nils-herrmann/clean_scirepeval huggingface dataset:
Joint Common Architecture Demonstration Lessons Learned - Sikorsky/Boeing Perspective

The Joint Common Architecture (JCA) Demonstration project exercised the Future Airborne Capability Environment (FACETM) Technical Standard and tools as well as the JCA model-driven approach to gather lessons learned and mature the approaches. Sikorsky/Boeing was selected as one of two vendors to develop a software component and its reusable test component to be integrated onto multiple undisclosed Operating Environments (OEs). The model-driven approach was partially successful but required significantly more detail than was initially provided by the Government. The FACE Standard was effective in enabling portability of the software and the test component onto each OE. FACE Verification was pursued and process improvements were identified. Application of the FACE Transport Services did not introduce timing latencies that would impact safety-critical applications. The quality of the lessons learned throughout this effort highlight the importance of demonstrations involving independent teams in order to mature these approaches.
Brain Ischemic Stroke Segmentation : A Survey

In this survey paper a comprehensive survey of segmentation techniques used for localizing brain stroke area (differentiating it with normal region of brain) has been presented. In recent times, automated systems are preferred over manual and semi automated systems. Segmentation or localization is not a challenge, challenge lies behind producing an automated, reproducible and clinically accepted method which works well in case of abnormalities too and can be used for further processing like computing volume, growth prediction and treatment etc. As in today's time there is no place for erroneous interpretations but large number of stroke cases overburden the radiologists and may result in wrong analysis. So, an efficient automated segmentation method is need of an hour and is preferred over conventional systems. The most relevant methods whether manual, semi automated or automated for segmentation are discussed. So, it sets up a picture where there is scope of improvement to reach a goal.
Applying software model checking to PALS systems

Physically Asynchronous/Logically Synchronous (PALS) is an architecture pattern that allows developers to design and verify a system as though all nodes executed synchronously. The correctness of PALS protocol was formally verified. However, the implementation of PALS adds additional code that is otherwise not needed. In our case, we have a middleware (PALSWare) that supports PALS systems. In this paper, we introduce a verification framework that shows how we can apply Software Model Checking (SMC) to verify a PALS system at the source code level. SMC is an automated and exhaustive source code checking technology. Compared to verifying (hardware or software) models, verifying the actual source code is more useful because it minimizes any chance of false interpretation and eliminates the possibility of missing software bugs that were absent in the model but introduced during implementation. In other words, SMC reduces the semantic gap between what is verified and what is executed. Our approach is compositional, i.e., the verification of PALSWare is done separately from applications. Since PALSWare is inherently concurrent, to verify it via SMC we must overcome the statespace explosion problem, which arises from concurrency and asynchrony. To this end, we develop novel simplification abstractions, prove their soundness, and then use these abstractions to reduce the verification of a system with many threads to verifying a system with a relatively small number of threads. When verifying an application, we leverage the (already verified) synchronicity guarantees provided by the PALSWare to reduce the verification complexity significantly. Thus, our approach uses both “abstraction” and “composition”, the two main techniques to reduce statespace explosion. This separation between verification of PALSWare and applications also provides better management against upgrades to either. We validate our approach by verifying the current PALSWare implementation, and several PALSWare-based distributed real time applications.
A CMOS Energy Harvesting and Imaging (EHI) Active Pixel Sensor (APS) Imager for Retinal Prosthesis

A CMOS image sensor capable of imaging and energy harvesting on same focal plane is presented for retinal prosthesis. The energy harvesting and imaging (EHI) active pixel sensor (APS) imager was designed, fabricated, and tested in a standard 0.5 μm CMOS process. It has 54 × 50 array of 21 × 21 μm2 EHI pixels, 10-bit supply boosted (SB) SAR ADC, and charge pump circuits consuming only 14.25 μW from 1.2 V and running at 7.4 frames per second. The supply boosting technique (SBT) is used in an analog signal chain of the EHI imager. Harvested solar energy on focal plane is stored on an off-chip capacitor with the help of a charge pump circuit with better than 70% efficiency. Energy harvesting efficiency of the EHI pixel was measured at different light levels. It was 9.4% while producing 0.41 V open circuit voltage. The EHI imager delivers 3.35 μW of power was delivered to a resistive load at maximum power point operation. The measured pixel array figure of merit (FoM) was 1.32 pW/frame/pixel while imager figure of merit (iFoM) including whole chip power consumption was 696 fJ/pixel/code for the EHI imager.
Identification and expansion of the tumorigenic lung cancer stem cell population

Lung carcinoma is often incurable and remains the leading cancer killer in both men and women. Recent evidence indicates that tumors contain a small population of cancer stem cells that are responsible for tumor maintenance and spreading. The identification of the tumorigenic population that sustains lung cancer may contribute significantly to the development of effective therapies. Here, we found that the tumorigenic cells in small cell and non-small cell lung cancer are a rare population of undifferentiated cells expressing CD133, an antigen present in the cell membrane of normal and cancer-primitive cells of the hematopoietic, neural, endothelial and epithelial lineages. Lung cancer CD133+ cells were able to grow indefinitely as tumor spheres in serum-free medium containing epidermal growth factor and basic fibroblast growth factor. The injection of 104 lung cancer CD133+ cells in immunocompromised mice readily generated tumor xenografts phenotypically identical to the original tumor. Upon differentiation, lung cancer CD133+ cells acquired the specific lineage markers, while loosing the tumorigenic potential together with CD133 expression. Thus, lung cancer contains a rare population of CD133+ cancer stem-like cells able to self-renew and generates an unlimited progeny of non-tumorigenic cells. Molecular and functional characterization of such a tumorigenic population may provide valuable information to be exploited in the clinical setting.
Optimization of EEG frequency bands for improved diagnosis of Alzheimer disease

Many clinical studies have shown that electroencephalograms (EEG) of Alzheimer patients (AD) often have an abnormal power spectrum. In this paper a frequency band analysis of AD EEG signals is presented, with the aim of improving the diagnosis of AD from EEG signals. Relative power in different EEG frequency bands is used as features to distinguish between AD patients and healthy control subjects. Many different frequency bands between 4 and 30Hz are systematically tested, besides the traditional frequency bands, e.g., theta band (4–8Hz). The discriminative power of the resulting spectral features is assessed through statistical tests (Mann-Whitney U test). Moreover, linear discriminant analysis is conducted with those spectral features. The optimized frequency ranges (4–7Hz, 8–15Hz, 19–24Hz) yield substantially better classification performance than the traditional frequency bands (4–8Hz, 8–12Hz, 12–30Hz); the frequency band 4–7Hz is the optimal frequency range for detecting AD, which is similar to the classical theta band. The frequency bands were also optimized as features through leave-one-out crossvalidation, resulting in error-free classification. The optimized frequency bands may improve existing EEG based diagnostic tools for AD. Additional testing on larger AD datasets is required to verify the effectiveness of the proposed approach.
Dynamic Graph-Based Software Watermarking

Watermarking embeds a secret message into a cover message. In media watermarking the secret is usually a copyright notice and the cover a digital image. Watermarking an object discourages intellectual property theft, or when such theft has occurred, allows us to prove ownership. The Software Watermarking problem can be described as follows. Embed a structure W into a program P such that: W can be reliably located and extracted from P even after P has been subjected to code transformations such as translation, optimization and obfuscation; W is stealthy; W has a high data rate; embedding W into P does not adversely affect the performance of P; and W has a mathematical property that allows us to argue that its presence in P is the result of deliberate actions. In this paper we describe a software watermarking technique in which a dynamic graph watermark is stored in the execution state of a program. Because of the hardness of pointer alias analysis such watermarks are difficult to attack automatically.
A Survey on Content-Aware Image and Video Retargeting

This survey introduces the current state of the art in image and video retargeting and describes important ideas and technologies that have influenced the recent work. Retargeting is the process of adapting an image or video from one screen resolution to another to fit different displays, for example, when watching a wide screen movie on a normal television screen or a mobile device. As there has been considerable work done in this field already, this survey provides an overview of the techniques. It is meant to be a starting point for new research in the field. We include explanations of basic terms and operators, as well as the basic workflow of the different methods.
Understanding and predicting classes of college students who use pornography

Despite the widespread acceptance and use of pornography, much remains unknown about the heterogeneity among consumers of pornography. Using a sample of 457 college students from a midwestern university in the United States, a latent profile analysis was conducted to identify unique classifications of pornography users considering motivations of pornography use, level of pornography use, age of user, degree of pornography acceptance, and religiosity. Results indicated three classes of pornography users: Porn Abstainers (n 1⁄4 285), Auto-Erotic Porn Users (n 1⁄4 85), and Complex Porn Users (n 1⁄4 87). These three classes of pornography use are carefully defined. The odds of membership in these three unique classes of pornography users was significantly distinguished by relationship status, selfesteem, and gender. These results expand what is known about pornography users by providing a more person-centered approach that is more nuanced in understanding pornography use. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit
Object Recognition and Tracking based on Object Feature Extracting

Object recognition and tracking is one of the most important task in the field of computer vision and surveillance system. Among many proposed, object tracking using a feature matching method is popular and accurate, however, it has a room to improve on high computational complexity and weak robustness in various environments. This paper proposes a robust object recognition and tracking method, which uses an advanced feature matching for the use of real time environment. The proposed method enables to recognize an object using invariant features, with reducing the dimension of feature descriptor, compared to the existing algorithm. The experimental results show that the proposed recognition and tracking method outperforms the conventional tracking approaches in terms of tracking accuracy and computing time.
3.3kV SiC MOSFETs designed for low on-resistance and fast switching

This paper discusses the latest developments in the optimization and fabrication of 3.3kV SiC vertical DMOSFETs. The devices show superior on-state and switching losses compared to the even the latest generation of 3.3kV fast Si IGBTs and promise to extend the upper switching frequency of high-voltage power conversion systems beyond several tens of kHz without the need to increase part count with 3-level converter stacks of faster 1.7kV IGBTs.
RNN-Based Sequence-Preserved Attention for Dependency Parsing

Recurrent neural networks (RNN) combined with attention mechanism has proved to be useful for various NLP tasks including machine translation, sequence labeling and syntactic parsing. The attention mechanism is usually applied by estimating the weights (or importance) of inputs and taking the weighted sum of inputs as derived features. Although such features have demonstrated their effectiveness, they may fail to capture the sequence information due to the simple weighted sum being used to produce them. The order of the words does matter to the meaning or the structure of the sentences, especially for syntactic parsing, which aims to recover the structure from a sequence of words. In this study, we propose an RNN-based attention to capture the relevant and sequence-preserved features from a sentence, and use the derived features to perform the dependency parsing. We evaluated the graph-based and transition-based parsing models enhanced with the RNN-based sequence-preserved attention on the both English PTB and Chinese CTB datasets. The experimental results show that the enhanced systems were improved with significant increase in parsing accuracy.
Exploiting Process Variations and Programming Sensitivity of Phase Change Memory for Reconfigurable Physical Unclonable Functions

Physical unclonable function (PUF) leverages the immensely complex and irreproducible nature of physical structures to achieve device authentication and secret information storage. To enhance the security and robustness of conventional PUFs, reconfigurable physical unclonable functions (RPUFs) with dynamically refreshable challenge-response pairs (CRPs) have emerged recently. In this paper, we propose two novel physically reconfigurable PUF (P-RPUF) schemes that exploit the process parameter variability and programming sensitivity of phase change memory (PCM) for CRP reconfiguration and evaluation. The first proposed PCM-based P-RPUF scheme extracts its CRPs from the measurable differences of the PCM cell resistances programmed by randomly varying pulses. An imprecisely controlled regulator is used to protect the privacy of the CRP in case the configuration state of the RPUF is divulged. The second proposed PCM-based RPUF scheme produces the random response by counting the number of programming pulses required to make the cell resistance converge to a predetermined target value. The merging of CRP reconfiguration and evaluation overcomes the inherent vulnerability of P-RPUF devices to malicious prediction attacks by limiting the number of accessible CRPs between two consecutive reconfigurations to only one. Both schemes were experimentally evaluated on 180-nm PCM chips. The obtained results demonstrated their quality for refreshable key generation when appropriate fuzzy extractor algorithms are incorporated.
ExploitMeter: Combining Fuzzing with Machine Learning for Automated Evaluation of Software Exploitability

Exploitable software vulnerabilities pose severe threats to its information security and privacy. Although a great amount of efforts have been dedicated to improving software security, research on quantifying software exploitability is still in its infancy. In this work, we propose ExploitMeter, a fuzzing-based framework of quantifying software exploitability that facilitates decision-making for software assurance and cyber insurance. Designed to be dynamic, efficient and rigorous, ExploitMeter integrates machine learning-based prediction and dynamic fuzzing tests in a Bayesian manner. Using 100 Linux applications, we conduct extensive experiments to evaluate the performance of ExploitMeter in a dynamic environment.
Online and offline character recognition: A survey

Handwritten character recognition has been one of the most fascinating research among the various researches in field of image processing. In Handwritten character recognition method the input is scanned from images, documents and real time devices like tablets, tabloids, digitizers etc. which are then interpreted into digital text. There are basically two approaches - Online Handwritten recognition which takes the input at run time and Offline Handwritten Recognition which works on scanned images. In this paper we have discussed the architecture, the steps involved, and the various proposed methodologies of offline and online character recognition along with their comparison and few applications.
Forensic Data Extraction and Analysis of Left Artifacts on emulated Android Phones: A Case Study of Instant Messaging Applications

The fast-growing nature of instant messaging applications usage on Android mobile devices brought about a proportional increase on the number of cyber-attack vectors that could be perpetrated on them. Android mobile phones store significant amount of information in the various memory partitions when Instant Messaging (IM) applications (WhatsApp, Skype, and Facebook) are executed on them. As a result of the enormous crimes committed using instant messaging applications, and the amount of electronic based traces of evidence that can be retrieved from the suspect’s device where an investigation could convict or refute a person in the court of law and as such, mobile phones have become a vulnerable ground for digital evidence mining. This paper aims at using forensic tools to extract and analyse left artefacts digital evidence from IM applications on Android phones using android studio as the virtual machine. Digital forensic investigation methodology by Bill Nelson was applied during this research. Some of the key results obtained showed how digital forensic evidence such as call logs, contacts numbers, sent/retrieved messages, and images can be mined from simulated android phones when running these applications. These artefacts can be used in the court of law as evidence during cybercrime investigation.
Understanding Benefit and Risk Framework of Fintech Adoption: Comparison of Early Adopters and Late Adopters

Financial technology (Fintech) service has recently become the focus of considerable attention. Although many researchers and practitioners believe that Fintech can reshape the future of the financial services industry, others are skeptical about the adoption of Fintech because of the considerable risks involved. Therefore, we need to better understand why users are willing or hesitant to adopt Fintech, wherein, positive and negative factors affect their adoption decision. Based on the net valence framework theoretically embedded in theory of reasoned action, we propose a benefit-risk framework which integrates positive and negative factors associated with its adoption. Based on the empirical data collected from 244 Fintech users, this study initially investigates whether perceived benefit and risk significantly impact Fintech adoption intention. We then examine whether the effect of perceived benefit and risk on Fintech adoption intention differs depending on the user types. Results show that legal risk has the biggest negative effect, whereas convenience has the strongest positive effect on Fintech adoption intention. The differences between early adopters and late adopters are driven by different factors.
Triboelectric nanogenerator based on vertical contact separation mode for energy harvesting

Triboelectric effect works on the principle of triboelectrification and electrostatic induction. This principle is used to generate voltage by converting mechanical energy into electrical energy. This paper presents the charging behavior of different capacitors by rubbing of two different materials using mechanical motion. The numerical and simulation modeling, describes the charging performance of a TENG with a bridge rectifier. It is also demonstrated that a 10 μF capacitor can be charged to a maximum of 24.04 volt in 300 seconds and it is also provide 2800 μJ/cm3 maximum energy density. Such system can be used for ultralow power electronic devices, biomedical devices and self-powered appliances etc.
High-resolution medical image synthesis using progressively grown generative adversarial networks

Generative adversarial networks (GANs) are a class of unsupervised machine learning algorithms that can produce realistic images from randomly-sampled vectors in a multi-dimensional space. Until recently, it was not possible to generate realistic high-resolution images using GANs, which has limited their applicability to medical images that contain biomarkers only detectable at native resolution. Progressive growing of GANs is an approach wherein an image generator is trained to initially synthesize low resolution synthetic images (8x8 pixels), which are then fed to a discriminator that distinguishes these synthetic images from real downsampled images. Additional convolutional layers are then iteratively introduced to produce images at twice the previous resolution until the desired resolution is reached. In this work, we demonstrate that this approach can produce realistic medical images in two different domains; fundus photographs exhibiting vascular pathology associated with retinopathy of prematurity (ROP), and multi-modal magnetic resonance images of glioma. We also show that fine-grained details associated with pathology, such as retinal vessels or tumor heterogeneity, can be preserved and enhanced by including segmentation maps as additional channels. We envisage several applications of the approach, including image augmentation and unsupervised classification of pathology.
APOE and Alzheimer’s Disease: Evidence Mounts that Targeting APOE4 may Combat Alzheimer’s Pathogenesis

Alzheimer’s disease (AD) is an immutable neurodegenerative disease featured by the two hallmark brain pathologies that are the extracellular amyloid ß (Aß) and intraneuronal tau protein. People carrying the APOE4 allele are at high risk of AD concerning the ones carrying the ε3 allele, while the ε2 allele abates risk. ApoE isoforms exert a central role in controlling the transport of brain lipid, neuronal signaling, mitochondrial function, glucose metabolism, and neuroinflammation. Regardless of widespread indispensable studies, the appropriate function of APOE in AD etiology stays ambiguous. Existing proof recommends that the disparate outcomes of ApoE isoforms on Aβ accretion and clearance have a distinct function in AD pathogenesis. ApoE–lipoproteins combine diverse cell-surface receptors to transport lipids and moreover to lipophilic Aβ peptide, that is believed to begin deadly events that generate neurodegeneration in the AD. ApoE has great influence in tau pathogenesis, tau-mediated neurodegeneration, and neuroinflammation, as well as α-synucleinopathy, lipid metabolism, and synaptic plasticity despite the presence of Aβ pathology. ApoE4 shows the deleterious effect for AD while the lack of ApoE4 is defensive. Therapeutic strategies primarily depend on APOE suggest to lessen the noxious effects of ApoE4 and reestablish the protective aptitudes of ApoE. This appraisal represents the critical interactions of APOE and AD pathology, existing facts on ApoE levels in the central nervous system (CNS), and the credible active stratagems for AD therapy by aiming ApoE. This review also highlighted utmost ApoE targeting therapeutic tactics that are crucial for controlling Alzheimer’s pathogenesis.