Reviewerly/exHarmony huggingface dataset:
Single image dehazing using improved cycleGAN

Haze is an aggregation of very fine, widely dispersed, solid and/or liquid particles suspended in the atmosphere. In this paper, we propose an end-to-end network for single image dehazing, which enhances the CycleGAN model by introducing a transformer architecture within the generator, which is specific for haze removal. The proposed model is trained in an unpaired fashion with clear and hazy images altogether and does not require pairs of hazy and corresponding ground-truth clear images. Furthermore, the proposed model does not depend on estimating the parameters of the atmospheric scattering model. Rather, it uses a K-estimation module as the generator’s transformer for complete end-to-end modeling. The feature transformer introduced in the proposed generator model transforms the encoded features into desired feature space and then feeds them into the CycleGAN decoder to create a clear image. In the proposed model we further modified the cycle consistency loss to include the SSIM loss along with pixel-wise mean loss to produce a new loss function specific for the reconstruction task, which enhances the performance of the proposed model. The model performs well even on the high-resolution images provided in the NTIRE 2019 challenge dataset for single image dehazing. Further, we perform experiments on NYU-Depth and reside beta datasets. Results of our experiments show the efficacy of the proposed approach compared to the state-of-the-art in removing the haze from the input image.
Seeing the Un-Scene: Learning Amodal Semantic Maps for Room Navigation

We introduce a learning-based approach for room navigation using semantic maps. Our proposed architecture learns to predict top-down belief maps of regions that lie beyond the agent's field of view while modeling architectural and stylistic regularities in houses. First, we train a model to generate amodal semantic top-down maps indicating beliefs of location, size, and shape of rooms by learning the underlying architectural patterns in houses. Next, we use these maps to predict a point that lies in the target room and train a policy to navigate to the point. We empirically demonstrate that by predicting semantic maps, the model learns common correlations found in houses and generalizes to novel environments. We also demonstrate that reducing the task of room navigation to point navigation improves the performance further.
Optimized HRNet for image semantic segmentation

With the rapid development of deep learning, image semantic segmentation has made great progress and become a hot topic in scene understanding of computer vision. In this paper, we propose an optimized high-resolution net (HRNet) for image semantic segmentation. Unlike traditional networks usually extract feature maps based on a high-to-low encoder, which may easily loss important shape and boundary details especially for the deeper layers with lower resolutions, our optimized HRNet can maintain high resolution features at all times using a relatively shallow and parallel network structure. To improve the ability of our model in better recognizing the objects with various scales and irregular shapes, we introduce a mixed dilated convolution (MDC) module, which can not only increase the diversity of the receptive fields, but also tackle the “gridding” problem commonly existing in the conventional dilated convolution. By minimizing fine detail lost based on a DUpsample strategy, we further develop a multi-level data-dependent feature aggregation (MDFA) module to enhance the capability of our network in better identifying the fine details especially for the small objects with fuzzy boundaries. We evaluate the optimized HRNet on four different datasets, including Cityscapes, Pascal VOC2012, CamVid and the KITTI. Experimental results validate the effectiveness of our method in improving the accuracy of image semantic segmentation. Comparisons with state-of-the-art methods also verify the advantages of our optimized HRNet in achieving better semantic segmentation performance.
Identification of Phyllosilicates in the Antarctic Environment Using ASTER Satellite Data: Case Study from the Mesa Range, Campbell and Priestley Glaciers, Northern Victoria Land

In Antarctica, spectral mapping of altered minerals is very challenging due to the remoteness and inaccessibility of poorly exposed outcrops. This investigation evaluates the capability of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite remote sensing imagery for mapping and discrimination of phyllosilicate mineral groups in the Antarctic environment of northern Victoria Land. The Mixture-Tuned Matched-Filtering (MTMF) and Constrained Energy Minimization (CEM) algorithms were used to detect the sub-pixel abundance of Al-rich, Fe3+-rich, Fe2+-rich and Mg-rich phyllosilicates using the visible and near-infrared (VNIR), short-wave infrared (SWIR) and thermal-infrared (TIR) bands of ASTER. Results indicate that Al-rich phyllosilicates are strongly detected in the exposed outcrops of the Granite Harbour granitoids, Wilson Metamorphic Complex and the Beacon Supergroup. The presence of the smectite mineral group derived from the Jurassic basaltic rocks (Ferrar Dolerite and Kirkpatrick Basalts) by weathering and decomposition processes implicates Fe3+-rich and Fe2+-rich phyllosilicates. Biotite (Fe2+-rich phyllosilicate) is detected associated with the Granite Harbour granitoids, Wilson Metamorphic Complex and Melbourne Volcanics. Mg-rich phyllosilicates are mostly mapped in the scree, glacial drift, moraine and crevasse fields derived from weathering and decomposition of the Kirkpatrick Basalt and Ferrar Dolerite. Chlorite (Mg-rich phyllosilicate) was generally mapped in the exposures of Granite Harbour granodiorite and granite and partially identified in the Ferrar Dolerite, the Kirkpatrick Basalt, the Priestley Formation and Priestley Schist and the scree, glacial drift and moraine. Statistical results indicate that Al-rich phyllosilicates class pixels are strongly discriminated, while the pixels attributed to Fe3+-rich class, Fe2+-rich and Mg-rich phyllosilicates classes contain some spectral mixing due to their subtle spectral differences in the VNIR+SWIR bands of ASTER. Results derived from TIR bands of ASTER show that a high level of confusion is associated with mafic phyllosilicates pixels (Fe3+-rich, Fe2+-rich and Mg-rich classes), whereas felsic phyllosilicates (Al-rich class) pixels are well mapped. Ground truth with detailed geological data, petrographic study and X-ray diffraction (XRD) analysis verified the remote sensing results. Consequently, ASTER image-map of phyllosilicate minerals is generated for the Mesa Range, Campbell and Priestley Glaciers, northern Victoria Land of Antarctica.
A high order PDE-constrained optimization for the image denoising problem

In the present work, we investigate the inverse problem of identifying simultaneously the denoised image and the weighting parameter that controls the balance between two diffusion operators for an evolutionary partial differential equation (PDE). The problem is formulated as a non-smooth PDE-constrained optimization model. This PDE is constructed by second- and fourth-order diffusive tensors that combines the benefits from the diffusion model of Perona–Malik in the homogeneous regions, the Weickert model near sharp edges and the fourth-order term in reducing staircasing. The existence and uniqueness of solutions for the proposed PDE-constrained optimization system are provided in a suitable Sobolev space. Also, an optimization problem for the determination of the weighting parameter is introduced based on the Primal–Dual algorithm. Finally, simulation results show that the obtained parameter usually coincides with the better choice related to the best restoration quality of the image.
Syntactic Structure Distillation Pretraining for Bidirectional Encoders

Textual representation learners trained on large amounts of data have achieved notable success on downstream tasks; intriguingly, they have also performed well on challenging tests of syntactic competence. Hence, it remains an open question whether scalable learners like BERT can become fully proficient in the syntax of natural language by virtue of data scale alone, or whether they still benefit from more explicit syntactic biases. To answer this question, we introduce a knowledge distillation strategy for injecting syntactic biases into BERT pretraining, by distilling the syntactically informative predictions of a hierarchical—albeit harder to scale—syntactic language model. Since BERT models masked words in bidirectional context, we propose to distill the approximate marginal distribution over words in context from the syntactic LM. Our approach reduces relative error by 2–21% on a diverse set of structured prediction tasks, although we obtain mixed results on the GLUE benchmark. Our findings demonstrate the benefits of syntactic biases, even for representation learners that exploit large amounts of data, and contribute to a better understanding of where syntactic biases are helpful in benchmarks of natural language understanding.
Three-Attention Mechanisms for One-Stage 3-D Object Detection Based on LiDAR and Camera

This article studies one-stage 3-D object detection based on light detection and ranging (LiDAR) point clouds and red-green-blue (RGB) images that aims to boost 3-D object detection accuracy based on three attention mechanisms. Currently, most of the previous works converted LiDAR point clouds into bird's-eye-view (BEV) images, achieving a significant performance. However, they still have a problem due to partial height information (z-axis value) loss during the conversion. To eliminate this problem, the height information of the LiDAR point clouds is projected onto an RGB image and embedded into the original RGB image to generate a new image, named RGB <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>D</b></sup> . This is the first attention mechanism to improve 3-D detection accuracy. Moreover, two other attention mechanisms extract more discriminative global and local features, respectively. Specifically, the global attention network is appended to a feature encoder, and the local attention network is used for the view-specific region of interest fusion. Massive experiments evaluated on the KITTI benchmark suite show that the proposed approach outperforms state-of-the-art LiDAR-Camera-based methods on the car class (easy, moderate, hard): 2-D (90.35%, 88.47%, 86.98%), 3-D (85.12%, 76.23%, 74.46%), and BEV (89.64%, 86.23%, 85.60%).
A POI group recommendation method in location-based social networks based on user influence

Group recommendation has attracted researchers' attention in various domains, specifically such approaches utilizing location-based social networks (LBSNs). However, point of interest (POI) group recommendation faces the challenge of aggregating diverse user preferences, while group members have different influences on the final decision of the group. Besides, the recommendation of spatial items is different from non-spatial items and the unique features of the spatial items such as distance must be considered in the recommendation. In this paper, a POI group recommendation method is proposed to tackle this problem. User influence is modeled fuzzy and taken into account the difference of users' personality and their preferences when are alone or in a group, by using historical check-in data in LBSNs and in terms of category, distance and time. The proposed method is integrated with the weighted average aggregation to improve the efficiency of the POI group recommendation. Experimental results in a real dataset show improvement in the accuracy of POI group recommendations in varying sizes of groups. The results also get better when the user influence is calculated using the fuzzy approach. Besides, studying user behavior differences to choose the place to visit when alone or in a group shows that i) the flexibility of users in distance is less than time and category. It is also in the category less than time. ii) Time has a greater range of behavioral change than distance and category. iii) Users who actively participate in group decision making have a more significant number of visits in groups than when they are alone.
The Impact of Selfish Mining on Bitcoin Network Performance

Selfish mining strategy allows miners to gain unfair advantage and excess revenue in Bitcoin network, but it also disrupts the normal operation of the network. In this work, we analyze the impact of selfish behavior on the Bitcoin network through a number of performance indicators such as network connectivity, block arrival rate, node response time, and block delivery time for selfish and honest blocks, respectively. We also discuss the probability of unintentional as well as intentional forks. We have found that the impact of selfish mining on network performance is noticeable, and in extreme cases, disproportional to the number of selfish miners or their hash power compared to honest nodes. Our analysis has also found that forking probability is dominated by intentional forking resulting from selfish behavior, which has the potential to increase the ledger inconsistency time and open the door to security attacks.
Only Time Can Tell: Discovering Temporal Data for Temporal Modeling

Understanding temporal information and how the visual world changes over time, is a fundamental ability of intelligent systems. In video understanding, temporal information is at the core of many current challenges, including compression, efficient inference, motion estimation or summarization. However, in current video datasets it has been observed that action classes can often be recognized without any temporal information, from a single frame of video. As a result, both benchmarking and training in these datasets may give an unintentional advantage to models with strong image understanding capabilities, as opposed to those with strong temporal understanding. In other words, current datasets may not reward good temporal understanding, potentially hindering progress. In this paper we address this problem head on by identifying action classes where temporal information is actually necessary to recognize them and call these "temporal classes". Selecting temporal classes using a computational method would bias the process. Instead, we propose a methodology based on a simple and effective human annotation experiment. We remove just the temporal information, by shuffling frames in time, and measure if the action can still be recognized. Classes that cannot be recognized when frames are not in order, are included in the temporal set. We observe that this set is statistically different from other static classes, and that performance in it correlates with a network's ability to capture temporal information. Thus we use it as a benchmark on current popular networks, which reveals a series of interesting facts, like inflated convolutions bias networks towards classes where motion is not important. We also explore the effect of training on the temporal set, and observe that this leads to better generalization in unseen classes, demonstrating the need for more temporal data. We hope that the proposed dataset of temporal categories will help guide future research in temporal modeling for better video understanding.
Text-to-Image Generation Grounded by Fine-Grained User Attention

Localized Narratives [28] is a dataset with detailed natural language descriptions of images paired with mouse traces that provide a sparse, fine-grained visual grounding for phrases. We propose TRECS, a sequential model that exploits this grounding to generate images. TRECS uses descriptions to retrieve segmentation masks and predict object labels aligned with mouse traces. These alignments are used to select and position masks to generate a fully covered segmentation canvas; the final image is produced by a segmentation-to-image generator using this canvas. This multi-step, retrieval-based approach outperforms existing direct text-to-image generation models on both automatic metrics and human evaluations: overall, its generated images are more photo-realistic and better match descriptions.
Softwarized IoT Network Immunity Against Eavesdropping With Programmable Data Planes

State-of-the-art mechanisms against eavesdropping first encrypt all packet payloads in the application layer and then split the packets into multiple network paths. However, versatile eavesdroppers could simultaneously intercept several paths to intercept all the packets, classify the packets into streams using transport fields, and analyze the streams by brute-force. In this article, we propose a programming protocol-independent packet processors (P4)-based network immune scheme (P4NIS) against the intractable eavesdropping. Specifically, P4NIS is equipped with three lines of defenses to provide a softwarized network immunity. Packets are successively processed by the third, second, and first line of defenses. The third line basically encrypts all packet payloads in the application layer using cryptographic mechanisms. Additionally, the second line re-encrypts all packet headers in the transport layer to distribute the packets from one stream into different streams, and disturbs eavesdroppers to classify the packets correctly. Besides, the second line adopts a programmable design for dynamically changing encryption algorithms. Complementally, the first line uses programmable forwarding policies which could split all the double-encrypted packets into different network paths disorderly. Using a paradigm of programmable data planes-P4, we implement P4NIS and evaluate its performances. Experimental results show that P4NIS can increase difficulties of eavesdropping and transmission throughput effectively compared with state-of-the-art mechanisms. Moreover, if P4NIS and state-of-the-art mechanisms have the same level of defending eavesdropping, P4NIS can decrease the encryption cost by 69.85%-81.24%.
Image super-resolution based on deep neural network of multiple attention mechanism

At present, the main super-resolution (SR) method based on convolutional neural network (CNN) is to increase the layer number of the network by skip connection so as to improve the nonlinear expression ability of the model. However, the network also becomes difficult to be trained and converge. In order to train a smaller but better performance SR model, this paper constructs a novel image SR network of multiple attention mechanism(MAMSR), which includes channel attention mechanism and spatial attention mechanism. By learning the relationship between the channels of the feature map and the relationship between the pixels in each position of the feature map, the network can enhance the ability of feature expression and make the reconstructed image more close to the real image. Experiments on public datasets show that our network surpasses some current state-of-the-art algorithms in PSNR, SSIM, and visual effects.
Trends and usage pattern of SPSS and Minitab Software in Scientific research

Abstract Most scientific research generates data. Analysis of the data from scientific research helps create new knowledge or a deep understanding of natural phenomena. Statistical software is used mainly in data analysis. SPSS and Minitab appear to be most popular, especially for those that could neither code nor mathematical inclined to handle advanced software such as R, MATLAB, Maple, etc. Trends and usage pattern of SPSS and Minitab Software in Scientific research was studied in this paper with the data obtained from the Scopus database. In their abstracts or keywords, documents that have mentioned SPSS were extracted for the years 2010 to 2019. Frequency analysis showed that the trend of using SPSS and Minitab is steadily increasing, although the use of Minitab is a fraction of SPSS. Minitab is mostly used in engineering, materials science, and computer science, while SPSS is mainly used in medicine, social science, and engineering. Analysis of the document type showed that SPSS and Minitab are mostly stated in abstracts or keywords of research articles, conference papers, review papers, and books indexed in Scopus.
Software Quality for AI: Where We Are Now?

Artificial Intelligence is getting more and more popular, being adopted in a large number of applications and technology we use on a daily basis. However, a large number of Artificial Intelligence applications are produced by developers without proper training on software quality practices or processes, and in general, lack in-depth knowledge regarding software engineering processes. The main reason is due to the fact that the machine-learning engineer profession has been born very recently, and currently there is a very limited number of training or guidelines on issues (such as code quality or testing) for machine learning and applications using machine learning code. In this work, we aim at highlighting the main software quality issues of Artificial Intelligence systems, with a central focus on machine learning code, based on the experience of our four research groups. Moreover, we aim at defining a shared research road map, that we would like to discuss and to follow in collaboration with the workshop participants. As a result, the software quality of AI-enabled systems is often poorly tested and of very low quality.
Federated Learning Over Noisy Channels: Convergence Analysis and Design Examples

Does Federated Learning (FL) work when <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">both</i> uplink and downlink communications have errors? How much communication noise can FL handle and what is its impact on the learning performance? This work is devoted to answering these practically important questions by explicitly incorporating both uplink and downlink noisy channels in the FL pipeline. We present several novel convergence analyses of FL over simultaneous uplink and downlink noisy communication channels, which encompass full and partial clients participation, direct model and model differential transmissions, and non-independent and identically distributed (IID) local datasets. These analyses characterize the sufficient conditions for FL over noisy channels to have the same convergence behavior as the ideal case of no communication error. More specifically, in order to maintain the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}({1}/{T})$ </tex-math></inline-formula> convergence rate of FED AVG with perfect communications, the uplink and downlink signal-to-noise ratio (SNR) for direct model transmissions should be controlled such that they scale as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(t^{2})$ </tex-math></inline-formula> where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${t}$ </tex-math></inline-formula> is the index of communication rounds, but can stay <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(1)$ </tex-math></inline-formula> (i.e., constant) for model differential transmissions. The key insight of these theoretical results is a “flying under the radar” principle – stochastic gradient descent (SGD) is an inherent noisy process and uplink/downlink communication noises can be tolerated as long as they do not dominate the time-varying SGD noise. We exemplify these theoretical findings with two widely adopted communication techniques – transmit power control and receive diversity combining – and further validate their performance advantages over the standard methods via numerical experiments using several real-world FL tasks.