Reviewerly/exHarmony huggingface dataset:
Coarse-to-fine auto-focus method using a white-and-red light source for white-light scanning interferometers

To realize high-efficiency automation for white-light scanning interferometers, auto-focusing is a critical technique, which is expected to have a large focusing range and high repeatability. In this paper, a coarse-to-fine auto-focusing method using a white-and-red LED light source is proposed. The approximate focus position is firstly searched efficiently in the coarse focusing using a re-blur sharpness algorithm, and then the optimal focusing position is determined with a full range scanning in the fine focusing. By combining the long coherence length of the red LED with the short coherence length of the white LED, the proposed auto-focusing method can realize a large focusing range up to 40 µm and achieve high repeatability within several nanometers level. The comparison experiments of sharpness algorithms and auto-focus experiments are carried out. The results show that the proposed method is robust, low-cost, and insensitive to noise, surface textures, and light intensity, which is highly suitable for the optical surface measurement with White-light scanning interferometer.
Capturing the few-shot class distribution: Transductive distribution optimization

Few-shot learning is challenging since only a few labeled samples are available for training a learning model. To alleviate the data limitation problem in few-shot learning, several works try to generate samples or features by learning a model or distribution. But complex models and biased estimation of class distribution hamper their interpretability and generalization ability, respectively. In this work, we propose a generation based Transductive Distribution Optimization (TDO) method, which introduces neither extra parameters nor complex models. We use a few labeled samples and some high-confident unlabeled samples of the target set to capture the distributions of the few-shot classes, and then generate sufficient samples from them to augment the labeled inputs. Our method can work with most pre-trained feature extractors and outperforms state-of-the-art methods with a simple linear classifier. The visualization of the generated samples shows that our method can capture an accurate distribution even though the few labeled samples deviate from the ground-truth distribution.
Gamma estimation method based on phase error coefficients vector in digital fringe projection system

For phase-measuring profilometry systems, gamma effect is important to the accuracy while its gamma factor cannot be measured goodly when there are a few images. To tackle the problem, this paper proposes a novel idea: calculate the phase errors coefficients vector (PECV) of a series of heuristic gamma values, and measure the distances from the PECV of the current measurement to those, respectively; then, the gamma corresponding to minimal distance is the measured gamma factor. The idea does not measure the gamma factor straightforwardly and novelly measures the phase error distances. The technique is fast and robust, and only needs at least six images. Experimental results demonstrate the feasibility and performance.
Continuous diagnosis and prognosis by controlling the update process of deep neural networks

Continuous diagnosis and prognosis are essential for critical patients. They can provide more opportunities for timely treatment and rational allocation. Although deep-learning techniques have demonstrated superiority in many medical tasks, they frequently forget, overfit, and produce results too late when performing continuous diagnosis and prognosis. In this work, we summarize the four requirements; propose a concept, continuous classification of time series (CCTS); and design a training method for deep learning, restricted update strategy (RU). The RU outperforms all baselines and achieves average accuracies of 90%, 97%, and 85% on continuous sepsis prognosis, COVID-19 mortality prediction, and eight disease classifications, respectively. The RU can also endow deep learning with interpretability, exploring disease mechanisms through staging and biomarker discovery. We find four sepsis stages, three COVID-19 stages, and their respective biomarkers. Further, our approach is data and model agnostic. It can be applied to other diseases and even in other fields.
Optimization of two-dimensional generalized partial response target using multi-layer perceptron for bit-patterned media recording systems

In magnetic storage systems, the read signal that is the output of the magnetic recording channel is passed through the equalizer to convert it into the desired partial response (PR) target signal. However, mapping between the output of the recording channel and the PR target signal is a non-linear problem. Therefore, many multilayer perceptron (MLP) equalizers have been proposed. In this paper, we propose an optimization scheme of two-dimensional generalized PR target using MLP for improving the detection performance of the bit-patterned magnetic recording (BPMR) systems. The simulation results show that the proposed scheme outperforms the previous methods.
Perspective Chapter: Audit Digitalization - Key Impacts on the Audit Profession

The rate of technological advancement has influenced existing digitalization programmes of audit processes. The adoption and use of emerging technologies in audit digitalization programmes present numerous opportunities for innovation and improvement. These opportunities come with many threats to the current ways of working, which impacts the audit profession, audit process, and auditor. This study is based on two qualitative data sets to identify the impact of digitalization on the audit industry and audit profession, specifically big data analytics, blockchain technology and artificial intelligence. Existing literature was used to identify areas where the biggest impacts were experienced. Based on the themes identified, semi-structured interviews were conducted in the financial services sector in South Africa to gain insights from industry lived experiences. The interviews focused on the impacts experienced and the realized and potential benefits. Data collected was synthesized to address the research questions of how the adoption of emerging technology will impact the audit industry and shape the audit process, audit profession, the role of an auditor, and aims to define the auditor of the future.
Adversary for Social Good: Leveraging Attribute-Obfuscating Attack to Protect User Privacy on Social Networks

As social networks become indispensable for people's daily lives, inference attacks pose significant threat to users' privacy where attackers can infiltrate users' information and infer their private attributes. In particular, social networks are represented as graph-structured data, maintaining rich user activities and complex relationships among them. This enables attackers to deploy state-of-the-art graph neural networks (GNNs) to automate attribute inference attacks for users' privacy disclosure. To address this challenge, in this paper, we leverage the vulnerability of GNNs to adversarial attacks, and propose a new graph adversarial method, called Attribute-Obfuscating Attack (AttrOBF) to mislead GNNs into misclassification and thus protect user attribute privacy against GNN-based inference attacks on social networks. Different from the prior attacks using perturbations on graph structure or node features, AttrOBF provides a more practical formulation by obfuscating optimal training user attribute values, and also advances the attribute obfuscation by solving the unavailability issue of test attribute annotations, black-box setting, bi-level optimization, and non-differentiable obfuscating operation. We demonstrate the effectiveness of AttrOBF on user attribute obfuscation by extensive experiments over three real-world social network datasets. We believe our work yields great potential of applying adversarial attacks to attribute protection on social networks.
A Cross-layer Plausibly Deniable Encryption System for Mobile Devices

Mobile computing devices have been used to store and process sensitive or even mission critical data. To protect sensitive data in mobile devices, encryption is usually incorporated into major mobile operating systems. However, traditional encryption can not defend against coercive attacks in which victims are forced to disclose the key used to decrypt the sensitive data. To combat the coercive attackers, plausibly deniable encryption (PDE) has been introduced which can allow the victims to deny the existence of the sensitive data. However, the existing PDE systems designed for mobile devices are either insecure (i.e., suffering from deniability compromises) or impractical (i.e., unable to be compatible with the storage architecture of mainstream mobile devices, not lightweight, or not user-oriented). In this work, we design CrossPDE, the first cross-layer mobile PDE system which is secure, being compatible with the storage architecture of mainstream mobile devices, lightweight as well as user-oriented. Our key idea is to intercept major layers of a mobile storage system, including the file system layer (preventing loss of hidden sensitive data and enabling users to use the hidden mode), the block layer (taking care of expensive encryption and decryption), and the flash translation layer (eliminating traces caused by the hidden sensitive data). Experimental evaluation on our real-world prototype shows that CrossPDE can ensure deniability with a modest decrease in throughput.
CTDRB: Controllable Timed Data Release Using Blockchains

The notion of Timed Data Release (TDR) supports time-based sensitive data protection in such a way that sensitive data can be accessed only after a prescribed amount of time has passed. With recent advancements in blockchain techniques, practical solutions to support decentralized TDR using blockchains (BTDR) is gaining importance. Briefly, such designs entrust blockchain decentralized networks to serve as a decentralized time agent to protect the data and release the data at a prescribed release time. However, as a variant of outsourced data management service, BTDR inherently incurs the tension between data confidentiality protection as well as data control. Unfortunately, the off-the-shelf arts only strive to protect the data without rigorous support for the control of data. In this paper, we design a controllable framework for BTDR called CTDRB. At a high level, CTDRB realizes data access control as well as data lifetime control while protecting data confidentiality. The novel technical contributions of CTDRB are three-fold: first, we adopt a temporal CP-ABE cryptographic scheme, serving as a basis, to enable the data access control; second, on top of such a design, we enable data lifetime control by carefully designing a time token control service on Ethereum. We then design two representative data lifetime control primitives, namely Data Revocation and Data Release Time Modification. The former refers to revoking the data before its prescribed release time while the latter modifies the release of data at a time ahead of its prescribed release time; third but not the least, we perform security analysis of CTDRB and implement it using the Ethereum blockchain. Our results show that CTDRB incurs only a moderate on-chain gas consumption and demonstrates high efficiency.
FairBlock: Preventing Blockchain Front-Running with Minimal Overheads

While blockchain systems are quickly gaining popularity, front-running remains a major obstacle to fair exchange. In this paper, we show how to apply identity-based encryption (IBE) to prevent front-running with minimal bandwidth overheads. In our approach, to decrypt a block of N transactions, the number of messages sent across the network only grows linearly with the size of decrypting committees, S. That is, to decrypt a set of N transactions sequenced at a specific block, a committee only needs to exchange S decryption shares (independent of N). In comparison, previous solutions are based on threshold decryption schemes, where each transaction in a block must be decrypted separately by the committee, resulting in bandwidth overhead of $$N \times S$$ . Along the way, we present a model for fair block processing and build a prototype implementation. We show that on a sample of 1000 messages with 1000 validators our system saves 42.53 MB of bandwidth which is 99.6% less compared with the standard threshold decryption paradigm.
Coreference Resolution for Cybersecurity Entity: Towards Explicit, Comprehensive Cybersecurity Knowledge Graph with Low Redundancy

Cybersecurity Knowledge Graph (CKG) has become an important structure to address the current cybersecurity crises and challenges, due to its powerful ability to model, mine, and leverage massive security intelligence data. To construct a comprehensive and explicit CKG with low redundancy, coreference resolution (CR) plays a crucial role as the core step in knowledge fusion. Although the research on coreference resolution techniques in Natural Language Processing (NLP) field has made notable achievements, there is still a great gap in the cybersecurity field. Therefore, the paper first investigates the effectiveness of the existing CR models on cybersecurity corpus and presents CyberCoref, an end-to-end coreference resolution model for cybersecurity entities. We propose an entity type prediction network that not only helps to improve mention representations and provide type consistency checks, but also enables the model to distinguish the coreference among different entity types and thus run the coreference resolution more granular. To overcome the problem of implicit contextual modeling adopted by the existing CR models, we innovative propose an explicit contextual modeling method for the coreference resolution task based on semantic text matching. Finally, we improve the span representation by introducing lexical and syntactic features. The experimental results demonstrate that CyberCoref improves the F1 values on the cybersecurity corpus by 6.9% compared to existing CR models.
Mitigation of noise in Josephson parametric oscillator by injection locking

Injection locking is a well-established technique widely used in optics as well as solid-state devices for efficient suppression of noise. We present the spectroscopic characterization of the effect of the injection-locking signal (ILS) in mitigating the phase noise of a Josephson parametric oscillator (JPO), whose output oscillating phase undergoes indeterministic switching between the bistable states with symmetry $\theta \rightarrow{\theta+\pi}$. With the injection of a weak locking signal, we measure the phase noise power spectral density of the self-sustained oscillator output state for different locking signal strengths. We observed suppression of phase noise by injection locking. As the ILS strength surpasses more than a few photons, the output state stays completely pinned to the locking phase of the ILS, and the random telegraphic noise due to the switching of the states is significantly suppressed.
Design of Confidence-Integrated Denoising Auto-Encoder for Personalized Top-N Recommender Systems

A recommender system not only “gains users’ confidence” but also helps them in other ways, such as reducing their time spent and effort. To gain users’ confidence, one of the main goals of recommender systems in an e-commerce industry is to estimate the users’ interest by tracking the users’ transactional behavior to provide a fast and highly related set of top recommendations out of thousands of products. The standard ranking-based models, i.e., the denoising auto-encoder (DAE) and collaborative denoising auto-encoder (CDAE), exploit positive-only feedback without utilizing the ratings’ ranks for the full set of observed ratings. To confirm the rank of observed ratings (either low or high), a confidence value for each rating is required. Hence, an improved, confidence-integrated DAE is proposed to enhance the performance of the standard DAE for solving recommender systems problems. The correctness of the proposed method is authenticated using two standard MovieLens datasets such as ML-1M and ML-100K. The proposed study acts as a vital contribution for the design of an efficient, robust, and accurate algorithm by learning prominent latent features used for fast and accurate recommendations. The proposed model outperforms the state-of-the-art methods by achieving improved P@10, R@10, NDCG@10, and MAP scores.
Improving Intent Classification Using Unlabeled Data from Large Corpora

Intent classification is a central component of a Natural Language Understanding (NLU) pipeline for conversational agents. The quality of such a component depends on the quality of the training data, however, for many conversational scenarios, the data might be scarce; in these scenarios, data augmentation techniques are used. Having general data augmentation methods that can generalize to many datasets is highly desirable. The work presented in this paper is centered around two main components. First, we explore the influence of various feature vectors on the task of intent classification using RASA’s text classification capabilities. The second part of this work consists of a generic method for efficiently augmenting textual corpora using large datasets of unlabeled data. The proposed method is able to efficiently mine for examples similar to the ones that are already present in standard, natural language corpora. The experimental results show that using our corpus augmentation methods enables an increase in text classification accuracy in few-shot settings. Particularly, the gains in accuracy raise up to 16% when the number of labeled examples is very low (e.g., two examples). We believe that our method is important for any Natural Language Processing (NLP) or NLU task in which labeled training data are scarce or expensive to obtain. Lastly, we give some insights into future work, which aims at combining our proposed method with a semi-supervised learning approach.
Blockchain Data Availability Scheme with Strong Data Privacy Protection

Blockchain, with its characteristics of non-tamperability and decentralization, has had a profound impact on various fields of society and has set off a boom in the research and application of blockchain technology. However, blockchain technology faces the problem of data availability attacks during its application, which greatly limits the scope and domain of blockchain applications. One of the most advantageous researches to address this problem is the scalable data availability solution that integrates coding theory design into the Merkle tree promise. Based on this scheme, this paper combines a zero-knowledge accumulator with higher efficiency and security with local repair coding, and proposes a data availability scheme with strong dataset privacy protection. The scheme first encodes the data block information on the blockchain to ensure tamper-proof data, and then uses a zero-knowledge accumulator to store the encoded data block information. Its main purpose is to use zero-knowledge property to protect the accumulation set information stored in the accumulator from being leaked and to ensure that no other information about the accumulation set is revealed during the data transmission. It fundamentally reduces the possibility of attackers generating fraudulent information by imitating block data and further resists data availability attacks.
Monocular Facial Presentation–Attack–Detection: Classifying Near-Infrared Reflectance Patterns

This paper presents a novel material spectroscopy approach to facial presentation–attack–defense (PAD). Best-in-class PAD methods typically detect artifacts in the 3D space. This paper proposes similar features can be achieved in a monocular, single-frame approach by using controlled light. A mathematical model is produced to show how live faces and their spoof counterparts have unique reflectance patterns due to geometry and albedo. A rigorous dataset is collected to evaluate this proposal: 30 diverse adults and their spoofs (paper-mask, display-replay, spandex-mask and COVID mask) under varied pose, position, and lighting for 80,000 unique frames. A panel of 13 texture classifiers are then benchmarked to verify the hypothesis. The experimental results are excellent. The material spectroscopy process enables a conventional MobileNetV3 network to achieve 0.8% average-classification-error rate, outperforming the selected state-of-the-art algorithms. This demonstrates the proposed imaging methodology generates extremely robust features.
An investigation of continuous-time quantum walk on hypercube in view of Cartesian product structure

In this paper, continuous-time quantum walk on hypercube is discussed in view of Cartesian product structure. We find that the [Formula: see text]-fold Cartesian power of the complete graph [Formula: see text] is the [Formula: see text]-dimensional hypercube, which give us new ideas for the study of quantum walk on hypercube. Combining the product structure, the spectral distribution of the graph and the quantum decomposition of the adjacency matrix, the probability amplitudes of the continuous-time quantum walker’s position at time [Formula: see text] are given, and it is discussed that the probability distribution for the continuous-time case is uniform when [Formula: see text]. The application of this product structure greatly improves the study of quantum walk on complex graphs, which has far-reaching influence and great significance.
Filter Pruning via Attention Consistency on Feature Maps

Due to the effective guidance of prior information, feature map-based pruning methods have emerged as promising techniques for model compression. In the previous works, the undifferentiated treatment of all information on feature maps amplifies the negative impact of noise and background information. To address this issue, a novel filter pruning strategy called Filter Pruning via Attention Consistency (FPAC) is proposed, and a simple and effective implementation method of FPAC is presented. FPAC is inspired by the notion that the attention of feature maps on one layer is in high consistency of spatial dimension. The experiments also show that feature maps with lower consistency are less important. Hence, FPAC measures the importance of filters by evaluating the attention consistency on the feature maps and then prunes the filters corresponding to feature maps with lower consistency. The present experiments on various datasets further confirm the effectiveness of FPAC. For instance, applying VGG-16 on CIFAR-10, the classification accuracy even increases from 93.96% to 94.03% with 58.1% FLOPs reductions. Furthermore, applying ResNet-50 on ImageNet achieves 45% FLOPs reductions with only 0.53% accuracy loss.