OpenRaiser/Intern-Atlas huggingface dataset:
Deconstructing and reconstructing word embedding algorithms
Venue: arXiv cs.CL 2019
Year: 2019

Uncontextualized word embeddings are reliable feature representations of words used to obtain high quality results for various NLP applications. Given the historical success of word embeddings in NLP, we propose a retrospective on some of the most well-known word embedding algorithms. In this work, we deconstruct Word2vec, GloVe, and others, into a common form, unveiling some of the necessary and sufficient conditions required for making performant word embeddings. We find that each algorithm: (1) fits vector-covector dot products to approximate pointwise mutual information (PMI); and, (2) modulates the loss gradient to balance weak and strong signals. We demonstrate that these two algorithmic features are sufficient conditions to construct a novel word embedding algorithm, Hilbert-MLE. We find that its embeddings obtain equivalent or better performance against other algorithms across 17 intrinsic and extrinsic datasets.
Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019
Venue: arXiv cs.LG 2019
Year: 2019

Financial time series forecasting is, without a doubt, the top choice of computational intelligence for finance researchers from both academia and financial industry due to its broad implementation areas and substantial impact. Machine Learning (ML) researchers came up with various models and a vast number of studies have been published accordingly. As such, a significant amount of surveys exist covering ML for financial time series forecasting studies. Lately, Deep Learning (DL) models started appearing within the field, with results that significantly outperform traditional ML counterparts. Even though there is a growing interest in developing models for financial time series forecasting research, there is a lack of review papers that were solely focused on DL for finance. Hence, our motivation in this paper is to provide a comprehensive literature review on DL studies for financial time series forecasting implementations. We not only categorized the studies according to their intended forecasting implementation areas, such as index, forex, commodity forecasting, but also grouped them based on their DL model choices, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Long-Short Term Memory (LSTM). We also tried to envision the future for the field by highlighting the possible setbacks and opportunities, so the interested researchers can benefit.
Reproducible Evaluation of Neural Network Based Channel Estimators And Predictors Using A Generic Dataset
Venue: arXiv cs.IT 2019
Year: 2019

A low-complexity neural network based approach for channel estimation was proposed recently, where assumptions on the channel model were incorporated into the design procedure of the estimator. Instead of using data from a measurement campaign as done in previous work, we evaluate the performance of the convolutional neural network (CNN) based channel estimator by using a reproducible mmWave environment of the DeepMIMO dataset. We further propose a neural network based predictor which is derived by starting from the linear minimum mean squared error (LMMSE) predictor. We start by deriving a weighted sum of LMMSE predictors which is motivated by the structure of the optimal MMSE predictor. This predictor provides an initialization (weight matrices, biases and activation function) to a feed-forward neural network based predictor. With a properly learned neural network, we show that it is possible to easily outperform the LMMSE predictor based on the Jakes assumption of the underlying Doppler spectrum in an reproducible indoor scenario of the DeepMIMO dataset.
Actin Networks Voltage Circuits
Venue: arXiv cs.ET 2019
Year: 2019

Starting with an experimentally observed networks of actin bundles, we model their network structure in terms of edges and nodes. We then compute and discuss the main electrical parameters, considering the bundles as electrical wires. A set of equations describing the network is solved with several initial conditions. Input voltages, that can be considered as information bits, are applied in a set of points and output voltages are computed in another set of positions. We consider both an idealized situation, where point-like electrodes can be inserted in any points of the bundles and a more realistic one, where electrodes lay on a surface and have typical dimensions available in the industry. We find that in both cases such a system can implement the main logical gates and a finite state machine.
Belief and plausibility measures for D numbers
Venue: arXiv cs.AI 2019
Year: 2019

As a generalization of Dempster-Shafer theory, D number theory provides a framework to deal with uncertain information with non-exclusiveness and incompleteness. However, some basic concepts in D number theory are not well defined. In this note, the belief and plausibility measures for D numbers have been proposed, and basic properties of these measures have been revealed as well.
Secure Wireless Internet of Things Communication using Virtual Private Networks
Venue: arXiv cs.NI 2019
Year: 2019

The Internet of Things (IoT) is an exploding market as well as a important focus area for research. Security is a major issue for IoT products and solutions, with several massive problems that are still commonplace in the field. In this paper, we have successfully minimized the risk of data eavesdropping and tampering over the network by securing these communications using the concept of tunneling. We have implemented this by connecting a router to the internet via a Virtual Private network while using PPTP and L2TP as the underlying protocols for the VPN and exploring their cost benefits, compatibility and most importantly, their feasibility. The main purpose of our paper is to try to secure IoT networks without adversely affecting the selling point of IoT.
Energy Efficiency Optimization: A New Trade-off Between Fairness and Total System Performance
Venue: arXiv cs.IT 2019
Year: 2019

The total energy efficiency (TEE), defined as the ratio between the total data rate and the total power consumption, is considered the most meaningful performance metric in terms of energy efficiency (EE). Nevertheless, it does not depend directly on the EE of each link and its maximization leads to unfairness between the links. On the other hand, the maximization of the minimum EE (MEE), i.e., the minimum of the EEs of all links, guarantees the fairest power allocation, but it does not contain any explicit information about the total system performance. The main trend in current research is to maximize TEE and MEE separately. Unlike previous contributions, this letter presents a general multi-objective approach for EE optimization that takes into account both TEE and MEE at the same time, and thus achieves various trade-off points in the MEE-TEE plane. Due to the nonconvex form of the resulting problem, we propose a low-complexity algorithm leveraging the theory of sequential convex optimization (SCO). Last but not least, we provide a novel theoretical result for the complexity of SCO algorithms.
Introduction to Computational Thinking: a new high school curriculum using CodeWorld
Venue: arXiv cs.CY 2019
Year: 2019

The Louisiana Department of Education partnered with the Gordon A. Cain Center at LSU to pilot a Computing High School Graduation Pathway. The first course in the pathway, Introduction to Computational Thinking (ICT), is designed to teach programming and reinforce mathematical practice skills of nine-grade students, with an emphasis on promoting higher order thinking. In 2017-18, about 200 students and five teachers participated in the pilot, in 2018-2019 the participation increased to 400 students, and in the current 2019-2020 year about 800 students in 11 schools are involved. Professional development starts with a five-week intensive summer institute, which is complemented with follow-up Saturday sessions and coaching support during the academic year. After describing the course content and briefly the teacher training, we discuss the data we have collected in the last two years. The overall student reception of the course has been positive, but the course was categorized by most students as hard. However, the Computing Attitude Survey analysis indicates that the difficulty of the course did not demotivate the students. The pre-post test content assessments show that students learned not only the language, but also general principles of programming, logic and modeling, as well as use of variables, expressions and functions. Lessons learned during the pilot phase motivated changes, such as emphasizing during PD the need to provide timely feedback to students, provide detailed rubrics for the projects and reorganize the lessons to increase the initial engagement with the material. After two years of running pilots, the course is becoming student-centered, where most of the code and image samples provided in the lessons are based on code created by previous students.
Visualizing structural balance in signed networks
Venue: arXiv cs.SI 2019
Year: 2019

Network visualization has established as a key complement to network analysis since the large variety of existing network layouts are able to graphically highlight different properties of networks. However, signed networks, i.e., networks whose edges are labeled as friendly (positive) or antagonistic (negative), are target of few of such layouts and none, to our knowledge, is able to show structural balance, i.e., the tendency of cycles towards including an even number of negative edges, which is a well-known theory for studying friction and polarization. In this work we present Structural-balance-viz: a novel visualization method showing whether a connected signed network is balanced or not and, in the latter case, how close the network is to be balanced. Structural-balance-viz exploits spectral computations of the signed Laplacian matrix to place network's nodes in a Cartesian coordinate system resembling a balance (a scale). Moreover, it uses edge coloring and bundling to distinguish positive and negative interactions. The proposed visualization method has characteristics desirable in a variety of network analysis tasks: Structural-balance-viz is able to provide indications of balance/polarization of the whole network and of each node, to identify two factions of nodes on the basis of their polarization, and to show their cumulative characteristics. Moreover, the layout is reproducible and easy to compare. Structural-balance-viz is validated over synthetic-generated networks and applied to a real-world dataset about political debates confirming that it is able to provide meaningful interpretations.
Smooth Fictitious Play in $N\times 2$ Potential Games
Venue: arXiv cs.GT 2019
Year: 2019

The paper shows that smooth fictitious play converges to a neighborhood of a pure-strategy Nash equilibrium with probability 1 in almost all $N\times 2$ ($N$-player, two-action) potential games. The neighborhood of convergence may be made arbitrarily small by taking the smoothing parameter to zero. Simple proof techniques are furnished by considering regular potential games.
On Multi-Cascade Influence Maximization: Model, Hardness and Algorithmic Framework
Venue: arXiv cs.SI 2019
Year: 2019

This paper studies the multi-cascade influence maximization problem, which explores strategies for launching one information cascade in a social network with multiple existing cascades. With natural extensions to the classic models, we first propose the independent multi-cascade model where the diffusion process is governed by the so-called activation function. We show that the proposed model is sufficiently flexible as it generalizes most of the existing cascade-based models. We then study the multi-cascade influence maximization problem under the designed model and provide approximation hardness under common complexity assumptions, namely Exponential Time Hypothesis and $NP \subseteq DTIME(n^{\poly \log n})$. Given the hardness results, we build a framework for designing heuristic seed selection algorithms with a testable data-dependent approximation ratio. The designed algorithm leverages upper and lower bounds, which reveal the key combinatorial structure behind the multi-cascade influence maximization problem. The performance of the framework is theoretically analyzed and practically evaluated through extensive simulations. The superiority of the proposed solution is supported by encouraging experimental results, in terms of effectiveness and efficiency.
SPEECHMINER: A Framework for Investigating and Measuring Speculative Execution Vulnerabilities
Venue: arXiv cs.CR 2019
Year: 2019

SPEculative Execution side Channel Hardware (SPEECH) Vulnerabilities have enabled the notorious Meltdown, Spectre, and L1 terminal fault (L1TF) attacks. While a number of studies have reported different variants of SPEECH vulnerabilities, they are still not well understood. This is primarily due to the lack of information about microprocessor implementation details that impact the timing and order of various micro-architectural events. Moreover, to date, there is no systematic approach to quantitatively measure SPEECH vulnerabilities on commodity processors. This paper introduces SPEECHMINER, a software framework for exploring and measuring SPEECH vulnerabilities in an automated manner. SPEECHMINER empirically establishes the link between a novel two-phase fault handling model and the exploitability and speculation windows of SPEECH vulnerabilities. It enables testing of a comprehensive list of exception-triggering instructions under the same software framework, which leverages covert-channel techniques and differential tests to gain visibility into the micro-architectural state changes. We evaluated SPEECHMINER on 9 different processor types, examined 21 potential vulnerability variants, confirmed various known attacks, and identified several new variants.
Alignment Free and Distortion Robust Iris Recognition
Venue: arXiv cs.CV 2019
Year: 2019

Iris recognition is a reliable personal identification method but there is still much room to improve its accuracy especially in less-constrained situations. For example, free movement of head pose may cause large rotation difference between iris images. And illumination variations may cause irregular distortion of iris texture. To match intra-class iris images with head rotation robustly, the existing solutions usually need a precise alignment operation by exhaustive search within a determined range in iris image preprosessing or brute force searching the minimum Hamming distance in iris feature matching. In the wild, iris rotation is of much greater uncertainty than that in constrained situations and exhaustive search within a determined range is impracticable. This paper presents a unified feature-level solution to both alignment free and distortion robust iris recognition in the wild. A new deep learning based method named Alignment Free Iris Network (AFINet) is proposed, which uses a trainable VLAD (Vector of Locally Aggregated Descriptors) encoder called NetVLAD to decouple the correlations between local representations and their spatial positions. And deformable convolution is used to overcome iris texture distortion by dense adaptive sampling. The results of extensive experiments on three public iris image databases and the simulated degradation databases show that AFINet significantly outperforms state-of-art iris recognition methods.
PointEval: On the Impact of Pointer Analysis Frameworks
Venue: arXiv cs.SE 2019
Year: 2019

Pointer analysis is a foundational analysis leveraged by various static analyses. Therefore, it gathered wide attention in research for decades. Some pointer analysis frameworks are based on succinct declarative specifications. However, these tools are heterogeneous in terms of the underlying intermediate representation (IR), heap abstraction, and programming methodology. This situation complicates a fair comparison of these frameworks and thus hinders further research. Consequently, the literature lacks an evaluation of the strengths and weaknesses of these tools. In this work, we evaluate two major frameworks for pointer analysis, WALA and Doop, on the DaCapo set of benchmarks. We compare the pointer analyses available in Wala and Doop, and conclude that---even though based on a declarative specification---Doop provides a better pointer analysis than Wala in terms of precision and scalability. We also compare the two IRs used in Doop, i.e., Jimple from the Soot framework and IR from the Wala framework. Our evaluation shows that in the majority of the benchmarks Soot's IR gives a more precise and scalable pointer analysis. Finally, we propose a micro-benchmark \emph{PointerBench}, for which we manually validate the points-to statistics to evaluate the results of these tools.
Zero knowledge proofs for cloud storage integrity checking
Venue: arXiv cs.CR 2019
Year: 2019

With the wide application of cloud storage, cloud security has become a crucial concern. Related works have addressed security issues such as data confidentiality and integrity, which ensure that the remotely stored data are well maintained by the cloud. However, how to define zero-knowledge proof algorithms for stored data integrity check has not been formally defined and investigated. We believe that it is important that the cloud server is unable to reveal any useful information about the stored data. In this paper, we introduce a novel definition of data privacy for integrity checks, which describes very high security of a zero-knowledge proof. We found that all other existing remote integrity proofs do not capture this feature. We provide a comprehensive study of data privacy and an integrity check algorithm that captures data integrity, confidentiality, privacy, and soundness.
A Method for Computing Class-wise Universal Adversarial Perturbations
Venue: arXiv cs.LG 2019
Year: 2019

We present an algorithm for computing class-specific universal adversarial perturbations for deep neural networks. Such perturbations can induce misclassification in a large fraction of images of a specific class. Unlike previous methods that use iterative optimization for computing a universal perturbation, the proposed method employs a perturbation that is a linear function of weights of the neural network and hence can be computed much faster. The method does not require any training data and has no hyper-parameters. The attack obtains 34% to 51% fooling rate on state-of-the-art deep neural networks on ImageNet and transfers across models. We also study the characteristics of the decision boundaries learned by standard and adversarially trained models to understand the universal adversarial perturbations.
Replicated Computational Results (RCR) Report for "Code Generation for Generally Mapped Finite Elements"
Venue: arXiv cs.MS 2019
Year: 2019

"Code Generation for Generally Mapped Finite Elements" includes performance results for the finite element methods discussed in that manuscript. The authors provided a Zenodo archive with the Firedrake components and dependencies used, as well as the scripts that generated the results. The software was installed on two similar platforms; then, new results were gathered and compared to the original results. After completing this process, the results have been deemed replicable by the reviewer.
A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression
Venue: arXiv cs.LG 2019
Year: 2019

Data privacy and security becomes a major concern in building machine learning models from different data providers. Federated learning shows promise by leaving data at providers locally and exchanging encrypted information. This paper studies the vertical federated learning structure for logistic regression where the data sets at two parties have the same sample IDs but own disjoint subsets of features. Existing frameworks adopt the first-order stochastic gradient descent algorithm, which requires large number of communication rounds. To address the communication challenge, we propose a quasi-Newton method based vertical federated learning framework for logistic regression under the additively homomorphic encryption scheme. Our approach can considerably reduce the number of communication rounds with a little additional communication cost per round. Numerical results demonstrate the advantages of our approach over the first-order method.
Adaptive Divergence for Rapid Adversarial Optimization
Venue: arXiv cs.LG 2019
Year: 2019

Adversarial Optimization (AO) provides a reliable, practical way to match two implicitly defined distributions, one of which is usually represented by a sample of real data, and the other is defined by a generator. Typically, AO involves training of a high-capacity model on each step of the optimization. In this work, we consider computationally heavy generators, for which training of high-capacity models is associated with substantial computational costs. To address this problem, we introduce a novel family of divergences, which varies the capacity of the underlying model, and allows for a significant acceleration with respect to the number of samples drawn from the generator. We demonstrate the performance of the proposed divergences on several tasks, including tuning parameters of a physics simulator, namely, Pythia event generator.
BenchCouncil's View on Benchmarking AI and Other Emerging Workloads
Venue: arXiv cs.PF 2019
Year: 2019

This paper outlines BenchCouncil's view on the challenges, rules, and vision of benchmarking modern workloads like Big Data, AI or machine learning, and Internet Services. We conclude the challenges of benchmarking modern workloads as FIDSS (Fragmented, Isolated, Dynamic, Service-based, and Stochastic), and propose the PRDAERS benchmarking rules that the benchmarks should be specified in a paper-and-pencil manner, relevant, diverse, containing different levels of abstractions, specifying the evaluation metrics and methodology, repeatable, and scaleable. We believe proposing simple but elegant abstractions that help achieve both efficiency and general-purpose is the final target of benchmarking in future, which may be not pressing. In the light of this vision, we shortly discuss BenchCouncil's related projects.
Fastened CROWN: Tightened Neural Network Robustness Certificates
Venue: arXiv cs.LG 2019
Year: 2019

The rapid growth of deep learning applications in real life is accompanied by severe safety concerns. To mitigate this uneasy phenomenon, much research has been done providing reliable evaluations of the fragility level in different deep neural networks. Apart from devising adversarial attacks, quantifiers that certify safeguarded regions have also been designed in the past five years. The summarizing work of Salman et al. unifies a family of existing verifiers under a convex relaxation framework. We draw inspiration from such work and further demonstrate the optimality of deterministic CROWN (Zhang et al. 2018) solutions in a given linear programming problem under mild constraints. Given this theoretical result, the computationally expensive linear programming based method is shown to be unnecessary. We then propose an optimization-based approach \textit{FROWN} (\textbf{F}astened C\textbf{ROWN}): a general algorithm to tighten robustness certificates for neural networks. Extensive experiments on various networks trained individually verify the effectiveness of FROWN in safeguarding larger robust regions.