OpenRaiser/Intern-Atlas huggingface dataset:
Theoretical Investigation of Composite Neural Network
Venue: arXiv cs.LG 2019
Year: 2019

This work theoretically investigates the performance of a composite neural network. A composite neural network is a rooted directed acyclic graph combining a set of pre-trained and non-instantiated neural network models, where a pre-trained neural network model is well-crafted for a specific task and targeted to approximate a specific function with instantiated weights. The advantages of adopting such a pre-trained model in a composite neural network are two folds. One is to benefit from other's intelligence and diligence, and the other is saving the efforts in data preparation and resources and time in training. However, the overall performance of composite neural network is still not clear. In this work, we prove that a composite neural network, with high probability, performs better than any of its pre-trained components under certain assumptions. In addition, if an extra pre-trained component is added to a composite network, with high probability the overall performance will be improved. In the empirical evaluations, distinctively different applications support the above findings.
Supervised Machine Learning based Ensemble Model for Accurate Prediction of Type 2 Diabetes
Venue: arXiv cs.LG 2019
Year: 2019

According to the American Diabetes Association(ADA), 30.3 million people in the United States have diabetes, but only 7.2 million may be undiagnosed and unaware of their condition. Type 2 diabetes is usually diagnosed for most patients later on in life whereas the less common Type 1 diabetes is diagnosed early on in life. People can live healthy and happy lives while living with diabetes, but early detection produces a better overall outcome on most patient's health. Thus, to test the accurate prediction of Type 2 diabetes, we use the patients' information from an electronic health records company called Practice Fusion, which has about 10,000 patient records from 2009 to 2012. This data contains individual key biometrics, including age, diastolic and systolic blood pressure, gender, height, and weight. We use this data on popular machine learning algorithms and for each algorithm, we evaluate the performance of every model based on their classification accuracy, precision, sensitivity, specificity/recall, negative predictive value, and F1 score. In our study, we find that all algorithms other than Naive Bayes suffered from very low precision. Hence, we take a step further and incorporate all the algorithms into a weighted average or soft voting ensemble model where each algorithm will count towards a majority vote towards the decision outcome of whether a patient has diabetes or not. The accuracy of the Ensemble model on Practice Fusion is 85\%, by far our ensemble approach is new in this space. We firmly believe that the weighted average ensemble model not only performed well in overall metrics but also helped to recover wrong predictions and aid in accurate prediction of Type 2 diabetes. Our accurate novel model can be used as an alert for the patients to seek medical evaluation in time.
Cyber-Security Internals of a Skoda Octavia vRS: A Hands on Approach
Venue: arXiv cs.CR 2019
Year: 2019

The convergence of information technology and vehicular technologies are a growing paradigm, allowing information to be sent by and to vehicles. This information can further be processed by the Electronic Control Unit (ECU) and the Controller Area Network (CAN) for in-vehicle communications or through a mobile phone or server for out-vehicle communication. Information sent by or to the vehicle can be life-critical (e.g. breaking, acceleration, cruise control, emergency communication, etc. . . ). As vehicular technology advances, in-vehicle networks are connected to external networks through 3 and 4G mobile networks, enabling manufacturer and customer monitoring of different aspects of the car. While these services provide valuable information, they also increase the attack surface of the vehicle, and can enable long and short range attacks. In this manuscript, we evaluate the security of the 2017 Skoda Octavia vRS 4x4. Both physical and remote attacks are considered, the key fob rolling code is successfully compromised, privacy attacks are demonstrated through the infotainment system, the Volkswagen Transport Protocol 2.0 is reverse engineered. Additionally, in-car attacks are highlighted and described, providing an overlook of potentially deadly threats by modifying ECU parameters and components enabling digital forensics investigation are identified.
Data assimilation in Agent-based models using creation and annihilation operators
Venue: arXiv cs.MA 2019
Year: 2019

Agent-based models are a powerful tool for studying the behaviour of complex systems that can be described in terms of multiple, interacting ``agents''. However, because of their inherently discrete and often highly non-linear nature, it is very difficult to reason about the relationship between the state of the model, on the one hand, and our observations of the real world on the other. In this paper we consider agents that have a discrete set of states that, at any instant, act with a probability that may depend on the environment or the state of other agents. Given this, we show how the mathematical apparatus of quantum field theory can be used to reason probabilistically about the state and dynamics the model, and describe an algorithm to update our belief in the state of the model in the light of new, real-world observations. Using a simple predator-prey model on a 2-dimensional spatial grid as an example, we demonstrate the assimilation of incomplete, noisy observations and show that this leads to an increase in the mutual information between the actual state of the observed system and the posterior distribution given the observations, when compared to a null model.
Distributed Quantization for Sparse Time Sequences
Venue: arXiv cs.IT 2019
Year: 2019

Analog signals processed in digital hardware are quantized into a discrete bit-constrained representation. Quantization is typically carried out using analog-to-digital converters (ADCs), operating in a serial scalar manner. In some applications, a set of analog signals are acquired individually and processed jointly. Such setups are referred to as distributed quantization. In this work, we propose a distributed quantization scheme for representing a set of sparse time sequences acquired using conventional scalar ADCs. Our approach utilizes tools from secure group testing theory to exploit the sparse nature of the acquired analog signals, obtaining a compact and accurate representation while operating in a distributed fashion. We then show how our technique can be implemented when the quantized signals are transmitted over a multi-hop communication network providing a low-complexity network policy for routing and signal recovery. Our numerical evaluations demonstrate that the proposed scheme notably outperforms conventional methods based on the combination of quantization and compressed sensing tools.
Cascaded Generation of High-quality Color Visible Face Images from Thermal Captures
Venue: arXiv cs.CV 2019
Year: 2019

Generating visible-like face images from thermal images is essential to perform manual and automatic cross-spectrum face recognition. We successfully propose a solution based on cascaded refinement network that, unlike previous works, produces high quality generated color images without the need for face alignment, large databases, data augmentation, polarimetric sensors, computationally-intense training, or unrealistic restriction on the generated resolution. The training of our solution is based on the contextual loss, making it inherently scale (face area) and rotation invariant. We present generated image samples of unknown individuals under different poses and occlusion conditions.We also prove the high similarity in image quality between ground-truth images and generated ones by comparing seven quality metrics. We compare our results with two state-of-the-art approaches proving the superiority of our proposed approach.
Performance Evaluation of Advanced Features in CUDA Unified Memory
Venue: arXiv cs.DC 2019
Year: 2019

CUDA Unified Memory improves the GPU programmability and also enables GPU memory oversubscription. Recently, two advanced memory features, memory advises and asynchronous prefetch, have been introduced. In this work, we evaluate the new features on two platforms that feature different CPUs, GPUs, and interconnects. We derive a benchmark suite for the experiments and stress the memory system to evaluate both in-memory and oversubscription performance. The results show that memory advises on the Intel-Volta/Pascal-PCIe platform bring negligible improvement for in-memory executions. However, when GPU memory is oversubscribed by about 50%, using memory advises results in up to 25% performance improvement compared to the basic CUDA Unified Memory. In contrast, the Power9-Volta-NVLink platform can substantially benefit from memory advises, achieving up to 34% performance gain for in-memory executions. However, when GPU memory is oversubscribed on this platform, using memory advises increases GPU page faults and results in considerable performance loss. The CUDA prefetch also shows different performance impact on the two platforms. It improves performance by up to 50% on the Intel-Volta/Pascal-PCI-E platform but brings little benefit to the Power9-Volta-NVLink platform.
Representing and Reasoning about Dynamic Code
Venue: arXiv cs.CR 2019
Year: 2019

Dynamic code, i.e., code that is created or modified at runtime, is ubiquitous in today's world. The behavior of dynamic code can depend on the logic of the dynamic code generator in subtle and non-obvious ways, with significant security implications, e.g., JIT compiler bugs can lead to exploitable vulnerabilities in the resulting JIT-compiled code. Existing approaches to program analysis do not provide adequate support for reasoning about such behavioral relationships. This paper takes a first step in addressing this problem by describing a program representation and a new notion of dependency that allows us to reason about dependency and information flow relationships between the dynamic code generator and the generated dynamic code. Experimental results show that analyses based on these concepts are able to capture properties of dynamic code that cannot be identified using traditional program analyses.
On Automating Conversations
Venue: arXiv cs.HC 2019
Year: 2019

From 2016 to 2018, we developed and deployed Chorus, a system that blends real-time human computation with artificial intelligence (AI) and has real-world, open conversations with users. We took a top-down approach that started with a working crowd-powered system, Chorus, and then created a framework, Evorus, that enables Chorus to automate itself over time. Over our two-year deployment, more than 420 users talked with Chorus, having over 2,200 conversation sessions. This line of work demonstrated how a crowd-powered conversational assistant can be automated over time, and more importantly, how such a system can be deployed to talk with real users to help them with their everyday tasks. This position paper discusses two sets of challenges that we explored during the development and deployment of Chorus and Evorus: the challenges that come from being an "agent" and those that arise from the subset of conversations that are more difficult to automate.
Real-Time Multi-Diver Tracking and Re-identification for Underwater Human-Robot Collaboration
Venue: arXiv cs.RO 2019
Year: 2019

Autonomous underwater robots working with teams of human divers may need to distinguish between different divers, e.g. to recognize a lead diver or to follow a specific team member. This paper describes a technique that enables autonomous underwater robots to track divers in real time as well as to reidentify them. The approach is an extension of Simple Online Realtime Tracking (SORT) with an appearance metric (deep SORT). Initial diver detection is performed with a custom CNN designed for realtime diver detection, and appearance features are subsequently extracted for each detected diver. Next, realtime tracking-by-detection is performed with an extension of the deep SORT algorithm. We evaluate this technique on a series of videos of divers performing human-robot collaborative tasks and show that our methods result in more divers being accurately identified during tracking. We also discuss the practical considerations of applying multi-person tracking to on-board autonomous robot operations, and we consider how failure cases can be addressed during on-board tracking.
Learning to Make Generalizable and Diverse Predictions for Retrosynthesis
Venue: arXiv cs.LG 2019
Year: 2019

We propose a new model for making generalizable and diverse retrosynthetic reaction predictions. Given a target compound, the task is to predict the likely chemical reactants to produce the target. This generative task can be framed as a sequence-to-sequence problem by using the SMILES representations of the molecules. Building on top of the popular Transformer architecture, we propose two novel pre-training methods that construct relevant auxiliary tasks (plausible reactions) for our problem. Furthermore, we incorporate a discrete latent variable model into the architecture to encourage the model to produce a diverse set of alternative predictions. On the 50k subset of reaction examples from the United States patent literature (USPTO-50k) benchmark dataset, our model greatly improves performance over the baseline, while also generating predictions that are more diverse.
Fine-Tuned Neural Models for Propaganda Detection at the Sentence and Fragment levels
Venue: arXiv cs.CL 2019
Year: 2019

This paper presents the CUNLP submission for the NLP4IF 2019 shared-task on FineGrained Propaganda Detection. Our system finished 5th out of 26 teams on the sentence-level classification task and 5th out of 11 teams on the fragment-level classification task based on our scores on the blind test set. We present our models, a discussion of our ablation studies and experiments, and an analysis of our performance on all eighteen propaganda techniques present in the corpus of the shared task.
Spatiotemporal Emotion Recognition using Deep CNN Based on EEG during Music Listening
Venue: arXiv cs.LG 2019
Year: 2019

Emotion recognition based on EEG has become an active research area. As one of the machine learning models, CNN has been utilized to solve diverse problems including issues in this domain. In this work, a study of CNN and its spatiotemporal feature extraction has been conducted in order to explore capabilities of the model in varied window sizes and electrode orders. Our investigation was conducted in subject-independent fashion. Results have shown that temporal information in distinct window sizes significantly affects recognition performance in both 10-fold and leave-one-subject-out cross validation. Spatial information from varying electrode order has modicum effect on classification. SVM classifier depending on spatiotemporal knowledge on the same dataset was previously employed and compared to these empirical results. Even though CNN and SVM have a homologous trend in window size effect, CNN outperformed SVM using leave-one-subject-out cross validation. This could be caused by different extracted features in the elicitation process.
Let's Gamble: Uncovering the Impact of Visualization on Risk Perception and Decision-Making
Venue: arXiv cs.HC 2019
Year: 2019

Data visualizations are standard tools for assessing and communicating risks. However, it is not always clear which designs are optimal or how encoding choices might influence risk perception and decision-making. In this paper, we report the findings of a large-scale gambling game that immersed participants in an environment where their actions impacted their bonuses. Participants chose to either enter a draw or receive guaranteed monetary gains based on five common visualization designs. By measuring risk perception and observing decision-making, we showed that icon arrays tended to elicit economically sound behavior. We also found that people were more likely to gamble when presented area proportioned triangle and circle designs. Using our results, we model risk perception and decisions for each visualization and provide a ranking to improve visualization selection.
Vanishing Nodes: Another Phenomenon That Makes Training Deep Neural Networks Difficult
Venue: arXiv cs.LG 2019
Year: 2019

It is well known that the problem of vanishing/exploding gradients is a challenge when training deep networks. In this paper, we describe another phenomenon, called vanishing nodes, that also increases the difficulty of training deep neural networks. As the depth of a neural network increases, the network's hidden nodes have more highly correlated behavior. This results in great similarities between these nodes. The redundancy of hidden nodes thus increases as the network becomes deeper. We call this problem vanishing nodes, and we propose the metric vanishing node indicator (VNI) for quantitatively measuring the degree of vanishing nodes. The VNI can be characterized by the network parameters, which is shown analytically to be proportional to the depth of the network and inversely proportional to the network width. The theoretical results show that the effective number of nodes vanishes to one when the VNI increases to one (its maximal value), and that vanishing/exploding gradients and vanishing nodes are two different challenges that increase the difficulty of training deep neural networks. The numerical results from the experiments suggest that the degree of vanishing nodes will become more evident during back-propagation training, and that when the VNI is equal to 1, the network cannot learn simple tasks (e.g. the XOR problem) even when the gradients are neither vanishing nor exploding. We refer to this kind of gradients as the walking dead gradients, which cannot help the network converge when having a relatively large enough scale. Finally, the experiments show that the likelihood of failed training increases as the depth of the network increases. The training will become much more difficult due to the lack of network representation capability.
Question Answering over Knowledge Graphs via Structural Query Patterns
Venue: arXiv cs.AI 2019
Year: 2019

Natural language question answering over knowledge graphs is an important and interesting task as it enables common users to gain accurate answers in an easy and intuitive manner. However, it remains a challenge to bridge the gap between unstructured questions and structured knowledge graphs. To address the problem, a natural discipline is building a structured query to represent the input question. Searching the structured query over the knowledge graph can produce answers to the question. Distinct from the existing methods that are based on semantic parsing or templates, we propose an effective approach powered by a novel notion, structural query pattern, in this paper. Given an input question, we first generate its query sketch that is compatible with the underlying structure of the knowledge graph. Then, we complete the query graph by labeling the nodes and edges under the guidance of the structural query pattern. Finally, answers can be retrieved by executing the constructed query graph over the knowledge graph. Evaluations on three question answering benchmarks show that our proposed approach outperforms state-of-the-art methods significantly.
Hetero-Center Loss for Cross-Modality Person Re-Identification
Venue: arXiv cs.CV 2019
Year: 2019

Cross-modality person re-identification is a challenging problem which retrieves a given pedestrian image in RGB modality among all the gallery images in infrared modality. The task can address the limitation of RGB-based person Re-ID in dark environments. Existing researches mainly focus on enlarging inter-class differences of feature to solve the problem. However, few studies investigate improving intra-class cross-modality similarity, which is important for this issue. In this paper, we propose a novel loss function, called Hetero-Center loss (HC loss) to reduce the intra-class cross-modality variations. Specifically, HC loss can supervise the network learning the cross-modality invariant information by constraining the intra-class center distance between two heterogenous modalities. With the joint supervision of Cross-Entropy (CE) loss and HC loss, the network is trained to achieve two vital objectives, inter-class discrepancy and intra-class cross-modality similarity as much as possible. Besides, we propose a simple and high-performance network architecture to learn local feature representations for cross-modality person re-identification, which can be a baseline for future research. Extensive experiments indicate the effectiveness of the proposed methods, which outperform state-of-the-art methods by a wide margin.
Weakly-Supervised Completion Moment Detection using Temporal Attention
Venue: arXiv cs.CV 2019
Year: 2019

Monitoring the progression of an action towards completion offers fine grained insight into the actor's behaviour. In this work, we target detecting the completion moment of actions, that is the moment when the action's goal has been successfully accomplished. This has potential applications from surveillance to assistive living and human-robot interactions. Previous effort required human annotations of the completion moment for training (i.e. full supervision). In this work, we present an approach for moment detection from weak video-level labels. Given both complete and incomplete sequences, of the same action, we learn temporal attention, along with accumulated completion prediction from all frames in the sequence. We also demonstrate how the approach can be used when completion moment supervision is available. We evaluate and compare our approach on actions from three datasets, namely HMDB, UCF101 and RGBD-AC, and show that temporal attention improves detection in both weakly-supervised and fully-supervised settings.
Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection
Venue: arXiv cs.CL 2019
Year: 2019

We present the shared task on Fine-Grained Propaganda Detection, which was organized as part of the NLP4IF workshop at EMNLP-IJCNLP 2019. There were two subtasks. FLC is a fragment-level task that asks for the identification of propagandist text fragments in a news article and also for the prediction of the specific propaganda technique used in each such fragment (18-way classification task). SLC is a sentence-level binary classification task asking to detect the sentences that contain propaganda. A total of 12 teams submitted systems for the FLC task, 25 teams did so for the SLC task, and 14 teams eventually submitted a system description paper. For both subtasks, most systems managed to beat the baseline by a sizable margin. The leaderboard and the data from the competition are available at http://propaganda.qcri.org/nlp4if-shared-task/.