Reviewerly/exHarmony huggingface dataset:
RoboTHOR: An Open Simulation-to-Real Embodied AI Platform

Visual recognition ecosystems (e.g. ImageNet, Pascal, COCO) have undeniably played a prevailing role in the evolution of modern computer vision. We argue that interactive and embodied visual AI has reached a stage of development similar to visual recognition prior to the advent of these ecosystems. Recently, various synthetic environments have been introduced to facilitate research in embodied AI. Notwithstanding this progress, the crucial question of how well models trained in simulation generalize to reality has remained largely unanswered. The creation of a comparable ecosystem for simulation-to-real embodied AI presents many challenges: (1) the inherently interactive nature of the problem, (2) the need for tight alignments between real and simulated worlds, (3) the difficulty of replicating physical conditions for repeatable experiments, (4) and the associated cost. In this paper, we introduce RoboTHOR to democratize research in interactive and embodied visual AI. RoboTHOR offers a framework of simulated environments paired with physical counterparts to systematically explore and overcome the challenges of simulation-to-real transfer, and a platform where researchers across the globe can remotely test their embodied models in the physical world. As a first benchmark, our experiments show there exists a significant gap between the performance of models trained in simulation when they are tested in both simulations and their carefully constructed physical analogs. We hope that RoboTHOR will spur the next stage of evolution in embodied computer vision.
Epipolar Transformers

A common approach to localize 3D human joints in a synchronized and calibrated multi-view setup consists of two-steps: (1) apply a 2D detector separately on each view to localize joints in 2D, and (2) perform robust triangulation on 2D detections from each view to acquire the 3D joint locations. However, in step 1, the 2D detector is limited to solving challenging cases which could potentially be better resolved in 3D, such as occlusions and oblique viewing angles, purely in 2D without leveraging any 3D information. Therefore, we propose the differentiable “epipolar transformer”, which enables the 2D detector to leverage 3D-aware features to improve 2D pose estimation. The intuition is: given a 2D location p in the current view, we would like to first find its corresponding point p' in a neighboring view, and then combine the features at p' with the features at p, thus leading to a 3D-aware feature at p. Inspired by stereo matching, the epipolar transformer leverages epipolar constraints and feature matching to approximate the features at p'. Experiments on InterHand and Human3.6M show that our approach has consistent improvements over the baselines. Specifically, in the condition where no external data is used, our Human3.6M model trained with ResNet-50 backbone and image size 256 x 256 outperforms state-of-the-art by 4.23 mm and achieves MPJPE 26.9 mm.
Single-Stage Semantic Segmentation From Image Labels

Recent years have seen a rapid growth in new approaches improving the accuracy of semantic segmentation in a weakly supervised setting, i.e. with only image-level labels available for training. However, this has come at the cost of increased model complexity and sophisticated multi-stage training procedures. This is in contrast to earlier work that used only a single stage -- training one segmentation network on image labels -- which was abandoned due to inferior segmentation accuracy. In this work, we first define three desirable properties of a weakly supervised method: local consistency, semantic fidelity, and completeness. Using these properties as guidelines, we then develop a segmentation-based network model and a self-supervised training scheme to train for semantic masks from image-level annotations in a single stage. We show that despite its simplicity, our method achieves results that are competitive with significantly more complex pipelines, substantially outperforming earlier single-stage methods.
A context-aware citation recommendation model with BERT and graph convolutional networks

With the tremendous growth in the number of scientific papers being published, searching for references while writing a scientific paper is a time-consuming process. A technique that could add a reference citation at the appropriate place in a sentence will be beneficial. In this perspective, the context-aware citation recommendation has been researched for around two decades. Many researchers have utilized the text data called the context sentence, which surrounds the citation tag, and the metadata of the target paper to find the appropriate cited research. However, the lack of well-organized benchmarking datasets, and no model that can attain high performance has made the research difficult. In this paper, we propose a deep learning-based model and well-organized dataset for context-aware paper citation recommendation. Our model comprises a document encoder and a context encoder. For this, we use graph convolutional networks layer, and bidirectional encoder representations from transformers, a pre-trained model of textual data. By modifying the related PeerRead dataset, we propose a new dataset called FullTextPeerRead containing context sentences to cited references and paper metadata. To the best of our knowledge, this dataset is the first well-organized dataset for a context-aware paper recommendation. The results indicate that the proposed model with the proposed datasets can attain state-of-the-art performance and achieve a more than 28% improvement in mean average precision and recall@k.
Deep learning and wavelet transform integrated approach for short-term solar PV power prediction

A novel short-term solar power prediction model is presented in this work, by utilizing the learning ability of Long-Shot-Term-Memory network (LSTM) based deep learning (DL) technique and the concept of wavelet transform (WT). In this proposed WT-LSTM model, the WT is used to decompose the recorded solar energy time-series data into different frequency series followed by the statistical feature extraction process. The LSTM with dropout based DL model is proposed to predict the futuristic value of solar energy generation in different time-horizon (hourly and day basis), where the statistical WT based features combined with several other meteorological factors such as temperature, wind speed, pressure, cloudy-index, humidity and altimeter index are modelled as input to the LSTM model. The efficiency of the suggested WT-LSTM model has been proved by comparing statistical performance measures in terms of RMSE, MAPE, MAE and R2 score, with other contemporary machine learning and deep-learning based models.
A Siamese CNN for Image Steganalysis

Image steganalysis is a technique for detecting data hidden in images. Recent research has shown the powerful capabilities of using convolutional neural networks (CNN) for image steganalysis. However, due to the particularity of steganographic signals, there are still few reliable CNN-based methods for applying steganalysis to images of arbitrary size. In this paper, we address this issue by exploring the possibility of exploiting a network for steganalyzing images of varying sizes without retraining its parameters. On the assumption that natural image noise is similar between different image sub-regions, we propose an end-to-end, deep learning, novel solution for distinguishing steganography images from normal images that provides satisfying performance. The proposed network first takes the image as the input, then identifies the relationships between the noise of different image sub-regions, and, finally, outputs the resulting classification based upon them. Our algorithm adopts a Siamese, CNN-based architecture, which consists of two symmetrical subnets with shared parameters, and contains three phases: preprocessing, feature extraction, and fusion/classification. To validate the network, we generated datasets composed of steganography images with multiple sizes and their corresponding normal images sourced from BOSSbase 1.01 and ALASKA #2. Experimental results produced by the data generated by various methods show that our proposed network is well-generalized and robust.
Microwave Quantum Link between Superconducting Circuits Housed in Spatially Separated Cryogenic Systems

Superconducting circuits are a strong contender for realizing quantum computing systems, and are also successfully used to study quantum optics and hybrid quantum systems. However, their cryogenic operation temperatures and the current lack of coherence-preserving microwave-to-optical conversion solutions have hindered the realization of superconducting quantum networks either spanning different cryogenics systems or larger distances. Here, we report the successful operation of a cryogenic waveguide coherently linking transmon qubits located in two dilution refrigerators separated by a physical distance of five meters. We transfer qubit states and generate entanglement on-demand with average transfer and target state fidelities of 85.8 % and 79.5 %, respectively, between the two nodes of this elementary network. Cryogenic microwave links do provide an opportunity to scale up systems for quantum computing and create local area quantum communication networks over length scales of at least tens of meters.
Optimized container scheduling for data-intensive serverless edge computing

Operating data-intensive applications on edge systems is challenging, due to the extreme workload and device heterogeneity, as well as the geographic dispersion of compute and storage infrastructure. Serverless computing has emerged as a compelling model to manage the complexity of such systems, by decoupling the underlying infrastructure and scaling mechanisms from applications. Although serverless platforms have reached a high level of maturity, we have found several limiting factors that inhibit their use in an edge setting. This paper presents a container scheduling system that enables such platforms to make efficient use of edge infrastructures. Our scheduler makes heuristic trade-offs between data and computation movement, and considers workload-specific compute requirements such as GPU acceleration. Furthermore, we present a method to automatically fine-tune the weights of scheduling constraints to optimize high-level operational objectives such as minimizing task execution time, uplink usage, or cloud execution cost. We implement a prototype that targets the container orchestration system Kubernetes, and deploy it on an edge testbed we have built. We evaluate our system with trace-driven simulations in different infrastructure scenarios, using traces generated from running representative workloads on our testbed. Our results show that (a) our scheduler significantly improves the quality of task placement compared to the state-of-the-art scheduler of Kubernetes, and (b) our method for fine-tuning scheduling parameters helps significantly in meeting operational goals.
Deep Transfer Learning-Based Downlink Channel Prediction for FDD Massive MIMO Systems

Artificial intelligence (AI) based downlink channel state information (CSI) prediction for frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems has attracted growing attention recently. However, existing works focus on the downlink CSI prediction for the users under a given environment and is hard to adapt to users in new environment especially when labeled data is limited. To address this issue, we formulate the downlink channel prediction as a deep transfer learning (DTL) problem, and propose the direct-transfer algorithm based on the fully-connected neural network architecture, where the network is trained in the manner of classical deep learning and is then fine-tuned for new environments. To further improve the transfer efficiency, we propose the meta-learning algorithm that trains the network by alternating inner-task and across-task updates and then adapts to a new environment with a small number of labeled data. Simulation results show that the direct-transfer algorithm achieves better performance than the deep learning algorithm, which implies that the transfer learning benefits the downlink channel prediction in new environments. Moreover, the meta-learning algorithm significantly outperforms the direct-transfer algorithm, which validates its effectiveness and superiority.
Horizontal and vertical crossover of Harris hawk optimizer with Nelder-Mead simplex for parameter estimation of photovoltaic models

An improved Harris hawks optimization is proposed in this work to facilitate the simulation of an efficient photovoltaic system and extraction of unknown parameters, which combines horizontal and vertical crossover mechanism of the crisscross optimizer and Nelder-Mead simplex algorithm, named CCNMHHO. In CCNMHHO, the cores appeared in the crisscross optimizer are utilized to enrich the information exchange between the individuals and avoid the problem of dimensional stagnation of individuals all through the iterations. Hence, it enhances to change to improve the population quality and prevent the shortcoming of falling into a local optimum. In contrast, the Nelder-Mead simplex algorithm is employed in the proposed CCNMHHO methodology. Nelder-Mead simplex helps to improve individual searching capabilities in performing the local search phase and showing a faster convergence to optimal values. Compared to some algorithms that have a competitive performance in dealing with this type of problem, CCNMHHO has a faster convergence speed, and it shows high stability. In different environments, the experimental data obtained by this improved Harris hawks Optimization can reveal a high agreement with the measurement data. The experimental results show that the proposed method not only is very competitive in extracting the unknown parameters of different PV models compared to other state-of-the-art algorithms but also perform well in dealing with the complex outdoor environments such as different temperature and radiance. Therefore, we observed that the CCNMHHO could be considered as a reliable and efficient method in solving a class of cases for the assessment of unknown parameters of solar cells and photovoltaic models. For post-publication guidance, supports, and materials for this research, please refer to the supporting homepage: http://aliasgharheidari.com.
Applicability of mobile contact tracing in fighting pandemic (COVID-19): Issues, challenges and solutions

Contact Tracing is considered as the first and the most effective step towards containing an outbreak, as resources for mass testing and large quantity of vaccines are highly unlikely available for immediate utilization. Effective contact tracing can allow societies to reopen from lock-down even before availability of vaccines. The objective of mobile contact tracing is to speed up the manual interview based contact tracing process for containing an outbreak efficiently and quickly. In this article, we throw light on some of the issues and challenges pertaining to the adoption of mobile contact tracing solutions for fighting COVID-19. In essence, we proposed an Evaluation framework for mobile contact tracing solutions to determine their usability, feasibility, scalability and effectiveness. We evaluate some of the already proposed contact tracing solutions in light of our proposed framework. Furthermore, we present possible attacks that can be launched against contact tracing solutions along with their necessary countermeasures to thwart any possibility of such attacks.
Over-the-Air Federated Learning From Heterogeneous Data

Federated learning (FL) is a framework for distributed learning of centralized models. In FL, a set of edge devices train a model using their local data, while repeatedly exchanging their trained updates with a central server. This procedure allows tuning a centralized model in a distributed fashion without having the users share their possibly private data. In this paper, we focus on over-the-air (OTA) FL, which has been suggested recently to reduce the communication overhead of FL due to the repeated transmissions of the model updates by a large number of users over the wireless channel. In OTA FL, all users simultaneously transmit their updates as analog signals over a multiple access channel, and the server receives a superposition of the analog transmitted signals. However, this approach results in the channel noise directly affecting the optimization procedure, which may degrade the accuracy of the trained model. We develop a Convergent OTA FL (COTAF) algorithm which enhances the common local stochastic gradient descent (SGD) FL algorithm, introducing precoding at the users and scaling at the server, which gradually mitigates the effect of the noise. We analyze the convergence of COTAF to the loss minimizing model and quantify the effect of a statistically heterogeneous setup, i.e. when the training data of each user obeys a different distribution. Our analysis reveals the ability of COTAF to achieve a convergence rate similar to that achievable over error-free channels. Our simulations demonstrate the improved convergence of COTAF over vanilla OTA local SGD for training using non-synthetic datasets. Furthermore, we numerically show that the precoding induced by COTAF notably improves the convergence rate and the accuracy of models trained via OTA FL.
Accuracy Comparison Across Face Recognition Algorithms: Where Are We on Measuring Race Bias?

Previous generations of face recognition algorithms differ in accuracy for images of different races (race bias). Here, we present the possible underlying factors (data-driven and scenario modeling) and methodological considerations for assessing race bias in algorithms. We discuss data-driven factors (e.g., image quality, image population statistics, and algorithm architecture), and scenario modeling factors that consider the role of the “user” of the algorithm (e.g., threshold decisions and demographic constraints). To illustrate how these issues apply, we present data from four face recognition algorithms (a previous-generation algorithm and three deep convolutional neural networks, DCNNs) for East Asian and Caucasian faces. First, dataset difficulty affected both overall recognition accuracy and race bias, such that race bias increased with item difficulty. Second, for all four algorithms, the degree of bias varied depending on the identification decision threshold. To achieve equal false accept rates (FARs), East Asian faces required higher identification thresholds than Caucasian faces, for all algorithms. Third, demographic constraints on the formulation of the distributions used in the test, impacted estimates of algorithm accuracy. We conclude that race bias needs to be measured for individual applications and we provide a checklist for measuring this bias in face recognition algorithms.
A comparative review on mobile robot path planning: Classical or meta-heuristic methods?

The involvement of Meta-heuristic algorithms in robot motion planning has attracted the attention of researchers in the robotics community due to the simplicity of the approaches and their effectiveness in the coordination of the agents. This study explores the implementation of many meta-heuristic algorithms, e.g. Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA) in multiple motion planning scenarios. The study provides comparison between multiple meta-heuristic approaches against a set of well-known conventional motion planning and navigation techniques such as Dijkstra’s Algorithm (DA), Probabilistic Road Map (PRM), Rapidly Random Tree (RRT) and Potential Field (PF). Two experimental environments with difficult to manipulate layouts are used to examine the feasibility of the methods listed. several performance measures such as total travel time, number of collisions, travel distances, energy consumption and displacement errors are considered for assessing feasibility of the motion planning algorithms considered in the study. The results show the competitiveness of meta-heuristic approaches against conventional methods. Dijkstra ’s Algorithm (DA) is considered a benchmark solution and Constricted Particle Swarm Optimization (CPSO) is found performing better than other meta-heuristic approaches in unknown environments.
Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors

We present a systematic study of the transferability of adversarial attacks on state-of-the-art object detection frameworks. Using standard detection datasets, we train patterns that suppress the objectness scores produced by a range of commonly used detectors, and ensembles of detectors. Through extensive experiments, we benchmark the effectiveness of adversarially trained patches under both white-box and black-box settings, and quantify transferability of attacks between datasets, object classes, and detector models. Finally, we present a detailed study of physical world attacks using printed posters and wearable clothes, and rigorously quantify the performance of such attacks with different metrics.
Decision analysis related to the renewable energy investments in Turkey based on a Fuzzy AHP-EDAS-Fuzzy FMEA approach

Due to the factors such as technological developments, industrialization, population growth, etc. the energy need in the world is constantly increasing and the available resources are insufficient to meet this need. Therefore, the search for alternative energy sources has accelerated recently and this situation has directed the countries to renewable energy. Although the use of renewable energy had gone until before of the first industrial revolution, it has become a very popular area today due to significantly eliminating the increasing concerns related to the security of energy supply, being environmentally friendly, and has positive economic effects. For this reason, nowadays countries form their strategies, policies, and goals related to the energy sector with a focus on renewable energy and make serious investments in this field. Owing to the current importance of the issue, in this study it is aimed to determine how the optimum investment decisions could be taken at the renewable energy sector in Turkey. Five renewable energy alternatives which are hydropower, solar energy, wind energy, geothermal energy and biomass energy, were taken into consideration. First of all, using the Fuzzy Analytical Hierarchy Process and the Evaluation Based on Distance from Average Solution methods, the importance weights of renewable energy alternatives were found and they were prioritized. Afterwards, the risks of renewable energy alternatives were found with the Fuzzy Failure Mode and Effect Analysis. Finally, the best alternatives were determined by comparing the evaluation results. When the results of study were analyzed it was clearly seen Turkey should focus on hydropower and wind energy in renewable energy investments.
Clustering Driven Deep Autoencoder for Video Anomaly Detection

Because of the ambiguous definition of anomaly and the complexity of real data, video anomaly detection is one of the most challenging problems in intelligent video surveillance. Since the abnormal events are usually different from normal events in appearance and/or in motion behavior, we address this issue by designing a novel convolution autoencoder architecture to separately capture spatial and temporal informative representation. The spatial part reconstructs the last individual frame (LIF), while the temporal part takes consecutive frames as input and RGB difference as output to simulate the generation of optical flow. The abnormal events which are irregular in appearance or in motion behavior lead to a large reconstruction error. Besides, we design a deep k-means cluster to force the appearance and the motion encoder to extract common factors of variation within the dataset. Experiments on some publicly available datasets demonstrate the effectiveness of our method with the state-of-the-art performance.