OpenRaiser/Intern-Atlas huggingface dataset:
Introducing the structural bases of typicality effects in deep learning
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we hypothesize that the effects of the degree of typicality in natural semantic categories can be generated based on the structure of artificial categories learned with deep learning models. Motivated by the human approach to representing natural semantic categories and based on the Prototype Theory foundations, we propose a novel Computational Prototype Model (CPM) to represent the internal structure of semantic categories. Unlike other prototype learning approaches, our mathematical framework proposes a first approach to provide deep neural networks with the ability to model abstract semantic concepts such as category central semantic meaning, typicality degree of an object's image, and family resemblance relationship. We proposed several methodologies based on the typicality's concept to evaluate our CPM-model in image semantic processing tasks such as image classification, a global semantic description, and transfer learning. Our experiments on different image datasets, such as ImageNet and Coco, showed that our approach might be an admissible proposition in the effort to endow machines with greater power of abstraction for the semantic representation of objects' categories.
BumbleBee: A Transformer for Music
Venue: arXiv cs.SD 2021
Year: 2021

We will introduce BumbleBee, a transformer model that will generate MIDI music data . We will tackle the issue of transformers applied to long sequences by implementing a longformer generative model that uses dilating sliding windows to compute the attention layers. We will compare our results to that of the music transformer and Long-Short term memory (LSTM) to benchmark our results. This analysis will be performed using piano MIDI files, in particular , the JSB Chorales dataset that has already been used for other research works (Huang et al., 2018)
A repeated-measures study on emotional responses after a year in the pandemic
Venue: arXiv cs.CL 2021
Year: 2021

The introduction of COVID-19 lockdown measures and an outlook on return to normality are demanding societal changes. Among the most pressing questions is how individuals adjust to the pandemic. This paper examines the emotional responses to the pandemic in a repeated-measures design. Data (n=1698) were collected in April 2020 (during strict lockdown measures) and in April 2021 (when vaccination programmes gained traction). We asked participants to report their emotions and express these in text data. Statistical tests revealed an average trend towards better adjustment to the pandemic. However, clustering analyses suggested a more complex heterogeneous pattern with a well-coping and a resigning subgroup of participants. Linguistic computational analyses uncovered that topics and n-gram frequencies shifted towards attention to the vaccination programme and away from general worrying. Implications for public mental health efforts in identifying people at heightened risk are discussed. The dataset is made publicly available.
A Dataset and Method for Hallux Valgus Angle Estimation Based on Deep Learing
Venue: arXiv cs.CV 2021
Year: 2021

Angular measurements is essential to make a resonable treatment for Hallux valgus (HV), a common forefoot deformity. However, it still depends on manual labeling and measurement, which is time-consuming and sometimes unreliable. Automating this process is a thing of concern. However, it lack of dataset and the keypoints based method which made a great success in pose estimation is not suitable for this field.To solve the problems, we made a dataset and developed an algorithm based on deep learning and linear regression. It shows great fitting ability to the ground truth.
MAFIA: Machine Learning Acceleration on FPGAs for IoT Applications
Venue: arXiv cs.AR 2021
Year: 2021

Recent breakthroughs in ML have produced new classes of models that allow ML inference to run directly on milliwatt-powered IoT devices. On one hand, existing ML-to-FPGA compilers are designed for deep neural-networks on large FPGAs. On the other hand, general-purpose HLS tools fail to exploit properties specific to ML inference, thereby resulting in suboptimal performance. We propose MAFIA, a tool to compile ML inference on small form-factor FPGAs for IoT applications. MAFIA provides native support for linear algebra operations and can express a variety of ML algorithms, including state-of-the-art models. We show that MAFIA-generated programs outperform best-performing variant of a commercial HLS compiler by 2.5x on average.
Duplicate-sensitivity Guided Transformation Synthesis for DBMS Correctness Bug Detection
Venue: arXiv cs.SE 2021
Year: 2021

Database Management System (DBMS) plays a core role in modern software from mobile apps to online banking. It is critical that DBMS should provide correct data to all applications. When the DBMS returns incorrect data, a correctness bug is triggered. Current production-level DBMSs still suffer from insufficient testing due to the limited hand-written test cases. Recently several works proposed to automatically generate many test cases with query transformation, a process of generating an equivalent query pair and testing a DBMS by checking whether the system returns the same result set for both queries. However, all of them still heavily rely on manual work to provide a transformation which largely confines their exploration of the valid input query space. This paper introduces duplicate-sensitivity guided transformation synthesis which automatically finds new transformations by first synthesizing many candidates then filtering the nonequivalent ones. Our automated synthesis is achieved by mutating a query while keeping its duplicate sensitivity, which is a necessary condition for query equivalence. After candidate synthesis, we keep the mutant query which is equivalent to the given one by using a query equivalent checker. Furthermore, we have implemented our idea in a tool Eqsql and used it to test the production-level DBMSs. In two months, we detected in total 30 newly confirmed and unique bugs in MySQL, TiDB and CynosDB.
Feature Pyramid Network for Multi-task Affective Analysis
Venue: arXiv cs.CV 2021
Year: 2021

Affective Analysis is not a single task, and the valence-arousal value, expression class, and action unit can be predicted at the same time. Previous researches did not pay enough attention to the entanglement and hierarchical relation of these three facial attributes. We propose a novel model named feature pyramid networks for multi-task affect analysis. The hierarchical features are extracted to predict three labels and we apply a teacher-student training strategy to learn from pretrained single-task models. Extensive experiment results demonstrate the proposed model outperforms other models. This is a submission to The 2nd Workshop and Competition on Affective Behavior Analysis in the wild (ABAW). The code and model are available for research purposes at https://github.com/ryanhe312/ABAW2-FPNMAA.
Complete Scanning Application Using OpenCv
Venue: arXiv cs.CV 2021
Year: 2021

In the following paper, we have combined the various basic functionalities provided by the NumPy library and OpenCv library, which is an open source for Computer Vision applications, like conversion of colored images to grayscale, calculating threshold, finding contours and using those contour points to take perspective transform of the image inputted by the user, using Python version 3.7. Additional features include cropping, rotating and saving as well. All these functions and features, when implemented step by step, results in a complete scanning application. The applied procedure involves the following steps: Finding contours, applying Perspective transform and brightening the image, Adaptive Thresholding and applying filters for noise cancellation, and Rotation features and perspective transform for a special cropping algorithm. The described technique is implemented on various samples.
Daany -- DAta ANalYtics on .NET
Venue: arXiv cs.SE 2021
Year: 2021

Daany is .NET and cross platform data analytics and linear algebra library written in C# supposed to be a tool for data preparation, feature engineering and other kind of data transformations and feature engineering. The library is implemented on top of .NET Standard 2.1 and supports .NET Core 3.0 and above separated on several Visual Studio projects that can be installed as a NuGet package. The library implements DataFrame as the core component with extensions of a set of data science and linear algebra features. The library contains several implementation of time series decomposition (SSA, STL ARIMA), optimization methods (SGD) as well as plotting support. The library also implements set of features based on matrix, vectors and similar linear algebra operations. The main part of the library is the Daany.DataFrame with similar implementation that can be found in python based Pandas library. The paper presents the main functionalities and the implementation behind the Daany packages in the form of developer guide and can be used as manual in using the Daany in every-day work. To end this the paper shows the list of papers used the library.
Identification of Gait Phases with Neural Networks for Smooth Transparent Control of a Lower Limb Exoskeleton
Venue: arXiv cs.RO 2021
Year: 2021

Lower limbs exoskeletons provide assistance during standing, squatting, and walking. Gait dynamics, in particular, implies a change in the configuration of the device in terms of contact points, actuation, and system dynamics in general. In order to provide a comfortable experience and maximize performance, the exoskeleton should be controlled smoothly and in a transparent way, which means respectively, minimizing the interaction forces with the user and jerky behavior due to transitions between different configurations. A previous study showed that a smooth control of the exoskeleton can be achieved using a gait phase segmentation based on joint kinematics. Such a segmentation system can be implemented as linear regression and should be personalized for the user after a calibration procedure. In this work, a nonlinear segmentation function based on neural networks is implemented and compared with linear regression. An on-line implementation is then proposed and tested with a subject.
A note on 'Variable Neighborhood Search Based Algorithms for Crossdock Truck Assignment'
Venue: arXiv cs.DM 2021
Year: 2021

Some implementations of variable neighborhood search based algorithms were presented in \emph{Cécilia Daquin, Hamid Allaoui, Gilles Goncalves and Tienté Hsu, Variable neighborhood search based algorithms for crossdock truck assignment, RAIRO-Oper. Res., 55 (2021) 2291-2323}. This work is based on model in \emph{Zhaowei Miao, Andrew Lim, Hong Ma, Truck dock assignment problem with operational time constraint within crossdocks, European Journal of Operational Research 192 (1), 2009, 105-115 }m which has been proven to be incorrect. We reiterate and elaborate on the deficiencies in the latter and show that the authors in the former were already aware of the deficiencies in the latter and the proposed minor amendment does not overcome any of such deficiencies.
Use of Affective Visual Information for Summarization of Human-Centric Videos
Venue: arXiv cs.CV 2021
Year: 2021

Increasing volume of user-generated human-centric video content and their applications, such as video retrieval and browsing, require compact representations that are addressed by the video summarization literature. Current supervised studies formulate video summarization as a sequence-to-sequence learning problem and the existing solutions often neglect the surge of human-centric view, which inherently contains affective content. In this study, we investigate the affective-information enriched supervised video summarization task for human-centric videos. First, we train a visual input-driven state-of-the-art continuous emotion recognition model (CER-NET) on the RECOLA dataset to estimate emotional attributes. Then, we integrate the estimated emotional attributes and the high-level representations from the CER-NET with the visual information to define the proposed affective video summarization architectures (AVSUM). In addition, we investigate the use of attention to improve the AVSUM architectures and propose two new architectures based on temporal attention (TA-AVSUM) and spatial attention (SA-AVSUM). We conduct video summarization experiments on the TvSum database. The proposed AVSUM-GRU architecture with an early fusion of high level GRU embeddings and the temporal attention based TA-AVSUM architecture attain competitive video summarization performances by bringing strong performance improvements for the human-centric videos compared to the state-of-the-art in terms of F-score and self-defined face recall metrics.
Algorithmic aspects of quasi-kernels
Venue: arXiv cs.DM 2021
Year: 2021

In a digraph, a quasi-kernel is a subset of vertices that is independent and such that every vertex can reach some vertex in that set via a directed path of length at most two. Whereas Chvátal and Lovász proved in 1974 that every digraph has a quasi-kernel, very little is known so far about the complexity of finding small quasi-kernels. In 1976 Erdős and Székely conjectured that every sink-free digraph $D = (V, A)$ has a quasi-kernel of size at most $|V|/2$. Obviously, if $D$ has two disjoint quasi-kernels then it has a quasi-kernel of size at most $|V|/2$, and in 2001 Gutin, Koh, Tay and Yeo conjectured that every sink-free digraph has two disjoint quasi-kernels. Yet, they constructed in 2004 a counterexample, thereby disproving this stronger conjecture. We shall show that, not only sink-free digraphs occasionally fail to contain two disjoint quasi-kernels, but it is computationally hard to distinguish those that do from those that do not. We also prove that the problem of computing a small quasi-kernel is polynomial time solvable for orientations of trees but is computationally hard in most other cases (and in particular for restricted acyclic digraphs).
Investigating the Sense of Presence Between Handcrafted and Panorama Based Virtual Environments
Venue: arXiv cs.HC 2021
Year: 2021

Virtual Reality applications are becoming increasingly mature. The requirements and complexity of such systems is steadily increasing. Realistic and detailed environments are often omitted in order to concentrate on the interaction possibilities within the application. Creating an accurate and realistic virtual environment is not a task for laypeople, but for experts in 3D design and modeling. To save costs and avoid hiring experts, panorama images are often used to create realistic looking virtual environments. These images can be captured and provided by non-experts. Panorama images are an alternative to handcrafted 3D models in many cases because they offer immersion and a scene can be captured in great detail with the touch of a button. This work investigates whether it is advisable to recreate an environment in detail by hand or whether it is recommended to use panorama images for virtual environments in certain scenarios. For this purpose, an interactive virtual environment was created in which a handmade 3D environment is almost indistinguishable from an environment created with panorama images. Interactive elements were added and a user study was conducted to investigate the effect of both environments to the user. The study conducted indicates that panorama images can be a useful substitute for 3D modeled environments.
A Multi-Protocol, Secure, and Dynamic Data Storage Integration Frameworkfor Multi-tenanted Science Gateway Middleware
Venue: arXiv cs.DC 2021
Year: 2021

Science gateways are user-centric, end-to-end cyberinfrastructure for managing scientific data and executions of computational software on distributed resources. In order to simplify the creation and management of science gateways, we have pursued a multi-tenanted, platform-as-a-service approach that allows multiple gateway front-ends (portals) to be integrated with a consolidated middleware that manages the movement of data and the execution of workflows on multiple back-end scientific computing resources. An important challenge for this approach is to provide an end-to-end data movement and management solution that allows gateway users to integrate their own data stores with the gateway platform. These user-provided data stores may include commercial cloud-based object store systems, third-party data stores accessed through APIs such as REST endpoints, and users' own local storage resources. In this paper, we present a solution design and implementation based on the integration of a managed file transfer (MFT) service (Airavata MFT) into the platform.
AI and the future of pharmaceutical research
Venue: arXiv cs.CY 2021
Year: 2021

This paper examines how pharmaceutical Artificial Intelligence advancements may affect the development of new drugs in the coming years. The question was answered by reviewing a rich body of source material, including industry literature, research journals, AI studies, market reports, market projections, discussion papers, press releases, and organizations' websites. The paper argues that continued innovation in pharmaceutical AI will enable rapid development of safe and effective therapies for previously untreatable diseases. A series of major points support this conclusion: The pharmaceutical industry is in a significant productivity crisis today, and AI-enabled research methods can be directly applied to reduce the time and cost of drug discovery projects. The industry already reported results such as a 10-fold reduction in drug molecule discovery times. Numerous AI alliances between industry, governments, and academia enabled utilizing proprietary data and led to outcomes such as the largest molecule toxicity database to date or more than 200 drug safety predictive models. The momentum was recently increased by the involvement of tech giants combined with record rounds of funding. The long-term effects will range from safer and more effective therapies, through the diminished role of pharmaceutical patents, to large-scale collaboration and new business strategies oriented around currently untreatable diseases. The paper notes that while many reviewed resources seem to have overly optimistic future expectations, even a fraction of these developments would alleviate the productivity crisis. Finally, the paper concludes that the focus on pharmaceutical AI put the industry on a trajectory towards another significant disruption: open data sharing and collaboration.
Manifold Hypothesis in Data Analysis: Double Geometrically-Probabilistic Approach to Manifold Dimension Estimation
Venue: arXiv cs.LG 2021
Year: 2021

Manifold hypothesis states that data points in high-dimensional space actually lie in close vicinity of a manifold of much lower dimension. In many cases this hypothesis was empirically verified and used to enhance unsupervised and semi-supervised learning. Here we present new approach to manifold hypothesis checking and underlying manifold dimension estimation. In order to do it we use two very different methods simultaneously - one geometric, another probabilistic - and check whether they give the same result. Our geometrical method is a modification for sparse data of a well-known box-counting algorithm for Minkowski dimension calculation. The probabilistic method is new. Although it exploits standard nearest neighborhood distance, it is different from methods which were previously used in such situations. This method is robust, fast and includes special preliminary data transformation. Experiments on real datasets show that the suggested approach based on two methods combination is powerful and effective.
Artificial intelligence across company borders
Venue: arXiv cs.CY 2021
Year: 2021

Artificial intelligence (AI) has become a valued technology in many companies. At the same time, a substantial potential for utilizing AI \emph{across} company borders has remained largely untapped. An inhibiting factor concerns disclosure of data to external parties, which raises legitimate concerns about intellectual property rights, privacy issues, and cybersecurity risks. Combining federated learning with domain adaptation can provide a solution to this problem by enabling effective cross-company AI without data disclosure. In this Viewpoint, we discuss the use, value, and implications of this approach in a cross-company setting.
Analysis of geospatial behaviour of visitors of urban gardens: is positioning via smartphones a valid solution?
Venue: arXiv cs.CY 2021
Year: 2021

Tracking locations is practical and speditive with smartphones, as they are omnipresent devices, relatively cheap, and have the necessary sensors for positioning and networking integrated in the same box. Nowadays recent models have GNSS antennas capable of receiving multiple constellations. In the proposed work we test the hypothesis that GNSS positions directly recorded by smartphones can be a valid solution for spatial analysis of people's behaviour in an urban garden. Particular behaviours can be linked to therapeutic spots that promote health and well-being of visitors. Three parts are reported: (i) assessment of the accuracy of the positions relative to a reference track, (ii) implementation of a framework for automating transmission and processing of the location information, (iii) analysis of preferred spots via spatial analytics. Different devices were used to survey at different times and with different methods, i.e. in the pocket of the owner or on a rigid frame. Accuracy was estimated using distance of each located point to the reference track, and precision was estimated with static multiple measures. A chat-bot through the Telegram application was implemented to allow users to send their data to a centralized computing environment thus automating the spatial analysis. Results report a horizontal accuracy below ~2.3 m at 95% confidence level, without significant difference between surveys, and very little differences between devices. GNSS-only and assisted navigation with telephone cells also did not show significant difference. Autocorrelation of the residuals over time and space showed strong consistency of the residuals, thus proving a valid solution for spatial analysis of walking behaviour.
EEG-ConvTransformer for Single-Trial EEG based Visual Stimuli Classification
Venue: arXiv cs.CV 2021
Year: 2021

Different categories of visual stimuli activate different responses in the human brain. These signals can be captured with EEG for utilization in applications such as Brain-Computer Interface (BCI). However, accurate classification of single-trial data is challenging due to low signal-to-noise ratio of EEG. This work introduces an EEG-ConvTranformer network that is based on multi-headed self-attention. Unlike other transformers, the model incorporates self-attention to capture inter-region interactions. It further extends to adjunct convolutional filters with multi-head attention as a single module to learn temporal patterns. Experimental results demonstrate that EEG-ConvTransformer achieves improved classification accuracy over the state-of-the-art techniques across five different visual stimuli classification tasks. Finally, quantitative analysis of inter-head diversity also shows low similarity in representational subspaces, emphasizing the implicit diversity of multi-head attention.