Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Proof Theory of Property-Based Testing of Coinductive Speciﬁcations, or: PBT to Inﬁnity and beyond

Reasoning about inﬁnite computations via coinduction and corecursion has an ever increasing relevance in formal methods and, in particular, in the semantics of programming languages, starting from [16]; see also [13] for a compelling example — and, of course, coinduction underlies (the meta-theory of) process calculi. This was acknowledged by researchers in proof assistants, who promptly provided support for coinduction and corecursion from the early 90’s on, see [19, 10] for the beginning of the story concerning the most popular frameworks. It also became apparent that tools that search for refutations/counter-examples of conjectures prior to attempting a formal proof are invaluable: this is particularly true in PL theory, where proofs tend to be shallow but may have hundreds of cases. One such approach is property-based testing (PBT), which employs automatic test data generation to try and refute executable speciﬁcations. Pioneered by QuickCheck for functional programming [7], it has now spread to most major proof assistants [4, 18].
Thermal Stresses in a NanoComposite Slab Due to Non-Fourier Heat transfer

Multi-layer metal thin-films are widely used in engineering applications since a single metal layer often cannot satisfy all mechanical, thermal and electrical requirements. A better understanding of energy transfer in such multi-layer systems is critical in many applications. Thermal stresses, especially in micro/nanostructures, microfilms, semiconductors, are among the important factors that affect the life of the material of these devises. These stresses become very significant in the applications involving the ultra-fast heating rate and large temperature differences or gradients. In this work the hyperbolic heat conduction model was used to determine the temperature distribution in a thin rapidly heated two-layer slab. Using these temperature results, thermal stresses generated within the slab were calculated numerically using the Finite Difference technique after the transient deflections of the plate were calculated. The effect of ratio of thermal conductivity, and the dimensionless time on the temperature distribution and thermal stresses were studied Keywords—Composite slab, thermal stresses, hyperbolic heat conduction.
Multidimensional diffusion processes in dynamic online networks

We develop a dynamic matched sample estimation algorithm to distinguish peer influence and homophily effects on item adoption decisions in dynamic networks, with numerous items diffusing simultaneously. We infer preferences using a machine learning algorithm applied to previous adoption decisions, and we match agents using those inferred preferences. We show that ignoring previous adoption decisions leads to significantly overestimating the role of peer influence in the diffusion of information, mistakenly confounding influence-based contagion with diffusion driven by common preferences. Our matching-on-preferences algorithm with machine learning reduces the relative effect of peer influence on item adoption decisions in this network significantly more than matching on earlier adoption decisions, as well other observable characteristics. We also show significant and intuitive heterogeneity in the relative effect of peer influence.
SeMA: Extending and Analyzing Storyboards to Develop Secure Android Apps

Mobile apps provide various critical services, such as banking, communication, and healthcare. To this end, they have access to our personal information and have the ability to perform actions on our behalf. Hence, securing mobile apps is crucial to ensuring the privacy and safety of its users. Recent research efforts have focused on developing solutions to secure mobile ecosystems (i.e., app platforms, apps, and app stores), specifically in the context of detecting vulnerabilities in Android apps. Despite this attention, known vulnerabilities are often found in mobile apps, which can be exploited by malicious apps to harm the user. Further, fixing vulnerabilities after developing an app has downsides in terms of time, resources, user inconvenience, and information loss. In an attempt to address this concern, we have developed SeMA, a mobile app development methodology that builds on existing mobile app design artifacts such as storyboards. With SeMA, security is a first-class citizen in an app's design -- app designers and developers can collaborate to specify and reason about the security properties of an app at an abstract level without being distracted by implementation level details. Our realization of SeMA using Android Studio tooling demonstrates the methodology is complementary to existing design and development practices. An evaluation of the effectiveness of SeMA shows the methodology can detect and help prevent 49 vulnerabilities known to occur in Android apps. Further, a usability study of the methodology involving ten real-world developers shows the methodology is likely to reduce the development time and help developers uncover and prevent known vulnerabilities while designing apps.
Eye-dominance-guided Foveated Rendering

Optimizing rendering performance is critical for a wide variety of virtual reality (VR) applications. Foveated rendering is emerging as an indispensable technique for reconciling interactive frame rates with ever-higher head-mounted display resolutions. Here, we present a simple yet effective technique for further reducing the cost of foveated rendering by leveraging ocular dominance – the tendency of the human visual system to prefer scene perception from one eye over the other. Our new approach, eye-dominance-guided foveated rendering (EFR), renders the scene at a lower foveation level (with higher detail) for the dominant eye than the non-dominant eye. Compared with traditional foveated rendering, EFR can be expected to provide superior rendering performance while preserving the same level of perceived visual quality.
Decentralized gradient methods: does topology matter?

Consensus-based distributed optimization methods have recently been advocated as alternatives to parameter server and ring all-reduce paradigms for large scale training of machine learning models. In this case, each worker maintains a local estimate of the optimal parameter vector and iteratively updates it by averaging the estimates obtained from its neighbors, and applying a correction on the basis of its local dataset. While theoretical results suggest that worker communication topology should have strong impact on the number of epochs needed to converge, previous experiments have shown the opposite conclusion. This paper sheds lights on this apparent contradiction and show how sparse topologies can lead to faster convergence even in the absence of communication delays.
Clinical Annotation Research Kit (CLARK): Computable Phenotyping Using Machine Learning

Computable phenotypes are algorithms that translate clinical features into code that can be run against electronic health record (EHR) data to define patient cohorts. However, computable phenotypes that only make use of structured EHR data do not capture the full richness of a patient’s medical record. While natural language processing (NLP) methods have shown success in extracting clinical features from text, the use of such tools has generally been limited to research groups with substantial NLP expertise. Our goal was to develop an open-source phenotyping software, Clinical Annotation Research Kit (CLARK), that would enable clinical and translational researchers to use machine learning–based NLP for computable phenotyping without requiring deep informatics expertise. CLARK enables nonexpert users to mine text using machine learning classifiers by specifying features for the software to match in clinical notes. Once the features are defined, the user-friendly CLARK interface allows the user to choose from a variety of standard machine learning algorithms (linear support vector machine, Gaussian Naïve Bayes, decision tree, and random forest), cross-validation methods, and the number of folds (cross-validation splits) to be used in evaluation of the classifier. Example phenotypes where CLARK has been applied include pediatric diabetes (sensitivity=0.91; specificity=0.98), symptomatic uterine fibroids (positive predictive value=0.81; negative predictive value=0.54), nonalcoholic fatty liver disease (sensitivity=0.90; specificity=0.94), and primary ciliary dyskinesia (sensitivity=0.88; specificity=1.0). In each of these use cases, CLARK allowed investigators to incorporate variables into their phenotype algorithm that would not be available as structured data. Moreover, the fact that nonexpert users can get started with machine learning–based NLP with limited informatics involvement is a significant improvement over the status quo. We hope to disseminate CLARK to other organizations that may not have NLP or machine learning specialists available, enabling wider use of these methods.
Real-Time PCR Analysis of Metabolic Pathway of PHB in Acidiphilium

The time, yield, and related genes expression of PHB accumulation of Acidiphilium cryptum DX1-1 were investigated under four different initial C/N ratios, 1.2, 2.4, 7.5, and 24. The results of time and yield of poly-β-hydroxybutyrate (PHB) accumulation show that the initial C/N ratio of 2.4 was optimum for strain DX1-1 to accumulate PHB, but both higher and lower initial C/N ratios did not favor that process. Based on the genome of Acidiphilium cryptum JF-5, 13 PHB accumulation related genes in strain JF-5 were chosen and successfully cloned from strain DX1-1. The differential expressions of the 13 functional genes, in different C/N ratios as cited above, were then studied by real-time PCR. The results show that all the 13 genes were most upregulated when the initial C/N ratio was 2.4, and among which the gene Acry_3030 encoding poly-β-hydroxybutyrate polymerase and Acry_0626 encoding acetyl-CoA synthetase were much more upregulated than the other genes, which proved that they play the most important role for PHB accumulation, and acetate is the main initial substance for PHB accumulation for strain DX1-1. Potential regulatory motifs analysis showed that the genes related to PHB accumulation are regulated by different promoters and that the motif had weak similarity to the model promoters, suggesting that PHB metabolism in Acidiphilium cryptum may be mediated by a different mechanism.
Green Synthesis and High Efficacy Method for Reduced Graphene Oxide by Zataria Multiflora Extract

There are many different methods for producing nanoparticles, but the use of plants due to the low cost and environmentally friendly nature of nanoparticle synthesis is very much considered. The biological synthesis of reduced graphene oxide by extracts of Zataria multiflora cultivar is reported in this work. In this study, Zataria multiflora extract was used as a reducing agent for the production of nano-graphene oxide in high yield and avoids the use of toxic and environmentally hazardous reducing agents commonly used in the reduction processes of GO. The reduced graphene oxide and optimization of reaction conditions were monitored by UV and analyzed for determination of size and morphology of reduced graphene oxide with Transmission Electron Microscopy (TEM), Raman spectra, Fourier Transform-infrared spectroscopy and X-ray diffraction devices (XRD) respectively. Keyword: Green synthesis, Zataria multiflora, Reduced graphene oxide, Graphene oxide
Study of phylogenetic consistency, structure and origin of the pink berries in Great and Little Sippewissett salt marsh

In the past 20 years pink microbial aggregates from the Little and Great Sippewissett salt marsh have been studied in the microbial diversity course, Woods Hole, Massachusetts. Clone libaries revealed that the aggregates may be dominated by two main phylotypes 1) Halochromatium sp. , belonging to the purple sulfur bacteria of the Gammaproteobacteria, and 2) Desulfofustis sp. , characterized as a sulfate-reducing bacterium in the Deltaproteobacteria. Furthermore, the biomass of the aggregates was found to be dominated by two morphotypes: 1) large, spherical and pigmented cells and 2) smaller, rod-shaped bacteria. The physical arrangement and their phylogenetic relationship to the Gammaand Delatproteobacteria, respectively, have been demonstrated. In this study, cluster-specific FISH-probes for the Halochromatium-related and the Desulfofustis-related phylotypes of the berries were designed to serve the confirmation that these phylotypes are forming the main biomass of the berry aggregate. Furthermore, they were used for the specific description of cell arrangement within the aggregate and were tested on water and sand samples in order to reveal the putative presence of free-living stages of the phylotypes. By this, putative early stages of aggregate formation were found in pink sand banks, giving insights into the potential origin of the berries. Isolation of the putative sulfatereducing bacteria from the berries should provide a tool for studying the metabolites used and products excreted by these organisms, which could help to understand the physiological interconnection of the two dominating organisms in the aggregate.
PNet: An Efficient Network for Pneumonia Detection

Pneumonia is a common lung disease and affects millions of people worldwide each year. The chest X-ray is one of the most commonly accessible radiological examinations for screening and diagnosing lung diseases. Usually, chest X-ray images (CXRs) are taken at physical examination and evaluated by radiologists. The unbalance between tremendous numbers of CXRs and limited number of radiologists need to be solved by Computer Assisted Diagnosis (CAD). As deep neural networks have shown promising results in CAD, we implemented a deep learning-based framework, PNet, for pneumonia detection. 10784 chest X-ray images collected at the Shenzhen No.2 People's Hospital were employed for training and evaluation. The experimental results showed that the proposed PNet (1,695,777 parameters) achieved higher accuracy and F1 score than classical networks like AlexNet (42,725,889 parameters) and VGG16 (27,560,769 parameters).
HIL Based Solution for ADAS Software Development and Verification

The automotive, as one of the fastest growing and largest industries, is finding more and more interest in developing software for its innovating solutions. New features and modules such as digital cockpit, driver assistance algorithms and many more are based on software solutions. Thus, there is considerable interest in this industry in computer aided tools that support rapid development of new products, where the majority of the tools require expensive PC hardware support. This paper describes solution that enables use of affordable PC hardware as a new method for the design and validation of advanced software algorithms based on the hardware-in-the-loop (HIL) simulator. Simulator is used to verify the different software modules and algorithms under development for new electronic control units (ECU), where the development time and mainly the validation phase is carried out faster in a more cost effective and controllable manner.
Participant Service Quality Aware Data Collecting Mechanism With High Coverage for Mobile Crowdsensing

A large number of participants are required to complete specific tasks to sense data and obtain the sensing information in Mobile Crowdsensing. In order to ensure the real-time effectiveness and comprehensiveness of the sensing information, this paper proposes a participant service quality aware data collecting mechanism with high coverage. Firstly, the service quality is measured by the willingness and regional preference of participants to analyze the real-time effectiveness of the sensing data. Then the sensing data coverage is evaluated according to the number of target points covered by the participants during the execution of sensing tasks. Furthermore, the efficiency of the participants is determined by service quality and data coverage under the condition of a limited platform budget. Finally, an iterative greedy algorithm is designed to select the sensing data from the participants set with the highest efficiency, so the coverage of sensing data can be efficiently optimized. The results demonstrate that the proposed data collecting mechanism can reduce the total reward for participants, make the tasks accomplished more efficiently, and effectively collect sensing data with high coverage.
Recognition and Location of Mixed-type Patterns in Wafer Bin Maps

Wafer map analysis is one of the most critical steps for monitoring wafer quality and tracking failures in the semiconductor manufacturing process. Defective dies on wafer bin maps (WBMs) usually cluster into specific spatial patterns, which contain critical information for cause identification and yield improvement. This paper proposes an effective pattern recognition and location approach for both single pattern and mixed-type pattern maps. A convolutional neural network is introduced to classify WBMs with the single pattern or mixed-type (non-overlapped) patterns after seed filling segmentation. Meanwhile, WBMs with overlapped patterns are located and classified through template matching method. Further, for assessing the impact of each pattern, the pattern sizes are calculated after pattern location. The overall classification accuracy in two real-world datasets reaches 91.2% and 84.3%, which demonstrates the proposed approach has excellent classification performance for WBMs with mixed-type patterns.
Decoding Scheduling for Unequal Error Protection Quasi-Cyclic Low-Density Parity-Check Codes

Algebraic constructions of quasi-cyclic (QC) low-density parity-check (LDPC) codes with unequal error protection (UEP) capabilities have recently been proposed, which can provide different error-correcting capabilities for different parts of the coded bits. However, conventional decoding algorithms are not designed for LDPC codes with UEP. In this paper, we propose three decoding schemes for UEP QC-LDPC codes extended from existing scheduling strategies, tailored to the special structures of the parity-check matrices. Simulation results show that the proposed schemes can provide improved error rate performance especially for coded bits on low protection levels. The decoding convergence can also be accelerated, thereby reducing the computational complexity.
mManagement: Wi-Fi Hotspot based Attendance Application using Android Smartphone

mManagement is new area for managing the business and human resource tasks that offers services by advance communication technologies and smart devices. Attendance is a hectic and time-consuming process in our daily lives. To resolve this, we proposed and developed a fully automated android smartphone application. It marks and tracks attendance as mManagement solution with an additional Wi-Fi hotspot feature. The objective of our application is to optimize the performance of taking attendance in an efficient and effective way. It reminds the students/attendees about timings of upcoming class via broadcasting a notification to attendees of that specific class. The app not only notes the attendance, but also track the presence of attendee, by automatically marking their attendance at predefined intervals. To evaluate our application, we tested the app in small conference halls, and it out-performed many existing android applications, especially applications based on Bluetooth connectivity. Our app works over the coverage area of maximum 24.5 meters radius in closed area, by marking attendance of 10 attendees simultaneously.
Predicting Execution Time of CUDA Kernels with Unified Memory Capability

Nowadays, GPUs are known as one of the most important, most remarkable, and perhaps most popular computing platforms. In recent years, GPUs have increasingly been considered as co-processors and accelerators. Along with growing technology, Graphics Processing Units (GPUs) with more advanced features and capabilities are manufactured and launched by the world's largest commercial companies. Unified memory is one of these new features introduced on the latest generations of Nvidia GPUs which allows programmers to write a program considering the uniform memory shared between CPU and GPU. This feature makes programming considerably easier. The present study introduces this new feature and its attributes. In addition, a model is proposed to predict the execution time of applications if using unified memory style programming based on the information of non-unified style implementation. The proposed model can predict the execution time of a kernel with an average accuracy of 87.60%.
Adapting of Non-Metallic Cookware for Induction Heating Technology via Thin-Layer Non-Magnetic Conductive Coatings

We analyze the feasibility of heating non-metallic cookware, unappropriate for heating by means of induced currents, with the purpose of extending the applicability range of the current induction heating cooktops. In order to turn materials as glass, ceramic, wood or plastic into suitable for the induction heating technology, we propose the use of thin layers of a metal (not necessarily a ferromagnetic material) which can be deposited on a surface by means of a thin or thick layer technology. For this purpose, the inductive performance of these layers is investigated by means of an analytical electromagnetic model, finite element simulations and experimental measurements. Calculations point out that for a specific induction arrangement working at a fixed frequency, it exists a thickness which maximizes the induction efficiency for each layer material. The suitability of this result is tested by means of a set of samples with copper thin layers whose thicknesses range from one hundred of nanometers to tens of micrometers, which are implemented using a phase vapor deposition (PVD) technology. The obtained induction efficiency and equivalent resistance are compared with those obtained with conventional ferromagnetic materials. As a proof of concept, the inner and outer bottoms of two glass pots are covered with a copper layer of $2~{\mathrm {\mu }}\text{m}$ , and $1.5~{\mathrm {\mu }}\text{m}$ , respectively, and 1 kW is inductively supplied by means of a series resonant inverter, reaching the boiling water conditions.
Robotic Biceps Exercise Machine: Hardware Using Series Elastic Actuator and Control With Disturbance Observer

Resistance training is a popular form of exercise owing to its health-related and athletic performance benefits. Robotic exercise machines using human-robot interaction are a promising solution for designing resistance training programs that successfully achieve training gains. We propose a robotic biceps exercise machine that generates a variable resistance force profile and controls the interaction force corresponding to the profile through a range of motion of exercises. A series elastic actuator measures and controls the resistance force. A novel cascade control structure comprising an inner velocity and outer force control is presented. The inner loop disturbance on the dynamics and outer loop disturbance on the kinematics are eliminated by a disturbance observer (DOB) in each loop. The performance of the proposed force control scheme is validated by comparisons with the conventional DOB and proportional-integral (PI) control schemes. Additionally, the resistance force profile and interaction force of a conventional robotic biceps exercise machine are analyzed experimentally.