OpenRaiser/Intern-Atlas huggingface dataset:
Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses
Venue: arXiv cs.LG 2018
Year: 2018

The effectiveness of learning in massive open online courses (MOOCs) can be significantly enhanced by introducing personalized intervention schemes which rely on building predictive models of student learning behaviors such as some engagement or performance indicators. A major challenge that has to be addressed when building such models is to design handcrafted features that are effective for the prediction task at hand. In this paper, we make the first attempt to solve the feature learning problem by taking the unsupervised learning approach to learn a compact representation of the raw features with a large degree of redundancy. Specifically, in order to capture the underlying learning patterns in the content domain and the temporal nature of the clickstream data, we train a modified auto-encoder (AE) combined with the long short-term memory (LSTM) network to obtain a fixed-length embedding for each input sequence. When compared with the original features, the new features that correspond to the embedding obtained by the modified LSTM-AE are not only more parsimonious but also more discriminative for our prediction task. Using simple supervised learning models, the learned features can improve the prediction accuracy by up to 17% compared with the supervised neural networks and reduce overfitting to the dominant low-performing group of students, specifically in the task of predicting students' performance. Our approach is generic in the sense that it is not restricted to a specific supervised learning model nor a specific prediction task for MOOC learning analytics.
Consensus and Disagreement of Heterogeneous Belief Systems in Influence Networks
Venue: arXiv cs.SI 2018
Year: 2018

Recently, an opinion dynamics model has been proposed to describe a network of individuals discussing a set of logically interdependent topics. For each individual, the set of topics and the logical interdependencies between the topics (captured by a logic matrix) form a belief system. We investigate the role the logic matrix and its structure plays in determining the final opinions, including existence of the limiting opinions, of a strongly connected network of individuals. We provide a set of results that, given a set of individuals' belief systems, allow a systematic determination of which topics will reach a consensus, and which topics will disagreement in arise. For irreducible logic matrices, each topic reaches a consensus. For reducible logic matrices, which indicates a cascade interdependence relationship, conditions are given on whether a topic will reach a consensus or not. It turns out that heterogeneity among the individuals' logic matrices, including especially differences in the signs of the off-diagonal entries, can be a key determining factor. This paper thus attributes, for the first time, a strong diversity of limiting opinions to heterogeneity of belief systems in influence networks, in addition to the more typical explanation that strong diversity arises from individual stubbornness.
Analyzing Popularity of Software Testing Careers in Canada
Venue: arXiv cs.CY 2018
Year: 2018

Software testing is critical to prevent software failures. Therefore, research has been carried out in testing but that is largely limited to the process and technology dimensions and has not sufficiently addressed the human dimension. Even though there are reports about inadequacies of testing professionals and their skills, only a few studies have tackled the problem. Therefore, we decided to explore the human dimension. We started with the basic problem that plagues the testing profession, the shortage of talent, by asking why do students and professionals are reluctant to consider testing careers, what can be done about that, and is the problem specific to locales or spread across the globe? This paper focusses on these questions. The study was carried out in one college in Canada and its findings are compared with a college in India. Studies in more colleges is required to develop acceptable national views. It also may help to study this phenomenon in more countries and develop global perspectives on the issue. However, the study certainly offers useful insights and helps educators and industry leaders to come up with an action plan to change the outlook towards testers in industry and in computer science and software engineering programs, and put the software testing profession under a new light. That could increase the number of software engineers deciding on testing as a career of their choice, could increase the quality of software testing, and improve the overall productivity, and turnaround time of software development activity.
Deep Learning Framework for Wireless Systems: Applications to Optical Wireless Communications
Venue: arXiv cs.IT 2018
Year: 2018

Optical wireless communication (OWC) is a promising technology for future wireless communications owing to its potentials for cost-effective network deployment and high data rate. There are several implementation issues in the OWC which have not been encountered in radio frequency wireless communications. First, practical OWC transmitters need an illumination control on color, intensity, and luminance, etc., which poses complicated modulation design challenges. Furthermore, signal-dependent properties of optical channels raise non-trivial challenges both in modulation and demodulation of the optical signals. To tackle such difficulties, deep learning (DL) technologies can be applied for optical wireless transceiver design. This article addresses recent efforts on DL-based OWC system designs. A DL framework for emerging image sensor communication is proposed and its feasibility is verified by simulation. Finally, technical challenges and implementation issues for the DL-based optical wireless technology are discussed.
Comparing several calculi for first-order infinite-valued Łukasiewicz logic
Venue: arXiv cs.LO 2018
Year: 2018

From the viewpoint of provability, we compare some Gentzen-type hypersequent calculi for first-order infinite-valued Łukasiewicz logic and for first-order rational Pavelka logic with each other and with Hájek's Hilbert-type calculi for these logics. The key aspect of our comparison is a density elimination proof for one of the hypersequent calculi considered.
MMM: May I Mine Your Mind?
Venue: arXiv cs.CY 2018
Year: 2018

Consider the following set-up for the plot of a possible future episode of the TV series Black Mirror: human brains can be connected directly to the net and MiningMind Inc. has developed a technology that merges a reward system with a cryptojacking engine that uses the human brain to mine cryptocurrency (or to carry out some other mining activity). Part of our brain will be committed to cryptographic calculations (mining), leaving the remaining part untouched for everyday operations, i.e., for our brain's normal daily activity. In this short paper, we briefly argue why this set-up might not be so far fetched after all, and explore the impact that such a technology could have on our lives and our society.
Profit and Strategic Analysis for MNO-MVNO Partnership
Venue: arXiv cs.NI 2018
Year: 2018

We consider a mobile market driven by two Mobile Network Operators (MNOs) and a new competitor Mobile Virtual Network Operator (MVNO). The MNOs can partner with the entrant MVNO by leasing network resources; however, the MVNO can also rely on other technologies such as free WiFi access points. Moreover, in addition to its connectivity offer, the MVNO can also draw indirect revenues from services due to its brand. In that framework including many access technologies and several revenue sources, a possible partner MNO will then have to decide which wholesale price to charge the MVNO for its resources. This multi-actor context, added to the need to consider both wholesale and retail markets, represents a new challenge for the underlying decision-making process. In this paper, the optimal price setting is formulated as a multi-level optimization problem which enables us to derive closed-form expressions for the optimal MNOs wholesale prices and the optimal MVNO retail price. The price attractivity of the MVNO is also evaluated in terms of its indirect revenues and the proportion of resources leased from possible partner MNOs. Finally, through a game-theoretical approach, we characterize the scenario where both MNOs partner with the MVNO as the unique Nash equilibrium under appropriate conditions.
The dangerous path towards your own cryptography method
Venue: arXiv cs.CR 2018
Year: 2018

Would you like to have your own cryptography method? Experts say you should not do it. If you think you can develop a better cryptography method anyway. We present a brief discussion about some well known cryptography methods and how our model fails against the traditional attacks. We do not want to discourage anybody, we just want to show that, despite of the importance of developing better cryptography models, it is a very hard task.
Discrete model for cloud computing: Analysis of data security and data loss
Venue: arXiv cs.DC 2018
Year: 2018

Cloud computing is recognized as one of the most promising solutions to information technology, e.g., for storing and sharing data in the web service which is sustained by a company or third party instead of storing data in a hard drive or other devices. It is essentially a physical storage system which provides large storage of data and faster computing to users over the Internet. In this cloud system, the third party allows to preserve data of clients or users only for business purpose and also for a limited period of time. The users are used to share data confidentially among themselves and to store data virtually to save the cost of physical devices as well as the time. In this paper, we propose a discrete dynamical system for cloud computing and data management of the storage service between a third party and users. A framework, comprised of different techniques and procedures for distribution of storage and their implementation with users and the third party is given. For illustration purpose, the model is considered for two users and a third party, and its dynamical properties are briefly analyzed and discussed. It is shown that the discrete system exhibits periodic, quasiperiodic and chaotic states. The latter discerns that the cloud computing system with distribution of data and storage between users and the third party may be secured. Some issues of data security are discussed and a random replication scheme is proposed to ensure that the data loss can be highly reduced compared to the existing schemes in the literature.
A Polynomial Time Algorithm for Maximum Likelihood Estimation of Multivariate Log-concave Densities
Venue: arXiv cs.DS 2018
Year: 2018

We study the problem of computing the maximum likelihood estimator (MLE) of multivariate log-concave densities. Our main result is the first computationally efficient algorithm for this problem. In more detail, we give an algorithm that, on input a set of $n$ points in $\mathbb{R}^d$ and an accuracy parameter $ε>0$, it runs in time $\text{poly}(n, d, 1/ε)$, and outputs a log-concave density that with high probability maximizes the log-likelihood up to an additive $ε$. Our approach relies on a natural convex optimization formulation of the underlying problem that can be efficiently solved by a projected stochastic subgradient method. The main challenge lies in showing that a stochastic subgradient of our objective function can be efficiently approximated. To achieve this, we rely on structural results on approximation of log-concave densities and leverage classical algorithmic tools on volume approximation of convex bodies and uniform sampling from convex sets.
U-PoT: A Honeypot Framework for UPnP-Based IoT Devices
Venue: arXiv cs.CR 2018
Year: 2018

The ubiquitous nature of the IoT devices has brought serious security implications to its users. A lot of consumer IoT devices have little to no security implementation at all, thus risking user's privacy and making them target of mass cyber-attacks. Indeed, recent outbreak of Mirai botnet and its variants have already proved the lack of security on the IoT world. Hence, it is important to understand the security issues and attack vectors in the IoT domain. Though significant research has been done to secure traditional computing systems, little focus was given to the IoT realm. In this work, we reduce this gap by developing a honeypot framework for IoT devices. Specifically, we introduce U-PoT: a novel honeypot framework for capturing attacks on IoT devices that use Universal Plug and Play (UPnP) protocol. A myriad of smart home devices including smart switches, smart bulbs, surveillance cameras, smart hubs, etc. uses the UPnP protocol. Indeed, a simple search on Shodan IoT search engine lists 1,676,591 UPnP devices that are exposed to public network. The popularity and ubiquitous nature of UPnP-based IoT device necessitates a full-fledged IoT honeypot system for UPnP devices. Our novel framework automatically creates a honeypot from UPnP device description documents and is extendable to any device types or vendors that use UPnP for communication. To the best of our knowledge, this is the first work towards a flexible and configurable honeypot framework for UPnP-based IoT devices. We released U-PoT under an open source license for further research and created a database of UPnP device descriptions. We also evaluated our framework on two emulated deices. Our experiments show that the emulated devices are able to mimic the behavior of a real IoT device and trick vendor-provided device management applications or popular IoT search engines while having minimal performance ovherhead.
On the Capacity of Locally Decodable Codes
Venue: arXiv cs.IT 2018
Year: 2018

A locally decodable code (LDC) maps $K$ source symbols, each of size $L_w$ bits, to $M$ coded symbols, each of size $L_x$ bits, such that each source symbol can be decoded from $N \leq M$ coded symbols. A perfectly smooth LDC further requires that each coded symbol is uniformly accessed when we decode any one of the messages. The ratio $L_w/L_x$ is called the symbol rate of an LDC. The highest possible symbol rate for a class of LDCs is called the capacity of that class. It is shown that given $K, N$, the maximum value of capacity of perfectly smooth LDCs, maximized over all code lengths $M$, is $C^*=N\left(1+1/N+1/N^2+\cdots+1/N^{K-1}\right)^{-1}$. Furthermore, given $K, N$, the minimum code length $M$ for which the capacity of a perfectly smooth LDC is $C^*$ is shown to be $M = N^K$. Both of these results generalize to a broader class of LDCs, called universal LDCs. The results are then translated into the context of PIR$_{\max}$, i.e., Private Information Retrieval subject to maximum (rather than average) download cost metric. It is shown that the minimum upload cost of capacity achieving PIR$_{\max}$ schemes is $(K-1)\log N$. The results also generalize to a variation of the PIR problem, known as Repudiative Information Retrieval (RIR).
Benchmark Dataset for Automatic Damaged Building Detection from Post-Hurricane Remotely Sensed Imagery
Venue: arXiv cs.CV 2018
Year: 2018

Rapid damage assessment is of crucial importance to emergency responders during hurricane events, however, the evaluation process is often slow, labor-intensive, costly, and error-prone. New advances in computer vision and remote sensing open possibilities to observe the Earth at a different scale. However, substantial pre-processing work is still required in order to apply state-of-the-art methodology for emergency response. To enable the comparison of methods for automatic detection of damaged buildings from post-hurricane remote sensing imagery taken from both airborne and satellite sensors, this paper presents the development of benchmark datasets from publicly available data. The major contributions of this work include (1) a scalable framework for creating benchmark datasets of hurricane-damaged buildings and (2) public sharing of the resulting benchmark datasets for Greater Houston area after Hurricane Harvey in 2017. The proposed approach can be used to build other hurricane-damaged building datasets on which researchers can train and test object detection models to automatically identify damaged buildings.
LAMP: Prompt Layer 7 Attack Mitigation with Programmable Data Planes
Venue: arXiv cs.NI 2018
Year: 2018

While there are various methods to detect application layer attacks or intrusion attempts on an individual end host, it is not efficient to provide all end hosts in the network with heavy-duty defense systems or software firewalls. In this work, we leverage a new concept of programmable data planes, to directly react on alerts raised by a victim and prevent further attacks on the whole network by blocking the attack at the network edge. We call our design LAMP, Layer 7 Attack Mitigation with Programmable data planes. We implemented LAMP using the P4 data plane programming language and evaluated its effectiveness and efficiency in the Behavioral Model (bmv2) environment.
A corpus of precise natural textual entailment problems
Venue: arXiv cs.CL 2018
Year: 2018

In this paper, we present a new corpus of entailment problems. This corpus combines the following characteristics: 1. it is precise (does not leave out implicit hypotheses) 2. it is based on "real-world" texts (i.e. most of the premises were written for purposes other than testing textual entailment). 3. its size is 150. The corpus was constructed by taking problems from the Real Text Entailment and discovering missing hypotheses using a crowd of experts. We believe that this corpus constitutes a first step towards wide-coverage testing of precise natural-language inference systems.
Shared Bottleneck Detecction Based on Trend Line Regression for Multipath Transmission
Venue: arXiv cs.NI 2018
Year: 2018

The current deployed multipath congestion control algorithms couple all the subflows together to avoid bandwidth occupation aggressiveness if the subflows of multipath transmission protocol share common bottleneck with single path TCP. The coupled congestion control algorithms can guarantee well fairness property in common bottleneck but result in rate increase conservativeness in none-sharing bottleneck situation. Thus, the throughput of multipath session can be further improved when combing with effective shared bottleneck detection mechanism. This paper proposes a delay trend line regression method to detect if flows share common bottleneck. Deduced from TCP fluid model, the packet round trip delay signal shows linear increase property during the queue building up process of the narrowest link and the delay trend line slopes of two flows are in close proximity if they traverse the same bottleneck link. The proposed method is implemented on multipath QUIC golang codebase and extensive simulations are performed to validate its effectiveness in detecting out flows traversing common bottleneck. If the subflows are detected out via a common bottleneck, the sender would perform coupled congestion control algorithm and perform congestion control seperately on flow level in none sharing bottleneck case. Results show a multipath session with two subflows can obtain 74\% gain on average in throughput compared with single path connection when Linked Increases Algorithm (LIA) is in combination with trend line regession shared bottlenck detection algorithm in none shared bottleneck, and show well fairness property in common bottleneck scenarios.
Multi-hypothesis contextual modeling for semantic segmentation
Venue: arXiv cs.CV 2018
Year: 2018

Semantic segmentation (i.e. image parsing) aims to annotate each image pixel with its corresponding semantic class label. Spatially consistent labeling of the image requires an accurate description and modeling of the local contextual information. Segmentation result is typically improved by Markov Random Field (MRF) optimization on the initial labels. However this improvement is limited by the accuracy of initial result and how the contextual neighborhood is defined. In this paper, we develop generalized and flexible contextual models for segmentation neighborhoods in order to improve parsing accuracy. Instead of using a fixed segmentation and neighborhood definition, we explore various contextual models for fusion of complementary information available in alternative segmentations of the same image. In other words, we propose a novel MRF framework that describes and optimizes the contextual dependencies between multiple segmentations. Simulation results on two common datasets demonstrate significant improvement in parsing accuracy over the baseline approaches.
Detecting Reliable Novel Word Senses: A Network-Centric Approach
Venue: arXiv cs.CL 2018
Year: 2018

In this era of Big Data, due to expeditious exchange of information on the web, words are being used to denote newer meanings, causing linguistic shift. With the recent availability of large amounts of digitized texts, an automated analysis of the evolution of language has become possible. Our study mainly focuses on improving the detection of new word senses. This paper presents a unique proposal based on network features to improve the precision of new word sense detection. For a candidate word where a new sense (birth) has been detected by comparing the sense clusters induced at two different time points, we further compare the network properties of the subgraphs induced from novel sense cluster across these two time points. Using the mean fractional change in edge density, structural similarity and average path length as features in an SVM classifier, manual evaluation gives precision values of 0.86 and 0.74 for the task of new sense detection, when tested on 2 distinct time-point pairs, in comparison to the precision values in the range of 0.23-0.32, when the proposed scheme is not used. The outlined method can therefore be used as a new post-hoc step to improve the precision of novel word sense detection in a robust and reliable way where the underlying framework uses a graph structure. Another important observation is that even though our proposal is a post-hoc step, it can be used in isolation and that itself results in a very decent performance achieving a precision of 0.54-0.62. Finally, we show that our method is able to detect the well-known historical shifts in 80% cases.
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection
Venue: arXiv cs.LG 2018
Year: 2018

Unsupervised learning can leverage large-scale data sources without the need for annotations. In this context, deep learning-based auto encoders have shown great potential in detecting anomalies in medical images. However, state-of-the-art anomaly scores are still based on the reconstruction error, which lacks in two essential parts: it ignores the model-internal representation employed for reconstruction, and it lacks formal assertions and comparability between samples. We address these shortcomings by proposing the Context-encoding Variational Autoencoder (ceVAE) which combines reconstruction- with density-based anomaly scoring. This improves the sample- as well as pixel-wise results. In our experiments on the BraTS-2017 and ISLES-2015 segmentation benchmarks, the ceVAE achieves unsupervised ROC-AUCs of 0.95 and 0.89, respectively, thus outperforming state-of-the-art methods by a considerable margin.