Reviewerly/exHarmony huggingface dataset:
Feature extraction for subtle anomaly detection using semi-supervised learning

The demand for automated and effective monitoring techniques has soared with the increased digitization of industrial monitoring systems. State-of-the-art machine learning methods are effectively detecting abrupt changes in system states. However, these methods lack comparable maturity in detecting subtle changes that may be signs of incipient faults. This manuscript argues that the current anomaly detection methods can be enhanced by exploring weak patterns to enable subtle variation detection. Specifically, the concept of semi-supervised learning is employed, with labels representing knowledge about some anomalous conditions of a system. The basic idea is to extract a candidate set of weak patterns discarded by state-of-the-art baselining algorithms. With few labeled anomalous data, the algorithm selects the weak patterns and allows for their possible fusion using the highest sensitivity to the labeled anomalies. The method's applicability is demonstrated using a representative pressurized water reactor (PWR) model simulated by Dymola.
Intellectual Property Law

<italic>Intellectual Property Law</italic> provides a detailed analysis of intellectual property law with reference to a wide range of academic opinion, giving a broad context for exploring the key principles of the subject. The book covers a number of areas of intellectual property law including copyright, patents, the legal regulation of designs, trade marks and passing off, confidential information, misuse of private information, and litigation and remedies. In this sixth edition, the introduction has been updated to take account of the United Kingdom leaving the European Union. Important developments covered include CJEU decisions on the concept of a copyright work, communication to the public, digital exhaustion and defences, and the Court of Appeal’s analysis of joint authorship.
Possibility of detecting the gravity of an object frozen in a spatial superposition by the Zeno effect

While quantum probes surely feel gravity, no source of gravity has been prepared in a delocalized quantum state yet. Two basic questions need to be addressed: how to delocalize a mass sufficiently large to generate detectable gravity and, once that state has been prepared, how to fight localization by decoherence. We propose to fight decoherence by freezing the source in the desired state through the Zeno effect. Successful implementation can be verified by scattering a probe in the effective potential generated by the source. Besides putting forward the idea, we provide an estimation of the values of the parameters required for the proposal to be feasible. Overall, the proposal seems as challenging as other existing ones, although the specific challenges are different (e.g., no entanglement needs to be preserved or detected, but the Zeno freezing must be implemented).
ECO-GNN: Signoff Power Prediction Using Graph Neural Networks with Subgraph Approximation

Modern electronic design automation flows depend on both implementation and signoff tools to perform timing-constrained power optimization through Engineering Change Orders (ECOs), which involve gate sizing and threshold-voltage ( V th )-assignment of standard cells. However, the signoff ECO optimization is highly time-consuming, and the power improvement is hard to predict in advance. Ever since the industrial benchmarks released by the ISPD-2012 gate-sizing contest, active research has been conducted extensively to improve the optimization process. Nonetheless, previous works were mostly based on heuristics or analytical methods whose timing models were oversimplified and lacked of formal validations from commercial signoff tools. In this article, we propose ECO-graph neural networks (GNN), a transferable graph-learning-based framework, which harnesses GNNs to perform commercial-quality signoff power optimization through discrete ( V th -assignment. One of the highlights of our framework is that it generates tool-accurate optimization results instantly on unseen netlists that are not utilized in the training process. Furthermore, we propose a subgraph approximation technique to improve training and inferencing time of the proposed GNN model. We show that design instances with non-overlapping subgraphs can be optimized in parallel so as to improve the inference time of the learning-based model. Finally, we implement a GNN-based explanation method to interpret the optimization results achieved by our framework. Experimental results on 14 industrial designs, including a RISC-V-based multi-core system and the renowned ISPD-2012 benchmarks, demonstrate that our framework achieves up to 14× runtime improvement with similar signoff power optimization quality compared with Synopsys PrimeTime , an industry-leading signoff tool.
Success probability for postselective transformations of pure quantum states

The situation is studied when an ensemble of pure quantum states is mapped onto another ensemble of pure quantum states. In general, this operation may not be done with unit probability. In this work, the bounds for success probability of such operation are provided, with closed-form expressions involving the Gram matrices and quantum max-relative entropy. We also discuss a partial version of this operation which has arbitrary success probability.
Using Zero-Knowledge to Reconcile Law Enforcement Secrecy and Fair Trial Rights in Criminal Cases

The use of hidden investigative software to collect evidence of crimes presents courts with a recurring dilemma: On the one hand, there is often clear public interest in keeping the software hidden to preserve its effectiveness in fighting crimes. On the other hand, criminal defendants have rights to inspect and challenge the full evidence against them, including law enforcement's investigative methods. In fact, in the U.S. adversarial legal system, the defendant's rights to scrutinize the government's tools are crucial to the truth-seeking process and to keeping law enforcement conduct lawful and constitutional. Presently, courts balance these conflicting interests on a case-by-case basis through evidentiary privilege law, often voicing their frustration with the challenging dilemma they face. We demonstrate how judicious use of a sophisticated cryptographic tool called Zero Knowledge Proofs (ZKPs) could help to mitigate this dilemma: Based on actual court cases where evidence was collected using a modified version of a peer-to-peer software, we demonstrate how law enforcement could, in these cases, augment their investigative software with a ZKP-based mechanism that would allow them to later provide full responses to challenges made by a defense expert -- and allow a defense expert to independently verify law enforcement claims -- while keeping the software hidden. We demonstrate the technical feasibility of our mechanism via a proof-of-concept implementation. We also propose legal analysis that justifies its use, discusses its merits, and considers the legal implications that the very existence of such a mechanism might have, even in cases where it has not been used. Our proof-of-concept may also extend to other verification dilemmas in the legal landscape.
Location-Aware Augmented-Reality for Predicting Sea Level Rise in Situ

Public awareness of climate change's growing impact on coastal areas is vital for assessing and managing future environmental changes in order to restrain its effect. Accessibility to climate change data and evident display of its consequences can promote social engagement. Augmented Reality (AR) technology can efficiently communicate scientific data, advocating the environmental message. However, AR comes with usability and interaction challenges especially outdoors inbright sun light, noise and traffic. This paper presents a location-aware, interactive visualization of the predicted sea level rise on the coastal area of the old city of Chania in Crete, Greece. Usability challenges in relation to AR operating outdoors are tackled in relation to non-obstructive virtual content placement and interaction, while the integration of external GPS data enable accurate geo-location. An interactive system enables the user to witness the sea level rise impact on site, in real-time, up to the year 2100 prompting immediate awareness of future environmental changes and associated hazards.
Minimum-Violation Velocity Planning with Temporal Logic Constraints

Motion planning for autonomous agents requires compliance with a variety of constraints. However, in some situations, all constraints cannot be simultaneously satisfied, e.g., due to the misbehavior of other traffic participants. In such situations, one strategy for guaranteeing a feasible behavior of the system is deliberately ignoring less important constraints while maintaining the satisfaction of more important ones. We address this problem by presenting a novel velocity planner for an autonomous shuttle that violates temporal logic constraints as little as possible in the sense that important constraints are violated last. In particular, we provide an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A$</tex> *-based velocity planner that minimally violates a hierarchically ordered set of constraints formalized in signal temporal logic. We apply our approach to challenging scenarios from the CommonRoad benchmark suite, showing that our proposed method yields easily interpretable and explainable decisions.
Results of Approbation of the Method for Predicting the Classification Accuracy of Multichannel Images

The results of verification of the method for predicting the classification accuracy for three-channel remote sensing images are presented. This study is carried out based on pixel-by-pixel classification according to the maximum likelihood criterion. As a criterion for classification accuracy, the weighted total probability of correct recognition of classes is taken, where the weight coefficients are a priori probabilities of the occurrence of objects from the corresponding classes. The results of comparison of statistical estimates of a priori probabilities of classes and their predictions by the Monte Carlo method using “hard” and fuzzy classification are presented. Empirical dependences of the relative errors of prediction of the probabilities of correct recognition on the sample size are obtained. It is shown that predictions of the total probability of correct recognition, provided that the random sample is representative and of sufficient volume, produce the specified reliability. At the same time, the volume of processed data is significantly (at least by 1-2 orders of magnitude) reduced. This, in turn, leads to the corresponding reduction in the complexity of calculations and the time spent on obtaining predictions for the desired criteria.
Synthesis of a Two Cascade Neural Network for Time Series Forecasting

The paper suggests the method for the synthesis of a two-cascade neural network for time series forecasting. The first cascade contains the basic (popular) time series forecast models with the optimal parameters for the given forecast step that allow the implementation in neural basis. The first cascade includes the basic models from the given system of models that are important for the improvement of the forecast quality. Each model of the first cascade of the neural network determines one coordinate of the weight vector of the neural element of the second cascade that is found as a result of their joint learning.
Method of automatic identification of ruler scales on images to solve the problem of estimating the geometric dimensions of certain objects

Detection of pixels to real-world unit correspondence on the image is an important step for the further analysis of the geometrical characteristics of the objects on the image. In this paper, I propose an iterative method of automatic identification of the ruler scales on the image based on digit recognition. Experiments showed a mean absolute error of 6.63 px and median absolute error of 4 px on high-resolution images <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(3104\mathrm{x}1746\ \text{px})$</tex> compared to the manual measurement results. I focus on the medical domain images, but this method can be used in many others domains like forensic, museum business, etc.
Detection and Classification of Road Damage Using CNN with Hyperparameter Optimization

Accidents in Indonesia are caused by various factors, including damage. To reduce these factors, manual surveys are carried out periodically to detect road damage, but manual surveys make road repairs hampered and uneven. This study looks at road damage detection systems to help classify and assess road conditions, as well as evaluation and assessment materials for use by relevant agencies. To determine the type of damage, the data will be processed using the Convolutional Neural Network (CNN) algorithm and the SSD V2 model. It is then transferred to the website via FTP (File Transfer Protocol) (FTP). Damage image, location, classification results, and degree of damage depending on the Surface Distress Index (SDI) approach is one of the data displayed on the website. The research was conducted using two methods, namely changing the hyperparameter values on MobileNet SSD V2 and comparing the two models MobileNet SSD V2 and MobileNet SSD V1. The test results demonstrate the best learning rate and model for detecting road damage. The MobileNet V2 model is the most accurate with tuning hyperparameters and by using step 500,000 and batch size 8 obtained optimal results and used in the detection of road damage.
Comparison and integration of tactical path planning approaches for Urban Air Mobility

This paper develops several tactical path planning strategies specifically tailored for urban air mobility. The developed strategies not only account for obstacles and traffic, but also consider other relevant constraints to ensure a safe flight in urban scenarios such as risk and weather information and vehicle specifics and limitations (maximum altitude, flight path angle and endurance). In addition, the problem of ensuring reliable navigation performance is taken into account at planning level. Three tactical strategies are accounted for, which either update the time history on the trajectory or spatially modify the three-dimensional path by acting at local or global level. The three approaches are compared in the results to assess their performance as a function of the conflict geometry.
The Discrepancies of Online Translation-Machine Performances: A Mini-Test on Object Language and Metalanguage

This research has explored the discrepancies in online translation-machine performances through a mini-test on object language and metalanguage translation. Within this framework, object language is the verbal language with its literal meaning or denotation, as it factually is, while metalanguage is the language with its figurative meaning or connotation, which results from human creative imagination. The words sitting duck as a duck which is sitting (bebek duduk) is an object language while sitting duck as an easy target (sasaran empuk) is a metalanguage. This research has gone through six methods: online observation, online-machine translation, auto-expert judgment, verification, classification, and interpretation. The discrepancies within this quasi-qualitative research are obtained from verification on four set-up aspects of 10 corpus data, i.e. object language alone, object language within context, metalanguage alone, and metalanguage within context. Upon the analyses of English-Indonesian translation performances by Google Translate, Bing Microsoft Translator, Yandex Translate, and Systran Translate, the high-percentage discrepancies of translation machine performances mostly happen in the translations of metalanguage, while the translations of object language are successful enough with low-percentage discrepancies. Upon the findings of this research, online translation should be developed and improved within the aspects of metalanguage in the target language. Theoretically, the findings propose the inclusion of object language and metalanguage in the online translation machines, while empirically they challenge more metalanguage words to exercise the appropriacy of online translation machines for their better service ahead. This study can also be a research model for other languages in the world in the context of online translationmachine performances.
Infinite Spaces Using Recursive Portals

One of the strongest aspects of virtual reality (VR) hardware and applications is the immersion it provides to the user. While combination of techniques such as real-walking for locomotion, head movement for gaze direction and hand detection for controls are great for immersive experience; when the real space and the virtual environment does not match, developers must resort to alternative locomotion techniques such as teleportation, controller-assisted movement or in-place walking via external devices. Despite lifting the restrictions on physical spaces, these techniques may need expensive or cumbersome hardware, cause nausea and dizziness on the user or simply diminish the immersion. We propose a portal based environment design method and a modern rendering engine to visualize the created scenes. Our method uses the same physical space by overlapping multiple regions of the virtual environment on top of each other while keeping the affine properties of the scene. The user can walk between different virtual rooms while staying in a limited physical space. We also conduct a user study to compare user experience to state-of-the-art physical-virtual environment mapping method; in which, the users strongly favored our method.
Privacy Preserving Classification via Machine Learning Model Inference on Homomorphic Encrypted Medical Data

This paper studies the use of homomorphic encryption to preserve privacy when using machine learning classifiers. The paper compares different parameters and explores drawbacks in terms of accuracy, speed, and packet size when using encrypted data versus unencrypted data by using a client-server use case. These comparisons were performed during multiple tests on different datasets with different sizes and complexity. These tests show it is possible to do machine learning with homomorphic encrypted data without losing accuracy. However, the increased processing time, data size and communication time have to be considered.
Comparison of Heuristic and Metaheuristic Algorithms

The development of computer technologies day by day has enabled the use of new technological approaches in solving many problems. Technological innovations are used to solve many problems, especially thanks to hardware devices that develop in capacity, and algorithms that continue to develop rapidly. These technologies are used to provide significant time and/or performance gains in solving problems. The great change in software technologies has led to the emergence of many smart algorithms. With the development of artificial intelligence approaches, algorithms inspired by many nature and natural events have been used in the solution stages of problems. These approaches, which take the intuitive movements of living things as an example, have revealed a new solution approach in addition to the mathematical and statistical models used for problem-solving. These algorithms, which are called heuristic algorithms, aim to create the most appropriate solution set by considering the time and/or performance gains of the solution sets. The use of these algorithms, which can be used in solving many problems from production to design, from optimization problems to classification problems, is quite common. With the development of heuristic algorithms, new approaches such as meta-heuristic and hyper-heuristic algorithms have been introduced. In our study, a detailed examination has been made of these algorithms, which are classified as heuristic algorithms, and especially heuristic algorithms have been compared with meta-heuristic (meta-heuristic) algorithms, and details on their similarities and differences have been tried to be presented. Algorithms are shown by classifying them according to their structures. In particular, the basic features of heuristic and meta-heuristic algorithms such as search space, performance, workspace, search behaviors, search process, simplicity, reliability, flexibility, and initial requirements have been examined and the similarities and differences between them have been tried to be shown with examples. Information on current algorithms published in recent years and their applicability for solving problems are also given.
Derivation of the Utility Evaluation Indicators of the Air Force LVC Synthetic Battlefield Training System

The Armed Forces are training and practicing by various training assets. In order to achieve a specific training purpose, the composition of training assets forms one training system. When the form provides training purpose by all training systems that is recognized as simulations which is composed by actual weapons systems(live) such as aircraft, virtual systems such as simulators, and constructive systems such as wargame models, and research on system linkage and integration is underway to maximize each advantage.BR The purpose of this study is to derive a utility evaluation indicators of the LVC(Live, Virtual, Constructive) system. The indicators to evaluate the utility of the LVC training system can be composed of sub-items. In this study, evaluation indicators are derived through several surveys and statistical verification by various experts using the Delphi technique, and hierarchically organized to enable future system evaluation and further research.
Real-time siamese multiple object tracker with enhanced proposals

Maintaining the identity of multiple objects in real-time video is a challenging task, as it is not always feasible to run a detector on every frame. Thus, motion estimation systems are often employed, which either do not scale well with the number of targets or produce features with limited semantic information. To solve the aforementioned problems and allow the tracking of dozens of arbitrary objects in real-time, we propose SiamMOTION. SiamMOTION includes a novel proposal engine that produces quality features through an attention mechanism and a region-of-interest extractor fed by an inertia module and powered by a feature pyramid network. Finally, the extracted tensors enter a comparison head that efficiently matches pairs of exemplars and search areas, generating quality predictions via a pairwise depthwise region proposal network and a multi-object penalization module. SiamMOTION has been validated on five public benchmarks, achieving leading performance against current state-of-the-art trackers. Code available at: https://github.com/lorenzovaquero/SiamMOTION