Miracle-dz/UniFAR-FTUs huggingface dataset:
Data Driven Product Portfolio Analysis of Electric Motors Based on Product Platforms Using Knowledge-Based Systems

For a company it is necessary to know, which products can be configured using carry-over-parts or the same technology. This can become quite relevant in the context of automobile electrification, where complex mechatronic systems are used. Consisting of various mechanical components, these systems perform the required function while being actuated by electronically controlled motors. To solve this, a novel mechanism for data driven portfolio analysis based on product platforms using knowledge-based systems is proposed in this paper. Further, the mechanism is tested by applying it to an electrical motors' case study. Using this method, all possible combinations of a product platform are calculated and finally displayed in different product portfolios. Additionally, all the non-feasible motor designs are removed from the solutions portfolio using the acquired knowledge base and performing design checks. The latter are employed for penalising and eliminating from the pareto-front of the designs, which violate the thermal, mechanical and acoustic constraints. The generated product portfolio can be used further to expand the systems engineering collaboration and support decision-making.
Risk-Efficient Bayesian Data Synthesis for Privacy Protection

Statistical agencies utilize models to synthesize respondent-level data for release to the public for privacy protection. In this study, we efficiently induce privacy protection into any Bayesian synthesis model by employing a pseudo-likelihood that exponentiates each likelihood contribution by an observation record-indexed weight ∈[0,1], defined to be inversely proportional to the identification risk for that record. We start with the marginal probability of identification risk for a record, which is composed as the probability that the identity of the record may be disclosed. Our application to the Consumer Expenditure Surveys (CE) of the U.S. Bureau of Labor Statistics demonstrates that the marginally risk-weighted synthesizer provides an overall improved privacy protection. However, the identification risks actually increase for some moderate-risk records after risk-weighted pseudo-posterior estimation synthesis owing to increased isolation after weighting, a phenomenon we label “whack-a-mole. ” We proceed to construct a weight for each record from a collection of pairwise identification risk probabilities with other records, where each pairwise probability measures the joint probability of reidentification of the pair of records, which mitigates the whack-a-mole issue and produces a more efficient set of synthetic data with lower risk and higher utility for the CE data.
Rotational Doppler cooling and heating

Rotational Doppler cooling and heating are generalized from their translational counterparts, revealing dissimilar physics. Doppler cooling is a widely used technique to laser cool atoms, molecules, and nanoparticles by exploiting the Doppler shift associated with translational motion. The rotational Doppler effect arising from rotational coordinate transformation should similarly enable optical manipulation of the rotational motion of nanosystems. Here, we show that rotational Doppler cooling and heating (RDC and RDH) effects embody rich and unexplored physics, including an unexpected strong dependence on particle morphology. For geometrically constrained particles, cooling and heating are observed at red- or blue-detuned laser frequencies relative to particle resonances. In contrast, for nanosystems that can be modeled as solid particles, RDH appears close to resonant illumination, while detuned frequencies produce cooling of rotation. We further predict that RDH can lead to optomechanical spontaneous chiral symmetry breaking, where an achiral particle under linearly polarized illumination starts spontaneously rotating. Our results open up new exciting possibilities to control the rotational motion of nanosystems.
Speech Quality Enhancement for In-Ear Microphone Based on Neural Network

Speech captured by an in-ear microphone placed inside an occluded ear has a high signal-to-noise ratio; however, it has different sound characteristics compared to normal speech captured through air conduction. In this study, a method for blind speech quality enhancement is proposed that can convert speech captured by an in-ear microphone to one that resembles normal speech. The proposed method estimates an inputdependent enhancement function by using a neural network in the feature domain and enhances the captured speech via time-domain filtering. Subjective and objective evaluations confirm that the speech enhanced using our proposed method sounds more similar to normal speech than that enhanced using conventional equalizer-based methods. key words: in-ear microphone, neural network, noise-free speech, speech quality enhancement
Adaptive Distributed Caching for Scalable Machine Learning Services

Applications for Internet-enabled devices use machine learning to process captured data to make intelligent decisions or provide information to users. Typically, the computation to process the data is executed in cloud-based backends. The devices are used for sensing data, offloading it to the cloud, receiving responses and acting upon them. However, this approach leads to high end-to-end latency due to communication over the Internet. This dissertation proposes reducing this response time by minimizing offloading, and pushing computation close to the source of the data, i.e. to edge servers and devices themselves. To adapt to the resource constrained environment at the edge, it presents an approach that leverages spatiotemporal locality to push subparts of the model to the edge. This approach is embodied in a distributed caching framework, Cachier. Cachier is built upon a novel caching model for recognition, and is distributed across edge servers and devices. The analytical caching model for recognition provides a formulation for expected latency for recognition requests in Cachier. The formulation incorporates the effects of compute time and accuracy. It also incorporates network conditions, thus providing a method to compute expected response times under various conditions. This is utilized as a cost function by Cachier, at edge servers and devices. By analyzing requests at the edge server, Cachier caches relevant parts of the trained model at edge servers, which is used to respond to requests, minimizing the number of requests that go to the cloud. Then, Cachier uses context-aware prediction to prefetch parts of the trained model onto devices. The requests can then be processed on the devices, thus minimizing the number of offloaded requests. Finally, Cachier enables cooperation between nearby devices to allow exchanging prefetched data, reducing the dependence on remote servers even further. The efficacy of Cachier is evaluated by using it with an art recognition application. The application is driven using real world traces gathered at museums. By conducting a large-scale study with different control variables, we show that Cachier can lower latency, increase scalability and decrease infrastructure resource usage, while maintaining high accuracy.
The Role of Software-Defined Networking in Cellular Networks

In this paper, we discuss how SDN can contribute to enhance future cellular networks. We first present SDN and describe its characteristics. Then, we explore how SDN can be used to improve current cellular networks, analyzing the advantages and disadvantages, while highlighting some use cases. Finally, we conclude this work exposing some challenges that still require further research to take full advantage of SDN in cellular networks.
Design space exploration of Ferroelectric FET based Processing-in-Memory DNN Accelerator

In this letter, we quantify the impact of device limitations on the classification accuracy of an artificial neural network, where the synaptic weights are implemented in a Ferroelectric FET (FeFET) based in-memory processing architecture. We explore a design-space consisting of the resolution of the analog-to-digital converter, number of bits per FeFET cell, and the neural network depth. We show how the system architecture, training models and overparametrization can address some of the device limitations.
A Novel Privacy Preserving Framework for Large Scale Graph Data Publishing

The need to efficiently store and query large scale graph datasets is evident in the growing number of data-intensive applications, particularly to maximize the mining of intelligence from these data (e.g., to inform decision making). However, directly releasing graph dataset for analysis may leak sensitive information of an individual even if the graph is anonymized, as demonstrated by the re-identification attacks on the DBpedia datasets. A key challenge in the design of graph sanitization methods is scalability, as existing execution models generally have significant memory requirements. In this paper, we propose a novel <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="ding-ieq1-2931903.gif"/></alternatives></inline-formula>-decomposition algorithm and define a new information loss matrix designed for utility measurement in massively large graph datasets. We also propose a novel privacy preserving framework that can be seamlessly integrated with graph storage, anonymization, query processing, and analysis. Our experimental studies show that the proposed solution achieves privacy-preserving, utility, and efficiency.
Toward efficient online scheduling for large-scale distributed machine learning system

viii CHAPTER 1. OVERVIEW . 1 1.
ERIM: Secure, Efficient In-process Isolation with Memory Protection Keys (MPK)

Isolating sensitive state and data can increase the security and robustness of many applications. Examples include protecting cryptographic keys against exploits like OpenSSL's Heartbleed bug or protecting a language runtime from native libraries written in unsafe languages. When runtime references across isolation boundaries occur relatively infrequently, then conventional page-based hardware isolation can be used, because the cost of kernel- or hypervisor-mediated domain switching is tolerable. However, some applications, such as the isolation of cryptographic session keys in network-facing services, require very frequent domain switching. In such applications, the overhead of kernel- or hypervisor-mediated domain switching is prohibitive. In this paper, we present ERIM, a novel technique that provides hardware-enforced isolation with low overhead on x86 CPUs, even at high switching rates (ERIM's measured overhead is less than 1% for 100,000 switches per second). The key idea is to combine protection keys (MPKs), a feature recently added to x86 that allows protection domain switches in userspace, with binary inspection to prevent circumvention. We show that ERIM can be applied with little effort to new and existing applications, doesn't require compiler changes, can run on a stock Linux kernel, and has low runtime overhead even at high domain switching rates.
Energy Evaluations for Wireless IPv6 Sensor Nodes

Typical Wireless Sensor Networks (WSN) include some energy autonomous nodes and main powered router nodes. To meet a reasonable lifetime, i.e. years, the autonomous devices have to encompass strong energy constraints. Our target network architecture consists in such wireless autonomous nodes connected to a backbone based on PLC nodes. Our given building automation application scenario runs a 10 minutes periodic probes sampling which reports a data frame over the Internet using IPv6. For this purpose, our WSN nodes use open RPL/ContikiOS softwares and typical oﬀ the shelf electronic components. In order to optimize the node energy consumption we pro-pose a systemic analysis that includes all the software layers and hardware components. The energy distribution among the diﬀerent components and the critical software parameters are weighted against the global energy consumption. Thanks to measurements and technical data, we propose a simple model that allows to identify pitfalls and determine optimized solutions. Following our established guidelines, we believe our future WSN monitoring platform will achieve more than 10 years lifetime with the target scenario.
Dependability analysis for characterizing Google cluster reliability

Cloud solutions are emerging as a new suitable way of transforming traditional IT data centers to highly available and reliable computing resources for hosting critical applications and data. However, software and hardware failures are a common problem in cloud datacenters that can lead to harmful damages. In this paper, we analyze the physical server failures in the Google cloud datacenter. We study the Google cluster properties to investigate the relationship among physical servers' failure rate and jobs failure events. The failure rate of Google cluster executed jobs and servers is taken into consideration during a 29‐day period.
Bloch wave approach to almost periodic homogenization and approximations of effective coefficients

Bloch wave homogenization is a spectral method for obtaining effective coefficients for periodically heterogeneous media. This method hinges on the direct integral decomposition of periodic operators, which is not available in a suitable form for almost periodic operators. In particular, the notion of Bloch eigenvalues and eigenvectors does not exist for almost periodic operators. However, we are able to recover the homogenization result in this case, by employing a sequence of periodic approximations to almost periodic operators. We also establish a rate of convergence for approximations of homogenized tensors for a class of almost periodic media. The results are supported by a numerical study.
Developing Design Methods - a Conceptual Requirement Framework

Design methods can provide valuable support in structuring and solving complex product design problems. However, the application and the transfer of methods from academia to industry is limited. To date, research has tended to focus on solving this through improved method selection, method adaptation and training. The development of design methods itself has attracted surprisingly low attention. This paper closes this gap and adds a quite new perspective of systematic requirement management of method development. However, the variety of methods, method users and application contexts is a key challenge and does not allow for a universal set of requirements. Thus, this paper transfers the concept of solution-neutral requirements frameworks, which are established in product design, to method development. The framework is derived from analysing and structuring different requirements found in literature. Different requirement sub-/categories allow for accommodating the varying levels of detail of requirements. The framework works like a checklist and helps design researchers to consider the most important requirement categories, which subsequently can be detailed project-specifically.
Using Surrogate Road Network for Map-Matching: A Sensitivity Analysis of Positional Accuracy

Map-matching GPS traces on a reference road network often increases point positional accuracy, especially in urban environment where satellite signal is frequently obstructed by buildings. However, with receivers improvement, road data errors may no longer be ipso facto considered as negligible in front of GPS trace errors. This raises the question of sensitivity of map-matching output precision to input network geometric quality. We address the problem by attempting to relate gain in precision with a network quality index, based on two commonly used measures : averaged Hausdorff distance and areal difference. Analysis has been conducted by performing both GPS traces simulation and network perturbation. Results highlighted that the areal difference (for which we demonstrate that it can be considered as a distance) seems to be better related to map-matching output errors. We also provide an upper bound on the impact of using a surrogate reference network and we apply it to a practical case study.
The Research Process for the Principles of Complex System Description with the Reference to the Energy Generation System

The objective of the study, which we would like to initiate by this paper, is to research the easy to apply rules for the complex system description. These rules will address the problems of the system's complexity which will be disclosed applying the approach presented in this paper. The particularity of our approach is the way to identify and to formulate the key problems of the system's complexity. The starting point for the identification of the problems is the group of the system study tools including e.g. System Dynamics, IDEFO, OTSM-TRIZ methods. Through the study of these tools it is possible to disclose the problems addressed by these tools and also the problems arising due to the interaction between the tool and the user applying it. The whole process of the research is being recorded using the model of contradiction as a formulation of a problem and the network of contradictions as a tool to picture the interrelations between problems. The network of contradictions which is one of the OTSM-TRIZ concepts is applied here as a way to obtain the single page overview of the research process and also as the powerful platform for the further research towards the principles of complex system description. The main solution of the presented study are first interim results presented as a network of contradictions constructed from the disclosed problems of system's complexity. The problems interconnected in the network are valid for the real complex system of the energy generation system for the urban area including CHP plants, applied by us as a reference case study. In further research we are going to show that addressing the problems of system's complexity identified here, it is possible to describe the complex system using a few simple to apply principles. After performing further research, the practical result will be the decrease in the data treatment expenses e.g. time, computing capacity, human involvement.
Stock market trading simulations: Assessing the impact on student learning

Abstract In higher education, business students with different learning styles may not all respond successfully to a straight lecture format. The authors analyze the impact that an experiential learning activity (an optional stock market trading simulation) has demonstrated on student success in a college investments course. Findings suggest that students who engage in the experiential learning component earn higher overall grades and demonstrate substantially higher grade improvements over the course of the semester than do those who do not participate in the simulation. This study reinforces the existing literature and supports the hypothesis that experiential learning components do aid in student learning.
Good Fibrations through the Modal Prism

Homotopy type theory is a formal language for doing abstract homotopy theory --- the study of identifications. But in unmodified homotopy type theory, there is no way to say that these identifications come from identifying the path-connected points of a space. In other words, we can do abstract homotopy theory, but not algebraic topology. Shulman's Real Cohesive HoTT remedies this issue by introducing a system of modalities that relate the spatial structure of types to their homotopical structure. In this paper, we develop a theory of modal fibrations for a general modality, and apply it in particular to the shape modality of Real Cohesion. We then give examples of modal fibrations in Real Cohesive HoTT, and develop the theory of covering spaces.
Path-oriented random testing through iterative partitioning (IP-PRT)

: Path-oriented random testing aims at generating a uniformly distributed sequence of test data from a program input domain space to traverse a desired execution path of the program. To this aim, this article proposes a new algorithm to refine a program inputs domain space from invalid subdomains not covering the path. The validity of the subdomains is checked by a constraint propagation method against the path constraints (PCs). The proposed algorithm uses a divide-and-conquer technique to iteratively split the inputs domain into subdomains and each time refutes those subdomains that are inconsistent with the PCs. The remaining shrunken subdomains provide all possible test data covering the desired path. Obviously, the more accurate the input domain is, the more effective test data will result. Experiments show the proposed method outperformed other related methods on a set of classical benchmark programs.
ROLLING PROCESS SIMULATION USING A DEVELOPED EULERIAN FORMULATION

In this paper, an Eulerian formulation is developed from the more general Arbitrary Lagrangian Eulerian (ALE) formulation and is used in rolling process simulation. Starting from the basic principles of continuum mechanics, a consistent ALE formulation is derived. An Eulerian formulation is then obtained by fixing the finite element mesh in space. A finite element program based on the Eulerian formulation has been developed. The program is used to simulate a steady state rolling process. The roll pressure distribution is compared with published experimental results. Comparisons reveal the effectiveness of the developed formulation.
Predict Emoji Combination with Retrieval Strategy

As emojis are widely used in social media, people not only use an emoji to express their emotions or mention things but also extend its usage to represent complicate emotions, concepts or activities by combining multiple emojis. In this work, we study how emoji combination, a consecutive emoji sequence, is used like a new language. We propose a novel algorithm called Retrieval Strategy to predict what emoji combination follows given a short text as context. Our algorithm treats emoji combinations as phrase in language, ranking sets of emoji combinations like retrieving words from dictionary. We show that our algorithm largely improves the F1 score from 0.141 to 0.204 on emoji combination prediction task.
BGADAM: Boosting based Genetic-Evolutionary ADAM for Convolutional Neural Network Optimization

Among various optimization algorithms, ADAM can achieve outstanding performance and has been widely used in model learning. ADAM has the advantages of fast convergence with both momentum and adaptive learning rate. For deep neural network learning problems, since their objective functions are nonconvex, ADAM can also get stuck in local optima easily. To resolve such a problem, the genetic evolutionary ADAM (GADAM) algorithm, which combines the ADAM and genetic algorithm, was introduced in recent years. To further maximize the advantages of the GADAM model, we propose to implement the boosting strategy for unit model training in GADAM. In this paper, we introduce a novel optimization algorithm, namely Boosting based GADAM (BGADAM). We will show that after adding the boosting strategy to the GADAM model, it can help unit models jump out the local optima and converge to better solutions.