Reviewerly/exHarmony huggingface dataset:
An Efficient Organization Method for Large-Scale and Long Time-Series Remote Sensing Data in a Cloud Computing Environment

Historical Earth observation (EO) data have played an important role in long-term scientific and environmental monitoring.The effective organization of large-scale and longterm remote-sensing data to achieve efficient retrieval and access has become one of the important issues.However, inherent big data characteristics, such as a large scale, and asymmetrical temporal and spatial distributions, have caused problems with the efficiency of data retrieval and access.Therefore, this study proposes an efficient data organization method for use in a cloudcomputing environment that has two aims.First, it addresses the problem of low retrieval efficiency.An asymmetrical index model for the image metadata is constructed that is based on a unified spatio-temporal grid coding; a pre-partitioning mechanism under the HBase architecture is established to realize the uniform storage of the metadata with an asymmetrical spatiotemporal distribution and to avoid retrieval efficiency bottlenecks caused by a load imbalance.Second, it addresses low access efficiency.By dividing the remote-sensing image into tiles, a unified spatiotemporal code is established for each tile, and a consistent hash operation is performed; tiles with similar hash values are stored in the same or adjacent HDFS (Hadoop Distributed File System) nodes.In this way, tiles with temporal or spatial correlations can be gathered and stored, and lots of disk seeks can be avoided during retrieval, thereby greatly improving the data access efficiency.Comparative experiments showed that the data organization method can effectively improve the retrieval and access efficiencies of large-scale and long time-series remotesensing data in a cloud-computing environment.
Towards Visual Semantics

Abstract Lexical Semantics is concerned with how words encode mental representations of the world, i.e., concepts . We call this type of concepts, classification concepts . In this paper, we focus on Visual Semantics , namely, on how humans build concepts representing what they perceive visually. We call this second type of concepts, substance concepts . As shown in the paper, these two types of concepts are different and, furthermore, the mapping between them is many-to-many. In this paper we provide a theory and an algorithm for how to build substance concepts which are in a one-to-one correspondence with classifications concepts, thus paving the way to the seamless integration between natural language descriptions and visual perception. This work builds upon three main intuitions: (i) substance concepts are modeled as visual objects , namely, sequences of similar frames, as perceived in multiple encounters ; (ii) substance concepts are organized into a visual subsumption hierarchy based on the notions of and ; (iii) the human feedback is exploited not to name objects, but, rather, to align the hierarchy of substance concepts with that of classification concepts. The learning algorithm is implemented for the base case of a hierarchy of depth two. The experiments, though preliminary, show that the algorithm manages to acquire the notions of and with reasonable accuracy, this despite seeing a small number of examples and receiving supervision on a fraction of them.
Interlocutors’ Age Impacts Teenagers’ Online Writing Style: Accommodation in Intra- and Intergenerational Online Conversations

The present study examines how teenagers adapt their language use to that of their conversation partner (i.e., the linguistic phenomenon of accommodation) in interactions with peers (intragenerational communication) and with older interlocutors (intergenerational communication). We analyze a large corpus of Flemish teenagers’ conversations on Facebook Messenger and WhatsApp, which appear to be highly peer-oriented. With Poisson models, we examine whether the teenage participants adjust their writing style to older interlocutors. The same trend emerges for three sets of prototypical markers of the informal online genre: teenagers insert significantly fewer of these markers when interacting with older interlocutors, thus matching their interlocutors’ style and increasing linguistic similarity. Finally, the analyses reveal subtle differences in accommodation patterns for the distinct linguistic variables with respect to the impact of the teenagers’ sociodemographic profiles and their interlocutors’ age.
Speaker Gender Classification in Mono-Language and Cross-Language Using BLSTM Network

Determination of speaker gender from a speech is an important subject, where can be applied to various real-world applications. This study aims to investigate the robustness of classifying gender with different speakers who speak multi-languages, specifically Arabic and English, by using the Bidirectional Long Short-Term Memory (BLSTM) network model-based classifier. For that purpose, this paper presents the mono-language and cross-language gender classification results obtained using the BLSTM model from Arabic and English language datasets were selected from the open-source Mozilla Common Voice (MCV) corpus. The proposed model has mainly based on the speech signal features, including the Gammatone Cepstral Coefficient (GTCC), Spectral Entropy, Pitch, Harmonic Ratio, Mel Frequency Cepstral Coefficient (MFCC), and Mel Spectrum. The results showed the highest accuracy of 91.76% for the Arabic-speakers model and 86.53% for the English-speakers model.
EADAS: Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming

Mobile networks equipped with edge computing nodes enable access to information that can be leveraged to assist client-based adaptive bitrate (ABR) algorithms in making better adaptation decisions to improve both Quality of Experience (QoE) and fairness. For this purpose, we propose a novel on-the-fly edge mechanism, named EADAS (Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming), located at the edge node that assists and improves the ABR decisions on-the-fly. EADAS proposes (i) an edge ABR algorithm to improve QoE and fairness for clients and (ii) a segment prefetching scheme. The results show a QoE increase of 4.6%, 23.5%, and 24.4% and a fairness increase of 11%, 3.4%, and 5.8% when using a buffer-based, a throughput-based, and a hybrid ABR algorithm, respectively, at the client compared with client-based algorithms without EADAS. Moreover, QoE and fairness among clients can be prioritized using parameters of the EADAS algorithm according to service providers’ requirements.
Weakly Supervised Relative Spatial Reasoning for Visual Question Answering

Vision-and-language (V&L) reasoning necessitates perception of visual concepts such as objects and actions, understanding semantics and language grounding, and reasoning about the interplay between the two modalities. One crucial aspect of visual reasoning is spatial understanding, which involves understanding relative locations of objects, i.e. implicitly learning the geometry of the scene. In this work, we evaluate the faithfulness of V&L models to such geometric understanding, by formulating the prediction of pair-wise relative locations of objects as a classification as well as a regression task. Our findings suggest that state-of-the-art transformer-based V&L models lack sufficient abilities to excel at this task. Motivated by this, we design two objectives as proxies for 3D spatial reasoning (SR) – object centroid estimation, and relative position estimation, and train V&L with weak supervision from off-the-shelf depth estimators. This leads to considerable improvements in accuracy for the "GQA" visual question answering challenge (in fully supervised, few-shot, and O.O.D settings) as well as improvements in relative spatial reasoning. Code and data will be released here.
Unified concept and assertion detection using contextual multi-task learning in a clinical decision support system

Assertions, such as negation and speculation, alter the meaning of clinical findings ('concepts') in Electronic Health Records. Accurate assertion detection is vital to the identification of target findings in clinical decision support systems. Diverse clinical concepts and assertion modifiers embedded within longer sentences add to the challenge of error-free detection. Recent approaches leveraging biomedical contextual embeddings lead to standalone concept and assertion models that do not effectively utilize inter-task knowledge transfer. We propose a novel neural model integrating task-specific fine-tuning and multi-task learning in a coherent framework based on the hierarchical relationship between the tasks. We show that such a unified framework enhances both the tasks using several real-world clinical notes' datasets (n2c2 2010, n2c2 2012, NegEx). Concept task performance enhanced by +1.69 F1 on n2c2 2010 and +2.96 F1 on n2c2 2012 compared to standalone baselines. Assertion recognition improved by +2.89 F1 and +3.77 F1, respectively. Negation detection under low-resource settings increased significantly (+2.4 F1, p-value = 3.11E-05, McNemar's test), demonstrating the impact of inter-task knowledge transfer. The integrated architecture enhanced the generalization performance of speculation detection (+2.09 F1). To the best of our knowledge, this model is the first demonstration of a contextual multi-task system for unified detection of concepts and assertions in clinical decision support applications.
CEGAR-Tableaux: Improved Modal Satisfiability via Modal Clause-Learning and SAT

We present CEGAR-Tableaux, a tableaux-like method for propositional modal logics utilising SAT-solvers, modal clause-learning and multiple optimisations from modal and description logic tableaux calculi. We use the standard Counter-example Guided Abstract Refinement (CEGAR) strategy for SAT-solvers to mimic a tableau-like search strategy that explores a rooted tree-model with the classical propositional logic part of each Kripke world evaluated using a SAT-solver. Unlike modal SAT-solvers and modal resolution methods, we do not explicitly represent the accessibility relation but track it implicitly via recursion. By using “satisfiability under unit assumptions”, we can iterate rather than “backtrack” over the satisfiable diamonds at the same modal level (context) of the tree model with one SAT-solver. By keeping modal contexts separate from one another, we add further refinements for reflexivity and transitivity which manipulate modal contexts once only. Our solver CEGARBox is, overall, the best for modal logics K, KT and S4 over the standard benchmarks, sometimes by orders of magnitude.
A spaCy-based tool for extracting variability from NL requirements

In previous work, we have shown that ambiguity detection in requirements can also be used as a way to capture latent aspects of variability. Natural Language Processing (NLP) tools have been used for a lexical analysis aimed at ambiguity indicators detection, and we have studied the necessary adaptations to those tools for pointing at potential variability, essentially by adding specific dictionaries for variability. We have identified also some syntactic rules able to detect potential variability, such as disjunction between nouns or pairs of indicators in a subordinate proposition. This paper describes a new prototype NLP tool, based on the spaCy library, specifically designed to detect variability. The prototype is shown to preserve the same recall exhibited by previously used lexical tools, with a higher precision.
Development of a Face Recognition System and Its Intelligent Lighting Compensation Method for Dark-Field Application

A face recognition system which uses 3D lighting estimation and optimal lighting compensation for dark-field application is proposed. To develop the proposed system, which can realize people identification in a near scene dark-field environment, a light-emitting diode (LED) overhead light, eight LED wall lights, a visible light binocular camera, and a control circuit are used. First, 68 facial landmarks are detected and their coordinates in both image as well as camera coordinate systems are computed. Second, a 3D morphable model (3DMM) is developed after considering facial shadows, and a transformation matrix between the 3DMM and camera coordinate systems is estimated. Third, to assess lighting uniformity, 30 evaluation points are selected from the face. Sequencing computations of LED radiation intensity, ray reflection luminance, camera response, and face lighting uniformity are then carried out. Ray occlusion is processed using a simplified 3D face model. Fourth, an optimal lighting compensation is realized: the overhead light is used for flood lighting, and the wall lights are employed as meticulous lighting. A genetic algorithm then is used to identify the optimal lighting of the wall lights. Finally, an Eigenface method is used for face recognition. The results show that our system and method can improve face recognition accuracy by >10% compared to traditional recognition methods.
Plenoptic 2.0 Intra Coding Using Imaging Principle

Plenoptic 2.0 videos that are captured by focused plenoptic cameras outperform the traditional plenoptic videos with higher spatial resolution of rendered subaperture images and more effective light field sampling. However, its distinct imaging principle causes different pixel intensity distribution and complex macropixel structures, which results in ineffectiveness in coding them using the existing plenoptic video compression methods. Thus, a novel compression method is proposed in this paper to dramatically improve the intra prediction efficiency for multi-focus plenoptic 2.0 video coding. It consists of two new intra prediction modes: imaging-principle-guided static prediction (ISP) and imaging-principle-guided zoomed prediction (IZP). In ISP, the prediction candidates spatially positioned by plenoptic 2.0 imaging principle are weighted by minimizing boundary error to generate the predicted samples. In IZP, the spatial positions and scales of the prediction candidates vary according to the focusing status and the focal length of the microlens that generates the pixels in the prediction candidates. The combination of two modes generalizes plenoptic 2.0 video coding for multi-focus plenoptic cameras. The proposed approach outperforms high efficiency video coding (HEVC) standard by an average of 15.37% bitrate reduction under All Intra configuration. It also outperforms other state-of-the-art methods with obvious compression efficiency improvements.
CoMSum and SIBERT: A Dataset and Neural Model for Query-Based Multi-document Summarization

Document summarization compress source document(s) into succinct and information-preserving text. A variant of this is query-based multi-document summarization (q mds) that targets summaries to providing specific informational needs, contextualized to the query. However, the progress in this is hindered by limited availability to large-scale datasets. In this work, we make two contributions. First, we propose an approach for automatically generated dataset for both extractive and abstractive summaries and release a version publicly. Second, we design a neural model SIBERT for extractive summarization that exploits the hierarchical nature of the input. It also infuses queries to extract query-specific summaries. We evaluate this model on CoMSum dataset showing significant improvement in performance. This should provide a baseline and enable using CoMSum for future research on q mds.
Optical Neural Network Based on Synthetic Nonlinear Photonic Lattices

We reveal that a synthetic photonic lattice based on coupled optical loops can be utilized as a feed-forward neural network for processing of optical pulse sequences in time domain. As a proof of concept, we train the optical system to restore an initial shape of the pulse train from the signal distorted due to linear dispersion in a fiber-optic link. We also show efficient training of the optical network with an intrinsic Kerr-type nonlinearity for the realization of target nonlinear transmission functions and inference functionality for the discrimination of different pulse sequences. The theoretical modeling is performed under practical conditions and can guide future experimental realizations.
Understanding Kinematics of the Orthogonal Cutting Using Digital Image Correlation—Measurement and Analysis

Abstract With the development of advanced image correlation and high-speed filming techniques, the kinematic field during the cutting process can be experimentally determined including the velocity and strain rate fields. As known, the setting parameters for the digital image correlation (DIC) as well as the optical parameters of the given camera and lighting system have a great influence on the spatial resolution and accuracy of the DIC results. In this study, the speckle pattern in terms of speckle size and intensity distribution are analyzed when using two different surface preparation methods. Moreover, the influences of the subset sizes for the image correlation on the strain rate are numerically studied. Interlaboratory measurements of the kinematic field during the orthogonal cutting of AISI 4140 were conducted with two different in-situ imaging setups. The material flow near the cutting tool edge determined from the velocity field is compared with the numerical simulation. The stagnation zone which is commonly found in the numerical simulation of the cutting process using a chamfered cubic boron nitride (CBN) tool was not observed in the experiments. Furthermore, slip-line fields were constructed from the experimentally determined strain rate components, from which the boundary conditions along the chip-free and chip-tool interface were derived.
Medical Image Encryption Based on Hybrid AES with Chaotic Map

Abstract Patient privacy and image protection is an obligation. Data privacy, data protection, and security must be provided by using encryption to ensure confidentiality. Currently, there are numerous standard traditional encryption algorithms. Most of these are suitable for a text file. It is challenging to employ these algorithms for images or videos directly because of strong adjacent pixels correlations. Decreasing the correlation among the surrounding pixels reduces the detail. An algorithm based on a quadratic map is applied as a preprocessing step to nullify the relationship between pixels and reduce the entropy. The AES image encryption is performed for confusion and diffusion, which are necessary for confidentiality. The security analysis findings indicate that the sensitive encryption and decryption techniques are highly dependent on any improvement in the key. The encryption solution is broad enough to avoid brute-force attacks. Thus, during the transfer of medical images over the network, protection can become an issue.
Stereo disparity optimization with depth change constraint based on a continuous video

Three-dimensional reconstruction based on stereo vision technology is an important research direction in the field of computer vision, and has a wide range of applications in industrial measurement, medical image reconstruction, cultural relic preservation, robot navigation, virtual reality and other fields. However, the three-dimensional reconstruction of moving objects usually has poor accuracy, low efficiency and poor visualization effect due to the image noise, motion blur, complex and time-consuming calculation etc. In this article, a disparity optimization method based on depth change constraint is proposed, which utilizes the correlation of the adjacent frames in the continuous video sequence to eliminate mismatches and correct the wrong disparity values by introducing a depth change constraint threshold. The experiments on the video images which are taken by a binocular stereo vision system demonstrate that our method of removing incorrect matches bears satisfactory results and it can greatly improve the effect of the three-dimensional reconstruction of the moving objects.
FastAER Det: Fast Aerial Embedded Real-Time Detection

Automated detection of objects in aerial imagery is the basis for many applications, such as search and rescue operations, activity monitoring or mapping. However, in many cases it is beneficial to employ a detector on-board of the aerial platform in order to avoid latencies, make basic decisions within the platform and save transmission bandwidth. In this work, we address the task of designing such an on-board aerial object detector, which meets certain requirements in accuracy, inference speed and power consumption. For this, we first outline a generally applicable design process for such on-board methods and then follow this process to develop our own set of models for the task. Specifically, we first optimize a baseline model with regards to accuracy while not increasing runtime. We then propose a fast detection head to significantly improve runtime at little cost in accuracy. Finally, we discuss several aspects to consider during deployment and in the runtime environment. Our resulting four models that operate at 15, 30, 60 and 90 FPS on an embedded Jetson AGX device are published for future benchmarking and comparison by the community.
Multi visual and textual embedding on visual question answering for blind people

Visual impairment community, especially blind people have a thirst for assistance from advanced technologies for understanding and answering the image. Through the development and intersection between vision and language, Visual Question Answering (VQA) is to predict an answer from a textual question on an image. It is essential and ideal to help blind people with capturing the image and answering their questions automatically. Traditional approaches often utilize the strength of convolution and recurrent networks, which requires a great effort for learning and optimizing. A key challenge in VQA is finding an effective way to extract and combine textual and visual features. To take advantage of previous knowledge in different domains, we propose BERT-RG, the delicate integration of pre-trained models into feature extractors, which relies on the interaction between residual and global features in the image and linguistic features in the question. Moreover, our architecture integrates a stacked attention mechanism that exploits the relationship between textual and visual objects. Specifically, the partial regions of images interact with partial keywords in question to enhance the text-vision representation. Besides, we also propose a novel perspective by considering a specific question type in VQA. Our proposal is significantly meaningful enough to develop a specialized system instead of putting forth the effort to dig for unlimited and unrealistic approaches. Experiments on VizWiz-VQA, a practical benchmark dataset, show that our proposed model outperforms existing models on the VizWiz VQA dataset in the Yes/No question type.