OpenRaiser/Intern-Atlas huggingface dataset:
A Strategic Cyber Crime and Security Awareness Information System using a Dedicated Portal
Venue: arXiv cs.CY 2019
Year: 2019

A real time portal (www.ganamoscybersecure.org) to enlighten people on how to protect their data in the web, the strategies adopted by cyber criminals to succeed in exploiting their victims as well as the mistakes made by people and organizations that make them prone to the menace of cyber crime has been designed in this paper. Critical observations were made over time on the degree of awareness and attitudes of people and organizations in some parts of South Africa and Nigeria towards information security. Interviews were conducted using structured questionnaire to elicit information. The outcome showed that most individuals and organizations lack adequate awareness on strategies adopted by cyber criminals, and pay little attention to securing their online data. The application of the services provided by the portal designed would be a sine qua non for contending with the menace of cyber crime.act:
Rapid Prototyping Formal Systems in MMT: 5 Case Studies
Venue: arXiv cs.LO 2019
Year: 2019

Logical frameworks are meta-formalisms in which the syntax and semantics of object logics and related formal systems can be defined. This allows object logics to inherit implementations from the framework including, e.g., parser, type checker, or module system. But if the desired object logic falls outside the comfort zone of the logical framework, these definitions may become cumbersome or infeasible. Therefore, the MMT system abstracts even further than previous frameworks: it assumes no type system or logic at all and allows its kernel algorithms to be customized by almost arbitrary sets of rules. In particular, this allows implementing standard logical frameworks like LF in MMT. But it does so without chaining users to one particular meta-formalism: users can flexibly adapt MMT whenever the object logic demands it. In this paper, we present a series of case studies that do just that, defining increasingly complex object logics in MMT. We use elegant declarative logic definitions wherever possible, but inject entirely new rules into the kernel when necessary. Our experience shows that the MMT approach allows deriving prototype implementations of very diverse formal systems very easily and quickly.
A Weakly Initial Algebra for Higher-Order Abstract Syntax in Cedille
Venue: arXiv cs.LO 2019
Year: 2019

Cedille is a relatively recent tool based on a Curry-style pure type theory, without a primitive datatype system. Using novel techniques based on dependent intersection types, inductive datatypes with their induction principles are derived. One benefit of this approach is that it allows exploration of new or advanced forms of inductive datatypes. This paper reports work in progress on one such form, namely higher-order abstract syntax (HOAS). We consider the nature of HOAS in the setting of pure type theory, comparing with the traditional concept of environment models for lambda calculus. We see an alternative, based on what we term Kripke function-spaces, for which we can derive a weakly initial algebra in Cedille. Several examples are given using the encoding.
Soft Prototyping Camera Designs for Car Detection Based on a Convolutional Neural Network
Venue: arXiv cs.CV 2019
Year: 2019

Imaging systems are increasingly used as input to convolutional neural networks (CNN) for object detection; we would like to design cameras that are optimized for this purpose. It is impractical to build different cameras and then acquire and label the necessary data for every potential camera design; creating software simulations of the camera in context (soft prototyping) is the only realistic approach. We implemented soft-prototyping tools that can quantitatively simulate image radiance and camera designs to create realistic images that are input to a convolutional neural network for car detection. We used these methods to quantify the effect that critical hardware components (pixel size), sensor control (exposure algorithms) and image processing (gamma and demosaicing algorithms) have upon average precision of car detection. We quantify (a) the relationship between pixel size and the ability to detect cars at different distances, (b) the penalty for choosing a poor exposure duration, and (c) the ability of the CNN to perform car detection for a variety of post-acquisition processing algorithms. These results show that the optimal choices for car detection are not constrained by the same metrics used for image quality in consumer photography. It is better to evaluate camera designs for CNN applications using soft prototyping with task-specific metrics rather than consumer photography metrics.
Patients, Primary Care, and Policy: Simulation Modeling for Health Care Decision Support
Venue: arXiv cs.MA 2019
Year: 2019

Demand for health care is constantly increasing due to the ongoing demographic change, while at the same time health service providers face difficulties in finding skilled personnel. This creates pressure on health care systems around the world, such that the efficient, nationwide provision of primary health care has become one of society's greatest challenges. Due to the complexity of health care systems, unforeseen future events, and a frequent lack of data, analyzing and optimizing the performance of health care systems means tackling a wicked problem. To support this task for primary care, this paper introduces the hybrid agent-based simulation model SiM-Care. SiM-Care models the interactions of patients and primary care physicians on an individual level. By tracking agent interactions, it enables modelers to assess multiple key indicators such as patient waiting times and physician utilization. Based on these indicators, primary care systems can be assessed and compared. Moreover, changes in the infrastructure, patient behavior, and service design can be directly evaluated. To showcase the opportunities offered by SiM-Care and aid model validation, we present a case study for a primary care system in Germany. Specifically, we investigate the effects of an aging population, a decrease in the number of physicians, as well as the combined effects.
Toward a view-based data cleaning architecture
Venue: arXiv cs.DB 2019
Year: 2019

Big data analysis has become an active area of study with the growth of machine learning techniques. To properly analyze data, it is important to maintain high-quality data. Thus, research on data cleaning is also important. It is difficult to automatically detect and correct inconsistent values for data requiring expert knowledge or data created by many contributors, such as integrated data from heterogeneous data sources. An example of such data is metadata for scientific datasets, which should be confirmed by data managers while handling the data. To support the efficient cleaning of data by data managers, we propose a data cleaning architecture in which data managers interactively browse and correct portions of data through views. In this paper, we explain our view-based data cleaning architecture and discuss some remaining issues.
Matching in the Air: Optimal Analog Beamforming under Angular Spread
Venue: arXiv cs.IT 2019
Year: 2019

Gbps wireless transmission over long distance at high frequency bands has great potential for 5G and beyond, as long as high beamforming gain could be delivered at affordable cost to combat the severe path loss. With limited number of RF chains, the effective beamwidth of a high gain antenna will be "widened" by channel angular spread, resulting in gain reduction. In this paper, we formulate the analog beamforming as a constrained optimization problem and present closed form solution that maximizes the effective beamforming gain. The optimal beam pattern of antenna array turns out to "match" the channel angular spread, and the effectiveness of the theoretical results has been verified by numerical evaluation via exhaustive search and system level simulation using 3D channel models. Furthermore, we propose an efficient angular spread estimation method using as few as three power measurements and validate its accuracy by lab measurements using a $16{\times}16$ phased array at 28 GHz. The capability of estimating angular spread and matching the beam pattern on the fly enables high effective gain using low cost analog/hybrid beamforming implementation, and we demonstrate a few examples where substantial gain can be achieved through array geometry optimization.
Deep Learning for Whole Slide Image Analysis: An Overview
Venue: arXiv cs.CV 2019
Year: 2019

The widespread adoption of whole slide imaging has increased the demand for effective and efficient gigapixel image analysis. Deep learning is at the forefront of computer vision, showcasing significant improvements over previous methodologies on visual understanding. However, whole slide images have billions of pixels and suffer from high morphological heterogeneity as well as from different types of artefacts. Collectively, these impede the conventional use of deep learning. For the clinical translation of deep learning solutions to become a reality, these challenges need to be addressed. In this paper, we review work on the interdisciplinary attempt of training deep neural networks using whole slide images, and highlight the different ideas underlying these methodologies.
Facial Emotion Recognition Using Deep Learning
Venue: arXiv cs.CV 2019
Year: 2019

We aim to construct a system that captures real-world facial images through the front camera on a laptop. The system is capable of processing/recognizing the captured image and predict a result in real-time. In this system, we exploit the power of deep learning technique to learn a facial emotion recognition (FER) model based on a set of labeled facial images. Finally, experiments are conducted to evaluate our model using largely used public database.
Stochastic Channel-Based Federated Learning for Medical Data Privacy Preserving
Venue: arXiv cs.LG 2019
Year: 2019

Artificial neural network has achieved unprecedented success in the medical domain. This success depends on the availability of massive and representative datasets. However, data collection is often prevented by privacy concerns and people want to take control over their sensitive information during both training and using processes. To address this problem, we propose a privacy-preserving method for the distributed system, Stochastic Channel-Based Federated Learning (SCBF), which enables the participants to train a high-performance model cooperatively without sharing their inputs. Specifically, we design, implement and evaluate a channel-based update algorithm for the central server in a distributed system, which selects the channels with regard to the most active features in a training loop and uploads them as learned information from local datasets. A pruning process is applied to the algorithm based on the validation set, which serves as a model accelerator. In the experiment, our model presents better performances and higher saturating speed than the Federated Averaging method which reveals all the parameters of local models to the server when updating. We also demonstrate that the saturating rate of performance could be promoted by introducing a pruning process. And further improvement could be achieved by tuning the pruning rate. Our experiment shows that 57% of the time is saved by the pruning process with only a reduction of 0.0047 in AUCROC performance and a reduction of 0.0068 in AUCPR.
Diversifying Topic-Coherent Response Generation for Natural Multi-turn Conversations
Venue: arXiv cs.CL 2019
Year: 2019

Although response generation (RG) diversification for single-turn dialogs has been well developed, it is less investigated for natural multi-turn conversations. Besides, past work focused on diversifying responses without considering topic coherence to the context, producing uninformative replies. In this paper, we propose the Topic-coherent Hierarchical Recurrent Encoder-Decoder model (THRED) to diversify the generated responses without deviating the contextual topics for multi-turn conversations. In overall, we build a sequence-to-sequence net (Seq2Seq) to model multi-turn conversations. And then we resort to the latent Variable Hierarchical Recurrent Encoder-Decoder model (VHRED) to learn global contextual distribution of dialogs. Besides, we construct a dense topic matrix which implies word-level correlations of the conversation corpora. The topic matrix is used to learn local topic distribution of the contextual utterances. By incorporating both the global contextual distribution and the local topic distribution, THRED produces both diversified and topic-coherent replies. In addition, we propose an explicit metric (\emph{TopicDiv}) to measure the topic divergence between the post and generated response, and we also propose an overall metric combining the diversification metric (\emph{Distinct}) and \emph{TopicDiv}. We evaluate our model comparing with three baselines (Seq2Seq, HRED and VHRED) on two real-world corpora, respectively, and demonstrate its outstanding performance in both diversification and topic coherence.
Artificial intelligence for elections: the case of 2019 Argentina primary and presidential election
Venue: arXiv cs.SI 2019
Year: 2019

We use a method based on machine learning, big-data analytics, and network theory to process millions of messages posted in Twitter to predict election outcomes. The model has achieved accurate results in the current Argentina primary presidential election on August 11, 2019 by predicting the large difference win of candidate Alberto Fernandez over president Mauricio Macri; a result that none of the traditional pollsters in that country was able to predict, and has led to a major bond market collapse. We apply the model to the upcoming Argentina presidential election on October 27, 2019 yielding the following results: Fernandez 47.5%, Macri 30.9% and third party 21.6%. Our method improves over traditional polling methods which are based on direct interactions with small number of individuals that are plagued by ever declining response rates, currently falling in the low single digits. They provide a reliable polling method that can be applied not only to predict elections but to discover any trend in society, for instance, what people think about climate change, politics or education.
Interpretable Text Classification Using CNN and Max-pooling
Venue: arXiv cs.CL 2019
Year: 2019

Deep neural networks have been widely used in text classification. However, it is hard to interpret the neural models due to the complicate mechanisms. In this work, we study the interpretability of a variant of the typical text classification model which is based on convolutional operation and max-pooling layer. Two mechanisms: convolution attribution and n-gram feature analysis are proposed to analyse the process procedure for the CNN model. The interpretability of the model is reflected by providing posterior interpretation for neural network predictions. Besides, a multi-sentence strategy is proposed to enable the model to beused in multi-sentence situation without loss of performance and interpret ability. We evaluate the performance of the model on several classification tasks and justify the interpretable performance with some case studies.
Emotion recognition with 4kresolution database
Venue: arXiv cs.CV 2019
Year: 2019

Classifying the human emotion through facial expressions is a big topic in both the Computer Vision and Deep learning fields. Human emotion can be classified as one of the basic emotion types like being angry, happy or dimensional emotion with valence and arousal values. There are a lot of related challenges in this topic, one of the most famous challenges is called the 'Affect-in-the-wild Challenge'(Aff-Wild Challenge). It is the first challenge on the estimation of valence and arousal in-the-wild. This project is an extension of the Aff-wild Challenge. Aff-wild database was created using images with a mean resolution of 607*359, I and Dimitrios sought to find out the performance of the model that is trained on a database that contains4K resolution in-the-wild images. Since there is no existing database to satisfy the requirement, I built this database from scratch with help from Dimitrios and trained neural network models with different hyperparameters on this database. I used network models likeVGG16, AlexNet, ResNet and also some pre-trained models like Ima-geNet VGG. I compared the results of the different network models alongside the results from the Aff-wild database to exploit the optimal model for my database.
Identifying Unknown Instances for Autonomous Driving
Venue: arXiv cs.CV 2019
Year: 2019

In the past few years, we have seen great progress in perception algorithms, particular through the use of deep learning. However, most existing approaches focus on a few categories of interest, which represent only a small fraction of the potential categories that robots need to handle in the real-world. Thus, identifying objects from unknown classes remains a challenging yet crucial task. In this paper, we develop a novel open-set instance segmentation algorithm for point clouds which can segment objects from both known and unknown classes in a holistic way. Our method uses a deep convolutional neural network to project points into a category-agnostic embedding space in which they can be clustered into instances irrespective of their semantics. Experiments on two large-scale self-driving datasets validate the effectiveness of our proposed method.
Community-Level Anomaly Detection for Anti-Money Laundering
Venue: arXiv cs.LG 2019
Year: 2019

Anomaly detection in networks often boils down to identifying an underlying graph structure on which the abnormal occurrence rests on. Financial fraud schemes are one such example, where more or less intricate schemes are employed in order to elude transaction security protocols. We investigate the problem of learning graph structure representations using adaptations of dictionary learning aimed at encoding connectivity patterns. In particular, we adapt dictionary learning strategies to the specificity of network topologies and propose new methods that impose Laplacian structure on the dictionaries themselves. In one adaption we focus on classifying topologies by working directly on the graph Laplacian and cast the learning problem to accommodate its 2D structure. We tackle the same problem by learning dictionaries which consist of vectorized atomic Laplacians, and provide a block coordinate descent scheme to solve the new dictionary learning formulation. Imposing Laplacian structure on the dictionaries is also proposed in an adaptation of the Single Block Orthogonal learning method. Results on synthetic graph datasets comprising different graph topologies confirm the potential of dictionaries to directly represent graph structure information.
Handheld Mobile Photography in Very Low Light
Venue: arXiv cs.CV 2019
Year: 2019

Taking photographs in low light using a mobile phone is challenging and rarely produces pleasing results. Aside from the physical limits imposed by read noise and photon shot noise, these cameras are typically handheld, have small apertures and sensors, use mass-produced analog electronics that cannot easily be cooled, and are commonly used to photograph subjects that move, like children and pets. In this paper we describe a system for capturing clean, sharp, colorful photographs in light as low as 0.3~lux, where human vision becomes monochromatic and indistinct. To permit handheld photography without flash illumination, we capture, align, and combine multiple frames. Our system employs "motion metering", which uses an estimate of motion magnitudes (whether due to handshake or moving objects) to identify the number of frames and the per-frame exposure times that together minimize both noise and motion blur in a captured burst. We combine these frames using robust alignment and merging techniques that are specialized for high-noise imagery. To ensure accurate colors in such low light, we employ a learning-based auto white balancing algorithm. To prevent the photographs from looking like they were shot in daylight, we use tone mapping techniques inspired by illusionistic painting: increasing contrast, crushing shadows to black, and surrounding the scene with darkness. All of these processes are performed using the limited computational resources of a mobile device. Our system can be used by novice photographers to produce shareable pictures in a few seconds based on a single shutter press, even in environments so dim that humans cannot see clearly.
Extending Event Detection to New Types with Learning from Keywords
Venue: arXiv cs.LG 2019
Year: 2019

Traditional event detection classifies a word or a phrase in a given sentence for a set of predefined event types. The limitation of such predefined set is that it prevents the adaptation of the event detection models to new event types. We study a novel formulation of event detection that describes types via several keywords to match the contexts in documents. This facilitates the operation of the models to new types. We introduce a novel feature-based attention mechanism for convolutional neural networks for event detection in the new formulation. Our extensive experiments demonstrate the benefits of the new formulation for new type extension for event detection as well as the proposed attention mechanism for this problem.
HRL4IN: Hierarchical Reinforcement Learning for Interactive Navigation with Mobile Manipulators
Venue: arXiv cs.LG 2019
Year: 2019

Most common navigation tasks in human environments require auxiliary arm interactions, e.g. opening doors, pressing buttons and pushing obstacles away. This type of navigation tasks, which we call Interactive Navigation, requires the use of mobile manipulators: mobile bases with manipulation capabilities. Interactive Navigation tasks are usually long-horizon and composed of heterogeneous phases of pure navigation, pure manipulation, and their combination. Using the wrong part of the embodiment is inefficient and hinders progress. We propose HRL4IN, a novel Hierarchical RL architecture for Interactive Navigation tasks. HRL4IN exploits the exploration benefits of HRL over flat RL for long-horizon tasks thanks to temporally extended commitments towards subgoals. Different from other HRL solutions, HRL4IN handles the heterogeneous nature of the Interactive Navigation task by creating subgoals in different spaces in different phases of the task. Moreover, HRL4IN selects different parts of the embodiment to use for each phase, improving energy efficiency. We evaluate HRL4IN against flat PPO and HAC, a state-of-the-art HRL algorithm, on Interactive Navigation in two environments - a 2D grid-world environment and a 3D environment with physics simulation. We show that HRL4IN significantly outperforms its baselines in terms of task performance and energy efficiency. More information is available at https://sites.google.com/view/hrl4in.
A Robot's Expressive Language Affects Human Strategy and Perceptions in a Competitive Game
Venue: arXiv cs.HC 2019
Year: 2019

As robots are increasingly endowed with social and communicative capabilities, they will interact with humans in more settings, both collaborative and competitive. We explore human-robot relationships in the context of a competitive Stackelberg Security Game. We vary humanoid robot expressive language (in the form of "encouraging" or "discouraging" verbal commentary) and measure the impact on participants' rationality, strategy prioritization, mood, and perceptions of the robot. We learn that a robot opponent that makes discouraging comments causes a human to play a game less rationally and to perceive the robot more negatively. We also contribute a simple open source Natural Language Processing framework for generating expressive sentences, which was used to generate the speech of our autonomous social robot.