Miracle-dz/UniFAR-FTUs huggingface dataset:
Diagnosing Double Faulty Chains through Failing Bit Separation

Scan chain diagnosis plays a key role in ramping up production yield. However, this is challenged by high test compression ratios of modern designs, increasing the probability of multiple faulty chains feeding the same compressor. In our analyzed silicon data, we observe that 8.76% of scan chain failures have such two faulty chains. We propose a technique to help address this problem, separating the superposition of chain fault effects to diagnose these chips. This technique first uses jump simulation to identify and classify failures that are attributable to only one of the faulty chains. It then uses commercial tools to diagnose the classified failures of each chain individually. Experiments are conducted on both simulated and silicon test data to show the efficacy of our technique, and the proposed method showed improvements over standard diagnosis with commercial tools in resolution (2.38 candidates) and accuracy (92.0%). This method was also applied on industrial chips with potentially systematic double faulty chain failures.
Borel measurable mappings for nonseparable metric spaces

The main object of this paper is the extension of part of the basic theory of Borel measurable mappings, from the "classical" separable metric case, to general metric spaces. Although certain results of the standard theory are known to fail in the absence of separability, we show that they continue to hold for the class of "adiscrete" mappings. This class is shown to be quite extensive, containing the continuous mappings, all mappings with a separable range, and any Borel measurable mappings whose domain is a Borel subset of a complete metric space. The last result is a consequence of our Basic Theorem which gives a topological characterization of those collections which are the inverse image of an open discrete collection under a Borel measurable mapping. Such collections are shown to possess a strong type of a-discrete refinement. The properties of a-discrete mappings together with the known properties of "locally Borel" sets allow us to extend, to general metric spaces, well-known techniques used for separable spaces. The basic properties of "complex" and "product" mappings, well known for separable spaces, are proved for general metric spaces for the class of a-discrete mappings. A consequence of these is a strengthening of the basic theorem of the structure theory of nonseparable Borel sets due to A. H. Stone. Finally, the classical continuity properties of Borel measurable mappings are extended, and, in particular, a generalization of the famous theorem of Baire on the points of discontinuity of a mapping of class 1 is obtained. Introduction. In this paper we are primarily concerned with extending part of the "classical" (separable metric) theory of Borel measurable mappings to general metric spaces. Some work of this nature has already been done-notably the results of Montgomery in [10], the related work of Kuratowski in [7], and the work of Stone in [13]. It has long been suspected (and in some cases known, e.g. [13, p. 29] and [6, p. 488]) that certain basic results of the standard theory will not extend to nonseparable spaces, at least without imposing some further conditions upon the mappings (or perhaps the spaces themselves). Here we consider mappingsf: X -* Y which are "u-discrete" in the sense that there exists a u-discrete collection R of subsets of X such that to each open Vc Y there corresponds a subcollection Rv of Received by the editors June 17, 1970. AMS 1969 subject classifications. Primary 0440, 2820, 5460; Secondary 2810, 5435.
A Survey of Orientation and Mobility Specialist’s Use of Telepractice During COVID-19

Introduction: This survey sought to establish a baseline for the remote delivery of services (telepractice) by orientation and mobility (O&M) specialists nationwide, which became pervasive in 2020 as a result of the COVID-19 pandemic. Method: An online survey was distributed using professional email lists and then used snowball sampling to obtain a convenience sample. The survey was completed by 66 O&M specialists using telepractice at the time of the survey, the primary criterion for inclusion. Results: Engaging in telepractice was a direct result of the pandemic for 90.77% of the participants. Most professionals’ caseloads remained relatively similar to the size they were prior to the pandemic (69.70%), and instruction used a one-on-one model (90.77%). For the most part, professionals were teaching conceptual knowledge rather than actual travel skills using video conferencing software. Most participants indicated they had not received training in telepractice (81.25%). Only 20.00% of participants found telepractice for O&M satisfactory, but 26.16% of participants indicated they would probably continue using telepractice after the pandemic. Most participants (72.13%) were unsure if they were covered by professional liability insurance. Discussion: Most participants were thrust into telepractice and very few received training in telepractice. It is likely that the tools used were tools of convenience. Despite a lack of preparation and lukewarm satisfaction levels, a noteworthy percentage of respondents intend to continue to use telepractice after the end of the pandemic. The liability risks associated with this new model have not been widely assessed. Implications for Practitioners: The most effective tools for O&M telepractice have not yet been identified. Practitioners and researchers can work together to develop and promote promising practices and tools for O&M telepractice. Professional liability should always be investigated before providing services. A tool for professionals to assess risk should be developed.
Low Complexity Approaches for End-to-End Latency Prediction

Software Defined Networks have opened the door to statistical and AI-based techniques to improve efficiency of networking. Especially to ensure a certain Quality of Service (QoS) for specific applications by routing packets with awareness on content nature (VoIP, video, files, etc.) and its needs (latency, bandwidth, etc.) to use efficiently resources of a network.Predicting various Key Performance Indicators (KPIs) at any level may handle such problems while preserving network bandwidth.The question addressed in this work is the design of efficient and low-cost algorithms for KPI prediction, implementable at the local level. We focus on end-to-end latency prediction, for which we illustrate our approaches and results on a public dataset from the recent international challenge on GNN [1]. We propose several low complexity, locally implementable approaches, achieving significantly lower wall time both for training and inference, with marginally worse prediction accuracy compared to state-of-the-art global GNN solutions.
S-MKI: Incremental Dense Semantic Occupancy Reconstruction Through Multi-Entropy Kernel Inference

Autonomous robots are often required to acquire high-level prior knowledge by continuously reconstructing the semantics and geometry of the surrounding scene, which is the basis of exploration and planning. Most existing continuous semantic mapping algorithms cannot distinguish potential differences in voxels, resulting in an over-inflated map. Furthermore, fixed-size query ranges introduce high computational complexity. Based on the limitation of over-inflation and inefficiency, this paper proposes a novel incremental continuous semantic occupancy mapping algorithm (S-MKI). The key innovation of this work comes from the two models in the preprocessing stage. On the one hand, Redundant Voxel Filter Model utilizes context entropy to filter out redundant voxels to improve the confidence of the final map, where objects have accurate boundaries with sharp edges. On the other hand, Adaptive Kernel Length Model adaptively adjusts the kernel length with class entropy, which reduces the inherent amount of training data. The final multientropy kernel inference function is formulated to integrate these two models to infer sparse noisy sensor data into dense accurate 3D maps. Experimental results conducted in both indoors and outdoors datasets validate that S-MKI outperforms existing methods.
A Heuristic Approach to Conformity Assessment by Clamp-On Testing of a Multi-Rod Earthing System

The paper presents an approach to conformity assessment of a multi-rod earthing system based on the experience of testing of several multi-rod earth fields using the clamp-on testing method. The test results are compared with the results obtained by an alternative method. A multiplier for reduction of the results of the clamp-on method is proposed.
Light trapping in thin-film silicon solar cells with photonic structures

Efficient photovoltaic conversion of solar energy requires optimization of both light absorption and carrier collection. This manuscript reviews theoretical studies of thin-film silicon solar cells with various kinds of ordered and disordered photonic structures. Light trapping capabilities of these systems are analyzed by means of rigorous coupled-wave analysis and compared with the so-called Lambertian limit as given by a fully randomizing light scatterer. The best photonic structures are found to require proper combinations of order and disorder, and can be fabricated starting from pre-patterned rough substrates. Carrier collection is studied by means of analytic models and by full electro-optical simulations. The results indicate that thin-film silicon solar cells can outperform bulk ones with comparable material quality, provided surface recombination is kept below a critical level, which is compatible with present-day surface passivation technologies.
Toroidal Origami Monotrack: Mechanism to Realize Smooth Driving and Bending for Closed-Skin-Drive Robots

We propose a novel toroidal origami monotrack capable of smooth-skin driving and bending for closed-skin-drive robots. Monotracks are a promising solution for achieving high mobility in unstructured environments. Toroidal-drive mechanisms enable whole skin drive; however, conventional methods experience unexpected wrinkling and buckles that lead to a large resistance. In this study, we propose an origami bellows structure with multiple rollers that can maintain the skin tension and deal with the cause of large friction between the skin and the body. The origami structure design method is presented, and the bending angle range and required drive force were derived through a theoretical analysis. The validity of the effectiveness of the concept was verified through prototype testing.
Performance Evaluation of Elastic Optical Networks under Scenarios with Unequal Distribution of Service Types per Route Length

This paper addresses the problem of heterogeneously distributed service types according to the route lengths and the impact on the EON performance. Blocking probability in different aspects of the network, such as network-related, inter-service, and considering specific route lengths, is evaluated according to a methodology that changes the percentage of services in the routes with specific lengths, keeping a fixed network load. A 4-node bidirectional ring topology is used to present the methodology and evaluate the initial scenarios. However, this strategy can be generalized to other scenarios, standing as a helpful tool to assess the coexistence of diverse modulation formats.
Autonomous Path Planner Vehicle Using Raspberry PI

The Autonomous Vehicle (AV) is an intelligent cognitive engineering system that processes streams of observations from various onboard sources, such as cameras, ultrasonic sensors, GPS units, radars, LiDARs, and/or inertial sensors, to guide itself automatically. This allows the AV to avoid collisions, park itself, and navigate to its destination without human intervention. Every day, the most current vehicle series makes little but significant improvements to their vehicles. The most important step in overcoming difficulties with this project is the design of the routes that will be taken. The most important aspect is broken down in this article, and it may be possible to get a superior solution for transportation as well as many other sectors that may help the society in a significant amount with services such as an advanced driving assistance system (ADAS). Within the scope of the project, we will make use of a GPS signal to ascertain the destination in order to enable AV to automatically ascertain the route. An ultrasonic sensor will detect whether or not there is an obstacle in the way of the vehicle. If it senses an impediment in front of the sensor, such as another car, a pedestrian, or another item, the vehicle will be aware of it and make an effort to avoid colliding with it. Using the instructions for the Arduino and the raspberry pi, it will identify the alternate path that leads to its target that is the shortest. There are a variety of techniques that may be used in order to correctly locate the destination. Some of these strategies include simultaneous localization and mapping (SLAM). Planning your route using GPS technology might be one of the most efficient methods to go to any location. If the route planning is successful, there is still a chance that the issue can be resolved. The fundamental objective of this article is to map out the route that AV will take so that we may successfully complete the mission.
Combinatorial studies for determining properties of thin-film gold–cobalt alloys

A library of gold–cobalt alloys was synthesized by combinatorial methods to explore potential contact materials for microfabricated microrelays. After a compositionally graded film was deposited, it was subjected to heat treatments to create precipitates and to promote precipitation hardening. Using a high-throughput screening method, the film was then characterized for mechanical hardness, sheet resistance, composition, and microstructure by using nanoindentation, four-point probe, x-ray photoelectron spectroscopy, and transmission electron microscopy. The hardness exhibited a linear behavior from pure gold to pure cobalt from 2 to 9 GPa. The microstructure included a metastable gold–silicide with a grain size that seems dependent on the amount of cobalt. From this combinatorial method, we gain an understanding of the material’s structure–property relationship and can illuminate the link between mechanical and electrical properties to composition. This work presents the experiments and techniques for map.
Tree‐structured complex wavelet‐based Bayesian compressive sensing method

Using complex wavelets, the tree-structured complex wavelet Bayesian compressive sensing method is proposed. The hidden Markov tree is used for inter-scale relations of wavelet coefficients. The spike-slab distribution is considered for the prior. From simulation results, the number of measurements and the value of reconstruction error reduce by 25% and 65%, respectively.
Comment on ''An Investigation into the Performance of Particle Swarm Optimization with Various Chaotic Maps''

This paper researched three definitions of Gauss map and found that the definition of “Gauss map” in the paper of Arasomwan and Adewumi may be incoherent with other publications. In addition, we analyzed the difference of continuous Gauss map and the floating-point Gauss map, and we pointed out that the floating-point simulation behaved significantly differently from the continuous Gauss map.
Intelligent Cockpit for Intelligent Vehicle in Metaverse: A Case Study of Empathetic Auditory Regulation of Human Emotion

Advances in technologies, such as intelligent connected vehicles and the metaverse are driving the rapid development of automotive intelligent cockpits. From the perspective of the cyber–physical–social system (CPSS), this study proposed the intelligent cockpit composition framework which includes three layers of perception, cognition and decision, and interaction. Meanwhile, we also describe the relationship between the intelligent cockpit framework and the outside environment. The framework can dynamically perceive and understand humans, and provide feedback on the understanding results, which is beneficial to provide a safe, efficient, and enjoyable experience for humans in the intelligent cockpit. In the cognition and decision layers of the proposed framework, we design a case study of active empathetic auditory regulation of driver anger, focusing on improving road traffic safety. We conducted an in-depth interview experiment and designed two auditory regulation materials of active empathy speech and text-to-speech (TTS) speech. Next, 30 participants were recruited, and they completed a total of 240 anger-regulated driving experiments in the straight and obstacle avoidance scenarios. Finally, we quantitatively analyzed and compared the participants’ subjective feelings, physiological changes, driving behaviors, and driving risks, as well as validated the driver anger regulation quality of AES and TTS. The proposed research methods results are beneficial to the design of future intelligent cockpit emotion regulation systems, toward a better intelligent cockpit.
Shape and Motion Optimization of Rigid Planar Effectors for Contact Trajectory Satisfaction

We propose a framework for co-optimizing the shape and motion of rigid robotic effectors for planar tasks. While planning object and robot-object contact trajectories is extensively studied, designing an effector that can execute the planned trajectories receives less attention. As such, our framework synthesizes an object trajectory and object-effector contact trajectory into an effector trajectory and shape that (a) does not penetrate the object, (b) makes contact with the object as specified, and (c) optimizes a user-specified objective. This simplifies manipulator control by encoding task-specific contact information in the effector's geometry. Our key insight is posing these requirements as constraints in the effector's reference frame, preventing the need for explicit parameterization of the effector shape. This prevents artificial restrictions on the shape design space. Importantly, it also facilitates posing the shape and motion design problem as a tractable nonlinear program. Our method is particularly useful for problems where the shape of the effector surface must be precisely chosen to achieve a task. We apply our method to several such problems, including jar-opening and picking up objects in constrained spaces. We evaluate the performance and computational cost of our method, and provide a physical experiment of a robotic arm picking up a screwdriver from a table with a designed tool.
A Meta-learning approach to Predict Non-performing Loans in Sri Lankan Financial Institutions

Most financial institutions make the majority of their income from loan interest. However, due to the current financial crisis in Sri Lanka, non-performing loans have been the focus of financial industry concerns. Before the crisis, financial institutions were more ready to lend to businesses and individuals. Since the crisis, the rate of non-performing loans has increased, limiting the company’s growth. Predicting the likelihood of nonperforming loans can help in lowering this credit risk. Therefore, this paper presents a machine learning approach to predict nonperforming loans of a financial company in Sri Lanka. Moreover, this study also attempts to develop a meta-model that combines various classifiers, including K-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine, and Naïve Bayes. The meta-model is also compared with the baseline models. The predictive performance of all models is compared using accuracy, precision, recall, F1-score, and AUROC score. At the end of the study, it was identified that the meta-learning model is the most effective model to handle this case, with a classification accuracy of 93.09%, precision of 82.68%, recall of 92.17%, F1 score of 86.24%, AUROC score of 96.0%, sensitivity of 92.44% and specificity of 93.17%.
Realization algorithm for determining generalized state-space representations

A realization algorithm is presented that determines a generalized state-space representation from a so-called column-pseudoproper right MFD. This algorithm is a generalization of the realization algorithm given by Wolovich and is proved to be controllable in the Rosenbrock sense. Moreover, the dimension of the realization is equal to the determinantal degree of the given right MFD in a generalized sense.
Data-driven Kalman Filter with Kernel-based Koopman Operators for Nonlinear Robot Systems

Designing the Kalman filter for nonlinear robot systems with theoretical guarantees is challenging, especially when the dynamics model is unavailable. This paper proposes a data-driven Kalman filter algorithm using kernel-based Koop-man operators for unknown nonlinear robot systems. First, the Koopman operator using sparse kernel-based extended dynamic decomposition (EDMD) is presented to learn the unknown dynamics with input-output datasets. Unlike classic EDMD, which requires manual selection of kernel functions, our approach automatically constructs kernel functions using an approximate linear dependency analysis method. The resulting Koopman model is a linear dynamic evolution in the kernel space, enabling us to address the nonlinear filtering problem using the standard linear Kalman filter design process. Despite this, our approach generates a nonlinear filtering law thanks to the adopted nonlinear kernel functions. Finally, the effectiveness of the proposed approach is validated by simulated experiments.
Construction Method Of Fuzzy Decision Trees For Identification The Computer System State

The efficiency of using fuzzy decision trees to identify the state of a computer system has been studied. The method of fuzzy classification is offered. Based on it, a model was created, trained and tested with real data According to the results of the research, a method of identifying the state of a computer system using the classifier of fuzzy decision trees with a special procedure for the formation of fuzzy sets and membership functions is proposed. The software in which the offered technique of the decision of a problem of identification of a condition of computer system is realized and investigated is developed. The evaluation of the efficiency of the developed classifier in comparison with standard machine learning algorithms is performed. Prospects for further research are the development of an ensemble of decision-making and fuzzy decision-making trees based on boosting and bagging using meta-algorithm improvements and a method for calculating classifier weights, as well as a special final decision-making procedure based on common and fuzzy classifier decisions.