nils-herrmann/clean_scirepeval huggingface dataset:
MADAM: A Multi-level Anomaly Detector for Android Malware

Currently, in the smartphone market, Android is the platform with the highest share. Due to this popularity and also to its open source nature, Android-based smartphones are now an ideal target for attackers. Since the number of malware designed for Android devices is increasing fast, Android users are looking for security solutions aimed at preventing malicious actions from damaging their smartphones. In this paper, we describe MADAM, a Multi-level Anomaly Detector for Android Malware. MADAM concurrently monitors Android at the kernel-level and user-level to detect real malware infections using machine learning techniques to distinguish between standard behaviors and malicious ones. The first prototype of MADAM is able to detect several real malware found in the wild. The device usability is not affected by MADAM due to the low number of false positives generated after the learning phase.
Hoffa’s fat pad abnormalities, knee pain and magnetic resonance imaging in daily practice

Hoffa's (infrapatellar) fat pad (HFP) is one of the knee fat pads interposed between the joint capsule and the synovium. Located posterior to patellar tendon and anterior to the capsule, the HFP is richly innervated and, therefore, one of the sources of anterior knee pain. Repetitive local microtraumas, impingement, and surgery causing local bleeding and inflammation are the most frequent causes of HFP pain and can lead to a variety of arthrofibrotic lesions. In addition, the HFP may be secondarily involved to menisci and ligaments disorders, injuries of the patellar tendon and synovial disorders. Patients with oedema or abnormalities of the HFP on magnetic resonance imaging (MRI) are often symptomatic; however, these changes can also be seen in asymptomatic patients. Radiologists should be cautious in emphasising abnormalities of HFP since they do not always cause pain and/or difficulty in walking and, therefore, do not require therapy. Teaching Points • Hoffa's fat pad (HFP) is richly innervated and, therefore, a source of anterior knee pain. • HFP disorders are related to traumas, involvement from adjacent disorders and masses. • Patients with abnormalities of the HFP on MRI are often but not always symptomatic. • Radiologists should be cautious in emphasising abnormalities of HFP.
Hot topics-eye-gaze computer interfaces: what you look at is what you get

As computers are becoming more powerful, the critical bottleneck in their use is often in the user interface, not in the computer processing. Research in human-computer interaction that seeks to increase the communication bandwidth between the user and the machine by using input from the user's eye movement is discussed. The speed potential, processing stages, interaction techniques, and problems associated with these eye-gaze interfaces are described.<<ETX>>
Pipeline Item-Based Collaborative Filtering Based on MapReduce

As we all know, it is an era of information explosion, in which we always get huge amounts of information. Therefore, it is in urgent need of picking out the useful and interesting information quickly. In order to solve this serious problem, recommendation system arises at the historic moment. Among the existing recommendation algorithms, the item-based collaborative filtering recommendation algorithm is the most widely used one. Its principle is based on the user's evaluation of items. The purpose is to find the similarity between users, and recommend items to the target user according to the records of the similar users. However, the number of customers and products keeps increasing at a high rate, which increases the cost to find out the recommendation list for each user. The efficiency of a single common computer will not satisfy the requirement and the super computer will cost too much. In order to solve the problem, we propose to use MapReduce to implement the recommendation system. Besides, we distribute the job to some computer clusters and the input file of the current computer cluster only relies on the previous one or the origin input. So the pipeline technology will be adopted to improve the efficiency further. The experiment shows that the method can merge the ability of some common PC to process large-scale data in a short time.
Deep Hierarchical Network With Line Segment Learning for Quantitative Analysis of Facial Palsy

We propose the deep hierarchical network (DHN) for the quantitative analysis of facial palsy. Facial palsy, also known as Bell’s palsy, is the most common type of facial nerve palsy that results in the loss of muscle control in the affected facial regions. Typical symptoms include facial deformity and facial expression dysfunction. To the best of our best knowledge, all approaches for the automatic detection of facial palsy consider hand-crafted features. This paper reports the first deep-learning-based approach developed for the real-time quantitative analysis of facial palsy. The proposed DHN consists of three component networks: the first detects the subject’s face, the second detects the facial landmarks and line segments on the detected face, and the third detects the local palsy regions. The first component network is built on the YOLO2 detector. The second component network is developed on a fused network architecture that incorporates a line segment learning network for locating the facial landmarks and line segments. The third component network is developed on an object detection network with the line-segment-embedded input that combines the landmarked region and the line segments detected by the second component network. The novelties of this research include: 1) the modification of a state-of-the-art edge detector for extracting the facial line segments; 2) the embedding of the line segment learning for the detection of facial landmarks and local palsy regions; 3) the quantitative description of the facial palsy syndrome intensity; and 4) the release of the first clinically labeled database, the YouTube Facial Palsy (YFP) database. The making of the YFP database solves the issue that previous methods were all evaluated on proprietary databases, making the comparison of different methods extremely difficult. The YFP database includes 32 videos of 21 patients collected from YouTube and labeled by clinic specialists. To enhance the robustness against facial expression variations, we include the CK+ facial expression database in the training. We show that the proposed DHN not only just detects the local palsy regions but also captures the intensity of the facial palsy syndrome over time, enabling the quantitative description of the syndrome. The experiments show that the proposed approach offers an accurate and efficient real-time solution for facial palsy analysis.
Regulation of Platelet Derived Growth Factor Signaling by Leukocyte Common Antigen-related (LAR) Protein Tyrosine Phosphatase: A Quantitative Phosphoproteomics Study.

Intracellular signaling pathways are reliant on protein phosphorylation events that are controlled by a balance of kinase and phosphatase activity. Although kinases have been extensively studied, the role of phosphatases in controlling specific cell signaling pathways has been less so. Leukocyte common antigen-related protein (LAR) is a member of the LAR subfamily of receptor-like protein tyrosine phosphatases (RPTPs). LAR is known to regulate the activity of a number of receptor tyrosine kinases, including platelet-derived growth factor receptor (PDGFR). To gain insight into the signaling pathways regulated by LAR, including those that are PDGF-dependent, we have carried out the first systematic analysis of LAR-regulated signal transduction using SILAC-based quantitative proteomic and phosphoproteomic techniques. We haveanalyzed differential phosphorylation between wild-type mouse embryo fibroblasts (MEFs) and MEFs in which the LAR cytoplasmic phosphatase domains had been deleted (LARΔP), and found a significant change in abundance of phosphorylation on 270 phosphosites from 205 proteins because of the absence of the phosphatase domains of LAR. Further investigation of specific LAR-dependent phosphorylation sites and enriched biological processes reveal that LAR phosphatase activity impacts on a variety of cellular processes, most notably regulation of the actin cytoskeleton. Analysis of putative upstream kinases that may play an intermediary role between LAR and the identified LAR-dependent phosphorylation events has revealed a role for LAR in regulating mTOR and JNK signaling.
High gain broadband monopole antenna for wireless communications

In this paper, a wideband monopole antenna with high gain characteristics has been proposed. Number of slits was introduced at the far radiating edge to transform it to multiple monopole radiators. Partial ground plane has been used to widen the bandwidth while by inserting suitable slits at the radiating edges return loss and bandwidth has been improved. The proposed antenna provides high gain up to 13.2dB and the achieved impedance bandwidth is wider than an earlier reported design.  Epoxy with dielectric constant 4.4 and loss tangent 0.02 has been used as substrate material. Antenna has been simulated using HFSS (High Frequency Structure Simulator) as a 3D electromagnetic field simulator, based on finite element method. A good settlement has been found between simulated and measured results. The proposed design is suitable for GSM (890-960MHz), GPS (L1:1575.42MHz, L2:1227.60MHz, L3:1381.05MHz, L4:1379.913MHz, L5:1176.45MHz), DCS (1710-1880MHz), PCS (1850-1990MHz), UMTS(1920-2170MHz), Wi-Fi/WLAN/Hiper LAN/IEEE 802.11 2.4GHz (2412-2484MHz), 3.6GHz (3657.5-3690.0MHz) and 4.9/5.0GHz (4915-5825MHz), Bluetooth (2400-2484MHz), WiMAX 2.3GHz (2.3-2.5GHz), 2.5GHz (2500-2690 MHz), 3.3GHz, 3.5GHz (3400-3600MHz) and 5.8GHz (5.6-5.9GHz) & LTE applications.
Deep Alignment Network: A Convolutional Neural Network for Robust Face Alignment

In this paper, we propose Deep Alignment Network (DAN), a robust face alignment method based on a deep neural network architecture. DAN consists of multiple stages, where each stage improves the locations of the facial landmarks estimated by the previous stage. Our method uses entire face images at all stages, contrary to the recently proposed face alignment methods that rely on local patches. This is possible thanks to the use of landmark heatmaps which provide visual information about landmark locations estimated at the previous stages of the algorithm. The use of entire face images rather than patches allows DAN to handle face images with large variation in head pose and difficult initializations. An extensive evaluation on two publicly available datasets shows that DAN reduces the state-of-the-art failure rate by up to 70%. Our method has also been submitted for evaluation as part of the Menpo challenge.
Incomplete Follow-up Question Resolution using Retrieval based Sequence to Sequence Learning

Intelligent personal assistants (IPAs) and interactive question answering (IQA) systems frequently encounter incomplete follow-up questions. The incomplete follow-up questions only make sense when seen in conjunction with the conversation context: the previous question and answer. Thus, IQA and IPA systems need to utilize the conversation context in order to handle the incomplete follow-up questions and generate an appropriate response. In this work, we present a retrieval based sequence to sequence learning system that can generate the complete (or intended) question for an incomplete follow-up question (given the conversation context). We can train our system using only a small labeled dataset (with only a few thousand conversations), by decomposing the original problem into two simpler and independent problems. The first problem focuses solely on selecting the candidate complete questions from a library of question templates (built offline using the small labeled conversations dataset). In the second problem, we re-rank the selected candidate questions using a neural language model (trained on millions of unlabelled questions independently). Our system can achieve a BLEU score of 42.91, as compared to 29.11 using an existing generation based approach. We further demonstrate the utility of our system as a plug-in module to an existing QA pipeline. Our system when added as a plug-in module, enables Siri to achieve an improvement of 131.57% in answering incomplete follow-up questions.
Customer satisfaction in E-Commerce A case study of China and Bangladesh

As information technology and the popularity of Internet technology and in-depth applications, e-commerce is at unprecedented pace. People become more and more the focus of attention. At present, relatively fast development of e-commerce activities are online sales, online promotions, and online services. Globalization of electronic commerce as the development of enterprises provided many opportunities, but in developing country’s electricity business is still in the initial stage of development, how to improve e-commerce environment of consumer satisfaction, consumer loyalty and thus, related to the electron the performance of business enterprises. Therefore, to the upsurge in e-commerce for more benefits, for many enterprises, there is still need for careful analysis of the business environment electricity consumer behavior, understanding the factors that affect their consumption and thus the basis of network marketing the characteristics of the network setup customer satisfaction evaluation index system, then the theory based on customer satisfaction, on this basis to take corresponding countermeasures, the development of effective and reasonable marketing strategy, E-commerce can improve business performance, and promote the sound development of their self. At present, domestic and international network of scholars on consumer psychology, motivation and behavior have more exposition, however, how in ecommerce environment impact factors of customer satisfaction and how to improve ecommerce customer satisfaction studies are not many see. This article is from the analysis the impact of e-commerce network environment factors in consumer satisfaction.
Robust Top-k Multiclass SVM for Visual Category Recognition

Classification problems with a large number of classes inevitably involve overlapping or similar classes. In such cases it seems reasonable to allow the learning algorithm to make mistakes on similar classes, as long as the true class is still among the top-k (say) predictions. Likewise, in applications such as search engine or ad display, we are allowed to present k predictions at a time and the customer would be satisfied as long as her interested prediction is included. Inspired by the recent work of , we propose a very generic, robust multiclass SVM formulation that directly aims at minimizing a weighted and truncated combination of the ordered prediction scores. Our method includes many previous works as special cases. Computationally, using the Jordan decomposition Lemma we show how to rewrite our objective as the difference of two convex functions, based on which we develop an efficient algorithm that allows incorporating many popular regularizers (such as the l2 and l1 norms). We conduct extensive experiments on four real large-scale visual category recognition datasets, and obtain very promising performances.
IoT Big-Data Centred Knowledge Granule Analytic and Cluster Framework for BI Applications: A Case Base Analysis.

The current rapid growth of Internet of Things (IoT) in various commercial and non-commercial sectors has led to the deposition of large-scale IoT data, of which the time-critical analytic and clustering of knowledge granules represent highly thought-provoking application possibilities. The objective of the present work is to inspect the structural analysis and clustering of complex knowledge granules in an IoT big-data environment. In this work, we propose a knowledge granule analytic and clustering (KGAC) framework that explores and assembles knowledge granules from IoT big-data arrays for a business intelligence (BI) application. Our work implements neuro-fuzzy analytic architecture rather than a standard fuzzified approach to discover the complex knowledge granules. Furthermore, we implement an enhanced knowledge granule clustering (e-KGC) mechanism that is more elastic than previous techniques when assembling the tactical and explicit complex knowledge granules from IoT big-data arrays. The analysis and discussion presented here show that the proposed framework and mechanism can be implemented to extract knowledge granules from an IoT big-data array in such a way as to present knowledge of strategic value to executives and enable knowledge users to perform further BI actions.
Chemical and molecular mechanisms of antioxidants: experimental approaches and model systems

Free radicals derived from oxygen, nitrogen and sulphur molecules in the biological system are highly active to react with other molecules due to their unpaired electrons. These radicals are important part of groups of molecules called reactive oxygen/nitrogen species (ROS/RNS), which are produced during cellular metabolism and functional activities and have important roles in cell signalling, apoptosis, gene expression and ion transportation. However, excessive ROS attack bases in nucleic acids, amino acid side chains in proteins and double bonds in unsaturated fatty acids, and cause oxidative stress, which can damage DNA, RNA, proteins and lipids resulting in an increased risk for cardiovascular disease, cancer, autism and other diseases. Intracellular antioxidant enzymes and intake of dietary antioxidants may help to maintain an adequate antioxidant status in the body. In the past decades, new molecular techniques, cell cultures and animal models have been established to study the effects and mechanisms of antioxidants on ROS. The chemical and molecular approaches have been used to study the mechanism and kinetics of antioxidants and to identify new potent antioxidants. Antioxidants can decrease the oxidative damage directly via reacting with free radicals or indirectly by inhibiting the activity or expression of free radical generating enzymes or enhancing the activity or expression of intracellular antioxidant enzymes. The new chemical and cell-free biological system has been applied in dissecting the molecular action of antioxidants. This review focuses on the research approaches that have been used to study oxidative stress and antioxidants in lipid peroxidation, DNA damage, protein modification as well as enzyme activity, with emphasis on the chemical and cell-free biological system.
Bioactive fungal polysaccharides as potential functional ingredients in food and nutraceuticals.

Fungal bioactive polysaccharides deriving mainly from the Basidiomycetes family (and some from the Ascomycetes) and medicinal mushrooms have been well known and widely used in far Asia as part of traditional diet and medicine, and in the last decades have been the core of intense research for the understanding and the utilization of their medicinal properties in naturally produced pharmaceuticals. In fact, some of these biopolymers (mainly β-glucans or heteropolysaccharides) have already made their way to the market as antitumor, immunostimulating or prophylactic drugs. The fact that many of these biopolymers are produced by edible mushrooms makes them also very good candidates for the formulation of novel functional foods and nutraceuticals without any serious safety concerns, in order to make use of their immunomodulating, anticancer, antimicrobial, hypocholesterolemic, hypoglycemic and health-promoting properties. This article summarizes the most important properties and applications of bioactive fungal polysaccharides and discusses the latest developments on the utilization of these biopolymers in human nutrition.
Large-scale functional connectivity networks in the rodent brain

Resting-state functional Magnetic Resonance Imaging (rsfMRI) of the human brain has revealed multiple large-scale neural networks within a hierarchical and complex structure of coordinated functional activity. These distributed neuroanatomical systems provide a sensitive window on brain function and its disruption in a variety of neuropathological conditions. The study of macroscale intrinsic connectivity networks in preclinical species, where genetic and environmental conditions can be controlled and manipulated with high specificity, offers the opportunity to elucidate the biological determinants of these alterations. While rsfMRI methods are now widely used in human connectivity research, these approaches have only relatively recently been back-translated into laboratory animals. Here we review recent progress in the study of functional connectivity in rodent species, emphasising the ability of this approach to resolve large-scale brain networks that recapitulate neuroanatomical features of known functional systems in the human brain. These include, but are not limited to, a distributed set of regions identified in rats and mice that may represent a putative evolutionary precursor of the human default mode network (DMN). The impact and control of potential experimental and methodological confounds are also critically discussed. Finally, we highlight the enormous potential and some initial application of connectivity mapping in transgenic models as a tool to investigate the neuropathological underpinnings of the large-scale connectional alterations associated with human neuropsychiatric and neurological conditions. We conclude by discussing the translational potential of these methods in basic and applied neuroscience.
Polyp Detection via Imbalanced Learning and Discriminative Feature Learning

Recent achievement of the learning-based classification leads to the noticeable performance improvement in automatic polyp detection. Here, building large good datasets is very crucial for learning a reliable detector. However, it is practically challenging due to the diversity of polyp types, expensive inspection, and labor-intensive labeling tasks. For this reason, the polyp datasets usually tend to be imbalanced, i.e., the number of non-polyp samples is much larger than that of polyp samples, and learning with those imbalanced datasets results in a detector biased toward a non-polyp class. In this paper, we propose a data sampling-based boosting framework to learn an unbiased polyp detector from the imbalanced datasets. In our learning scheme, we learn multiple weak classifiers with the datasets rebalanced by up/down sampling, and generate a polyp detector by combining them. In addition, for enhancing discriminability between polyps and non-polyps that have similar appearances, we propose an effective feature learning method using partial least square analysis, and use it for learning compact and discriminative features. Experimental results using challenging datasets show obvious performance improvement over other detectors. We further prove effectiveness and usefulness of the proposed methods with extensive evaluation.
MOICA: A novel multi-objective approach based on imperialist competitive algorithm

A novel multi-objective evolutionary algorithm (MOEA) is developed based on Imperialist Competitive Algorithm (ICA), a newly introduced evolutionary algorithm (EA). Fast non-dominated sorting and the Sigma method are employed for ranking the solutions. The algorithm is tested on six well-known test functions each of them incorporate a particular feature that may cause difficulty to MOEAs. The numerical results indicate that MOICA shows significantly higher efficiency in terms of accuracy and maintaining a diverse population of solutions when compared to the existing salient MOEAs, namely fast elitism non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO). Considering the computational time, the proposed algorithm is slightly faster than MOPSO and significantly outperforms NSGA-II. KEYWORD Multi-objective Imperialist Competitive Algorithm, Multi-objective optimization, Pareto front.