nils-herrmann/clean_scirepeval huggingface dataset:
An Advanced Approach for Implementation of Audio Steganography: Modified LSB Algorithm

Steganography is a branch of information security which deals with transmission of message without being detected. Message, to be send, is embedded in a cover file. Different types of digital can be used as cover object, we used (.WAV) audio as our cover file in the research work. The objective of steganography is to shield the fact that the message exists in the transmission medium. Many algorithms have so far derived for this purpose can be categorized in terms of their embedding technique, time and space complexity. LSB is the acronym of „Least Significant Bit‟, is one of the algorithm that is considered as the easiest in way of hiding information in a digital media, also it has good efficiency. It perform its task by embedding secret message in the least significant bits of each data sample of audio file. Ease of cracking this algorithm makes it more prone to visual and statistical attacks. Keeping this in mind few improvisation are being done on LSB algorithm that reduces the ease of cracking message. Modified version of LSB algorithm which we call as „MODIFIED LSB ALGORITHM‟ uses the pseudo-random number generator to spread the secret message over the cover in a random manner. This algorithm will be more immune to statistical attacks without affecting its efficiency significantly.
Gene selection using hybrid particle swarm optimization and genetic algorithm

Selecting high discriminative genes from gene expression data has become an important research. Not only can this improve the performance of cancer classification, but it can also cut down the cost of medical diagnoses when a large number of noisy, redundant genes are filtered. In this paper, a hybrid Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) method is used for gene selection, and Support Vector Machine (SVM) is adopted as the classifier. The proposed approach is tested on three benchmark gene expression datasets: Leukemia, Colon and breast cancer data. Experimental results show that the proposed method can reduce the dimensionality of the dataset, and confirm the most informative gene subset and improve classification accuracy.
A 15-bit 28nm CMOS fully-integrated 1.6W digital power amplifier for LTE IoT

This paper presents a 15-bit digital power amplifier (DPA) with 1.6W saturated output power. The topology of the polar switched-current DPA is discussed together with the architecture of the output transformer which is implemented in BEOL as well as in WLCSP metal layers. The chip is fabricated in a standard 28nm CMOS process and exhibits an EVM of 3.6%, E-UTRA ACLR of 34.1dB, output noise of −145.7dBc/Hz at 45 MHz offset and 22.4% DPA efficiency when generating a 26.8dBm LTE-1.4 output signal at 2.3GHz. The total area of the DPA is 0.5mm2.
SHALLOW LEARNING FOR DEEP NETWORKS

Shallow supervised 1-hidden layer neural networks have a number of favorable properties that make them easier to interpret, analyze, and optimize than their deep counterparts, but lack their representational power. Here we use 1-hidden layer learning problems to sequentially build deep networks layer by layer, which can inherit properties from shallow networks. Contrary to previous approaches using shallow networks, we focus on problems where deep learning is reported as critical for success. We thus study CNNs on two large-scale image recognition tasks: ImageNet and CIFAR-10. Using a simple set of ideas for architecture and training we find that solving sequential 1-hidden-layer auxiliary problems leads to a CNN that exceeds AlexNet performance on ImageNet. Extending our training methodology to construct individual layers by solving 2-and-3-hidden layer auxiliary problems, we obtain an 11-layer network that exceeds VGG-11 on ImageNet obtaining 89.8% top-5 single crop.To our knowledge, this is the first competitive alternative to end-to-end training of CNNs that can scale to ImageNet. We conduct a wide range of experiments to study the properties this induces on the intermediate layers.
Gut-liver axis, cirrhosis and portal hypertension: the chicken and the egg.

The term gut-liver axis is used to highlight the close anatomical and functional relationship between the intestine and the liver. The intestine has a highly specialized epithelial membrane which regulates transport across the mucosa. Due to dysbiosis, impairment of the intestinal barrier and altered immunity status, bacterial products can reach the liver through the portal vein, where they are recognized by specific receptors, activate the immune system and lead to a proinflammatory response. Gut microbiota and bacterial translocation play an important role in the pathogenesis of chronic liver diseases, including alcoholic and non-alcoholic fatty liver disease, cirrhosis, and its complications, such as portal hypertension, spontaneous bacterial peritonitis and hepatic encephalopaty. The gut microbiota also plays a critical role as a modulator of bile acid metabolism which can also influence intestinal permeability and portal hypertension through the farnesoid-X receptor. On the other hand, cirrhosis and portal hypertension affect the microbiota and increase translocation, leading to a "chicken and egg" situation, where translocation increases portal pressure, and vice versa. A myriad of therapies targeting gut microbiota have been evaluated specifically in patients with chronic liver disease. Further studies targeting intestinal microbiota and its possible hemodynamic and metabolic effects are needed. This review summarizes the current knowledge about the role of gut microbiota in the pathogenesis of chronic liver diseases and portal hypertension.
VISUAL-BASED AUTOMATIC COIN COUNTING SYSTEM

Coin counting has been a practical issue in various financial transactions. Before the arrival of coin counting machine people used to count the coins manually. This is a very time consuming and tedious job for those who handle the work. Mistakes are common due to various reasons such as; eye tiredness, fatigue, too many coins cause confuses in eyes and etc. In this paper a visual-based simple coin counting system is described. This system performs its task by using an image captured from a webcam. Image processing techniques such as gray image conversion and thresholding to a binary image were employed. Classification of coins based on value was done by analyzing statistical properties and connected components. The results show the effectiveness of the algorithm in evaluating the coin counting.
1-Point RANSAC for EKF Filtering . Application to Real-Time Structure from Motion and Visual Odometry

Random Sample Consensus (RANSAC) has become one of the most successful techniques for robust estimation from a data set that may contain outliers. It works by constructing model hypotheses from random minimal data subsets and evaluating their validity from the support of the whole data. In this paper we present a novel combination of RANSAC plus Extended Kalman Filter (EKF) that uses the available prior probabilistic information from the EKF in the RANSAC model hypothesize stage. This allows the minimal sample size to be reduced to one, resulting in large computational savings without the loss of discriminative power. 1-Point RANSAC is shown to outperform both in accuracy and computational cost the Joint Compatibility Branch and Bound (JCBB) algorithm, a gold-standard technique for spurious rejection within the EKF framework. Two visual estimation scenarios are used in the experiments: First, six degrees of freedom motion estimation from a monocular sequence (Structure from Motion). Here, a new method for benchmarking six DOF visual estimation algorithms based on the use of high resolution images is presented, validated and used to show the superiority of 1-Point RANSAC. Second, we demonstrate long-term robot trajectory estimation combining monocular vision and wheel odometry (Visual Odometry). Here, a comparison against GPS shows an accuracy comparable to state-of-the-art visual odometry methods.
Approaches for Neural-Network Language Model Adaptation

Language Models (LMs) for Automatic Speech Recognition (ASR) are typically trained on large text corpora from news articles, books and web documents. These types of corpora, however, are unlikely to match the test distribution of ASR systems, which expect spoken utterances. Therefore, the LM is typically adapted to a smaller held-out in-domain dataset that is drawn from the test distribution. We propose three LM adaptation approaches for Deep NN and Long Short-Term Memory (LSTM): (1) Adapting the softmax layer in the Neural Network (NN); (2) Adding a non-linear adaptation layer before the softmax layer that is trained only in the adaptation phase; (3) Training the extra non-linear adaptation layer in pre-training and adaptation phases. Aiming to improve upon a hierarchical Maximum Entropy (MaxEnt) second-pass LM baseline, which factors the model into word-cluster and word models, we build an NN LM that predicts only word clusters. Adapting the LSTM LM by training the adaptation layer in both training and adaptation phases (Approach 3), we reduce the cluster perplexity by 30% on a held-out dataset compared to an unadapted LSTM LM. Initial experiments using a state-of-the-art ASR system show a 2.3% relative reduction in WER on top of an adapted MaxEnt LM.
Cross-Correlation Based Face Recognition Scheme – A Review

The face recognition systems are used to recognize the person by using merely a person’s image. The face detection scheme is the primary method which is used to extract the region of interest (ROI). The ROI is further processed under the face recognition scheme. In the proposed model, we are going to use the cross-correlation algorithm along with the viola jones for the purpose of face recognition to recognize the person. The proposed model is proposed using the Cross-correlation algorithm along with cross correlation scheme in order to recognize the person by evaluating the facial features.
Inferring Anchor Links Based on Social Network Structure

Nowadays, people usually participate in multiple social networks simultaneously, e.g., Facebook and Twitter. Formally, the correspondences of the accounts that belong to the same user are defined as anchor links, and the networks aligned by anchor links can be denoted as aligned networks. In this paper, we study the problem of anchor link prediction (ALP) across a pair of aligned networks based on social network structure. First, three similarity metrics (CPS, CCS, and CPS+) are proposed. Different from the previous works, we focus on the theoretical guarantees of our metrics. We prove mathematically that the node pair with the maximum CPS or CPS+ should be an anchor link with high probability and a correctly predicted anchor link must have a high value of CCS. Second, using the CPS+ and CCS, we present a two-stage iterative algorithm CPCC to solve the problem of the ALP. More specifically, we present an early termination strategy to make a tradeoff between precision and recall. At last, a series of experiments are conducted on both synthetic and real-world social networks to demonstrate the effectiveness of the CPCC.
Graph Neural Networks for IceCube Signal Classification

Tasks involving the analysis of geometric (graph-and manifold-structured) data have recently gained prominence in the machine learning community, giving birth to a rapidly developing field of geometric deep learning. In this work, we leverage graph neural networks to improve signal detection in the IceCube neutrino observatory. The IceCube detector array is modeled as a graph, where vertices are sensors and edges are a learned function of the sensors spatial coordinates. As only a subset of IceCubes sensors is active during a given observation, we note the adaptive nature of our GNN, wherein computation is restricted to the input signal support. We demonstrate the effectiveness of our GNN architecture on a task classifying IceCube events, where it outperforms both a traditional physics-based method as well as classical 3D convolution neural networks.
Information Recovery in Shuffled Graphs via Graph Matching

While many multiple graph inference methodologies operate under the implicit assumption that an explicit vertex correspondence is known across the vertex sets of the graphs, in practice these correspondences may only be partially or errorfully known. Herein, we provide an information theoretic foundation for understanding the practical impact that errorfully observed vertex correspondences can have on subsequent inference, and the capacity of graph matching methods to recover the lost vertex alignment and inferential performance. Working in the correlated stochastic blockmodel setting, we establish a duality between the loss of mutual information due to an errorfully observed vertex correspondence and the ability of graph matching algorithms to recover the true correspondence across graphs. In the process, we establish a phase transition for graph matchability in terms of the correlation across graphs, and we conjecture the analogous phase transition for the relative information loss due to shuffling vertex labels. We demonstrate the practical effect that graph shuffling—and matching—can have on subsequent inference, with examples from two sample graph hypothesis testing and joint spectral graph clustering.
A Chinese Mind-Body Exercise Improves Self-Control of Children with Autism: A Randomized Controlled Trial

Self-control problems commonly manifest as temper outbursts and repetitive/rigid/impulsive behaviors, in children with autism spectrum disorders (ASD), which often contributes to learning difficulties and caregiver burden. The present study aims to compare the effect of a traditional Chinese Chan-based mind-body exercise, Nei Yang Gong, with that of the conventional Progressive Muscle Relaxation (PMR) technique in enhancing the self-control of children with ASD. Forty-six age- and IQ-matched ASD children were randomly assigned to receive group training in Nei Yang Gong (experimental group) or PMR (control group) twice per week for four weeks. The participants' self-control was measured by three neuropsychological tests and parental rating on standardized questionnaires, and the underlying neural mechanism was assessed by the participants' brain EEG activity during an inhibitory-control task before and after intervention. The results show that the experimental group demonstrated significantly greater improvement in self-control than the control group, which concurs with the parental reports of reduced autistic symptoms and increased control of temper and behaviors. In addition, the experimental group showed enhanced EEG activity in the anterior cingulate cortex, a region that mediates self-control, whereas the PMR group did not. The present findings support the potential application of Chinese Chan-based mind-body exercises as a form of neuropsychological rehabilitation for patients with self-control problems.  ChiCTR-TRC-12002561; URL: www.chictr.org.
Churn analysis of online social network users using data mining techniques

A churn is defined as the loss of a user in an online social network (OSN). Detecting and analyzing user churn at an early stage helps to provide timely delivery of retention solutions (e.g., interventions, customized services, and better user interfaces) that are useful for preventing users from churning. In this paper we develop a prediction model based on a clustering scheme to analyze the potential churn of users. In the experiment, we test our approach on a real-name OSN which contains data from 77,448 users. A set of 24 attributes is extracted from the data. A decision tree classifier is used to predict churn and non-churn users of the future month. In addition, k-means algorithm is employed to cluster the actual churn users into different groups with different online social networking behaviors. Results show that the churn and nonchurn prediction accuracies of ∼65% and ∼77% are achieved respectively. Furthermore, the actual churn users are grouped into five clusters with distinguished OSN activities and some suggestions of retaining these users are provided.
A comparison of strategies to develop a customer default scoring model

Behavioural scoring models are generally used to estimate the probability that a customer of a financial institution who owns a credit product will default on this product in a fixed time horizon. However, one single customer usually purchases many credit products from an institution while behavioural scoring models generally treat each of these products independently. In order to make credit risk management easier and more efficient, it is interesting to develop customer default scoring models. These models estimate the probability that a customer of a certain financial institution will have credit issues with at least one product in a fixed time horizon. In this study, three strategies to develop customer default scoring models are described. One of the strategies is regularly utilized by financial institutions and the other two will be proposed herein. The performance of these strategies is compared by means of an actual data bank supplied by a financial institution and a Monte Carlo simulation study.
Clustering and visualization of mobile application permissions for end users and malware analysts

Application permissions at the core of Android security mechanism are the first leading transparent feature for users to assess any mobile application before download or installation and for experts to analyse any malware. Representing vast, dispersed permissions and achieving clarity is not a trivial matter. In this study, we first examined Android permissions, their groups and formal representations with the limitations. We also surveyed limited studies on clustering/visualization of permissions. We grouped 251 Android permissions into 12 clusters semantically and proposed a new visualization approach that looks more conventional to both end users and experts and helps comprehending permissions easily and quickly. We applied the proposed clustering and visualization on calculated discriminative malign permissions concept for malware analysis and demonstrated potential effectiveness of the approach. Our approach improves expressing and understanding of large number of mobile application permissions in a better context, provides more understanding and insight, and helps interpreting or inferring interesting patterns related to permissions for malware classification.
Tackling the challenge of selective analytical clean-up of complex natural extracts: the curious case of chlorophyll removal.

Alkaline saponification is often used to remove interfering chlorophylls and lipids during carotenoids analysis. However, saponification also hydrolyses esterified carotenoids and is known to induce artifacts. To avoid carotenoid artifact formation during saponification, Larsen and Christensen (2005) developed a gentler and simpler analytical clean-up procedure involving the use of a strong basic resin (Ambersep 900 OH). They hypothesised a saponification mechanism based on their Liquid Chromatography-Photodiode Array (LC-PDA) data. In the present study, we show with LC-PDA-accurate mass-Mass Spectrometry that the main chlorophyll removal mechanism is not based on saponification, apolar adsorption or anion exchange, but most probably an adsorption mechanism caused by H-bonds and dipole-dipole interactions. We showed experimentally that esterified carotenoids and glycerolipids were not removed, indicating a much more selective mechanism than initially hypothesised. This opens new research opportunities towards a much wider scope of applications (e.g. the refinement of oils rich in phytochemical content).
Dynamic Models of Segregation in Small-World Networks

Schelling (1969, 1971a,b, 1978) considered a simple proximity model of segregation where individual agents only care about the types of people living in their own local geographical neighborhood, the spatial structure being represented by oneor two-dimensional lattices. In this paper, we argue that segregation might occur not only in the geographical space, but also in social environments. Furthermore, recent empirical studies have documented that social interaction structures are well-described by small-world networks. We generalize Schelling’s model by allowing agents to interact in small-world networks instead of regular lattices. We study two alternative dynamic models where agents can decide to move either arbitrarily far away (global model) or are bound to choose an alternative location in their social neighborhood (local model). Our main result is that the system attains levels of segregation that are in line with those reached in the lattice-based spatial proximity model. Thus, Schelling’s original results seem to be robust to the structural properties of the network.
Atypical perception in autism: A failure of perceptual specialization?

We examined whether reduced perceptual specialization underlies atypical perception in autism spectrum disorder (ASD) testing classifications of stimuli that differ either along integral dimensions (prototypical integral dimensions of value and chroma), or along separable dimensions (prototypical separable dimensions of value and size). Current models of the perception of individuals with an ASD would suggest that on these tasks, individuals with ASD would be as, or more, likely to process dimensions as separable, regardless of whether they represented separable or integrated dimensions. In contrast, reduced specialization would propose that individuals with ASD would respond in a more integral manner to stimuli that differ along separable dimensions, and at the same time, respond in a more separable manner to stimuli that differ along integral dimensions. A group of nineteen adults diagnosed with high functioning ASD and seventeen typically developing participants of similar age and IQ, were tested on speeded and restricted classifications tasks. Consistent with the reduced specialization account, results show that individuals with ASD do not always respond more analytically than typically developed (TD) observers: Dimensions identified as integral for TD individuals evoke less integral responding in individuals with ASD, while those identified as separable evoke less analytic responding. These results suggest that perceptual representations are more broadly tuned and more flexibly represented in ASD.
Unthule: An Incremental Graph Construction Process for Robust Road Map Extraction from Aerial Images

The availability of highly accurate maps has become crucial due to the increasing importance of location-based mobile applications as well as autonomous vehicles. However, mapping roads is currently an expensive and humanintensive process. High-resolution aerial imagery provides a promising avenue to automatically infer a road network. Prior work uses convolutional neural networks (CNNs) to detect which pixels belong to a road (segmentation), and then uses complex post-processing heuristics to infer graph connectivity [4, 10]. We show that these segmentation methods have high error rates (poor precision) because noisy CNN outputs are difficult to correct. We propose a novel approach, Unthule, to construct highly accurate road maps from aerial images. In contrast to prior work, Unthule uses an incremental search process guided by a CNN-based decision function to derive the road network graph directly from the output of the CNN. We train the CNN to output the direction of roads traversing a supplied point in the aerial imagery, and then use this CNN to incrementally construct the graph. We compare our approach with a segmentation method on fifteen cities, and find that Unthule has a 45% lower error rate in identifying junctions across these cities.