OpenRaiser/Intern-Atlas huggingface dataset:
Technical Report: Using Loop Scopes with for-Loops
Venue: arXiv cs.PL 2019
Year: 2019

Loop scopes have been shown to be a helpful tool in creating sound loop invariant rules which do not require program transformation of the loop body. Here we extend this idea from while-loops to for-loops and also present sound loop unrolling rules for while- and for-loops, which require neither program transformation of the loop body, nor the use of nested modalities. This approach allows for-loops to be treated as first-class citizens -- rather than the usual approach of transforming for-loops into while-loops -- which makes semi-automated proofs easier to follow for the user, who may need to provide help in order to finish the proof.
Achieving Vanishing Rate Loss in Decentralized Network MIMO
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we analyze a Network MIMO channel with 2 Transmitters (TXs) jointly serving 2 users, where each TX has a different multi-user Channel State Information (CSI), potentially with a different accuracy. Recently it was shown the surprising result that this decentralized setting can attain the same Degrees-of-Freedom (DoF) as its genie-aided centralized counterpart in which both TXs share the best-quality CSI. However, the DoF derivation alone does not characterize the actual rate and the question was left open as to how big the rate gap between the centralized and the decentralized settings was going to be. In this paper, we considerably strengthen the previous intriguing DoF result by showing that it is possible to achieve asymptotically the same sum rate as that attained by Zero-Forcing (ZF) precoding in a centralized setting endowed with the best-quality CSI. This result involves a novel precoding scheme which is tailored to the decentralized case. The key intuition behind this scheme lies in the striking of an asymptotically optimal compromise between i) realizing high enough precision ZF precoding while ii) maintaining consistent-enough precoding decisions across the non-communicating cooperating TXs.
Agile Collaboration for Distributed Teams
Venue: arXiv cs.SE 2019
Year: 2019

Editor Introduction: Today software engineering is characterized by two strong trends: agile and distributed. Both together are increasingly demanded and challenge teams and projects due to lack of discipline, insufficient transparency, agile "ping pong" and thus overheads and rework. Authors Fabio Calefato and I describe current technologies and tools for agile collaboration. I look forward to hearing from both readers and prospective column authors about this column and the technologies you want to know more about. -- Christof Ebert
The Capacity of Multi-round Private Information Retrieval from Byzantine Databases
Venue: arXiv cs.IT 2019
Year: 2019

In this work, we investigate the capacity of private information retrieval (PIR) from $N$ replicated databases, where a subset of the databases are untrustworthy (byzantine) in their answers to the query of the user. We allow for multi-round queries and demonstrate that the identities of the byzantine databases can be determined with a small additional download cost. As a result, the capacity of the multi-round PIR with byzantine databases (BPIR) reaches that of the robust PIR problem when the number of byzantine databases is less than the number of trustworthy databases.
SUMNet: Fully Convolutional Model for Fast Segmentation of Anatomical Structures in Ultrasound Volumes
Venue: arXiv cs.CV 2019
Year: 2019

Ultrasound imaging is generally employed for real-time investigation of internal anatomy of the human body for disease identification. Delineation of the anatomical boundary of organs and pathological lesions is quite challenging due to the stochastic nature of speckle intensity in the images, which also introduces visual fatigue for the observer. This paper introduces a fully convolutional neural network based method to segment organ and pathologies in ultrasound volume by learning the spatial-relationship between closely related classes in the presence of stochastically varying speckle intensity. We propose a convolutional encoder-decoder like framework with (i) feature concatenation across matched layers in encoder and decoder and (ii) index passing based unpooling at the decoder for semantic segmentation of ultrasound volumes. We have experimentally evaluated the performance on publicly available datasets consisting of $10$ intravascular ultrasound pullback acquired at $20$ MHz and $16$ freehand thyroid ultrasound volumes acquired $11 - 16$ MHz. We have obtained a dice score of $0.93 \pm 0.08$ and $0.92 \pm 0.06$ respectively, following a $10$-fold cross-validation experiment while processing frame of $256 \times 384$ pixel in $0.035$s and a volume of $256 \times 384 \times 384$ voxel in $13.44$s.
Discovering Eastern European PCs by hacking them. Today
Venue: arXiv cs.CY 2019
Year: 2019

Computer science would not be the same without personal computers. In the West the so called PC revolution started in the late '70s and has its roots in hobbyists and do-it-yourself clubs. In the following years the diffusion of home and personal computers has made the discipline closer to many people. A bit later, to a lesser extent, yet in a similar way, the revolution took place also in East European countries. Today, the scenario of personal computing has completely changed, however the computers of the '80s are still objects of fascination for a number of retrocomputing fans who enjoy using, programming and hacking the old 8-bits. The paper highlights the continuity between yesterday's hobbyists and today's retrocomputing enthusiasts, particularly focusing on East European PCs. Besides the preservation of old hardware and software, the community is engaged in the development of emulators and cross compilers. Such tools can be used for historical investigation, for example to trace the origins of the BASIC interpreters loaded in the ROMs of East European PCs.
We Are Not Your Real Parents: Telling Causal from Confounded using MDL
Venue: arXiv cs.LG 2019
Year: 2019

Given data over variables $(X_1,...,X_m, Y)$ we consider the problem of finding out whether $X$ jointly causes $Y$ or whether they are all confounded by an unobserved latent variable $Z$. To do so, we take an information-theoretic approach based on Kolmogorov complexity. In a nutshell, we follow the postulate that first encoding the true cause, and then the effects given that cause, results in a shorter description than any other encoding of the observed variables. The ideal score is not computable, and hence we have to approximate it. We propose to do so using the Minimum Description Length (MDL) principle. We compare the MDL scores under the models where $X$ causes $Y$ and where there exists a latent variables $Z$ confounding both $X$ and $Y$ and show our scores are consistent. To find potential confounders we propose using latent factor modeling, in particular, probabilistic PCA (PPCA). Empirical evaluation on both synthetic and real-world data shows that our method, CoCa, performs very well -- even when the true generating process of the data is far from the assumptions made by the models we use. Moreover, it is robust as its accuracy goes hand in hand with its confidence.
Domain Adaptation for sEMG-based Gesture Recognition with Recurrent Neural Networks
Venue: arXiv cs.LG 2019
Year: 2019

Surface Electromyography (sEMG/EMG) is to record muscles' electrical activity from a restricted area of the skin by using electrodes. The sEMG-based gesture recognition is extremely sensitive of inter-session and inter-subject variances. We propose a model and a deep-learning-based domain adaptation method to approximate the domain shift for recognition accuracy enhancement. Analysis performed on sparse and HighDensity (HD) sEMG public datasets validate that our approach outperforms state-of-the-art methods.
A Lower Bound on the Error Exponent of Linear Block Codes over the Erasure Channel
Venue: arXiv cs.IT 2019
Year: 2019

A lower bound on the maximum likelihood (ML) decoding error exponent of linear block code ensembles, on the erasure channel, is developed. The lower bound turns to be positive, over an ensemble specific interval of erasure probabilities, when the ensemble weight spectral shape function tends to a negative value as the fractional codeword weight tends to zero. For these ensembles we can therefore lower bound the block-wise ML decoding threshold. Two examples are presented, namely, linear random parity-check codes and fixed-rate Raptor codes with linear random precoders. While for the former a full analytical solution is possible, for the latter we can lower bound the ML decoding threshold on the erasure channel by simply solving a 2 x 2 system of nonlinear equations.
Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy
Venue: arXiv cs.CV 2019
Year: 2019

In recent years, endomicroscopy has become increasingly used for diagnostic purposes and interventional guidance. It can provide intraoperative aids for real-time tissue characterization and can help to perform visual investigations aimed for example to discover epithelial cancers. Due to physical constraints on the acquisition process, endomicroscopy images, still today have a low number of informative pixels which hampers their quality. Post-processing techniques, such as Super-Resolution (SR), are a potential solution to increase the quality of these images. SR techniques are often supervised, requiring aligned pairs of low-resolution (LR) and high-resolution (HR) images patches to train a model. However, in our domain, the lack of HR images hinders the collection of such pairs and makes supervised training unsuitable. For this reason, we propose an unsupervised SR framework based on an adversarial deep neural network with a physically-inspired cycle consistency, designed to impose some acquisition properties on the super-resolved images. Our framework can exploit HR images, regardless of the domain where they are coming from, to transfer the quality of the HR images to the initial LR images. This property can be particularly useful in all situations where pairs of LR/HR are not available during the training. Our quantitative analysis, validated using a database of 238 endomicroscopy video sequences from 143 patients, shows the ability of the pipeline to produce convincing super-resolved images. A Mean Opinion Score (MOS) study also confirms this quantitative image quality assessment.
Development of verification system of socio-demographic data of virtual community member
Venue: arXiv cs.CY 2019
Year: 2019

The important task of developing verification system of data of virtual community member on the basis of computer-linguistic analysis of the content of a large sample of Ukrainian virtual communities is solved. The subject of research is methods and tools for verification of web-members socio-demographic characteristics based on computer-linguistic analysis of their communicative interaction results. The aim of paper is to verifying web-user personal data on the basis of computer-linguistic analysis of web-members information tracks. The structure of verification software for web-user profile is designed for a practical implementation of assigned tasks. The method of personal data verification of web-members by analyzing information track of virtual community member is conducted. For the first time the method for checking the authenticity of web members personal data, which helped to design of verification tool for socio-demographic characteristics of web-member is developed. The verification system of data of web-members, which forms the verified socio-demographic profiles of web-members, is developed as a result of conducted experiments. Also the user interface of the developed verification system web-members data is presented. Effectiveness and efficiency of use of the developed methods and means for solving tasks in web-communities administration is proved by their approbation. The number of false results of verification system is 18%.
Analyzing Branch-and-Bound Algorithms for the Multiprocessor Scheduling Problem
Venue: arXiv cs.DS 2019
Year: 2019

The Multiprocessor Scheduling Problem (MSP) is an NP-Complete problem with significant applications in computer and operations systems. We provide a survey of the wide array of polynomial-time approximation, heuristic, and meta-heuristic based algorithms that exist for solving MSP. We also implement Fujita's state-of-the-art Branch-and-Bound algorithm and evaluate the benefit of using Fujita's binary search bounding method instead of the Fernandez bound. We find that in fact Fujita's method does not offer any improvement over the Fernandez bound on our data set.
Large-Scale NOMA: Promises for Massive Machine-Type Communication
Venue: arXiv cs.NI 2019
Year: 2019

We investigate on large-scale deployment of non-orthogonal multiple access (NOMA) for improved spectral and power efficiency in cellular networks to provide massive wireless connectivity (e.g. for machine-type communication [mMTC]). First, we describe the basics of single-antenna NOMA technology and its extension to co-located multiple-antenna NOMA as well as coordinated multipoint transmission (CoMP)-enabled NOMA technologies. Then we discuss some of the practical challenges of large-scale deployment of NOMA such as the inter-NOMA-interference (INI), inter-cell interference, and hardware implementation complexity. To this end, we present one key enabling technique to overcome the challenges of large-scale deployment of NOMA. Generally speaking, for a feasible large-scale NOMA implementation, sophisticated diversity enhancing techniques can be used to compensate for the degradation in coding gain and to decrease the complexity resulting from excessive INI and increased level of required successive interference cancellation (SIC). Furthermore, to massively extend NOMA over the network coverage area, NOMA transmitters have to cooperate in a generalized manner to serve all nearby users simultaneously.
Complete weight enumerators of a class of linear codes with two or three weights
Venue: arXiv cs.IT 2019
Year: 2019

We construct a class of linear codes by choosing a proper defining set and determine their complete weight enumerators and weight enumerators. The results show that they are at most three-weight codes and they are suitable for applications in secret sharing schemes. We give an extension of the results raised by Wang et al.(2017).
Generation High resolution 3D model from natural language by Generative Adversarial Network
Venue: arXiv cs.GR 2019
Year: 2019

We present a method of generating high resolution 3D shapes from natural language descriptions. To achieve this goal, we propose two steps that generating low resolution shapes which roughly reflect texts and generating high resolution shapes which reflect the detail of texts. In a previous paper, the authors have shown a method of generating low resolution shapes. We improve it to generate 3D shapes more faithful to natural language and test the effectiveness of the method. To generate high resolution 3D shapes, we use the framework of Conditional Wasserstein GAN. We propose two roles of Critic separately, which calculate the Wasserstein distance between two probability distribution, so that we achieve generating high quality shapes or acceleration of learning speed of model. To evaluate our approach, we performed quantitive evaluation with several numerical metrics for Critic models. Our method is first to realize the generation of high quality model by propagating text embedding information to high resolution task when generating 3D model.
A $(4+ε)$-approximation for $k$-connected subgraphs
Venue: arXiv cs.DS 2019
Year: 2019

We obtain approximation ratio $2(2+\frac{1}{\ell})$ for the (undirected) $k$-Connected Subgraph problem, where $\ell \approx \frac{1}{2} (\log_k n-1)$ is the largest integer such that $2^{\ell-1} k^{2\ell+1} \leq n$. For large values of $n$ this improves the $6$-approximation of Cheriyan and Végh when $n =Ω(k^3)$, which is the case $\ell=1$. For $k$ bounded by a constant we obtain ratio $4+ε$. For large values of $n$ our ratio matches the best known ratio $4$ for the augmentation version of the problem, as well as the best known ratios for $k=6,7$. Similar results are shown for the problem of covering an arbitrary crossing supermodular biset function.
Hidden Treasures - Recycling Large-Scale Internet Measurements to Study the Internet's Control Plane
Venue: arXiv cs.NI 2019
Year: 2019

Internet-wide scans are a common active measurement approach to study the Internet, e.g., studying security properties or protocol adoption. They involve probing large address ranges (IPv4 or parts of IPv6) for specific ports or protocols. Besides their primary use for probing (e.g., studying protocol adoption), we show that - at the same time - they provide valuable insights into the Internet control plane informed by ICMP responses to these probes - a currently unexplored secondary use. We collect one week of ICMP responses (637.50M messages) to several Internet-wide ZMap scans covering multiple TCP and UDP ports as well as DNS-based scans covering > 50% of the domain name space. This perspective enables us to study the Internet's control plane as a by-product of Internet measurements. We receive ICMP messages from ~171M different IPs in roughly 53K different autonomous systems. Additionally, we uncover multiple control plane problems, e.g., we detect a plethora of outdated and misconfigured routers and uncover the presence of large-scale persistent routing loops in IPv4.
Adversarial Pseudo Healthy Synthesis Needs Pathology Factorization
Venue: arXiv cs.CV 2019
Year: 2019

Pseudo healthy synthesis, i.e. the creation of a subject-specific `healthy' image from a pathological one, could be helpful in tasks such as anomaly detection, understanding changes induced by pathology and disease or even as data augmentation. We treat this task as a factor decomposition problem: we aim to separate what appears to be healthy and where disease is (as a map). The two factors are then recombined (by a network) to reconstruct the input disease image. We train our models in an adversarial way using either paired or unpaired settings, where we pair disease images and maps (as segmentation masks) when available. We quantitatively evaluate the quality of pseudo healthy images. We show in a series of experiments, performed in ISLES and BraTS datasets, that our method is better than conditional GAN and CycleGAN, highlighting challenges in using adversarial methods in the image translation task of pseudo healthy image generation.
Design and Decoding of Polar Codes for the Gaussian Multiple Access Channel
Venue: arXiv cs.IT 2019
Year: 2019

Massive machine-type communications (mMTC) is one of the key application scenarios for future 5G networks. Non-orthogonal multiple access (NOMA) is a promising technique for the use in mMTC scenario. In this paper, we investigate NOMA schemes based on polar codes. We compare two possible decoding techniques: joint successive cancellation algorithm and joint iterative algorithm. In order to optimize the codes (choose frozen bits) we propose a special and efficient design algorithm. We investigate the performance of the resulting scheme in the Gaussian multiple access channel (GMAC) by means of simulations. The scheme is shown to outperform LDPC based solution by approximately 1 dB and to be close to the achievability bound for GMAC.
Malware Detection Using Dynamic Birthmarks
Venue: arXiv cs.CR 2019
Year: 2019

In this paper, we explore the effectiveness of dynamic analysis techniques for identifying malware, using Hidden Markov Models (HMMs) and Profile Hidden Markov Models (PHMMs), both trained on sequences of API calls. We contrast our results to static analysis using HMMs trained on sequences of opcodes, and show that dynamic analysis achieves significantly stronger results in many cases. Furthermore, in contrasting our two dynamic analysis techniques, we find that using PHMMs consistently outperforms our analysis based on HMMs.
Android Malware Detection Using Autoencoder
Venue: arXiv cs.CR 2019
Year: 2019

Smartphones have become an intrinsic part of human's life. The smartphone unifies diverse advanced characteristics. It enables users to store various data such as photos, health data, credential bank data, and personal information. The Android operating system is the prevalent mobile operating system and, in the meantime, the most targeted operating system by malware developers. Recently the unparalleled development of Android malware put pressure on researchers to propose effective methods to suppress the spread of the malware. In this paper, we propose a deep learning approach for Android malware detection. The proposed approach investigates five different feature sets and applies Autoencoder to identify malware. The experimental results show that the proposed approach can identify malware with high accuracy.
Measuring Effectiveness of Video Advertisements
Venue: arXiv cs.CV 2019
Year: 2019

Advertisements are unavoidable in modern society. Times Square is notorious for its incessant display of advertisements. Its popularity is worldwide and smaller cities possess miniature versions of the display, such as Pittsburgh and its digital works in Oakland on Forbes Avenue. Tokyo's Ginza district recently rose to popularity due to its upscale shops and constant onslaught of advertisements to pedestrians. Advertisements arise in other mediums as well. For example, they help popular streaming services, such as Spotify, Hulu, and Youtube TV gather significant streams of revenue to reduce the cost of monthly subscriptions for consumers. Ads provide an additional source of money for companies and entire industries to allocate resources toward alternative business motives. They are attractive to companies and nearly unavoidable for consumers. One challenge for advertisers is examining a advertisement's effectiveness or usefulness in conveying a message to their targeted demographics. Rather than constructing a single, static image of content, a video advertisement possesses hundreds of frames of data with varying scenes, actors, objects, and complexity. Therefore, measuring effectiveness of video advertisements is important to impacting a billion-dollar industry. This paper explores the combination of human-annotated features and common video processing techniques to predict effectiveness ratings of advertisements collected from Youtube. This task is seen as a binary (effective vs. non-effective), four-way, and five-way machine learning classification task. The first findings in terms of accuracy and inference on this dataset, as well as some of the first ad research, on a small dataset are presented. Accuracies of 84\%, 65\%, and 55\% are reached on the binary, four-way, and five-way tasks respectively.
Extension of Convolutional Neural Network with General Image Processing Kernels
Venue: arXiv cs.CV 2019
Year: 2019

We applied pre-defined kernels also known as filters or masks developed for image processing to convolution neural network. Instead of letting neural networks find its own kernels, we used 41 different general-purpose kernels of blurring, edge detecting, sharpening, discrete cosine transformation, etc. for the first layer of the convolution neural networks. This architecture, thus named as general filter convolutional neural network (GFNN), can reduce training time by 30% with a better accuracy compared to the regular convolutional neural network (CNN). GFNN also can be trained to achieve 90% accuracy with only 500 samples. Furthermore, even though these kernels are not specialized for the MNIST dataset, we achieved 99.56% accuracy without ensemble nor any other special algorithms.