Reviewerly/exHarmony huggingface dataset:
Semantic Guided Long Range Stereo Depth Estimation for Safer Autonomous Vehicle Applications

Autonomous vehicles in intelligent transportation systems must be able to perform reliable and safe navigation. This necessitates accurate object detection, which is commonly achieved by high-precision depth perception. Existing stereo vision-based depth estimation systems generally involve computation of pixel correspondences and estimation of disparities between rectified image pairs. The estimated disparity values will be converted into depth values in downstream applications. As most applications often work in the depth domain, the accuracy of depth estimation is often more compelling than disparity estimation. However, at large distances (> 50m), the accuracy of disparity estimation does not directly translate to the accuracy of depth estimation. In the context of learning-based stereo systems, this is mainly due to biases imposed by the choices of the disparity-based loss function and the training data. Consequently, the learning algorithms often produce unreliable depth estimates of under-represented foreground objects, particularly at large distances. To resolve this issue, we first analyze the effect of those biases and then propose a pair of depth-based loss functions for foreground objects and background separately. These loss functions can be tuned and can balance the inherent bias of the stereo learning algorithms. The efficacy of our solution is demonstrated by an extensive set of experiments, which are benchmarked against state of the art. We show on the KITTI 2015 benchmark that our proposed solution yields substantial improvements in disparity and depth estimation, particularly for objects located at distances beyond 50 meters, outperforming the previous state of the art by 10%.
Joint Wavelet Sub-Bands Guided Network for Single Image Super-Resolution

Since deep convolutional neural network (CNN) has achieved excellent results in single image super-resolution (SISR), an increasing number of methods based on CNN have been proposed. Most CNN-based methods are devoted to finding mapping based on pixel intensity while ignoring the importance of frequency information, which can reflect semantic information of images on different bands. This leads to less effectiveness in the reconstruction of high-frequency details. To address this problem, we propose a novel CNN-based super-resolution method named joint wavelet sub-bands guided network (JWSGN). We separate the different frequency information of the image by the WT and then recover this information by a multi-branch network. To recover finer edge details, we propose an edge extraction module, which estimates an edge feature map by using the similarity of all high-frequency sub-bands and then corrects the high-frequency features recovered from each branch by exploiting the edge feature map. Furthermore, we use the complementary relationship between different frequencies to calibrate the high-frequency sub-bands. Finally, the high-resolution image is obtained by inverse wavelet transform. Both qualitative and quantitative experiments show that our method performs excellent performance with the guidance of the edge extraction module.
Question Answering Over Knowledge Graphs: A Case Study in Tourism

Over the recent years, a large number of knowledge graphs (KGs) have been developed to store and present small and medium enterprises&#x2019; data in the form of subject-predicate-object triples. The KGs are not easily accessible to end-users because they need an understanding of query languages and KGs&#x2019; structures. To assist end-users in accessing these KGs, question answering over KGs targets to provide answers for natural language questions (NLQs). This paper proposes an approach to answer questions over small and medium scaled KGs based on graph isomorphism in two phases: (1) offline phase and (2) semantic parsing phase. In the offline phase, a semi-automated solution is proposed to generate NLQs and their answers, which are used to train machine learning models employed in the next phase. In the semantic parsing phase, a given input NLQ is mapped into a query pattern according to its grammatical structure. Each query pattern contains some slots that need to be filled with corresponding entities, classes and relations from the KG. While string and semantic similarity metrics are applied to identify the entities and classes, the probability distribution of the relations is used to extract the relations. The Cartesian product of the identified entities, classes and relations is utilized to fill the slots, derive SPARQL queries and finally retrieve the answers. To evaluate the proposed approach, we use SalzburgerLand KG, a real KG describing touristic entities of the region of Salzburg, Austria. Our results show that the approach improves the end-to-end user experience in terms of interactive question answering and performance.
How Confident Was Your Reviewer? Estimating Reviewer Confidence from Peer Review Texts

The scholarly peer-reviewing system is the primary means to ensure the quality of scientific publications. An area or program chair relies on the reviewer's confidence score to address conflicting reviews and borderline cases. Usually, reviewers themselves disclose how confident they are in reviewing a certain paper. However, there could be inconsistencies in what reviewers self-annotate themselves versus how the preview text appears to the readers. This is the job of the area or program chair to consider such inconsistencies and make a reasonable judgment. Peer review texts could be a valuable source of Natural Language Processing (NLP) studies, and the community is uniquely poised to investigate some inconsistencies in the paper vetting system. Here in this work, we attempt to automatically estimate how confident was the reviewer directly from the review text. We experiment with five data-driven methods: Linear Regression, Decision Tree, Support Vector Regression, Bidirectional Encoder Representations from Transformers (BERT), and a hybrid of Bidirectional Long-Short Term Memory (BiLSTM) and Convolutional Neural Networks (CNN) on Bidirectional Encoder Representations from Transformers (BERT), to predict the confidence score of the reviewer. Our experiments show that the deep neural model grounded on BERT representations generates encouraging performance.
Reducing Multiple Occurrences of Meta-Mark Selection in Relational Data Watermarking

Contrary to multimedia data watermarking approaches, it is not recommended that relational data watermarking techniques consider sequential selection for marks in the watermark and embedding locations in the digital asset being protected. Indeed, considering the database relations&#x2019; elements, i.e., tuple and attributes, when watermarking techniques are based on sequential processes, watermark detection can be easily compromised by performing subset reverse order attacks. As a result, attackers can obtain owner evidence-free high-quality data since no data modifications for mark removing are required for the malicious operation to succeed. A standard solution to this problem has been pseudo-random selection, which however often leads to choosing the same marks multiple times, ignoring others, thus compromising the embedding of the entire watermark. This work proposes an engine that contributes to control marks&#x2019; recurrent selection, allowing marks excluded by previous approaches to be considered. The experiments performed show a dramatic improvement of the embedded watermark quality when the proposed engine is included in watermarking techniques&#x2019; architecture. They also provide evidence that this proposal leads to higher resilience against common malicious operations such as subset and superset attacks.
Best Practices for a Handwritten Text Recognition System

Handwritten text recognition has been developed rapidly in the recent years, following the rise of deep learning and its applications. Though deep learning methods provide notable boost in performance concerning text recognition, non-trivial deviation in performance can be detected even when small pre-processing or architectural/optimization elements are changed. This work follows a "best practice" rationale; highlight simple yet effective empirical practices that can further help training and provide well-performing handwritten text recognition systems. Specifically, we considered three basic aspects of a deep HTR system and we proposed simple yet effective solutions: 1) retain the aspect ratio of the images in the preprocessing step, 2) use max-pooling for converting the 3D feature map of CNN output into a sequence of features and 3) assist the training procedure via an additional CTC loss which acts as a shortcut on the max-pooled sequential features. Using these proposed simple modifications, one can attain close to state-of-the-art results, while considering a basic convolutional-recurrent (CNN+LSTM) architecture, for both IAM and RIMES datasets. Code is available at https://github.com/georgeretsi/HTR-best-practices/ .
Optimal ABox Repair w.r.t. Static $$\mathcal {EL}$$ TBoxes: From Quantified ABoxes Back to ABoxes

Errors in Description Logic (DL) ontologies are often detected when a reasoner computes unwanted consequences. The question is then how to repair the ontology such that the unwanted consequences no longer follow, but as many of the other consequences as possible are preserved. The problem of computing such optimal repairs was addressed in our previous work in the setting where the data (expressed by an ABox) may contain errors, but the schema (expressed by an $$\mathcal {EL} $$ TBox) is assumed to be correct. Actually, we consider a generalization of ABoxes called quantified ABoxes (qABoxes) both as input for and as result of the repair process. Using qABoxes for repair allows us to retain more information, but the disadvantage is that standard DL systems do not accept qABoxes as input. This raises the question, investigated in the present paper, whether and how one can obtain optimal repairs if one restricts the output of the repair process to being ABoxes. In general, such optimal ABox repairs need not exist. Our main contribution is that we show how to decide the existence of optimal ABox repairs in exponential time, and how to compute all such repairs in case they exist.
Robots as Welfare Technologies to Reduce Falls Amongst Older Adults: An Explorative Study from Norway

This paper investigates robots as Welfare Technologies (WT), in Norway. Previous studies show that Norway follows the demographic trends around the world regarding the aging of the population, the shortage of nurses, and thus, increased costs due to co-morbidity and multiple chronic diseases. The research question addressed is: Is the notion of WT challenged by the adoption of intelligent robots within home care, and if yes, how? To explore robots as WT, we focused specifically on the case of robots as safety alarms for fall prevention, detection, and prediction as part of the Vulnerability in Robot Society research project, and by drawing on the previous research project, namely Multimodal Elderly Care Systems project. At the basis of our theoretical framework, the paper is anchored into the Scandinavian notion of welfare technology. To achieve this, we analyzed data from different research activities (n = 10, hs = 25) through thematic analysis. Findings show that current WT for fall detection has design and technical limitations, whereas robots as advanced Artificial Intelligence (AI) systems could potentially be a solution. We then discuss the findings in the light of the welfare concept. Finally, we conclude that robots as WT does not differ from other technologies, with one exception: they differ from the traditional way of providing (home-)care, bringing in both challenges related to privacy and safety, but also opportunities for reduced costs, personalized, adapted, and higher quality care in the heart of the home.
ML-Based QoE Estimation in 5G Networks Using Different Regression Techniques

Monitoring and providing customers with a satisfying Quality of Experience (QoE) is a crucial business incentive for mobile network operators (MNOs). While the MNO is capable of monitoring a vast amount of network-related key performance indicators (KPIs), it typically does not have access to application-specific performance metrics. Among others, this is due to practical obstacles, such as missing standardized interfaces between the network and the application. Existing QoE models allow to map collected KPIs to the user-perceived quality. However, they are not dynamic, cumbersome to obtain, and often rely on application-level information, such as the stalling duration in the case of video streaming. The 5G networking architecture provides new features which can potentially overcome current limitations of in-network QoE monitoring. More specifically, the Application Function (AF) provides a standardized interface for communicating between 5G systems and third parties, such as application providers. The Network Data Analytics Function (NWDAF) is capable of collecting a vast number of network statistics from other 5G network functions and is dedicated to training and deploying Machine Learning (ML) models. This opens new possibilities, unimaginable for earlier mobile network generations, to dynamically learn the relationship between network KPIs and QoE by utilizing ML. Besides elaborating on how the new capabilities introduced with 5G can support an ML-based QoE estimation, we perform a simulation-based feasibility study which evaluates the estimation accuracy of different state-of-the-art regression techniques. In addition, we discuss them with respect to various qualitative aspects from an MNO’s point of view.
A Secured Quantum Two-Bit Commitment Protocol for Communication Systems

Bit commitment is an essential primitive in quantum cryptography. It is the basic building block of many cryptographic protocols in which it provides security over distrustful parties. This paper proposes a secured quantum two-bit commitment protocol for any two classical bits. The protocol contains two parties - a committer and a receiver - who share both quantum and classical communication channels. The main phases of this protocol are commitment and revealing phases where the concealing and binding conditions are proved in order to accept the commitment message. The proposed framework utilizes different security layers to hide specific quantum states in a superposition alongside trivial one qubit operations. The secured unitary transformations are applied by the two parties to meet the concealing and binding conditions. To verify the success of the proposed protocol, the measured output of the two parties are compared. The protocol operates under the assumption that the two parties might cheat and the possible existence of an eavesdropper.
Blockchain Impacts on Global Supply Chain Operational and Managerial Business Value Processes

The purpose of this article is to investigate the impact of blockchain on global supply chain operational and managerial processes. This article reviews and classifies the existing empirical evidence of blockchain applications in the global suppbly chain. We utilize the Mooney <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> framework to organize this evidence into operational and managerial business value processes and the effects—automational, informational, and transformational. Our findings indicate that blockchain is not following the Mooney <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> stage model proposed in 1996 since the empirical evidence is evenly spread between the three effect categories. In addition, we have identified possible reasons why blockchain is not following the Mooney <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> stage model. Our study concludes with nine propositions as a guide on how to facilitate blockchain deployment and future research. Limitations of this article concerning blockchain are the lack of consistency and understanding of standards and the lack of information on blockchain implementation, which is referred to as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">“grey space.”</i> Blockchain today is where radio-frequency identification (RFID) was in 2002 and the bar code was in 1980. Therefore, future research work could be expanded to investigate case studies of blockchain implementations as they become available across the global supply chain. For practitioners, the empirical evidence presented in this article can help identify applications and implementation steps for building the business case where blockchain can have a greater impact on the organization. This article fills a void in the literature by providing practitioners and academics with a better understanding of blockchain and its applications for implementation.
SemCKD: Semantic Calibration for Cross-Layer Knowledge Distillation

Knowledge distillation is a technique to enhance the generalization ability of a student model by exploiting outputs from a teacher model. Recently, feature-map based variants explore knowledge transfer between manually assigned teacher-student pairs in intermediate layers for further improvement. However, layer semantics may vary in different neural networks, resulting in performance degeneration due to negative regularization from semantic mismatch in manual layer associations. To address this issue, we propose semantic calibration for cross-layer knowledge distillation (SemCKD), which automatically assigns proper target layers of the teacher model for each student layer with an attention mechanism. With a learned attention distribution, each student layer distills knowledge contained in multiple teacher layers rather than a specific intermediate layer for appropriate cross-layer supervision. We further provide theoretical analysis of the association weights and conduct extensive experiments to demonstrate the effectiveness of our approach. On average, SemCKD improves the student Top-1 classification accuracy by 4.27% across twelve different teacher-student model combinations on CIFAR-100. 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/DefangChen/SemCKD</uri> .
Introducing Fuzzy Cognitive Map for predicting Engine’s Health Status

Predictive maintenance (PdM) is an open issue in industrial and manufacturing systems. Remaining Useful Life (RUL) estimation is a significant concern in PdM, which corresponds to the engine’s remaining time to perform its intended function. This work proposes Fuzzy Cognitive Maps (FCMs) as a Health Indicator (HI) prognostics method for engines’ RUL prediction. For the FCM model design, we apply, test, and compare four different training algorithms to determine the model parameters’ values that lead to the optimal prediction accuracy. In order to test the results obtained from the FCM-based approach, we compare them with those derived from an Artificial Neural Network (ANN), and we verify that the use of FCMs results in a decent engine’s health status predictor comparable to a machine learning method.
Few-Shot Class-Incremental Learning via Compact and Separable Features for Fine-Grained Vehicle Recognition

Most of the existing deep learning-based fine-grained vehicle recognition methods collect a large-scale training set in advance and train a model based on the closed-world assumption. However, in the real world, new classes of vehicles are released over time but it is difficult to collect sufficient labeled data for new classes, which results in a typical few-shot class-incremental learning problem (FSCIL). To solve this problem, this work proposes a compact and separable feature learning method (CSFL) which exploits a decoupled learning scheme to prevent the feature extractor from updating during class-incremental learning. CSFL trains an initial model to learn discriminative features of fine-grained vehicles using deep metric learning. Then an incremental linear discriminant analysis algorithm is applied to the learned features to further discriminate potential confused classes. Specifically, the decoupled components share the same objective of enhancing intra-class compactness and inter-class separability, which is beneficial for classification. Extensive experiments on three fine-grained vehicle datasets demonstrate that the proposed CSFL achieves better results than state-of-the-art incremental learning methods, validating the importance of compact and separable features in the problem of FSCIL.
3-D Structured Light Scanning With Phase Domain-Modulated Fringe Patterns

Existing 3-D structured light (SL) scanning methods require a high number of images (multifrequency phase shifting, MF-PS) or embed signals in the space domain (space domain modulation phase shifting, SDM-PS) to conduct phase unwrapping. These methods are either movement sensitive (for MF-PS) or low in accuracy (for SDM-PS). In this work, a new 3-D SL scanning method is proposed to use the theoretical minimum of three images only. Unlike existing methods that directly embed signals in the space domain, the assistance signals are modulated in the phase domain of PS images, inspired by the phase-shift keying (PSK) theory. Phase calculation in the proposed phase domain modulation phase shifting (PDM-PS) method is independent of embedded assistance signals. The signal to noise ratio (SNR) of phase codewords and the accuracy of phase unwrapping and 3-D reconstruction are well retained. Experimental results demonstrated that the proposed PDM-PS method using only three images was able to achieve comparable 3-D measurement accuracy as the traditional MF-PS method (three images vs. nine images, 0.03 mm vs. 0.02 mm); and both using three images, the proposed PDM-PS method outperformed the traditional SDM-PS method in terms of measurement accuracy (0.03 mm vs. 0.07 mm).
Robust Correlation Tracking in Unmanned Aerial Vehicle Videos via Deep Target-Specific Rectification Networks

Unmanned Aerial Vehicle (UAV) Videos have received active research attention in the remote sensing field by taking full advantage of the bird&#x2019;s eye view offered by UAV. At the same time, visual tracking approaches based on discriminative correlation filters (DCF) have recently achieved increasing popularity and success. Despite their success, the tracking robustness and accuracy of existing DCF-based trackers are hard to promote in challenging tracking scenarios of aerial videos due to excessive reliance on the response map and fixed linear model update strategy. To resolve these issues, we propose a robust DCF-based tracking framework via an effective pretrained rectification network for UAV-based remote sensing. Specifically, the target-specific rectification network is offline trained to discriminatively classify the target and background. During the online tracking stage, the DCF module performs fast inference to obtain the potential locations of the target. After that, the deep rectification network evaluates the correlation-specific proposals offered by the DCF module and provides precise tracking results. Besides, to achieve a robust and adaptive model update strategy, we propose to finetune both the DCF module and rectification network according to the classification confidence of the estimated result. Extensive experimental results on recent UAV benchmarks demonstrate that our method achieves better performance than other competing algorithms.