Reviewerly/exHarmony huggingface dataset:
Photonic neuromorphic technologies in optical communications

Abstract Machine learning (ML) and neuromorphic computing have been enforcing problem-solving in many applications. Such approaches found fertile ground in optical communications, a technological field that is very demanding in terms of computational speed and complexity. The latest breakthroughs are strongly supported by advanced signal processing, implemented in the digital domain. Algorithms of different levels of complexity aim at improving data recovery, expanding the reach of transmission, validating the integrity of the optical network operation, and monitoring data transfer faults. Lately, the concept of reservoir computing (RC) inspired hardware implementations in photonics that may offer revolutionary solutions in this field. In a brief introduction, I discuss some of the established digital signal processing (DSP) techniques and some new approaches based on ML and neural network (NN) architectures. In the main part, I review the latest neuromorphic computing proposals that specifically apply to photonic hardware and give new perspectives on addressing signal processing in optical communications. I discuss the fundamental topologies in photonic feed-forward and recurrent network implementations. Finally, I review the photonic topologies that were initially tested for channel equalization benchmark tasks, and then in fiber transmission systems, for optical header recognition, data recovery, and modulation format identification.
Graph Complemented Latent Representation for Few-Shot Image Classification

Few-shot learning is a tough topic to solve since obtaining a large number of training samples in real applications is challenging. It has attracted increasing attention recently. Meta-learning is a prominent way to address this issue, intending to adapt predictors as base-learners to new tasks swiftly. However, a key challenge of meta-learning is its lack of expressive capacity, which stems from the difficulty of extracting general information from a small number of training samples. As a result, the generalizability of meta-learners trained from high-dimensional parameter spaces is frequently limited. To learn a better representation, we propose a graph complemented latent representation (GCLR) network for few-shot image classification. In particular, we embed the representation into a latent space, in which the latent codes are reconstructed using variational information to enrich the representation. In this way, the latent representation can achieve better generalizability. Another benefit is that, because the latent space is formed using variational inference, it cooperates well with various base-learners, boosting robustness. To make full use of the relation between samples in each category, a graph neural network (GNN) is also incorporated to improve relation mining. Consequently, our end-to-end framework delivers competitive performance on three few-shot learning benchmarks for image classification.
Decompose to Adapt: Cross-Domain Object Detection Via Feature Disentanglement

Recent advances in unsupervised domain adaptation (UDA) techniques have witnessed great success in cross-domain computer vision tasks, enhancing the generalization ability of data-driven deep learning architectures by bridging the domain distribution gaps. For the UDA-based cross-domain object detection methods, the majority of them alleviate the domain bias by inducing the domain-invariant feature generation via adversarial learning strategy. However, their domain discriminators have limited classification ability due to the unstable adversarial training process. Therefore, the extracted features induced by them cannot be perfectly domain-invariant and still contain domain-private factors, bringing obstacles to further alleviate the cross-domain discrepancy. To tackle this issue, we design a Domain Disentanglement Faster-RCNN (DDF) to eliminate the source-specific information in the features for detection task learning. Our DDF method facilitates the feature disentanglement at the global and local stages, with a Global Triplet Disentanglement (GTD) module and an Instance Similarity Disentanglement (ISD) module, respectively. By outperforming state-of-the-art methods on four benchmark UDA object detection tasks, our DDF method is demonstrated to be effective with wide applicability.
UULPN: An ultra-lightweight network for human pose estimation based on unbiased data processing

Most top-performed human pose estimation methods tend to have a high computational load, which is difficult to transform to resource-limited platforms. To conquer this issue, we propose an ultra-lightweight human pose estimation method based on unbiased data processing called UULPN. Firstly, we design a lightweight bottleneck block with a re-parameterized structure. Through simple linear operations, it generates a large number of feature maps and increases the diversity of feature maps. Secondly, we introduce a multi-branch structure and a single-branch structure in the bottleneck block. In the training phase, a multi-branch structure is adopted to increase the prediction accuracy. In the deploying phase, a single-branch structure is used to improve the model inference speed. These two structures realize the decoupling of the training phase and the deployment phase through the reparameterization technology. In the case of decreased computational cost, they have increased the predicted accuracy. Finally, we present a novel unbiased data processing method to solve quantization errors, which are introduced in the process of image encoding and decoding. Extensive experiment results on the MPII and COCO pose estimation benchmarks indicate that UULPN achieves almost equivalent results with the state-of-the-art methods with less computational cost. In particular, the computational cost of UULPN is almost 31% of HRNet, and the estimated accuracy on the COCO val2017 dataset is up to 74.1%, which is almost the same as HRNet-W32 at the resolution of 256 × 192. It shows that the research further develops in depth, which is of great significance. The code and the proposed method are available on https://github.com/Johnren1111/UULPN.
3D multi-scale residual fully convolutional neural network for segmentation of extremely large-sized kidney tumor

We propose a novel deep neural network, the 3D Multi-Scale Residual Fully Convolutional Neural Network (3D-MS-RFCNN) to improve segmentation in extremely large-sized kidney tumors.The multi-scale approach with a deep neural network is applied to capture global contextual features. Our method, 3D-MS-RFCNN, consists of two encoders and one decoder as a single complete network. One of the encoders is designed for capturing global contextual information by using the low-resolution, down-sampled data from input images. In the decoder, features from the encoder for global contextual features are concatenated with up-sampled features from the previous layer and features from the other encoder. Ensemble learning strategy is also applied.We evaluated the performance of our proposed method using the KiTS public dataset and the in-house hospital dataset. When compared with the state-of-the-art method, Res3D U-Net, our model, 3D-MS-RFCNN, demonstrated greater accuracy (0.9390 dice score for KiTS dataset and 0.8575 dice score for external dataset) for segmenting extremely large-sized kidney tumors.Our proposed network shows significantly improved segmentation performance of extremely large-sized targets. This study can be usefully employed in the field of medical image analysis.
A Comparison of TCN and LSTM Models in Detecting Anomalies in Time Series Data

There exist several data-driven approaches that enable us model time series data including traditional regression-based modeling approaches (i.e., ARIMA). Recently, deep learning techniques have been introduced and explored in the context of time series analysis and prediction. A major research question to ask is the performance of these many variations of deep learning techniques in predicting time series data. This paper compares two prominent deep learning modeling techniques. The Recurrent Neural Network (RNN)-based Long Short-Term Memory (LSTM) and the convolutional Neural Network (CNN)-based Temporal Convolutional Networks (TCN) are compared and their performance and training time are reported. According to our experimental results, both modeling techniques per-form comparably having TCN-based models outperform LSTM slightly. Moreover, the CNN-based TCN model builds a stable model faster than the RNN-based LSTM models.
A central bank digital currency in a heterogeneous monetary union: Managing the effects on the bank lending channel

A major obstacle for the implementation of a general-purpose central bank digital currency (CBDC) is the risk of bank disintermediation, potentially jeopardizing financial stability and the bank lending channel of monetary transmission. By adapting the theoretical framework of Dutkowsky and VanHoose (2018b, 2020) to the euro area, this study investigates and clarifies the conditions under which a digital euro could be introduced on a large scale without leading to bank disintermediation or a credit crunch. First, the central bank would require proper mechanisms to manage the volume and the user cost of CBDC in circulation. Second, the central bank should continue to facilitate access to its long-term lending facilities, to provide banks with a funding source alternative to client deposits at an equivalent cost. Depending on its design, a digital euro could improve bank profitability and competitiveness by absorbing large amounts of idle (and expensive) excess reserves without penalizing lending, while incentivizing bank digitalization.
Protecting Against Threats to Information Security: An Attitudinal Ambivalence Perspective

A popular information security-related motivation theory is the Protection Motivation Theory (PMT) that has been studied extensively in many information security contexts with promising results. However, prior studies have found inconsistent findings regarding the relationships within PMT. To shed light on these inconsistent findings, we introduce the attitudinal ambivalence theory to open the black box within PMT. We tested our model on data collect ed from 1,383 individuals facing potential cyberattacks of their emails in a field experiment. The results of polynomial regression with response surface analysis showed that attitudinal ambivalence is generated from the opposition between an individual’s evaluations of maladaptive rewards and social norms (i.e., descriptive norm and subjective norm). This attitudinal ambivalence, in turn, affects individuals’ evaluations of their coping appraisal process and protection motivation, and ultimately protection behavior. We discuss the theoretical and managerial implications of identifying the determinants and outcomes of attitudinal ambivalence in the information security context. From a theoretical standpoint, our work contributes to the information security literature by incorporating attitudinal ambivalence, which arises from the intrapersonal and interpersonal appraisal processes, into PMT. From a practical standpoint, our work provides insights into designing effective fear appeals to avoid triggering attitudinal ambivalence and thus encouraging adoption of security protection behavior.
Powerline Tracking with Event Cameras

Autonomous inspection of powerlines with quadrotors is challenging. Flights require persistent perception to keep a close look at the lines. We propose a method that uses event cameras to robustly track powerlines. Event cameras are inherently robust to motion blur, have low latency, and high dynamic range. Such properties are advantageous for autonomous inspection of powerlines with drones, where fast motions and challenging illumination conditions are ordinary. Our method identifies lines in the stream of events by detecting planes in the spatio-temporal signal, and tracks them through time. The implementation runs onboard and is capable of detecting multiple distinct lines in real time with rates of up to 320 thousand events per second. The performance is evaluated in real-world flights along a powerline. The tracker is able to persistently track the powerlines, with a mean lifetime of the line 10× longer than existing approaches.
Mobile-assisted or paper-based? The influence of the reading medium on the reading comprehension of English as a foreign language

The issues of whether mobile-assisted English as a foreign language (EFL) reading is effective have become increasingly important for EFL teachers and policy-makers. However, empirical studies investigating EFL students’ reading comprehension performance on mobile phones and students’ perceptions of these reading formats are lacking. The present research employed questionnaire surveys and semistructured interviews to investigate whether differences exist between mobile-assisted and paper-based EFL reading in terms of participants’ reading comprehension accuracy, reading speed, and reading strategy used (i.e. general, problem-solving or support strategies) and explore EFL learners’ perceptions of mobile-assisted EFL reading. A sample of 81 first-year college students participated in a quasiexperiment, 6 participants participated in a semistructured interview, and a sample of an additional 10 students participated in a pilot study. The analyses, including qualitative descriptions, mixed-design analysis of variance (ANOVA), paired-sample t-tests, and independent-sample t-tests, indicated that participants achieved superior reading comprehension accuracy when reading from printed paper compared to reading from mobile phones. Superior reading speed from paper was observed only when participants were instructed to read and complete the comprehension test the first time, while no difference in speed between the two reading media was observed the second time. Additionally, participants’ use of reading strategies during mobile-assisted EFL reading was not as effective as that during paper reading. Regarding participants’ preferences, most participants (79%) preferred reading from paper over mobile phones because hard copies provided a better reading experience and promoted active reading engagement. However, participants held positive attitudes toward mobile-assisted EFL learning and wanted to continue to read from mobile phones to assist their foreign language learning due to their convenient built-in functions and portability. The implications for the use of mobile devices to assist language pedagogical practice are discussed.
Efficient Network Monitoring Applications in the Kernel with eBPF and XDP

Continuous traffic monitoring and analytics are fundamental to the operation of today’s networks. Network telemetry allows for performing fine-grained analytics on network flow or packet records for various use cases including intrusion detection and traffic engineering. While some analytics tasks can be offloaded to programmable switches, ultimately, telemetry data needs to be processed by analytics applications in software. These applications are highly specialized, and running many such applications concurrently to achieve high coverage is expensive. To reduce the resource footprint of software network analytics, we present a novel network monitoring primitive that consolidates logic which all monitoring applications require. The primitive can (partially) be offloaded to a SmartNIC and triggers applications only when required based on high-level traffic metrics, avoiding unnecessary and redundant computations. We identify eBPF and XDP as a natural fit for this task, and implement a prototype of our system on top of this novel technology. Our evaluation shows that the combination of conditional execution of analytics tasks and the use of modern packet I/O technologies not relying on expensive busy polling (e.g., as in DPDK) significantly reduces the resource footprint of performing continuous network analytics.
Few-Shot Learning for Fine-Grained Signal Modulation Recognition Based on Foreground Segmentation

Automotive radars have faced to various jamming signals with different types and parameters. One counter measure is to identify the fine-grained modulation types of these jamming signals, which can provide supplementary information for intercepting. Most existing signal modulation recognition methods attempt to establish a machine-learning mechanism by training with a large number of annotated samples, which is hardly applicable in real-world electronic reconnaissance scenarios where only a few samples can be intercepted in advance. Few-Shot Learning aims to learn from base classes with many samples and infuse this knowledge to support classes with only a few samples, thus realizing model generalization. In this paper, inspired by the fact that the energy distribution of the signal of interest is always concentrated in time-frequency images, a novel few-shot learning framework based on foreground segmentation is proposed, which benefits from its powerful filtering to remove the noise and clutter from background. The experimental results show that the proposed method can achieve excellent performance for fine-grained signal modulation recognition even with only one support sample and is robust under different signal-to-noise-ratio conditions.
Learning Controllable Content Generators

It has recently been shown that reinforcement learning can be used to train generators capable of producing high-quality game levels, with quality defined in terms of some user-specified heuristic. To ensure that these generators' output is sufficiently diverse (that is, not amounting to the reproduction of a single optimal level configuration), the generation process is constrained such that the initial seed results in some variance in the generator's output. However, this results in a loss of control over the generated content for the human user. We propose to train generators capable of producing controllably diverse output, by making them “goal-aware.” To this end, we add conditional inputs representing how close a generator is to some heuristic, and also modify the reward mechanism to incorporate that value. Testing on multiple domains, we show that the resulting level generators are capable of exploring the space of possible levels in a targeted, controllable manner, producing levels of comparable quality as their goal-unaware counterparts, that are diverse along designer-specified dimensions.
An automated extract method refactoring approach to correct the long method code smell

Long Method is amongst the most common code smells in software systems. Despite various attempts to detect the long method code smell, few automated approaches are presented to refactor this smell. Extract Method refactoring is mainly applied to eliminate the Long Method smell. However, current approaches still face serious problems such as insufficient accuracy in detecting refactoring opportunities, limitations on correction types, the need for human intervention in the refactoring process, and lack of attention to object-oriented principles, mainly single responsibility and cohesion–coupling principles. This paper aims to automatically identify and refactor the long method smells in Java codes using advanced graph analysis techniques, addressing the aforementioned difficulties. First, a graph representing project entities is created. Then, long method smells are detected, considering the methods’ dependencies and sizes. All possible refactorings are then extracted and ranked by a modularity metric, emphasizing high cohesion and low coupling classes for the detected methods. Finally, a proper name is assigned to the extracted method based on its responsibility. Subsequently, the best destination class is determined such that design modularity is maximized. Experts’ opinion is used to evaluate the proposed approach on five different Java projects. The results show the applicability of the proposed method in establishing the single responsibility principle with a 21% improvement compared to the state-of-the-art extract method refactoring approaches.
Blockchain Adoption for Provenance and Traceability in the Retail Food Supply Chain

Blockchain has evolved as one of the disruptive technologies in the landscape of business. The study aims to investigate drivers of consumer adoption of blockchain for product origin and track to trace history before making a purchase. An extended technology adoption model (TAM) has been proposed to examine the consumer perspective for blockchain adoption in the food supply chain. Based on the survey of 208 retail consumers the proposed model was validated using variance-based structure equation modeling. Findings of the study emphasize the significant role of perceived security and privacy in developing trust, ease of use, and usefulness of blockchain-enabled systems. The relationship between perceived ease of use and attitude is mediated through perceived usefulness. The strong influence of attitude on adoption intention represents the consumer interest for blockchain to understand the product provenance. Study provides vital insights for successful blockchain implementation to enhance supply chain effectiveness.
A Minority Class Boosted Framework for Adaptive Access Control Decision-Making

Access control is an effective way to prevent data exfiltration from insiders. Recently, machine learning algorithms have been widely used in access control decision-making. However, these algorithms usually fail to consider the dynamic class imbalance in access control problems and thus achieve poor performance on minority classes. In addition, concept drift problems caused by evolving user and resource attributes, user behaviours and access environments are also challenges to tackle. This paper proposes a minority class boosted framework for adaptive access control methods. Specifically, this framework uses a continuous incremental batch learning strategy instead of a batch learning approach to handle the concept drift problem adaptively. Furthermore, a boosting window (BW) algorithm within the framework is specially designed to boost the performance of the minority class, thus, to decrease false positive decisions. The proposed framework is evaluated on a well-known Amazon employee access dataset and results demonstrate the effectiveness and flexibility of the proposed framework and BW algorithm.
Social Networks Analysis

Supported by advances in technology, simulation-, scenario- and game-based assessments (DiCerbo KE, Behrens JT, Implications of the digital ocean on current and future assessment. In: Lissitz R (eds) Computers and their impact on state assessment: recent history and predictions for the future. Information Age Publishing, Charlotte, pp 273–306, 2012; Mislevy RJ, Oranje A, Bauer MI, Von Davier A, Hao J, Corrigan S, . . . John M, Psychometric considerations in game-based assessment. GlassLab Report, 2014) provide opportunities for the students to interact with complex tasks. Rich process data can be collected during the assessment, such as log data of student response actions (e.g., Zhu M, Shu Z, von Davier AA, J Educ Measur 53(2):190–211, 2016b), keystroke data (e.g., Almond R, Deane P, Quinlan T, Wagner M, Sydorenko T, A preliminary analysis of keystroke log data from a timed writing task. In Educational testing service research report series (No. RR-12-23), pp. 1–61, 2012. https://doi.org/10.1002/j.2333-8504.2012.tb02305.x), and eye-tracking data (e.g., Tai RH, Loehr JF, Brigham FJ, Int J Res Method Educ 29(2):185–208, 2006. https://doi.org/10.1080/17437270600891614). Process data record the series of activities conducted by students during problem-solving processes and contain information not represented in the final answers. One useful direction in which to study process data is to explore how students transit from one action to other actions, or from one state to other states. In this chapter, we introduce the basic concepts and methods of Social Network Analysis (SNA) and discuss related applications in visualizing and analyzing process data using SNA to understand the transitions in process data.