Miracle-dz/UniFAR-FTUs huggingface dataset:
Beyond sets with atoms: definability in first order logic

Sets with atoms serve as an alternative to ZFC foundations for mathematics, where some infinite, though highly symmetric sets, behave in a finitistic way. Therefore, one can try to carry over analysis of the classical algorithms from finite structures to some infinite structures. Recent results show that this is indeed possible and leads to many practical applications. In this paper we shall take another route to finite analysis of infinite sets, which extends and sheds more light on sets with atoms. As an application of our theory we give a characterisation of languages recognized by automata definable in fragments of first-order logic.
Evaluating Side Channel Resilience in iPhone 5c Unlock Scenarios

iOS is one of the most secure operating systems based on policies created and enforced by Apple. Though not impervious or free from vulnerabilities, iOS has remained resilient to many attacks partially based on lower market share of devices, but primarily because of tight controls placed on iOS development and application deployment. Locked iOS devices pose a specific hard problem for both law enforcement and corporate IT dealing with malicious insiders or intrusion scenarios. The need to recover forensic data from locked iOS devices has been of public interest for some time. In this paper, we describe a case study analysis of the iPhone 5c model and our attempts to use electromagnetic (EM) fault-injection as a side channel means to unlock the device. Based on our study, we report on our unsuccessful attempts in unlocking a locked iPhone 5c using this side channel-based approach. As a contribution, we provide initial analysis of the iPhone 5c processor’s spectral mapping under different states, a brief survey of published techniques related to iPhone unlock scenarios, and a set of lessons learned and recommended best practices for other researchers who are interested in future EM-based iOS studies.
Forecasting residential gas demand: machine learning approaches and seasonal role of temperature forecasts

Gas demand forecasting is a critical task for energy providers as it impacts on pipe reservation and stock planning. In this paper, the one-day-ahead forecasting of residential gas demand at country level is investigated by implementing and comparing five models: Ridge Regression, Gaussian Process (GP), k-Nearest Neighbour, Artificial Neural Network (ANN), and Torus Model. Italian demand data from 2007 to 2017 are used for training and testing the proposed algorithms. The choice of the relevant covariates and the most significant aspects of the pre-processing and feature extraction steps are discussed in-depth, lending particular attention to the role of one-day-ahead temperature forecasts. Our best model, in terms of Root Mean Squared Error (RMSE), is the ANN, closely followed by the GP. If the Mean Absolute Error (MAE) is taken as an error measure, the GP becomes the best model, although by a narrow margin. A main novel contribution is the development of a model describing the propagation of temperature errors to gas forecasting errors that is successfully validated on experimental data. Being able to predict the quantitative impact of temperature forecasts on gas forecasts could be useful in order to assess potential improvement margins associated with more sophisticated weather forecasts. On the Italian data, it is shown that temperature forecast errors account for some 18\% of the mean squared error of gas demand forecasts provided by ANN.
Acquisition and Interpretation of Mandarin Speech Prosody by Native Speakers and Cantonese Learners

This study aims to test the ability to match acoustic cues to different focus types and positions by advanced Cantonese L2 learners of Mandarin under the modalities of auditory-only and visual-auditory. Following the design by [29], participants were instructed to make a 5-Likert response to rate their preferences for the conversations they heard. Results show that visual-aids facilitated the perception of prosody; L2 learners showed fewer difficulties in differentiating narrow and contrastive focus than native Mandarin speakers. These findings provide significances for prosodic perception, second language acquisition and bilingual education.
Infección diseminada por Staphylococcus aureus positivo para leucocidina de Panton-Valentine en un niño

RESUMEN La leucocidina de Panton-Valentine (LPV) es una exotoxina producida por muchas cepas de Staphylococcus aureus, y un importante factor de virulencia. Una infección por S. aureus positivo para LPV deriva en infecciones rápidas y graves de partes blandas y neumonía necrosante en adolescentes sanos, y la tasa de mortalidad es elevada. Presentamos el caso de un paciente de 12 años hospitalizado por fiebre, dificultad respiratoria y coxalgia en el que se identificó neumonía necrosante con embolia pulmonar séptica, absceso del psoas, celulitis y osteomielitis. En el hemocultivo del paciente se aisló S. aureus sensible a la meticilina (SASM) positivo para LPV. Palabras clave: Staphylococcus aureus, leucocidina de PantonValentine, neumonía necrosante, sepsis.
Novel Adaptive Generative Adversarial Network for Voice Conversion

Voice Conversion (VC) converts the speaking style of a source speaker to the speaking style of a target speaker by preserving the linguistic content of a given speech utterance. Recently, Cycle Consistent Adversarial Network (CycleGAN), and its variants have become popular for non-parallel VC tasks. However, CycleGAN uses two different generators and discriminators. In this paper, we introduce a novel Adaptive Generative Adversarial Network (AdaGAN) for non-parallel VC task, which effectively requires single generator, and two discriminators for transferring the style from one speaker to another while preserving the linguistic content in the converted voices. To the best of authors' knowledge, this is the first study of its kind to introduce a new Generative Adversarial Network (GAN)-based architecture (i.e., AdaGAN) in machine learning literature, and the first attempt to apply this architecture for nonparallel VC task. In this paper, we compared the results of the AdaGAN w.r. t. state-of-the-art CycleGAN architecture. Detailed subjective and objective tests are carried out on the publicly available VC Challenge 2018 corpus. In addition, we perform three statistical analysis which show effectiveness of AdaGAN over CycleGAN for parallel-data free one-to-one VC. For inter-gender and intra-gender VC, We observe that the AdaGAN yield objective results that are comparable to the CycleGAN, and are superior in terms of subjective evaluation. A subjective evaluation shows that AdaGAN outperforms CycleGAN-VC in terms of naturalness, sound quality, and speaker similarity. AdaGAN was preferred 58.33% and 41% time more over CycleGAN in terms of speaker similarity and sound quality, respectively.
Location Privacy Protection in Mobile Social Networks Based on l-Diversity

In recent years, location-based service has been widely used in social networks. While more and more users enjoy the convenience of location-based services, the user's location privacy is also facing various threats of privacy disclosure. Aiming at the problem of location privacy disclosure in mobile social network applications, this paper proposed a location privacy protection method for multi-sensitive attributes based on l-diversity privacy protection model, and protected the user's location information in client side and server respectively. On the client side, the decomposition algorithm of minimum distance grouping is used to lighten the location data, which makes the processed data satisfy the l1-diversity principle and upload the data to the server in the form of QIT1(Quasi-Identifier attribute Table) and ST^1(Sensitive attribute Table) to achieve the initial protection of the user's location data. On the server side, the minimum selectivity priority strategy was adopted to form the l2-diversity group satisfying the multi-sensitive attributes, and the data was uploaded in the form of QIT2 and ST2 to further protect the user location data (where l_1< l_2). The experimental results show that this method not only can effectively protect location privacy data, but also has high data availability.
Deep Learning Based Plant Disease Detection for Smart Agriculture

Deep learning is a promising approach for fine- grained disease severity classification for smart agriculture, as it avoids the labor-intensive feature engineering and segmentation-based threshold. In this work, we first propose a Densely Connected Convolutional Networks (DenseNet) based transfer learning method to detect the plant diseases, which expects to run on edge servers with augmented computing resources. Then, we propose a lightweight Deep Neural Networks (DNN) approach that can run on Internet of Things (IoT) devices with constrained resources. To reduce the size and computation cost of the model, we further simplify the DNN model and reduce the size of input sizes. The proposed models are trained with different image sizes to find the appropriate size of the input images. Experiment results are provided to evaluate the performance of the proposed models based on real- world dataset, which demonstrate the proposed models can accurately detect plant disease using low computational resources.
A Survey of Computation-Driven Data Encoding

Although the metal-oxide-semiconductor field-effect transistor (MOSFET) has been the dominant device for modern very-large scale integration (VLSI) circuits for more than six decades, with the dawning of a post-Moore era, researchers are trying to find replacements. A foundation of modern digital computing is the encoding of digital values through a binary radix representation. However, as we enter into the post-Moore era, the challenges of increasing power density, signal noise, and device unreliability raise the question of whether this basic way of encoding data is still the best choice, particularly with novel electronic devices. Prior work has shown that binary radix encoding has some disadvantages. We argue that it is crucial to rethink the necessity of using this representation in the post-Moore era. In this paper, we review some recent development on computation-driven data encoding. We begin with stochastic encoding, a representation proposed a long time ago, discussing both its advantages and disadvantages. Then, we review several recent breakthroughs with variations of stochastic encoding that mitigate many of its disadvantages. Finally, we conclude the paper by extrapolating future directions for effective computation-driven data encoding.
Low Complexity Detection Scheme for Fast Target in Randomized Stepped Frequency

In randomized stepped frequency radar, two-dimensional matched filtering is performed to detect the target and estimate the range and radial velocity of target. However, if the region of target velocity to be considered is very wide, thus, target maneuvers very quickly, matched filtering should be performed to the corresponding wide velocity region. Then, grating lobe occurs periodically in the velocity axis, thereby causing a problem of finding a mainlobe in the grating lobe. In this study, we propose a two-stage low complexity detection algorithm for high-speed target. In the first stage, the coarse detection which determines the velocity region including mainlobe is performed. Then, fine detection is processed by performing 2D matched filtering in the reduced range-velocity region in the second stage. By dividing the target detection into two steps, computational complexity can be drastically reduced. The performance of the proposed technique is verified through simulation results.
Intelligent Spectrum Sensing: When Reinforcement Learning Meets Automatic Repeat Sensing in 5G Communications

Spectrum sensing, which helps to resolve the coexistence issue and optimize spectrum efficiency, plays an important role in future wireless communication systems. However, the upcoming 5G communication involves diversified scenarios with different characteristics and diverse requirements. This tendency makes it difficult for spectrum sensing methods to flexibly serve various applications while maintaining satisfactory performance. Motivated by such a challenge, this article combines the reinforcement learning concept with spectrum sensing technique, seeking a feasible way to adaptively deploy spectrum sensing configurations so as to optimize system performance under multifarious scenarios in 5G communications. In this article, we first categorize several major optimization targets for spectrum sensing in future communications. Then we elaborate the full details of the proposed sensing technique. Three dedicated modes with respective optimization objectives are designed thereafter. Numerical results manifest that the proposed sensing technique has the capability of adapting to various scenarios, which is appealing in practice.
Measuring Compute-Reuse Opportunities for Video Processing Acceleration

Increases in bandwidth for mobile and land-line networks enables devices to communicate video at higher image and audio resolution. Processing these video streams is of interest for a wide-ranging collection of applications including robotic recognition and navigation, video security surveillance, action recognition and augmented virtual reality. The advent of massively parallel Graphical Processing Units (GPUs) has met many computing challenges in processing video in real time. Yet, GPU computing works best when the algorithm and structure of the image data can be regularized into locally-independent compute threads. When these regularity constraints do not hold or if the computational complexity of the local thread is high, GPU computing solutions can be overwhelmed by the computational task at hand. This article provides initial research results to explore an alternative approach for computation classically referred to as “memoization. ” Here input and output values of computationally expensive functions are stored on a “memo” Look-Up Table (LUT) and when an input re-occurs, the corresponding output value is taken from the LUT without computation. Research work implements a memoization system to investigate the extent of compute re-use that exists within streaming video data. Insights are provided specifying new techniques that efficiently transform traditional memoization approaches into approximate memoization methods which open up new design spaces to explore the tradeoff between accuracy and computational cost. Here one may enhance compute re-use by allowing similar input values to map to the same output having an end-effect similar to approximate computing. Experimental results for 2D convolution (stencil() function) on data from six videos shows reuse of 50% with no loss in the computation accuracy. Our experimental results also show approximate memoization increases reuse to rates ranging from 75% up to 99%.
Sound Source Tracking by incorporating Target Motion estimated by Visual Trackers

It is one of the fundamental features for intelligent robots to track plural targets around them to recognize the environment, and various sensors including visual and acoustic devices have been utilized. Sound source tracking is one of such trackers required by the systems with microphones, but sound signals may include pauses when the system is not able to perceive the signal from the source, or multiple sound sources may locate very close to each other which makes it impossible for the system to distinguish sources only by acoustic information. In order to overcome this difficulty, this paper proposes to utilize visual trackers to estimate the motion of sources and to utilize them to track dynamic acoustic targets. The proposed algorithm incorporates Kalman filter that takes the motion vector from the visual trackers based on Kernelized Correlation Filters with estimating the uncertainty of the association between audio and visual features. Experiments to track dynamic acoustic sources validate the proposed method, which is also shown in this paper.
Minimization of Actuator’s Driving Force of Chuck-type Hand with Three Parallel Stick Fingers through Grasp Optimization

For realizing a versatile, flexible robotic assembly system in manufacturing, development of a versatile robotic hand with minimally necessary mechanism has been expected. As a simple hand for this demand, we are currently aiming at developing a chuck-type hand with three parallel stick fingers which can achieve a target assembly task. Toward the optimal design of the hand, it is necessary to know the required driving force of an actuator to be used. This paper presents a method to compute the magnitude of the minimally necessary driving force of the actuator for the hand through grasp optimization.
Optimal Computation Offloading with a Shared MEC Center: A Mean Field Game Approach

In this paper, we consider the scenario of a multi-cell 5G ultra-dense network where a shared mobile edge computing (MEC) center is built. Each smart mobile device is required to offload its computation-heavy tasks to the MEC center, within a predefined time, at a minimal cost. We firstly model the problem as a N-users non-cooperative dynamic stochastic game (DSG). Bypassing the complexity of analyzing a Nash equilibrium, we turn a N-body problem into an equivalent mean field game, which is a 2-body problem only. Then, the related Hamilton-Jacobi-Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations are deduced, and the optimal offloading strategy is obtained by solving these two partial differential equations (PDEs) interactively. Numerical results reveal that the optimal offloading strategy can reduce the average system cost by at least 36:5% compared to the maximum rate offloading strategy under the given latency constraint.
The Corporate Entrepreneur: A driving force for strategic renewal and radical innovation in established companies Linking pin and entrepreneurial leader as success factor of semi-autonomous teams

corporate entrepreneurship is one tool for established companies to strengthen their capabilities for strategic renewal and innovativeness. The question, however, which factors are influencing the success of a corporate entrepreneurship initiative requires further attention. The corporate entrepreneur who is acting as both the leader of embedded entrepreneurial teams and linking pin to the corporate, is providing one possible perspective. Based on 6 interviews conducted in 6 German organizations this study contributes to the understanding of the role of the corporate entrepreneur and how this role can be distinguished from other roles in the context of innovation.
Cloud-based Mobile and Web application to support managing the treatment of Tuberculosis

The nature of tuberculosis, a serious chronic disease, requires regular, continuous drug-based treatment, periodic diagnostic monitoring for serious side effects on liver function and proper supportive dietary nutrition. The app is developed to be very simple and explicit for patients to feed their data and the web interface is to optimize doctor's time in following up on the treatment of the case. Use of the app is expected to solve problems related to the administration of TB treatment, like migration, infectiveness for health and care providers etc.
Proposal for 3D-printed pneumatic artificial muscles -Effect of leaf spring stiffness on contraction amount and contraction force-

In this paper, we describe the performance of a pneumatic artificial muscle integrally molded by a 3D printer that can mold materials with different stiffnesses.In the recent years, soft and hard materials can be integrally molded as the performances of 3D printers improve. For this reason, attention has been focused on the integral molding of actuators by using 3D printers. In this study, an artificial muscle is integrally molded by a state-of-the-art 3D printer, and the amount of contraction and contraction force generated are experimentally measured. In particular, the integrally molded artificial muscle formulated in this study is a pleated artificial muscle that does not require high material stretching performance. When the stiffness of the leaf spring material constituting the 3D printable artificial muscle is changed in a stepwise manner, the generated contraction amount and force were measured. From these measurement results, the usefulness of this actuator was confirmed by comparing the generated force with that of a pneumatic piston with the same inner diameter.
PBRnet: Pyramidal Bounding Box Refinement to Improve Object Localization Accuracy

Many recently developed object detectors focused on coarse-to-fine framework which contains several stages that classify and regress proposals from coarse-grain to fine-grain, and obtains more accurate detection gradually. Multi-resolution models such as Feature Pyramid Network(FPN) integrate information of different levels of resolution and effectively improve the performance. Previous researches also have revealed that localization can be further improved by: 1) using fine-grained information which is more translational variant; 2) refining local areas which is more focused on local boundary information. Based on these principles, we designed a novel boundary refinement architecture to improve localization accuracy by combining coarse-to-fine framework with feature pyramid structure, named as Pyramidal Bounding Box Refinement network(PBRnet), which parameterizes gradually focused boundary areas of objects and leverages lower-level feature maps to extract finer local information when refining the predicted bounding boxes. Extensive experiments are performed on the MS-COCO dataset. The PBRnet brings a significant performance gains by roughly 3 point of $mAP$ when added to FPN or Libra R-CNN. Moreover, by treating Cascade R-CNN as a coarse-to-fine detector and replacing its localization branch by the regressor of PBRnet, it leads an extra performance improvement by 1.5 $mAP$, yielding a total performance boosting by as high as 5 point of $mAP$.
Emerging classes of antioxidant to cancer therapy: a review of clinical and experimental studies

This review mainly focuses on the relation between antioxidants with cancer therapy. Antioxidants have been reported to play an essential role to reduce free radical species. Free radicals commonly cause oxidative damage which is a common factor in the aging process, and also the vital factor of formation, and development of major disease specially cancer. Although, since last many decades several antioxidants belong to natural and synthetic origin have been tested in clinical trials against oxidative stress, however these clinical trials end up with undesirable effects. This review also complied with the most recent findings of oxidative stress, highlighting of free racial production, and its related oxidative damage at cellular and molecular level, with the new and existing natural and synthetic classes of free radical scavenger and their related clinical trials.