Reviewerly/exHarmony huggingface dataset:
Popularity sensitive and domain-aware summarization for web tables

Table summarization can be of great help, which generates a concise and informative overview of a table to assist users to understand the table easily and unambiguously. A high-quality summary needs to have two desirable properties: presenting notable entities in the table and achieving broad coverage and high diversity on domains. However, notability and domain are often neglected in table summarization. Thus in this paper, we present a framework of domain-aware table summarization that is able to: (1) identify notable entities using a popularity-sensitive notability evaluation algorithm, (2) find core domains with a measurement of domain centrality, (3) and output the final high-quality summary using a three-phase clustering based algorithm. The experimental results show that our summarization method outperforms state-of-the-art methods by 9.62%, 2.78% and 6.77% on metrics coverage, diversity, and notability, respectively. We also conduct a user study to demonstrate that people can improve the accuracy of understanding tables by 17% with the help of our summarization technique.
Digital claustrophobia: Affective responses to digital design decisions

This research introduces the concept of ‘digital claustrophobia,’ examining how stable individual differences in claustrophobic tendencies can generate affective reactions to spatial elements online. The findings indicate that elevated claustrophobic tendencies lead to emotional discomfort and negatively moderate spatial constraint effects on website evaluations and anticipated product satisfaction. A series of exploratory studies establishes that (1) a common measure of individual differences in claustrophobia applies in digital commerce contexts, (2) claustrophobia alters reactions to spatial constraints, which changes downstream consequences such as mood, website design evaluation, and anticipated product satisfaction, and (3) these effects persist for more claustrophobic individuals in spatially constrained digital interfaces even when general affective measures such as mood are explicitly primed. This work is the first to investigate the role of claustrophobic tendencies in e-commerce environments in the context of anticipated satisfaction and suggests a foundation for numerous future research avenues.
Inaudible sound covert channel with anti-jamming capability: Attacks vs. countermeasure

When an inaudible sound covert channel (ISCC) attack is launched inside a computer system, sensitive data are converted to inaudible sound waves and then transmitted. The receiver at the other end picks up the sound signal, from which the original sensitive data can be recovered. As a forceful countermeasure against the ISCC attack, strong noise can be used to jam the channel and literally shut down any possible sound data transmission. In this paper, enhanced ISCC whose transmission frequency can be dynamically changed is proposed. Essentially, if the transmitter detects that the covert channel is being jammed, the transmitter and receiver both will switch to another available frequency and re-establish their communications, following the proposed communications protocol. Experimental results show that the proposed enhanced ISCC can remain connected even in the presence of a strong jamming noise source. Correspondingly, a detection method based on frequency scanning is proposed to help to combat such an anti-jamming sound channel. With the proposed countermeasure, the bit error rate (BER) of the data communications over enhanced ISCC soars to more than 48%, essentially shutting down the data transmission, and thus neutralizing the security threat.
MAP inference algorithms without approximation for collective graphical models on path graphs via discrete difference of convex algorithm

Abstract Collective graphical model (CGM) is a probabilistic model that provides a framework for analyzing aggregated count data. Maximum a posteriori (MAP) inference of unobserved variables under given observations is one of the essential operations in CGM. Because the MAP inference problem is known to be NP-hard in general, the current mainstream approach is to solve an alternative problem obtained by approximating the objective function and applying continuous relaxation. However, this approach has two significant drawbacks. First, the quality of the solution deteriorates when the values in the count data are negligible due to the inaccuracy of Stirling’s approximation. Second, the application of continuous relaxation causes the violation of integrality constraints. This paper proposes novel algorithms for MAP inference in CGMs on path graphs to overcome these problems. Our method is based on the discrete difference of convex algorithm (DCA); DCA is a general framework to minimize the sum of a convex function and a concave function by repeatedly minimizing surrogate functions. Utilizing the particular structure of path graphs, we efficiently solve the surrogate function minimization by minimum convex cost flow algorithms. Furthermore, our approach also leads to a new method of solving another important task; MAP inference of the sample size in CGM on path graphs. Our method is naturally applicable to this task, allowing us to design very efficient algorithms. Experimental results on synthetic and real-world datasets show the effectiveness of the proposed algorithms.
Metrology Challenges in Quantum Key Distribution

Abstract The metrology of the QKD devices and systems grows increasingly important in recent years not only because of the needs for conformance and performance testing in the standardization, but more importantly, imperfect implementation of the devices and systems or deviations from the theoretical models, which could be exploited by eavesdropper, should be carefully characterised to avoid the so-called side channel attack. In this paper, we review the recent advances in many aspects of the QKD metrology in both fibre based QKD and free space QKD systems, including a cutting edge metrology facility development and application, traceable calibration methods, and practical device characterising technologies, all of which have been contributed by the metrology communities and relative institutions.
Laser line interferometric sensor for multipoint velocity and distance measurements with camera based 3D calibrations

Optical, non-contact velocity and position measurement techniques offer great opportunities of in-process monitoring for instance in turning lathes and microfluidics. This work presents a novel laser line interferometric sensor, which enables simultaneous multipoint velocity and distance measurements. The sensor system is developed with cylindrical lens based measurement volume expansion and line camera based full field detection. Meanwhile, a matrix camera based calibration method is presented for an essential 3D evaluation of fringe spacings in the measurement volume. Experimental results show that the sensor allows synchronous multipoint velocity and distance measurements with a submicron distance uncertainty and a relative velocity uncertainty of 10<sup>−3</sup> .
Automatic robotic trajectory planning assisted by laser projection measurement

Recently, the full-area defect inspection of high-performance optical components such as large telescope mirrors is urgently demanded. An industrial robotic arm is suitable for conducting the scanning movement of defect inspection systems, and another monitoring system is needed to guide the moving trajectory of the robotic arm. An efficient and precise guiding system is developed based on a laser projection measuring system. After the calibration of the measuring system, real-time point clouds of the component under test can be acquired. Denoising and registration of the point clouds are conducted to align the robot coordinate system with the workpiece coordinate system. Then, the scanning inspection can be conducted all over the component under test. Experimental results demonstrate that the system has high efficiency and accuracy within 17.59 μm
Calibration of digital speckle pattern interferometry based on three-axis angle motions measurement theory

Digital speckle pattern interferometry (DSPI) is a full-field optical testing technique that can be used to measure tiny deformations and strains. It has been widely used in aerospace, precision manufacturing and other fields. However, the lack of effective calibration method has prevented the wider adoption of this technique. In the measurement process of DSPI, there are phase shift errors, phase noise, phase map processing algorithm errors, geometric sensitivity factors miscalibration, etc., which will lead to the final measurement error. Item-by-item calibration of the aforementioned error sources faces many difficulties in implementation and does not work well. Comprehensive calibration would be a better solution to minimize the measurement error but it is hard to perform due to the lack of suitable references for deformation measurement. In this paper, a comprehensive calibration method based on the theory of three-axis angle motions measurement using DSPI has been proposed. The tiny three-axis angle motions are loaded by Piezoelectric actuators and measured using a DSPI device based on the DSPI three-axis angle motions measurement theory. A multi-axis interferometry is used to measure the three-axis angle motions simultaneously and its output is used as the measurement reference. Because the angle motions of a rigid body instead of the deformations of an elastic body are measured, the measurement reference is readily available, yielding the successful precision calibration of the DSPI.
FPGA-enabled accurate angle-power matching method in MEMS mirrors-based structure light projection system

Three-dimensional (3D) measurement based on structured light is widely applied in the diverse fields of industrial detection, face recognition, reverse engineering, and so on. Compared with typical dynamic structured light fringe projection system using DLP or DOE, the system based on MEMS mirror has the advantages of small size, low cost, no optical magnifying lens, no need for focusing, and a large field of view angle range, which can achieve sub-millimeter or even higher accuracy. In MEMS mirror-based system, structured light patterns are formed by matching the mirror angle and laser output power and accurate identification of the angle is key point for achievement of a high pattern quality. There is a big challenge in identifying the MEMS mirror’s angle since the pulse signal for driving the mirror is very narrow, around 20 ns. For meeting this requirement, a high speed and accurate FPGA is utilized to observe the narrow pulse signal of the MEMS mirror and simultaneously control the laser power output, generating precise sinusoidal fringes. A 250 MHz clock frequency for observing pulse signals position based on logic voltage comparison under an LVCMOS33 standard, successfully identifying the narrow pulse signal. This ensures a high accuracy of the light patterns. Total harmonic distortion (THD) is used to evaluate the sinusoidal property of the structured light patterns and results verify that the THD of the MEMS mirror structure light projection system controlled by FPGA is less than 5%.
Deep learning profilometry for single-frame absolute 3D shape reconstruction

Recovering high-precision 3D information of dynamic scenes from single-frame fringe pattern is a major challenge in the field of fringe projection profilometry (FPP). Inspired by the successful application of deep learning in the field of FPP, we achieve single-frame, high-precision 3D measurement through the combination of data driven and physical model-based approaches. More specifically, we utilize deep learning with powerful feature extraction ability to reduce the number of fringe images required for phase demodulation to the physical limit. Then stereo phase unwrapping (SPU) approach based on geometric constraint is used to unwrap the high frequency wrapped phases obtained from deep learning, which maximizes the efficiency of FPP without projecting additional auxiliary patterns. Experimental results demonstrate that our method can realize high-precision 3D measurement only by a single projection, overcoming the motion sensitivity problem compared to traditional methods in dynamic scenes.
The influence of phase-shifting algorithms on the instrument transfer function in a Fizeau interferometer

The phase-shifting algorithms are essential for a Fizeau interferometer to reconstruct the topography of the optical element’s surface or wavefront. There are differences between different algorithms for reconstruction results, especially for the suppression of noise. To acquire a more accurate Instrument transfer function (ITF) which reflects the axial spatial frequency response of a Fizeau interferometer, the algorithm transfer function which represents the characteristics of the calculation process in spatial frequency was proposed. In this paper, numerical simulations calculated and analyzed several transfer functions of the well-known phase-shifting algorithms. Then, the ITFs of a step plate with a height of 118 nm were measured with different algorithms by experiments and the results were analyzed. The simulations and experimental results indicate that the phase-shifting algorithm has an effect on the ITF measurement but it is not a key factor affecting the ITF measurement.
High-accuracy digital volume correlation-based point cloud registration for 3D reconstruction

Three-dimensional (3D) reconstruction plays an important role in intelligent manufacturing, industrial inspection and reverse modeling. The model accuracy of 3D reconstruction has important influence on the final product quality and reliability, and point cloud registration is the key to 3D reconstruction, whose registration accuracy directly affects the final 3D reconstruction accuracy. There are many researches on point cloud registration algorithms, but the existing point cloud methods often have the disadvantages of low accuracy and slow speed when registering large point clouds. To meet this challenge, it is proposed that a high-accuracy point cloud registration method by digital volume correlation (DVC). Firstly, source point cloud and target point cloud are down sampled by voxel grid filter. Subsequently, the intrinsic shape signatures (ISS) feature is used to extract the feature points and random sample consensus (RANSAC) algorithm is used for coarse registration with ISS feature points. Finally, the point clouds are converted into voxels with gray-value information, which will be used for DVC calculation to obtain higher accuracy point cloud registration results. Experimental results show that our method can achieve high precision registration of large point clouds and ensure sufficient registration speed.
Voxel grid performer: efficient radiance fields generalization for human novel view synthesis

Novel view synthesis is a long-standing problem. Despite the rapid development of neural radiance field (nerf), in terms of rendering dynamic human body, NeRF still cannot achieve a good trade-off in precision and efficiency. In this paper, we aim at synthesizing a free-viewpoint video of an arbitrary human performers in an efficient way, only requiring a sparse number of camera views as inputs and skirting per-case fine-tuning. Recently, several works have addressed this problem by learning person-specific neural radiance fields (NeRF) to capture the appearance of a particular human. In parallel, some work proposed to use pixel-aligned features to generalize radiance fields to arbitrary new scenes and objects. Adopting these generalization approchs to human achieve reasonable rendering result. However, due to the difficulties of modeling the complex appearance of human and the dynamic sense, it is challenging to train nerf well in an efficient way. We find that the slow convergence of the human body reconstruction model is largely due to the nerf representation. In this work, we introduce a voxel grid based representation for human view synthesis, termed Voxel Grid Performer(VGP). Specifically, a sparse voxel grid is designed to represent the density and color in every space voxel, which enable better performance and less computation than conventional nerf optimization. We perform extensive experiments on both seen human performer and unseen human performer, demonstrating that our approach surpasses nerf-based methods on a wide variety of metrics. Code and data will be made available at https://github.com/fanzhongyi/vgp.
Probability-distribution-based Gamma correction method in fringe projection profilometry

This paper proposed a Gamma effect correction method based on the probability distribution function (PDF) for suppressing phase nonlinearity error in fringe projection profilometry. In this work, the periodicity of phase with Gamma effect is first analyzed, and nonlinear wrapped phase is modeled as superposition of normal wrapped phase with a sinusoidal function. Afterwards, a series of reference phases with Gamma factors ranging from 1 to 3 is constructed and the corresponding PDF is calculated. Then the optimal precoding factor γp of projected fringe is obtained by applying Jensen-Shannon (JS) divergence matching between the PDF of measured phase and constructed reference phases. In simulations, the availability of proposed method is investigated, where RMSE decreases from 0.218rad to 0.016rad. In experiments, the turbine blade is tested and compared with calibrated values where RMS of measured deviations after correction has decreased from 0.269mm to 0.095mm. All the investigations have proved the high reliability of proposed method.
A Vision-Based Segmentation Technique Using HSV and YCbCr Color Model

Human interaction with computing devices have evolved rapidly since its inception. The current trend of accepting input has shifted to gestures. These gestures are recorded either by sensor devices or by vision-based devices. The main challenge for vision-based systems is the segmentation of the limb performing the action. This chapter presents a method for achieving the accurate segmentation of the limb which can aid in improving accurate gesture recognition. The proposed method uses features such as an HSV and a YCbCr color space model for limb segmentation. An extensive review of the different methodologies for the same is also included.
LGC-ShQ

We present LGC-ShQ, a new ECN-based congestion control mechanism for datacenters. LGC-ShQ relies on ECN feedback from a Shadow Queue, and it uses ECN not only to decrease the rate, but it also increases the rate in relation to this signal. Real-life tests in a Linux testbed show that LGC-ShQ keeps the real queue at low levels while achieving good link utilization and fairness.
The October 2022 Issue

Before we present the content of this issue, we want to make an announcement. We are delighted to introduce a new journal titled "Proceedings of the ACM on Networking" (PACMNET). PACMNET is among the last journals joining the recently launched Proceedings of the ACM (PACM) series. The goal of the PACM series is to showcase the highest quality research conducted in diverse areas of computer science as represented by the ACM Special Interest Groups (SIGs), SIGCOMM in our case.
Proposing My Reading Experience Questionnaire (MREQ) as An Instrument to Measure Reading Motivation and Reading Comprehension

The reading experience in the Malaysian context is often perceived and understood as being about reading in specific languages. Particularly, research in the field of English as a Second Language is replete with work on reading motivation. However, in this paper, it is argued that reading needs to be seen as going beyond language, setting reading as an experience rather than an ability. This paper therefore sets out to validate the My Reading Experience Questionnaire (MREQ) through a discussion of the questionnaire design as well as the statistical data validation based on the questionnaire items. From there, reading motivation levels of primary school children in Malaysia is analysed. 544 primary school children aged 8 and 11-yearsold completed the MREQ . Findings from the questionnaire were matched with the Malay language reading comprehension levels of these children. Both the validation and comparisons showed that the MREQ not only gauges reading motivation levels but also reveals the complexities that come with Malaysia’s multilingual literacy context in schools. This paper concludes by proposing for the MREQ to continue to be used especially with how its design is underpinned by sociocultural theories of language and literacy. Keywords: reading experience; survey; motivation; multimodality; primary school
Transfer learning data adaptation using conflation of low‐level textural features

Abstract Adapting the target dataset for a pre‐trained model is still challenging. These adaptation problems result from a lack of adequate transfer of traits from the source dataset; this often leads to poor model performance resulting in trial and error in selecting the best‐performing pre‐trained model. This paper introduces the conflation of source domain low‐level textural features extracted using the first layer of the pre‐trained model. The extracted features are compared to the conflated low‐level features of the target dataset to select a higher‐quality target dataset for improved pre‐trained model performance and adaptation. From comparing the various probability distance metrics, Kullback‐Leibler is adopted to compare the samples from both domains. We experiment on three publicly available datasets and two ImageNet pre‐trained models used in past studies for results comparisons. This proposed approach method yields two categories of the target samples with those with lower Kullback‐Leibler values giving better accuracy, precision and recall. The samples with the lower Kullback‐Leibler values give a higher margin accuracy rate of 0.22%–9.15%, thereby leading to better model adaptation and easier model selection process for the target transfer learning datasets and tasks.
Non-Face-to-Face Learner Mutual Learning Study: Focusing on the University Liberal Arts Writing Teaching Method

The purpose of this study is to propose the use of open bulletin boards to compensate for the lack of communication due to changes in the classroom environment as a necessary teaching method in the untact period. A special communication space is required because a non face-to-face learning environment negatively affects learners' achievement of learning goals compared to face-to-face classes in university writing classes. The open bulletin board is a space where teachers and learners can all share creative ideas. The entire writing process for a semester can be recorded, and peer-to-peer learning is possible by sharing data. In addition, it is effective for learner-centered teaching methods for learners to reflect on their entire writing process, and for learners to read and evaluate the writings of fellow learners as examples of various pieces of writing. The teaching method, which consists of an open bulletin board as a point of contact, is meaningful as an alternative to the lack of communication.