nils-herrmann/clean_scirepeval huggingface dataset:
COVERT: Compositional Analysis of Android Inter-App Permission Leakage

Android is the most popular platform for mobile devices. It facilitates sharing of data and services among applications using a rich inter-app communication system. While access to resources can be controlled by the Android permission system, enforcing permissions is not sufficient to prevent security violations, as permissions may be mismanaged, intentionally or unintentionally. Android's enforcement of the permissions is at the level of individual apps, allowing multiple malicious apps to collude and combine their permissions or to trick vulnerable apps to perform actions on their behalf that are beyond their individual privileges. In this paper, we present COVERT, a tool for compositional analysis of Android inter-app vulnerabilities. COVERT's analysis is modular to enable incremental analysis of applications as they are installed, updated, and removed. It statically analyzes the reverse engineered source code of each individual app, and extracts relevant security specifications in a format suitable for formal verification. Given a collection of specifications extracted in this way, a formal analysis engine (e.g., model checker) is then used to verify whether it is safe for a combination of applications-holding certain permissions and potentially interacting with each other-to be installed together. Our experience with using COVERT to examine over 500 real-world apps corroborates its ability to find inter-app vulnerabilities in bundles of some of the most popular apps on the market.
Learning to Solve NP-Complete Problems - A Graph Neural Network for the Decision TSP

Graph Neural Networks (GNN) are a promising technique for bridging differential programming and combinatorial domains. GNNs employ trainable modules which can be assembled in different configurations that reflect the relational structure of each problem instance. In this paper, we show that GNNs can learn to solve, with very little supervision, the decision variant of the Traveling Salesperson Problem (TSP), a highly relevant NP-Complete problem. Our model is trained to function as an effective message-passing algorithm in which edges (embedded with their weights) communicate with vertices for a number of iterations after which the model is asked to decide whether a route with cost < C exists. We show that such a network can be trained with sets of dual examples: given the optimal tour cost C∗, we produce one decision instance with target cost x% smaller and one with target cost x% larger than C∗. We were able to obtain 80% accuracy training with −2%,+2% deviations, and the same trained model can generalize for more relaxed deviations with increasing performance. We also show that the model is capable of generalizing for larger problem sizes. Finally, we provide a method for predicting the optimal route cost within 2% deviation from the ground truth. In summary, our work shows that Graph Neural Networks are powerful enough to solve NP-Complete problems which combine symbolic and numeric data.
Realizing underwater communication through magnetic induction

The majority of the work on underwater communication has mainly been based on acoustic communication. Acoustic communication faces many known problems, such as high propagation delays, very low data rates, and highly environment-dependent channel behavior. In this article, to address these shortcomings, magnetic induction is introduced as a possible communication paradigm for underwater applications. Accordingly, all research challenges in this regard are explained. Fundamentally different from the conventional underwater communication paradigm, which relies on EM, acoustic, or optical waves, the underwater MI communications rely on the time varying magnetic field to covey information between the transmitting and receiving parties. MI-based underwater communications exhibit several unique and promising features such as negligible signal propagation delay, predictable and constant channel behavior, sufficiently long communication range with high bandwidth, as well as silent and stealth underwater operations. To fully utilize the promising features of underwater MI-based communications, this article introduces the fundamentals of underwater MI communications, including the MI channel models, MI networking protocols design, and MI-based underwater localization.
Corpus-based HIT-MW database for offline recognition of general-purpose Chinese handwritten text

A Chinese handwriting database named HIT-MW is presented to facilitate the offline Chinese handwritten text recognition. Both the writers and the texts for handcopying are carefully sampled with a systematic scheme. To collect naturally written handwriting, forms are distributed by postal mail or middleman instead of face to face. The current version of HIT-MW includes 853 forms and 186,444 characters that are produced under an unconstrained condition without preprinted character boxes. The statistics show that the database has an excellent representation of the real handwriting. Many new applications concerning real handwriting recognition can be supported by the database.
Crowdsourcing-based Evaluation of Privacy in HDR Images

The ability of High Dynamic Range imaging (HDRi) to capture details in high-contrast environments, making both dark and bright regions clearly visible, has a strong implication on privacy. However, the extent to which HDRi affects privacy when it is used instead of typical Standard Dynamic Range imaging (SDRi) is not yet clear. In this paper, we investigate the effect of HDRi on privacy via crowdsourcing evaluation using the Microworkers platform. Due to the lack of HDRi standard privacy evaluation dataset, we have created such dataset containing people of varying gender, race, and age, shot indoor and outdoor and under large range of lighting conditions. We evaluate the tone-mapped versions of these images, obtained by several representative tone-mapping algorithms, using subjective privacy evaluation methodology. Evaluation was performed using crowdsourcing-based framework, because it is a popular and effective alternative to traditional lab-based assessment. The results of the experiments demonstrate a significant loss of privacy when even tone-mapped versions of HDR images are used compared to typical SDR images shot with a standard exposure.
Robust Stochastic Approximation Approach to Stochastic Programming

In this paper we consider optimization problems where the objective function is given in a form of the expectation. A basic difficulty of solving such stochastic optimization problems is that the involved multidimensional integrals (expectations) cannot be computed with high accuracy. The aim of this paper is to compare two computational approaches based on Monte Carlo sampling techniques, namely, the stochastic approximation (SA) and the sample average approximation (SAA) methods. Both approaches, the SA and SAA methods, have a long history. Current opinion is that the SAA method can efficiently use a specific (say, linear) structure of the considered problem, while the SA approach is a crude subgradient method, which often performs poorly in practice. We intend to demonstrate that a properly modified SA approach can be competitive and even significantly outperform the SAA method for a certain class of convex stochastic problems. We extend the analysis to the case of convex-concave stochastic saddle point problems and present (in our opinion highly encouraging) results of numerical experiments.
EMPLOYING DESIGN AND DEVELOPMENT RESEARCH (DDR) APPROACHES IN THE DESIGN AND DEVELOPMENT OF ONLINE ARABIC VOCABULARY LEARNING GAMES PROTOTYPE

The design and development research, first proposed by Brown and Collins in the 1990s, is currently among the well-known methods in educational research to test theory and validate its practicality. The method is also known as developmental research, design research, design-based research, formative research and design-cased and possesses conceptual underpinning and practical aspects of the ‘what’ and ‘how’ of ‘doing’. This paper describes the design and development of a web-based Arabic vocabulary learning games prototype incorporating the methods and approaches of DDR for elementary learners in Centre for Foundation Studies (CFS), International Islamic University Malaysia (IIUM). The effort in generating this learning prototype attempts to design, develop and integrate a game-based learning application in an online platform. It is meant to provide an interactive learning experience for learners who have been through traditional non-computer based Arabic teaching and learning methods. Their feedbacks and responses are then gathered and analysed as the formative evaluation for the design and development principles. The exploration of the potential of use for the game-based learning prototype in teaching and learning in real setting by various experts, learners and teachers was also among the steps taken to evaluate the usability and practicality of the prototype during the design and development phases.
BM3D filter in salt-and-pepper noise removal

There is a significant recent advance in filtering of the salt-and-pepper noise for digital images. However, almost all recent schemes for filtering of this type of noise are not taking into an account the shape of objects (in particular edges) in images. We have applied the block-matching and 3D filtering (BM3D) scheme in order to refine the output of the decision-based/adaptive median techniques. Obtained results are excellent, surpassing current state-of-the-art for about 2 dB for both grayscale and color images.
New Generation Sensor Web Enablement

Many sensor networks have been deployed to monitor Earth's environment, and more will follow in the future. Environmental sensors have improved continuously by becoming smaller, cheaper, and more intelligent. Due to the large number of sensor manufacturers and differing accompanying protocols, integrating diverse sensors into observation systems is not straightforward. A coherent infrastructure is needed to treat sensors in an interoperable, platform-independent and uniform way. The concept of the Sensor Web reflects such a kind of infrastructure for sharing, finding, and accessing sensors and their data across different applications. It hides the heterogeneous sensor hardware and communication protocols from the applications built on top of it. The Sensor Web Enablement initiative of the Open Geospatial Consortium standardizes web service interfaces and data encodings which can be used as building blocks for a Sensor Web. This article illustrates and analyzes the recent developments of the new generation of the Sensor Web Enablement specification framework. Further, we relate the Sensor Web to other emerging concepts such as the Web of Things and point out challenges and resulting future work topics for research on Sensor Web Enablement.
Thickness and Permittivity Measurement in Multi-Layered Dielectric Structures Using Complementary Split-Ring Resonators

This paper presents a non-invasive microwave method based on a square-shaped complementary split-ring resonator (CSRR) to measure the thickness and permittivity of multilayer dielectric structures. The CSRR sensor is etched on the ground plane of a microstrip line. The change of resonance frequency depends on the thickness and permittivity of the multilayer dielectric sample below the ground plane. For resolution analysis, the resonance frequency shifts caused by a variation of permittivity (Δε = 0.01) and thickness (Δd=0.01 mm) in the detection layer were compared across various design dimensions. Sensor size optimization improved the resolution in permittivity and thickness measurement by 66% and 37%, respectively. Subsequently, the permittivity and thickness resolution was improved by 28% and 16%, respectively, by optimizing the separation of the etched CSRRs. The analysis results show that a CSRR sensor can be designed with excellent resolution in core layer permittivity changes and thickness resolution in multilayered dielectric structures.
Denial of Service Defence for Resource Availability in Wireless Sensor Networks

Wireless sensor networks (WSN) over the years have become one of the most promising networking solutions with exciting new applications for the near future. Its deployment has been enhanced by its small, inexpensive, and smart sensor nodes, which are easily deployed, depending on its application and coverage area. Common applications include its use for military operations, monitoring environmental conditions (such as volcano detection, agriculture, and management), distributed control systems, healthcare, and the detection of radioactive sources. Notwithstanding its promising attributes, security in WSN is a big challenge and remains an ongoing research trend. Deployed sensor nodes are vulnerable to various security attacks due to its architecture, hostile deployment location, and insecure routing protocol. Furthermore, the sensor nodes in WSNs are characterized by their resource constraints, such as, limited energy, low bandwidth, short communication range, limited processing, and storage capacity, which have made the sensor nodes an easy target. Therefore, in this paper, we present a review of denial of service attacks that affect resource availability in WSN and their countermeasure by presenting a taxonomy. Future research directions and open research issues are also discussed.
LOW CORRELATION MIMO ANTENNAS WITH NEGATIVE GROUP DELAY

A key feature of upcoming 4G wireless communication systems is multiple-input-multiple-output (MIMO) technology. To make the best use of MIMO, the antenna correlation between adjacent antennas should be low (< 0.5). In this context, we propose a correlation reduction technique suitable for closely spaced antennas (distance, d < λ/40). This technique reduces mutual coupling between antennas and concurrently uncorrelates antennas’ radiation characteristics by inducing the negative group delay at the target frequency. The validity of the technique is demonstrated with a USB dongle MIMO antenna designed for LTE 700 MHz band. Measurement results show that the antenna correlation is reduced more than 40% using the proposed technique.
The Anisotropic Noise in Stochastic Gradient Descent : Its Behavior of Escaping from Minima and Regularization Effects

Understanding the behavior of stochastic gradient descent (SGD) in the context of deep neural networks has raised lots of concerns recently. Along this line, we theoretically study a general form of gradient based optimization dynamics with unbiased noise, which unifies SGD and standard Langevin dynamics. Through investigating this general optimization dynamics, we analyze the behavior of SGD on escaping from minima and its regularization effects. A novel indicator is derived to characterize the efficiency of escaping from minima through measuring the alignment of noise covariance and the curvature of loss function. Based on this indicator, two conditions are established to show which type of noise structure is superior to isotropic noise in term of escaping efficiency. We further show that the anisotropic noise in SGD satisfies the two conditions, and thus helps to escape from sharp and poor minima effectively, towards more stable and flat minima that typically generalize well. We verify our understanding through comparing this anisotropic diffusion with full gradient descent plus isotropic diffusion (i.e. Langevin dynamics) and other types of position-dependent noise.
A pediatric robotic thumb exoskeleton for at-home rehabilitation: The Isolated Orthosis for Thumb Actuation (IOTA)

In this paper, we present the design of a thumb exoskeleton for pediatric at-home rehabilitation. Pediatric disorders, such as cerebral palsy (CP) and stroke, can result in thumb in palm deformity greatly limiting hand function. This not only limits children's ability to perform activities of daily living but also limits important motor skill development. Specifically, the device, dubbed IOTA (Isolated Orthosis for Thumb Actuation) is a 2-DOF thumb exoskeleton that can actuate the carpometacarpal (CMC) and metacarpalphalangeal (MCP) joints through ranges of motion required for activities of daily living. The device consists of a lightweight hand-mounted mechanism that can be custom secured and aligned to the wearer. The mechanism is actuated via flexible cables that connect to a portable control box. Embedded encoders and bend sensors monitor the two degrees of freedom of the thumb and flexion/extension of the wrist. Using this platform, a number of control modes can be implemented that will enable the device to be intuitively controlled by a patient to assist with opposition grasp, fine motor control, and ultimately facilitate motor recovery. We envision this at-home device augmenting the current in-clinic therapy and enabling tele-rehabilitation where a clinician can remotely monitor a patient's usage and performance.
Theoretical perspectives on the relation between catastrophizing and pain.

The tendency to "catastrophize" during painful stimulation contributes to more intense pain experience and increased emotional distress. Catastrophizing has been broadly conceived as an exaggerated negative "mental set" brought to bear during painful experiences. Although findings have been consistent in showing a relation between catastrophizing and pain, research in this area has proceeded in the relative absence of a guiding theoretical framework. This article reviews the literature on the relation between catastrophizing and pain and examines the relative strengths and limitations of different theoretical models that could be advanced to account for the pattern of available findings. The article evaluates the explanatory power of a schema activation model, an appraisal model, an attention model, and a communal coping model of pain perception. It is suggested that catastrophizing might best be viewed from the perspective of hierarchical levels of analysis, where social factors and social goals may play a role in the development and maintenance of catastrophizing, whereas appraisal-related processes may point to the mechanisms that link catastrophizing to pain experience. Directions for future research are suggested.
A Pattern Recognition System for Malicious PDF Files Detection

Malicious PDF files have been used to harm computer security during the past two-three years, and modern antivirus are proving to be not completely effective against this kind of threat. In this paper an innovative technique, which combines a feature extractor module strongly related to the structure of PDF files and an effective classifier, is presented. This system has proven to be more effective than other stateof-the-art research tools for malicious PDF detection, as well as than most of antivirus in commerce. Moreover, its flexibility allows adopting it either as a stand-alone tool or as plug-in to improve the performance of an already installed antivirus.
Artificial intelligence theory (Basic concepts)

Taking the bionic approach as a basis, the article discusses the main concepts of the theory of artificial intelligence as a field of knowledge, which studies the principles of creation and functioning of intelligent systems based on multidimensional neural-like growing networks. The general theory of artificial intelligence includes the study of neural-like elements and multidimensional neural-like growing networks, temporary and long term memory, study of the functional organization of the “brain” of the artificial intelligent systems, of the sensor system, modulating system, motor system, conditioned and unconditioned reflexes, reflexes arc (ring), motivation, purposeful behavior, of “thinking”, “consciousness”, “subconscious and artificial personality developed as a result of training and education”.
The TypTop System: Personalized Typo-Tolerant Password Checking

Password checking systems traditionally allow login only if the correct password is submitted. Recent work on typo-tolerant password checking suggests that usability can be improved, with negligible security loss, by allowing a small number of typographical errors. Existing systems, however, can only correct a handful of errors, such as accidentally leaving caps lock on or incorrect capitalization of the first letter in a password. This leaves out numerous kinds of typos made by users, such as transposition errors, substitutions, or capitalization errors elsewhere in a password. Some users therefore receive no benefit from existing typo-tolerance mechanisms.
 We introduce personalized typo-tolerant password checking. In our approach, the authentication system learns over time the typos made by a specific user. In experiments using Mechanical Turk, we show that 45% of users would benefit from personalization. Therefore, we design a system, called TypTop, that securely implements personalized typo-tolerance. Underlying TypTop is a new stateful password-based encryption scheme that can be used to store recent failed login attempts. Our formal analysis shows that security in the face of an attacker that obtains the state of the system reduces to the difficulty of a brute-force dictionary attack against the real password. We implement TypTop for Linux and Mac OS login and report on a proof-of-concept deployment.
Wideband coplanar waveguide RF probe pad to microstrip transitions without via holes

A novel via-less coplanar waveguide (CPW) to microstrip transition is discussed and design rules based on simulations and experimental results are presented. This transition demonstrates a maximum insertion loss of 1 dB over the frequency range from 10 GHz to 40 GHz with a value of 0.4 dB at 20 GHz. This transition could find a variety of applications due to its compatibility with RF systems-on-a chip, low loss performance, low cost and its ease of fabrication.
REAL-TIME MULTI-PATIENT MONITORING SYSTEM USING ARM AND WIRELESS SENSOR NETWORK

Mobile Multi patient monitoring device has become increasingly important in Hospital wards to record real-time data during normal activity for better treatment. However, the current quality and reliability have not been satisfactory due to the size, weight, distance of coverage and also high power consumption. This paper provides a solution for enhancing the reliability, flexibility by improving the performance and power management of the real-time multi-patient monitoring system (MPMS). In the current proposed system the patient health is continuously monitored by the MPMS and the acquired data is transmitted to a centralized ARM server using Wireless Sensor Networks. A ZigBee node is connected to every patient monitor system which will send the patient's vital information .Upon system boot up, the mobile patient monitor system will continuously monitor the patients vital parameters like Heart Beat, body temperature etc and will periodically send those parameters to a centralized server using ZigBee node configured as co-coordinator. If a particular patient’s health parameter falls below the threshold value, a buzzer alert is triggered by the ARM server. Along with a buzzer an automated SMS is posted to the pre-configured Doctors mobile number using a standard GSM module interfaced to the ARM server. The Doctor is continuously connected to the ARM server using GSM Module and he/she can get a record of a particular patient’s information by just posting a SMS message to the centralized ARM server. This will reduce treatment time, cost and power consumption to a greater extent. At the same time, the efficiency of examining ward will be improved by making the system more real-time and robust. KeywordsMobile Multi patient monitoring system ,ARM, ZigBee .GSM Module