Reviewerly/exHarmony huggingface dataset:
Quantum Resource Counts for Operations Constructed from an Addition Circuit

An adversary armed with a quantum computer has algorithms at their disposal, which are capable of breaking our current methods of encryption. Even with the birth of post-quantum cryptography, some of best cryptanalytic algorithms are still quantum. Lattice reduction algorithms require several arithmetic operations such as addition, multiplication, and division. Resource counts of quantum circuits determine the practicality of simulating an operation on a quantum computer. Each of the previous operations are constructed from a single addition circuit designed by Takahashi et al. and implemented in the open-source software framework for quantum computing, ProjectQ. We also include a novel approach to designing a circuit for dot product which can be used in Gram-Schmidt orthogonalization, an algorithm often found in lattice reduction. Tiepelt and Szenpieniec provide a quantum circuit for the Gram-Schmidt orthogonalization algorithm. We design modifications to their circuit to better suit the implemented arithmetic operation circuits. Resource estimations are then given for small-dimensional cases.
Self-centralized jointly sparse maximum margin criterion for robust dimensionality reduction

Linear discriminant analysis (LDA) is among the most popular supervised dimensionality reduction algorithms, which has been largely followed in the fields of pattern recognition and data mining. However, LDA has three major drawbacks. One is the challenge brought by small-sample-size (SSS) problem; second makes it sensitive to outliers due to the use of squared L2-norms in the scatter loss evaluation; the third is the case that the feature loadings in projection matrix are relatively redundant and there is a risk of overfitting. In this paper, we put forward a novel functional expression for LDA, which combines maximum margin criterion (MMC) with a weighted strategy formulated by L1,2-norms to against outliers. Meanwhile, we simultaneously realize the adaptive calculation of weighted intra-class and global centroid to further reduce the influence of outliers, and employ the L2,1-norm to constrain row sparsity so that subspace learning and feature selection could be performed cooperatively. Besides, an effective alternating iterative algorithm is derived and its convergence is verified. From the complexity analysis, our proposed algorithm can deal with large-scale data processing. Our proposed model can address the sensitivity problem of outliers and extract the most representative features while preventing overfitting effectively. Experiments performed on several benchmark databases demonstrate that the proposed algorithm is more effective than some other state-of-the-art methods and has better generalization performance.
Analysis of brain MRI images for Tumor segmentation using Fuzzy C means Algorithm

In general, the amount of information present in biomedical images is more and it is a very challenging task to handle these kinds of images in wireless communication. Image retrieval is one of the most emerging technologies for telemedicine to handle the medical data of images (MRI and CT). Brain tumor segmentation is that the vital method to portrait the beginning level of tumor. Magnifying the tumor is being an enormous challenge due to the complex characteristics of the MRI Images which provides high intensive, divergent and uncertain boundaries. In this paper, the tumor present in the brain MRI is segmented using a new formulation technique of FCM (fuzzy C means algorithm) called PIGFCM algorithm has been introduced, followed by an image de-noising technique, which is an inevitable pre-processing step in image processing. Image de-noising technique is employed using PSNLM filter (i.e. Pre-Smooth Non-Local Means filter), which is used for denoising the Rician noise (noise present in the image, which is difficult to remove), and the fuzzy algorithm used in the proposed method is called as the PIGFCM algorithm, which is a reformulation of the FCM (fuzzy C means algorithm), which incorporates good quality in the segmentation process. PIGFCM algorithm utilizes the prior information of the tumor classes. The proposed technique has better de-noising results, substantial superior segmentation accuracy and good speed. The PSNR and the NMSE of the results are also calculated.
Channel based Threshold Segmentation of Multi-Class Cervical Cancer using Mean and Standard Deviation on Pap Smear Images

Cervical cancer is the second most dangerous metastatic tumor where grows in a woman's cervix. Pap smear is the simplest screening test for the detection of cancer at the starting stage. Cervical cancer has two types of Normality and abnormality cancer which contains the cell and cytoplasm in the same structure. It is difficult to identify a cancerous nucleus in the cell. In Image processing, various algorithms are used to segment the nucleus alone in microscopic images. The primary scope of this paper is focusing on Pre-processing, Segmentation and Feature Extraction with six levels of Pap images. The performance evaluation has been calculated based on the segmentation results. In the first phase, pre-processing used mean filters to remove noise and enhanced with CLAHE. In the second phase, Segmentation used threshold value by taking the sum of three channels with the proposed methodology in mean and standard deviation. In the third phase, Properties of image regions, Shapes, Textures and some statistical features are extracted after segmentation. To evaluate performance measure used SSIM for each type of cancerous segmentation that is compared with K-means and Fuzzy C-means algorithm. Thus, the proposed work of separation and addition of RGB channel based segmentation gives the best results for nucleus segmentation in Pap smear images. The accuracy level 94.60 % has been obtained by using SVM and KNN classification for 182 Pap smear images with a class of six labels. Matlab R2016a is used as a programming tool.
Variable-Radix Coding of the Reals

Recently proposed real number systems like Posits and Elias codes make use of tapered accuracy resulting from variable-length coding of exponents and significands. Several quite different interpretations of these number systems have been provided, though most often these rely on some combination of fixed- and variable-length codes for exponent and significand. We provide a new perspective on these number systems that unifies known representations while suggesting new ones. Our framework is based on multibit radix representations that encode the exponent in unary, the leading nonzero digit in a variablelength code, and the remaining digits in fixed-length binary code. We show how Posits, the various Elias codes, and ieee 754 like representations can be expressed in this framework. Moreover, we show that Posits and the Elias γ and δ codes represent the leading digit using the canonical Huffman code for a probability distribution given by Benford's law, which governs the probability of leading digits. We further show that Posits correspond to the use of a fixed radix while Elias δ and ω codes are based on simple sequences of increasing radix. Our approach provides for an intuitive and uniform framework for representing numbers that reveals a visual mapping between codewords and the binary representation of real numbers obscured by prior frameworks. This new interpretation suggests a generalization of Posits and other number systems and provides simple rules for designing information-theoretically optimal codes.
Adaptive Filtering of 4-D Light Field Images for Depth-Based Image Enhancement

A novel method is described for adaptive filtering of light fields to enhance objects at a given depth. Using the frequency domain of an epipolar-plane image (EPI) to select the minimum and maximum depths of an object of interest (OOI) allows greater selectivity over traditional methods and the ability to re-focus a light field as the scene changes. This method is executed on real light fields, where depth information is extracted and used for depth filtering. A light field video is used to show that as an OOI moves to varying depths in a scene, the performance of a fixed-depth filter decreases when compared to an adaptive-depth filter. This method is shown to be robust in an environment where an OOI is moving to different depths in relation to the camera, and has implications in tasks where objects must be identified by their depth, such as in robotics or autonomous vehicles.
A Self Adaptive FCM Cluster Forests Based Feature Selection

Ensemble clustering refers to combine many clustering methods to produce better results. In this context, we propose a new clustering ensemble method inspired from cluster forests (CF) based Self-Adaptive Fuzzy C-Means (SAFCM) method. Firstly, unsupervised feature selection methodology based on the building of best variables on simulated datasets. Next, we ameliorate the CF algorithm with the integration of SAFCM to find also the best number of K groups. Finally, the modified version normalized cuts spectral clustering (Ncut) is applied to general grouping. The proposed algorithm was tested on datasets from UCI Machine Learning Repository. The experimental results indicate that our proposed method outperforms both different clustering algorithms in terms of clustering quality.
Vanishing region loss for crowd density estimation

Crowd density estimation is a crucial component in surveillance systems to construct safe and efficient urban environments. Due to perspective distortion, individuals in crowd scenes diminish in size as they converge toward the vanishing point. Hence, there are significant visual variations in individuals’ size and appearance, which may lead to inaccurate estimations of crowd counts. This paper proposes an intuitive and effective loss function for the error estimation of crowd counts, particularly in vanishing crowd regions. Specifically, estimation errors in vanishing crowd regions are used to refine and generate network filters that are adaptive toward perspective distortion during network training. Extensive experiments on the challenging UCF-QNRF, WorldExpo, and ShanghaiTech benchmark datasets demonstrate the effectiveness of our novel loss function for training a network to achieve accurate crowd density estimation, particularly in the presence of perspective distortion.
Stable relations and abstract interpretation of higher-order programs

We present a novel denotational semantics for the untyped call-by-value λ-calculus, where terms are interpreted as stable relations , i.e. as binary relations between substitutions and values, enjoying a monotonicity property. The denotation captures the input-output behaviour of higher-order programs, and is proved sound and complete with respect to the operational semantics. The definition also admits a presentation as a program logic. Following the principles of abstract interpretation, we use our denotational semantics as a collecting semantics to derive a modular relational analysis for higher-order programs. The analysis infers equalities between the arguments of a program and its result—a form of frame condition for functional programs.
Estimating the privacy of quantum-random numbers

We analyze the information an attacker can obtain on the numbers generated by a user by measurements on a subsystem of a system consisting of two entangled two-level systems. The attacker and the user make measurements on their respective subsystems, only. Already the knowledge of the density matrix of the subsystem of the user completely determines the upper bound on the information accessible to the attacker. We compare and contrast this information to the appropriate bounds provided by quantum state discrimination.
AST[AR] – Towards Using Augmented Reality and Abstract Syntax Trees for Teaching Data Structures To Novice Programmers

Augmented Reality (AR) is one of the emerging technologies which helps in enhancing user experience by providing a composite view of the real-world objects. It supports the user with superimposed information on top of the object, thus creating a live experience. Besides, coding is pervasive in today's world, and there is a strong need for well-equipped developers with thorough theoretical and practical knowledge. Data Structures and Abstract Syntax Trees are two of the fundamental topics of the computer science domain. Data structures help in the organization of data, whereas, Abstract Syntax Trees (ASTs) enable better comprehension of source code by syntactically analyzing it. In this paper, we propose an augmented reality-based software application AST[AR] to teach data structures with the help of ASTs. We conducted a user experience study with 30 volunteers and got positive feedback from 80.6% of the participants. We believe that this software application will help in bifold learning of data structures as well as ASTs with exciting and enjoyable user experience.
Compact Interchannel Sampling Difference Descriptor for Color Texture Classification

Many representation methods were built for gray image textures. However, they are not effective for color textures in general. To alleviate this problem, in this paper we propose a novel Compact Interchannel Sampling Difference Descriptor (CISDD) for color texture classification. In particular, considering sampling-based method can capture more directional information, we first use a heavy-tailed distribution, t-distribution to generate sample points in the image patch to calculate the micro-block difference. Then we model the interchannel relationship of color texture image by using dense micro-block differences. Furthermore, we utilize principal component analysis (PCA) to reduce the dimensions of the features encoded by the Fisher vector, and construct a Compact Interchannel Sampling Difference Descriptor (CISDD) for representing color texture image. Finally, experimental results on five published standard texture datasets (KTH-TIPS, VisTex, CUReT, USPTex and Colored Brodatz) reveal that CISDD is effective and outperforms thirteen representative color texture classification methods.
An Energy-Efficient Accelerator Architecture with Serial Accumulation Dataflow for Deep CNNs

Convolutional Neural Networks (CNNs) have shown outstanding accuracy for many vision tasks during recent years. When deploying CNNs on portable devices and embedded systems, however, the large number of parameters and computations result in long processing time and low battery life. An important factor in designing CNN hardware accelerators is to efficiently map the convolution computation onto hardware resources. In addition, to save battery life and reduce energy consumption, it is essential to reduce the number of DRAM accesses since DRAM consumes orders of magnitude more energy compared to other operations in hardware. In this paper, we propose an energy-efficient architecture which maximally utilizes its computational units for convolution operations while requiring a low number of DRAM accesses. The implementation results show that the proposed architecture performs one image recognition task using the VGGNet model with a latency of 393 ms and only 251.5 MB of DRAM accesses.
Versatile Multipurpose Crashproof UAV: Machine Learning and IoT approach

Technological advancements in the drone sector posed an arduous challenge of enhancing the collisiontolerance competence of an Unmanned Aerial Vehicle(UAV). This paper presents the design and system integration of an IoT-enabled UAV which comprises a 3D designed and printed spherical frame wound across the UAV consisting of a high definition camera that demonstrates processing of the video feed captured using machine learning algorithm. A truncated icosahedron shaped protective frame is designed such that it can bounce and roll in the near proximity of objects as well as humans, thereby proving to be crash-resistant. Hence, it offers close scrutinization, surveying and inspection of various structures and analyses them using a machine learning model. Another novel feature of this UAV is the sensor module composed of various detachable sensors used for applicationspecific purposes like gas-leakage detection, air-quality monitoring, temperature, humidity etc. in confined and complex environments. These features of the UAV, on collaborating with various indoor and outdoor applications contribute towards the versatility of this drone. The UAV is integrated with LoRa modules and is used for seamless connectivity and networking over astonishingly great distances. The final prototype of this design was successfully flight tested numerous times and was found to be efficient, robust and stable.
Adaptive Ciphertext Policy Attribute Based Encryption Scheme for Internet of Things Devices Using Decision Tree

The Internet of Things (IoT) has recently become a hot spot for researchers and its industrial importance is growing exponentially day after day.Statistics show that the number of IoT devices will reach fifty billion by 2020.IoT applications are backed through clouds where data is stored and processed by gigantic processing systems and accessed only by authorized users.In the present work, we outsource the encryption and decryption of ABE operations to proxy servers in order to reduce the computation overhead of encryption and decryption.However, in some cases performing full encryption is more worthy than partial encryption if IoT resources are fit to perform this encryption thus, our scheme allows to adaptively switch from partial encryption to full encryption based on the resources that available and the number of attributes in access tree.The decision maker component (manager) uses decision tree to select the appropriate scheme based on the context at encryption time.Finally, we evaluate the performance of our scheme with other scheme proposed for constrained devices.
Towards Team Formation in Software Development: A Case Study of Moodle

Software development is a team-based intensive activity where various skills (e.g. technical and analysis skills) are required to deliver high quality outcomes. An effective team member assignment is thus a crucial process. In this paper, we propose to adopt the existing machine learning approach for team recommendation to recommend software team members who are suitable for a given task. The approach take both individual strength and collaborative efficiency among team members into account to give a recommendation. We evaluate the approach on the Moodle project, well-known open source software project. The evaluation results show that the adopted approach yields a better recommendation performance compared to the baseline (i.e. random assignment approach).
Transparent IFC Enforcement: Possibility and (In)Efficiency Results

Information Flow Control (IFC) is a collection of techniques for ensuring a no-write-down no-read-up style security policy known as noninterference. Traditional methods for both static (e.g. type systems) and dynamic (e.g. runtime monitors) IFC suffer from untenable numbers of false alarms on real-world programs. Secure Multi-Execution (SME) promises to provide secure information flow control without modifying the behaviour of already secure programs, a property commonly referred to as transparency. Implementations of SME exist for the web in the form of the FlowFox browser and as plug-ins to several programming languages. Furthermore, SME can in theory work in a black-box manner, meaning that it can be programming language agnostic, making it perfect for securing legacy or third-party systems. As such SME, and its variants like Multiple Facets (MF) and Faceted Secure Multi-Execution (FSME), appear to be a family of panaceas for the security engineer. The question is, how come, given all these advantages, that these techniques are not ubiquitous in practice? The answer lies, partially, in the issue of runtime and memory overhead. SME and its variants are prohibitively expensive to deploy in many non-trivial situations. The natural question is why is this the case? On the surface, the reason is simple. The techniques in the SME family all rely on the idea of multi-execution, running all or parts of a program multiple times to achieve noninterference. Naturally, this causes some overhead. However, the predominant thinking in the IFC community has been that these overheads can be overcome. In this paper we argue that there are fundamental reasons to expect this not to be the case and prove two key theorems: (1) All transparent enforcement is polynomial time equivalent to multi-execution. (2) All black-box enforcement takes time exponential in the number of principals in the security lattice. Our methods also allow us to answer, in the affirmative, an open question about the possibility of secure and transparent enforcement of a security condition known as Termination Insensitive Noninterference.
Fault Tolerance in Cloud Computing- An Algorithmic Approach

Cloud Computing can be deliberated one of the revolutionary technology in the digital world in recent days. Cloud Computing has begun a new revolution not only technically but also economically in the computer based world. Employing cloud computing facilities has plentiful benefits such as the lessening of costs, enlargement of efficiency, central promotion of soft wares, limitless storage capacity, relaxed access and the independency of mentioned services from the hardware. But there are some problems and challenges in cloud computing. Fault tolerance can be mentioned as the one of the main restrictions. It becomes a critical requirement for attaining high performance in cloud computing. In this paper, various type of techniques of generating the capacity of Fault Tolerance has been pointed out. Subsequently, abstraction and algorithms of them are stated. The procedures of handling failure at any level has been described with flowchart in this paper. The fault tolerance architecture has been compared in terms of the type of the practical policies applied in the cloud computing system architecture, fault detection and fault recovery.
An AR-Based Case Study of Using Textual and Collaborative Scaffolding for Students with Different Self-Efficacy to Learn Lever Principles

With the deepening application of augmented reality (AR) in education, researches pay more attention to the evaluation of learning results rather than consider how to use teaching strategies to support personalized teaching. This study developed an AR science curriculum incorporating textual and collaborative scaffolding, and taught it in elementary school science classes to help students learn about scientific knowledge of lever principle. To explore the effects of different self-efficacy and scaffolding teaching strategies on students' learning conceptions and cognitive load, a two times two experiment was conducted on an elementary school. The results show that the use of textual and collaborative scaffolding in AR teaching can help students with low self-efficacy to pay attention to the conceptions of deep learning and effectively reduce the cognitive load of students.
Towards efficient interdisciplinary authoring of industrial augmented reality applications

The application of Augmented Reality (AR) supports process optimization in various areas. The beneficial characteristics are particularly evident in the industrial context, where AR can lead to an efficiency increase in the entire product life cycle. However, developing AR applications results in a challenging process, characterized by a role shift based on complex interaction modeling. We present an approach for efficient interdisciplinary authoring of industrial AR applications. Interaction Stories are introduced to enable UI designers and domain experts to create interaction concepts and participate in the development process. A concept for interdisciplinary collaboration enables the constant exchange between actors of different competence levels. The simplification of interaction modeling and the interdisciplinary collaboration are designed to assemble actors to enable an efficient iterative AR development process.