nils-herrmann/clean_scirepeval huggingface dataset:
Years of Biometric Research : Accomplishments , Challenges , and Opportunities

Biometric recognition refers to the automated recognition of individuals based on their biological and behavioral characteristics such as fingerprint, face, iris, and voice. The first scientific paper on automated fingerprint matching was published by Mitchell Trauring in the journal Nature in 1963. The first objective of this paper is to document the significant progress that has been achieved in the field of biometric recognition in the past 50 years since Trauring’s landmark paper. This progress has enabled current state-of-the-art biometric systems to accurately recognize individuals based on biometric trait(s) acquired under controlled environmental conditions from cooperative users. Despite this progress, a number of challenging issues continue to inhibit the full potential of biometrics to automatically recognize humans. The second objective of this paper is to enlist such challenges, analyze the solutions proposed to overcome them, and highlight the research opportunities in this field. One of the foremost challenges is the design of robust algorithms for representing and matching biometric samples obtained from uncooperative subjects under unconstrained environmental conditions (e.g., recognizing faces in a crowd). In addition, fundamental questions such as the distinctiveness and persistence of biometric traits need greater attention. Problems related to the security of biometric data and robustness of the biometric system against spoofing and obfuscation attacks, also remain unsolved. Finally, larger system-level issues like usability, user privacy concerns, integration with the end application, and return on investment have not been adequately addressed. Unlocking the full potential of biometrics through inter-disciplinary research in the above areas will not only lead to widespread adoption of this promising technology, but will also result in wider user acceptance and societal impact.
DEEProtect: Enabling Inference-based Access Control on Mobile Sensing Applications

Personal sensory data is used by context-aware mobile applications to provide utility. However, the same data can also be used by an adversary to make sensitive inferences about a user thereby violating her privacy. We present DEEProtect, a framework that enables a novel form of inference control, in which mobile apps with access to sensor data are limited (provably) in their ability to make inferences about user’s sensitive data and behavior. DEEProtect adopts a two-layered privacy strategy. First, it leverages novel autoencoder techniques to perform data minimization and limits the amount of information being shared; the learning network is used to derive a compact representation of sensor data consisting only of features relevant to authorized utility-providing inferences. Second, DEEProtect obfuscates the previously learnt features, thereby providing an additional layer of protection against sensitive inferences. Our framework supports both conventional as well as a novel relaxed notion of local differential privacy that enhances utility. Through theoretical analysis and extensive experiments using real-world datasets, we demonstrate that when compared to existing approaches DEEProtect provides provable privacy guarantees with up to 8x improvement in utility. Finally, DEEProtect shares obfuscated but raw sensor data reconstructed from the perturbed features, thus requiring no changes to the existing app interfaces.
Three-dimensional cell aggregates composed of HUVECs and cbMSCs for therapeutic neovascularization in a mouse model of hindlimb ischemia.

The proximity of cells in three-dimensional (3D) organization maximizes the cell-cell communication and signaling that are critical for cell function. In this study, 3D cell aggregates composed of human umbilical vein endothelial cells (HUVECs) and cord-blood mesenchymal stem cells (cbMSCs) were used for therapeutic neovascularization to rescue tissues from critical limb ischemia. Within the cell aggregates, homogeneously mixed HUVECs and cbMSCs had direct cell-cell contact with expressions of endogenous extracellular matrices and adhesion molecules. Although dissociated HUVECs/cbMSCs initially formed tubular structures on Matrigel, the grown tubular network substantially regressed over time. Conversely, 3D HUVEC/cbMSC aggregates seeded on Matrigel exhibited an extensive tubular network that continued to expand without regression. Immunostaining experiments show that, by differentiating into smooth muscle cell (SMC) lineages, the cbMSCs stabilize the HUVEC-derived tubular network. The real-time PCR analysis results suggest that, through myocardin, TGF-β signaling regulates the differentiation of cbMSCs into SMCs. Transplantation of 3D HUVEC/cbMSC aggregates recovered blood perfusion in a mouse model of hindlimb ischemia more effectively compared to their dissociated counterparts. The experimental results confirm that the transplanted 3D HUVEC/cbMSC aggregates enhanced functional vessel formation within the ischemic limb and protected it from degeneration. The 3D HUVEC/cbMSC aggregates can therefore facilitate the cell-based therapeutic strategies for modulating postnatal neovascularization.
A Dynamic Online Traffic Classification Methodology Based on Data Stream Mining

Recently, traffic classification (TC) becomes more and more important for network management and measurement tasks. The new-coming machine learning based classification methods can achieve high classification accuracy and fast identification ability; however, all these related TC methods up to now always have the assumption of the stability of classification model constituted from network traffic. It is not true since seldom real-world traffic is static. In this paper, we make a first step towards classifying dynamic online traffic in a data stream perspective to handle the dynamic real-time network traffic. In this paper, we validate the dynamic feature of real-world traffic for the first time, using concept drift from two different levels: overall traffic level and application level. The conclusion convinces us that the user behavior reflected in traffic can vary dramatically due to different conditions and different periods. We then propose a novel integrated dynamic online traffic classification framework; called DSTC (Data Stream based Traffic Classification). This DSTC differs from previous work since it aims to deal with dynamic traffic with online identification ability. It is a more realistic framework in which training phase can go simultaneously with classification phase and more accurate training model can be constructed with the feedback from classification result. Experiment results have shown that DSTC can have a high stable classification accuracy of above 95% for network traffic with different periods and user conditions, while accuracy for the traditional classification methodology can vary from 81% to 97% when dealing with different traffic.
Encouraging Physical Activity in Patients With Diabetes: Intervention Using a Reinforcement Learning System

Regular physical activity is known to be beneficial for people with type 2 diabetes. Nevertheless, most of the people who have diabetes lead a sedentary lifestyle. Smartphones create new possibilities for helping people to adhere to their physical activity goals through continuous monitoring and communication, coupled with personalized feedback.



The aim of this study was to help type 2 diabetes patients increase the level of their physical activity.



We provided 27 sedentary type 2 diabetes patients with a smartphone-based pedometer and a personal plan for physical activity. Patients were sent short message service messages to encourage physical activity between once a day and once per week. Messages were personalized through a Reinforcement Learning algorithm so as to improve each participant's compliance with the activity regimen. The algorithm was compared with a static policy for sending messages and weekly reminders.



Our results show that participants who received messages generated by the learning algorithm increased the amount of activity and pace of walking, whereas the control group patients did not. Patients assigned to the learning algorithm group experienced a superior reduction in blood glucose levels (glycated hemoglobin [HbA1c]) compared with control policies, and longer participation caused greater reductions in blood glucose levels. The learning algorithm improved gradually in predicting which messages would lead participants to exercise.



Mobile phone apps coupled with a learning algorithm can improve adherence to exercise in diabetic patients. This algorithm can be used in large populations of diabetic patients to improve health and glycemic control. Our results can be expanded to other areas where computer-led health coaching of humans may have a positive impact. Summary of a part of this manuscript has been previously published as a letter in Diabetes Care, 2016.
Feature Reduction Using Standard Deviation with Different Subsets Selection in Sentiment Analysis

The genesis of the internet and web has created huge information on the web, including users’ digital or textual opinions and reviews. This leads to compiling many features in document-level. Consequently, we will have a high-dimensional feature space. In this paper, we propose an algorithm based on standard deviation method to solve the high-dimensional feature space. The algorithm constructs feature subsets based on dispersion of features. In other words, algorithm selects the features with higher value of standard deviation for construction of the subsets. To do this, the paper presents an experiment of performance estimation on sentiment analysis dataset using ensemble of classifiers when dimensionality reduction is performed on the input space using three different methods. Also different types of base classifiers and classifier combination rules were used.
How Current Android Malware Seeks to Evade Automated Code Analysis

First we report on a new threat campaign, underway in Korea, which infected around 20,000 Android users within two months. The campaign attacked mobile users with malicious applications spread via different channels, such as email attachments or SMS spam. A detailed investigation of the Android malware resulted in the identification of a new Android malware family Android/BadAccents. The family represents current state-of-the-art in mobile malware development for banking trojans. Second, we describe in detail the techniques this malware family uses and confront them with current state-of-the-art static and dynamic codeanalysis techniques for Android applications. We highlight various challenges for automatic malware analysis frameworks that significantly hinder the fully automatic detection of malicious components in current Android malware. Furthermore, the malware exploits a previously unknown tapjacking vulnerability in the Android operating system, which we describe. As a result of this work, the vulnerability, affecting all Android versions, will be patched in one of the next releases of the Android Open Source Project.
Structure and Innervation of the Equine Supraspinous and Interspinous Ligaments.

Pain related to the osseous thoracolumbar spine is common in the equine athlete, with minimal information available regarding soft tissue pathology. The aims of this study were to describe the anatomy of the equine SSL and ISL (supraspinous and interspinous ligaments) in detail and to assess the innervation of the ligaments and their myofascial attachments including the thoracolumbar fascia. Ten equine thoracolumbar spines (T15-L1) were dissected to define structure and anatomy of the SSL, ISL and adjacent myofascial attachments. Morphological evaluation included histology, electron microscopy and immunohistochemistry (S100 and Substance P) of the SSL, ISL, adjacent fascial attachments, connective tissue and musculature. The anatomical study demonstrated that the SSL and ISL tissues merge with the adjacent myofascia. The ISL has a crossing fibre arrangement consisting of four ligamentous layers with adipose tissue axially. A high proportion of single nerve fibres were detected in the SSL (mean = 2.08 fibres/mm2 ) and ISL (mean = 0.75 fibres/mm2 ), with the larger nerves located between the ligamentous and muscular tissue. The oblique crossing arrangement of the fibres of the ISL likely functions to resist distractive and rotational forces, therefore stabilizing the equine thoracolumbar spine. The dense sensory innervation within the SSL and ISL could explain the severe pain experienced by some horses with impinging dorsal spinous processes. Documentation of the nervous supply of the soft tissues associated with the dorsal spinous processes is a key step towards improving our understanding of equine back pain.
The visual perception of 3D shape

A fundamental problem for the visual perception of 3D shape is that patterns of optical stimulation are inherently ambiguous. Recent mathematical analyses have shown, however, that these ambiguities can be highly constrained, so that many aspects of 3D structure are uniquely specified even though others might be underdetermined. Empirical results with human observers reveal a similar pattern of performance. Judgments about 3D shape are often systematically distorted relative to the actual structure of an observed scene, but these distortions are typically constrained to a limited class of transformations. These findings suggest that the perceptual representation of 3D shape involves a relatively abstract data structure that is based primarily on qualitative properties that can be reliably determined from visual information.
Double-spending prevention for Bitcoin zero-confirmation transactions

Zero-confirmation transactions, i.e. transactions that have been broadcast but are still pending to be included in the blockchain, have gained attention in order to enable fast payments in Bitcoin, shortening the time for performing payments. Fast payments are desirable in certain scenarios, for instance, when buying in vending machines, fast food restaurants, or withdrawing from an ATM. Despite being quickly propagated through the network, zero-confirmation transactions are not protected against double-spending attacks, since the double-spending protection Bitcoin offers relies on the blockchain and, by definition, such transactions are not yet included in it. In this paper, we propose a double-spending prevention mechanism for Bitcoin zero-confirmation transactions. Our proposal is based on exploiting the flexibility of the Bitcoin scripting language together with a well-known vulnerability of the ECDSA signature scheme to discourage attackers from performing such an attack.
Grounding Referring Expressions in Images by Variational Context

We focus on grounding (i.e., localizing or linking) referring expressions in images, e.g., "largest elephant standing behind baby elephant". This is a general yet challenging vision-language task since it does not only require the localization of objects, but also the multimodal comprehension of context - visual attributes (e.g., "largest", "baby") and relationships (e.g., "behind") that help to distinguish the referent from other objects, especially those of the same category. Due to the exponential complexity involved in modeling the context associated with multiple image regions, existing work oversimplifies this task to pairwise region modeling by multiple instance learning. In this paper, we propose a variational Bayesian method, called Variational Context, to solve the problem of complex context modeling in referring expression grounding. Our model exploits the reciprocal relation between the referent and context, i.e., either of them influences estimation of the posterior distribution of the other, and thereby the search space of context can be greatly reduced. We also extend the model to unsupervised setting where no annotation for the referent is available. Extensive experiments on various benchmarks show consistent improvement over state-of-the-art methods in both supervised and unsupervised settings. The code is available at https://github.com/yuleiniu/vc/.
Traffic Congestion Analysis Visualisation Tool

The rapid growth of urban population and numbers of private cars in this modern era, results in increasingly urgent transportation problem in cities throughout the world. Road traffic congestion is an omnipresent problem, which leads to delays, time loss, human stress, energy consumption, environmental pollution e.c.t. In order to decrease traffic congestion, there is a need for simulating and optimizing traffic control and improving traffic management. There are different ways for traffic congestion monitoring and analysis such as using video monitoring and surveillance systems, or static and dynamic sensors which allow traffic management in real time. There are also other methods using non real time analysis where traf?c congestion can be extracted from historical patterns of traf?c congestion. The historical patterns can be gained from the stored travel time and speed data. The goal of enhancing driver convenience is achieved by providing applications based on road traffic condition that mainly identifies congestion status. This paper presents a web application which uses live traffic congestion data from Google Maps traffic layer for real time congestion calculation. A technique utilized for estimating the level of congestion is image processing. The main objective is to provide an automated and yet interactive visualization tool for congestion analysis in real time. The aim is reducing the traffic congestion on roads which will lead to decrease in the number of accidents. It can provide important data which can help road traffic management. Thus, it is mainly dedicated to traffic managers, operators and analysts. Nevertheless it can be implemented also by road users. Unlike most sensor based applications, it makes quantified congestion data available even in regions with limited traffic data information.
Lightweight S-Box Architecture for Secure Internet of Things

Lightweight cryptographic solutions are required to guarantee the security of Internet of Things (IoT) pervasiveness. Cryptographic primitives mandate a non-linear operation. The design of a lightweight, secure, non-linear 4 × 4 substitution box (S-box) suited to Internet of Things (IoT) applications is proposed in this work. The structure of the 4 × 4 S-box is devised in the finite fields GF (24) and GF ((22)2). The finite field S-box is realized by multiplicative inversion followed by an affine transformation. The multiplicative inverse architecture employs Euclidean algorithm for inversion in the composite field GF ((22)2). The affine transformation is carried out in the field GF (24). The isomorphic mapping between the fields GF (24) and GF ((22)2) is based on the primitive element in the higher order field GF (24). The recommended finite field S-box architecture is combinational and enables sub-pipelining. The linear and differential cryptanalysis validates that the proposed S-box is within the maximal security bound. It is observed that there is 86.5% lesser gate count for the realization of sub field operations in the composite field GF ((22)2) compared to the GF (24) field. In the PRESENT lightweight cipher structure with the basic loop architecture, the proposed S-box demonstrates 5% reduction in the gate equivalent area over the look-up-table-based S-box with TSMC 180 nm technology.
Neural modeling for time series: A statistical stepwise method for weight elimination

Many authors use feedforward neural networks for modeling and forecasting time series. Most of these applications are mainly experimental, and it is often difficult to extract a general methodology from the published studies. In particular, the choice of architecture is a tricky problem. We try to combine the statistical techniques of linear and nonlinear time series with the connectionist approach. The asymptotical properties of the estimators lead us to propose a systematic methodology to determine which weights are nonsignificant and to eliminate them to simplify the architecture. This method (SSM or statistical stepwise method) is compared to other pruning techniques and is applied to some artificial series, to the famous Sunspots benchmark, and to daily electrical consumption data.
The Effect of Mulligan Mobilization Technique in Older Adults with Neck Pain: A Randomized Controlled, Double-Blind Study

The purpose of this study was to examine the effect of Mulligan mobilization technique (MMT) on pain, range of motion (ROM), functional level, kinesiophobia, depression, and quality of life (QoL) in older adults with neck pain (NP).



Forty-two older adults with NP were included in the study, and they were randomly divided into two groups: traditional physiotherapy (TP) group and traditional physiotherapy-Mulligan mobilization (TPMM) group. Treatment program was scheduled for 10 sessions. Participants were assessed in terms of pain, ROM, functional level, kinesiophobia, depression, and QoL both pre- and posttreatment.



Pain, ROM, functional level, kinesiophobia, depression, and QoL improved in both groups following treatment (p < 0.05). When comparing effects of these two treatment programs, it was observed that the TPMM group had a better outcome (p < 0.05) in terms of ROM, kinesiophobia, depression, and QoL.



In older adults with NP, MMT has been found to have significant effects on pain, ROM, functional level, kinesiophobia, depression, and QoL as long as it is performed by a specialist. "
Geriatric Depression in Family Medicine

Elderly persons often suffer from depression, without anyone around them noticing. Depression is more common at physically ill elderly person then at their physically healthy contemporary. It is important mental health problem of developed society, because it is still faintly revealed thus insufficiently treated.



To explore the existence of geriatric depression in elderly persons living on their own and those who live in family environment.



The research included 200 elderly respondents, experimental group made of elderly persons (>65 years) living alone. Control group included elderly persons living in a family environment. Universal geriatric questionnaire was made for this research. To assess the presence of depression at respondents we used "The scale of geriatric depression".



The average age (±SD) was 75,4±6,2 years in the experimental group, while in the control group the average age was 74,9±5,6 years. In the experimental group there is significantly larger number of elderly persons that are neglected (p=0,001). Elderly respondents surrounded by loneliness are more depressive than elderly living in the family environment. Statistically geriatric depression is significantly connected with inability for everyday activities, with decreased result of cognitive abilities and indicated result of dementia (P=0.001).



Depression is an important mental health problem of the developed society, because it is still faintly discovered and by that insufficiently treated. Organizing approach to different aspects of geriatric health, doctors of the primary protection can improve care of their elderly patients.