OpenRaiser/Intern-Atlas huggingface dataset:
The Evolving Ecosystem of Predatory Journals: A Case Study in Indian Perspective
Venue: arXiv cs.DL 2019
Year: 2019

Digital advancement in scholarly repositories has led to the emergence of a large number of open access predatory publishers that charge high article processing fees from authors but fail to provide necessary editorial and publishing services. Identifying and blacklisting such publishers has remained a research challenge due to the highly volatile scholarly publishing ecosystem. This paper presents a data-driven approach to study how potential predatory publishers are evolving and bypassing several regularity constraints. We empirically show the close resemblance of predatory publishers against reputed publishing groups. In addition to verifying standard constraints, we also propose distinctive signals gathered from network-centric properties to understand this evolving ecosystem better.
Hierarchical Back Projection Network for Image Super-Resolution
Venue: arXiv cs.CV 2019
Year: 2019

Deep learning based single image super-resolution methods use a large number of training datasets and have recently achieved great quality progress both quantitatively and qualitatively. Most deep networks focus on nonlinear mapping from low-resolution inputs to high-resolution outputs via residual learning without exploring the feature abstraction and analysis. We propose a Hierarchical Back Projection Network (HBPN), that cascades multiple HourGlass (HG) modules to bottom-up and top-down process features across all scales to capture various spatial correlations and then consolidates the best representation for reconstruction. We adopt the back projection blocks in our proposed network to provide the error correlated up and down-sampling process to replace simple deconvolution and pooling process for better estimation. A new Softmax based Weighted Reconstruction (WR) process is used to combine the outputs of HG modules to further improve super-resolution. Experimental results on various datasets (including the validation dataset, NTIRE2019, of the Real Image Super-resolution Challenge) show that our proposed approach can achieve and improve the performance of the state-of-the-art methods for different scaling factors.
Recursive Style Breach Detection with Multifaceted Ensemble Learning
Venue: arXiv cs.CL 2019
Year: 2019

We present a supervised approach for style change detection, which aims at predicting whether there are changes in the style in a given text document, as well as at finding the exact positions where such changes occur. In particular, we combine a TF.IDF representation of the document with features specifically engineered for the task, and we make predictions via an ensemble of diverse classifiers including SVM, Random Forest, AdaBoost, MLP, and LightGBM. Whenever the model detects that style change is present, we apply it recursively, looking to find the specific positions of the change. Our approach powered the winning system for the PAN@CLEF 2018 task on Style Change Detection.
Matching Patterns with Variables
Venue: arXiv cs.DS 2019
Year: 2019

A pattern p (i.e., a string of variables and terminals) matches a word w, if w can be obtained by uniformly replacing the variables of p by terminal words. The respective matching problem, i.e., deciding whether or not a given pattern matches a given word, is generally NP-complete, but can be solved in polynomial-time for classes of patterns with restricted structure. In this paper we overview a series of recent results related to efficient matching for patterns with variables, as well as a series of extensions of this problem.
Trimmed Action Recognition, Dense-Captioning Events in Videos, and Spatio-temporal Action Localization with Focus on ActivityNet Challenge 2019
Venue: arXiv cs.CV 2019
Year: 2019

This notebook paper presents an overview and comparative analysis of our systems designed for the following three tasks in ActivityNet Challenge 2019: trimmed action recognition, dense-captioning events in videos, and spatio-temporal action localization.
Simple Swarm Foraging Algorithm Based on Gradient Computation
Venue: arXiv cs.RO 2019
Year: 2019

Swarm foraging is a common test case application for multi-robot systems. In this paper we present a novel algorithm for controlling swarm robots with limited communication range and storage capacity to efficiently search for and retrieve targets within an unknown environment. In our approach, robots search using random walk and adjust their turn probability based on attraction and repulsion signals they sense from other robots. We compared our algorithm with five different variations reflecting absence or presence of attractive and/or repulsive communication signals. Our results show that best performance is achieved when both signals are used by robots for communication. Furthermore, we show through hardware experiments how the communication model we used in the simulation could be realized on real robots.
Mobility based network lifetime in wireless sensor networks: A review
Venue: arXiv cs.NI 2019
Year: 2019

Increasingly emerging technologies in micro-electromechanical systems and wireless communications allows a mobile wireless sensor networks (MWSN) to be a more and more powerful mean in many applications such as habitat and environmental monitoring, traffic observing, battlefield surveillance, smart homes and smart cities. Nevertheless, due to sensor battery constraints, energy-efficiently operating a MWSN is paramount importance in those applications; and a plethora of approaches have been proposed to elongate the network longevity at most possible. Therefore, this paper provides a comprehensive review on the developed methods that exploit mobility of sensor nodes and/or sink(s) to effectively maximize the lifetime of a MWSN. The survey systematically classifies the algorithms into categories where the MWSN is equipped with mobile sensor nodes, one mobile sink or multiple mobile sinks. How to drive the mobile sink(s) for energy efficiency in the network is also fully reviewed and reported.
On-board Deep Q-Network for UAV-assisted Online Power Transfer and Data Collection
Venue: arXiv cs.NI 2019
Year: 2019

Unmanned Aerial Vehicles (UAVs) with Microwave Power Transfer (MPT) capability provide a practical means to deploy a large number of wireless powered sensing devices into areas with no access to persistent power supplies. The UAV can charge the sensing devices remotely and harvest their data. A key challenge is online MPT and data collection in the presence of on-board control of a UAV (e.g., patrolling velocity) for preventing battery drainage and data queue overflow of the sensing devices, while up-to-date knowledge on battery level and data queue of the devices is not available at the UAV. In this paper, an on-board deep Q-network is developed to minimize the overall data packet loss of the sensing devices, by optimally deciding the device to be charged and interrogated for data collection, and the instantaneous patrolling velocity of the UAV. Specifically, we formulate a Markov Decision Process (MDP) with the states of battery level and data queue length of sensing devices, channel conditions, and waypoints given the trajectory of the UAV; and solve it optimally with Q-learning. Furthermore, we propose the on-board deep Q-network that can enlarge the state space of the MDP, and a deep reinforcement learning based scheduling algorithm that asymptotically derives the optimal solution online, even when the UAV has only outdated knowledge on the MDP states. Numerical results demonstrate that the proposed deep reinforcement learning algorithm reduces the packet loss by at least 69.2%, as compared to existing non-learning greedy algorithms.
Coverability is Undecidable in One-dimensional Pushdown Vector Addition Systems with Resets
Venue: arXiv cs.FL 2019
Year: 2019

We consider the model of pushdown vector addition systems with resets. These consist of vector addition systems that have access to a pushdown stack and have instructions to reset counters. For this model, we study the coverability problem. In the absence of resets, this problem is known to be decidable for one-dimensional pushdown vector addition systems, but decidability is open for general pushdown vector addition systems. Moreover, coverability is known to be decidable for reset vector addition systems without a pushdown stack. We show in this note that the problem is undecidable for one-dimensional pushdown vector addition systems with resets.
Using Trusted Execution Environments for Secure Stream Processing of Medical Data
Venue: arXiv cs.CR 2019
Year: 2019

Processing sensitive data, such as those produced by body sensors, on third-party untrusted clouds is particularly challenging without compromising the privacy of the users generating it. Typically, these sensors generate large quantities of continuous data in a streaming fashion. Such vast amount of data must be processed efficiently and securely, even under strong adversarial models. The recent introduction in the mass-market of consumer-grade processors with Trusted Execution Environments (TEEs), such as Intel SGX, paves the way to implement solutions that overcome less flexible approaches, such as those atop homomorphic encryption. We present a secure streaming processing system built on top of Intel SGX to showcase the viability of this approach with a system specifically fitted for medical data. We design and fully implement a prototype system that we evaluate with several realistic datasets. Our experimental results show that the proposed system achieves modest overhead compared to vanilla Spark while offering additional protection guarantees under powerful attackers and threat models.
NB-IoT Uplink Receiver Design and Performance Study
Venue: arXiv cs.NI 2019
Year: 2019

LTE Narrowband Internet of Things (NB-IoT) is a 3GPP defined cellular technology that is designed to enable connectivity to many low-cost and low power/throughput IoT devices running delay-tolerant applications. NB-IoT can coexist within LTE spectrum either in a standalone mode, in-band with LTE or in guard-band of LTE. With NB-IoT designed to operate in very low signal to noise power ratios, the uplink receiver design presents several challenges. In this paper the design and performance of a NB-IoT uplink receiver is studied in detail. First, receiver design for NB-IoT uplink channels, with corresponding mathematical analysis, is presented. Specifically, it is shown how the time/frequency structure of signals can be exploited to enhance the receiver performance. Second, the performance of each channel is characterized with both link level simulations and implementation on a commercially deployed Qualcomm(registered) FSM(Trade Mark) platform [1]. Comparisons against the 3GPP defined Radio Performance and Protocol aspect requirements are also provided. Finally, implementation details are addressed and discussions on proposed enhancements to NB-IoT in 3GPP Release 15 are provided. It is shown how the proposed receiver algorithms can be adopted to Release 15 enhancements with minor or no modifications. The work in this paper is of significance to system designers looking to implement efficient NB-IoT uplink receiver to coexist with legacy LTE systems.
Slicing the IO execution with ReLayTracer
Venue: arXiv cs.OS 2019
Year: 2019

Analyzing IO performance anomalies is a crucial task in various computing environments, ranging from large-scale cloud applications to desktop applications. However, the IO stack of modern operating systems is complicated, making it hard to understand the performance anomalies with existing tools. Kernel IO executions are frequently interrupted by internal kernel activities, requiring a sophisticated IO profile tool to deal with the noises. Furthermore, complicated interactions of concurrent IO requests cause different sources of tail latencies in kernel IO stack. As a consequence, developers want to know fine-grained latency profile across IO layers, which may differ in each IO requests. To meet the requirements, this paper suggests ReLayTracer, a per-request, per-layer IO profiler. ReLayTracer enables a detailed analysis to identify root causes of IO performance anomalies by providing per-layer latency distributions of each IO request, hardware performance behavior, and time spent by kernel activities such as an interrupt.
Danger of using fully homomorphic encryption: A look at Microsoft SEAL
Venue: arXiv cs.CR 2019
Year: 2019

Fully homomorphic encryption is a promising crypto primitive to encrypt your data while allowing others to compute on the encrypted data. But there are many well-known problems with fully homomorphic encryption such as CCA security and circuit privacy problem. Despite these problems, there are still many companies are currently using or preparing to use fully homomorphic encryption to build data security applications. It seems that the full homomorphic encryption is very close to practicality and these problems can be easily mitigated in implementation. Although the those problems are well known in theory, there is no public discussion of their actual impact on real application. Our research shows that there are many security pitfalls in fully homomorphic encryption from the perspective of practical application. The security problems of a fully homomorphic encryption in a real application is more severe than imagined. In this paper, we will take Microsoft SEAL as an examples to introduce the security pitfalls of fully homomorphic encryption from the perspective of implementation and practical application
Impact of HTTP Cookie Violations in Web Archives
Venue: arXiv cs.DL 2019
Year: 2019

Certain HTTP Cookies on certain sites can be a source of content bias in archival crawls. Accommodating Cookies at crawl time, but not utilizing them at replay time may cause cookie violations, resulting in defaced composite mementos that never existed on the live web. To address these issues, we propose that crawlers store Cookies with short expiration time and archival replay systems account for values in the Vary header along with URIs.
Modeling Music Modality with a Key-Class Invariant Pitch Chroma CNN
Venue: arXiv cs.SD 2019
Year: 2019

This paper presents a convolutional neural network (CNN) that uses input from a polyphonic pitch estimation system to predict perceived minor/major modality in music audio. The pitch activation input is structured to allow the first CNN layer to compute two pitch chromas focused on different octaves. The following layers perform harmony analysis across chroma and time scales. Through max pooling across pitch, the CNN becomes invariant with regards to the key class (i.e., key disregarding mode) of the music. A multilayer perceptron combines the modality activation output with spectral features for the final prediction. The study uses a dataset of 203 excerpts rated by around 20 listeners each, a small challenging data size requiring a carefully designed parameter sharing. With an R2 of about 0.71, the system clearly outperforms previous systems as well as individual human listeners. A final ablation study highlights the importance of using pitch activations processed across longer time scales, and using pooling to facilitate invariance with regards to the key class.
PolSAR Image Classification based on Polarimetric Scattering Coding and Sparse Support Matrix Machine
Venue: arXiv cs.CV 2019
Year: 2019

POLSAR image has an advantage over optical image because it can be acquired independently of cloud cover and solar illumination. PolSAR image classification is a hot and valuable topic for the interpretation of POLSAR image. In this paper, a novel POLSAR image classification method is proposed based on polarimetric scattering coding and sparse support matrix machine. First, we transform the original POLSAR data to get a real value matrix by the polarimetric scattering coding, which is called polarimetric scattering matrix and is a sparse matrix. Second, the sparse support matrix machine is used to classify the sparse polarimetric scattering matrix and get the classification map. The combination of these two steps takes full account of the characteristics of POLSAR. The experimental results show that the proposed method can get better results and is an effective classification method.
An IoT Based Framework For Activity Recognition Using Deep Learning Technique
Venue: arXiv cs.LG 2019
Year: 2019

Activity recognition is the ability to identify and recognize the action or goals of the agent. The agent can be any object or entity that performs action that has end goals. The agents can be a single agent performing the action or group of agents performing the actions or having some interaction. Human activity recognition has gained popularity due to its demands in many practical applications such as entertainment, healthcare, simulations and surveillance systems. Vision based activity recognition is gaining advantage as it does not require any human intervention or physical contact with humans. Moreover, there are set of cameras that are networked with the intention to track and recognize the activities of the agent. Traditional applications that were required to track or recognize human activities made use of wearable devices. However, such applications require physical contact of the person. To overcome such challenges, vision based activity recognition system can be used, which uses a camera to record the video and a processor that performs the task of recognition. The work is implemented in two stages. In the first stage, an approach for the Implementation of Activity recognition is proposed using background subtraction of images, followed by 3D- Convolutional Neural Networks. The impact of using Background subtraction prior to 3D-Convolutional Neural Networks has been reported. In the second stage, the work is further extended and implemented on Raspberry Pi, that can be used to record a stream of video, followed by recognizing the activity that was involved in the video. Thus, a proof-of-concept for activity recognition using small, IoT based device, is provided, which can enhance the system and extend its applications in various forms like, increase in portability, networking, and other capabilities of the device.
Pose Guided Fashion Image Synthesis Using Deep Generative Model
Venue: arXiv cs.CV 2019
Year: 2019

Generating a photorealistic image with intended human pose is a promising yet challenging research topic for many applications such as smart photo editing, movie making, virtual try-on, and fashion display. In this paper, we present a novel deep generative model to transfer an image of a person from a given pose to a new pose while keeping fashion item consistent. In order to formulate the framework, we employ one generator and two discriminators for image synthesis. The generator includes an image encoder, a pose encoder and a decoder. The two encoders provide good representation of visual and geometrical context which will be utilized by the decoder in order to generate a photorealistic image. Unlike existing pose-guided image generation models, we exploit two discriminators to guide the synthesis process where one discriminator differentiates between generated image and real images (training samples), and another discriminator verifies the consistency of appearance between a target pose and a generated image. We perform end-to-end training of the network to learn the parameters through back-propagation given ground-truth images. The proposed generative model is capable of synthesizing a photorealistic image of a person given a target pose. We have demonstrated our results by conducting rigorous experiments on two data sets, both quantitatively and qualitatively.
Crowdsourcing in the Absence of Ground Truth -- A Case Study
Venue: arXiv cs.HC 2019
Year: 2019

Crowdsourcing information constitutes an important aspect of human-in-the-loop learning for researchers across multiple disciplines such as AI, HCI, and social science. While using crowdsourced data for subjective tasks is not new, eliciting useful insights from such data remains challenging due to a variety of factors such as difficulty of the task, personal prejudices of the human evaluators, lack of question clarity, etc. In this paper, we consider one such subjective evaluation task, namely that of estimating experienced emotions of distressed individuals who are conversing with a human listener in an online coaching platform. We explore strategies to aggregate the evaluators choices, and show that a simple voting consensus is as effective as an optimum aggregation method for the task considered. Intrigued by how an objective assessment would compare to the subjective evaluation of evaluators, we also designed a machine learning algorithm to perform the same task. Interestingly, we observed a machine learning algorithm that is not explicitly modeled to characterize evaluators' subjectivity is as reliable as the human evaluation in terms of assessing the most dominant experienced emotions.
Reputation Systems -- Fair allocation of points to the editors in the collaborative community
Venue: arXiv cs.SE 2019
Year: 2019

In this paper we are trying to determine a scheme for the fair allocation of points to the contributors of the collaborative community. The major problem of fair allocation of points among the contributors is that we have to analyze the improvement in the versions of an article. Lets say there is a contribution of major change in content which is relevant vs the contribution of adding a single comma. Every contributor cannot be given the same points in such a case. There are many ways which can be used like number of changes in a new version. That might seem relevant but it becomes irrelevant in terms of correct content contribution and other significant changes. There is no AI system too which can detect such a change and award the points accordingly. So this problem of allocation of points to the contributors is presented by an algorithm with a theoretical proof. It relies on the interactive interaction of the users in the system which is trivial in case of big system design economies.
On the Tractability of Public Persuasion with No Externalities
Venue: arXiv cs.GT 2019
Year: 2019

Persuasion studies how a principal can influence agents' decisions via strategic information revelation --- often described as a signaling scheme --- in order to yield the most desirable equilibrium outcome. Recently, there has been a large body of algorithmic study of designing optimal public signaling schemes, a.k.a., public persuasion, which however is rifle with computational intractability results. In this paper, we design efficient and tight algorithms for public persuasion, and focus on a fundamental multi-agent persuasion model with no inter-agent externalities and binary actions introduced by Arieli and Babichenko. En route, we develop new algorithmic techniques which may be of independent interests. First, we prove that optimal public persuasion is fixed parameter tractable. Our main result here relies on an interesting connection to a basic question in combinatorial geometry: how many cells can $n$ hyperplanes divide $R^d$ into? We use this connection to show a new characterization of public persuasion, which then enables efficient algorithm design. Second, we relax agent incentives and show that optimal public persuasion admits a bi-criteria PTAS for monotone submodular objectives and this approximation is tight. To prove this result, we establish an intriguing "noise stability" property of submodular functions which strictly generalizes the key result of Cheraghchi et al., originally motivated by applications of learning submodular functions and differential privacy. Finally, motivated by automated persuasion implemented as software, we consider relaxing the equilibrium concept of the model to coarse correlated equilibrium. Here we use a sophisticated primal-dual analysis to establish the polynomial-time equivalence between optimal public persuasion and the combinatorial problem of directly maximizing the sender's objective minus any linear function.