Miracle-dz/UniFAR-FTUs huggingface dataset:
PPChecker: Towards Accessing the Trustworthiness of Android Apps’ Privacy Policies

Recent years have witnessed a sharp increase of malicious apps that steal users’ personal information. To address users’ concerns about privacy risks and to comply with data protection laws, more and more apps are supplied with privacy policies written in natural language to help users understand an app's privacy practices. However, little is known whether these privacy policies are trustworthy or not. Questionable privacy policies may be prepared by careless app developers or someone with malicious intention. In this paper, we carry out a systematic study on privacy policy by proposing a novel approach to automatically identify five kinds of problems in privacy policy. After tackling several challenging issues, we implement the approach in a system, named PPChecker, and evaluate it with real apps and their privacy policies. The experimental results show that PPChecker can effectively identify questionable privacy policies with high precision. Applying PPChecker to 2,500 popular apps, we find that 1,850 apps (i.e., 74.0 percent) have at least one kind of problems. This study sheds light on the research of improving and regulating apps’ privacy policies.
A High Frequency Transformer Isolation Ups System With 110V/220V Input Voltage

This work proposes an isolated double conversion UPS with power factor correction using a high frequency transformer, and input voltages equal to 110V/220V. The arrangement is suitable to rack structures because it has small size and reduced weight. For both input voltages, the proposed converter has almost the same efficiency processing the same output power. Other relevant features are soft commutation of the controlled switches in the chopper and the boost stage, simple control strategy that can be implemented with well-known integrated circuits, and the use of few batteries in series due to the step-up stage. Qualitative analysis and experimental results obtained from 2kVA laboratory prototype are presented. 1
The EnviDat Concept for an Institutional Environmental Data Portal

EnviDat is the environmental data portal developed by the Swiss Federal Institute for Forest, Snow and Landscape Research WSL. The strategic initiative EnviDat highlights the importance WSL lays on Research Data Management (RDM) at the institutional level and demonstrates the commitment to accessible research data in order to advance environmental science. EnviDat focuses on registering and publishing environmental data sets and provides unified and efficient access to the WSL’s comprehensive reservoir of environmental monitoring and research data. Research data management is organized in a decentralized manner where the responsibility to curate research data remains with the experts and the original data providers. EnviDat supports data producers and data users in registration, documentation, storage, publication, search and retrieval of a wide range of heterogeneous data sets from the environmental domain. Innovative features include (i) a flexible, three-layer metadata schema, (ii) an additive data discovery model that considers spatial data and (iii) a DataCRediT mechanism designed for specifying data authorship. In addition, the overall user-friendly appearance in EnviDat provides an important opportunity for showcasing WSL research activities and results. The EnviDat portal builds on a conceptual system consisting of a core system, a set of guiding principles and a number of key services. Its development closely follows the conceptual framework, being guided by principles towards the ultimate goal of providing useful services for researchers.
DVB-T: the COFDM-based system for terrestrial television

In early 1996, the European DVB Project finalized what is now called the “Common 2k/8k Specification”. This specification is another important corner-stone of digital video broadcasting in Europe and numerous other countries in the world. The terrestrial system, termed DVB-T, is based on the channel-coding algorithms the DVB Project had earlier devised for digital television on cable and satellite. The modulation scheme that is used for DVB-T is OFDM (orthogonal frequency division multiplexing), which has been assigned the name of COFDM in conjunction with the channel coding. Two variants are described, one employing 1705 individual carriers in an 8 MHz channel (“2k variant”), the other employing 6817 carriers (“8k variant”). For each of the variants four specific types of guard interval have been defined. Each individual carrier can be modulated either by QPSK, 16QAM, 64-QAM or hierarchical modulation.
Data-driven Approaches to Author’s Profiling Identification for Russian Texts on Base of Complex Machine Learning Models in Combinations with Siamese Networks

In this work data-driven approaches to author’s profiling identification for Russian texts are investigated on base of a united data corpus. This corpus has been specially collected by crowdsourcing, and currently contains texts from 1161 men and 2043 women. The adaptation of complicated models, based on convolutional neural networks, gradient boosting methods, LSTM, Siamese networks along with different input data and features (morphological data, vector of character n-grams frequencies, Linguistic Inquiry and Word Count and others) to form the vector of derived features in order to identify gender and age of the author of text is described. The method to improve the accuracy using coding by the Siamese network is presented and analyzed.
Adaptive Filtering for Attitude Estimation System in Harsh Vibration Environment

This paper presents the adaptive algorithm for a low-cost and low-weight attitude estimation system of the light aircraft using a MEMS (Micro-Electro-Mechanical System) inertial sensor. The proposed approach relies on two parts: 1) Pre-processing of accelerometer sensor data measured in harsh environment such as strong vibration. This approach utilizes sinusoidal estimation to continuously adapt the filtering bandwidth of the accelerometer’s data in order to reduce the effects of vibration and sensor noise before attitude estimation is processed 2) Design of adaptive complementary filter for an aiding system using accelerometer and gyro sensor. This approach relies on a gain-scheduled complementary filter, augmented by an angular rate-based switching architecture. The cutoff frequency of the filter is determined adaptively under varying light aircraft dynamics. The proposed method is applied to light aircraft and the experimental data demonstrate the effectiveness of this approach.
THE INFLUENCE OF SPEECH PRODUCTION EXPERIENCE ON THE SIZE AND THE STRUCTURE OF THE SPEECH MOTOR PROGRAM

Schema theory (1975) proposed that information about relative timing and force of movements and the order of motor events is stored in Generalized Motor Programs (GMPs). Because some researchers (e. g., Lofqvist, 1991; Max & Caruso, 1997) observed consistent relative timing information in some, but not all, speech rate contexts, this study attempted to provide an alternative explanation for these inconsistent findings of proportional relationships in the trajectories of speech movements. Motivated by Verwey and colleagues (1995, 1996; 1996), who observed changes in production modes from preparing a key press motor response in advance to preparing it in a concurrent manner as the sequence length increased, this study proposes two possible reasons for increased variability in movement trajectories: various motor program sizes and changes in the production mode between advance programming and concurrent programming. The current study hypothesizes that more experienced speakers preserve more proportional relationship information, utilize larger size stored motor programs, and make more flexible switches in their production modes. Twenty-four native Mandarin and twenty-four non-Mandarin male speakers (19-30 years of age) with normal speech and language functions were recruited. They produced three-syllable, six-syllable, and nine-syllable length Mandarin tone sequences. Interactions between Group and Sequence Length Conditions were investigated in the hierarchical generalized linear model. Several timing, GMP error, and parameter error measurements were examined. Significant interactions were observed between Group and Sequence Length Condition on the GMP errors per syllable, Hamming distance difference per syllable between slope and parsons’ code measurements, and Hamming distance per syllable for parsons’ code measurement. In addition, many other significant Group and Sequence Length Condition main or simple main effects were observed. Results revealed that once motor programs are retrieved, they are executed without being reparameterized. The existence of GMP for lexical tones was supported. Also, it appeared that both native Mandarin and non-Mandarin speakers could switch between advance programming and concurrent programming as the sequence length increased. The timing of this switch occurred later in more-experienced speakers. Furthermore, the attempt to concatenate motor programs appeared to increase variability in movement outcome trajectories, supporting the hypotheses of this study.
Patient-Specific Computational Modeling of Cardiac Mechanics

Computational models of cardiac mechanics, personalized to a patient, offer access to mechanical information above and beyond direct medical imaging. Additionally, such models can be used to optimize and plan therapies in-silico, thereby reducing risks and improving patient outcome. Model personalization has traditionally been achieved by data assimilation, which is the tuning or optimization of model parameters to match patient observations. Current data assimilation procedures for cardiac mechanics are limited in their ability to efficiently handle high dimensional parameters. This restricts parameter spatial resolution, and thereby the ability of a personalized model to account for heterogeneities that are often present in a diseased or injured heart. In this paper we address this limitation by proposing an adjoint-gradient based data assimilation method that can efficiently handle high-dimensional parameters. We test this procedure on a synthetic data set, and provide a clinical example with a dyssynchronous left ventricle with highly irregular motion. Our results show that the method efficiently handles a high dimensional optimization parameter, and produces an excellent agreement for personalized models to both synthetic and clinical data. *Both of these authors contributed equally to this work.
Automatic classification of microglial cells' state using stacked denoising auto-encoders

As a class of cells composing the Central Nervous System, microglia is responsible for its maintenance and immunological defense. A cell of such class may be found in three distinct states (resting, transition and active) and the state reflects what is occurring on the Central Nervous System; particularly, it may indicate the beginning of the development of a neurodegenerative disease. In this dissertation, we present the first study for the automatic recognition of microglial cells’ state using stacked denoising auto-encoders. In order to obtain the most appropriate recognition model, we resort to different strategies, namely, to image pre-processing, to artificial increase of the dataset (using image rotations) and to solving sub problems of the original problem. We also apply transfer learning considering five source problems. The obtained results show that the transition state is the most hard to recognize. In terms of accuracy, a performance of approximately 64% is achieved.
Pareto Optimal Allocation under Compact Uncertain Preferences

The assignment problem is one of the most well-studied settings in multi-agent resource allocation. Aziz, de Haan, and Rastegari (2017) considered this problem with the additional feature that agents’ preferences involve uncertainty. In particular, they considered two uncertainty models neither of which is necessarily compact. In this paper, we focus on three uncertain preferences models whose size is polynomial in the number of agents and items. We consider several interesting computational questions with regard to Pareto optimal assignments. We also present some general characterization and algorithmic results that apply to large classes of uncertainty models.
Design of Power Hardware-in-the-Loop Simulations for Integrated Starter–Generator Systems

The integrated starter–generator system (ISGS) is a combination of starter and generator for independent power systems in transportation. It replaces both a conventional starter and generator with a single set of highly integrated devices. A power hardware in the loop simulation that is flexible and can include the hardware under test is used to test the ISGS under representative field conditions. This paper utilizes the special structure of the ISGS and proposes an ISGS emulator (ISGSE) to develop and test the converter. The proposed ISGSE can be used to test a variety of motor drives or rectifiers including dynamic capabilities without necessitating a connection to a large motor load. To emulate the ISGS, the structure and operation principle in different modes are introduced in detail. Also, the issue of stability and accuracy is discussed in this paper. Detailed simulation and experimental comparisons are carried out between the ISGS and the ISGSE, which validates the proposed ISGSE as an effective tool for designing and testing new motor drives.
Implementation of Parametric Haar-like Transformations on FPGA

Linear transformations are used in many algorithms and applications that are implemented in hardware. In this master’s thesis a parametrized representation, called a parametric Haar-like transformation, is presented for a family of Haar-like linear transformations. An advantage of this parametric representation is that it can be implemented using a unified hardware architecture. The aim of this thesis is to study how the hardware architectures for parametric Haar-like transformations could be efficiently implemented as a part of a larger FPGA based system. First hardware architectures for the transformations were investigated in VHDL and the final synthesizable RTL models were implemented with HLS. With HLS complex and real number implementations for flat hierarchy and class based hierarchy style descriptions were created for a variety of transformation sizes and synthesized to an Intel Stratix 10 FPGA. The synthesized implementations were characterized with respect to speed, latency, throughput and resource usage. In general, the class based hierarchies were found to be more suitable for FPGA implementations when increased throughput and faster clock rates are wanted. The flat hierarchies had a more algorithmic implementation style and were found to have slower clock rates and decreased throughput. Although, the flat hierarchies did consume less resources on the FPGA than the class based implementations.
A Novel Analytical Framework for Massive Grant-Free NOMA

In this paper, we consider a massive grant-free non-orthogonal multiple access (GF-NOMA) scheme, where devices have strict latency requirements and no retransmission opportunities are available. Each device chooses a pilot sequence from a predetermined set as its signature and transmits its selected pilot and data simultaneously. A collision occurs when two or more devices choose the same pilot sequence. Existing GF-NOMA schemes assume that a collision of at least one pair of users entails a collision for all simultaneously transmitting users, which is sub-optimal in terms of individual outage and system throughput. For that, we propose a novel framework, where collisions are treated as interference to the remaining received signals. With the aid of Poisson point processes and ordered statistics, we derive simplified expressions that can well approximate the outage probability and throughput of the system for both successive joint decoding (SJD) and successive interference cancellation (SIC). Numerical results verify the accuracy of our analytical expressions. For low data rate transmissions, results show that the performance of SIC is close to that of SJD in terms of outage probability, for packet arrival rates up to 10 packets per slot. However, SJD can achieve almost double the throughput of SIC and is, thus, far more superior.
Myocardial Segmentation in MR images using Convolutional Neural Networks

A convolutional neural network for automatic myocardial segmentation of MR images is described and implemented, based on the readily available architecture SegNet. A general network trained on both end-systolic and end-diastolic images is determined to be superior to the networks trained on the separate data. The evaluation of myocardial segmentation is discussed, as well as the importance of manual, visual inspection.
An Unified Representation of Source Code Authoring Workflows

: Existing approaches to modeling software development processes mostly deal with high-level processes at the level of project management. There are speciﬁc tasks that involve the analysis of processes at the level of writing and modifying the program code, but they lack a common reusable modeling framework. We suggest that a model of source code editing workﬂow would be beneﬁcial for many tasks, from defect prediction to teaching programming to novices. We propose a uniﬁed approach that combines several levels of annotations, from keyboard events to task tracker issues and project planning.
Looking Beyond the Gesture: Vocabulary Acceptability Criteria for Gesture Elicitation Studies

The choice of what gestures should be part of a gesture language is a critical step in the design of gesturebased interfaces. This step is especially important when time and accuracy are key factors of the user experience, such as gestural interfaces in vehicle control and sterile control of a picture archiving and communication system (PACS) in the operating room (OR). Agreement studies are commonly used to find the gesture preference of the end users. These studies hypothesize that the best available gesture lexicon is the one preferred by a majority. However, these agreement approaches cannot offer a metric to assess the qualitative aspects of gestures. In this work, we propose an experimental framework to quantify, compare and evaluate gestures. This framework is grounded in the expert knowledge of speech and language professionals (SLPs). The development consisted of three studies: 1) Creation, 2) Evaluation and 3) Validation. In the creation study, we followed an adapted version of the Delphi’s interview/discussion procedure with SLPs. The purpose was to obtain the Vocabulary Acceptability Criteria (VAC) to evaluate gestures. Next, in the evaluation study, a modified method of pairwise comparisons was used to rank and quantify the gestures based on each criteria (VAC). Lastly, in the validation study, we formulated an odd one out procedure, to prove that the VAC values of a gesture are representative and sufficiently distinctive, to select that particular gesture from a pool of gestures. We applied this framework to the gestures obtained from a gesture elicitation study conducted with nine neurosurgeons, to control an imaging software. In addition, 29 SLPs comprising of 17 experts and 12 graduate students participated in the VAC study. The best lexicons from the available pool were obtained through both agreement and VAC metrics. We used binomial tests to show that the results obtained from the validation procedure are significantly better than the baseline. These results verify our hypothesis that the VAC are representative of the gestures and the subjects should be able to select the right gesture given its VAC values.
Coding dosage of teachers’ implementation of activities using ICAP : A video analysis

The ultimate goal of professional development is to facilitate teachers’ spontaneous transfer of knowledge gained from education research to their teaching practice in order to improve student outcomes. Researchers in the field of teacher professional development agree that professional education should situate learning opportunities for teachers in the context of their work (e.g., Ball & Cohen, 1999; Kazemi & Hubbard, 2008) and that a professional development “curriculum” should be grounded in the tasks, questions, and problems of practice (Ball & Cohen, 1999, p. 20). Our intervention, instructing in-service teachers about the ICAP (InteractiveConstructive-Active-Passive) framework and its implementation, afforded them the opportunity to apply a theoretical framework in the context of their everyday activities. The instruction was grounded in the tasks that teachers perform on a frequent basis—designing ways to engage their students in learning. Our intervention was an effort to address the ubiquitous question of how student learning can be improved via teachers’ instantiation of activities that elicit increased student engagement. In this chapter, we will report our methodology and results from a video analysis conducted to assess teachers’ transfer of their learning about the ICAP framework from this professional development activity to their implementation of ICAP activities in the classroom.
Using Calibrated Parts and Integral Surface Analysis to Investigate Dimensional CT Measurements

The application of computed tomography (CT) is growing rapidly in the field of dimensional metrology. In the current state of the art, however, CT-systems cannot provide the same accuracy as tactile coordinate measuring systems, and one of the main reasons is the high level of noise generated in CT measurements. This paper presents an experimental approach to investigate the influence of the extraction operation on geometrical evaluations performed with CT-systems. The methodology consists in performing systematic experiments using different setup parameters on calibrated workpieces; and comparing characteristics of the extracted integral features in both the space and the frequency domains. The approach was used to analyse a set of CT measurement data extracted with different setup parameters and evaluated with different verification operators. The results demonstrate that distortions generated during the extraction operation with CT systems may have great impact on the results of geometrical evaluations. In this context, the analysis of the integral extracted features obtained with systematic experiments showed to be a useful tool to evaluate the quality of the extraction operation, and thus, to define the setup parameters in order to improve the accuracy of geometrical evaluations.