Reviewerly/exHarmony huggingface dataset:
Physical Layer-Aware Digital-Analog Co-Design for Photonic Convolution Neural Network

Photonic convolution neural network (CNN) has shown tremendous capability to provide Tera-level operations/sec (OPS) for streaming object detection with ultra-low energy consumption. However, due to the nature of analog photonic computing which is constrained by accumulated noise from various sources, its physical operating precision is significantly lower than that of electronic artificial intelligence (AI) accelerators powered by tensor processing unit (TPU) and graphic processing unit (GPU). In this article, we propose a robust digital-analog co-designed photonic CNN using error learning and Kirsch edge enhancement. Experimental results show that, nearly 96.1% recognition accuracy, compared with 34.2% in baseline photonic CNN, can be achieved at global 2-bit precision through physical error learning in dense layer and edge enhancement by photonic Kirsch operator. The proposed photonic CNN architecture is promising to provide 10 Tera-OPS scale computing speed deployed by the state of art PAM-4 optical modules with high energy efficiency.
Multi-Target Extractor and Detector for Unknown-Number Speaker Diarization

Strong representations of target speakers can help extract important information about speakers and detect corresponding temporal regions in multi-speaker conversations. In this study, we propose a neural architecture that simultaneously extracts speaker representations consistent with the speaker diarization objective and detects the presence of each speaker on a frame-by-frame basis regardless of the number of speakers in a conversation. A speaker representation (called z-vector) extractor and a time-speaker contextualizer, implemented by a residual network and processing data in both temporal and speaker dimensions, are integrated into a unified framework. Tests on the CALLHOME corpus show that our model outperforms most of the methods proposed so far. Evaluations in a more challenging case with simultaneous speakers ranging from 2 to 7 show that our model achieves 6.4% to 30.9% relative diarization error rate reductions over several typical baselines.
Motorized Measurement of Deformation on Surface of Revolution With 2-D Laser Profiler

The 2D laser profiler potentially provides an efficient way to measure the profile of a deformed surface of revolution. However, the application is constrained by its finite measurement range, as well as the accumulated errors during device installation, workpiece placement and 3D reconstruction. In this work, an integrated measurement system is designed with high-precision rotary table (HRT) and a motorized vertical stage (MVS), so that in-situ scanning measurement is achieved by coordinated motion of the workpiece and the 2D laser profiler. An extrinsic calibration method is proposed between the 2D laser profiler and the HRT with fittings of a plane and a circle curve by the Leica absolute tracker (LAT), so that large tolerance of installation error is allowed for both MVS and HRT. Eventually, the point cloud of the workpiece with respect to the HRT is virtually relocated on the CAD model by Iterative Closest Point (ICP) algorithm with kd-tree construction, so that the amount of deformation is correctly assessed even when the deformed workpiece is arbitrarily clamped by the 3-jaw chuck. The experiment is conducted on the testbed for measuring the deformation of a wheel rim, which verifies the effectiveness of the developed system and proposed method.
A Group Decision-Making Method Based on the Experts’ Behavior During the Debate

Debate is a process consisting in arriving at a reasoned opinion on a proportion in which individuals must be truly capable of defending their own judgments. It has been used within group decision-making (GDM) problems to help experts make better decisions. However, whether experts engage in a vigorous debate, it can result in the use of aggressive language that may diminish consensus, which is the major objective of GDM. To avoid it, we present a novel method for GDM problems that can identify aggressive comments during the debate by incorporating a classifier based on sentiment analysis techniques. According to the information extracted during the debate, two procedures are developed to assign weights to the experts, which are used to introduce two new consensus measures and to make the final decision. Unlike the existing GDM methods, this new one can take advantage of the information extracted during the debate (i.e., experts’ behavior) throughout the decision process, making it in rapport with real-world GDM processes.
P3SNet: Parallel Pyramid Pooling Stereo Network

In autonomous driving and advanced driver assistance systems (ADAS), stereo matching is a challenging research topic. Recent work has shown that high-accuracy disparity maps can be obtained with end-to-end training with the help of deep convolutional neural networks from stereo images. However, many of these methods suffer from long run-time for real-time studies. Therefore, in this paper, we introduce P3SNet, which can generate both real-time results and competitive disparity maps to the state-of-the-art. P3SNet architecture consists of two main modules: parallel pyramid pooling and hierarchical disparity aggregation. The parallel pyramid pooling structure makes it possible to obtain local and global information intensively from its multi-scale features. The hierarchical disparity aggregation provides multi-scale disparity maps by using a coarse-to-fine training strategy with the help of the costs obtained from multi-scale features. The proposed approach was evaluated on several benchmark datasets. The results on all datasets showed that the proposed P3SNet achieved better or competitive results while having lower runtime. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/aemlek/P3SNet</uri> .
A PID Based Meta-Learning Method About Space Non-Cooperative Active Object Tracking

In recent years, more and more scholars pay attention to the problem of active object tracking, which has a wide range of application scenarios. Active tracking of non-cooperative objects in space was a good application example, which has important implications for the exploration of the space environment. Datasets of the space objects are difficult to collect, thus traditional machine learning is difficult to complete space tasks, which needs train from scratch, sufficient datasets and lots of training time. Thus, we propose a PID based for Meta-Learning method for space non-cooperative active object tracking, which overcome the shortcoming of few space datasets, and save a lot of training time. We incorporate PID into the meta-learning algorithm, past and change information of gradients to update the meta-learning parameters, and reducing greatly the overshoot problem of meta-learning and accelerating the optimization process of meta-learning. Our algorithm can help train a better initialized active object tracking model due to the differentiation and integration of gradients, which can retain more information and improve stability. Our PID based meta-learning method was based on reinforcement learning, which can help achieve automated and optimal object tracking. The initial model of our method can track a wide variety of objects without train from scratch. Through a large number of comparative experiments, our method has good results in tracking speed and tracking accuracy with longer episodes and higher rewards.
Multicomponent Adversarial Domain Adaptation: A General Framework

Domain adaptation (DA) aims to transfer knowledge from one source domain to another different but related target domain. The mainstream approach embeds adversarial learning into deep neural networks (DNNs) to either learn domain-invariant features to reduce the domain discrepancy or generate data to fill in the domain gap. However, these adversarial DA (ADA) approaches mainly consider the domain-level data distributions, while ignoring the differences among components contained in different domains. Therefore, components that are not related to the target domain are not filtered out. This can cause a negative transfer. In addition, it is difficult to make full use of the relevant components between the source and target domains to enhance DA. To address these limitations, we propose a general two-stage framework, named multicomponent ADA (MCADA). This framework trains the target model by first learning a domain-level model and then fine-tuning that model at the component-level. In particular, MCADA constructs a bipartite graph to find the most relevant component in the source domain for each component in the target domain. Since the nonrelevant components are filtered out for each target component, fine-tuning the domain-level model can enhance positive transfer. Extensive experiments on several real-world datasets demonstrate that MCADA has significant advantages over state-of-the-art methods.
Function Call Graph Context Encoding for Neural Source Code Summarization

Source code summarization is the task of writing natural language descriptions of source code. The primary use of these descriptions is in documentation for programmers. Automatic generation of these descriptions is a high value research target due to the time cost to programmers of writing these descriptions themselves. In recent years, a confluence of software engineering and artificial intelligence research has made inroads into automatic source code summarization through applications of neural models of that source code. However, an Achilles’ heel to a vast majority of approaches is that they tend to rely solely on the context provided by the source code being summarized. But empirical studies in program comprehension are quite clear that the information needed to describe code much more often resides in the context in the form of Function Call Graph surrounding that code. In this paper, we present a technique for encoding this call graph context for neural models of code summarization. We implement our approach as a supplement to existing approaches, and show statistically significant improvement over existing approaches. In a human study with 20 programmers, we show that programmers perceive generated summaries to generally be as accurate, readable, and concise as human-written summaries.
A General Reconstruction Framework for Deflectometric Measurement Based on Nonuniform B-Splines

Phase measuring deflectometry (PMD) is a powerful tool well suited for precision measurement of optical surfaces. However, accurate and efficient surface reconstruction from gradients is challenging for the PMD. Because most reconstruction algorithms are not well adapted to complex surface forms with local undulations, and a large number of tedious operations are demanded for calculating the ray intersections. Besides, the format of the reconstructed data is usually not compatible to the machine tools and additional format conversion is required, which increases the computational effort and lengthens the error propagation chain. Therefore, a general reconstruction framework is proposed for the deflectometric measurement based on the non-uniform B-spline (NUBS), which embeds the NUBS into the PMD framework to enhance the local representativity. The correspondences between the points on the screen, camera and workpiece can be established by the parameter mapping instead of by the ray intersections. Simulations and experiments prove the effectiveness of the proposed method, and the measurement accuracy can achieve a level of one hundred nanometers in practice.
Real-Time Image and Video Processing Applications Using Raspberry Pi

Image and video processing applications have significant importance in many areas which are the industrial and medical applications, especially the vehicular technology. To provide safe driving, Driving Assistance Systems (DAS) including detection and tracking of a road situation, objects and behavior of the driver should be considered by using the image processing algorithms. Since these applications need the processors having high speed and power, a Raspberry Pi platform meeting these specifications is used in this study. A Canny edge detector which is one of the important edge detection methods, face and eye detection, the road line tracking, motion detection, and object detection applications are handled. Conversely the studies in the literature which are separately examined these techniques, all of them are realized as a real-time based on the Rapberry Pi according to a video image taken by a camera. For this reason, OpenCv and TensorFlow libraries having widely computer vision algorithms and Python software language are used. It is observed that the results of all image and video processing applications are successful and satisfying. As a result, a safe and comfortable driving can be provided thanks to the proposed methods.
A unified construction of weightwise perfectly balanced Boolean functions

At Eurocrypt 2016, Méaux et al. presented FLIP, a new family of stream ciphers that aimed to enhance the efficiency of homomorphic encryption frameworks. Motivated by FLIP, recent research has focused on the study of Boolean functions with good cryptographic properties when restricted to subsets of the space F2n. If an n-variable Boolean function has the property of balancedness when restricted to each set of vectors with fixed Hamming weight between 1 and n−1, it is a weightwise perfectly balanced (WPB) Boolean function. In the literature, a few algebraic constructions of WPB functions are known, in which there are some constructions that use iterative method based on functions with low degrees of 1, 2, or 4. In this paper, we generalize the iterative method and contribute a unified construction of WPB functions based on functions with algebraic degrees that can be any power of 2. For any given positive integer d not larger than m, we first provide a class of 2m-variable Boolean functions with a degree of 2d−1. Utilizing these functions, we then present a construction of 2m-variable WPB functions gm;d. In particular, gm;d includes four former classes of WPB functions as special cases when d=1,2,3,m. When d takes other integer values, gm;d has never appeared before. In addition, we prove the algebraic degree of the constructed WPB functions and compare the weightwise nonlinearity of WPB functions known so far in 8 and 16 variables.
iStream Player: A Versatile Video Player Framework

The increasing demand for video streaming in all forms draws significant research and development attention, especially on the client-side for adaptive streaming services like DASH and HLS. However, the implementation challenges in developing and validating new client-side solutions within a full-stack video player pose a major obstacle. State-of-the-art open-source video players, such as DASH.js, VLC, and GPAC, were designed for specific purposes and are difficult to extend and modify for video streaming research. To address this issue, we propose iStream Player, a versatile video player framework featuring fully extendable and independent micro-modules similar to Lego blocks. Constructing a video player in iStream Player is as simple as assembling Lego pieces. Our case studies demonstrate that it is effortless to create a diverse range of players by making only minor changes, such as extending or replacing only one or two micro-modules. As a result, iStream Player significantly reduces the time and effort required to develop and validate new solutions, providing researchers and developers in the video streaming field with a shared platform to explore and to share their innovative ideas.
Improving ABR Performance for Short Video Streaming Using Multi-Agent Reinforcement Learning with Expert Guidance

In the realm of short video streaming, popular adaptive bitrate (ABR) algorithms developed for classical long video applications suffer from catastrophic failures because they are tuned to solely adapt bitrates. Instead, short video adaptive bitrate (SABR) algorithms have to properly determine which video at which bitrate level together for content prefetching, without sacrificing the users' quality of experience (QoE) and yielding noticeable bandwidth wastage jointly. Unfortunately, existing SABR methods are inevitably entangled with slow convergence and poor generalization. Thus, in this paper, we propose Incendio, a novel SABR framework that applies Multi-Agent Reinforcement Learning (MARL) with Expert Guidance to separate the decision of video ID and video bitrate in respective buffer management and bitrate adaptation agents to maximize the system-level utilized score modeled as a compound function of QoE and bandwidth wastage metrics. To train Incendio, it is first initialized by imitating the hand-crafted expert rules and then fine-tuned through the use of MARL. Results from extensive experiments indicate that Incendio outperforms the current state-of-the-art SABR algorithm with a 53.2% improvement measured by the utility score while maintaining low training complexity and inference time.
Discrete limited attentional collaborative filtering for fast social recommendation

Over the last few years, social recommendation has attracted tremendous attention due to the ever-growing online social platform such as Twitter and Facebook. However, as the number of users increases rapidly, recommendation efficiency has become the bottleneck of many existing social recommender systems due to the computation and storage of real-valued models. For addressing the efficiency problem, recent researches resolve it by introducing hashing technique into social recommender systems. By mapping real values to discrete values, the computational speed is guaranteed as well as the storage cost is reduced. Nevertheless, these methods suffer from two critical limitations: (1) The inevitable quantization loss brought by hash function decreases recommendation accuracy to a certain extent. (2) The original social relations contain massive noise that may result in sub-optimal accuracy of recommendation without considering the fact that people can only pay attention to a small number of their friends. Therefore, to tackle the above limitations and have a better tradeoff between accuracy and efficiency, in this paper, we propose a novel social recommendation method called Discrete Limited Attentional Collaborative Filtering (DLACF), which models recommendation objective with limited attention as a constrained mix-integer optimization problem. Since the original problem is NP-hard, we further devise a computationally efficient optimization algorithm to learn the binary codes as well as to estimate the best influential friends. Experimental results conducted on two real-world datasets demonstrate the effectiveness of our proposed model, achieving the averaged improvement of 118.7% and 54.7% compared to state-of-the-art discrete methods.
Green Digitalization? Agriculture 4.0 and the Challenges of Environmental Governance in Brazil

Brazil is an agricultural powerhouse and a global frontrunner in several crops, including soybeans, coffee, maize, and sugarcane. However, this position has taken its toll on the country’s environmental sustainability over the years, resulting in deforestation, soil acidification, loss of biodiversity, and other environmental hazards. Recently, a new technological paradigm has emerged in Brazil’s agriculture, consisting of the intensified use of digital technologies in various production stages. Some of these technologies include artificial intelligence, the Internet of Things, big data, and blockchain, resulting in the so-called Agriculture 4.0. This chapter provides an institutionalist account of Agriculture 4.0 in Brazil, focusing on the challenges and opportunities borne by this new paradigm to the country’s environmental governance architecture. Our analysis suggests that Agriculture 4.0 has been adopted slowly but progressively in the country. While some hi-tech portions of the agro-industry have adopted digitalization to improve productivity, they have mostly neglected the possibilities of Agriculture 4.0 to improve sustainability governance. Moreover, except for a few high-capitalized small farmers, digital farming is far from being a reality for the majority of Brazil’s family farmers, particularly those located in the North and Northeast regions.
Highlighting the Challenges of Blinks in Eye Tracking for Interactive Systems

Eye tracking is the basis for many intelligent systems to predict user actions. A core challenge with eye-tracking data is that it inherently suffers from missing data due to blinks. Approaches such as intent prediction and user state recognition process gaze data using neural networks; however, they often have difficulty handling missing information. In an effort to understand how prior work dealt with missing data, we found that researchers often simply ignore missing data or adopt use-case-specific approaches, such as artificially filling in missing data. This inconsistency in handling missing data in eye tracking hinders the development of effective intelligent systems for predicting user actions and limits reproducibility. Furthermore, this can even lead to incorrect results. Thus, this lack of standardization calls for investigating possible solutions to improve the consistency and effectiveness of processing eye-tracking data for user action prediction.
Demonstrating Eye Movement Biometrics in Virtual Reality

Thanks to the eye-tracking sensors that are embedded in emerging consumer devices like the Vive Pro Eye, we demonstrate that it is feasible to deliver user authentication via eye movement biometrics.
Privacy in Eye Tracking Research with Stable Diffusion

Image-generative models take textual prompts as input and generate almost arbitrary image content based on the underlying training data. This technology is rapidly developing and produces better results with each new generation of trained models. Apart from the application to create artwork, we see potential in deploying such models for eye-tracking research with respect to anonymizing content in visual stimuli. One feature of such models is the ability to take an image as input and adjust content according to a prompt. Hence, privacy-preserving visualization of stimuli can be achieved for static images and videos by slightly adjusting content to anonymize persons, text, and other sensible sources. In this work, we will discuss how this process can be applied to the presentation and dissemination of results with respect to privacy issues resulting from eye-tracking experiments.
Analysing the API learning process through the use of eye tracking

We conducted an exploratory study in which participants had to acquire knowledge of unfamiliar application programming interfaces (APIs) in order to complete two programming tasks. Eye tracking was used to monitor participants’ attention throughout the study. We analysed the learning process using the COIL model, which includes three stages: Information Collection, Information Organisation and Solution Testing. Using this model, we describe patterns in the sequences of actions derived from the data. We discuss whether the Solution Testing stage has two distinct functions: constructing the solution and gathering information simultaneously. This would be an exploratory Solution Testing Stage. In addition, the use of eye tracking provided further insight into the API learning process.