Reviewerly/exHarmony huggingface dataset:
Enabling Cloud Applications to Negotiate Multiple Resources in a Cost-Efficient Manner

Cloud applications can achieve similar performance with diverse multi-resource configurations, allowing cloud service providers to benefit from optimal resource allocation for reducing their operation cost. This paper aims to solve the problem of multi-resource negotiation with considerations of both the service-level agreement (SLA) and the cost efficiency so that the performance requirement for cloud services is satisfied and the cost of resource usage is also minimized. The performance and resource demand are usually application-dependent, making the optimization problem complicated, especially when the dimension of multi-resource configurations is large. To this end, we use reinforcement learning to solve the optimal problem of multi-resource configuration with simultaneous optimization of the learning efficiency and performance guarantee. The developed prototype named SmartYARN is an extended Apache YARN equipped with our learning algorithm which can enable cloud applications to negotiate multiple resources cost-effectively. The extensive evaluations with four typical benchmarks show that SmartYARN performs well in reducing the cost of resource usage while maintaining compliance with the SLA constraints of cloud service simultaneously.
Efficient Local Statistical Analysis via Point-Wise Histograms in Tetrahedral Meshes and Curvilinear Grids

Local histograms (i.e., point-wise histograms computed from local regions of mesh vertices) have been used in many data analysis and visualization applications. Previous methods for computing local histograms mainly work for regular or rectilinear grids only. In this paper, we develop theory and novel algorithms for computing local histograms in tetrahedral meshes and curvilinear grids. Our algorithms are theoretically sound and efficient, and work effectively and fast in practice. Our main focus is on scalar fields, but the algorithms also work for vector fields as a by-product with small, easy modifications. Our methods can benefit information theoretic and other distribution-driven analysis. The experiments demonstrate the efficacy of our new techniques, including a utility case study on tetrahedral vector field visualization.
Integer programming ensemble of temporal relations classifiers

The extraction of temporal events from text and the classification of temporal relations among both temporal events and time expressions are major challenges for the interface of data mining and natural language processing. We present an ensemble method, which reconciles the outputs of multiple heterogenous classifiers of temporal expressions. We use integer programming, a constrained optimisation technique, to improve on the best result of any individual classifier by choosing consistent temporal relations from among those recommended by multiple classifiers. Our ensemble method is conceptually simple and empirically powerful. It allows us to encode knowledge about the structure of valid temporal expressions as a set of constraints. It obtains new state-of-the-art results on two recent natural language processing challenges, SemEval-2013 TempEval-3 (Temporal Annotation) and SemEval-2016 Task 12 (Clinical TempEval), with F1 scores of 0.3915 and 0.595 respectively.
User Grouping for Sharing Services with Capacity Limit

Sharing a service among multiple users could bring benefit to users by reducing their service price and also benefit service providers by allowing them to make more profit. Shared services usually have a capacity limit. To construct a win-win situation between users and providers through service sharing, it is necessary to reasonably organize users with similar service requests into groups under the limits of group sizes. This paper explores methods to enable user grouping for sharing services with the limit on the group size. Based on user request descriptions and evaluation, a connection relation is built to present which two users could be in a group. Semi-groups can be derived from the connection relation and two grouping schemes satisfying the group size limit are further determined through proposed algorithms to meet different service expectations. Finally, a case study and a simulation are given to demonstrate the effectiveness of the proposed methods in grouping users to provide higher service benefits to both users and providers.
Two-Dimensional Block Trees

Abstract The Block Tree is a data structure for representing repetitive sequences in compressed space, which reaches space comparable with that of Lempel–Ziv compression while retaining fast direct access to any position in the sequence. In this paper, we generalize Block Trees to two dimensions, in order to exploit repetitive patterns in the representation of images, matrices and other kinds of bidimensional data. We demonstrate the practicality of the two-dimensional Block Trees (2D-BTs) in representing the adjacency matrices of Web graphs, and raster images in GIS applications. For this purpose, we integrate our 2D-BT with the $k^2$-tree—an efficient structure that exploits clustering and sparseness to compress adjacency matrices—so that it also exploits repetitive patterns. Our experiments show that this structure uses 60–80% of the space of the original $k^2$-tree, while being 30% faster to three times slower when accessing cells.
Machine Learning-Based Subjective Quality Estimation for Video Streaming Over Wireless Networks

Video streaming services top the internet traffic surging forward a competitive environment to impart best quality of experience (QoE) to the users. The standard codecs utilized in video transmission systems eliminate the spatiotemporal redundancies in order to decrease the bandwidth requirement. This may adversely affect the perceptual quality of videos. To rate a video quality both subjective and objective parameters can be used. So, it is essential to construct frameworks which will measure integrity of video just like humans. This chapter focuses on application of machine learning to evaluate the QoE without requiring human efforts with higher accuracy of 86% and 91% employing the linear and support vector regression respectively. Machine learning model is developed to forecast the subjective quality of H.264 videos obtained after streaming through wireless networks from the subjective scores.
Spatial Isolation Implies Zero Knowledge Even in a Quantum World

Zero knowledge plays a central role in cryptography and complexity. The seminal work of Ben-Or et al. (STOC 1988) shows that zero knowledge can be achieved unconditionally for any language in NEXP , as long as one is willing to make a suitable physical assumption : if the provers are spatially isolated, then they can be assumed to be playing independent strategies. Quantum mechanics, however, tells us that this assumption is unrealistic, because spatially-isolated provers could share a quantum entangled state and realize a non-local correlated strategy. The MIP * model captures this setting. In this work, we study the following question: Does spatial isolation still suffice to unconditionally achieve zero knowledge even in the presence of quantum entanglement? We answer this question in the affirmative: we prove that every language in NEXP has a 2-prover zero knowledge interactive proof that is sound against entangled provers; that is, NEXP ⊆ ZK-MIP * . Our proof consists of constructing a zero knowledge interactive probabilistically checkable proof with a strong algebraic structure, and then lifting it to the MIP * model. This lifting relies on a new framework that builds on recent advances in low-degree testing against entangled strategies, and clearly separates classical and quantum tools. Our main technical contribution is the development of new algebraic techniques for obtaining unconditional zero knowledge; this includes a zero knowledge variant of the celebrated sumcheck protocol, a key building block in many probabilistic proof systems. A core component of our sumcheck protocol is a new algebraic commitment scheme, whose analysis relies on algebraic complexity theory.
Effective Parallelization of a High-Order Graph Matching Algorithm for GPU Execution

High-order graph matching is a feature-matching algorithm that uses geometric information among features. This algorithm is more robust to repetitive patterns or unclear areas than first-order matching that uses only feature descriptors. However, the processing speed of high-order matching is very slow because of its high computational complexity. To accelerate its speed, this paper proposes a new parallelization algorithm of high-order matching for GPU execution. The obstacle for parallelization is the write collision caused by multiple threads that must simultaneously update the data at the same memory location. In high-order matching, multiple formulations of the objective function can generate the same solution. By taking advantage of this property, the proposed algorithm replaces the operation causing write collision with another operation eliminating the collision while generating the same solution. The proposed algorithm is tested with GTX 960 and takes 31.3 ms, which is 68 times faster than the execution time with a CPU and approximately three times faster than that with a straightforward parallelization for the same GPU.
Look-Aside at Your Own Risk: Privacy Implications of DNSSEC Look-Aside Validation

The Domain Name System Security Extension (DNSSEC) leverages public-key cryptography to provide data integrity, source authentication, and denial of existence for DNS responses. To complement DNSSEC operations, DNSSEC Look-aside Validation (DLV) is designed for alternative off-path validation. Although DNS privacy attracts a lot of attention, the privacy implications of DLV are not fully investigated and understood. In this paper, we take a first in-depth look into DLV, highlighting its lax specifications and privacy implications. By performing extensive experiments over datasets of domain names under comprehensive experimental settings, our findings firmly confirm the privacy leakages caused by DLV. We discover that a large number of domains that should not be sent to DLV servers are being leaked. We explore the root causes, including the lax specifications of DLV. We also propose two approaches to fix the privacy leakages. Our approaches require trivial modifications to the existing DNS standards, and we demonstrate their cost in terms of latency and communication.
Volumetric reconstruction of thermal-depth fused 3D models for occluded body posture estimation

Reliable and effective occluded skeletal posture estimation is a challenging problem for vision-based modalities that do not provide inter-surface imaging. These include both visible-light and depth-based devices that all existing pose estimation techniques are derived from. This fundamental limitation in skeletal tracking is due to the inability of these techniques to penetrate occluding surface materials to derive joint configurations that are not directly visible to the camera. In this work, we present a new method of estimating skeletal posture in occluded applications using both depth and thermal imaging through volumetric modeling and introduce a new occluded ground-truth tracking method inspired by modern motion capture solutions for tracking occluded joint positions. Using this integrated volumetric model, we utilize Convolutional Neural Networks to characterize and identify volumetric thermal distributions that match trained skeletal posture estimates which includes disconnected skeletal definitions and allows correct posture estimation in highly ambiguous cases. We demonstrate this approach by accurately identifying common sleep postures that present challenging cases for current skeletal joint estimation techniques and evaluate the use of volumetric thermal models for various sleep-study related applications.
Ecological Data Archiving and Sharing

New scientific discoveries and knowledge emerge when the existing corpus of data, information and knowledge is archived, discoverable and interpretable. Data sharing perceptions and practices have evolved over the last several decades in response to the emergence of large research programs and networks, as well as requirements from research sponsors, institutions and publishers. Five practices that facilitate effective data sharing are described herein: (1) creating and following a data management plan; (2) adhering to a reasonable data sharing and attribution license; (3) comprehensively documenting the data following community standards and best practices; (4) protecting and making available data, metadata, and algorithms and workflows via a trusted community data repository; and (5) disseminating and advertising the existence of the data. Preparing a preservation-ready data product for submission to a repository requires that the data contributor logically, consistently and clearly name and describe the data package, including the variables, files and algorithms and workflows. Furthermore, the data should be quality assured, completely and comprehensively documented, and protected throughout the research. Data repositories often have specific data organization and submission guidelines and play an important role in preserving and disseminating data that may represent a valuable scientific resource for decades to come.
Anonymous Single-Round Server-Aided Verification

Server-Aided Verification (SAV) is a method that can be employed to speed up the process of verifying signatures by letting the verifier outsource part of its computation load to a third party. Achieving fast and reliable verification under the presence of an untrusted server is an attractive goal in cloud computing and internet of things scenarios. In this paper, we describe a simple framework for SAV where the interaction between a verifier and an untrusted server happens via a single-round protocol. We propose a security model for SAV that refines existing ones and includes the new notions of SAV -anonymity and extended unforgeability. In addition, we apply our definitional framework to provide the first generic transformation from any signature scheme to a single-round SAV scheme that incorporates verifiable computation. Our compiler identifies two independent ways to achieve SAV-anonymity: computationally, through the privacy of the verifiable computation scheme, or unconditionally, through the adaptibility of the signature scheme. Finally, we define three novel instantiations of SAV schemes obtained through our compiler. Compared to previous works, our proposals are the only ones which simultaneously achieve existential unforgeability and soundness against collusion.
Characterization of some minimal codes for secret sharing

Recently, several authors used linear codes to construct secret sharing schemes. It is known that if each nonzero codeword of a code [Formula: see text] is minimal, then the dual code [Formula: see text] is suitable for secret sharing. To seek such codes Ashikhmin–Barg give a sufficient condition from weights; in [Formula: see text] code [Formula: see text], let [Formula: see text] and [Formula: see text] be the minimum and maximum nonzero weights, respectively. If [Formula: see text] then all nonzero codewords of [Formula: see text] are minimal. In this paper, a necessary and sufficient condition is given for self-dual codes and for MDS codes to verify the inequality (*). Special codes are examined and applied for secret sharing schemes.
Structure of repeated-root constacyclic codes of length 8ℓmpn

Constacyclic codes form an important class of linear codes which is remarkable generalization of cyclic and negacyclic codes. In this paper, we assume that [Formula: see text] is the finite field of order [Formula: see text] where [Formula: see text] is a power of the prime [Formula: see text] and [Formula: see text] are distinct odd primes, and [Formula: see text] are positive integers. We determine generator polynomials of all constacyclic codes of length [Formula: see text] over the finite field [Formula: see text] We also determine their dual codes.
Quantum teleportation with infinite reference-frame uncertainty

We present two new schemes for quantum teleportation between parties whose local reference frames are misaligned by the action of a compact Lie group G. These schemes require no prior alignment of reference frames and are unaffected by arbitrary changes in reference frame alignment during execution, suiting them to situations of rapid reference frame drift. Our tight scheme yields improved purity compared to standard teleportation, in some cases substantially --- this includes the case of qubit teleportation under arbitrary SU(2) reference frame uncertainty--- while communicating no information about either party's reference frame alignment at any time. Our perfect scheme performs perfect teleportation, but does communicate some reference frame information. The mathematical foundation of these schemes is a unitary error basis permuted up to a phase by the conjugation action of a finite subgroup of G.
On the Maximum Size of Block Codes Subject to a Distance Criterion

We establish a general formula for the maximum size of finite length block codes with minimum pairwise distance no less than d. The achievability argument involves an iterative construction of a set of radius-d balls, each centered at a codeword. We demonstrate that the number of such balls that cover the entire code space cannot exceed this maximum size. Our approach can be applied to codes i) with elements over arbitrary code alphabets, and ii) under a broad class of distance measures. Our formula indicates that the maximum code size can be fully characterized by the cumulative distribution function of the distance measure evaluated at two independent and identically distributed random codewords. When the two random codewords assume a uniform distribution over the entire code alphabet, our formula recovers and thus naturally generalizes the Gilbert-Varshamov (GV) lower bound. Finally, we extend our study to the asymptotic setting.
On codes over ℤp2 and its covering radius

This paper gives lower and upper bounds on the covering radius of codes over [Formula: see text] with respect to Lee distance. We also determine the covering radius of various repetition codes over [Formula: see text]
Geology and distribution of heavy metals in topsoil, Kuala Krai, Kelantan

Abstract The study for geology of Guchil, Kuala Krai, Kelantan was carried out approximately 25 km 2 in and around Guchil. Preliminary research and field studies conducted to produce geological map of Guchil, Kuala krai, Kelantan. Based on the field study carried, carbonaceous mudstone interbedded with greywacke sandstone, shale and ignimbrite distribution of rock found in this study. Meanwhile, investigation on distribution and potential heavy metals (Cu, Fe, Mn, Pb and Zn) in topsoil Guchil, Kuala Krai, Kelantan was conducted. Total of nine soil sample in and around Guchil undergo soil digestion and analyzed by Inductively Coupled Plasma Spectroscopy (ICP-OES). The heavy metals concentration in the topsoil was varied between 21176-138962; 649-8411; 11-505; 43-455; and 11-115 mg/kg for Fe, Pb, Mn, Zn and Cu respectively. The average concentration of heavy metals in this study followed the order of Fe &gt; Pb &gt; Mn &gt; Zn &gt; Cu. The spatial distribution pattern shows similar distribution pattern of heavy metals in residential area. The concentration of Cu, Fe, Mn, Pb and Zn from the soil samples compared with naturally occurring value of contaminated land management and control guidelines of Department of Environment Malaysia (DOE), 2009 and Barceloux (1999) as well as with permissible limit values in soil of Polish Ministry of the Environment, 2002. From this assessment method, highly potential element was Pb followed by Zn and Cu. Besides that soil characteristic of this study shows that soil pH which is acidic condition with low total organic matter. The soil fractions tend to dominated by clay fraction in topsoil.
Biopen–Fusing password choice and biometric interaction at presentation level

The paper presents experiments with the home-made, low-cost prototype of a sensor-equipped pen for handwriting-based biometric authentication. The pen allows to capture the dynamics of user writing on normal paper, while producing a kind of password (passphrase) chosen in advance. The use of a word of any length instead of the user's signature makes the approach more robust to spoofing, since there is no repetitive pattern to steal. Moreover, if the template gets violated, this is much less harmful than signature catch. The entailed sensors are a pair of accelerometer and gyroscope and a pressure sensor. The aim is a natural yet precise interaction, that allows recognizing the user by the signals recorded while producing a specific word chosen during enrollment and possibly changed later. The pen can be exploited in a number of applications requiring user recognition, yet relieving from the need to learn complex procedures, and to undergo critical capture operations. The approach fuses the use of a kind of password, though not necessarily complex as those requested by traditional approaches, and biometric recognition. The novelty with respect to most proposals in literature is the combination of three elements at once: the matching of any handwritten text instead of user signature, the on-line capture of seven sensor signals to recognize handwriting dynamics (three from accelerometer, three from gyroscope and one from pressure sensor), and the use of normal paper instead of a digitizing tablet. Presented experiments test two different recognition techniques, implemented by two modules that can be alternatively plugged into the system. An SVM-based verification module entails to extract the most relevant features from writing dynamics, and to acquire a sufficient amount of enrolling data (30 samples per user) to train an SVM for each user. A pure Dynamic Time Warping (DTW) verification module does not require such training, and is tested using either a gallery with the same number of templates per user as those used for SVM training, or with a gallery containing a much lower number of templates per user (namely 5). Obtained results encourage further investigation of lightweight strategies for written password dynamics recognition.
Association Rules and Frequent Patterns

Large datasets of transactional records in the form of co-occurring events, variables or features may contain interesting knowledge in terms of implicit relations and patterns. Association Rule Mining (ARM) is the systematic extraction of frequent patterns from data in the form of rules that exposes and explicitly represents the relation between variables. It is a ‘descriptive’ data mining technique, which provides a compact and high level description of interesting patterns found in historical data. ARM has been successfully employed in many application domains such as market basket analysis, Web user behaviour, substructures of online social networks, intrusion detection in communication networks, co-expression of genes in bioinformatics, substructures of molecular compounds in chemoinformatics, etc. The information represented by means of association rules can be used as the basis for decisions, to discover regularities in the data and, in general, to formulate new scientific hypotheses driven by the data. ARM is computationally hard to solve and practical applications require critical design choices in the data analysis workflow, including data pre-processing, the data layout, algorithm selection and tuning of the algorithm’s parameters. Many ARM algorithms have been proposed for more than two decades: Some are suitable for specific data formats, properties or database layouts, others solve a reduced or an extended formulation of the ARM problem for improving efficiency or the interestingness of the result. In this article the ARM problem is introduced and its complexity discussed, the most important algorithms and the extended formulations are briefly described, and some applications are finally provided.