OpenRaiser/Intern-Atlas huggingface dataset:
Harassment detection: a benchmark on the #HackHarassment dataset
Venue: arXiv cs.CL 2016
Year: 2016

Online harassment has been a problem to a greater or lesser extent since the early days of the internet. Previous work has applied anti-spam techniques like machine-learning based text classification (Reynolds, 2011) to detecting harassing messages. However, existing public datasets are limited in size, with labels of varying quality. The #HackHarassment initiative (an alliance of 1 tech companies and NGOs devoted to fighting bullying on the internet) has begun to address this issue by creating a new dataset superior to its predecssors in terms of both size and quality. As we (#HackHarassment) complete further rounds of labelling, later iterations of this dataset will increase the available samples by at least an order of magnitude, enabling corresponding improvements in the quality of machine learning models for harassment detection. In this paper, we introduce the first models built on the #HackHarassment dataset v1.0 (a new open dataset, which we are delighted to share with any interested researcherss) as a benchmark for future research.
A Large Scale Corpus of Gulf Arabic
Venue: arXiv cs.CL 2016
Year: 2016

Most Arabic natural language processing tools and resources are developed to serve Modern Standard Arabic (MSA), which is the official written language in the Arab World. Some Dialectal Arabic varieties, notably Egyptian Arabic, have received some attention lately and have a growing collection of resources that include annotated corpora and morphological analyzers and taggers. Gulf Arabic, however, lags behind in that respect. In this paper, we present the Gumar Corpus, a large-scale corpus of Gulf Arabic consisting of 110 million words from 1,200 forum novels. We annotate the corpus for sub-dialect information at the document level. We also present results of a preliminary study in the morphological annotation of Gulf Arabic which includes developing guidelines for a conventional orthography. The text of the corpus is publicly browsable through a web interface we developed for it.
Directed Graph-based Wireless EEG Sensor Channel Selection Approach for Cognitive Task Classification
Venue: arXiv cs.HC 2016
Year: 2016

Wireless electroencephalogram (EEG) sensors have been successfully applied in many medical and computer brain interface classifications. A common characteristic of wireless EEG sensors is that they are low powered devices, and hence an efficient usage of sensor energy resources is critical for any practical application. One way of minimizing energy consumption by the EEG sensors is by reducing the number of EEG channels participating in the classification process. For the purpose of classifying EEG signals, we propose a directed acyclic graph (DAG)-based channel selection algorithm. To achieve this objective, the EEG sensor channels are first realized in a complete undirected graph, where each channel is represented by a node. An edge between any two nodes indicates the collaboration between these nodes in identifying the system state; and the significance of this collaboration is quantified by a weight assigned to the edge. The complete graph is then reduced into a directed acyclic graph that encodes the knowledge of the non-increasing order of the channel ranking for each cognitive task. The channel selection algorithm utilizes this directed graph to find a maximum path such that the total weight of this path satisfies a predefined threshold. It has been demonstrated experimentally that channel utilization has been reduced by 50% in the worst case scenario for a three-state system and an EEG sensor with 14 channels; and the best classification accuracy obtained is 81%.
Autonomous collision attack on OCSP services
Venue: arXiv cs.CR 2016
Year: 2016

The paper describes two important design flaws in Online Certificate Status Protocol (OCSP), a protocol widely used in PKI environments for managing digital certificates' credibility in real time. The flaws significantly reduce the security capabilities of the protocol, and can be exploited by a malicious third party to generate forged signed certificate statuses and, in the worst scenario, forged certificates. Description of the flaws, along with expected exploitation routes, consequences for consuming application layer protocols, and proposed countermeasures, is given.
Using Spatial Pooler of Hierarchical Temporal Memory to classify noisy videos with predefined complexity
Venue: arXiv cs.CV 2016
Year: 2016

This paper examines the performance of a Spatial Pooler (SP) of a Hierarchical Temporal Memory (HTM) in the task of noisy object recognition. To address this challenge, a dedicated custom-designed system based on the SP, histogram calculation module and SVM classifier was implemented. In addition to implementing their own version of HTM, the authors also designed a profiler which is capable of tracing all of the key parameters of the system. This was necessary, since an analysis and monitoring of the system performance turned out to be extremely difficult using conventional testing and debugging tools. The system was initially trained on artificially prepared videos without noise and then tested with a set of noisy video streams. This approach was intended to mimic a real life scenario where an agent or a system trained to deal with ideal objects faces a task of classifying distorted and noisy ones in its regular working conditions. The authors conducted a series of experiments for various macro parameters of HTM SP, as well as for different levels of video reduction ratios. The experiments allowed them to evaluate the performance of two different system setups (i.e. 'Multiple HTMs' and 'Single HTM') under various noise conditions with 32--frame video files. Results of all the tests were compared to SVM baseline setup. It was determined that the system featuring SP is capable of achieving approximately 12 times the noise reduction for a video signal with with distorted bits accounting for 13\% of the total. Furthermore, the system featuring SP performed better also in the experiments without a noise component and achieved a max F1 score of 0.96. The experiments also revealed that a rise of column and synapse number of SP has a substantial impact on the performance of the system. Consequently, the highest F1 score values were obtained for 256 and 4096 synapses and columns respectively.
A Heuristic Method of Generating Diameter 3 Graphs for Order/Degree Problem
Venue: arXiv cs.DS 2016
Year: 2016

We propose a heuristic method that generates a graph for order/degree problem. Target graphs of our heuristics have large order (> 4000) and diameter 3. We describe the ob- servation of smaller graphs and basic structure of our heuristics. We also explain an evaluation function of each edge for efficient 2-opt local search. Using them, we found the best solutions for several graphs.
On locality of Generalized Reed-Muller codes over the broadcast erasure channel
Venue: arXiv cs.IT 2016
Year: 2016

One to Many communications are expected to be among the killer applications for the currently discussed 5G standard. The usage of coding mechanisms is impacting broadcasting standard quality, as coding is involved at several levels of the stack, and more specifically at the application layer where Rateless, LDPC, Reed Solomon codes and network coding schemes have been extensively studied, optimized and standardized in the past. Beyond reusing, extending or adapting existing application layer packet coding mechanisms based on previous schemes and designed for the foregoing LTE or other broadcasting standards; our purpose is to investigate the use of Generalized Reed Muller codes and the value of their locality property in their progressive decoding for Broadcast/Multicast communication schemes with real time video delivery. Our results are meant to bring insight into the use of locally decodable codes in Broadcasting.
Wav2Letter: an End-to-End ConvNet-based Speech Recognition System
Venue: arXiv cs.LG 2016
Year: 2016

This paper presents a simple end-to-end model for speech recognition, combining a convolutional network based acoustic model and a graph decoding. It is trained to output letters, with transcribed speech, without the need for force alignment of phonemes. We introduce an automatic segmentation criterion for training from sequence annotation without alignment that is on par with CTC while being simpler. We show competitive results in word error rate on the Librispeech corpus with MFCC features, and promising results from raw waveform.
Segmentation and Classification of Skin Lesions for Disease Diagnosis
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, a novel approach for automatic segmentation and classification of skin lesions is proposed. Initially, skin images are filtered to remove unwanted hairs and noise and then the segmentation process is carried out to extract lesion areas. For segmentation, a region growing method is applied by automatic initialization of seed points. The segmentation performance is measured with different well known measures and the results are appreciable. Subsequently, the extracted lesion areas are represented by color and texture features. SVM and k-NN classifiers are used along with their fusion for the classification using the extracted features. The performance of the system is tested on our own dataset of 726 samples from 141 images consisting of 5 different classes of diseases. The results are very promising with 46.71% and 34% of F-measure using SVM and k-NN classifier respectively and with 61% of F-measure for fusion of SVM and k-NN.
A Characterization of Quasi-Decreasingness
Venue: arXiv cs.LO 2016
Year: 2016

In 2010 Schernhammer and Gramlich showed that quasi-decreasingness of a DCTRS R is equivalent to μ-termination of its context-sensitive unraveling Ucs(R) on original terms. While the direction that quasi-decreasingness of R implies μ-termination of Ucs(R) on original terms is shown directly; the converse - facilitating the use of context-sensitive termination tools like MU-TERM and VMTL - employs the additional notion of context-sensitive quasi-reductivity of R. In the following, we give a direct proof of the fact that μ-termination of Ucs(R) on original terms implies quasi-decreasingness of R. Moreover, we report our experimental findings on DCTRSs from the confluence problems database (Cops), extending the experiments of Schernhammer and Gramlich.
Libraries' Metadata as Data in the Era of the Semantic Web: Modeling a Repository of Master Theses and PhD Dissertations for the Web of Data
Venue: arXiv cs.DL 2016
Year: 2016

This study argues that metadata of library catalogs can stand autonomously, providing valuable information detached from the resources they point to and, therefore, could be used as data in the context of the Semantic Web. We present an analysis of this perception followed by an implementation proposal for a Master's thesis and PhD dissertation repository. The analysis builds on the flexibility of the Resource Description Framework (RDF) and takes into account the Functional Requirements for Bibliographic Records (FRBR) and Functional Requirements for Authority Data (FRAD) in order to reveal the latent academic network by linking its entities to a meaningful and computationally processable set. Current library catalogs retrieve documents to find answers, whereas in our approach catalogs can provide answers that could not be found in any specific document.
A Bayesian Lower Bound for Parameter Estimation of Poisson Data Including Multiple Changes (extended)
Venue: arXiv cs.IT 2016
Year: 2016

This paper derives lower bounds for the mean square errors of parameter estimators in the case of Poisson distributed data subjected to multiple abrupt changes. Since both change locations (discrete parameters) and parameters of the Poisson distribution (continuous parameters) are unknown, it is appropriate to consider a mixed Cramer-Rao/Weiss-Weinstein bound for which we derive closed-form expressions and illustrate its tightness by numerical simulations.
Cooperative Phoneotypes: Exploring Phone-based Behavioral Markers of Cooperation
Venue: arXiv cs.SI 2016
Year: 2016

Cooperation is a fundamental human concept studied across multiple social and biological disciplines. Traditional methods for eliciting an individual's propensity to cooperate have included surveys and laboratory experiments and multiple such studies have connected an individual's cooperation level with her social behavior. We describe a novel approach to model an individual's cooperation level based on her phoneotype i.e. a composite of an individual's traits as observable via a mobile phone. This phone sensing-based method can potentially complement surveys, thus providing a cheaper, faster, automated method for generating insights into cooperation levels of users. Based on a 10-week field study involving 54 participants, we report that: (1) multiple phone-based signals were significantly associated with participant's cooperation attitudes; and (2) combining phone-based signals yielded a predictive model with AUCROC of 0.945 that performed significantly better than a comparable demography-based model at predicting individual cooperation propensities. The results pave the way for individuals and organizations to identify more cooperative peers in personal, social, and commerce related settings.
LTE in Unlicensed Bands is neither Friend nor Foe to Wi-Fi
Venue: arXiv cs.NI 2016
Year: 2016

Proponents of deploying LTE in the 5 GHz band for providing additional cellular network capacity have claimed that LTE would be a better neighbour to Wi-Fi in the unlicensed band, than Wi-Fi is to itself. On the other side of the debate, the Wi-Fi community has objected that LTE would be highly detrimental to Wi-Fi network performance. However, there is a lack of transparent and systematic engineering evidence supporting the contradicting claims of the two camps, which is essential for ascertaining whether regulatory intervention is in fact required to protect the Wi-Fi incumbent from the new LTE entrant. To this end, we present a comprehensive coexistence study of Wi-Fi and LTE-in-unlicensed, surveying a large parameter space of coexistence mechanisms and a range of representative network densities and deployment scenarios. Our results show that, typically, harmonious coexistence between Wi-Fi and LTE is ensured by the large number of 5 GHz channels. For the worst-case scenario of forced co-channel operation, LTE is sometimes a better neighbour to Wi-Fi - when effective node density is low - but sometimes worse - when density is high. We find that distributed interference coordination is only necessary to prevent a "tragedy of the commons" in regimes where interference is very likely. We also show that in practice it does not make a difference to the incumbent what kind of coexistence mechanism is added to LTE-in-unlicensed, as long as one is in place. We therefore conclude that LTE is neither friend nor foe to Wi-Fi in the unlicensed bands in general. We submit that the systematic engineering analysis exemplified by our case study is a best-practice approach for supporting evidence-based rulemaking by the regulator.
Co-active Learning to Adapt Humanoid Movement for Manipulation
Venue: arXiv cs.RO 2016
Year: 2016

In this paper we address the problem of robot movement adaptation under various environmental constraints interactively. Motion primitives are generally adopted to generate target motion from demonstrations. However, their generalization capability is weak while facing novel environments. Additionally, traditional motion generation methods do not consider the versatile constraints from various users, tasks, and environments. In this work, we propose a co-active learning framework for learning to adapt robot end-effector's movement for manipulation tasks. It is designed to adapt the original imitation trajectories, which are learned from demonstrations, to novel situations with various constraints. The framework also considers user's feedback towards the adapted trajectories, and it learns to adapt movement through human-in-the-loop interactions. The implemented system generalizes trained motion primitives to various situations with different constraints considering user preferences. Experiments on a humanoid platform validate the effectiveness of our approach.
In-place Graph Rewriting with Interaction Nets
Venue: arXiv cs.PL 2016
Year: 2016

An algorithm is in-place, or runs in-situ, when it does not need any additional memory to execute beyond a small constant amount. There are many algorithms that are efficient because of this feature, therefore it is an important aspect of an algorithm. In most programming languages, it is not obvious when an algorithm can run in-place, and moreover it is often not clear that the implementation respects that idea. In this paper we study interaction nets as a formalism where we can see directly, visually, that an algorithm is in-place, and moreover the implementation will respect that it is in-place. Not all algorithms can run in-place however. We can nevertheless still use the same language, but now we can annotate parts of the algorithm that can run in-place. We suggest an annotation for rules, and give an algorithm to find this automatically through analysis of the interaction rules.
Towards Energy-Efficient Communication Management in the Distributed Control of Networked Cyber-Physical Systems
Venue: arXiv cs.SY 2016
Year: 2016

In this paper, we study the distributed control of networked cyber-physical systems when a much more energy-efficient distributed communication management strategy is proposed to solve the well-studied consensus problem. In contrast to the existing potential-based network topology control method, the proposed topology control method is based on the variation of communication ranges such that each agent can control its ad hoc communication range. The proposed network topology control technique can not only guarantee network connectivity but also reduce the communication energy. We apply the new network topology control technique, based on variable communication ranges, in a well-studied consensus problem, where the communication range for each agent is designed locally along with a new bounded control algorithm. Theoretical analysis is then provided to show that the proposed network topology control technique can guarantee consensus with bounded communication energy consumption. Finally, simulation examples are provided to show the effectiveness of the proposed energy-efficient distributed topology control technique.
Power Allocation and Effective Capacity of AF Successive Relays
Venue: arXiv cs.IT 2016
Year: 2016

In the relay based telecommunications with $K$ relays between the source and destination, $K+1$ time or frequency slots are required for a single frame transmission. However, without the relays, only one time or frequency slot is used for a single frame transmission. Therefore, despite the benefits of relaying systems, this type of communications is not efficient from the spectral efficiency viewpoint. One solution to reduce this issue might be the full-duplex (FD) relays. An old technique which is reconsidered recently to improve the spectral efficiency of telecommunication systems. However, FD relays have a certain complexity, so, some similar techniques such as successive relays with nearly the same performance but less complexity is taken into account now. In successive relaying systems, two relays between the source and destination are employed which receive the transmitted frames from the source and relay it to the destination successively. This structure generally acts like an FD relays. In this paper, the effective capacity performance of an amplify and forward (AF) successive relaying systems with power allocation strategy at the relays are studied perfectly. However, while the inter-rely interference (IRI) between two successive relays has to be managed well, the power allocation and the effective capacity is derived under different assumptions about the IRI. In this way, we assume weak or strong, short or long-term constraints on the IRI. Then we extract the optimal transmitted power at the relay to maximize the effective capacity under these constraints.
Hacking of the AES with Boolean Functions
Venue: arXiv cs.CR 2016
Year: 2016

One of the major issues of cryptography is the cryptanalysis of cipher algorithms. Cryptanalysis is the study of methods for obtaining the meaning of encrypted information, without access to the secret information that is normally required. Some mechanisms for breaking codes include differential cryptanalysis, advanced statistics and brute-force. Recent works also attempt to use algebraic tools to reduce the cryptanalysis of a block cipher algorithm to the resolution of a system of quadratic equations describing the ciphering structure. In our study, we will also use algebraic tools but in a new way: by using Boolean functions and their properties. A Boolean function is a function from $F_2^n\to F_2$ with $n>1$, characterized by its truth table. The arguments of Boolean functions are binary words of length $n$. Any Boolean function can be represented, uniquely, by its algebraic normal form which is an equation which only contains additions modulo 2 - the XOR function - and multiplications modulo 2 - the AND function. Our aim is to describe the AES algorithm as a set of Boolean functions then calculate their algebraic normal forms by using the Möbius transforms. After, we use a specific representation for these equations to facilitate their analysis and particularly to try a combinatorial analysis. Through this approach we obtain a new kind of equations system. This equations system is more easily implementable and could open new ways to cryptanalysis.
3D Simulation for Robot Arm Control with Deep Q-Learning
Venue: arXiv cs.RO 2016
Year: 2016

Recent trends in robot arm control have seen a shift towards end-to-end solutions, using deep reinforcement learning to learn a controller directly from raw sensor data, rather than relying on a hand-crafted, modular pipeline. However, the high dimensionality of the state space often means that it is impractical to generate sufficient training data with real-world experiments. As an alternative solution, we propose to learn a robot controller in simulation, with the potential of then transferring this to a real robot. Building upon the recent success of deep Q-networks, we present an approach which uses 3D simulations to train a 7-DOF robotic arm in a control task without any prior knowledge. The controller accepts images of the environment as its only input, and outputs motor actions for the task of locating and grasping a cube, over a range of initial configurations. To encourage efficient learning, a structured reward function is designed with intermediate rewards. We also present preliminary results in direct transfer of policies over to a real robot, without any further training.