantity Qt also decreases gradually. Since the decreasing rate of Wt is greater than that of Qt, there is a unit drainage that makes the total groundwater discharge equal to rainfall supplement. (2) A separated tunnel under construction in Fuzhou City is taken as an example; the limit drainage q0 of the separated double tunnel is 0.48 m3/(m·d) considering ecological groundwater table.
Influences of True Triaxial Loading‐Unloading Stress Paths on Mechanical Properties and Wave Velocity of Coal Samples subject to Risk of Rock Burst
Authors: Shen Wei , Liu Guang-Jian , Dou Lin-Ming , Gong Si-Yuan , He Hu
Published: September 2021

To study fracture evolution and peak stress in burst risk coal samples (BRCSs) under true triaxial loading and unloading conditions, experimental and numerical research was applied to BRCSs under true triaxial stress paths entailing “x‐direction displacement fixed, y‐direction loading, z‐direction unloading.” Both the experimental and the numerical results demonstrated that the peak stress borne by the BRCSs was not only affected by the initial stress but also had a negative exponential relationship with the ratio of the unloading rate and the loading rate (RURLR); therefore, peak stress equations of BRCSs under true triaxial loading and unloading conditions were established. The triaxial stress‐time curves obtained by experiments and simulations exhibited an “elasticity‐yield‐destruction” phase, and the characteristics of the yield phase were determined by the RURLR. A typical BRCS was selected for velocity tomographic imaging to analyze the fracture evolution characteristics under true triaxial loading and unloading. The results showed that when the BRCS was subjected to a triaxial state of stress, the high‐ and low‐velocity regions existed alternately due to the presence of the crack; during the elastic phase, the crack closed during loading in the previous phase was reopened upon unloading, so that the velocity of the sample decreased and a wide range of low‐velocity regions could be formed; when entering the yield phase, the original crack continued to expand into a hole‐through crack, leading to wider extreme values and ranges of these low‐ and high‐velocity regions; at the breaking phase, multiple microcracks were generated around the hole‐through cracks, decreasing the overall velocity, and showing point distributions characteristics of high‐ and low‐velocity regions. Overall, many low‐velocity regions with similar normal directions to the unloading direction were formed; these correlated well with macrofractures (postfailure).
Grade Prediction in Blended Learning Using Multisource Data
Authors: Chen Ling-qing , Wu Mei-ting , Pan Li-fang , Zheng Ru-bin
Published: September 2021

Today, blended learning is widely carried out in many colleges. Different online learning platforms have accumulated a large number of fine granularity records of students’ learning behavior, which provides us with an excellent opportunity to analyze students’ learning behavior. In this paper, based on the behavior log data in four consecutive years of blended learning in a college’s programming course, we propose a novel multiclassification frame to predict students’ learning outcomes. First, the data obtained from diverse platforms, i.e., MOOC, Cnblogs, Programming Teaching Assistant (PTA) system, and Rain Classroom, are integrated and preprocessed. Second, a novel error-correcting output codes (ECOC) multiclassification framework, based on genetic algorithm (GA) and ternary bitwise calculator, is designed to effectively predict the grade levels of students by optimizing the code-matrix, feature subset, and binary classifiers of ECOC. Experimental results show that the proposed algorithm in this paper significantly outperforms other alternatives in predicting students’ grades. In addition, the performance of the algorithm can be further improved by adding the grades of prerequisite courses.
Corporate Social Responsibility Disclosure, Debt Financing Costs, and Innovation Capacity
Authors: Miao Yang , Zhou Xiaoxue , Dai Xin
Published: September 2021

The challenges of financing have been troubling the development of Chinese enterprises, especially private enterprises. This paper aims to examine the antecedents and consequences of debt financing costs. Drawing on a sample of Chinese A-share listed companies in Shanghai and Shenzhen from 2009 to 2018, the study examined the relationship between corporate social responsibility disclosure, debt financing costs, and innovation capacity. This paper found that the quality of CSR disclosure is negatively related to the cost of debt financing and examined the situational differences in the impact of CSR disclosure quality under different property rights, different disclosure forms, and different forensic effects. Moreover, it is found that the negative effect of CSR disclosure quality on debt financing cost helps to induce the innovation capacity of enterprises. The findings of this paper have certain reference value for corporate improvement of social responsibility disclosure.
Probabilistic Linguistic‐Based Group DEMATEL Method with Both Positive and Negative Influences
Authors: Sun Yonghe , Zhang Siyu , Huang Zihang , Miao Bin
Published: September 2021

Decision‐making trial and evaluation laboratory (DEMATEL) is a widely accepted factor analysis algorithm for complex systems. The rationality of the evaluation scale is the basis of sound DEMATEL decision‐making. Unfortunately, the existing evaluation scales of DEMATEL failed to reasonably distinguish and describe the positive and negative influences between factors. Generally, the positive and negative influences between factors should be considered at the same time. In other words, negative influence between factors should not be directly ignored, which is improper and unrealistic. To better address this issue, we extend the evaluation scale of DEMATEL. We also integrate the scale‐based group DEMATEL method with probabilistic linguistic term sets (PLTSs) to increase its effectiveness, which allows experts to express incomplete and uncertain linguistic preferences in DEMATEL decision‐making. An experts’ subjective weight adjustment method based on the similarity degree between PLTSs is introduced to determine experts’ weights. Finally, an algorithm of probabilistic linguistic‐based group DEMATEL method with both positive and negative influences is summarized, and an example is used to illustrate the proposed method and demonstrate its superiority. Our results demonstrate that the method proposed in this paper deals reasonably with realistic problems.
Experimental Study on the Spanwise Correlation of Vortex‐Induced Force Using Large‐Scale Section Model
Authors: Li Chunguang , Mao Yu , Han Yan , Li Kai , Cai C.S.
Published: September 2021

To investigate the spanwise correlation of vortex‐induced forces (VIF) of a typical section of a streamlined box girder, wind tunnel tests of simultaneous measurement of force and displacement responses of a sectional model were conducted in a smooth flow. The spanwise correlation of VIF and pressure coefficients on the measurement points of an oscillating main deck were analyzed in both the time domain and frequency domain, respectively. The research results indicated that the spanwise correlation of VIF and pressure coefficients on the measurement points were related to the amplitudes of vortex‐induced vibration (VIV), both of them weakened with the increase of spanwise distance; the maximum value of spanwise correlation coefficient is situated at the ascending stage of the lock‐in region, rather than at the extreme amplitude point. The amplitudes of VIV showed different impacts on the spanwise correlation of pressure coefficients on the measurement points of the upper and lower surfaces, for which the maximum value of the spanwise correlation coefficients is located at the extreme amplitude point and the ascending stage of the lock‐in region, respectively. Furthermore, the spanwise correlation of the pressure coefficients decreases continually from the upstream to downstream of the main deck; large coherence of vortex‐induced forces and pressure appears around the frequency of vortex shedding, and the coherence of VIF and pressure becomes smaller with the increase in the spanwise distance.
Determinants of Smallholder Farmers’ Participation in Improved Sheep Production: The Case of Doyogena District, Kembata Tembaro Zone, Southern Ethiopia
Authors: Mathewos Teketel , Temesgen Daniel , Hamza Dereje , Fesseha Haben
Published: September 2021

Background. Improved sheep production practices are becoming relevant, but smallholder farmers’ involvement in improved sheep production was below expectations and detailed studies were restricted on the determinants of the participation of smallholder farmers in improved sheep production. This research was conducted to examine the determinants of the involvement of smallholder farmers in enhanced sheep production in the study area. Methods. Multi-stages sampling techniques were used for this study. Firstly, improved sheep production had a big effect on the incomes of households of participants and nonparticipants. Finally, three kebeles were chosen by basic random sampling, and the third was picked for systematic sampling by 144 survey respondents. Quantitative and qualitative data were gathered from primary and secondary sources. Data collection techniques were undertaken by surveys, focus group discussions, and key informants. Quantitative data were used to evaluate descriptive statistics, such as average, frequency, standard deviation, scope, and inferential t-test and chi-square statistics, and a logit model. Qualitative data obtained from focus group discussion and key informants were analyzed by narrative and used for survey data triangulation. Results. Out of the 144 samples, 51 were participants and 93 were nonparticipants. Participation determinants found in this research field were household labor, age, communication frequency with developers, membership in cooperatives, land ownership, participation in credit, and off-farm income. Improved sheep production had a major effect on the incomes of households of participants and nonparticipants. Multiple determinants typically affect the role of smallholder farmers in raising sheep production. Conclusion. Future initiatives under a successful policy should aim at accelerating agricultural and rural growth by efficiently leveraging enhanced sheep capacity in particular in the study region and Ethiopia in general.
A Network Pharmacology and Molecular Docking Strategy to Explore Potential Targets and Mechanisms Underlying the Effect of Curcumin on Osteonecrosis of the Femoral Head in Systemic Lupus Erythematosus
Authors: Kang Pan , Wu Zhiming , Zhong Yue , Wang Zihao , Zhou Chi , Huo Shaochuan , Guo Hai , Li Songtao , Xu Kun , Liu Lingyun , Chen Shuai , Tang Hongyu , Wang Haibin
Published: September 2021

Background. Systemic lupus erythematosus (SLE) is a refractory immune disease, which is often complicated with osteonecrosis of the femoral head (ONFH). Curcumin, the most active ingredient of Curcuma longa with a variety of biological activities, has wide effects on the body system. The study is aimed at exploring the potential therapeutic targets underlying the effect of curcumin on SLE‐ONFH by utilizing a network pharmacology approach and molecular docking strategy. Methods. Curcumin and its drug targets were identified using network analysis. First, the Swiss target prediction, GeneCards, and OMIM databases were mined for information relevant to the prediction of curcumin targets and SLE‐ONFH‐related targets. Second, the curcumin target gene, SLE‐ONFH shared gene, and curcumin‐SLE‐ONFH target gene networks were created in Cytoscape software followed by collecting the candidate targets of each component by R software. Third, the targets and enriched pathways were examined by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Eventually, a gene‐pathway network was constructed and visualized by Cytoscape software; key potential central targets were verified and checked by molecular docking and literature review. Results. 201 potential targets of curcumin and 170 related targets involved in SLE‐ONFH were subjected to network analysis, and the 36 intersection targets indicated the potential targets of curcumin for the treatment of SLE‐ONFH. Additionally, for getting more comprehensive and accurate candidate genes, the 36 potential targets were determined to be analyzed by network topology and 285 candidate genes were obtained finally. The top 20 biological processes, cellular components, and molecular functions were identified, when corrected by a P value ≤ 0.05. 20 related signaling pathways were identified by KEGG analysis, when corrected according to a Bonferroni P value ≤ 0.05. Molecular docking showed that the top three genes (TP53, IL6, VEGFA) have good binding force with curcumin; combined with literature review, some other genes such as TNF, CCND1, CASP3, and MMP9 were also identified. Conclusion. The present study explored the potential targets and signaling pathways of curcumin against SLE‐ONFH, which could provide a better understanding of its effects in terms of regulating cell cycle, angiogenesis, immunosuppression, inflammation, and bone destruction.
An End-to-End Learning-Based Row-Following System for an Agricultural Robot in Structured Apple Orchards
Authors: Huang Peichen , Zhu Lixue , Zhang Zhigang , Yang Chenyu
Published: September 2021

A row-following system based on end-to-end learning for an agricultural robot in an apple orchard was developed in this study. Instead of dividing the navigation into multiple traditional subtasks, the designed end-to-end learning method maps images from the camera directly to driving commands, which reduces the complexity of the navigation system. A sample collection method for network training was also proposed, by which the robot could automatically drive and collect data without an operator or remote control. No hand labeling of training samples is required. To improve the network generalization, methods such as batch normalization, dropout, data augmentation, and 10-fold cross-validation were adopted. In addition, internal representations of the network were analyzed, and row-following tests were carried out. Test results showed that the visual navigation system based on end-to-end learning could guide the robot by adjusting its posture according to different scenarios and successfully passing through the tree rows.
A Few‐Shot Learning‐Based Siamese Capsule Network for Intrusion Detection with Imbalanced Training Data
Authors: Wang Zu-Min , Tian Ji-Yu , Qin Jing , Fang Hui , Chen Li-Ming
Published: September 2021

Network intrusion detection remains one of the major challenges in cybersecurity. In recent years, many machine‐learning‐based methods have been designed to capture the dynamic and complex intrusion patterns to improve the performance of intrusion detection systems. However, two issues, including imbalanced training data and new unknown attacks, still hinder the development of a reliable network intrusion detection system. In this paper, we propose a novel few‐shot learning‐based Siamese capsule network to tackle the scarcity of abnormal network traffic training data and enhance the detection of unknown attacks. In specific, the well‐designed deep learning network excels at capturing dynamic relationships across traffic features. In addition, an unsupervised subtype sampling scheme is seamlessly integrated with the Siamese network to improve the detection of network intrusion attacks under the circumstance of imbalanced training data. Experimental results have demonstrated that the metric learning framework is more suitable to extract subtle and distinctive features to identify both known and unknown attacks after the sampling scheme compared to other supervised learning methods. Compared to the state‐of‐the‐art methods, our proposed method achieves superior performance to effectively detect both types of attacks.
Pollution Characteristics and Risk Assessment of Heavy Metal Elements in Sediment in the West Lake of Hengshui Lake
Authors: Zhang Zhongqiang , Wei Shuzhen , Liu Jianxiao
Published: September 2021

21 sampling points were set in the West Lake of Hengshui Lake including 20 fish ponds and 1 natural pond to analyze the contents distribution of heavy metal elements including Cr, Zn, Pb, Cu, Cd, Hg, As, and Sb in sediment. The degree of heavy metal pollution was evaluated by the geoaccumulation index (Igeo) method. The results showed that the concentrations of Cr, Zn, Pb, Cu, Cd, and Hg at in all sampling points of the fish ponds were lower than soil risk screening values of agricultural land in soil environmental quality: risk control standard for soil contamination of agricultural land (trial) (GB 15618‐2018). The concentrations of As at 5 sampling points were higher than soil risk screening values and lower than soil risk control values of agricultural land in GB 15618‐2018. The concentrations of Sb were lower than soil risk screening values of construction land in soil environmental quality: risk control standard for soil contamination of development land (trial) (GB 36600‐2018). The evaluation results of the geoaccumulation index method showed that the pollution degrees of Cd, Pb, and Hg were moderately contaminated in general, the pollution degree of Cu was uncontaminated to moderately contaminated in general, the pollution degrees of Cr and As were uncontaminated to moderately contaminated in some sampling points, and Zn and Sb could be regarded as uncontaminated in sediment of the fish ponds in the West Lake of Hengshui Lake. Otherwise, the average contents of other heavy metal elements in sediment of the fish ponds in the West Lake of Hengshui Lake were higher than the background value of soil in Hebei Province (BVSH) except for Zn and Sb. The average contents of seven heavy metal elements in sediment of the natural pond in the West Lake of Hengshui Lake were lower than BVSH except for Cr. The result can be regarded as no pollution in the natural pond because the values of Igeo were all lower than 0. The heavy metal pollution in the sediment of the West Lake of Hengshui Lake may be due to the introduction of feeding. The water environment of the West Lake of Hengshui Lake can be effectively improved by cleaning up the sediment of fish ponds and “returning fishing to wetland.”
Endoscopic Submucosal Dissection for Resolution of a Symptomatic Pancreatic Rest in a Pediatric Patient: A Case Report and Literature Review
Authors: Camacho-Gomez Sandra Mabel , Moreau Chris , Noel James , Noel Robert Adam , Patel Sandeep
Published: September 2021

The pancreatic rest, aberrant, or heterotopic pancreas is a normal function pancreas found in the submucosal layer of the greater curvature of the gastric antrum and occasionally in the duodenum. Most of the patients are asymptomatic and the finding is usually incidental. We describe the case of a child with abdominal pain and history of recurrent ulcers that necessitated esophagogastroduodenoscopy and further evaluation with endoscopic ultrasound that confirmed a submucosal lesion consistent with a pancreatic rest. Endoscopic submucosal dissection was performed without complication, and complete symptom resolution was achieved after dissection of the pancreatic rest.
Changes in the Structures and Directions of Rock Excavation Research from 1999 to 2020: A Bibliometric Study
Authors: Shi Dongping , Xie Chengyu , Xiong Lichun
Published: September 2021

Rock excavation has been the hot spot and frontier of scientific research. Rock excavation research is in a period of rapid development. The bibliographies included in ISI Web of Knowledge database from 1999–2020 were used as data samples, and the collected data were analyzed by literature co‐citation and cluster analysis using CiteSpace and VOSviewer information visualization techniques and dynamic network analysis tools. A knowledge map of the evolution of bibliometric research development is drawn to reveal the representative literature in the field of bibliometrics. The hot areas of bibliometric research are introduced. The development trend of bibliometrics is proposed. The results of the study show that the amount of the literature on rock excavation is growing rapidly. A large amount of the foreign literature is available in China, the United States, Australia, Canada, France, and other countries. The main included journals are Tunnelling and Underground Space Technology, International Journal of Rock Mechanics and Mining Sciences, “Rock Mechanics and Rock Engineering,” “Engineering Geology,” and “Bulletin of Engineering Geology and the Environment.” Keyword co‐occurrence analysis includes the following contents: rock damage constitutive model, excavation damage area, numerical simulation, stability analysis support, and prediction technology of rock.