nils-herrmann/clean_scirepeval huggingface dataset:
Achieving Flexible and Self-Contained Data Protection in Cloud Computing

For enterprise systems running on public clouds in which the servers are outside the control domain of the enterprise, access control that was traditionally executed by reference monitors deployed on the system servers can no longer be trusted. Hence, a self-contained security scheme is regarded as an effective way for protecting outsourced data. However, building such a scheme that can implement the access control policy of the enterprise has become an important challenge. In this paper, we propose a self-contained data protection mechanism called RBAC-CPABE by integrating role-based access control (RBAC), which is widely employed in enterprise systems, with the ciphertext-policy attribute-based encryption (CP-ABE). First, we present a data-centric RBAC (DC-RBAC) model that supports the specification of fine-grained access policy for each data object to enhance RBAC’s access control capabilities. Then, we fuse DC-RBAC and CP-ABE by expressing DC-RBAC policies with the CP-ABE access tree and encrypt data using CP-ABE. Because CP-ABE enforces both access control and decryption, access authorization can be achieved by the data itself. A security analysis and experimental results indicate that RBAC-CPABE maintains the security and efficiency properties of the CP-ABE scheme on which it is based, but substantially improves the access control capability. Finally, we present an implemented framework for RBAC-CPABE to protect privacy and enforce access control for data stored in the cloud.
Dataflow design for optimal incremental SVM training

This paper proposes a new parallel architecture for incremental training of a Support Vector Machine (SVM), which produces an optimal solution based on manipulating the Karush-Kuhn-Tucker (KKT) conditions. Compared to batch training methods, our approach avoids re-training from scratch when training dataset changes. The proposed architecture is the first to adopt an efficient dataflow organisation. The main novelty is a parametric description of the parallel dataflow architecture, which deploys customisable arithmetic units for dense linear algebraic operations involved in updating the KKT conditions. The proposed architecture targets on-line SVM training applications. Experimental evaluation with real world financial data shows that our architecture implemented on Stratix-V FPGA achieved significant speedup against LIBSVM on Core i7-4770 CPU.
An Improved Harris-Affine Invariant Interest Point Detector

Interest point detection is essential process for many computer vision applications, which must provide invariant points to several image variations, such as, rotation, zoom, blur, illumination variation and change of viewpoints. Harris-Affine detector is considered as one of the most effective interest point detectors, although it still presents vulnerability to some image. This paper proposes an improved Harris-affine interest point detector based on two-dimensional weight Atomic functions to improve the repeatability and stability of detected points of Harris-Affine detector, in which the Gaussian kernel is replaced by the ) (x up Atomic Function. Evaluation results show that the new image interest point detector, called Atomic Harris-Affine detector, improves the repeatability in about 40% compared with the conventional Harris-Affine detector, under several conditions such as blurring, illumination changes, change of viewpoint, as well as other geometrical and affine transformations.  interest points detector; Atomic function; Harris-Affine detector; repeatability; convergence rate
Efficacy of Texture, Shape, and Intensity Feature Fusion for Posterior-Fossa Tumor Segmentation in MRI

Our previous works suggest that fractal texture feature is useful to detect pediatric brain tumor in multimodal MRI. In this study, we systematically investigate efficacy of using several different image features such as intensity, fractal texture, and level-set shape in segmentation of posterior-fossa (PF) tumor for pediatric patients. We explore effectiveness of using four different feature selection and three different segmentation techniques, respectively, to discriminate tumor regions from normal tissue in multimodal brain MRI. We further study the selective fusion of these features for improved PF tumor segmentation. Our result suggests that Kullback-Leibler divergence measure for feature ranking and selection and the expectation maximization algorithm for feature fusion and tumor segmentation offer the best results for the patient data in this study. We show that for T1 and fluid attenuation inversion recovery (FLAIR) MRI modalities, the best PF tumor segmentation is obtained using the texture feature such as multifractional Brownian motion (mBm) while that for T2 MRI is obtained by fusing level-set shape with intensity features. In multimodality fused MRI (T1, T2, and FLAIR), mBm feature offers the best PF tumor segmentation performance. We use different similarity metrics to evaluate quality and robustness of these selected features for PF tumor segmentation in MRI for ten pediatric patients.
Back to the Future: The Story of Squeak - A Usable Smalltalk Written in Itself

To achieve practical performance, a translator produces an equivalent C program whose performance is comparable to commercial Smalltalks.Other noteworthy aspects of Squeak include: a compact object format that typically requires only a single word of overhead per object; a simple yet efficient incremental garbage collector for 32-bit direct pointers; efficient bulk-mutation of objects; extensions of BitBlt to handle color of any depth and anti-aliased image rotation and scaling; and real-time sound and music synthesis written entirely in Smalltalk.
A system of portable ECG monitoring based on Bluetooth mobile phone

Aiming at diagnosing and preventing the cardiovascular disease, a portable ECG monitoring system based on Bluetooth mobile phones is presented. The system consists of some novel dry skin electrodes, an ECG monitoring circuit and a smart phone. The weak ECG signals extracted from the dry electrode can be amplified, band-pass filtered, analog-digital converted and so on. Finally it is sent to the mobile phone by Bluetooth technology for real-time display on screen. The core ECG monitoring circuit is composed of a CMOS preamplifier ASIC designed by ourselves, a band-pass filter, a microcontroller and a Bluetooth module. The volume is 5.5 cm × 3.4 cm × 1.6 cm, weight is only 20.76 g (without batteries), and power consumption is 115 mW. The tests show that the system can operate steadily, precisely and display the ECG in real time.
Enforcing security, safety and privacy for the Internet of Things

The connecting of physical units, such as thermostats, medical devices and self-driving vehicles, to the Internet is happening very quickly and will most likely continue to increase exponentially for some time to come. Valid concerns about security, safety and privacy do not appear to be hampering this rapid growth of the so-called Internet of Things (IoT). There have been many popular and technical publications by those in software engineering, cyber security and systems safety describing issues and proposing various “fixes.” In simple terms, they address the “why” and the “what” of IoT security, safety and privacy, but not the “how.” There are many cultural and economic reasons why security and privacy concerns are relegated to lower priorities. Also, when many systems are interconnected, the overall security, safety and privacy of the resulting systems of systems generally have not been fully considered and addressed. In order to arrive at an effective enforcement regime, we will examine the costs of implementing suitable security, safety and privacy and the economic consequences of failing to do so. We evaluated current business, professional and government structures and practices for achieving better IoT security, safety and privacy, and found them lacking. Consequently, we proposed a structure for ensuring that appropriate security, safety and privacy are built into systems from the outset. Within such a structure, enforcement can be achieved by incentives on one hand and penalties on the other. Determining the structures and rules necessary to optimize the mix of penalties and incentives is a major goal of this paper.
Communication complexity of document exchange

We address the problem of minimizing the communication involved in the exchange of similar documents. We consider two users, A and B, who hold documents x and y respectively. Neither of the users has any information about the other’s document. They exchange messages so that B computes x; it may be required that A compute y as well. Our goal is to design communication protocols with the main objective of minimizing the total number of bits they exchange; other objectives are minimizing the number of rounds and the complexity of internal computations. An important notion which determines the efficiency of the protocols is how one measures the distance between x and y. We consider several metrics for measuring this distance, namely the Hamming metric, the Levenshtein metric (edit distance), and a new LZ metric, which is introduced in this paper. We show how to estimate the distance between x and y using a single message of logarithmic size. For each metric, we present the first communication-efficient protocols, which often match the corresponding lower bounds. In consequence, we obtain error-correcting codes for these error models which correct up to d errors in n characters using O(d·polylog(n)) bits. Our most interesting methods use a new transformation from LZ distance to Hamming distance.
GeoDesc: Learning Local Descriptors by Integrating Geometry Constraints

Learned local descriptors based on Convolutional Neural Networks (CNNs) have achieved significant improvements on patch-based benchmarks, whereas not having demonstrated strong generalization ability on recent benchmarks of image-based 3D reconstruction. In this paper, we mitigate this limitation by proposing a novel local descriptor learning approach that integrates geometry constraints from multi-view reconstructions, which benefits the learning process in terms of data generation, data sampling and loss computation. We refer to the proposed descriptor as GeoDesc, and demonstrate its superior performance on various large-scale benchmarks, and in particular show its great success on challenging reconstruction tasks. Moreover, we provide guidelines towards practical integration of learned descriptors in Structurefrom-Motion (SfM) pipelines, showing the good trade-off that GeoDesc delivers to 3D reconstruction tasks between accuracy and efficiency.
Design and Characterization of a Soft Multi-Axis Force Sensor Using Embedded Microfluidic Channels

Thin, highly compliant sensing skins could provide valuable information for a host of grasping and locomotion tasks with minimal impact on the host system. We describe the design, fabrication, and characterization of a novel soft multi-axis force sensor made of highly deformable materials. The sensor is capable of measuring normal and in-plane shear forces. This soft sensor is composed of an elastomer (modulus: 69 kPa) with embedded microchannels filled with a conductive liquid. Depending on the magnitude and the direction of an applied force, all or part of the microchannels will be compressed, changing their electrical resistance. The two designs presented in this paper differ in their flexibility and channel configurations. The channel dimensions are approximately 200 × 200 μm and 300 × 700 μm for the two prototypes, respectively. The overall size of each sensor is 50 × 60 × 7 mm. The first prototype demonstrated force sensitivities along the two principal in-plane axes of 37.0 and -28.6 mV/N. The second prototype demonstrated the capability to detecting and differentiating normal and in-plane forces. In addition, this paper presents the results of a parameter study for different design configurations.
Voice Content Matching System for Quran Readers

In Islamic religion, mistakes in recitation of holy Quran (the sacred book of Muslims) are forbidden. Mistakes can be missing words, verse, misreading Harakat (pronunciations, punctuations, and accents). Thus, a hafiz/reciter who memorizes the holy Quran, needs other hafiz/tutor who listens the recitation and points oral mistakes. Due to the seriously commitment, the availability and expertise of a hafiz are also questionable. A listener can also make mistakes while hearing imputable to environmental interruptions like noise, attention. In order to tackle this issue, we designed, developed, and tested the E-hafiz system. E-hafiz is based on Mel-Frequency Cepstral Coefficient (MFCC) technique to extract voice features from Quranic verse recitation and maps them with the data collected during the training phase. Any mismatch mistake is pointed out. Testing results of short verses of Quran using the E-hafiz system are very encouraging.
"Accessibility Came by Accident": Use of Voice-Controlled Intelligent Personal Assistants by People with Disabilities

From an accessibility perspective, voice-controlled, home-based intelligent personal assistants (IPAs) have the potential to greatly expand speech interaction beyond dictation and screen reader output. To examine the accessibility of off-the-shelf IPAs (e.g., Amazon Echo) and to understand how users with disabilities are making use of these devices, we conducted two exploratory studies. The first, broader study is a content analysis of 346 Amazon Echo reviews that include users with disabilities, while the second study more specifically focuses on users with visual impairments, through interviews with 16 current users of home-based IPAs. Findings show that, although some accessibility challenges exist, users with a range of disabilities are using the Amazon Echo, including for unexpected cases such as speech therapy and support for caregivers. Richer voice-based applications and solutions to support discoverability would be particularly useful to users with visual impairments. These findings should inform future work on accessible voice-based IPAs.
Representational Learning with Extreme Learning Machine for Big Data Liyanaarachchi

Restricted Boltzmann Machines (RBM) and auto encoders, learns to represent features in a dataset meaningfully and used as the basic building blocks to create deep networks. This paper introduces Extreme Learning Machine based Auto Encoder (ELM-AE), which learns feature representations using singular values and is used as the basic building block for Multi Layer Extreme Learning Machine (ML-ELM). ML-ELM performance is better than auto encoders based deep networks and Deep Belief Networks (DBN), while in par with Deep Boltzmann Machines (DBM) for MNIST dataset. However MLELM is significantly faster than any state−of−the−art deep networks.
The Pirogoff amputation for necrosis of the forefoot: a case report.

T he Pirogoff amputation, removal of the forefoot and talus followed by calcaneotibial arthrodesis, produces a lower extremity with a minimum loss of length that is capable of bearing full weight. Although the technique itself is not new, patients who have already undergone amputation of the contralateral leg may benefit particularly from this littleused amputation. Painless weight-bearing is essential for the patient who needs to retain the ability to make indoor transfers independently of helpers or a prosthesis. As the number of patients with peripheral vascular disease continues to increase, this amputation should be in the armamentarium of the treating orthopaedic surgeon. Our primary indication for a Pirogoff amputation is a forefoot lesion that is too extensive for reconstruction or nonoperative treatment because of gangrene or infection, as occurs in patients with diabetes or arteriosclerosis. Other causes, such as trauma, malignancy, osteomyelitis, congenital abnormalities, and rare cases of frostbite, are also considered. To enhance the success rate, we only perform surgery if four criteria are met: (1) the blood supply to the soft tissues and the calcaneal region should support healing, (2) there should be no osteomyelitis of the distal part of the tibia or the calcaneus, (3) the heel pad should be clinically viable and painless, and (4) the patient should be able to walk with two prostheses after rehabilitation. Warren mentioned uncontrolled diabetes mellitus, severe Charcot arthropathy of the foot, and smoking as relative contraindications. There are other amputation options. In developed countries, the most common indication for transtibial amputation is arteriosclerosis (>90%). Although the results of revascularization operations and interventional radiology are promising, amputation remains the only option for 40% of all patients with severe ischemia. Various types of amputation of the lower extremity have been described. The advantages and disadvantages have to be considered and discussed with the patient. For the Syme ankle disarticulation, amputation is performed at the level of the talocrural joint and the plantar fat pad is dissected from the calcaneus and is preserved. Woundhealing and proprioception are good, but patients have an inconvenient leg-length discrepancy and in some cases the heel is not pain-free on weight-bearing. Prosthetic fitting can be difficult because of a bulbous distal end or shift of the plantar fat pad. However, the latter complication can be prevented in most cases by anchoring the heel pad to the distal aspect of
Children's trust in previously inaccurate informants who were well or poorly informed: when past errors can be excused.

Past research demonstrates that children learn from a previously accurate speaker rather than from a previously inaccurate one. This study shows that children do not necessarily treat a previously inaccurate speaker as unreliable. Rather, they appropriately excuse past inaccuracy arising from the speaker's limited information access. Children (N= 67) aged 3, 4, and 5 years aimed to identify a hidden toy in collaboration with a puppet as informant. When the puppet had previously been inaccurate despite having full information, children tended to ignore what they were told and guess for themselves: They treated the puppet as unreliable in the longer term. However, children more frequently believed a currently well-informed puppet whose past inaccuracies arose legitimately from inadequate information access.
Developing Adaptive Business Intelligence Systems for Agile Supply Chain Analytics

Agile supply chain analytics is about ability to adapt business intelligence (BI) systems to meet the changing needs of the supply network and its environment. Agile BI incorporates both technical and process-oriented approach to development, management, and delivery of BI solutions. To achieve agile BI, supply chains must provide and establish the necessary infrastructure, architecture, tools and processes in a way that enables them to deliver BI systems capable of quickly and effectively adapting to the rapidly changing business needs and environment factors. However, most supply chains still lack methods, processes and tools to successfully design and implement these systems. The paper first provides an analysis of current situation and a concise background research, followed by the introduction of the integrated and adaptive supply chain business intelligence model. This model enables design of pervasive analytical systems for collaborative decision-making, monitoring and management of the supply network. The main components of the model are described, as well as the architecture and features of the specialized supply chain intelligence web portal which demonstrates usefulness and applicability of the proposed model. This system allows creation of a new breed of flexible and agile BI systems which ultimately results in more efficient, responsive and adaptive supply chains.
Sparse coding based visual tracking: Review and experimental comparison

Recently, sparse coding has been successfully applied in visual tracking. The goal of this paper is to review the state-of-the-art tracking methods based on sparse coding. We first analyze the benefits of using sparse coding in visual tracking and then categorize these methods into appearance modeling based on sparse coding (AMSC) and target searching based on sparse representation (TSSR) as well as their combination. For each categorization, we introduce the basic framework and subsequent improvements with emphasis on their advantages and disadvantages. Finally, we conduct extensive experiments to compare the representative methods on a total of 20 test sequences. The experimental results indicate that: (1) AMSC methods significantly outperform TSSR methods. (2) For AMSC methods, both discriminative dictionary and spatial order reserved pooling operators are important for achieving high tracking accuracy. (3) For TSSR methods, the widely used identity pixel basis will degrade the performance when the target or candidate images are not aligned well or severe occlusion occurs. (4) For TSSR methods, ‘1 norm minimization is not necessary. In contrast, ‘2 norm minimization can obtain comparable performance but with lower computational cost. The open questions and future research topics are also discussed. & 2012 Elsevier Ltd. All rights reserved.
Automatic Generation of Test Cases

muchine, " a progrum f o r uutomuticully generating syntacticully correct progrums (test cusrs> f o r checking compiler front ends. The notion of " clynumic grammur " is introduced und is used in a syntax-defining notution thut procides f o r context-sensitiuity. Exurnples demonstrute use of the syntax machine. The " syntax machine " discussed here automatically generates random test cases for any suitably defined programming language.' The test cases it produces are syntactically valid programs. But they are not " meaningful, " and if an attempt is made to execute them, the results are unpredictable and uncheckable. For this reason, they are less valuable than handwritten test cases. However, as an inexhaustible source of new test material, the syntax machine has shown itself to be a valuable tool. In the following sections, we characterize the use of this tool in testing different types of language processors, introduce the concept of " dynamic grammar " of a programming language, outline the structure of the system, and show what the syntax machine does by means of some examples. Test cases Test cases for a language processor are programs written following the rules of the language, as documented. The test cases, when processed, should give known results. If this does not happen, then either the processor or its documentation is in error. We can distinguish three categories of language processors and assess the usefulness of the syntax machine for testing them. For an interpreter, the syntax machine test cases are virtually useless,
A Survey of Semantic Image and Video Annotation Tools

The availability of semantically annotated image and video assets constitutes a critical prerequisite for the realisation of intelligent knowledge management services pertaining to realistic user needs. Given the extend of the challenges involved in the automatic extraction of such descriptions, manually created metadata play a significant role, further strengthened by their deployment in training and evaluation tasks related to the automatic extraction of content descriptions. The different views taken by the two main approaches towards semantic content description, namely the Semantic Web and MPEG-7, as well as the traits particular to multimedia content due to the multiplicity of information levels involved, have resulted in a variety of image and video annotation tools, adopting varying description aspects. Aiming to provide a common framework of reference and furthermore to highlight open issues, especially with respect to the coverage and the interoperability of the produced metadata, in this chapter we present an overview of the state of the art in image and video annotation tools.