OpenRaiser/Intern-Atlas huggingface dataset:
Convergence and Sample Complexity of SGD in GANs
Venue: arXiv cs.LG 2020
Year: 2020

We provide theoretical convergence guarantees on training Generative Adversarial Networks (GANs) via SGD. We consider learning a target distribution modeled by a 1-layer Generator network with a non-linear activation function $ϕ(\cdot)$ parametrized by a $d \times d$ weight matrix $\mathbf W_*$, i.e., $f_*(\mathbf x) = ϕ(\mathbf W_* \mathbf x)$. Our main result is that by training the Generator together with a Discriminator according to the Stochastic Gradient Descent-Ascent iteration proposed by Goodfellow et al. yields a Generator distribution that approaches the target distribution of $f_*$. Specifically, we can learn the target distribution within total-variation distance $ε$ using $\tilde O(d^2/ε^2)$ samples which is (near-)information theoretically optimal. Our results apply to a broad class of non-linear activation functions $ϕ$, including ReLUs and is enabled by a connection with truncated statistics and an appropriate design of the Discriminator network. Our approach relies on a bilevel optimization framework to show that vanilla SGDA works.
Adaptive Neural Architectures for Recommender Systems
Venue: arXiv cs.IR 2020
Year: 2020

Deep learning has proved an effective means to capture the non-linear associations of user preferences. However, the main drawback of existing deep learning architectures is that they follow a fixed recommendation strategy, ignoring users' real time-feedback. Recent advances of deep reinforcement strategies showed that recommendation policies can be continuously updated while users interact with the system. In doing so, we can learn the optimal policy that fits to users' preferences over the recommendation sessions. The main drawback of deep reinforcement strategies is that are based on predefined and fixed neural architectures. To shed light on how to handle this issue, in this study we first present deep reinforcement learning strategies for recommendation and discuss the main limitations due to the fixed neural architectures. Then, we detail how recent advances on progressive neural architectures are used for consecutive tasks in other research domains. Finally, we present the key challenges to fill the gap between deep reinforcement learning and adaptive neural architectures. We provide guidelines for searching for the best neural architecture based on each user feedback via reinforcement learning, while considering the prediction performance on real-time recommendations and the model complexity.
A compact sequence encoding scheme for online human activity recognition in HRI applications
Venue: arXiv cs.CV 2020
Year: 2020

Human activity recognition and analysis has always been one of the most active areas of pattern recognition and machine intelligence, with applications in various fields, including but not limited to exertion games, surveillance, sports analytics and healthcare. Especially in Human-Robot Interaction, human activity understanding plays a crucial role as household robotic assistants are a trend of the near future. However, state-of-the-art infrastructures that can support complex machine intelligence tasks are not always available, and may not be for the average consumer, as robotic hardware is expensive. In this paper we propose a novel action sequence encoding scheme which efficiently transforms spatio-temporal action sequences into compact representations, using Mahalanobis distance-based shape features and the Radon transform. This representation can be used as input for a lightweight convolutional neural network. Experiments show that the proposed pipeline, when based on state-of-the-art human pose estimation techniques, can provide a robust end-to-end online action recognition scheme, deployable on hardware lacking extreme computing capabilities.
"A cold, technical decision-maker": Can AI provide explainability, negotiability, and humanity?
Venue: arXiv cs.CY 2020
Year: 2020

Algorithmic systems are increasingly deployed to make decisions in many areas of people's lives. The shift from human to algorithmic decision-making has been accompanied by concern about potentially opaque decisions that are not aligned with social values, as well as proposed remedies such as explainability. We present results of a qualitative study of algorithmic decision-making, comprised of five workshops conducted with a total of 60 participants in Finland, Germany, the United Kingdom, and the United States. We invited participants to reason about decision-making qualities such as explainability and accuracy in a variety of domains. Participants viewed AI as a decision-maker that follows rigid criteria and performs mechanical tasks well, but is largely incapable of subjective or morally complex judgments. We discuss participants' consideration of humanity in decision-making, and introduce the concept of 'negotiability,' the ability to go beyond formal criteria and work flexibly around the system.
Scalable Data Discovery Using Profiles
Venue: arXiv cs.DB 2020
Year: 2020

We study the problem of discovering joinable datasets at scale. This is, how to automatically discover pairs of attributes in a massive collection of independent, heterogeneous datasets that can be joined. Exact (e.g., based on distinct values) and hash-based (e.g., based on locality-sensitive hashing) techniques require indexing the entire dataset, which is unattainable at scale. To overcome this issue, we approach the problem from a learning perspective relying on profiles. These are succinct representations that capture the underlying characteristics of the schemata and data values of datasets, which can be efficiently extracted in a distributed and parallel fashion. Profiles are then compared, to predict the quality of a join operation among a pair of attributes from different datasets. In contrast to the state-of-the-art, we define a novel notion of join quality that relies on a metric considering both the containment and cardinality proportions between candidate attributes. We implement our approach in a system called NextiaJD, and present extensive experiments to show the predictive performance and computational efficiency of our method. Our experiments show that NextiaJD obtains similar predictive performance to that of hash-based methods, yet we are able to scale-up to larger volumes of data. Also, NextiaJD generates a considerably less amount of false positives, which is a desirable feature at scale.
Displacement-Invariant Cost Computation for Efficient Stereo Matching
Venue: arXiv cs.CV 2020
Year: 2020

Although deep learning-based methods have dominated stereo matching leaderboards by yielding unprecedented disparity accuracy, their inference time is typically slow, on the order of seconds for a pair of 540p images. The main reason is that the leading methods employ time-consuming 3D convolutions applied to a 4D feature volume. A common way to speed up the computation is to downsample the feature volume, but this loses high-frequency details. To overcome these challenges, we propose a \emph{displacement-invariant cost computation module} to compute the matching costs without needing a 4D feature volume. Rather, costs are computed by applying the same 2D convolution network on each disparity-shifted feature map pair independently. Unlike previous 2D convolution-based methods that simply perform context mapping between inputs and disparity maps, our proposed approach learns to match features between the two images. We also propose an entropy-based refinement strategy to refine the computed disparity map, which further improves speed by avoiding the need to compute a second disparity map on the right image. Extensive experiments on standard datasets (SceneFlow, KITTI, ETH3D, and Middlebury) demonstrate that our method achieves competitive accuracy with much less inference time. On typical image sizes, our method processes over 100 FPS on a desktop GPU, making our method suitable for time-critical applications such as autonomous driving. We also show that our approach generalizes well to unseen datasets, outperforming 4D-volumetric methods.
Automatic Extraction of Ranked SNP-Phenotype Associations from Literature through Detecting Neural Candidates, Negation and Modality Markers
Venue: arXiv cs.CL 2020
Year: 2020

Genome-wide association (GWA) constitutes a prominent portion of studies which have been conducted on personalized medicine and pharmacogenomics. Recently, very few methods have been developed for extracting mutation-diseases associations. However, there is no available method for extracting the association of SNP-phenotype from text which considers degree of confidence in associations. In this study, first a relation extraction method relying on linguistic-based negation detection and neutral candidates is proposed. The experiments show that negation cues and scope as well as detecting neutral candidates can be employed for implementing a superior relation extraction method which outperforms the kernel-based counterparts due to a uniform innate polarity of sentences and small number of complex sentences in the corpus. Moreover, a modality based approach is proposed to estimate the confidence level of the extracted association which can be used to assess the reliability of the reported association. Keywords: SNP, Phenotype, Biomedical Relation Extraction, Negation Detection.
Cross-Modal Terrains: Navigating Sonic Space through Haptic Feedback
Venue: arXiv cs.HC 2020
Year: 2020

This paper explores the idea of using virtual textural terrains as a means of generating haptic profiles for force-feedback controllers. This approach breaks from the para-digm established within audio-haptic research over the last few decades where physical models within virtual environments are designed to transduce gesture into sonic output. We outline a method for generating multimodal terrains using basis functions, which are rendered into monochromatic visual representations for inspection. This visual terrain is traversed using a haptic controller, the NovInt Falcon, which in turn receives force information based on the grayscale value of its location in this virtual space. As the image is traversed by a performer the levels of resistance vary, and the image is realized as a physical terrain. We discuss the potential of this approach to afford engaging musical experiences for both the performer and the audience as iterated through numerous performances.
Classification of Multimodal Hate Speech -- The Winning Solution of Hateful Memes Challenge
Venue: arXiv cs.CL 2020
Year: 2020

Hateful Memes is a new challenge set for multimodal classification, focusing on detecting hate speech in multimodal memes. Difficult examples are added to the dataset to make it hard to rely on unimodal signals, which means only multimodal models can succeed. According to Kiela,the state-of-the-art methods perform poorly compared to humans (64.73% vs. 84.7% accuracy) on Hateful Memes. I propose a new model that combined multimodal with rules, which achieve the first ranking of accuracy and AUROC of 86.8% and 0.923 respectively. These rules are extracted from training set, and focus on improving the classification accuracy of difficult samples.
CORRIDRONE: Corridors for Drones, An Adaptive On-Demand Multi-Lane Design and Testbed
Venue: arXiv cs.RO 2020
Year: 2020

In this article, a novel drone skyway framework called CORRIDRONE is proposed. As the name suggests, this represents virtual air corridors for point-to-point safe passage of multiple drones. The corridors are not permanent but can be set up on demand. A few such scenarios could be those in warehouse/factory floors, package delivery, shore-to-ship delivery, border patrol, etc. Several factors play major roles in the planning and design of such aerial passages. The proposed framework includes many novel features which aid safe and efficient integration of UAVs into the airspace with already available technologies. A several kilometres long test bed is proposed to be set-up at the 1500 acres Challekere campus of Indian Institute of Science, in the state of Karnataka, to design and test the infrastructure required for CORRIDRONE.
Biomedical Knowledge Graph Refinement with Embedding and Logic Rules
Venue: arXiv cs.AI 2020
Year: 2020

Currently, there is a rapidly increasing need for high-quality biomedical knowledge graphs (BioKG) that provide direct and precise biomedical knowledge. In the context of COVID-19, this issue is even more necessary to be highlighted. However, most BioKG construction inevitably includes numerous conflicts and noises deriving from incorrect knowledge descriptions in literature and defective information extraction techniques. Many studies have demonstrated that reasoning upon the knowledge graph is effective in eliminating such conflicts and noises. This paper proposes a method BioGRER to improve the BioKG's quality, which comprehensively combines the knowledge graph embedding and logic rules that support and negate triplets in the BioKG. In the proposed model, the BioKG refinement problem is formulated as the probability estimation for triplets in the BioKG. We employ the variational EM algorithm to optimize knowledge graph embedding and logic rule inference alternately. In this way, our model could combine efforts from both the knowledge graph embedding and logic rules, leading to better results than using them alone. We evaluate our model over a COVID-19 knowledge graph and obtain competitive results.
Retracing the Flow of the Stream: Investigating Kodi Streaming Services
Venue: arXiv cs.MM 2020
Year: 2020

Kodi is of one of the world's largest open-source streaming platforms for viewing video content. Easily installed Kodi add-ons facilitate access to online pirated videos and streaming content by facilitating the user to search and view copyrighted videos with a basic level of technical knowledge. In some countries, there have been paid child sexual abuse organizations publishing/streaming child abuse material to an international paying clientele. Open source software used for viewing videos from the Internet, such as Kodi, is being exploited by criminals to conduct their activities. In this paper, we describe a new method to quickly locate Kodi artifacts and gather information for a successful prosecution. We also evaluate our approach on different platforms; Windows, Android and Linux. Our experiments show the file location, artifacts and a history of viewed content including their locations from the Internet. Our approach will serve as a resource to forensic investigators to examine Kodi or similar streaming platforms.
Efficient Depth Completion Using Learned Bases
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we propose a new global geometry constraint for depth completion. By assuming depth maps often lay on low dimensional subspaces, a dense depth map can be approximated by a weighted sum of full-resolution principal depth bases. The principal components of depth fields can be learned from natural depth maps. The given sparse depth points are served as a data term to constrain the weighting process. When the input depth points are too sparse, the recovered dense depth maps are often over smoothed. To address this issue, we add a colour-guided auto-regression model as another regularization term. It assumes the reconstructed depth maps should share the same nonlocal similarity in the accompanying colour image. Our colour-guided PCA depth completion method has closed-form solutions, thus can be efficiently solved and is significantly more accurate than PCA only method. Extensive experiments on KITTI and Middlebury datasets demonstrate the superior performance of our proposed method.
Parallel Scheduling Self-attention Mechanism: Generalization and Optimization
Venue: arXiv cs.LG 2020
Year: 2020

Over the past few years, self-attention is shining in the field of deep learning, especially in the domain of natural language processing(NLP). Its impressive effectiveness, along with ubiquitous implementations, have aroused our interest in efficiently scheduling the data-flow of corresponding computations onto architectures with many computing units to realize parallel computing. In this paper, based on the theory of self-attention mechanism and state-of-the-art realization of self-attention in language models, we propose a general scheduling algorithm, which is derived from the optimum scheduling for small instances solved by a satisfiability checking(SAT) solver, to parallelize typical computations of self-attention. Strategies for further optimization on skipping redundant computations are put forward as well, with which reductions of almost 25% and 50% of the original computations are respectively achieved for two widely-adopted application schemes of self-attention. With the proposed optimization adopted, we have correspondingly come up with another two scheduling algorithms. The proposed algorithms are applicable regardless of problem sizes, as long as the number of input vectors is divisible to the number of computing units available in the architecture. Due to the complexity of proving the correctness of the algorithms mathematically for general cases, we have conducted experiments to reveal their validity, together with the superior quality of the solutions provided by which, by solving SAT problems for particular instances.
It's a Thin Line Between Love and Hate: Using the Echo in Modeling Dynamics of Racist Online Communities
Venue: arXiv cs.CL 2020
Year: 2020

The (((echo))) symbol -- triple parenthesis surrounding a name, made it to mainstream social networks in early 2016, with the intensification of the U.S. Presidential race. It was used by members of the alt-right, white supremacists and internet trolls to tag people of Jewish heritage -- a modern incarnation of the infamous yellow badge (Judenstern) used in Nazi-Germany. Tracking this trending meme, its meaning, and its function has proved elusive for its semantic ambiguity (e.g., a symbol for a virtual hug). In this paper we report of the construction of an appropriate dataset allowing the reconstruction of networks of racist communities and the way they are embedded in the broader community. We combine natural language processing and structural network analysis to study communities promoting hate. In order to overcome dog-whistling and linguistic ambiguity, we propose a multi-modal neural architecture based on a BERT transformer and a BiLSTM network on the tweet level, while also taking into account the users ego-network and meta features. Our multi-modal neural architecture outperforms a set of strong baselines. We further show how the the use of language and network structure in tandem allows the detection of the leaders of the hate communities. We further study the ``intersectionality'' of hate and show that the antisemitic echo correlates with hate speech that targets other minority and protected groups. Finally, we analyze the role IRA trolls assumed in this network as part of the Russian interference campaign. Our findings allow a better understanding of recent manifestations of racism and the dynamics that facilitate it.
Development of Dynamic Local Area Network (LAN) Based Mock Board Examination System
Venue: arXiv cs.CY 2020
Year: 2020

Purpose-Mock board exam is necessary to identify if the students are ready to take the board exam. However, preparing for the examination is not easy. It takes too much time to release the result, given that the College has limited personnel. Thus, the proponents developed the Dynamic Local Area Network (LAN) Based Mock Board Examination System that makes the preparation and checking easy. Method-The proponents followed the iterative waterfall model to develop the system efficiently. Some criteria of ISO 25010 were adopted in the evaluation instrument. Simultaneously, evaluators are composed of program chairperson, College of Computer Studies (CCS) faculty, board program students, and alumni. Results-The result of 4.91 in Functional Suitability, 4.87 in Performance Efficiency, 4.91 in Usability, 4.90 in Security, and 4.92 in Maintainability shows that the system is fully functional and is usable by any board program. Conclusion The computer-based examination, implemented through LAN, can simplify administering personnel of MinSCAT mock board examination. The development of the "Dynamic LAN Based Mock Board Examination System" is a great help to the MinSCAT and their board program graduates if it is implemented. Recommendations-The system has hidden weaknesses that were only identified in actual operation. Thus, further testing is needed, like beta testing, to identify and correct it for better performance. After beta-testing, the system can be improved through iteration of the Waterfall Model phases. Research Implications-The developed system can improve the mock board examination process and ease preparing this significant examination. Moreover, it immediately provides a result that helps the examinees to identify their weaknesses and do a further review to master it.
ASIAVR: Asian Studies Virtual Reality Game a Learning Tool
Venue: arXiv cs.CY 2020
Year: 2020

The study aims to develop an application that will serve as an alternative learning tool for learning Asian Studies. The delivery of lessons into a virtual reality game depends on the pace of students. The developed application comprises several more features that enable users to get valuable information from an immersive environment. The researchers used Rapid Application Development (RAD) in developing the application. It follows phases such as requirement planning, user design, construction, and cutover. Two sets of questionnaires were developed, one for the teachers and another for the students. Then, testing and evaluation were conducted through purposive sampling to select the respondents. The application was overall rated as 3.56 which is verbally interpreted as very good. The result was based on the system evaluation using ISO 9126 in terms of functionality, usability, content, reliability, and performance. The developed application meets the objectives to provide an alternative learning tool for learning Asian Studies. The application is well commended and accepted by the end-users to provide an interactive and immersive environment for students to learn at their own pace. Further enhancement of the audio, gameplay, and graphics of the tool. Schools should take into consideration the adoption of the Asian Studies Virtual Reality is a good alternative tool for their teachers and students to teach and learn Asian Studies. The use of more 3D objects relevant to the given information to enhance the game experience may be considered. A databank for the quiz questions that will be loaded into the game should also be considered.
AGenT Zero: Zero-shot Automatic Multiple-Choice Question Generation for Skill Assessments
Venue: arXiv cs.CY 2020
Year: 2020

Multiple-choice questions (MCQs) offer the most promising avenue for skill evaluation in the era of virtual education and job recruiting, where traditional performance-based alternatives such as projects and essays have become less viable, and grading resources are constrained. The automated generation of MCQs would allow assessment creation at scale. Recent advances in natural language processing have given rise to many complex question generation methods. However, the few methods that produce deployable results in specific domains require a large amount of domain-specific training data that can be very costly to acquire. Our work provides an initial foray into MCQ generation under high data-acquisition cost scenarios by strategically emphasizing paraphrasing the question context (compared to the task). In addition to maintaining semantic similarity between the question-answer pairs, our pipeline, which we call AGenT Zero, consists of only pre-trained models and requires no fine-tuning, minimizing data acquisition costs for question generation. AGenT Zero successfully outperforms other pre-trained methods in fluency and semantic similarity. Additionally, with some small changes, our assessment pipeline can be generalized to a broader question and answer space, including short answer or fill in the blank questions.