Reviewerly/exHarmony huggingface dataset:
Unmanned Aerial Vehicles (UAVs) Battery Power Anomaly Detection Using Temporal Convolutional Network with Simple Moving Average Algorithm

Nowadays, the widespread use of UAVs in various fields has led to studies on many issues related to UAVs in the literature. In particular, any deterioration that may occur in UAV systems and their inability to detect them, cause uncontrolled accidents and financial losses. In this study, temporal convolutional network and moving average are used to identify anomalies in instantaneous battery power consumption of UAVs. In time series prediction, usually preprocessed data with various statistical methods are used as inputs to the neural networks. In this study, both the power consumption data obtained from the UAV battery sensors and the calculated simple moving average data are given as multi-variate inputs to the temporal convolutional network. In the simulation results, the effect of using the simple moving average data together with the data from the UAVs sensor, the instantaneous battery power consumption prediction and the detection of anomalies have been examined. The simulation results showed that the combined use of simple moving average data and data from UAVs sensors helps to achieve more successful results in instantaneous power consumption prediction and anomaly detection with temporal convolutional network.
Robust network traffic identification with graph matching

Network traffic identification is of great value as an effective way for network management. Typically, intelligent classifiers are deployed for advanced network protection, including intrusion traffic detection and network behavior monitoring etc. Robust network traffic classifiers with generalization ability should also ensure stable performance in various network environments. Unfortunately, although the existing network traffic classifiers can achieve the claimed performance when initialized and tested in invariant network environments with stable attribute distributions, they are inclined to fail when adapting to varying practical networks and suffer from significant performance degradation. Based on analysis of representative state-of-the-art classifiers with respect to their transferability, we arrive at that feature distribution of the same class is vulnerable to the changes of networking which account for the degradation of most existing methods. To tackle the issues, we propose a weakly-supervised network traffic classification method based on graph matching. Specifically, network sessions are aggregated to several clusters with the extracted principal features through weakly-supervised clustering. Moreover, we measure the correlations between clusters from the same networks to construct the similarity graphs. Clusters from the testing network are associated with those from the initial network by the carefully designed graph matching algorithm, so that the testing clusters can be labeled according to the associated ones in the initialized network. Our method shows eye-catching robustness, achieving an accuracy of 88.19% when practically deployed in different networks, which significantly outperforms the existing approaches.
A Dataset for Analysing Complex Document Layouts in the Digital Humanities and Its Evaluation with Krippendorff’s Alpha

We introduce a new research resource in the form of a high-quality, domain-specific dataset for analysing the document layout of historical documents. The dataset provides an instance segmentation ground truth with 19 classes based on historical layout structures that stem (a) from the publication production process and the respective genres (life sciences, architecture, art, decorative arts, etc.) and, (b) from selected text registers (such as monograph, trade journal, illustrated magazine). Altogether, the dataset contains more than 52,000 instances annotated by experts. A baseline has been tested with the well-known Mask R-CNN and compared to the state-of-the-art model VSR [55]. Inspired by evaluation practices from the field of Natural Language Processing (NLP), we have developed a new method for evaluating annotation consistency. Our method is based on Krippendorff’s alpha (K- $$\alpha $$ ), a statistic for quantifying the so-called “inter-annotator-agreement”. In particular, we propose an adaptation of K- $$\alpha $$ that treats annotations as a multipartite graph for assessing the agreement of a variable number of annotators. The method is adjustable with regard to evaluation strictness, and it can be used in 2D or 3D as well as for a variety of tasks such as semantic segmentation, instance segmentation, and 3D point cloud segmentation.
Real-Time Visualization of Stream-Based Monitoring Data

Abstract Stream-based runtime monitors are used in safety-critical applications such as Unmanned Aerial Systems (UAS) to compute comprehensive statistics and logical assessments of system health that provide the human operator with critical information in hand-over situations. In such applications, a visual display of the monitoring data can be much more helpful than the textual alerts provided by a more traditional user interface. This visualization requires extensive real-time data processing, which includes the synchronization of data from different streams, filtering and aggregation, and priorization and management of user attention. We present a visualization approach for the RTLola monitoring framework. Our approach is based on the principle that the necessary data processing is the responsibility of the monitor itself, rather than the responsibility of some external visualization tool. We show how the various aspects of the data transformation can be described as RTLola stream equations and linked to the visualization component through a bidirectional synchronous interface. In our experience, this approach leads to highly informative visualizations as well as to understandable and easily maintainable monitoring code.
Online Marker-Free Extrinsic Camera Calibration Using Person Keypoint Detections

Calibration of multi-camera systems, i.e. determining the relative poses between the cameras, is a prerequisite for many tasks in computer vision and robotics. Camera calibration is typically achieved using offline methods that use checkerboard calibration targets. These methods, however, often are cumbersome and lengthy, considering that a new calibration is required each time any camera pose changes. In this work, we propose a novel, marker-free online method for the extrinsic calibration of multiple smart edge sensors, relying solely on 2D human keypoint detections that are computed locally on the sensor boards from RGB camera images. Our method assumes the intrinsic camera parameters to be known and requires priming with a rough initial estimate of the camera poses. The person keypoint detections from multiple views are received at a central backend where they are synchronized, filtered, and assigned to person hypotheses. We use these person hypotheses to repeatedly solve optimization problems in the form of factor graphs. Given suitable observations of one or multiple persons traversing the scene, the estimated camera poses converge towards a coherent extrinsic calibration within a few minutes. We evaluate our approach in real-world settings and show that the calibration with our method achieves lower reprojection errors compared to a reference calibration generated by an offline method using a traditional calibration target.
Bousi<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si139.svg" display="inline" id="d1e1172"><mml:mo>∼</mml:mo></mml:math>Prolog: Design and implementation of a proximity-based fuzzy logic programming language

The fuzzy logic programming language Bousi∼Prolog extends Prolog with a weak unification algorithm based on proximity relations and truth degree annotations. The weak unification algorithm makes the search for answers more flexible, while rule annotations make possible knowledge-based applications where the rules may be uncertain. In this paper, after recalling the main concepts supporting this language, we detail its design and implementation. We describe the implementation of its operational semantics, which is based on compiling programs and queries into Prolog, and those important features that makes it more applicable: fuzzy sets, integration with WordNet and efficiency techniques. The result is a high-level open-source implementation of the Bousi∼Prolog system, written on top of SWI-Prolog, and publicly available. We also summarise some experiments measuring its performance compared to other systems.
Speech Recognition Lab

Automatic speech recognition (ASR) in the military domain can enable voice user interfaces of command & control information systems (C2IS) and, thus, improve the usability of C2IS. It can also serve intelligence (COMINT) by supporting the monitoring of radio communication. However, military use cases are challenging as ASR has to cope with adverse conditions due to speech variations, noise and other channel disturbances, as well as strong requirements regarding reliability and robustness. Generic ASR solutions do not meet these requirements sufficiently. Therefore, tailor-fit ASR solutions have to be provided. In order to enable the quick development and testing of such solutions for specific use cases, we designed a Speech Recognition Lab (SR Lab). A demonstrator of the SR Lab and its components has been implemented and will be described in this paper.
Derivation of a Semiclassical Model for a Transmon Capacitively Coupled to a Transmission Line

Accurately controlling the state of a transmon qubit via applied microwave pulses is essential in successfully running quantum computers that use these qubits. Currently, control pulses are designed using classical simulations of time-dependent quantum systems that neglect any back-action of the transmon on the transmission lines that deliver the microwaves. In this work, we develop a semiclassical model for a transmon capacitively coupled to a transmission line that treats the transmon quantum mechanically and the transmission line classically. This model can be used in the future to study the detailed interactions of microwave control pulses with transmon qubits to better optimize the control pulses. We show results from a classical model of this system to highlight that the back-action of the transmon may not be negligible for common experimental parameters, emphasizing the need for more rigorous models as experimental parameters are changed to accommodate scaling in device density.
CLOSED: A Cloud-Edge Dynamic Collaborative Strategy for Complex Event Detection

When incoming events arrive too quickly, edge nodes cannot process all events since they only have limited resources. Therefore, we propose a CLoud-edge dynamic cOllaborative Strategy for complex Event Detection (CLOSED). We consider the case where a composite event may involve primitive events from more than one edge node and model the composite event detection task allocation as an optimization problem to minimize the communication costs for both event detection and event notification among cloud centers and edge nodes. We propose the MINimized Event Transmission time-based detection node selection algorithm (MINET) to solve this problem. Moreover, we propose a Reward-based composite event detection Task Migration Algorithm (RTMA) to migrate event detection tasks when edge nodes are overloaded. By evaluating CLOSED on a simulated dataset and different workload patterns , we prove that this strategy can significantly improve event detection efficiency.
srVPA: A Multi-Domain Conversational Service Recommendation Approach

With the widespread use of Virtual Personal Assistants (VPAs) in people’s daily lives, research on conversational recommender systems (CRSs) has become a hot topic in services computing. A key challenge is to reduce search space of candidate services for providing satisfying recommendations in fewer dialogue rounds. Firstly, user portraits are used to record long-term user preferences for assisting personalized recommendations; however, traditional label-based user portraits cannot reach at higher precision of quantified preferences and higher timeliness of preference updates along with time. For this challenge, this paper imports Multi-domain Dialogue-based User Portrait (MDUP) model to record multi-domain numerical preferences and continuously accumulates long-term preference updates, so that candidate services can be matched more precisely. Secondly, recommender systems usually compose massive services from multiple domains to meet coarse-grained user needs, and in order to improve the search, filtering and sorting efficiency of services during dialogue, this paper models potential connections among services of different domains by introducing service feature dependency to effectively compress the scale of candidate services. Combined the two perspectives together, this paper proposes a new service pre-sorting algorithm and a novel dialogue policy (intra-domain feedback-based iteration and inter-domain backtracking), then introduces architecture and working process of a novel service recommendation VPA (srVPA). Experiment demonstrates that compared with existing CRSs, the importation of MDUP and service feature dependency into srVPA significantly improve dialogue efficiency and user satisfaction on recommendation results.
Parameterized Computing Module Generator Based on a Systolic Array

In this paper, the use of systolic arrays for data processing in the training or executing neural networks is explored. Two types of systolic arrays were developed, and a comparison on spending resources (ALM) and result calculation time was made. The comparison was conducted with two variable parameters of the input matrices: the number of rows of the first matrix and the number of columns of the second matrix. It is shown that (depending on the available resources) one of the methods for calculating the result can be used to synthesize the systolic array module: 1) to generate a systolic array of a given size and multiply matrices in which the first of them does not exceed the array size; 2) to synthesize a systolic array of a limited size and perform the multiplication of two matrices using the “Divide-and-Conquer” algorithm.
TS-InvarNet: Anomaly Detection and Localization based on Tempo-spatial KPI Invariants in Distributed Services

Modern industrial systems are often large-scale distributed systems composed of dozens to thousands of services, leading to difficulty in anomaly detection and localization. KPIs (Key Performance Indicators) record the states of different services and are presented as time series, which reflect the status of the system. However, due to the dynamic and complex periodic patterns embedded in KPIs, pinpointing anomalous behavior of these multivariate time series data quickly and accurately is a challenging problem. The current state-of-the-art deep-learning-based anomaly detection methods model global inter-KPI dependency, causing the limited ability to detect local subtle anomalies and poor interpretability. In practice, interpreting anomalies can accelerate problem localization and further troubleshooting. In this study, we propose TS-InvarNet, an interpretable end-to-end anomaly detection and diagnosis framework based on tempo-spatial KPI invariants. Extensive empirical studies on three real-world industrial datasets and a widely-used open-source system demonstrate that TS-InvarNet can outperform state-of-the-art baseline methods in detection and diagnosis performance. Specifically, TS-InvarNet increases best F1-scores by up to 27% compared to the baselines.
Noun Compound Interpretation With Relation Classification and Paraphrasing

Noun compounds are abundant in various languages and their interpretations have been applied in a wide range of NLP tasks. However, most existing work only uses relation classification- or paraphrasing-based methods to model this problem, failing in coverage or accuracy. We argue that the above two approaches are complementary to each other for the noun compound interpretation. In this paper, we propose a two-phase strategy to solve this task. The first phase is to perform the relation classification sub-task with a novel multi-view representation learning model. When noun compounds are predicted as the non-semantic relation, i.e., NA, or the confidence scores are below the threshold, the second phase, namely paraphrasing, will be triggered to interpret noun compounds with a contrastive slot filling method. To evaluate the effectiveness of our methods, we construct the largest Chinese dataset for noun compound interpretation in the life service domain. The experimental results on our constructed and public datasets prove the effectiveness of our solution. Furthermore, the online A/B testing on Meituan APP suggests that the Query View Click-Through Rate increases by 0.91% when noun compounds are used to enrich semantic information of items with the help of their interpretations on the platform.
Green Financial Health Risk Early Monitoring of Commercial Banks Based on Neural Network Model in a Small Sample Environment

Financial innovations emerge in an endless stream, and it is difficult for the regulatory measures and efforts of banks in various countries and the credit risk management level of commercial banks themselves to adapt to the increasingly complex risk environment faced by banks. In the process of building GFR (green financial risk) mixed governance model, the division of powers and responsibilities of governance subjects should be effectively defined. Therefore, it is very necessary to comprehensively and systematically study and grasp the characteristics, performance, and causes of commercial banks' GFR and build an early-warning model of commercial banks' GFR to comprehensively monitor the risks of banks, so as to reduce risks and avoid crises. Therefore, this paper uses the forward three-layer BPNN (BP neural network) technology to establish a real-time warning model of commercial banks' GFR. IL (input layer) to HL (hidden layer) adopts Sigmoid function, while HL to OL (output layer) function adopts linear function Purelin function. The results show that the test result of this method is greatly improved compared with the traditional method, and the correct rate is increased from 81.27% to 94.38%. It shows that the model in this paper has achieved a good warning effect of GFR for commercial banks.
Secure Hierarchical Deterministic Wallet Supporting Stealth Address

Over the past decade, cryptocurrency has been undergoing a rapid development. Digital wallet, as the tool to store and manage the cryptographic keys, is the primary entrance for the public to access cryptocurrency assets. Hierarchical Deterministic Wallet (HDW), proposed in Bitcoin Improvement Proposal 32 (BIP32), has attracted much attention and been widely used in the community, due to its virtues such as easy backup/recovery, convenient cold-address management, and supporting trust-less audits and applications in hierarchical organizations. While HDW allows the wallet owner to generate and manage his keys conveniently, Stealth Address (SA) allows a payer to generate fresh address (i.e., public key) for the receiver without any interaction, so that users can achieve "one coin each address" in a very convenient manner, which is widely regarded as a simple but effective way to protect user privacy. Consequently, SA has also attracted much attention and been widely used in the community. However, as so far, there is not a secure wallet algorithm that provides the virtues of both HDW and SA. Actually, even for standalone HDW, to the best of our knowledge, there is no strict definition of syntax and models that captures the functionality and security (i.e., safety of coins and privacy of users) requirements that practical scenarios in cryptocurrency impose on wallet. As a result, the existing wallet algorithms either have (potential) security flaws or lack crucial functionality features. In this work, after investigating HDW and SA comprehensively and deeply, we formally define the syntax and security models of Hierarchical Deterministic Wallet supporting Stealth Address (HDWSA), capturing the functionality and security (including safety and privacy) requirements imposed by the practice in cryptocurrency, which include all the versatile functionalities that lead to the popularity of HDW and SA as well as all the security guarantees that underlie these functionalities. We propose a concrete HDWSA construction and prove its security in the random oracle model. We implement our scheme and the experimental results show that the efficiency is suitable for typical cryptocurrency settings.
Towards Practical Homomorphic Time-Lock Puzzles: Applicability and Verifiability

Time-lock puzzle schemes allow one to encrypt messages for the future. More concretely, one can efficiently generate a time-lock puzzle for a secret/solution s, such that s remains hidden until a specified time T has elapsed, even for any parallel adversaries. However, since computation on secrets within multiple puzzles can be performed only when all of these puzzles are solved, the usage of classical time-lock puzzles is greatly limited. Homomorphic time-lock puzzle (HTLP) schemes were thus proposed to allow evaluating functions over puzzles directly without solving them. However, although efficient HTLP schemes exist, more improvements are still needed for practicability. In this paper, we improve HTLP schemes to broaden their application scenarios from the aspects of applicability and verifiability. In terms of applicability, we design the first multiplicatively HTLP scheme with the solution space over $$\mathbb {Z}_n^*$$ , which is more expressible than the original one, e.g., representing integers. Then, to fit HTLP into scenarios requiring verifiability that is missing in existing schemes, we propose three simple and fast protocols for both the additively HTLP scheme and our multiplicatively HTLP scheme, respectively. The first two protocols allow a puzzle solver to convince others of the correctness of the solution or the invalidity of the puzzle so that others do not need to solve the puzzle themselves. The third protocol allows a puzzle generator to prove the validity of his puzzles. It is shown that a puzzle in our scheme is only 1.25 KB, and one multiplication on puzzles takes simply 0.01 ms. Meanwhile, the overhead of each protocol is less than 0.6KB in communication and 40 ms in computation. Hence, HTLP still demonstrates excellent efficiency in both communication and computation with these versatile properties.
De-noising of salt and pepper noise using deep learning-based alpha-guided grey wolf optimization

The real images are undermined by salt and pepper noise due to uproarious sensors or communication errors. In this paper, a hybrid SAR image noise reduction using the adaptive pulse-coupled neural network (APCNN) which is optimized by an alpha-guided grey wolf optimizer (AgGWO) in the shearlet transform domain has been proposed. The shearlet transform is utilized to decompose the input SAR image. After the completion of AgGWO optimization, PCNN filtering strategy has been utilized to supplant the noisy pixels into related information pixel components, from which a restoration of the noise reduced images can be obtained. This proposed methodology efficiently resolves the difficulties that emerge from the basic PCNN parameter determination problem. In this methodology, the noisy pixels are secluded well from the image and original noiseless pixels are reestablished well, which can prompt better conservation of edges of an image. This proposed APCNN-AgGWO method has also been compared with other existing noise reduction methods and it yields superior de-noising impact, in terms of structural similarity index measure (SSIM) of 98.46%, peak signal-to-noise ratio (PSNR) of 39.49%, and standard deviation (STD) of 38.64%.