nils-herrmann/clean_scirepeval huggingface dataset:
Median filtering forensics in digital images based on frequency-domain features

Tampering detection has been increasingly attracting attention in the field of digital forensics. As a popular nonlinear smoothing filter, median filtering is often used as a post-processing operation after image forgeries such as copy-paste forgery (including copy-move and image splicing), which is of particular interest to researchers. To implement the blind detection of median filtering, this paper proposes a novel approach based on a frequency-domain feature coined the annular accumulated points (AAP). Experimental results obtained on widely used databases, which consists of various real-world photos, show that the proposed method achieves outstanding performance in distinguishing median-filtered images from original images or images that have undergone other types of manipulations, especially in the scenarios of low resolution and JPEG compression with a low quality factor. Moreover, our approach remains reliable even when the feature dimension decreases to 5, which is significant to save the computing time required for classification, demonstrating its great advantage to be applied in real-time processing of big multimedia data.
Polarity detection of Kannada documents

Document polarity detection is a part of sentiment analysis where a document is classified as a positive polarity document or a negative polarity document. The applications of polarity detection are content filtering and opinion mining. Content filtering of negative polarity documents is an important application to protect children from negativity and can be used in security filters of organizations. In this paper, dictionary based method using polarity lexicon and machine learning algorithms are applied for polarity detection of Kannada language documents. In dictionary method, a manually created polarity lexicon of 5043 Kannada words is used and compared with machine learning algorithms like Naïve Bayes and Maximum Entropy. It is observed that performance of Naïve Bayes and Maximum Entropy is better than dictionary based method with accuracy of 0.90, 0.93 and 0.78 respectively.
Psychosocial predictors of fruit and vegetable consumption in adults a review of the literature.

Adequate fruit and vegetable intake has been found to promote health and reduce the risk of several cancers and chronic diseases. Understanding the psychological determinants of fruit and vegetable intake is needed to design effective intervention programs.



Papers published in English from 1994 to 2006 that described the relationship between psychosocial predictors and fruit and vegetable intake in adults were reviewed. Studies and their constructs were independently rated based on the direction of significant effects, quality of execution, design suitability, and frequency. Methodology from the Guide to Community Preventive Services was used to systematically review and synthesize findings.



Twenty-five psychosocial constructs spanning 35 studies were reviewed (14 prospective and 21 cross-sectional/descriptive studies). Strong evidence was found for self-efficacy, social support, and knowledge as predictors of adult fruit and vegetable intake. Weaker evidence was found for variables including barriers, intentions, attitudes/beliefs, stages of change, and autonomous motivation.



The findings underscore the need to design future behavioral interventions that use strong experimental designs with efficacious constructs and to conduct formal mediation analyses to determine the strength of these potential predictors of fruit and vegetable intake.
Walking through Architectural Spaces: The Impact of Interior Forms on Human Brain Dynamics

Neuroarchitecture uses neuroscientific tools to better understand architectural design and its impact on human perception and subjective experience. The form or shape of the built environment is fundamental to architectural design, but not many studies have shown the impact of different forms on the inhabitants' emotions. This study investigated the neurophysiological correlates of different interior forms on the perceivers' affective state and the accompanying brain activity. To understand the impact of naturalistic three-dimensional (3D) architectural forms, it is essential to perceive forms from different perspectives. We computed clusters of form features extracted from pictures of residential interiors and constructed exemplary 3D room models based on and representing different formal clusters. To investigate human brain activity during 3D perception of architectural spaces, we used a mobile brain/body imaging (MoBI) approach recording the electroencephalogram (EEG) of participants while they naturally walk through different interior forms in virtual reality (VR). The results revealed a strong impact of curvature geometries on activity in the anterior cingulate cortex (ACC). Theta band activity in ACC correlated with specific feature types (rs (14) = 0.525, p = 0.037) and geometry (rs (14) = -0.579, p = 0.019), providing evidence for a role of this structure in processing architectural features beyond their emotional impact. The posterior cingulate cortex and the occipital lobe were involved in the perception of different room perspectives during the stroll through the rooms. This study sheds new light on the use of mobile EEG and VR in architectural studies and provides the opportunity to study human brain dynamics in participants that actively explore and realistically experience architectural spaces.
Time distortion when users at-risk for social media addiction engage in non-social media tasks.

There is a growing concern over the addictiveness of Social Media use. Additional representative indicators of impaired control are needed in order to distinguish presumed social media addiction from normal use.



(1) To examine the existence of time distortion during non-social media use tasks that involve social media cues among those who may be considered at-risk for social media addiction. (2) To examine the usefulness of this distortion for at-risk vs. low/no-risk classification.



We used a task that prevented Facebook use and invoked Facebook reflections (survey on self-control strategies) and subsequently measured estimated vs. actual task completion time. We captured the level of addiction using the Bergen Facebook Addiction Scale in the survey, and we used a common cutoff criterion to classify people as at-risk vs. low/no-risk of Facebook addiction.



The at-risk group presented significant upward time estimate bias and the low/no-risk group presented significant downward time estimate bias. The bias was positively correlated with Facebook addiction scores. It was efficacious, especially when combined with self-reported estimates of extent of Facebook use, in classifying people to the two categories.



Our study points to a novel, easy to obtain, and useful marker of at-risk for social media addiction, which may be considered for inclusion in diagnosis tools and procedures.
SMALLHOLDER COMMERCIALIZATION OF MAIZE AND SOCIAL CAPITAL IN THE EASTERN CAPE PROVINCE OF SOUTH AFRICA

Smallholder subsistence agriculture in the rural Eastern Cape Province is recognised as one of the major contributors to food security among the resourced-poor household. However, subsistence agriculture is thought to be unsustainable in the ever changing social, economic and political environment, and climate. This has contributed greatly to stagnate and widespread poverty among smallholder farmers in the Eastern Cape. For a sustainable transition from subsistence to smallholder commercial farming, strategies like accumulated social capital through rural farmer groups/cooperatives have been employed by the government and NGOs. These strategies have yielded mixed results of failed and successful farmer groups/cooperatives. Therefore, this study was aimed at establishing the impact of social capital on farmers’ household commercialization level of maize in addition to farm/farmer characteristics. The findings of this study established that smallholders’ average household commercialization index (HCI) of maize was 45%. Household size, crop sales, source of irrigation water, and bonding social capital had a positive and significant impact on HCI of maize while off-farm incomes and social values had a negative and significant impact on the same. Thus, innovation, adoption and use of labour saving technology, improved access to irrigation water and farmers’ access to trainings in relation to strengthening group cohesion are crucial in promoting smallholder commercial farming of maize in the study area.
Embedded Real-Time Object Detection for a UAV Warning System

In this paper, we demonstrate and evaluate a method to perform real-time object detection on-board a UAV using the state of the art YOLOv2 object detection algorithm running on an NVIDIA Jetson TX2, an GPU platform targeted at power constrained mobile applications that use neural networks under the hood. This, as a result of comparing several cutting edge object detection algorithms. Multiple evaluations we present provide insights that help choose the optimal object detection configuration given certain frame rate and detection accuracy requirements. We propose how this setup running on-board a UAV can be used to process a video feed during emergencies in real-time, and feed a decision support warning system using the generated detections.
Enhancement of security using cryptographic techniques

The paper will describe various types of security issues which include confidentality, integrity and availability of data. There exists various threats to security issues traffic analysis, snooping, spoofing, denial of service attack etc. The asymmetric key encryption techniques may provide a higher level of security but compared to the symmetric key encryption Although we have existing techniques symmetric and assymetric key cryptography methods but there exists security concerns. A brief description of proposed framework is defined which uses the random combination of public and private keys. The mechanisms includes: Integrity, Availability, Authentication, Nonrepudiation, Confidentiality and Access control which is achieved by private-private key model as the user is restricted both at sender and reciever end which is restricted in other models. A review of all these systems is described in this paper.
Visual mental imagery: A view from artificial intelligence

This article investigates whether, and how, an artificial intelligence (AI) system can be said to use visual, imagery-based representations in a way that is analogous to the use of visual mental imagery by people. In particular, this article aims to answer two fundamental questions about imagery-based AI systems. First, what might visual imagery look like in an AI system, in terms of the internal representations used by the system to store and reason about knowledge? Second, what kinds of intelligent tasks would an imagery-based AI system be able to accomplish? The first question is answered by providing a working definition of what constitutes an imagery-based knowledge representation, and the second question is answered through a literature survey of imagery-based AI systems that have been developed over the past several decades of AI research, spanning task domains of: 1) template-based visual search; 2) spatial and diagrammatic reasoning; 3) geometric analogies and matrix reasoning; 4) naive physics; and 5) commonsense reasoning for question answering. This article concludes by discussing three important open research questions in the study of visual-imagery-based AI systems-on evaluating system performance, learning imagery operators, and representing abstract concepts-and their implications for understanding human visual mental imagery.
Adapting Mask-RCNN for Automatic Nucleus Segmentation

Automatic segmentation of microscopy images is an important task in medical image processing and analysis. Nucleus detection is an important example of this task. Mask-RCNN is a recently proposed state-of-the-art algorithm for object detection, object localization, and object instance segmentation of natural images. In this paper we demonstrate that Mask-RCNN can be used to perform highly effective and efficient automatic segmentations of a wide range of microscopy images of cell nuclei, for a variety of cells acquired under a variety of conditions.
CAMY: applying a pet dog analogy to everyday ubicomp products

Ubicomp products have become more important in providing emotional experiences as users increasingly assimilate these products into their everyday lives. In this paper, we explored a new design perspective by applying a pet dog analogy to support emotional experience with ubicomp products. We were inspired by pet dogs, which are already intimate companions to humans and serve essential emotional functions in daily live. Our studies involved four phases. First, through our literature review, we articulated the key characteristics of pet dogs that apply to ubicomp products. Secondly, we applied these characteristics to a design case, CAMY, a mixed media PC peripheral with a camera. Like a pet dog, it interacts emotionally with a user. Thirdly, we conducted a user study with CAMY, which showed the effects of pet-like characteristics on users' emotional experiences, specifically on intimacy, sympathy, and delightedness. Finally, we presented other design cases and discussed the implications of utilizing a pet dog analogy to advance ubicomp systems for improved user experiences.
Share a pie?: Privacy-Preserving Knowledge Base Export through Count-min Sketches

Knowledge base (KB) sharing among parties has been proven to be beneficial in several scenarios. However such sharing can arise considerable privacy concerns depending on the sensitivity of the information stored in each party's KB. In this paper, we focus on the problem of exporting a (part of a) KB of a party towards a receiving one. We introduce a novel solution that enables parties to export data in a privacy-preserving fashion, based on a probabilistic data structure, namely the \emph{count-min sketch}. With this data structure, KBs can be exported in the form of key-value stores and inserted into a set of count-min sketches, where keys can be sensitive and values are counters. Count-min sketches can be tuned to achieve a given key collision probability, which enables a party to deny having certain keys in its own KB, and thus to preserve its privacy. We also introduce a metric, the γ-deniability (novel for count-min sketches), to measure the privacy level obtainable with a count-min sketch. Furthermore, since the value associated to a key can expose to linkage attacks, noise can be added to a count-min sketch to ensure controlled error on retrieved values. Key collisions and noise alter the values contained in the exported KB, and can affect negatively the accuracy of a computation performed on the exported KB. We explore the tradeoff between privacy preservation and computation accuracy by experimental evaluations in two scenarios related to malware detection.
Network Intrusion Detection Using a HNB Binary Classifier

Utilization of the data mining techniques in intrusion detection systems is common for the classification of the network events as either normal events or attack events. Naïve Bayes (NB) method is a simple, efficient and popular data mining method that is built on conditional independence of attributes assumption. Hidden Naïve Bayes (HNB) is an extended form of NB that keeps the NB's simplicity and efficiency while relaxing its independence assumption. Our experimental research claims that the HNB binary classifier model can be applied to intrusion detection problem. Experiment results using classic KDD 1999 Cup intrusion detection dataset indicate that HNB binary classifier has better performance in terms of detection accuracy compared to the traditional NB classifier.
Estimating Radio-Frequency Interference to an Antenna Due to Near-Field Coupling Using Decomposition Method Based on Reciprocity

In mixed radio-frequency (RF) and digital designs, noise from high-speed digital circuits can interfere with RF receivers, resulting in RF interference issues such as receiver desensitization. In this paper, an effective methodology is proposed to estimate the RF interference received by an antenna due to near-field coupling, which is one of the common noise-coupling mechanisms, using decomposition method based on reciprocity. In other words, the noise-coupling problem is divided into two steps. In the first step, the coupling from the noise source to a Huygens surface that encloses the antenna is studied, with the actual antenna structure removed, and the induced tangential electromagnetic fields due to the noise source on this surface are obtained. In the second step, the antenna itself with the same Huygens surface is studied. The antenna is treated as a transmitting one and the induced tangential electromagnetic fields on the surface are obtained. Then, the reciprocity theory is used and the noise power coupled to the antenna port in the original problem is estimated based on the results obtained in the two steps. The proposed methodology is validated through comparisons with full-wave simulations. It fits well with engineering practice, and is particularly suitable for prelayout wireless system design and planning.
Collecting and Processing Arabic Facebook Comments for Sentiment Analysis

Social networks platforms such as Facebook are becoming one of the most powerful sources for information. The produced and shared data are important in volume, in velocity and in variety. Processing these data in the raw state to extract useful information can be a very difficult task and a big challenge. Furthermore, the Arabic language under its modern standard or dialectal shape is one of the languages producing an important quantity of data in social networks and the least analyzed. The characteristics and the specificity of the Arabic language present a big challenge for sentiment analysis, especially if this analysis is performed on Arabic Facebook comments. In this paper, we present a methodology that we have elaborated, for collecting and preprocessing Facebook comments written in Modern Standard Arabic (MSA) or in Moroccan Dialectal Arabic (MDA) for Sentiment Analysis (SA) using supervised classification methods. In this methodology, we have detailed the processing applied to the comments’ text as well as various schemes of features’ construction (words or groups of words) useful for supervised sentiments’ classification. This methodology was tested on comments written in MSA or in MDA collected from Facebook for the sentiment analysis on a political phenomenon. The experiments’ results obtained are promising and this encourages us to continue working on this topic.
A Near-Optimum 13.56 MHz CMOS Active Rectifier With Circuit-Delay Real-Time Calibrations for High-Current Biomedical Implants

In this paper, a near-optimum active rectifier is proposed to achieve well-optimized power conversion efficiency (PCE) and voltage conversion ratio (VCR) under various process, voltage, temperature (PVT) and loading conditions. The near-optimum operation includes: eliminated reverse current loss and maximized conduction time achieved by the proposed sampling-based real-time calibrations with automatic circuit-delay compensation for both on-and off-time of active diodes considering PVT variations; and power stage optimizations with adaptive sizing over a wide loading range. The design is fabricated in TSMC 65 nm process with standard I/O devices. Measurement results show more than 36% and 17% improvement in PCE and VCR, respectively, by the proposed techniques. A peak PCE of 94.8% with an 80 Ω loading, a peak VCR of 98.7% with 1 kΩ loading, and a maximum output power of 248.1 mW are achieved with 2.5 V input amplitude.
Risk factors for accidental dural puncture during epidural anesthesia for laboring women.

Accidental dural puncture (ADP) during epidural analgesia is a debilitating complication. Symptoms of ADP post-dural puncture headache (PDPH) are headache while rising from supine to upright position, nausea, and neck stiffness. While age, gender and needle characteristics are established risk factors for ADP, little is known about risk factors in laboring women.



All cases of ADP during epidural analgesia treated with blood-patching during a 3-years period were retrospectively reviewed. Each case was matched to two controls according to delivery period.



Forty-nine cases of blood patches after ADP out 17 977 epidural anesthesia procedures were identified (0.27%). No differences were found between cases and controls with regards to body mass index, labor stage at time of epidural, length of second stage, location of epidural along the lumbar vertebrae, anesthesiologist's experience or time when epidural was done. In cases of ADP, significantly lower doses of local anesthetics were injected (10.9 versus 13.5 cc, p < 0.001); anesthesiologists reported significantly more trials of epidurals (70 versus 2.8% more than one trial, p < 0.001), more patient movement during the procedure (13 versus 0%, p < 0.001), more intra-procedure suspicion of ADP (69 versus 0%, p < 0.001) and more cases where CSF/blood was drawn with the syringe (57 versus 2.4%, p < 0.001).



ADP during labor is a rare but debilitating complication. Risk factors for this iatrogenic complication include patient movement and repeated epidural trials. Intra-procedure identification of ADP is common, allowing early intervention with blood patching where indicated.
TraNNsformer: Neural network transformation for memristive crossbar based neuromorphic system design

Implementation of Neuromorphic Systems using post Complementary Metal-Oxide-Semiconductor (CMOS) technology based Memristive Crossbar Array (MCA) has emerged as a promising solution to enable low-power acceleration of neural networks. However, the recent trend to design Deep Neural Networks (DNNs) for achieving human-like cognitive abilities poses significant challenges towards the scalable design of neuromorphic systems (due to the increase in computation/storage demands). Network pruning [7] is a powerful technique to remove redundant connections for designing optimally connected (maximally sparse) DNNs. However, such pruning techniques induce irregular connections that are incoherent to the crossbar structure. Eventually they produce DNNs with highly inefficient hardware realizations (in terms of area and energy). In this work, we propose TraNNsformer — an integrated training framework that transforms DNNs to enable their efficient realization on MCA-based systems. TraNNsformer first prunes the connectivity matrix while forming clusters with the remaining connections. Subsequently, it retrains the network to fine tune the connections and reinforce the clusters. This is done iteratively to transform the original connectivity into an optimally pruned and maximally clustered mapping. We evaluated the proposed framework by transforming different Multi-Layer Perceptron (MLP) based Spiking Neural Networks (SNNs) on a wide range of datasets (MNIST, SVHN and CIFAR10) and executing them on MCA-based systems to analyze the area and energy benefits. Without accuracy loss, TraNNsformer reduces the area (energy) consumption by 28%–55% (49%–67%) with respect to the original network. Compared to network pruning, TraNNsformer achieves 28%–49% (15%–29%) area (energy) savings. Furthermore, TraNNsformer is a technology-aware framework that allows mapping a given DNN to any MCA size permissible by the memristive technology for reliable operations.