nils-herrmann/clean_scirepeval huggingface dataset:
µ-FTL: : a memory-efficient flash translation layer supporting multiple mapping granularities

NAND flash memory is being widely adopted as a storage medium for embedded devices. FTL (Flash Translation Layer) is one of the most essential software components in NAND flash-based embedded devices as it allows to use legacy files systems by emulating the traditional block device interface on top of NAND flash memory.
 In this paper, we propose a novel FTL, called μ-FTL. The main design goal of μ-FTL is to reduce the memory foot-print as small as possible, while providing the best performance by supporting multiple mapping granularities based on variable-sized extents. The mapping information is managed by μ-Tree, which offers an efficient index structure for NAND flash memory. Our evaluation results show that μ-FTL significantly outperforms other block-mapped FTLs with the same memory size by up to 89.7%.
Deep Neural Networks and Hidden Markov Models in i-vector-based Text-Dependent Speaker Verification

Techniques making use of Deep Neural Networks (DNN) have recently been seen to bring large improvements in textindependent speaker recognition. In this paper, we verify that the DNN based methods result in excellent performances in the context of text-dependent speaker verification as well. We build our system on the previously introduced HMM based ivector approach, where phone models are used to obtain frame level alignment in order to collect sufficient statistics for ivector extraction. For comparison, we experiment with an alternative alignment obtained directly from the output of DNN trained for phone classification. We also experiment with DNN based bottleneck features and their combinations with standard cepstral features. Although the i-vector approach is generally considered not suitable for text-dependent speaker verification, we show that our HMM based approach combined with bottleneck features provides truly state-of-the-art performance on RSR2015 data.
DLIREC: Aspect Term Extraction and Term Polarity Classification System

This paper describes our system used in the Aspect Based Sentiment Analysis Task 4 at the SemEval-2014. Our system consists of two components to address two of the subtasks respectively: a Conditional Random Field (CRF) based classifier for Aspect Term Extraction (ATE) and a linear classifier for Aspect Term Polarity Classification (ATP). For the ATE subtask, we implement a variety of lexicon, syntactic and semantic features, as well as cluster features induced from unlabeled data. Our system achieves state-of-the-art performances in ATE, ranking 1st (among 28 submissions) and 2rd (among 27 submissions) for the restaurant and laptop domain respectively.
Information Systems Success Revisited

The DeLone & McLean IS Success Model has become a standard for the specification and justification of the measurement of the dependent variable in information systems research. Attempts to apply and test the model have resulted in both confirmation and challenges. This paper reviews and analyzes over 150 articles which have referenced the model over the past eight years in order to examine what more we have learned about measuring IS success. It highlights recent contributions to IS success measurement and proposes a Reformulated IS Success Model which recognizes and incorporates those contributions. The report concludes with recommendations for future IS success measurement.
Risk-Constrained Reinforcement Learning with Percentile Risk Criteria

In many sequential decision-making problems one is interested in minimizing an expected cumulative cost while taking into account risk, i.e., increased awareness of events of small probability and high consequences. Accordingly, the objective of this paper is to present efficient reinforcement learning algorithms for risk-constrained Markov decision processes (MDPs), where risk is represented via a chance constraint or a constraint on the conditional value-at-risk (CVaR) of the cumulative cost. We collectively refer to such problems as percentile risk-constrained MDPs. Specifically, we first derive a formula for computing the gradient of the Lagrangian function for percentile riskconstrained MDPs. Then, we devise policy gradient and actor-critic algorithms that (1) estimate such gradient, (2) update the policy in the descent direction, and (3) update the Lagrange multiplier in the ascent direction. For these algorithms we prove convergence to locally optimal policies. Finally, we demonstrate the effectiveness of our algorithms in an optimal stopping problem and an online marketing application.
A floating gate based 3D NAND technology with CMOS under array

NAND Flash has followed Moore's law of scaling for several generations. With the minimum half-pitch going below 20nm, transition to a 3D NAND cell is required to continue the scaling. This paper describes a floating gate based 3D NAND technology with superior cell characteristics relative to 2D NAND, and CMOS under array for high Gb/mm2 density.
Successful software projects and how to achieve them

veryone who ever taught project management seems to have a favorite disaster story, whether it's the new Denver airport baggage handling system, the London Stock Exchange, or the French Railways. Many of us would point to deficiencies in the software engineering activities that seemingly guarantee failure. It is indeed important that we understand how to engineer systems well, but we must also consider a wider viewpoint: success requires much more than good engineering. We must understand why we are engineering anything at all, and what the investment in money, time, and energy is all about. Who wants the software? In what context will it be applied? Who is paying for it and what do they hope to get from it? This special focus of IEEE Software takes that wider viewpoint and examines different views of how we can achieve success in software projects. The arguments and projects described here are not solely concerned with software but with the otherdeliverables that typically make for a required outcome: fully trained people; a new operational model for a business; the realization of an organizational strategy that depends on new information systems. What critical factors played a role in your last project success? Join us on this intellectual journey from soft ware engineering to business strategy.
SDSM: A secure data service mechanism in mobile cloud computing

To enhance the security of mobile cloud users, a few proposals have been presented recently. However we argue that most of them are not suitable for mobile cloud where mobile users might join or leave the mobile networks arbitrarily. In this paper, we design a secure mobile user-based data service mechanism (SDSM) to provide confidentiality and fine-grained access control for data stored in the cloud. This mechanism enables the mobile users to enjoy a secure outsourced data services at a minimized security management overhead. The core idea of SDSM is that SDSM outsources not only the data but also the security management to the mobile cloud in a trust way. Our analysis shows that the proposed mechanism has many advantages over the existing traditional methods such as lower overhead and convenient update, which could better cater the requirements in mobile cloud computing scenarios.
Describing Images using Inferred Visual Dependency Representations

The Visual Dependency Representation (VDR) is an explicit model of the spatial relationships between objects in an image. In this paper we present an approach to training a VDR Parsing Model without the extensive human supervision used in previous work. Our approach is to find the objects mentioned in a given description using a state-of-the-art object detector, and to use successful detections to produce training data. The description of an unseen image is produced by first predicting its VDR over automatically detected objects, and then generating the text with a template-based generation model using the predicted VDR. The performance of our approach is comparable to a state-ofthe-art multimodal deep neural network in images depicting actions.
On the Simple Economics of Advertising , Marketing , and Product Design By

We propose a framework for analyzing transformations of demand. Such transformations frequently stem from changes in the dispersion of consumers’ valuations, which lead to rotations of the demand curve. In many settings, profits are a U-shaped function of dispersion. High dispersion is complemented by niche production, and low dispersion is complemented by mass-market supply. We investigate numerous applications, including product design; advertising, marketing and sales advice; and the construction of quality-differentiated product lines. We also suggest a new taxonomy of advertising, distinguishing between hype, which shifts demand, and real information, which rotates demand.
A fuzzy logic based trust evaluation model for the selection of cloud services

Cloud computing enable users to avail the various services as per their wish. However, the quality about the service and its trust detail is not transparent to the user. In this paper, we propose a fuzzy logic based trust evaluation system that accepts the user's feedback in terms of fuzzy linguistic terms. The appropriate weight for the given feedback is calculated with the fuzzy inference system. Also, we have incorporated the fuzzy goal and constraint in our proposed model to predict the trust value with respect to the weight of feedback. The final trust value is derived by intersecting the goals and constraints of that service. A simulation on the MATLAB was performed and the results showed that, the proposed model can efficiently emulate the cloud user's feedback.
AsDroid: detecting stealthy behaviors in Android applications by user interface and program behavior contradiction

Android smartphones are becoming increasingly popular. The open nature of Android allows users to install miscellaneous applications, including the malicious ones, from third-party marketplaces without rigorous sanity checks. A large portion of existing malwares perform stealthy operations such as sending short messages, making phone calls and HTTP connections, and installing additional malicious components. In this paper, we propose a novel technique to detect such stealthy behavior. We model stealthy behavior as the program behavior that mismatches with user interface, which denotes the user's expectation of program behavior. We use static program analysis to attribute a top level function that is usually a user interaction function with the behavior it performs. Then we analyze the text extracted from the user interface component associated with the top level function. Semantic mismatch of the two indicates stealthy behavior. To evaluate AsDroid, we download a pool of 182 apps that are potentially problematic by looking at their permissions. Among the 182 apps, AsDroid reports stealthy behaviors in 113 apps, with 28 false positives and 11 false negatives.
Neural Networks in Civil Engineering : 1989 − 2000

The first journal article on neural network application in civil/structural engineering was published in this journal in 1989. This article reviews neural network articles published in archival research journals since then. The emphasis of the review is on the two fields of structural engineering and construction engineering and management. Neural networks articles published in other civil engineering areas are also reviewed, including environmental and water resources engineering, traffic engineering, highway engineering, and geotechnical engineering. The great majority of civil engineering applications of neural networks are based on the simple backpropagation algorithm. Applications of other recent, more powerful and efficient neural networks models are also reviewed. Recent works on integration of neural networks with other computing paradigms suchas genetic algorithm, fuzzy logic, andwavelet to enhance the performance of neural network models are presented.
Best Practices and Lessons Learned from Deploying and Operating Large-Scale Data-Centric Parallel File Systems

The Oak Ridge Leadership Computing Facility (OLCF) has deployed multiple large-scale parallel file systems (PFS) to support its operations. During this process, OLCF acquired significant expertise in large-scale storage system design, file system software development, technology evaluation, benchmarking, procurement, deployment, and operational practices. Based on the lessons learned from each new PFS deployment, OLCF improved its operating procedures, and strategies. This paper provides an account of our experience and lessons learned in acquiring, deploying, and operating large-scale parallel file systems. We believe that these lessons will be useful to the wider HPC community.
Beyond Memorability: Visualization Recognition and Recall

In this paper we move beyond memorability and investigate how visualizations are recognized and recalled. For this study we labeled a dataset of 393 visualizations and analyzed the eye movements of 33 participants as well as thousands of participant-generated text descriptions of the visualizations. This allowed us to determine what components of a visualization attract people's attention, and what information is encoded into memory. Our findings quantitatively support many conventional qualitative design guidelines, including that (1) titles and supporting text should convey the message of a visualization, (2) if used appropriately, pictograms do not interfere with understanding and can improve recognition, and (3) redundancy helps effectively communicate the message. Importantly, we show that visualizations memorable “at-a-glance” are also capable of effectively conveying the message of the visualization. Thus, a memorable visualization is often also an effective one.
Comparison of two Types of Surgery for Thoraco-Lumbar Burst Fractures: Combined Anterior and Posterior Stabilisation vs. Posterior Instrumentation Only

This retrospective study compares clinical outcome following two different types of surgery for thoracolumbar burst fractures. Forty-six patients with thoracolumbar burst fractures causing encroachment of the spinal canal greater than 50% were operated on within 30 days performing either: combined anterior decompression and stabilisation and posterior stabilisation (Group 1) or posterior distraction and stabilisation using pedicle instrumentation (AO internal fixator) (Group 2). We evaluated: neurological status (Frankel Grade), spinal deformities, residual pain, and complications. The average follow-up was 6 years. There were no significant differences between the patients in both groups concerning age, sex, cause of injury and the presence of other severe injuries. Neurological dysfunction was present in 39% of all cases. Bony union occurred in all patients. Loss of reduction greater than 5 degrees and instrumentation failure occurred significantly more often in Group 2 compared to Group 1, but the kyphosis angle at late follow-up did not differ between groups, due to some degree of overcorrection initially after surgery in Group 2. The clinical outcome was similar in both groups, and all but one patient with neurological deficits improved by at least one Frankel grade.  Indirect decompression of the spinal canal by posterior distraction and short-segment stabilisation with AO internal fixator is considered appropriate treatment for the majority of unstable thoracolumbar burst fractures. This is a less extensive surgical procedure than a combined anterior and posterior approach.
Cyber ethics and cyber crime: A deep dwelved study into legality, ransomware, underground web and bitcoin wallet

Future wars will be cyber wars and the attacks will be a sturdy amalgamation of cryptography along with malware to distort information systems and its security. The explosive Internet growth facilitates cyber-attacks. Web threats include risks, that of loss of confidential data and erosion of consumer confidence in e-commerce. The emergence of cyber hack jacking threat in the new form in cyberspace is known as ransomware or crypto virus. The locker bot waits for specific triggering events, to become active. It blocks the task manager, command prompt and other cardinal executable files, a thread checks for their existence every few milliseconds, killing them if present. Imposing serious threats to the digital generation, ransomware pawns the Internet users by hijacking their system and encrypting entire system utility files and folders, and then demanding ransom in exchange for the decryption key it provides for release of the encrypted resources to its original form. We present in this research, the anatomical study of a ransomware family that recently picked up quite a rage and is called CTB locker, and go on to the hard money it makes per user, and its source C&C server, which lies with the Internet's greatest incognito mode-The Dark Net. Cryptolocker Ransomware or the CTB Locker makes a Bitcoin wallet per victim and payment mode is in the form of digital bitcoins which utilizes the anonymity network or Tor gateway. CTB Locker is the deadliest malware the world ever encountered.
A clustered manycore processor architecture for embedded and accelerated applications

The Kalray MPPA-256 processor integrates 256 user cores and 32 system cores on a chip with 28nm CMOS technology. Each core implements a 32-bit 5-issue VLIW architecture. These cores are distributed across 16 compute clusters of 16+1 cores, and 4 quad-core I/O subsystems. Each compute cluster and I/O subsystem owns a private address space, while communication and synchronization between them is ensured by data and control Networks-On-Chip (NoC). The MPPA-256 processor is also fitted with a variety of I/O controllers, in particular DDR, PCI, Ethernet, Interlaken and GPIO. We demonstrate that the MPPA-256 processor clustered manycore architecture is effective on two different classes of applications: embedded computing, with the implementation of a professional H.264 video encoder that runs in real-time at low power; and high-performance computing, with the acceleration of a financial option pricing application. In the first case, a cyclostatic dataflow programming environment is utilized, that automates application distribution over the execution resources. In the second case, an explicit parallel programming model based on POSIX processes, threads, and NoC-specific IPC is used.
Synthesis of Modulated-Metasurface Antennas With Amplitude, Phase, and Polarization Control

An effective synthesis procedure for planar antennas realized with nonuniform metasurfaces (MTSs) excited by a point source is presented. This synthesis potentiates previous formulations by introducing a control of the amplitude of the aperture field while improving the polarization and phase performances. The class of MTS antennas we are dealing with is realized by using subwavelength patches of different dimensions printed on a grounded slab, illuminated by a transverse magnetic point source. These antennas are based on the interaction between a cylindrical surface-wave and the periodic modulation of the MTS, which leads to radiation through a leaky-wave (LW) effect. This new design method permits a systematic and simple synthesis of amplitude, phase, and polarization of the aperture field by designing the boundary conditions imposed by the MTS. The polarization control is based on the local value of the MTS anisotropy, the phase is controlled by the shape and periodicity of the modulation, and the amplitude is controlled by the local leakage attenuation parameter of the LW. The synthesis is based on analytical formulas derived by an adiabatic Floquet-wave expansion of currents and fields over the surface, which are simultaneously published in this journal issue. The effectiveness of the procedure is tested through several numerical examples involving realistic structures.
Shear stress concentrations in permanent magnet rotor sleeves

There is a strong trend in using permanent magnet synchronous machines for very high speed, high power applications due to their high efficiencies, versatility and compact nature. To increase power output for a given speed, rotor design becomes critical in order to maximize rotor volume and hence torque output for a given electrical loading and cooling capability. The two main constraints on rotor volume are mechanical, characterized by stresses in the rotor and resonant speeds of the rotor assembly. The level of mechanical stresses sustained in rotors increases with their radius and speed and, as this is pushed higher, previously minor effects become important in rotor design. This paper describes an observed shear stress concentration in sleeved permanent magnet rotors, caused by the Poisson effect, which can lead to magnet cracking and rotor failure. A simple analytical prediction of the peak shear stress is presented and methods for mitigating it are recommended.
STRING v10: protein–protein interaction networks, integrated over the tree of life

The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein-protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein-protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks.
MobDBTest: A machine learning based system for predicting diabetes risk using mobile devices

Diabetes mellitus (DM) is reaching possibly epidemic proportions in India. The degree of disease and destruction due to diabetes and its potential complications are enormous, and originated a significant health care burden on both households and society. The concerning factor is that diabetes is now being proven to be linked with a number of complications and to be occurring at a comparatively younger age in the country. In India, the migration of people from rural to urban areas and corresponding modification in lifestyle are all moving the degree of diabetes. Deficiency of knowledge about diabetes causes untimely death among the population at large. Therefore, acquiring a proficiency that should spread awareness about diabetes may affect the people in India. In this work, a mobile/android application based solution to overcome the deficiency of awareness about diabetes has been shown. The application uses novel machine learning techniques to predict diabetes levels for the users. At the same time, the system also provides knowledge about diabetes and some suggestions on the disease. A comparative analysis of four machine learning (ML) algorithms were performed. The Decision Tree (DT) classifier outperforms amongst the 4 ML algorithms. Hence, DT classifier is used to design the machinery for the mobile application for diabetes prediction using real world dataset collected from a reputed hospital in the Chhattisgarh state of India.