Reviewerly/exHarmony huggingface dataset:
A computational method for the European option price in an Internet of Things framework

In an Internet of Things (IoT) scenario, sensors and devices are able to: (i) extract information from real; (ii) storage them into a database; (iii) use this information for inferring results by the implementation of very efficient algorithms. In this paper we present a computational schema in which, in order to price financial European options, an IoT framework processes data, as financial assets, expiration day, interest rate, strike price. In particular, this procedure involves mathematical tools for the estimation of the volatility and for the calculation of the no arbitrage price. The mathematical model is translated into an algorithm that is implemented in a mobile software. To test its validity, we apply our schema to a real case.
Training convolutional neural network from multi-domain contour images for 3D shape retrieval

Recent vision-brain physiological experiments [65] have demonstrated that contours, and in particular, contour junctions present in the 2D images are very informative for revealing the 3D structure of the object. Inspired by this observation, we take 2D sketches (or 2D views of 3D sketches) and edge maps of 2D views of 3D models as a unified domain to train the Convolutional Neural Network (CNN). The CNN features are then used for 3D object representation. We show that the CNN can successfully learn the object structure from different types of clues. The performance of the proposed method demonstrates that the semantic gap between the 2D/3D sketches and the 3D models can be bridged without any cross-domain similarity learning. Experiments show that our approach significantly outperforms the state-of-the-art 2D/3D sketch-based 3D retrieval methods.
Hierarchical modular Bayesian networks for low-power context-aware smartphone

Nowadays, smartphone has a tremendous number of applications using sensors and devices for several applications such as healthcare and game. However, serious consideration to increase the duration of battery use of phone is required because of the limited battery capacity. In this paper, we propose a hybrid system to increase the longevity of phone with hierarchical modular Bayesian networks that recognize the user's contexts, and device management rules that infer the unnecessary devices in smartphone. Inferring the user's contexts with sensor data and considering the device status, the context inferred and user's tendency, we determine the superfluous devices that are consuming the battery as dispensable. The experiments with the real log data collected from 28 people for 6 months verify that the proposed system performs the accuracy of 85.68% and the reduction of battery consumption of about 6%.
Defending Distributed Systems Against Adversarial Attacks

Brief Biography: I am a postdoc in the Computer Sci- ence and Arti cial Intelligence Laboratory (CSAIL) at MIT, hosted by Professor Nancy Lynch. She received a Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign in 2017, supervised by Pro- fessor Nitin H. Vaidya. Her research intersects distributed systems, learning, security, and brain computing. She was the runner-up for the Best Student Paper Award at DISC 2016, and she received the 2015 Best Student Paper Award at SSS 2015. She received UIUC's Sundaram Seshu Interna- tional Student Fellowship for 2016, and was invited to par- ticipate in Rising Stars in EECS (2018). She has served on TPC for several conferences including ICDCS and ICDCN.
Tiered Cloud Storage via Two-Stage, Latency-Aware Bidding

In cloud storage, the digital data is stored in logical storage pools, backed by heterogeneous physical storage media and computing infrastructure that are managed by a cloud service provider (CSP). One of the key advantages of cloud storage is its elastic pricing mechanism, in which the users need only pay for the resources/services they actually use, e.g., depending on the storage capacity consumed, the number of file accesses per month, and the negotiated service level agreement. To balance the tradeoff between service performance and cost, CSPs often employ different storage tiers, for instance, cold storage and hot storage. Storing data in hot storage incurs high storage cost yet delivers low access latency, whereas cold storage is able to inexpensively store massive amounts of data and thus provides lower cost with higher latency. In this paper, we address a major challenge confronting the CSPs utilizing such tiered storage architecture-how to maximize their overall profit over a variety of storage tiers that offer distinct characteristics, as well as file placement and access request scheduling policies. To this end, we propose a scheme where the CSP offers a two-stage auction process for: 1) requesting storage capacity and 2) requesting accesses with latency requirements. Our two-stage bidding scheme provides a hybrid storage and access optimization framework with the objective of maximizing the CSP's total net profit over four dimensions: file acceptance decision, placement of accepted files, file access decision and access request scheduling policy. The proposed optimization is a mixed-integer nonlinear program that is hard to solve. We propose an efficient heuristic to relax the integer optimization and to solve the resulting nonlinear stochastic programs. The algorithm is evaluated under different scenarios and with different storage system parameters, and insightful numerical results are reported by comparing the proposed approach with other profit-maximization models. We see a profit increase of over 60% of our proposed method compared to other baseline algorithms in certain simulation scenarios.
Potential of Sentinel-2 data for alteration extraction in coal-bed methane reservoirs

This study illustrates the potential of alteration extraction in coal-bed methane (CBM) reservoirs using the recently available Sentinel-2 data. This study then evaluates the capabilities for mapping the altered minerals and vegetation. In the alteration mapping process, we separately analyzed the key remote sensing signatures of altered minerals and geobotanical anomalies based on the hydrocarbon micro-seepage theory. The diagnostic spectral characteristics of irons, clays and altered vegetation were concentrated and demonstrated the distribution of hydrocarbon micro-seepage. In the bare soil region, the altered minerals, including irons and clays were extracted through band math and principal component analysis (PCA) methods. In the vegetation area mapping, the diagnostic spectral feature parameters, such as the locations and slopes of three feature edges, were calculated. In addition, the mapping accuracy was assessed based on the extraction results of Hyperion data through full spectral profile matching method and the X-ray diffraction (XRD) analysis results. The results show that: 1) compared with band math and PCA methods, the different extraction methods were suitable for different minerals; 2) the extraction results of iron and clay minerals were most accurate (78.33% and 76.67%, respectively) with XRD analysis; 3) the highest rate of change of the feature edge slope was up to 39% with a reference spectrum; and 4) the distribution of alteration information was consistent with the Hudi coal mining area in Jincheng, Shanxi province. The potential geological application of Sentinel-2 data was revealed to identify the direction of CBM exploration in a large scale, highly efficient, convenient, and inexpensive way.
Simple very deep convolutional network for robust hand pose regression from a single depth image

We propose a novel approach for articulated hand pose estimation from a single depth image using a very deep convolutional network. For the first, a very deep network structure is designed to directly maps a single depth image to its corresponding 3D hand joint locations. This approach eliminates the necessity of hand-crafted intermediate features and sophisticated post-processing stages for robust and accurate hand pose estimation. We use Batch Normalization to accelerate training and prevent the objective function from getting stuck in poor local minima. We introduce a low-dimensional embedding forcing the network to learn the inherent constraints of hand joints, which helps to reduce the cost of reconstructing 3D hand poses from high-dimension feature space. We discuss the effect of the convolutional network depth on its accuracy under the hand pose regression setting. Quantitative assessments on two challenging datasets show that our proposed method gets competitive results to state-of-the-art approaches in terms of accuracy. Moreover, qualitative results also show that our proposed method is robust to some difficult hand poses.
Building a Planner: A Survey of Planning Systems Used in Commercial Video Games

In the last decade, many commercial video games have used planners instead of classical behavior trees or finite state machines to define agent behaviors. Planners allow looking ahead in time and can prevent some problems of purely reactive systems. Furthermore, some of them allow coordination of multiple agents. However, implementing a planner for highly dynamic environments such as video games is a difficult task. This paper aims to provide an overview of different elements of planners and the problems that developers might have when dealing with them. We identify the major areas of plan creation and execution, trying to guide developers through the process of implementing a planner and discuss possible solutions for problems that may arise in the following areas: environment, planning domain, goals, agents, actions, plan creation, and plan execution processes. Giving insights into multiple commercial games, we show different possibilities of solving such problems and discuss which solutions are better suited under specific circumstances, and why some academic approaches find a limited application in the context of commercial titles.
On the Design of Minimal-Cost Pipeline Systems Satisfying Hard/Soft Real-Time Constraints

Pipeline systems provide high throughput for applications by overlapping the executions of tasks. In the architectures with heterogeneity, two basic issues in the design of application-specific pipelines need to be studied: what type of functional unit to execute each task, and where to place buffers. Due to the increasing complexity of applications, pipeline designs face a bundle of problems. One of the most challenging problems is the uncertainty on the execution times, which makes the deterministic techniques inapplicable. In this paper, the execution times are modeled as random variables. Given an application, our objective is to construct the optimal pipeline, such that the total cost of the resultant pipeline can be minimized while satisfying the required timing constraints with the given guaranteed probability. We first prove the NP-hardness of the problem. Then, we present Mixed Integer Linear Programming (MILP) formulations to obtain the optimal solution. Due to the high time complexity of MILP, we devise an efficient (1+ε)-approximation algorithm, where the value of ε is less than 5 percent in practice. Experimental results show that our algorithms can achieve significant reductions in cost over the existing techniques, reaching up to 31.93 percent on average.
The Overhead from Combating Side-Channels in Cloud Systems Using VM-Scheduling

Recent work suggests that scheduling, with security as a consideration, can be effective in minimizing information leakage, via side-channels, that can exist when virtual machines (VMs) co-reside in clouds. We analyze the overhead that is incurred by such an approach. We first pose and answer a fundamental question: is the problem tractable? We show that the seemingly simpler sub-cases of initial placement and migration across only two equal-capacity servers are both intractable (NP-hard). However, a decision version of the general problem to which the optimization version is related polynomially is in NP. With these results as the basis, we make several other contributions. We revisit recent work that proposes a greedy algorithm for this problem, called Nomad. We establish that if P ≠ NP, then there exist infinitely many classes of input, each with an infinite number of inputs, for which a decrease in information leakage is possible, but Nomad provides none, let alone minimize it. We establish also that a mapping to Integer Linear Programming (ILP) in prior work is deficient in that the mapping can be inefficient (exponential-time), and therefore does not accurately convey the overhead of such an approach that, unlike Nomad, actually decreases information leakage. We present our efficient reductions to ILP and boolean satisfiability in conjunctive normal form (CNF-SAT). We have implemented these approaches and conducted an empirical assessment using the same ILP solver as prior work, and a SAT solver. Our analytical and empirical results more accurately convey the overhead that is incurred by an approach that actually provides security (decrease in information leakage).
An Instance Reservation Framework for Cost Effective Services in Geo-Distributed Data Centers

Infrastructure-as-a-Service clouds in geo-distributed data centers offer various pricing options, including on-demand and reserved instances, which provide an elastic and cost-effective infrastructure to support High Performance Computing (HPC) applications. In this paper, we propose an instance reservation based cloud service framework, modeling the cost-minimizing reservation decision issue as an NP-hard integer programming problem for distributed data centers. To ease its computation complexity, two algorithms are proposed to minimize the HPC service cost with the worst-case performance guarantees: an offline heuristic-greedy algorithm, and a rolling-horizon based online algorithm when only short-term demand prediction is available. Facing fluctuating demands, instance reservation in a single data center may incur the highly underutilized capacity. To address this issue for further cost reduction, we extend the scheme with a novel cloud broker federation based resource sharing mechanism, reallocating already reserved but unused instances to computation-intensive and short-lived tasks for continuous execution without interruption. Extensive evaluations driven by large-scale trace-based datasets demonstrate that the proposed mechanism can effectively handle large volumes of service requests, saving considerable service costs with higher reservation resource utilization.
Neurally-Guided Semantic Navigation in Knowledge Graph

In this big data era, knowledge becomes increasingly linked, along with the rapid growth in data volume. Connected knowledge is naturally represented and stored as knowledge graphs, which are of more and more importance for many frontier research areas such as machine intelligence. Effectively finding relations between entities in a large knowledge graph plays a key role in many knowledge graph applications, as the most valuable part of a knowledge graph is its rich connectedness, which captures rich information about the objects in the real world. However, due to the intrinsic complexity of real-world knowledge, finding semantically close relations by navigation in a large knowledge graph is very challenging. Canonical graph exploration methods inevitably result in combinatorial explosion especially when the paths connecting two entities are long: the search space is <inline-formula><tex-math notation="LaTeX">$O(d^l)$</tex-math></inline-formula> , where <inline-formula><tex-math notation="LaTeX">$d$</tex-math></inline-formula> is the average graph node degree and <inline-formula><tex-math notation="LaTeX">$l$</tex-math></inline-formula> is the length of the path. In this paper, we will systematically study the semantic navigation problem for large knowledge graphs. Inspired by AlphaGo, which was overwhelmingly successful in the game Go, we designed an efficient semantic navigation method based on a well-tailored Monte Carlo Tree Search algorithm with the unique characteristics of knowledge graphs considered. Extensive experiments on different real-life knowledge bases show that our method is not only effective but also very efficient.
Weak to Strong Detector Learning for Simultaneous Classification and Localization

This paper aims at learning discriminative part detectors with only image-level labels. To this end, we need to develop effective technologies for both pattern mining and detection learning. Different from previous methods, which train part detectors in one step, we divide the detector learning process into two stages and formulate it as a weak to strong learning framework. In particular, we first learn exemplar detectors from the unaligned patterns and perform a detector-based spectral clustering to produce weak detectors that are only responsible for a few discriminative patterns. In this way, the weak detectors are able to offer right initial patterns for strong detector learning. Second, we learn strong detectors with patterns discovered from the weak detectors, which we formulate as a confidence-loss sparse multiple instance learning (cls-MIL) task. The cls-MIL considers the diversity of positive samples while avoiding drifting away from the well localized ones by assigning a confidence value to each positive sample. The responses of the learned detectors produce an effective mid-level image representation for both image classification and object localization. Experiments conducted on benchmark data sets well demonstrate the superiority of our method over existing approaches.
Spectral primal spaces

Let [Formula: see text] be a mapping. Consider [Formula: see text] Then, according to Echi, [Formula: see text] is an Alexandroff topology. A topological space [Formula: see text] is called a primal space if its topology coincides with an [Formula: see text] for some mapping [Formula: see text]. We denote by [Formula: see text] the set of all fixed points of [Formula: see text], and [Formula: see text] the set of all periodic points of [Formula: see text]. The topology [Formula: see text] induces a preorder [Formula: see text] defined on [Formula: see text] by: [Formula: see text] if and only if [Formula: see text], for some integer [Formula: see text]. The main purpose of this paper is to provide necessary and sufficient algebraic conditions on the function [Formula: see text] in order to get [Formula: see text] (respectively, the one-point compactification of [Formula: see text]) a spectral topology. More precisely, we show the following results. (1) [Formula: see text] is spectral if and only if [Formula: see text] is a finite set and every chain in the ordered set [Formula: see text] is finite. (2) The one-point(Alexandroff) compactification of [Formula: see text] is a spectral topology if and only if [Formula: see text] and every nonempty chain of [Formula: see text] has a least element. (3) The poset [Formula: see text] is spectral if and only if every chain is finite. As an application the main theorem [12, Theorem 3. 5] of Echi–Naimi may be derived immediately from the general setting of the above results.
High Rate Robust Codes with Low Implementation Complexity

Robust codes C(n, k) <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> are nonlinear q-ary codes of dimension k and length n ≤ 2k. Robust codes can detect any error with nonzero probability; hence, they can effectively detect fault injection attacks. Most high rate robust codes are either restricted to certain ratios between n and k, or have relatively high hardware complexity. This paper presents new constructions for optimum or close to optimum low complexity high rate robust codes. These codes exist for any k and n. The hardware complexity of each construction is discussed, and a method to choose the one with the smallest implementation cost is presented.
A novel approach for extraction of Gaoshanhe-Group outcrops using Landsat Operational Land Imager (OLI) data in the heavily loess-covered Baoji District, Western China

We studied the applicability of Landsat Operational Land Imager (OLI) data for extraction of Gaoshanhe-Group outcrops in a heavily loess-covered area in the northern Baoji district of China, which is always a crucial but challenging issue in geological remote-sensing mapping. An integrated approach was employed to process the remote-sensing data: first, the interpretation target area of interest was clipped using certain slope and elevation difference thresholds (topographical analysis); second, band ratio (B4/B2) and fractal summation algorithms were conducted in succession to extract the bedrock-induced anomalies; third, spatial clumping and hot-spot analysis were performed in order to eliminate residual false anomalies; and finally, we introduced high-resolution Worldview 2 data for visual interpretation and further spectral analysis. The findings show that the rocky outcrops were somewhat spectrally indistinguishable, and they were identified mainly due to their generally moderate degree of spatial clustering. Thirteen anomalous pixels were extracted from nearly 200,000 pixels covering the study area, and our field reconnaissance verified the results. This research constitutes the first useful case study on the applicability of Landsat data-derived lithology interpretation in China’s loess plateau region.
Optimal linear secret sharing schemes for graph access structures on six participants

The efficiency of a secret sharing scheme is measured via its information ratio. The optimal information ratio (OIR) of an access structure is the infimum of the information ratios of all secret sharing schemes realizing it. When the infimum is taken over all linear secret sharing schemes, it is called the optimal linear information ratio (OLIR). We review the problem of finding the OIRs and OLIRs of graph access structures on six participants. Study of such access structures was initiated by van Dijk et al. (1998) [43]. In a sequence of works, the OIRs of nine access structures, out of the 18 initially unsolved non-isomorphic ones, were determined. Very recently, the known lower bounds on the OLIRs of the other nine cases have been improved (Farràs et al., 2018 [20]), determining the OLIRs for another two cases. In this paper, for each of the remaining seven cases, we provide a new upper bound on the OIR which matches that of the recently found lower bound on the OLIR. Improved upper bounds are achieved by constructing a linear secret sharing scheme, using a suitable decomposition technique for each access structure. A new decomposition technique, called (λ,ω)-weighted decomposition, is also introduced which is a generalization of all known decomposition methods.
Learning robust latent representation for discriminative regression

In this paper, we present a generic effective formulation, dubbed robust latent representation learning (RLRL), for discriminative linear regression. We formulate the RLRL optimization problem as a joint learning framework of discriminative latent feature selection and robust linear regression. Specifically, instead of directly projecting the original high-dimensional features onto a target space, RLRL learns discriminative latent representation by concurrently suppressing the redundant information from original features and constructing a robust latent subspace. To improve the effectiveness of the regression task, a capped lp-norm regression model is formulated for robust linear regression. Furthermore, RLRL incorporates learning latent representation and building marginalized regressing prediction into one framework for reducing the classification error of the regression model. Consequently, a general framework for simultaneous feature selection and robust regression is discussed and explicitly presented. An effective alternating optimization algorithm with efficient convergence is developed to solve the resulting optimization problem. Extensive experimental results conducted on diverse databases validate the effectiveness of the proposed RLRL method in comparison with state-of-the-art regression methods.