Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards a structure-borne sound source characterization for low-noise design

The concepts and approaches proposed for structure-borne sound source characterization can roughly be divided into three categories. Such are approaches that are primarily concerned with the acquisition of the required data, prediction methods that allow for a comparison of vibrational sources as well as theoretical concepts that deal with possible ways to process and view the source and receiver data in order to provide physical insight for design and optimization purposes. The present paper is directed towards the progress in the latter category. Among the approaches for low-noise design, the concept of interface mobilities oers the most general and straightforward physical interpretation combined with an enhanced numerical stability. The applicability of the interface mobility approach for the characterization of vibrational sources, however, relies upon the admissibility of neglecting the so-called cross-order terms. It is found that omitting the cross-order terms can result in misinterpretations at certain frequencies. On average, however, it is demonstrated that the cross-order terms are insignicant and can be neglected with good approximation. Owing to the resultant general applicability, the concept of interface mobilities appears suitable to form a basis for structure-borne sound source characterization for low-noise design.
Alphabet Soup: Learning Knowledge Graphs from Recipes on the Web

World knowledge (assertions such as “you can slice an apple”) is useful in a variety of applications ranging from annotating video segments to generating motion plans for robots. While people generally already have this kind of knowledge, giving a machine this insight is a challenging problem. However, with the growth of the internet, there has been an increasing amount of data to learn from; people post more pictures and write more text every day. From these, one relatively untapped source of data lies in the domain of cooking, in the form of cooking recipes. As instructional texts, recipes hold information about states of objects and what changes they undergo to reach a goal, and thus present an opportunity to learn world knowledge for applications in automatic illustrations and video annotation. I will present a method to try and extract this information from these data.
Evaluating the Impact of GUI Similarity between Android Applications to Measure their Functional Similarity

Finding similar or related Android applications is a feature in popular search engines. An app's appearance is usually the first indicator of similarity. In this paper, the impact of GUI similarity for Android applications in measuring their functional similarity is evaluated. Accordingly, a number of Android applications will be analyzed to identify their resources and extract the most commonly used appearance features from each app’s package kit (APK) and its xml layouts. An algorithm that automatically extracts these features is designed and developed. A sample of 50 Android apps from Google play store was chosen, and two separate experiments were performed: one using the presented method to measure appearance similarity, the second using one of the available methods to measure functional similarity, then the results were compared. Results show that there is a relationship between appearance and functional similarities, where a strong relationship exists between appearance similarity and most of the functional similarity anchors.
A formal approach to automatically analyse extra‐functional properties in mobile applications

This paper presents an integrated approach for testing mobile applications (apps) against a set of extra‐functional properties to be used by app developers. The approach starts with the (manual or automatic) extraction of the interaction model, that is, a formal model of the potential user interactions with the app. The model is constructed to allow a model checking tool to exhaustively extract the so‐called app user flows, that is, the sequences of user actions, that constitute the test cases. In the final step, the app user flows are executed on the app running on real devices. The resulting execution traces are enriched with different measures and verified against a set of extra‐functional properties of interest. The approach has been adapted to analyse several applications running at the same time with several devices supporting the applications. This paper presents the definition and formalization of both the modelling language for the interaction model and the specification language to represent the extra‐functional properties. It also describes a methodology for automatically extracting the model. Finally, it presents an implementation focused on Android apps, which is integrated in the TRIANGLE testing framework, and the evaluation of the approach. © 2019 The Authors. Software Testing, Verification & Reliability Published by John Wiley & Sons Ltd.
Real-Time Modeling of Audio Distortion Circuits with Deep Learning

This paper studies deep neural networks for modeling of audio distortion circuits. The selected approach is black-box modeling, which estimates model parameters based on the measured input and output signals of the device. Three common audio distortion pedals having a different circuit conﬁguration and their own distinctive sonic char-acter have been chosen for this study: the Ibanez Tube Screamer, the Boss DS-1, and the Electro-Harmonix Big Muff Pi. A feedforward deep neural network, which is a variant of the WaveNet architecture, is proposed for modeling these devices. The size of the receptive ﬁeld of the neural network is selected based on the measured impulse-response length of the circuits. A real-time implementation of the deep neural network is presented, and it is shown that the trained models can be run in real time on a mod-ern desktop computer. Furthermore, it is shown that three minutes of audio is a sufﬁcient amount of data for training the models. The deep neural network studied in this work is useful for real-time virtual analog modeling of nonlinear audio circuits.
Formal Verification of Digital Circuits Using Simulator with Mathematical Foundation

To ease hardware design process, circuits are normally designed in description languages such as Verilog and VHDL. The correctness of circuits is normally checked by exhaustive simulation in simulators such as Icarus and VCS. Both the description languages Verilog/VHDL and simulators Icarus/VCS do not have mathematical foundations and hence are not reliable and cannot be used to mathematically prove correctness of circuit designs. Hardware description languages with mathematical (formal) foundation such as VeriFormal, on the other hand, are more reliable, trustworthy and can be used for robust design. In this paper, we report our results of formal verifications of two simple hardware circuits designed in the formal description language VeriFormal. Using the VeriFormal simulator and the accompanied type checker tools, we prove reliability properties type safety, functional correctness and functional equivalence of the digital circuits.
A Simulation Study Comparing Different Statistical Approaches for the Identification of Predictive Biomarkers

Identification of relevant biomarkers that are associated with a treatment effect is one requirement for adequate treatment stratification and consequently to improve health care by administering the best available treatment to an individual patient. Various statistical approaches were proposed that allow assessing the interaction between a continuous covariate and treatment. Nevertheless, categorization of a continuous covariate, e.g., by splitting the data at the observed median value, appears to be very prevalent in practice. In this article, we present a simulation study considering data as observed in a randomized clinical trial with a time-to-event outcome performed to compare properties of such approaches, namely, Cox regression with linear interaction, Multivariable Fractional Polynomials for Interaction (MFPI), Local Partial-Likelihood Bootstrap (LPLB), and the Subpopulation Treatment Effect Pattern Plot (STEPP) method, and of strategies based on categorization of continuous covariates (splitting the covariate at the median, splitting at quartiles, and using an “optimal” split by maximizing a corresponding test statistic). In different scenarios with no interactions, linear interactions or nonlinear interactions, type I error probability and the power for detection of a true covariate-treatment interaction were estimated. The Cox regression approach was more efficient than the other methods for scenarios with monotonous interactions, especially when the number of observed events was small to moderate. When patterns of the biomarker-treatment interaction effect were more complex, MFPI and LPLB performed well compared to the other approaches. Categorization of data generally led to a loss of power, but for very complex patterns, splitting the data into multiple categories might help to explore the nature of the interaction effect. Consequently, we recommend application of statistical methods developed for assessment of interactions between continuous biomarkers and treatment instead of arbitrary or data-driven categorization of continuous covariates.
Unified Optical Scanning Technology

From the Publisher: A cohesive view of the expanding field of optical scanning, in a single compact volume Optical scanning is a systematic sampling of spatial information, transforming images or data to or from a temporal signal for electronic processing. The field of optical information handling has been developing rapidly, with many of its classical forms being transformed in new and fascinating ways. For instance, the once-discrete fields of serial and parallel optical information transfer are evolving into new hybrid systems as researchers add the flexibility of optical scanning for greater operational advantage in data manipulation and transfer. Insight for such creative advancement is fostered with the unification of unapparent or secluded concepts. Written by an award-winning leader in the field, this is a thoroughly integrated overview of the many facets and disciplines of optical scanning. It brings together scanning theory, scanned resolution, deflection and modulation devices, optical transfer techniques, positional error analysis and control, system architecture, new research, and the unification of complementary or analogous processes, such as active and passive scanning systems. Of particular utility to both practitioner and student are such features as: An overview of the technology and unifying principles, including active and passive scanning, optical transfer, and system architecture In-depth chapters on scanning theory and processes, scanned resolution, scanner devices and techniques, and the control of scanner beam misplacement A comprehensive review of the government-sponsored research of agile beam steering, now primed for commercial adaptation A unique focus on the Lagrange invariant and its revealing resolution invariant
Cooperative Secret Key Generation for Platoon-Based Vehicular Communications

In a vehicular platoon, the lead vehicle that is responsible for managing the platoon's moving directions and velocity periodically disseminates messages to the following automated vehicles in a multi-hop vehicular network. However, due to the broadcast nature of wireless channels, vehicle-to-vehicle (V2V) communications are vulnerable to eavesdropping and message modification. Generating secret keys by extracting the shared randomness in a wireless fading channel is a promising way for V2V communication security. We study a security scheme for platoon-based V2V communications, where the platooning vehicles generate a shared secret key based on the quantized fading channel randomness. To improve conformity of the generated key, the probability of secret key agreement is formulated, and a novel secret key agreement algorithm is proposed to recursively optimize the channel quantization intervals, maximizing the key agreement probability. Numerical evaluations demonstrate that the key agreement probability achieved by our security protocol given different platoon size, channel quality, and number of quantization intervals. Furthermore, by applying our security protocol, it is shown that the probability that the encrypted data being cracked by an eavesdropper is less than 5%.
Complexity science for the design of swarm robot control systems

Swarm robots and their neighbourhood structures are defined and illustrated. The robots interact through these neighbourhood structures. Elements of complexity science can be used in the design and control of swarm robot systems. These include the emergent behaviour from the local interaction of agents, chaotic dynamics, relational structures for representation, learning and co-evolution, and simulation science. The concepts are presented using an example of football playing robots.
AMR Compressed-Domain Analysis for Multimedia Forensics Double Compression Detection

An audio recording must be authentic to be admitted as evidence in a criminal prosecution so that the speech is saved with maximum fidelity and interpretation mistakes are prevented. AMR (adaptive multi-rate) encoder is a worldwide standard for speech compression and for GSM mobile network transmission, including 3G and 4G. In addition, such encoder is an audio file format standard with extension AMR which uses the same compression algorithm. Due to its extensive usage in mobile networks and high availability in modern smartphones, AMR format has been found in audio authenticity cases for forgery searching. Such exams compound the multimedia forensics field which consists of, among other techniques, double compression detection, i. e. , to determine if a given AMR file was decompressed and compressed again. AMR double compression detection is a complex engineering problem whose solution is still underway. In general terms, if an AMR file is double compressed, it is not an original one and it was likely doctored. The published works in literature about double compression detection are based on decoded waveform AMR files to extract features. In this paper, a new approach is proposed to AMR double compression detection which, in spite of processing decoded audio, uses its encoded version to extract compressed-domain linear prediction (LP) coefficient-based features. By means of feature statistical analysis, it is possible to show that they can be used to achieve AMR double compression detection in an effective way, so that they can be considered a promising path to solve AMR double compression problem by artificial neural networks.
Making 360$^{\circ}$ Video Watchable in 2D: Learning Videography for Click Free Viewing

360$^{\circ}$ video requires human viewers to actively control "where" to look while watching the video. Although it provides a more immersive experience of the visual content, it also introduces additional burden for viewers; awkward interfaces to navigate the video lead to suboptimal viewing experiences. Virtual cinematography is an appealing direction to remedy these problems, but conventional methods are limited to virtual environments or rely on hand-crafted heuristics. We propose a new algorithm for virtual cinematography that automatically controls a virtual camera within a 360$^{\circ}$ video. Compared to the state of the art, our algorithm allows more general camera control, avoids redundant outputs, and extracts its output videos substantially more efficiently. Experimental results on over 7 hours of real "in the wild" video show that our generalized camera control is crucial for viewing 360$^{\circ}$ video, while the proposed efficient algorithm is essential for making the generalized control computationally tractable.
Adaptive Neuro Fuzzy Inference System (ANFIS) approach for modeling paddy production data in Central Java

The aim of this research is to develop the procedure of constructing an adaptive neuro-fuzzy inference system (ANFIS) model for time series data. The procedure of development applies statistical inference for optimizing ANFIS architecture. In this study, the procedure of Lagrange multiplier (LM) test is used for selecting input variables. Firstly, several lags which are indicated significantly different to zero are divided into 2 clusters, and these lags are selected as optimal inputs of ANFIS based on LM test. Secondly, the cluster numbers of inputs are also determined by using LM-test procedure. Based on this result, a number of rule-bases are generated. The developed model is applied for forecasting paddy production data in Central Java. This study concluded that lag-1, lag-2 and lag-5 with 2 clusters are selected as the optimal inputs. The 1-1 and 2-2 rules are selected as optimal rules. Finally, the model can work well, and generates a very satisfying result in forecasting paddy production data. Based on the root mean squares error (RMSE) and mean absolute percentage error (MAPE) values, the ANFIS performance is better than performance of Autoregressive Integrated Moving Average (ARIMA) for forecasting.
PROPOSED METHOD OF INFORMATION HIDING IN IMAGE

Information hiding techniques have recently been useful in a many application areas, there are many techniques to achieve hiding data, and hiding text inside image is one field of them. In this paper we hiding our data in round of image without changing the details of image. The basic idea of the paper is to present a method that hides information in cover image using Least Significant Bit (LSB) of light part of image (.jpg) and coding method along with encryption so as to increase the security.
A distributed routing algorithm for multihop packet radio networks with uni- and bi-directional links

In multihop packet radio networks (PRNs) the nodes are not all within direct radio range of each other; to be able to forward the packets to their final destination the nodes must have enough knowledge about the network topology. This information is usually represented in the form of routing and distance tables. On a packet radio network the connectivity among the nodes can change due to several causes such as node or link failures and the presence of mobile nodes which can join or leave the network. This dynamic nature of the network topology requires that the tables at each node be continuously and accurately updated to avoid unnecessary delays or incorrect packet forwarding. The manner in which the packets are forwarded in the method proposed here is called incremental source routing, i.e. each node keeps a routing table which indicates the next hop (a node address) for each network final destination. Each packet is then delivered incrementally by the decisions made by each node in the path from source to destination. <<ETX>>
Aiming beyond the Obvious: Identifying Non-Obvious Cases in Semantic Similarity Datasets

Existing datasets for scoring text pairs in terms of semantic similarity contain instances whose resolution differs according to the degree of difficulty. This paper proposes to distinguish obvious from non-obvious text pairs based on superficial lexical overlap and ground-truth labels. We characterise existing datasets in terms of containing difficult cases and find that recently proposed models struggle to capture the non-obvious cases of semantic similarity. We describe metrics that emphasise cases of similarity which require more complex inference and propose that these are used for evaluating systems for semantic similarity.
Control and Operating Behavior of Continuously Variable Chain Transmissions

A new adaptive controller layout for chain converters for universal use in different applications and set-ups is being presented. Based on the control structure chosen for synchronization of the claw couplings of a chain converter type i-transmission during range shifts, a universal control layout was developed. Both the conventional clamping system for the well known PIVbased chain converter and a newly developed pressure controlled clamping system were taken into account. As the most important non-linearity the ratio of clamping forces between driving and driven pulley (ζ-value) for steady state operation was mapped in a characteristic diagram and is being adapted during normal operation. Besides improvements in performance and better availability of information for diagnostic purposes, an open loop control of the rate of ratio change becomes possible.
An approach to vibration control by stereo vision system in mobile manipulator

A mobile manipulator is composed of the vehicle and manipulator part to expand the workspace area. In case of the rough terrain, however, the tip motion of the mobile manipulator oscillates. To obtain the stable motion by the manipulator, it is necessary to suppress the oscillation. To address the above issue, this paper describes a novel strategy to estimate the oscillation of the mobile manipulator by the stereo camera mounted on the vehicle. To recognize the oscillation of the mobile manipulator, a landmark point fixed in the workspace coordinate is utilized. Then the landmark point is detected by the stereo camera. From the motion of the landmark points on the image plane of the camera, the motion of the mobile manipulator can be estimated. Here, to describe the vibration of the mobile manipulator which occurs by the unevenness of road surface, the oscillation model composed of rotational and translational joints is introduced. Furthermore a motion planning of the end-effector is described. Simulation results are shown to confirm the effectiveness of the proposed approach.
A Self-Reconfigurable System of Contents among Smart Devices

In this system, mobile devices are not independent, they can communicate with each other, one device’s change can affect the whole system or other devices. To achieve the above mentioned A Self-Reconfigurable System of Contents , through discover the device and connect process, to establish the connection between the mobile devices. After user assigns two dimension display type, the user can select content to input the system, contents are portioning and broadcast to devices. The system can self-reconfigure contents rapidly and exactly. This technique supports contents self-reconfiguration for devices remove, addition and position exchange. In this paper, when the user uses the hand contacts device, the device sends a signal to assist the system to detection device’s position. The system does not need to get accurate devices moving direction, just according to all changed devices position to judge where the devices destination is. This research develops an application according to this technique, and the real machine tests the application using Android platform. Some communication protocols and mathematical modeling methods are proposed. These methods can also be used in other Internet of Things (IoT) fields, such as Drones Navigation, Smart Home, and Informational City management.
MRI Brain Image Segmentation using Modified Fuzzy Logic Clustering (MFLC)

: Brain Tumor is a development of abnormal cells in the tissues of the brain and could be life menacing. It has critical impact on the person’s emotions, personality and quality of life. However, not all brain tumors are cancers. An early diagnosis increases the chance of a patient to survive. Various techniques were developed for this purpose. One of the common techniques for brain tumor recognition is MRI (Magnetic Resonance Imaging). The segmentation of MRI images is an important task to analyze brain changes. It aims to differentiate between normal and abnormal tissues in order to provide the corresponding treatments. However, ensuring an efficient and accurate segmentation is a challenging task due to the different tissues in the brain that varies in shape and size. In this paper, we propose a new technique called Modified Fuzzy Logic Clustering (MFLC) for brain tumor detection in MRI images. Our experimental results and analyses indicate that our proposed technique is superior in detecting brain tumors in comparison to K-means.