Miracle-dz/UniFAR-FTUs huggingface dataset:
IIITT@Dravidian-CodeMix-FIRE2021: Transliterate or translate? Sentiment analysis of code-mixed text in Dravidian languages

Sentiment analysis of social media posts and comments for various marketing and emotional purposes is gaining recognition. With the increasing presence of code-mixed content in various native languages, there is a need for ardent research to produce promising results. This research paper bestows a tiny contribution to this research in the form of sentiment analysis of code-mixed social media comments in the popular Dravidian languages Kannada, Tamil and Malayalam. It describes the work for the shared task conducted by Dravidian-CodeMix at FIRE 2021 by employing pre-trained models like ULMFiT and multilingual BERT fine-tuned on the code-mixed dataset, transliteration (TRAI) of the same, English translations (TRAA) of the TRAI data and the combination of all the three. The results are recorded in this research paper where the best models stood 4th, 5th and 10th ranks in the Tamil, Kannada and Malayalam tasks respectively.
Deep Joint Demosaicing and High Dynamic Range Imaging Within a Single Shot

Spatially varying exposure (SVE) is a promising choice for high-dynamic-range (HDR) imaging (HDRI). The SVE-based HDRI, which is called single-shot HDRI, is an efficient solution to avoid ghosting artifacts. However, it is very challenging to restore a full-resolution HDR image from a real-world image with SVE because: a) only one-third of pixels with varying exposures are captured by camera in a Bayer pattern, b) some of the captured pixels are over- and under-exposed. For the former challenge, a spatially varying convolution (SVC) is designed to process the Bayer images carried with varying exposures. For the latter one, an exposure-guidance method is proposed against the interference from over- and under-exposed pixels. Finally, a joint demosaicing and HDRI deep learning framework is formalized to include the two novel components and to realize an end-to-end single-shot HDRI. Experiments indicate that the proposed end-to-end framework avoids the problem of cumulative errors and surpasses the related state-of-the-art methods. Related codes and datasets will be provided at https://github.com/yilun-xu/SVEHDRI/.
Implications of stochastic effects for primordial black hole production in ultra-slow-roll inflation

We study the impact of stochastic noise on the generation of primordial black hole (PBH) seeds in ultra-slow-roll (USR) inflation with numerical simulations. We consider the non-linearity of the system by consistently taking into account the noise dependence on the inflaton perturbations, while evolving the perturbations on the coarse-grained background affected by the noise. We capture in this way the non-Markovian nature of the dynamics, and demonstrate that non-Markovian effects are subleading. Using the ΔN formalism, we find the probability distribution P(ℛ) of the comoving curvature perturbation ℛ. We consider inflationary potentials that fit the CMB and lead to PBH dark matter with i) asteroid, ii) solar, or iii) Planck mass, as well as iv) PBHs that form the seeds of supermassive black holes. We find that stochastic effects enhance the PBH abundance by a factor of 𝒪(10)–𝒪(108), depending on the PBH mass. We also show that the usual approximation, where stochastic kicks depend only on the Hubble rate, either underestimates or overestimates the abundance by orders of magnitude, depending on the potential. We evaluate the gauge dependence of the results, discuss the quantum-to-classical transition, and highlight open issues of the application of the stochastic formalism to USR inflation.
Eluding Secure Aggregation in Federated Learning via Model Inconsistency

Secure aggregation is a cryptographic protocol that securely computes the aggregation of its inputs. It is pivotal in keeping model updates private in federated learning. Indeed, the use of secure aggregation prevents the server from learning the value and the source of the individual model updates provided by the users, hampering inference and data attribution attacks. In this work, we show that a malicious server can easily elude secure aggregation as if the latter were not in place. We devise two different attacks capable of inferring information on individual private training datasets, independently of the number of users participating in the secure aggregation. This makes them concrete threats in large-scale, real-world federated learning applications. The attacks are generic and equally effective regardless of the secure aggregation protocol used They exploit a vulnerability of the federated learning protocol caused by incorrect usage of secure aggregation and lack of parameter validation. Our work demonstrates that current implementations of federated learning with secure aggregation offer only a ''false sense of security. ''
Quasifibrations of graphs to find symmetries and reconstruct biological networks

A fibration of graphs is a homomorphism that is a local isomorphism of in-neighborhoods. Recently, it has been shown that graph fibrations are useful tools to uncover symmetries and cluster synchronization in biological networks ranging from gene, protein, and metabolic networks to the brain. However, the inherent incompleteness and disordered nature of biological data preclude the application of the definition of fibration as it is. As a consequence, also the currently known algorithms to identify fibrations fail in these domains. In this paper, we introduce and develop systematically the theory of quasifibrations which attempts to capture more realistic patterns of quasi-symmetry in such networks. We provide an algorithmic solution to the problem of finding quasifibrations in networks where the existence of missing links and variability across samples preclude the identification of perfect fibration symmetries. We test our algorithm against other strategies to repair missing links in incomplete networks using real connectome data and synthetic networks. Quasifibrations can be applied to reconstruct any incomplete network structure characterized by underlying symmetrical and almost symmetrical clusters. The most direct application of our algorithms is that of helping researchers to find hidden symmetries in unknown (or partially unknown) networks, especially (but not exclusively) of biological nature.
Dynamic Placement of Rapidly Deployable Mobile Sensor Robots Using Machine Learning and Expected Value of Information

Although the Industrial Internet of Things has increased the number of sensors permanently installed in industrial plants, there will be gaps in coverage due to broken sensors or sparse density in very large plants, such as in the petrochemical industry. Modern emergency response operations are beginning to use Small Unmanned Aerial Systems (sUAS) that have the ability to drop sensor robots to precise locations. sUAS can provide longer-term persistent monitoring that aerial drones are unable to provide. Despite the relatively low cost of these assets, the choice of which robotic sensing systems to deploy to which part of an industrial process in a complex plant environment during emergency response remains challenging. This paper describes a framework for optimizing the deployment of emergency sensors as a preliminary step towards realizing the responsiveness of robots in disaster circumstances. AI techniques (Long short-term memory, 1-dimensional convolutional neural network, logistic regression, and random forest) identify regions where sensors would be most valued without requiring humans to enter the potentially dangerous area. In the case study described, the cost function for optimization considers costs of false-positive and false-negative errors. Decisions on mitigation include implementing repairs or shutting down the plant. The Expected Value of Information (EVI) is used to identify the most valuable type and location of physical sensors to be deployed to increase the decision-analytic value of a sensor network. This method is applied to a case study using the Tennessee Eastman process data set of a chemical plant, and we discuss implications of our findings for operation, distribution, and decision-making of sensors in plant emergency and resilience scenarios.
Automated scholarly paper review: Possibility and challenges

Peer review is a widely accepted mechanism for research evaluation, playing a pivotal role in scholarly publishing. However, criticisms have long been leveled on this mechanism, mostly because of its ineﬃciency and subjectivity. Recent years have seen the application of artiﬁcial intelligence (AI) in assisting the peer review process. Nonetheless, with the involvement of humans, such limitations remain inevitable. In this review paper, we propose the concept of automated scholarly paper review (ASPR) and review the relevant literature and technologies to discuss the possibility of achieving a full-scale computerized review process. We further look into the challenges in ASPR with the existing technologies. On the basis of the review and discussion, we conclude that there are already corresponding research and technologies at each stage of ASPR. This veriﬁes that ASPR can be realized in the long term as the relevant technologies continue to develop. The major diﬃculties in its realization lie in imperfect document parsing and representation, inadequate data, defected human-computer interaction and ﬂawed deep logical reasoning. In the foreseeable future, ASPR and peer review will coexist in a reinforcing manner before ASPR is able to fully undertake the reviewing workload from humans.
Laplacian on fuzzy de Sitter space

We study details of geometry of noncommutative de Sitter space: we determine the Riemann and Ricci curvature tensors, the energy and the Laplacian. We find, in particular, that fuzzy de Sitter space is an Einstein space, R ab = −3ζη ab . The Laplacian, defined in the noncommutative frame formalism, is not Hermitian and gives nonunitary evolution. When symmetrically ordered, it has the usual quadratic form Δ = Π a Π a (when acting on functions in representation space, Ψ∈H ): we find its eigenstates and discuss its spectrum. This result is a first step in a study of the scalar field Laplacian, Δ = [Π a , [Π a , ]], and its propagator.
Volumetric Parameterization of the Placenta to a Flattened Template

We present a volumetric mesh-based algorithm for parameterizing the placenta to a flattened template to enable effective visualization of local anatomy and function. MRI shows potential as a research tool as it provides signals directly related to placental function. However, due to the curved and highly variable in vivo shape of the placenta, interpreting and visualizing these images is difficult. We address interpretation challenges by mapping the placenta so that it resembles the familiar ex vivo shape. We formulate the parameterization as an optimization problem for mapping the placental shape represented by a volumetric mesh to a flattened template. We employ the symmetric Dirichlet energy to control local distortion throughout the volume. Local injectivity in the mapping is enforced by a constrained line search during the gradient descent optimization. We validate our method using a research study of 111 placental shapes extracted from BOLD MRI images. Our mapping achieves sub-voxel accuracy in matching the template while maintaining low distortion throughout the volume. We demonstrate how the resulting flattening of the placenta improves visualization of anatomy and function. Our code is freely available at https://github.com/mabulnaga/placenta-flattening.
A Comparative Study on Basic Elements of Deep Learning Models for Spatial-Temporal Traffic Forecasting

Traffic forecasting plays a crucial role in intelligent transportation systems. The spatial-temporal complexities in transportation networks make the problem especially challenging. The recently suggested deep learning models share basic elements such as graph convolution, graph attention, recurrent units, and/or attention mechanism. In this study, we designed an in-depth comparative study for four deep neural network models utilizing different basic elements. For base models, one RNN-based model and one attention-based model were chosen from previous literature. Then, the spatial feature extraction layers in the models were substituted with graph convolution and graph attention. To analyze the performance of each element in various environments, we conducted experiments on four real-world datasets highway speed, highway flow, urban speed from a homogeneous road link network, and urban speed from a heterogeneous road link network. The results demonstrate that the RNN-based model and the attention-based model show a similar level of performance for short-term prediction, and the attention-based model outperforms the RNN in longer-term predictions. The choice of graph convolution and graph attention makes a larger difference in the RNN-based models. Also, our modified version of GMAN shows comparable performance with the original with less memory consumption.
Reproducible and Adaptable Log Data Generation for Sound Cybersecurity Experiments

Artifacts such as log data and network traffic are fundamental for cybersecurity research, e.g., in the area of intrusion detection. Yet, most research is based on artifacts that are not available to others or cannot be adapted to own purposes, thus making it difficult to reproduce and build on existing work. In this paper, we identify the challenges of artifact generation with the goal of conducting sound experiments that are valid, controlled, and reproducible. We argue that testbeds for artifact generation have to be designed specifically with reproducibility and adaptability in mind. To achieve this goal, we present SOCBED, our proof-of-concept implementation and the first testbed with a focus on generating realistic log data for cybersecurity experiments in a reproducible and adaptable manner. SOCBED enables researchers to reproduce testbed instances on commodity computers, adapt them according to own requirements, and verify their correct functionality. We evaluate SOCBED with an exemplary, practical experiment on detecting a multi-step intrusion of an enterprise network and show that the resulting experiment is indeed valid, controlled, and reproducible. Both SOCBED and the log dataset underlying our evaluation are freely available.
Key research issues in the pulsed fast-neutron analysis technique for cargo inspection

Non-invasive inspection systems based on the use of fast neutrons are being studied for the inspection of large cargo containers. A key advantage of fast neutrons is their sensitivity to low-Z elements such as carbon, nitrogen, and oxygen, which are the primary constituents of explosives and narcotics. The high energy allows penetration of relatively large containers. The pulsed fast-neutron analysis (PFNA) technique is currently the baseline system. A workshop on the PFNA technique involving industrial, government, and university participants was held at Argonne National Laboratory in January 1994. The purpose of this workshop was to review the status of research on the key technical issues involved in PFNA, and to develop a list of those areas where additional modeling and/or experimentation were needed. The workshop also focused on development of a near-term experimental assessment program using existing prototypes and on development of a long-term test program at the Tacoma Testbed, where a PFNA prototype will be installed in 1995. A summary of conclusions reached at this workshop is presented. Results from analytic and Monte Carlo modeling of simplified PFNA systems are also presented.
Choose your programming copilot: a comparison of the program synthesis performance of github copilot and genetic programming

GitHub Copilot, an extension for the Visual Studio Code development environment powered by the large-scale language model Codex, makes automatic program synthesis available for software developers. This model has been extensively studied in the field of deep learning, however, a comparison to genetic programming, which is also known for its performance in automatic program synthesis, has not yet been carried out. In this paper, we evaluate GitHub Copilot on standard program synthesis benchmark problems and compare the achieved results with those from the genetic programming literature. In addition, we discuss the performance of both approaches. We find that the performance of the two approaches on the benchmark problems is quite similar, however, in comparison to GitHub Copilot, the program synthesis approaches based on genetic programming are not yet mature enough to support programmers in practical software development. Genetic programming usually needs a huge amount of expensive hand-labeled training cases and takes too much time to generate solutions. Furthermore, source code generated by genetic programming approaches is often bloated and difficult to understand. For future work on program synthesis with genetic programming, we suggest researchers to focus on improving the execution time, readability, and usability.
SFDM/CWDM of hollow-core photonic crystal fiber based etalon strain sensors with long cavity lengths

Spatial-frequency division multiplexing (SFDM)/coarse-wavelength division multiplexing (CWDM) of in-line fiber-optic etalon (ILFE) strain sensors, formed by a section of hollow-core photonic crystal fiber (HCPCF), is reported in this paper, for the first time to the best of our knowledge. Due to the low thermal expansion coefficient of HCPCF, such a strain sensor is much less sensitive to temperature change compared with conventional ILFE sensors. In the meanwhile, as the transmission loss of HCPCF is very low, the cavity length of the HCPCF sensor can be much longer than existing ILFEs, making it ideal for use in SFDM and offering great potential to realize multiplexing of a very large number of ILFE sensors. A SFDM/CWDM system with four HCPCF-based ILFE strain sensors is demonstrated and the experimental results show that a strain accuracy of ±5 can be achieved.
Distributionally robust expected residual minimization for stochastic variational inequality problems

The stochastic variational inequality problem (SVIP) is an equilibrium model that includes random variables and has been widely applied in various fields such as economics and engineering. Expected residual minimization (ERM) is an established model for obtaining a reasonable solution for the SVIP, and its objective function is an expected value of a suitable merit function for the SVIP. However, the ERM is restricted to the case where the distribution is known in advance. We extend the ERM to ensure the attainment of robust solutions for the SVIP under the uncertainty distribution (the extended ERM is referred to as distributionally robust expected residual minimization (DRERM), where the worst-case distribution is derived from the set of probability measures in which the expected value and variance take the same sample mean and variance, respectively). Under suitable assumptions, we demonstrate that the DRERM can be reformulated as a deterministic convex nonlinear semidefinite programming to avoid numerical integration.
Sentiment Analysis of Fashion Related Posts in Social Media

The role of social media in fashion industry has been blooming as the years have continued on. In this work, we investigate sentiment analysis for fashion related posts in social media platforms. There are two main challenges of this task. On the first place, information of different modalities must be jointly considered to make the final predictions. On the second place, some unique fashion related attributes should be taken into account. While most existing works focus on traditional multimodal sentiment analysis, they always fail to exploit the fashion related attributes in this task. We propose a novel framework that jointly leverages the image vision, post text, as well as fashion attribute modality to determine the sentiment category. One characteristic of our model is that it extracts fashion attributes and integrates them with the image vision information for effective representation. Furthermore, it exploits the mutual relationship between the fashion attributes and the post texts via a mutual attention mechanism. Since there is no existing dataset for this task, we prepare a large-scale sentiment analysis dataset of over 12k fashion related social media posts. Extensive experiments are conducted to demonstrate the effectiveness of our model.
Reliability Assessment of Distribution Systems including Microgrids

The electrical distribution system is moving toward a more decentralized, complex, and dynamic system. The system is experiencing a higher penetration of distributed energy resources, flexible resources, and active end-users, leading to an active distribution system. If these components are used actively, they can have a positive effect on the distribution system’s reliability. This paper aims to investigate how a microgrid with renewable energy sources and energy storage might influence the reliability of electricity supply in a radially operated distribution system. The reliability is investigated from both the distribution system perspective and the microgrid perspective with the application of different scenarios. A reliability assessment method for modern distribution systems based on Monte Carlo simulations is presented. The method includes load flow calculations to capture the behavior of the system and system components. The model is tested on the IEEE 33-bus network. The result is confirmed through statistical testing showing the statistical significance in providing support from the microgrid on the distribution system’s reliability. Keywords—Active Distribution Systems, Distribution System Reliability, Microgrid, Monte Carlo.
A Study on the Efficient Product Search Service for the Damaged Image Information

With the development of Information and Communication Technologies and the dissemination of smartphones, especially now that image search is possible through the internet, e-commerce markets are more activating purchasing services for a wide variety of products. However, it often happens that the image of the desired product is impaired and that the search engine does not recognize it properly. The idea of this study is to help search for products through image restoration using an image pre-processing and image inpainting algorithm for damaged images. It helps users easily purchase the items they want by providing a more accurate image search system. Besides, the system has the advantage of efficiently showing information by category, so that enables efficient sales of registered information.
Large-Scale Hyperspectral Image Clustering Using Contrastive Learning

Clustering of hyperspectral images is a fundamental but challenging task. The recent development of hyperspectral image clustering has evolved from shallow models to deep and achieved promising results in many benchmark datasets. However, their poor scalability, robustness, and generalization ability, mainly resulting from their offline clustering scenarios, greatly limit their application to large-scale hyperspectral data. To circumvent these problems, we present a scalable deep online clustering model, named Spectral-Spatial Contrastive Clustering (SSCC), based on self-supervised learning. Specifically, we exploit a symmetric twin neural network comprised of a projection head with a dimensionality of the cluster number to conduct dual contrastive learning from a spectral-spatial augmentation pool. We define the objective function by implicitly encouraging within-cluster similarity and reducing between-cluster redundancy. The resulting approach is trained in an end-to-end fashion by batch-wise optimization, making it robust in large-scale data and resulting in good generalization ability for unseen data. Extensive experiments on three hyperspectral image benchmarks demonstrate the effectiveness of our approach and show that we advance the state-of-the-art approaches by large margins.