OpenRaiser/Intern-Atlas huggingface dataset:
An Application of Random Walk on Fake Account Detection Problem: A Hybrid Approach
Venue: arXiv cs.SI 2019
Year: 2019

Social networks play a significant role in today's world. The importance of social networks, for example Facebook or Twitter, are undeniable. However, they also have many issues. One of which is the need for a defense mechanism against fake accounts. It is obviously not a trivial task to separate fake accounts from authentic ones. In this paper, we propose a ranking scheme, comprising of both graph based and feature based approaches to aid the detection of fake Facebook profiles. Utilizing Support Vector Machine (SVM) \cite{cortes1995} and SybilWalk \cite{JWZ17}, the model achieved high accuracy over the set of ten thousands Vietnamese Facebook accounts.
A Subword Level Language Model for Bangla Language
Venue: arXiv cs.CL 2019
Year: 2019

Language models are at the core of natural language processing. The ability to represent natural language gives rise to its applications in numerous NLP tasks including text classification, summarization, and translation. Research in this area is very limited in Bangla due to the scarcity of resources, except for some count-based models and very recent neural language models being proposed, which are all based on words and limited in practical tasks due to their high perplexity. This paper attempts to approach this issue of perplexity and proposes a subword level neural language model with the AWD-LSTM architecture and various other techniques suitable for training in Bangla language. The model is trained on a corpus of Bangla newspaper articles of an appreciable size consisting of more than 28.5 million word tokens. The performance comparison with various other models depicts the significant reduction in perplexity the proposed model provides, reaching as low as 39.84, in just 20 epochs.
Efficient Energy Harvesting in Wireless Sensor Networks of Smart Grid
Venue: arXiv cs.NI 2019
Year: 2019

Smart grids are becoming ubiquitous in recent time. With the progress of automation in this arena, it needs to be diagnosed for better performance and less failures. There are several options for doing that but we have seen from the past research that using Wireless Sensor Network (WSN) as the diagnosis framework would be the most promising option due to its diverse benefits. Several challenges such as effect of noise, lower speed, selective node replacement, complexity of logistics, and limited battery lifetime arise while using WSN as the framework. Limited battery lifetime has become one of the most significant issues to focus on to get rid of it. This article provides a model for replenishing the battery charge of the sensor nodes of wireless sensor network. We will use the model for sensor battery recharging in an efficient way so that no nodes become out of service after a while. We will be using mobile charger for this purpose. So, there may be some scope for improving the recharge interval for the mobile charger as well. This will be satisfied using optimum path calculation for each time the charger travels to the nodes. Our main objectives are to maximize the nodes battery utilization, distribute power effectively from the energy harvester, and minimize the distance between power source and cluster head. The simulation results show that the proposed approach successfully maximizes the utilization of the nodes battery while minimizes the waiting time for the sensor nodes to get recharged from the energy harvester.
MQTT-ST: a Spanning Tree Protocol for Distributed MQTT Brokers
Venue: arXiv cs.NI 2019
Year: 2019

MQTT, one of the most popular protocols for the IoT, works according to a publish/subscribe pattern in which multiple clients connect to a single broker, generally hosted in the cloud. However, such a centralised approach does not scale well considering the massive numbers of IoT devices forecasted in the next future, thus calling for distributed solutions in which multiple brokers cooperate together. Indeed, distributed brokers can be moved from traditional cloud-based infrastructure to the edge of the network (as it is envisioned by the upcoming MEC technology of 5G cellular networks), with clear improvements in terms of latency, for example. This paper proposes MQTT-ST, a protocol able to create such a distributed architecture of brokers, organized through a spanning tree. The protocol uses in-band signalling (i.e., reuses MQTT primitives for the control messages) and allows for full message replication among brokers, as well as robustness against failures. We tested MQTT-ST in different experimental scenarios and we released it as open-source project to allow for reproducible research.
Designing Accessible Visual Programming Tools for Children with Autism Spectrum Condition
Venue: arXiv cs.HC 2019
Year: 2019

Visual Programming Tools (VPTs) allow users to create interactive media projects such as games and animations using visual representations of programming concepts. Although VPTs have been shown to have huge potential for teaching children with cognitive impairments including those with Autism Spectrum Condition (ASC), research has shown that existing VPTs may not be accessible to them. Therefore, this study proposes a set of recommendations for the design of accessible VPTs for children with ASC. Recommendations were initially gathered and validated by interviewing experts (n=7). The interviews were thematically analysed to identify recommendations. A second set of interviews with a subset of the initial experts (n=3) was then conducted to validate the gathered recommendations. An examination of the available literature was then conducted to identify additional recommendations for the design of VPTs. These recommendations arose from those used for the design of other interactive applications for children with ASC (e.g. virtual environments, serious games) and not identified as part of those the initially gathered from interviews. A novel set of recommendations for the design of VPTs for children with ASC and additional cognitive impairments has been defined as the result of this study.
Reducing Honeypot Log Storage Capacity Consumption -- Cron Job with Perl-Script Approach
Venue: arXiv cs.CY 2019
Year: 2019

Honeypot is a decoy computer system that is used to attract and monitor hackers' activities in the network. The honeypot aims to collect information from the hackers in order to create a more secure system. However, the log file generated by honeypot can grow very large when heavy traffic occurred in the system, such as Distributed Denial of Services' (DDoS) attack. The DDoS possesses difficulty when it is being processed and analyzed by the network administrator as it required a lot of time and resources. Therefore, in this paper, we propose an approach to decrease the log size that is by using a Cron job that will run with a Perl-script. This approach parses the collected data into the database periodically to decrease the log size. Three DDoS attack cases were conducted in this study to show the increasing of the log size by sending a different amount of packet per second for 8 hours in each case. The results have shown that by utilizing the Cron job with Perl-script, the log size has been significantly reduced, the disk space used in the system has also decreased. Consequently, this approach capable of speeding up the process of parsing the log file into the database and thus, improving the overall system performance. This study contributes to providing a pathway in reducing honeypot log storage using the Cron job with Perl-Script.
Hacking Neural Networks: A Short Introduction
Venue: arXiv cs.CR 2019
Year: 2019

A large chunk of research on the security issues of neural networks is focused on adversarial attacks. However, there exists a vast sea of simpler attacks one can perform both against and with neural networks. In this article, we give a quick introduction on how deep learning in security works and explore the basic methods of exploitation, but also look at the offensive capabilities deep learning enabled tools provide. All presented attacks, such as backdooring, GPU-based buffer overflows or automated bug hunting, are accompanied by short open-source exercises for anyone to try out.
Domain Generalization Using a Mixture of Multiple Latent Domains
Venue: arXiv cs.CV 2019
Year: 2019

When domains, which represent underlying data distributions, vary during training and testing processes, deep neural networks suffer a drop in their performance. Domain generalization allows improvements in the generalization performance for unseen target domains by using multiple source domains. Conventional methods assume that the domain to which each sample belongs is known in training. However, many datasets, such as those collected via web crawling, contain a mixture of multiple latent domains, in which the domain of each sample is unknown. This paper introduces domain generalization using a mixture of multiple latent domains as a novel and more realistic scenario, where we try to train a domain-generalized model without using domain labels. To address this scenario, we propose a method that iteratively divides samples into latent domains via clustering, and which trains the domain-invariant feature extractor shared among the divided latent domains via adversarial learning. We assume that the latent domain of images is reflected in their style, and thus, utilize style features for clustering. By using these features, our proposed method successfully discovers latent domains and achieves domain generalization even if the domain labels are not given. Experiments show that our proposed method can train a domain-generalized model without using domain labels. Moreover, it outperforms conventional domain generalization methods, including those that utilize domain labels.
"Please enter your PIN" -- On the Risk of Bypass Attacks on Biometric Authentication on Mobile Devices
Venue: arXiv cs.HC 2019
Year: 2019

Nowadays, most mobile devices support biometric authentication schemes like fingerprint or face unlock. However, these probabilistic mechanisms can only be activated in combination with a second alternative factor, usually knowledge-based authentication. In this paper, we show that this aspect can be exploited in a bypass attack. In this bypass attack, the attacker forces the user to "bypass" the biometric authentication by, for example, resetting the phone. This forces the user to enter an easy-to-observe passcode instead. We present the threat model and provide preliminary results of an online survey. Based on our results, we discuss potential countermeasures. We conclude that better feedback design and security-optimized fallback mechanisms can help further improve the overall security of mobile unlock mechanisms while preserving usability.
A Multi-Task Gradient Descent Method for Multi-Label Learning
Venue: arXiv cs.LG 2019
Year: 2019

Multi-label learning studies the problem where an instance is associated with a set of labels. By treating single-label learning problem as one task, the multi-label learning problem can be casted as solving multiple related tasks simultaneously. In this paper, we propose a novel Multi-task Gradient Descent (MGD) algorithm to solve a group of related tasks simultaneously. In the proposed algorithm, each task minimizes its individual cost function using reformative gradient descent, where the relations among the tasks are facilitated through effectively transferring model parameter values across multiple tasks. Theoretical analysis shows that the proposed algorithm is convergent with a proper transfer mechanism. Compared with the existing approaches, MGD is easy to implement, has less requirement on the training model, can achieve seamless asymmetric transformation such that negative transfer is mitigated, and can benefit from parallel computing when the number of tasks is large. The competitive experimental results on multi-label learning datasets validate the effectiveness of the proposed algorithm.
Program synthesis performance constrained by non-linear spatial relations in Synthetic Visual Reasoning Test
Venue: arXiv cs.LG 2019
Year: 2019

Despite remarkable advances in automated visual recognition by machines, some visual tasks remain challenging for machines. Fleuret et al. (2011) introduced the Synthetic Visual Reasoning Test (SVRT) to highlight this point, which required classification of images consisting of randomly generated shapes based on hidden abstract rules using only a few examples. Ellis et al. (2015) demonstrated that a program synthesis approach could solve some of the SVRT problems with unsupervised, few-shot learning, whereas they remained challenging for several convolutional neural networks trained with thousands of examples. Here we re-considered the human and machine experiments, because they followed different protocols and yielded different statistics. We thus proposed a quantitative reintepretation of the data between the protocols, so that we could make fair comparison between human and machine performance. We improved the program synthesis classifier by correcting the image parsings, and compared the results to the performance of other machine agents and human subjects. We grouped the SVRT problems into different types by the two aspects of the core characteristics for classification: shape specification and location relation. We found that the program synthesis classifier could not solve problems involving shape distances, because it relied on symbolic computation which scales poorly with input dimension and adding distances into such computation would increase the dimension combinatorially with the number of shapes in an image. Therefore, although the program synthesis classifier is capable of abstract reasoning, its performance is highly constrained by the accessible information in image parsings.
Profile-based Resource Allocation for Virtualized Network Functions
Venue: arXiv cs.NI 2019
Year: 2019

The virtualization of compute and network resources enables an unseen flexibility for deploying network services. A wide spectrum of emerging technologies allows an ever-growing range of orchestration possibilities in cloud-based environments. But in this context it remains challenging to rhyme dynamic cloud configurations with deterministic performance. The service operator must somehow map the performance specification in the Service Level Agreement (SLA) to an adequate resource allocation in the virtualized infrastructure. We propose the use of a VNF profile to alleviate this process. This is illustrated by profiling the performance of four example network functions (a virtual router, switch, firewall and cache server) under varying workloads and resource configurations. We then compare several methods to derive a model from the profiled datasets. We select the most accurate method to further train a model which predicts the services' performance, in function of incoming workload and allocated resources. Our presented method can offer the service operator a recommended resource allocation for the targeted service, in function of the targeted performance and maximum workload specified in the SLA. This helps to deploy the softwarized service with an optimal amount of resources to meet the SLA requirements, thereby avoiding unnecessary scaling steps.
On the computation of counterfactual explanations -- A survey
Venue: arXiv cs.LG 2019
Year: 2019

Due to the increasing use of machine learning in practice it becomes more and more important to be able to explain the prediction and behavior of machine learning models. An instance of explanations are counterfactual explanations which provide an intuitive and useful explanations of machine learning models. In this survey we review model-specific methods for efficiently computing counterfactual explanations of many different machine learning models and propose methods for models that have not been considered in literature so far.
Beyond the Grounding Bottleneck: Datalog Techniques for Inference in Probabilistic Logic Programs (Technical Report)
Venue: arXiv cs.AI 2019
Year: 2019

State-of-the-art inference approaches in probabilistic logic programming typically start by computing the relevant ground program with respect to the queries of interest, and then use this program for probabilistic inference using knowledge compilation and weighted model counting. We propose an alternative approach that uses efficient Datalog techniques to integrate knowledge compilation with forward reasoning with a non-ground program. This effectively eliminates the grounding bottleneck that so far has prohibited the application of probabilistic logic programming in query answering scenarios over knowledge graphs, while also providing fast approximations on classical benchmarks in the field.
Novel Relay Selection Protocol for Cooperative Networks
Venue: arXiv cs.NI 2019
Year: 2019

Extensive research has been done to achieve better throughput and reliability in wireless networks, with focus on multiple-input multiple-output systems. Recently, cooperative networking techniques have been investigated to increase the performance of low-cost wireless systems by using the diversity created by different single antenna devices. However, cooperative networking requires a medium access control layer able to handle source-relay-destination communications. Wireless cooperative relaying poses several challenges, being the most important one related to the relay selection mechanism, especially in the presence of mobile nodes. This position paper aims to describe our findings towards development of an efficient relay selection algorithm.
On the Complexity of 2-Player Packing Games
Venue: arXiv cs.CC 2019
Year: 2019

We analyze the computational complexity of two 2-player games involving packing objects into a box. In the first game, players alternate drawing polycubes from a shared pile and placing them into an initially empty box in any available location; the first player who can't place another piece loses. In the second game, there is a fixed sequence of polycubes, and on a player's turn they drop the next piece in through the top of the box, after which it falls until it hits a previously placed piece (as in Tetris); the first player who can't place the next piece loses. We prove that in both games, deciding the outcome under perfect play is PSPACE-complete.
Unsupervised Representation Learning by Discovering Reliable Image Relations
Venue: arXiv cs.CV 2019
Year: 2019

Learning robust representations that allow to reliably establish relations between images is of paramount importance for virtually all of computer vision. Annotating the quadratic number of pairwise relations between training images is simply not feasible, while unsupervised inference is prone to noise, thus leaving the vast majority of these relations to be unreliable. To nevertheless find those relations which can be reliably utilized for learning, we follow a divide-and-conquer strategy: We find reliable similarities by extracting compact groups of images and reliable dissimilarities by partitioning these groups into subsets, converting the complicated overall problem into few reliable local subproblems. For each of the subsets we obtain a representation by learning a mapping to a target feature space so that their reliable relations are kept. Transitivity relations between the subsets are then exploited to consolidate the local solutions into a concerted global representation. While iterating between grouping, partitioning, and learning, we can successively use more and more reliable relations which, in turn, improves our image representation. In experiments, our approach shows state-of-the-art performance on unsupervised classification on ImageNet with 46.0% and competes favorably on different transfer learning tasks on PASCAL VOC.
Multiple Face Analyses through Adversarial Learning
Venue: arXiv cs.CV 2019
Year: 2019

This inherent relations among multiple face analysis tasks, such as landmark detection, head pose estimation, gender recognition and face attribute estimation are crucial to boost the performance of each task, but have not been thoroughly explored since typically these multiple face analysis tasks are handled as separate tasks. In this paper, we propose a novel deep multi-task adversarial learning method to localize facial landmark, estimate head pose and recognize gender jointly or estimate multiple face attributes simultaneously through exploring their dependencies from both image representation-level and label-level. Specifically, the proposed method consists of a deep recognition network R and a discriminator D. The deep recognition network is used to learn the shared middle-level image representation and conducts multiple face analysis tasks simultaneously. Through multi-task learning mechanism, the recognition network explores the dependencies among multiple face analysis tasks, such as facial landmark localization, head pose estimation, gender recognition and face attribute estimation from image representation-level. The discriminator is introduced to enforce the distribution of the multiple face analysis tasks to converge to that inherent in the ground-truth labels. During training, the recognizer tries to confuse the discriminator, while the discriminator competes with the recognizer through distinguishing the predicted label combination from the ground-truth one. Though adversarial learning, we explore the dependencies among multiple face analysis tasks from label-level. Experimental results on four benchmark databases, i.e., the AFLW database, the Multi-PIE database, the CelebA database and the LFWA database, demonstrate the effectiveness of the proposed method for multiple face analyses.
Configuration-dependent Fault Localization
Venue: arXiv cs.SE 2019
Year: 2019

In a buggy configurable system, configuration-dependent bugs cause the failures in only certain configurations due to unexpected interactions among features. Manually localizing configuration-dependent faults in configurable systems could be highly time-consuming due to their complexity. However, the cause of configuration-dependent bugs is not considered by existing automated fault localization techniques, which are designed to localize bugs in non-configurable code. Thus, their capacity for efficient configuration-dependent localization is limited. In this work, we propose CoFL, a novel approach to localize configuration-dependent bugs by identifying and analyzing suspicious feature interactions that potentially cause the failures in buggy configurable systems. We evaluated the efficiency of CoFL in fault localization of artificial configuration-dependent faults in a highly-configurable system. We found that CoFL significantly improves the baseline spectrum-based approaches. With CoFL, on average, the correctness in ranking the buggy statements increases more than 7 times, and the search space is significantly narrowed down, about 15 times.
Comments on the Du-Kakade-Wang-Yang Lower Bounds
Venue: arXiv cs.LG 2019
Year: 2019

Du, Kakade, Wang, and Yang recently established intriguing lower bounds on sample complexity, which suggest that reinforcement learning with a misspecified representation is intractable. Another line of work, which centers around a statistic called the eluder dimension, establishes tractability of problems similar to those considered in the Du-Kakade-Wang-Yang paper. We compare these results and reconcile interpretations.