OpenRaiser/Intern-Atlas huggingface dataset:
RTTD-ID: Tracked Captions with Multiple Speakers for Deaf Students
Venue: arXiv cs.HC 2019
Year: 2019

Students who are deaf and hard of hearing cannot hear in class and do not have full access to spoken information. They can use accommodations such as captions that display speech as text. However, compared with their hearing peers, the caption accommodations do not provide equal access, because they are focused on reading captions on their tablet and cannot see who is talking. This viewing isolation contributes to student frustration and risk of doing poorly or withdrawing from introductory engineering courses with lab components. It also contributes to their lack of inclusion and sense of belonging. We report on the evaluation of a Real-Time Text Display with Speaker-Identification, which displays the location of a speaker in a group (RTTD-ID). RTTD-ID aims to reduce frustration in identifying and following an active speaker when there are multiple speakers, e.g., in a lab. It has three different display schemes to identify the location of the active speaker, which helps deaf students in viewing both the speaker's words and the speaker's expression and actions. We evaluated three RTTD speaker identification methods: 1) traditional: captions stay in one place and viewers search for the speaker, 2) pointer: captions stay in one place, and a pointer to the speaker is displayed, and 3) pop-up: captions "pop-up" next to the speaker. We gathered both quantitative and qualitative information through evaluations with deaf and hard of hearing users. The users preferred the pointer identification method over the traditional and pop-up methods.
On a conjecture about a class of permutation quadrinomials
Venue: arXiv cs.IT 2019
Year: 2019

Very recently, Tu et al. presented a sufficient condition about $(a_1,a_2,a_3)$, see Theorem 1.1, such that $f(x) = x^{3\cdot 2^m} + a_1 x^{2^{m+1}+1}+ a_2 x^{2^m+2} + a_3 x^3$ is a class of permutation polynomials over $\gf_{2^{n}}$ with $n=2m$ and $m$ odd. In this present paper, we prove that the sufficient condition is also necessary.
Performance of Recommender Systems: Based on Content Navigator and Collaborative Filtering
Venue: arXiv cs.CY 2019
Year: 2019

In the world of big data, many people find it difficult to access the information they need quickly and accurately. In order to overcome this, research on the system that recommends information accurately to users is continuously conducted. Collaborative Filtering is one of the famous algorithms among the most used in the industry. However, collaborative filtering is difficult to use in online systems because user recommendation is highly volatile in recommendation quality and requires computation using large matrices. To overcome this problem, this paper proposes a method similar to database queries and a clustering method (Contents Navigator) originating from a complex network.
Pre-trained Language Model for Biomedical Question Answering
Venue: arXiv cs.CL 2019
Year: 2019

The recent success of question answering systems is largely attributed to pre-trained language models. However, as language models are mostly pre-trained on general domain corpora such as Wikipedia, they often have difficulty in understanding biomedical questions. In this paper, we investigate the performance of BioBERT, a pre-trained biomedical language model, in answering biomedical questions including factoid, list, and yes/no type questions. BioBERT uses almost the same structure across various question types and achieved the best performance in the 7th BioASQ Challenge (Task 7b, Phase B). BioBERT pre-trained on SQuAD or SQuAD 2.0 easily outperformed previous state-of-the-art models. BioBERT obtains the best performance when it uses the appropriate pre-/post-processing strategies for questions, passages, and answers.
Information Extraction Tool Text2ALM: From Narratives to Action Language System Descriptions
Venue: arXiv cs.AI 2019
Year: 2019

In this work we design a narrative understanding tool Text2ALM. This tool uses an action language ALM to perform inferences on complex interactions of events described in narratives. The methodology used to implement the Text2ALM system was originally outlined by Lierler, Inclezan, and Gelfond (2017) via a manual process of converting a narrative to an ALM model. It relies on a conglomeration of resources and techniques from two distinct fields of artificial intelligence, namely, natural language processing and knowledge representation and reasoning. The effectiveness of system Text2ALM is measured by its ability to correctly answer questions from the bAbI tasks published by Facebook Research in 2015. This tool matched or exceeded the performance of state-of-the-art machine learning methods in six of the seven tested tasks. We also illustrate that the Text2ALM approach generalizes to a broader spectrum of narratives.
A Tractable Logic for Molecular Biology
Venue: arXiv cs.LO 2019
Year: 2019

We introduce a logic for knowledge representation and reasoning on protein-protein interactions. Modulo a theory, formulas describe protein structures and dynamic changes. They can be composed in order to add or remove static and dynamic observations. A second-order circumscription operator then enables nonmonotonic reasoning on the changes implied by a formula. We introduce deduction rules that produce formulas which are, up to equivalence, in a first-order fragment with decidable satisfiability and validity. Importantly, the rules can produce circumscribed formulas.
Diffusion-Based Molecular Communication Channel in Presence of a Probabilistic Absorber: Single Receptor Model and Congestion Analysis
Venue: arXiv cs.ET 2019
Year: 2019

In this paper, a diffusion-based molecular communication channel is modeled in presence of a probabilistic absorber. The probabilistic absorber is an absorber which absorbs molecules upon collision with probability q. With random walk analysis, the discrete probability function of particle location in the presence of a probabilistic absorber can be found. Then, a continuous probability function is fitted to this Markov-based results with introducing several fitting parameters to the known probability function of particle location in an unbounded environment without an absorbing barrier. With this approach, a single receptor is modeled as an M/M/1/1 queue in which q represents the complementary blocking probability and the mean service time is the mean trafficking time. Therefore, we are able to model the stochastic nature of ligand-receptor binding, which comes from the incapability of a receptor to receive all molecules in its space; and also known as receptor occupancy. The proper consideration of the absorption effect leads to the accurate calculation of the concentration at the desired site, which is definitely less than the concentration obtained when neglecting it. These findings can have a crucial role in designing drug delivery systems in which determining the optimal rate of the drug transmitting nanomachines is critical to avoid toxicity while maintaining effectiveness.
Mutex Graphs and Multicliques: Reducing Grounding Size for Planning
Venue: arXiv cs.AI 2019
Year: 2019

We present an approach to representing large sets of mutual exclusions, also known as mutexes or mutex constraints. These are the types of constraints that specify the exclusion of some properties, events, processes, and so on. They are ubiquitous in many areas of applications. The size of these constraints for a given problem can be overwhelming enough to present a bottleneck for the solving efficiency of the underlying solver. In this paper, we propose a novel graph-theoretic technique based on multicliques for a compact representation of mutex constraints and apply it to domain-independent planning in ASP. As computing a minimum multiclique covering from a mutex graph is NP-hard, we propose an efficient approximation algorithm for multiclique covering and show experimentally that it generates substantially smaller grounding size for mutex constraints in ASP than the previously known work in SAT.
Quantified Constraint Handling Rules
Venue: arXiv cs.AI 2019
Year: 2019

We shift the QCSP (Quantified Constraint Satisfaction Problems) framework to the QCHR (Quantified Constraint Handling Rules) framework by enabling dynamic binder and access to user-defined constraints. QCSP offers a natural framework to express PSPACE problems as finite two-players games. But to define a QCSP model, the binder must be formerly known and cannot be built dynamically even if the worst case won't occur. To overcome this issue, we define the new QCHR formalism that allows to build the binder dynamically during the solving. Our QCHR models exhibit state-of-the-art performances on static binder and outperforms previous QCSP approaches when the binder is dynamic.
Reasoning in Highly Reactive Environments
Venue: arXiv cs.AI 2019
Year: 2019

The aim of my Ph.D. thesis concerns Reasoning in Highly Reactive Environments. As reasoning in highly reactive environments, we identify the setting in which a knowledge-based agent, with given goals, is deployed in an environment subject to repeated, sudden and possibly unknown changes. This is for instance the typical setting in which, e.g., artificial agents for video-games (the so called "bots"), cleaning robots, bomb clearing robots, and so on are deployed. In all these settings one can follow the classical approach in which the operations of the agent are distinguished in "sensing" the environment with proper interface devices, "thinking", and then behaving accordingly using proper actuators. In order to operate in an highly reactive environment, an artificial agent needs to be: 1. Responsive -> The agent must be able to react repeatedly and in a reasonable amount of time; 2. Elastic -> The agent must stay reactive also under varying workload; 3. Resilient -> The agent must stay responsive also in case of internal failure or failure of one of the programmed actions in the environment. Nowadays, thanks to new technologies in the field of Artificial Intelligence, it is already technically possible to create AI agents that are able to operate in reactive environments. Nevertheless, several issues stay unsolved, and are subject of ongoing research.
NatCSNN: A Convolutional Spiking Neural Network for recognition of objects extracted from natural images
Venue: arXiv cs.NE 2019
Year: 2019

Biological image processing is performed by complex neural networks composed of thousands of neurons interconnected via thousands of synapses, some of which are excitatory and others inhibitory. Spiking neural models are distinguished from classical neurons by being biological plausible and exhibiting the same dynamics as those observed in biological neurons. This paper proposes a Natural Convolutional Neural Network (NatCSNN) which is a 3-layer bio-inspired Convolutional Spiking Neural Network (CSNN), for classifying objects extracted from natural images. A two-stage training algorithm is proposed using unsupervised Spike Timing Dependent Plasticity (STDP) learning (phase 1) and ReSuMe supervised learning (phase 2). The NatCSNN was trained and tested on the CIFAR-10 dataset and achieved an average testing accuracy of 84.7% which is an improvement over the 2-layer neural networks previously applied to this dataset.
Probabilistic Atlases to Enforce Topological Constraints
Venue: arXiv cs.CV 2019
Year: 2019

Probabilistic atlases (PAs) have long been used in standard segmentation approaches and, more recently, in conjunction with Convolutional Neural Networks (CNNs). However, their use has been restricted to relatively standardized structures such as the brain or heart which have limited or predictable range of deformations. Here we propose an encoding-decoding CNN architecture that can exploit rough atlases that encode only the topology of the target structures that can appear in any pose and have arbitrarily complex shapes to improve the segmentation results. It relies on the output of the encoder to compute both the pose parameters used to deform the atlas and the segmentation mask itself, which makes it effective and end-to-end trainable.
Secure Computation of the kth-Ranked Element in a Star Network
Venue: arXiv cs.CR 2019
Year: 2019

We consider the problem of securely computing the kth-ranked element in a sequence of n private integers distributed among n parties. The kth-ranked element (e.g., minimum, maximum, median) is of particular interest in benchmarking, which allows a company to compare its own key performance indicator to the statistics of its peer group. The individual integers are sensitive data, yet the kth-ranked element is of mutual interest to the parties. Previous secure computation protocols for the kth-ranked element require a communication channel between each pair of parties. They do not scale to a large number of parties as they are highly interactive resulting in longer delays. Moreover, they are difficult to deploy as special arrangements are required between each pair of parties to establish a secure connection. A server model naturally fits with the client-server architecture of Internet applications in which clients are connected to the server and not to other clients. It can simplify secure computation by reducing the number of rounds, and as a result, improve its performance and scalability. In this model, there are communication channels only between each client and the server, while only clients provide inputs to the computation. Hence, it is a centralized communication pattern, i.e., a star network. We propose different approaches for privately computing the kth-ranked element in the server model, using either garbled circuits or threshold homomorphic encryption. Our schemes have a constant number of rounds and can compute the kth-ranked element within seconds for up to 50 clients in a WAN.
A Lexical, Syntactic, and Semantic Perspective for Understanding Style in Text
Venue: arXiv cs.CL 2019
Year: 2019

With a growing interest in modeling inherent subjectivity in natural language, we present a linguistically-motivated process to understand and analyze the writing style of individuals from three perspectives: lexical, syntactic, and semantic. We discuss the stylistically expressive elements within each of these levels and use existing methods to quantify the linguistic intuitions related to some of these elements. We show that such a multi-level analysis is useful for developing a well-knit understanding of style - which is independent of the natural language task at hand, and also demonstrate its value in solving three downstream tasks: authors' style analysis, authorship attribution, and emotion prediction. We conduct experiments on a variety of datasets, comprising texts from social networking sites, user reviews, legal documents, literary books, and newswire. The results on the aforementioned tasks and datasets illustrate that such a multi-level understanding of style, which has been largely ignored in recent works, models style-related subjectivity in text and can be leveraged to improve performance on multiple downstream tasks both qualitatively and quantitatively.
Towards Large-Scale Quantum Networks
Venue: arXiv cs.NI 2019
Year: 2019

The vision of a quantum internet is to fundamentally enhance Internet technology by enabling quantum communication between any two points on Earth. While the first realisations of small scale quantum networks are expected in the near future, scaling such networks presents immense challenges to physics, computer science and engineering. Here, we provide a gentle introduction to quantum networking targeted at computer scientists, and survey the state of the art. We proceed to discuss key challenges for computer science in order to make such networks a reality.
Design research, eHealth, and the convergence revolution
Venue: arXiv cs.HC 2019
Year: 2019

The Quadruple Aim is a framework which prioritizes four aims, or dimensions of performance, for innovating in the healthcare domain, respectively: 1) enhancing the individual experience of care; 2) improving the work life of health care clinicians and staff; 3) improving the health of populations; and 4) reducing the per capita cost of care. In this contribution, recent literature providing examples of design research in the eHealth domain is reviewed to answer the research question: in which measure has design research contributed to each of the four aims of eHealth innovation in the past five years?. The results of the review are presented and employed to draw three main observations: 1) design researchers in eHealth seem to be largely focused on improving experiences of care, either patients' or health professionals; 2) design researchers' contribution on reducing per capita costs of care appears to be less pronounced, which is outlined as a point for improvement; and 3) in a considerable amount of reviewed contributions, design researchers appear to be contributing to multiple aims at once. In this sub-group of reviewed contributions, several disciplinary areas and types of stakeholders interact and integrate through design research activities. The latter observation leads to a reflection on the strategic role of design research in the contexts of the convergence revolution and of the non-communicable disease crisis. Implications of this reflection for design researchers are recognized in the opportunity and timeliness to develop eHealth-specific ways to orchestrate design integration. A direction for further research in this sense is identified in the use of sensory and self-monitored data as a boundary object for eHealth innovation. The prospective value of this direction is finally exemplified through the case of blood pressure.
CASAD: CAN-Aware Stealthy-Attack Detection for In-Vehicle Networks
Venue: arXiv cs.CR 2019
Year: 2019

Nowadays, vehicles have complex in-vehicle networks (IVNs) with millions of lines of code controlling almost every function in the vehicle including safety-critical functions. It has recently been shown that IVNs are becoming increasingly vulnerable to cyber-attacks capable of taking control of vehicles, thereby threatening the safety of the passengers. Several countermeasures have been proposed in the literature in response to the arising threats, however, hurdle requirements imposed by the industry is hindering their adoption in practice. In particular, detecting attacks on IVNs is challenged by strict resource constraints and utterly complex communication patterns that vary even for vehicles of the same model. In addition, existing solutions suffer from two main drawbacks. First, they depend on the underlying vehicle configuration, and second, they are incapable of detecting certain attacks of a stealthy nature. In this paper, we propose CASAD, a CAN-Aware Stealthy-Attack Detection mechanism that does not abide by the strict specifications predefined for every vehicle model and addresses key real-world deployability challenges. Our fast, lightweight, and system-agnostic approach learns the normal behavior of IVN dynamics from historical data and detects deviations by continuously monitoring IVN traffic. We demonstrate the effectiveness of CASAD by conducting various experiments on a CAN bus prototype, a 2018 Volvo XC60, and publicly available data from two real vehicles. Our approach is experimentally shown to be effective against different attack scenarios, including the prompt detection of stealthy attacks, and has considerable potential applicability to real vehicles.
A polynomial time approximation schema for maximum k-vertex cover in bipartite graphs
Venue: arXiv cs.CC 2019
Year: 2019

The paper presents a polynomial time approximation schema for the edge-weighted version of maximum k-vertex cover problem in bipartite graphs.
Exploring Bit-Slice Sparsity in Deep Neural Networks for Efficient ReRAM-Based Deployment
Venue: arXiv cs.LG 2019
Year: 2019

Emerging resistive random-access memory (ReRAM) has recently been intensively investigated to accelerate the processing of deep neural networks (DNNs). Due to the in-situ computation capability, analog ReRAM crossbars yield significant throughput improvement and energy reduction compared to traditional digital methods. However, the power hungry analog-to-digital converters (ADCs) prevent the practical deployment of ReRAM-based DNN accelerators on end devices with limited chip area and power budget. We observe that due to the limited bit-density of ReRAM cells, DNN weights are bit sliced and correspondingly stored on multiple ReRAM bitlines. The accumulated current on bitlines resulted by weights directly dictates the overhead of ADCs. As such, bitwise weight sparsity rather than the sparsity of the full weight, is desirable for efficient ReRAM deployment. In this work, we propose bit-slice L1, the first algorithm to induce bit-slice sparsity during the training of dynamic fixed-point DNNs. Experiment results show that our approach achieves 2x sparsity improvement compared to previous algorithms. The resulting sparsity allows the ADC resolution to be reduced to 1-bit of the most significant bit-slice and down to 3-bit for the others bits, which significantly speeds up processing and reduces power and area overhead.
Using Synthetic Data and Deep Networks to Recognize Primitive Shapes for Object Grasping
Venue: arXiv cs.CV 2019
Year: 2019

A segmentation-based architecture is proposed to decompose objects into multiple primitive shapes from monocular depth input for robotic manipulation. The backbone deep network is trained on synthetic data with 6 classes of primitive shapes generated by a simulation engine. Each primitive shape is designed with parametrized grasp families, permitting the pipeline to identify multiple grasp candidates per shape primitive region. The grasps are priority ordered via proposed ranking algorithm, with the first feasible one chosen for execution. On task-free grasping of individual objects, the method achieves a 94% success rate. On task-oriented grasping, it achieves a 76% success rate. Overall, the method supports the hypothesis that shape primitives can support task-free and task-relevant grasp prediction.
Specification and Optimal Reactive Synthesis of Run-time Enforcement Shields
Venue: arXiv cs.LO 2019
Year: 2019

A system with sporadic errors (SSE) is a controller which produces high quality output but it may occasionally violate a critical requirement REQ(I,O). A run-time enforcement shield is a controller which takes (I,O) (coming from SSE) as its input, and it produces a corrected output O' which guarantees the invariance of requirement REQ(I,O'). Moreover, the output sequence O' must deviate from O "as little as possible" to maintain the quality. In this paper, we give a method for logical specification of shields using formulas of logic Quantified Discrete Duration Calculus (QDDC). The specification consists of a correctness requirement REQ as well as a hard deviation constraint HDC which must both be mandatorily and invariantly satisfied by the shield. Moreover, we also use quantitative optimization to give a shield which minimizes the expected value of cumulative deviation in an H-optimal fashion. We show how tool DCSynth implementing soft requirement guided synthesis can be used for automatic synthesis of shields from a given specification. Next, we give logical formulas specifying several notions of shields including the k-Stabilizing shield of Bloem "et al." as well as the Burst-error shield of Wu "et al.", and a new e,d-shield. Shields can be automatically synthesized for all these specifications using the tool DCSynth. We give experimental results showing the performance of our shield synthesis tool in relation to previous work. We also compare the performance of the shields synthesized under diverse hard deviation constraints in terms of their expected deviation and the worst case burst-deviation latency.