OpenRaiser/Intern-Atlas huggingface dataset:
Western ideological homogeneity in entrepreneurial finance research: Evidence from highly cited publications
Venue: arXiv cs.CY 2020
Year: 2020

Entrepreneurs play crucial roles in global sustainable development, but limited financial resources constrain their performance and survival rate. Entrepreneurial finance discipline is, therefore, born to explore the connection between finance and entrepreneurship. Despite the global presence of entrepreneurship, the literature of entrepreneurial finance is suspected to be Western ideologically homogenous. Thus, the objective of this study is to examine the existence of Western ideological homogeneity in entrepreneurial finance literature. Employing the mindsponge mechanism and bibliometric analyses (Y-index and social structure), we analyze 412 highly cited publications extracted from Web of Science database and find Western ideological dominance as well as weak tolerance towards heterogeneity in the set of core ideologies of entrepreneurial finance. These results are consistent across author-, institution-, and country-levels, which reveals strong evidence for the existence of Western ideological homogeneity in the field. We recommend editors, reviewers, and authors to have proactive actions to diversify research topics and enhancing knowledge exchange to avoid the shortfalls of ideological homogeneity. Moreover, the synthesis of mindsponge mechanism and bibliometric analyses are suggested as a possible way to evaluate the state of ideological diversity in other scientific disciplines.
Sentiment Analysis based Multi-person Multi-criteria Decision Making Methodology using Natural Language Processing and Deep Learning for Smarter Decision Aid. Case study of restaurant choice using TripAdvisor reviews
Venue: arXiv cs.CL 2020
Year: 2020

Decision making models are constrained by taking the expert evaluations with pre-defined numerical or linguistic terms. We claim that the use of sentiment analysis will allow decision making models to consider expert evaluations in natural language. Accordingly, we propose the Sentiment Analysis based Multi-person Multi-criteria Decision Making (SA-MpMcDM) methodology for smarter decision aid, which builds the expert evaluations from their natural language reviews, and even from their numerical ratings if they are available. The SA-MpMcDM methodology incorporates an end-to-end multi-task deep learning model for aspect based sentiment analysis, named DOC-ABSADeepL model, able to identify the aspect categories mentioned in an expert review, and to distill their opinions and criteria. The individual evaluations are aggregated via the procedure named criteria weighting through the attention of the experts. We evaluate the methodology in a case study of restaurant choice using TripAdvisor reviews, hence we build, manually annotate, and release the TripR-2020 dataset of restaurant reviews. We analyze the SA-MpMcDM methodology in different scenarios using and not using natural language and numerical evaluations. The analysis shows that the combination of both sources of information results in a higher quality preference vector.
Partial Reconfiguration for Design Optimization
Venue: arXiv cs.AR 2020
Year: 2020

FPGA designers have traditionally shared a similar design methodology with ASIC designers. Most notably, at design time, FPGA designers commit to a fixed allocation of logic resources to modules in a design. At runtime, some of the occupied resources could be left idle or under-utilized due to hard-to-avoid sources of inefficiencies (e.g., operation dependencies). With partial reconfiguration (PR), FPGA resources can be re-allocated over time. Therefore, using PR, a designer can attempt to reduce idleness and under-utilization with better area-time scheduling. In this paper, we explain when, how, and why PR-style designs can improve over the performance-area Pareto front of ASIC-style designs (without PR). We first introduce the concept of area-time volume to explain why PR-style designs can improve upon ASIC-style designs. We identify resource under-utilization as an opportunity that can be exploited by PR-style designs. We then present a first-order analytical model to help a designer decide if a PR-style design can be beneficial. When it is the case, the model points to the most suitable PR execution strategy and provides an estimate of the improvement. The model is validated in three case studies.
Survey of Spectrum Regulation for Intelligent Transportation Systems
Venue: arXiv cs.NI 2020
Year: 2020

As 5G communication technology develops, vehicular communications that require high reliability, low latency, and massive connectivity are drawing increasing interest from those in academia and industry. Due to these developing technologies, vehicular communication is not limited to vehicle components in the forms of Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I) networks, but has also been extended to connect with others, such as pedestrians and cellular users. Dedicated Short-Range Communications (DSRC) is the conventional vehicular communication standard for Intelligent Transportation Systems (ITS). More recently, the 3rd Generation Partnership Project introduced Cellular-Vehicle-to-Everything (C-V2X), a competitor to DSRC. Meanwhile, the Federal Communications Commission (FCC)issued a Notice of Proposed Rulemaking (NPRM) to consider deploying Unlicensed National Information Infrastructure (U-NII)devices in the ITS band with two interference mitigation approaches: Detect-and-Vacate (DAV)and Re-channelization (Re-CH). With multiple standard options and interference mitigation approaches, numerous regulatory taxonomies can be identified and notification of relevant technical challenges issued. However, these challenges are much broader than the current and future regulatory taxonomies pursued by the different countries involved. Because their plans differ, the technical and regulatory challenges vary. This paper presents a literature survey about the technical challenges, the current and future ITS band usage plans, and the major research testbeds for the U.S., Europe, China, Korea, and Japan. This survey shows that the most likely deployment taxonomies are (1) DSRC, C-V2X, and Wi-Fi with Re-CH; (2) DSRC and C-V2X with interoperation, and (3) C-V2X only. The most difficult technical challenge is the interoperability between the Wi-Fi-like DSRC and 4G LTE-like C-V2X.
Performance Evaluation of Orchestra Scheduling in Time Slotted Channel Hopping Networks
Venue: arXiv cs.NI 2020
Year: 2020

In this paper, we evaluate the performance of networks that use RPL (Routing Protocols for Low Power and Lossy Networks) with TSCH (Time Slotted Channel Hopping) and Orchestra (an autonomous method for building the TSCH schedule). We measure the performance in the transient state when a node dies (i.e., removed from the network) and determine how long it takes for the network to come back to a stable RPL tree and also what the impact is with respect to energy consumption. Our analysis shows that the Orchestra reduces the energy consumption when the RPL is in a transient state, like in the case of when one of the nodes die. Furthermore, we calculate the energy consumption in the transient state without using Orchestra, and then we make a comparison between both outcomes. We show that Orchestra reduces energy consumption by up to one-third compared to not using Orchestra.
KAPLAN: A 3D Point Descriptor for Shape Completion
Venue: arXiv cs.CV 2020
Year: 2020

We present a novel 3D shape completion method that operates directly on unstructured point clouds, thus avoiding resource-intensive data structures like voxel grids. To this end, we introduce KAPLAN, a 3D point descriptor that aggregates local shape information via a series of 2D convolutions. The key idea is to project the points in a local neighborhood onto multiple planes with different orientations. In each of those planes, point properties like normals or point-to-plane distances are aggregated into a 2D grid and abstracted into a feature representation with an efficient 2D convolutional encoder. Since all planes are encoded jointly, the resulting representation nevertheless can capture their correlations and retains knowledge about the underlying 3D shape, without expensive 3D convolutions. Experiments on public datasets show that KAPLAN achieves state-of-the-art performance for 3D shape completion.
Characterization of Assistive Robot Arm Teleoperation: A Preliminary Study to Inform Shared Control
Venue: arXiv cs.RO 2020
Year: 2020

Assistive robotic devices can increase the independence of individuals with motor impairments. However, each person is unique in their level of injury, preferences, and skills, which moreover can change over time. Further, the amount of assistance required can vary throughout the day due to pain or fatigue, or over longer periods due to rehabilitation, debilitating conditions, or aging. Therefore, in order to become an effective team member, the assistive machine should be able to learn from and adapt to the human user. To do so, we need to be able to characterize the user's control commands to determine when and how autonomy should change to best assist the user. We perform a 20 person pilot study in order to establish a set of meaningful performance measures which can be used to characterize the user's control signals and as cues for the autonomy to modify the level and amount of assistance. Our study includes 8 spinal cord injured and 12 uninjured individuals. The results unveil a set of objective, runtime-computable metrics that are correlated with user-perceived task difficulty, and thus could be used by an autonomy system when deciding whether assistance is required. The results further show that metrics which evaluate the user interaction with the robotic device, robot execution, and the perceived task difficulty show differences among spinal cord injured and uninjured groups, and are affected by the type of control interface used. The results will be used to develop an adaptable, user-centered, and individually customized shared-control algorithms.
MementoEmbed and Raintale for Web Archive Storytelling
Venue: arXiv cs.DL 2020
Year: 2020

For traditional library collections, archivists can select a representative sample from a collection and display it in a featured physical or digital library space. Web archive collections may consist of thousands of archived pages, or mementos. How should an archivist display this sample to drive visitors to their collection? Search engines and social media platforms often represent web pages as cards consisting of text snippets, titles, and images. Web storytelling is a popular method for grouping these cards in order to summarize a topic. Unfortunately, social media platforms are not archive-aware and fail to consistently create a good experience for mementos. They also allow no UI alterations for their cards. Thus, we created MementoEmbed to generate cards for individual mementos and Raintale for creating entire stories that archivists can export to a variety of formats.
Experiments in Extractive Summarization: Integer Linear Programming, Term/Sentence Scoring, and Title-driven Models
Venue: arXiv cs.IR 2020
Year: 2020

In this paper, we revisit the challenging problem of unsupervised single-document summarization and study the following aspects: Integer linear programming (ILP) based algorithms, Parameterized normalization of term and sentence scores, and Title-driven approaches for summarization. We describe a new framework, NewsSumm, that includes many existing and new approaches for summarization including ILP and title-driven approaches. NewsSumm's flexibility allows to combine different algorithms and sentence scoring schemes seamlessly. Our results combining sentence scoring with ILP and normalization are in contrast to previous work on this topic, showing the importance of a broader search for optimal parameters. We also show that the new title-driven reduction idea leads to improvement in performance for both unsupervised and supervised approaches considered.
The test set for the TransCoder system
Venue: arXiv cs.CL 2020
Year: 2020

The TransCoder system translates source code between Java, C++, and Python 3. The test set that was used to evaluate its quality is missing important features of Java, including the ability to define and use classes and the ability to call user-defined functions other than recursively. Therefore, the accuracy of TransCoder over programs with those features remains unknown.
Accurate and Efficient Intracranial Hemorrhage Detection and Subtype Classification in 3D CT Scans with Convolutional and Long Short-Term Memory Neural Networks
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we present our system for the RSNA Intracranial Hemorrhage Detection challenge. The proposed system is based on a lightweight deep neural network architecture composed of a convolutional neural network (CNN) that takes as input individual CT slices, and a Long Short-Term Memory (LSTM) network that takes as input feature embeddings provided by the CNN. For efficient processing, we consider various feature selection methods to produce a subset of useful CNN features for the LSTM. Furthermore, we reduce the CT slices by a factor of 2x, allowing ourselves to train the model faster. Even if our model is designed to balance speed and accuracy, we report a weighted mean log loss of 0.04989 on the final test set, which places us in the top 30 ranking (2%) from a total of 1345 participants. Although our computing infrastructure does not allow it, processing CT slices at their original scale is likely to improve performance. In order to enable others to reproduce our results, we provide our code as open source at https://github.com/warchildmd/ihd. After the challenge, we conducted a subjective intracranial hemorrhage detection assessment by radiologists, indicating that the performance of our deep model is on par with that of doctors specialized in reading CT scans. Another contribution of our work is to integrate Grad-CAM visualizations in our system, providing useful explanations for its predictions. We therefore consider our system as a viable option when a fast diagnosis or a second opinion on intracranial hemorrhage detection are needed.
SemEval-2020 Task 7: Assessing Humor in Edited News Headlines
Venue: arXiv cs.CL 2020
Year: 2020

This paper describes the SemEval-2020 shared task "Assessing Humor in Edited News Headlines." The task's dataset contains news headlines in which short edits were applied to make them funny, and the funniness of these edited headlines was rated using crowdsourcing. This task includes two subtasks, the first of which is to estimate the funniness of headlines on a humor scale in the interval 0-3. The second subtask is to predict, for a pair of edited versions of the same original headline, which is the funnier version. To date, this task is the most popular shared computational humor task, attracting 48 teams for the first subtask and 31 teams for the second.
Extracting actionable information from microtexts
Venue: arXiv cs.CL 2020
Year: 2020

Microblogs such as Twitter represent a powerful source of information. Part of this information can be aggregated beyond the level of individual posts. Some of this aggregated information is referring to events that could or should be acted upon in the interest of e-governance, public safety, or other levels of public interest. Moreover, a significant amount of this information, if aggregated, could complement existing information networks in a non-trivial way. This dissertation proposes a semi-automatic method for extracting actionable information that serves this purpose. First, we show that predicting time to event is possible for both in-domain and cross-domain scenarios. Second, we suggest a method which facilitates the definition of relevance for an analyst's context and the use of this definition to analyze new data. Finally, we propose a method to integrate the machine learning based relevant information classification method with a rule-based information classification technique to classify microtexts. Fully automatizing microtext analysis has been our goal since the first day of this research project. Our efforts in this direction informed us about the extent this automation can be realized. We mostly first developed an automated approach, then we extended and improved it by integrating human intervention at various steps of the automated approach. Our experience confirms previous work that states that a well-designed human intervention or contribution in design, realization, or evaluation of an information system either improves its performance or enables its realization. As our studies and results directed us toward its necessity and value, we were inspired from previous studies in designing human involvement and customized our approaches to benefit from human input.
Bayesian Optimization for Selecting Efficient Machine Learning Models
Venue: arXiv cs.LG 2020
Year: 2020

The performance of many machine learning models depends on their hyper-parameter settings. Bayesian Optimization has become a successful tool for hyper-parameter optimization of machine learning algorithms, which aims to identify optimal hyper-parameters during an iterative sequential process. However, most of the Bayesian Optimization algorithms are designed to select models for effectiveness only and ignore the important issue of model training efficiency. Given that both model effectiveness and training time are important for real-world applications, models selected for effectiveness may not meet the strict training time requirements necessary to deploy in a production environment. In this work, we present a unified Bayesian Optimization framework for jointly optimizing models for both prediction effectiveness and training efficiency. We propose an objective that captures the tradeoff between these two metrics and demonstrate how we can jointly optimize them in a principled Bayesian Optimization framework. Experiments on model selection for recommendation tasks indicate models selected this way significantly improves model training efficiency while maintaining strong effectiveness as compared to state-of-the-art Bayesian Optimization algorithms.
Removing Backdoor-Based Watermarks in Neural Networks with Limited Data
Venue: arXiv cs.CV 2020
Year: 2020

Deep neural networks have been widely applied and achieved great success in various fields. As training deep models usually consumes massive data and computational resources, trading the trained deep models is highly demanded and lucrative nowadays. Unfortunately, the naive trading schemes typically involves potential risks related to copyright and trustworthiness issues, e.g., a sold model can be illegally resold to others without further authorization to reap huge profits. To tackle this problem, various watermarking techniques are proposed to protect the model intellectual property, amongst which the backdoor-based watermarking is the most commonly-used one. However, the robustness of these watermarking approaches is not well evaluated under realistic settings, such as limited in-distribution data availability and agnostic of watermarking patterns. In this paper, we benchmark the robustness of watermarking, and propose a novel backdoor-based watermark removal framework using limited data, dubbed WILD. The proposed WILD removes the watermarks of deep models with only a small portion of training data, and the output model can perform the same as models trained from scratch without watermarks injected. In particular, a novel data augmentation method is utilized to mimic the behavior of watermark triggers. Combining with the distribution alignment between the normal and perturbed (e.g., occluded) data in the feature space, our approach generalizes well on all typical types of trigger contents. The experimental results demonstrate that our approach can effectively remove the watermarks without compromising the deep model performance for the original task with the limited access to training data.
Integrated monitoring of ice in selected Swiss lakes. Final project report
Venue: arXiv cs.CV 2020
Year: 2020

Various lake observables, including lake ice, are related to climate and climate change and provide a good opportunity for long-term monitoring. Lakes (and as part of them lake ice) is therefore considered an Essential Climate Variable (ECV) of the Global Climate Observing System (GCOS). Following the need for an integrated multi-temporal monitoring of lake ice in Switzerland, MeteoSwiss in the framework of GCOS Switzerland supported this 2-year project to explore not only the use of satellite images but also the possibilities of Webcams and in-situ measurements. The aim of this project is to monitor some target lakes and detect the extent of ice and especially the ice-on/off dates, with focus on the integration of various input data and processing methods. The target lakes are: St. Moritz, Silvaplana, Sils, Sihl, Greifen and Aegeri, whereby only the first four were mainly frozen during the observation period and thus processed. The observation period was mainly the winter 2016-17. During the project, various approaches were developed, implemented, tested and compared. Firstly, low spatial resolution (250 - 1000 m) but high temporal resolution (1 day) satellite images from the optical sensors MODIS and VIIRS were used. Secondly, and as a pilot project, the use of existing public Webcams was investigated for (a) validation of results from satellite data, and (b) independent estimation of lake ice, especially for small lakes like St. Moritz, that could not be possibly monitored in the satellite images. Thirdly, in-situ measurements were made in order to characterize the development of the temperature profiles and partly pressure before freezing and under the ice-cover until melting. This report presents the results of the project work.
Deep-Reinforcement-Learning-Based Semantic Navigation of Mobile Robots in Dynamic Environments
Venue: arXiv cs.RO 2020
Year: 2020

Mobile robots have gained increased importance within industrial tasks such as commissioning, delivery or operation in hazardous environments. The ability to autonomously navigate safely especially within dynamic environments, is paramount in industrial mobile robotics. Current navigation methods depend on preexisting static maps and are error-prone in dynamic environments. Furthermore, for safety reasons, they often rely on hand-crafted safety guidelines, which makes the system less flexible and slow. Visual based navigation and high level semantics bear the potential to enhance the safety of path planing by creating links the agent can reason about for a more flexible navigation. On this account, we propose a reinforcement learning based local navigation system which learns navigation behavior based solely on visual observations to cope with highly dynamic environments. Therefore, we develop a simple yet efficient simulator - ARENA2D - which is able to generate highly randomized training environments and provide semantic information to train our agent. We demonstrate enhanced results in terms of safety and robustness over a traditional baseline approach based on the dynamic window approach.