OpenRaiser/Intern-Atlas huggingface dataset:
Regularization for Deep Learning: A Taxonomy
Venue: arXiv cs.LG 2017
Year: 2017

Regularization is one of the crucial ingredients of deep learning, yet the term regularization has various definitions, and regularization methods are often studied separately from each other. In our work we present a systematic, unifying taxonomy to categorize existing methods. We distinguish methods that affect data, network architectures, error terms, regularization terms, and optimization procedures. We do not provide all details about the listed methods; instead, we present an overview of how the methods can be sorted into meaningful categories and sub-categories. This helps revealing links and fundamental similarities between them. Finally, we include practical recommendations both for users and for developers of new regularization methods.
Multilinear Class-Specific Discriminant Analysis
Venue: arXiv cs.CV 2017
Year: 2017

There has been a great effort to transfer linear discriminant techniques that operate on vector data to high-order data, generally referred to as Multilinear Discriminant Analysis (MDA) techniques. Many existing works focus on maximizing the inter-class variances to intra-class variances defined on tensor data representations. However, there has not been any attempt to employ class-specific discrimination criteria for the tensor data. In this paper, we propose a multilinear subspace learning technique suitable for applications requiring class-specific tensor models. The method maximizes the discrimination of each individual class in the feature space while retains the spatial structure of the input. We evaluate the efficiency of the proposed method on two problems, i.e. facial image analysis and stock price prediction based on limit order book data.
Towards A Usability Model for Software Development Process and Practice
Venue: arXiv cs.SE 2017
Year: 2017

Context/Background: process and practice adoption is a key element in modern software process improvement initiatives, and many of them fail. Goal: this paper presents a preliminary version of a usability model for software development process and practice. Method: this model integrates different perspectives, the ISO Standard on Sys- tems and Software Quality Models (ISO 25010) and classic usability literature. For illustrating the feasibility of the model, two experts applied it to Scrum. Results: metrics values were mostly positive and consistent between evaluators. Conclusions: we find the model feasible to use and potentially beneficial.
Louvre: Lightweight Ordering Using Versioning for Release Consistency
Venue: arXiv cs.AR 2017
Year: 2017

Fence instructions are fundamental primitives that ensure consistency in a weakly consistent shared memory multi-core processor. The execution cost of these instructions is significant and adds a non-trivial overhead to parallel programs. In a naive architecture implementation, we track the ordering constraints imposed by a fence by its entry in the reorder buffer and its execution overhead entails stalling the processor's pipeline until the store buffer is drained and also conservatively invalidating speculative loads. These actions create a cascading effect of increased overhead on the execution of the following instructions in the program. We find these actions to be overly restrictive and that they can be further relaxed thereby allowing aggressive optimizations. The current work proposes a lightweight mechanism in which we assign ordering tags, called versions, to load and store instructions when they reside in the load/store queues and the write buffer. The version assigned to a memory access allows us to fully exploit the relaxation allowed by the weak consistency model and restricts its execution in such a way that the ordering constraints by the model are satisfied. We utilize the information captured through the assigned versions to reduce stalls caused by waiting for the store buffer to drain and to avoid unnecessary squashing of speculative loads, thereby minimizing the re-execution penalty. This method is particularly effective for the release consistency model that employs uni-directional fence instructions. We show that this mechanism reduces the ordering instruction latency by 39.6% and improves program performance by 11% on average over the baseline implementation.
Computational Social Choice and Computational Complexity: BFFs?
Venue: arXiv cs.MA 2017
Year: 2017

We discuss the connection between computational social choice (comsoc) and computational complexity. We stress the work so far on, and urge continued focus on, two less-recognized aspects of this connection. Firstly, this is very much a two-way street: Everyone knows complexity classification is used in comsoc, but we also highlight benefits to complexity that have arisen from its use in comsoc. Secondly, more subtle, less-known complexity tools often can be very productively used in comsoc.
Generative Adversarial Source Separation
Venue: arXiv cs.SD 2017
Year: 2017

Generative source separation methods such as non-negative matrix factorization (NMF) or auto-encoders, rely on the assumption of an output probability density. Generative Adversarial Networks (GANs) can learn data distributions without needing a parametric assumption on the output density. We show on a speech source separation experiment that, a multi-layer perceptron trained with a Wasserstein-GAN formulation outperforms NMF, auto-encoders trained with maximum likelihood, and variational auto-encoders in terms of source to distortion ratio.
Rectangle Transformation Problem
Venue: arXiv cs.CG 2017
Year: 2017

In this paper, we propose the rectangle transformation problem (RTP) and its variants. RTP asks for a transformation by a rectangle partition between two rectangles of the same area. We are interested in the minimum RTP which requires to minimize the partition size. We mainly focus on the strict rectangle transformation problem (SRTP) in which rotation is not allowed in transforming. We show that SRTP has no finite solution if the ratio of the two parallel side lengths of input rectangles is irrational. So we turn to its complement denoted by SIRTP, in which case all side lengths can be assumed integral. We give a polynomial time algorithm ALGSIRTP which gives a solution at most $q/p+O(\sqrt{p})$ to SIRTP$(p,q)$ ($q\geq p$), where $p$ and $q$ are two integer side lengths of input rectangles $p\times q$ and $q\times p$, and so ALGSIRTP is a $O(\sqrt{p})$-approximation algorithm for minimum SIRTP$(p,q)$. On the other hand, we show that there is not constant solution to SIRTP$(p,q)$ for all integers $p$ and $q$ ($q>p$) even though the ratio $q/p$ is within any constant range. We also raise a series of open questions for the research along this line.
Sound Source Localization in a Multipath Environment Using Convolutional Neural Networks
Venue: arXiv cs.SD 2017
Year: 2017

The propagation of sound in a shallow water environment is characterized by boundary reflections from the sea surface and sea floor. These reflections result in multiple (indirect) sound propagation paths, which can degrade the performance of passive sound source localization methods. This paper proposes the use of convolutional neural networks (CNNs) for the localization of sources of broadband acoustic radiated noise (such as motor vessels) in shallow water multipath environments. It is shown that CNNs operating on cepstrogram and generalized cross-correlogram inputs are able to more reliably estimate the instantaneous range and bearing of transiting motor vessels when the source localization performance of conventional passive ranging methods is degraded. The ensuing improvement in source localization performance is demonstrated using real data collected during an at-sea experiment.
Quantitative Analysis of DoS Attacks and Client Puzzles in IoT Systems
Venue: arXiv cs.CR 2017
Year: 2017

Denial of Service (DoS) attacks constitute a major security threat to today's Internet. This challenge is especially pertinent to the Internet of Things (IoT) as devices have less computing power, memory and security mechanisms to mitigate DoS attacks. This paper presents a model that mimics the unique characteristics of a network of IoT devices, including components of the system implementing `Crypto Puzzles' - a DoS mitigation technique. We created an imitation of a DoS attack on the system, and conducted a quantitative analysis to simulate the impact such an attack may potentially exert upon the system, assessing the trade off between security and throughput in the IoT system. We model this through stochastic model checking in PRISM and provide evidence that supports this as a valuable method to compare the efficiency of different implementations of IoT systems, exemplified by a case study.
Demographics in Social Media Data for Public Health Research: Does it matter?
Venue: arXiv cs.CY 2017
Year: 2017

Social media data provides propitious opportunities for public health research. However, studies suggest that disparities may exist in the representation of certain populations (e.g., people of lower socioeconomic status). To quantify and address these disparities in population representation, we need demographic information, which is usually missing from most social media platforms. Here, we propose an ensemble approach for inferring demographics from social media data. Several methods have been proposed for inferring demographic attributes such as, age, gender and race/ethnicity. However, most of these methods require large volumes of data, which makes their application to large scale studies challenging. We develop a scalable approach that relies only on user names to predict gender. We develop three separate classifiers trained on data containing the gender labels of 7,953 Twitter users from Kaggle.com. Next, we combine predictions from the individual classifiers using a stacked generalization technique and apply the ensemble classifier to a dataset of 36,085 geotagged foodborne illness related tweets from the United States. Our ensemble approach achieves an accuracy, precision, recall, and F1 score of 0.828, 0.851, 0.852 and 0.837, respectively, higher than the individual machine learning approaches. The ensemble classifier also covers any user with an alphanumeric name, while the data matching approach, which achieves an accuracy of 0.917, only covers 67% of users. Application of our method to reports of foodborne illness in the United States highlights disparities in tweeting by gender and shows that counties with a high volume of foodborne-illness related tweets are heavily overrepresented by female Twitter users.
Virtual Differential Passivity based Control for Tracking of Flexible-joints Robots
Venue: arXiv cs.SY 2017
Year: 2017

Based on recent advances in contraction methods in systems and control, in this paper we present the virtual differential passivity based control (v-dPBC) technique. This is a constructive design method that combines the concept of virtual systems and of differential passivity. We apply the method to the tracking control problem of flexible joints robots (FJRs) which are formulated in the port-Hamiltonian (pH) framework. Simulations on a single flexible joint link are presented for showing the performance of a controller obtained with this approach.
VLSI Computational Architectures for the Arithmetic Cosine Transform
Venue: arXiv cs.AR 2017
Year: 2017

The discrete cosine transform (DCT) is a widely-used and important signal processing tool employed in a plethora of applications. Typical fast algorithms for nearly-exact computation of DCT require floating point arithmetic, are multiplier intensive, and accumulate round-off errors. Recently proposed fast algorithm arithmetic cosine transform (ACT) calculates the DCT exactly using only additions and integer constant multiplications, with very low area complexity, for null mean input sequences. The ACT can also be computed non-exactly for any input sequence, with low area complexity and low power consumption, utilizing the novel architecture described. However, as a trade-off, the ACT algorithm requires 10 non-uniformly sampled data points to calculate the 8-point DCT. This requirement can easily be satisfied for applications dealing with spatial signals such as image sensors and biomedical sensor arrays, by placing sensor elements in a non-uniform grid. In this work, a hardware architecture for the computation of the null mean ACT is proposed, followed by a novel architectures that extend the ACT for non-null mean signals. All circuits are physically implemented and tested using the Xilinx XC6VLX240T FPGA device and synthesized for 45 nm TSMC standard-cell library for performance assessment.
Deep Learning and Conditional Random Fields-based Depth Estimation and Topographical Reconstruction from Conventional Endoscopy
Venue: arXiv cs.CV 2017
Year: 2017

Colorectal cancer is the fourth leading cause of cancer deaths worldwide and the second leading cause in the United States. The risk of colorectal cancer can be mitigated by the identification and removal of premalignant lesions through optical colonoscopy. Unfortunately, conventional colonoscopy misses more than 20% of the polyps that should be removed, due in part to poor contrast of lesion topography. Imaging tissue topography during a colonoscopy is difficult because of the size constraints of the endoscope and the deforming mucosa. Most existing methods make geometric assumptions or incorporate a priori information, which limits accuracy and sensitivity. In this paper, we present a method that avoids these restrictions, using a joint deep convolutional neural network-conditional random field (CNN-CRF) framework. Estimated depth is used to reconstruct the topography of the surface of the colon from a single image. We train the unary and pairwise potential functions of a CRF in a CNN on synthetic data, generated by developing an endoscope camera model and rendering over 100,000 images of an anatomically-realistic colon. We validate our approach with real endoscopy images from a porcine colon, transferred to a synthetic-like domain, with ground truth from registered computed tomography measurements. The CNN-CRF approach estimates depths with a relative error of 0.152 for synthetic endoscopy images and 0.242 for real endoscopy images. We show that the estimated depth maps can be used for reconstructing the topography of the mucosa from conventional colonoscopy images. This approach can easily be integrated into existing endoscopy systems and provides a foundation for improving computer-aided detection algorithms for detection, segmentation and classification of lesions.
Bibliometric-Enhanced Information Retrieval: 5th International BIR Workshop
Venue: arXiv cs.IR 2017
Year: 2017

Bibliometric-enhanced Information Retrieval (BIR) workshops serve as the annual gathering of IR researchers who address various information-related tasks on scientific corpora and bibliometrics. The workshop features original approaches to search, browse, and discover value-added knowledge from scientific documents and related information networks (e.g., terms, authors, institutions, references). We welcome contributions elaborating on dedicated IR systems, as well as studies revealing original characteristics on how scientific knowledge is created, communicated, and used. In this paper we introduce the BIR workshop series and discuss some selected papers presented at previous BIR workshops.
Prototype Matching Networks for Large-Scale Multi-label Genomic Sequence Classification
Venue: arXiv cs.LG 2017
Year: 2017

One of the fundamental tasks in understanding genomics is the problem of predicting Transcription Factor Binding Sites (TFBSs). With more than hundreds of Transcription Factors (TFs) as labels, genomic-sequence based TFBS prediction is a challenging multi-label classification task. There are two major biological mechanisms for TF binding: (1) sequence-specific binding patterns on genomes known as "motifs" and (2) interactions among TFs known as co-binding effects. In this paper, we propose a novel deep architecture, the Prototype Matching Network (PMN) to mimic the TF binding mechanisms. Our PMN model automatically extracts prototypes ("motif"-like features) for each TF through a novel prototype-matching loss. Borrowing ideas from few-shot matching models, we use the notion of support set of prototypes and an LSTM to learn how TFs interact and bind to genomic sequences. On a reference TFBS dataset with $2.1$ $million$ genomic sequences, PMN significantly outperforms baselines and validates our design choices empirically. To our knowledge, this is the first deep learning architecture that introduces prototype learning and considers TF-TF interactions for large-scale TFBS prediction. Not only is the proposed architecture accurate, but it also models the underlying biology.
Synchronization in Networks of Diffusively Coupled Nonlinear Systems: Robustness Against Time-Delays
Venue: arXiv cs.SY 2017
Year: 2017

In this manuscript, we study the problem of robust synchronization in networks of diffusively time-delayed coupled nonlinear systems. In particular, we prove that, under some mild conditions on the input-output dynamics of the systems and the network topology, there always exists a unimodal region in the parameter space (coupling strength versus time-delay), such that if they belong to this region, the systems synchronize. Moreover, we show how this unimodal region scales with the network topology, which, in turn, provides useful insights on how to design the network topology to maximize robustness against time-delays. The results are illustrated by extensive simulation experiments of time-delayed coupled Hindmarsh-Rose neural chaotic oscillators.
A Sequential Matching Framework for Multi-turn Response Selection in Retrieval-based Chatbots
Venue: arXiv cs.CL 2017
Year: 2017

We study the problem of response selection for multi-turn conversation in retrieval-based chatbots. The task requires matching a response candidate with a conversation context, whose challenges include how to recognize important parts of the context, and how to model the relationships among utterances in the context. Existing matching methods may lose important information in contexts as we can interpret them with a unified framework in which contexts are transformed to fixed-length vectors without any interaction with responses before matching. The analysis motivates us to propose a new matching framework that can sufficiently carry the important information in contexts to matching and model the relationships among utterances at the same time. The new framework, which we call a sequential matching framework (SMF), lets each utterance in a context interacts with a response candidate at the first step and transforms the pair to a matching vector. The matching vectors are then accumulated following the order of the utterances in the context with a recurrent neural network (RNN) which models the relationships among the utterances. The context-response matching is finally calculated with the hidden states of the RNN. Under SMF, we propose a sequential convolutional network and sequential attention network and conduct experiments on two public data sets to test their performance. Experimental results show that both models can significantly outperform the state-of-the-art matching methods. We also show that the models are interpretable with visualizations that provide us insights on how they capture and leverage the important information in contexts for matching.
Joint Cache Resource Allocation and Request Routing for In-network Caching Services
Venue: arXiv cs.NI 2017
Year: 2017

In-network caching is recognized as an effective solution to offload content servers and the network. A cache service provider (SP) always has incentives to better utilize its cache resources by taking into account diverse roles that content providers (CPs) play, e.g., their business models, traffic characteristics, preferences. In this paper, we study the cache resource allocation problem in a Multi-Cache Multi-CP environment. We propose a cache partitioning approach, where each cache can be partitioned into slices with each slice dedicated to a content provider. We propose a content-oblivious request routing algorithm, to be used by individual caches, that optimizes the routing strategy for each CP. We associate with each content provider a utility that is a function of its content delivery performance, and formulate an optimization problem with the objective to maximize the sum of utilities over all content providers. We establish the biconvexity of the problem, and develop decentralized (online) algorithms based on convexity of the subproblem. The proposed model is further extended to bandwidth-constrained and minimum-delay scenarios, for which we prove fundamental properties, and develop efficient algorithms. Finally, we present numerical results to show the efficacy of our mechanism and the convergence of our algorithms.
Investigating the effect of social groups in uni-directional pedestrian flow
Venue: arXiv cs.MA 2017
Year: 2017

The influence of cohesion among members of dyads is investigated in scenarios characterized by uni-directional flow by means of a discrete model: a corridor and the egress from a room with a bottleneck of varying width are simulated. The model manages the dynamics of simulated group members with an adaptive mechanism, balancing the probability of movement according to the dispersion of the group; the cohesion mechanism is calibrated through the parameters $κ_c$ and $δ$. All scenarios are simulated with two procedures: (Proc. 1) population composed of individual pedestrians, in order to validate the simulation model and to provide baseline data; (Proc. 2) population including dyads (50% of the simulated pedestrians), in order to verify their impact. In the corridor scenario, the presence of dyads causes a reduction of the velocities and specific flow at medium-high densities. Egress from a square room with a unique central exit produces results in line with recent studies in the literature, but also shows that the dyads negatively affect the dynamics, leading generally to a slower walking speed and a lower pedestrian flow. Ignoring the presence of dyads would lead to an overestimation of egress flows.
Local Core Members Aided Community Structure Detection
Venue: arXiv cs.SI 2017
Year: 2017

The relationship of friends in social networks can be strong or weak. Some research works have shown that a close relationship between friends conducts good community structure. Based on this result, we propose an effective method in detecting community structure in social networks based on the closeness of relations among neighbors. This method calculates the gravity between each neighbor node to core nodes, then makes judgement if the node should be classified in the community or not, and finally form the process of community detection. The experimental results show that the proposed method can mine the social structure efficiently with a low computational complexity
Implicit Theories and Self-efficacy in an Introductory Programming Course
Venue: arXiv cs.CY 2017
Year: 2017

Contribution: This study examined student effort and performance in an introductory programming course with respect to student-held implicit theories and self-efficacy. Background: Implicit theories and self-efficacy shed a light into understanding academic success, which must be considered when developing effective learning strategies for programming. Research Questions: Are implicit theories of intelligence and programming, and programming-efficacy related to each other and student success in programming? Is it possible to predict student course performance using a subset of these constructs? Methodology: Two consecutive surveys (N=100 and N=81) were administered to non-CS engineering students in Işık University. Findings: Implicit theories and self-beliefs are interrelated and correlated with effort, performance, and previous failures in the course and students explain failure in programming course with "programming-aptitude is fixed" theory, and also that programming is a difficult task for themselves.