Reviewerly/exHarmony huggingface dataset:
Generating optimal path by level set approach for a mobile robot moving in static/dynamic environments

According to the analogy between the mobile robot navigation path and the heat transferring path under steady state, the robot path planning problem during navigation is converted into identify the heat transferring path that minimizes the thermal compliance across the analysis domain. A new path planning approach which combines the concept of growth simulation and level set based heat conduction topology optimization framework is adopted to determine the heat transferring path. By introducing the concept of growth simulation, the proposed approach could calculate a few steps of the navigation path, which is of great significance for online reactive navigation. The proposed approach could avoid local minima and search for the optimal growth orientation freely without constraints from background mesh since the inherent characteristics of heat conduction and the level set approach, respectively. A new reactive navigation algorithm based on the proposed path planning approach and the concept of temporarily safe path is proposed to navigate the mobile robot from the start point to the goal point in unknown dynamic environment with static and dynamic obstacles. Diverse simulation cases are carried to illustrate the effectiveness of the reactive navigation algorithm.
Eye Gaze Controlled Robotic Arm for Persons with Severe Speech and Motor Impairment

Recent advancements in the field of robotics offers new promises for people with different range of abilities although making a human robot interface for people with severe disabilities is challenging. This paper describes the design and development of an eye gaze controlled interface for users with severe speech and motor impairment to manipulate a robotic arm. Two user studies were reported on pick and drop and reachability studies involving users with severe speech and motor impairment. Using the eye gaze controlled interface users could undertake representative pick and drop task at an average duration less than 15 secs and reach a randomly designated target within 60 secs.
Zero shot learning based on class visual prototypes and semantic consistency

Zero shot classification is to recognize unseen images which are not present in training set, which is a quite difficult task. Traditional zero shot classification methods ignore the semantic inconsistencies between visual and semantic spaces which make those methods less effective. Projecting semantic representations to visual space can alleviate hubness problem. However, directly utilizing the semantic to visual mapping function learnt by seen classes to unseen classes will lead to domain shift problem. We propose a zero shot learning method which learns visual prototypes and preserves semantic consistency across visual and semantic spaces simultaneously, to handle semantic inconsistency problem and domain shift problem. The semantic consistency is represented by a shared sparse graph of visual space and semantic space. Our key insight is that the visual prototypes learning and the sparse graph learning are unified into a single process. Extensive experiments demonstrate that the results by the proposed method could be boosted significantly.
Beyond Microbenchmarks: The SPEC-RG Vision for a Comprehensive Serverless Benchmark

Serverless computing services, such as Function-as-a-Service (FaaS), hold the attractive promise of a high level of abstraction and high performance, combined with the minimization of operational logic. Several large ecosystems of serverless platforms, both open- and closed-source, aim to realize this promise. Consequently, a lucrative market has emerged. However, the performance trade-offs of these systems are not well-understood. Moreover, it is exactly the high level of abstraction and the opaqueness of the operational-side that make performance evaluation studies of serverless platforms challenging. Learning from the history of IT platforms, we argue that a benchmark for serverless platforms could help address this challenge. We envision a comprehensive serverless benchmark, which we contrast to the narrow focus of prior work in this area. We argue that a comprehensive benchmark will need to take into account more than just runtime overhead, and include notions of cost, realistic workloads, more (open-source) platforms, and cloud integrations. Finally, we show through preliminary real-world experiments how such a benchmark can help compare the performance overhead when running a serverless workload on state-of-the-art platforms.
Achieving Practical Symmetric Searchable Encryption With Search Pattern Privacy Over Cloud

Dynamic symmetric searchable encryption (SSE), which enables a data user to securely search and dynamically update the encrypted documents stored in a semi-trusted cloud server, has received considerable attention in recent years. However, the search and update operations in many previously reported SSE schemes will bring some additional privacy leakages, e.g., search pattern privacy, forward privacy and backward privacy. To the best of our knowledge, none of the existing dynamic SSE schemes preserves the search pattern privacy, and many backward private SSE schemes still leak some critical information, e.g., the identifiers containing a specific keyword currently in the database. Therefore, aiming at the above challenges, in this article, we design a practical SSE scheme, which not only supports the search pattern privacy but also enhances the backward privacy. Specifically, we first leverage the <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -anonymity and encryption to design an obfuscating technique. Then, based on the obfuscating technique, pseudorandom function and pseudorandom generator, we design a basic dynamic SSE scheme to support single keyword queries and simultaneously achieve search pattern privacy and enhanced backward privacy. Furthermore, we also extend our proposed scheme to support more efficient boolean queries. Security analysis demonstrates that our proposed scheme can achieve the desired privacy properties, and the extensive performance evaluations also show that our proposed scheme is indeed efficient in terms of communication overhead and computational cost.
Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance

We present a pairwise learning to rank approach based on a neural net, called DirectRanker, that generalizes the RankNet architecture. We show mathematically that our model is reflexive, antisymmetric, and transitive allowing for simplified training and improved performance. Experimental results on the LETOR MSLR-WEB10K, MQ2007 and MQ2008 datasets show that our model outperforms numerous state-of-the-art methods, while being inherently simpler in structure and using a pairwise approach only.
Reflectance-Guided Histogram Equalization and Comparametric Approximation

Existing image enhancement methods fall short of expectations because with them it is difficult to improve global and local image contrast simultaneously. To address this issue, we propose a histogram equalization-based method called RG-CACHE. It adapts to the data-dependent requirements of brightness enhancement and improves the visibility of details without losing the global contrast. RG-CACHE incorporates the spatial information provided by image context into density estimation for discriminative histogram equalization. To minimize the adverse effect of nonuniform illumination, we propose defining spatial information on the basis of image reflectance estimated with edge-preserving smoothing. RG-CACHE works particularly well for determining how the background brightness should be adaptively adjusted and for revealing useful image details hidden in the dark. To handle the loss of details due to the monotonicity of the intensity mapping function, we further propose a post-processing method to approximate RG-CACHE with a brightness transformation function corresponding to a parameterized camera response function. This method is called comparametric approximation. It takes into account a regression problem, in which the parameters of the camera response function are chosen so that the converted intensities are optimally matched to the image enhanced by RG-CACHE. Comparametric approximation is especially suitable for recovering useful image details that tend to be suppressed due to insufficient reflectance contrast.
Deep Battery Saver: End-to-End Learning for Power Constrained Contrast Enhancement

Due to the problems of power-hungry displays and limited battery life in electronic devices, the concept of “green computing,” which entails a reduction in power consumption, is proposed. One often seen green computing is the power-constrained contrast enhancement (PCCE), yet it is much more challenging because of the noticeable local intensity suppressions in images. This paper aims at developing an image-quality-lossless end-to-end learning network called deep battery saver to achieve power savings in emissive displays, i.e., produce power-saved images with high perceptual quality and less power consumption. Built upon the end-to-end network of the displayed image, we propose a variational loss function for enhancing the visual quality and suppressing the power consumption, simultaneously. The basic idea is to integrate both high-level perceptual losses and low-level pixel losses by a deep residual convolutional neural network (CNN) over a devised variational loss function with strong human perceptual consistency. Such deep residual CNN network leads to a visually pleasing image representation during the suppression of power consumption. Experimental results demonstrated the superiority of our deep battery saver to existing PCCE methods.
Validation of Large-Scale Classification Problem in Dendritic Neuron Model Using Particle Antagonism Mechanism

With the characteristics of simple structure and low cost, the dendritic neuron model (DNM) is used as a neuron model to solve complex problems such as nonlinear problems for achieving high-precision models. Although the DNM obtains higher accuracy and effectiveness than the middle layer of the multilayer perceptron in small-scale classification problems, there are no examples that apply it to large-scale classification problems. To achieve better performance for solving practical problems, an approximate Newton-type method-neural network with random weights for the comparison; and three learning algorithms including back-propagation (BP), biogeography-based optimization (BBO), and a competitive swarm optimizer (CSO) are used in the DNM in this experiment. Moreover, three classification problems are solved by using the above learning algorithms to verify their precision and effectiveness in large-scale classification problems. As a consequence, in the case of execution time, DNM + BP is the optimum; DNM + CSO is the best in terms of both accuracy stability and execution time; and considering the stability of comprehensive performance and the convergence rate, DNM + BBO is a wise choice.
Modified Sauvola binarization for degraded document images

The binarization of historical documents is a difficult job due to the presence of many degradations. Many existing local binarization techniques use certain manually adjusted parameters. The output of these techniques is much dependent on the value of these parameters. One of such parameters is window size which is kept fixed for the whole text image. The fixed window size will not be able to perform well for images having variable stroke widths and text sizes. The proposed binarization technique (Modified Sauvola) is the modification of state of art Sauvola’s binarization technique. It automatically computes window size dynamically across the image pixel to pixel using the stroke width transform (SWT). This led to reduction in number of manually adjusted parameters. The results are compared with the nine existing techniques using the quantitative measures: FM, PSNR, NRM, MPM, and DRD. The results show that the proposed method outperforms existing methods for images having variable stroke widths and text sizes.
Playing with machines: Using machine learning to understand automated copyright enforcement at scale

This article presents the results of methodological experimentation that utilises machine learning to investigate automated copyright enforcement on YouTube. Using a dataset of 76.7 million YouTube videos, we explore how digital and computational methods can be leveraged to better understand content moderation and copyright enforcement at a large scale.We used the BERT language model to train a machine learning classifier to identify videos in categories that reflect ongoing controversies in copyright takedowns. We use this to explore, in a granular way, how copyright is enforced on YouTube, using both statistical methods and qualitative analysis of our categorised dataset. We provide a large-scale systematic analysis of removals rates from Content ID’s automated detection system and the largely automated, text search based, Digital Millennium Copyright Act notice and takedown system. These are complex systems that are often difficult to analyse, and YouTube only makes available data at high levels of abstraction. Our analysis provides a comparison of different types of automation in content moderation, and we show how these different systems play out across different categories of content. We hope that this work provides a methodological base for continued experimentation with the use of digital and computational methods to enable large-scale analysis of the operation of automated systems.
Real Time Anomaly Detection Techniques Using PySpark Frame Work

The identification of anomaly in a network is a process of observing keenly the minute behavioral changes from the usual pattern followed. These are often referred with different names malware, exceptions, and anomaly or as outlier according to the dominion of the application. Though many works have emerged for the detection of the outlier, the identification of the abnormality in the multiple source data stream structure is still under research. To identify the abnormalities in the cloud data center that is encompassed with the multiple-source VMWare, by observing the behavioral changes in the load of the CPU, utilization of the memory etc. consistently the paper has developed a real time identification process. The procedure followed utilizes the PySpark to compute the batches of data and make predictions, with minimized delay. Further a flat-increment based clustering is used to frame the normal attributes in the PySpark Structure. The latencies in computing the tuple while clustering and predicting, was compared for PySpark, Storm and other dispersed structure that were used in processing the batches of data and was experimentally found that the processing time of tuple in a PySpark was much lesser compared to the other methods.
Scene Graph Inference via Multi-Scale Context Modeling

The scene graph generated for an image structurally represents its object interactions and it substantially aids image scene understanding. To the best of our knowledge, most current works on scene graph generation chiefly focus on pairwise object regions for object and relation inference while ignoring the global visual context outside of these regions. Guided by the intuition that object/relation inference can benefit from the visual context within an image, this paper proposes a multi-scale context modeling method, which can jointly discover and integrate the complementary object-centric and region-centric context for scene graph inference. While both the object-centric and region-centric contexts are separately modeled by their individual modules, a bi-directional message propagation strategy is designed to mutually reinforce the context modeling. A context-fused inference is then proposed to integrate the multi-scale context to guide scene graph inference. Extensive experiments establish that this method can achieve competitive performance compared to the state-of-the-art methods on three benchmarks. Additional ablation studies further validate its effectiveness. Code has been made available at: https://github.com/ningxu1990/MSCM.
Evaluation of Arabic to English Machine Translation Systems

Arabic machine translation has an important role in most NLP tasks. Many machine translation systems that support Arabic exist already; however the quality of the translation needs to be improved. In this paper, we review different research approaches for Arabic-to-English machine translation. The approaches use various evaluation methods, datasets, and tools to measure their performance. Moreover, this paper sheds light on several methods and assessment efforts, and future ideas to improve the machine translation quality of Arabic-to-English. The review results depict three major findings; first neural machine translation approaches outperform other approaches in many aspects. Second, the recently emerging attention-based approach is being useful to improve the performance of neural machine translation for all languages. Third, the translation performance quality depends on the quality of the dataset, well-behaved aligned corpus, and the evaluation technique used.
Optimized Transformer Models for FAQ Answering

Informational chatbots provide a highly effective medium for improving operational efficiency in answering customer queries for any enterprise. Chatbots are also preferred by users/customers since unlike other alternatives like calling customer care or browsing over FAQ pages, chatbots provide instant responses, are easy to use, are less invasive and are always available. In this paper, we discuss the problem of FAQ answering which is central to designing a retrieval-based informational chatbot. Given a set of FAQ pages s for an enterprise, and a user query, we need to find the best matching question-answer pairs from s. Building such a semantic ranking system that works well across domains for large QA databases with low runtime and model size is challenging. Previous work based on feature engineering or recurrent neural models either provides low accuracy or incurs high runtime costs. We experiment with multiple transformer based deep learning models, and also propose a novel MT-DNN (Multi-task Deep Neural Network)-based architecture, which we call Masked MT-DNN (or MMT-DNN). MMT-DNN significantly outperforms other state-of-the-art transformer models for the FAQ answering task. Further, we propose an improved knowledge distillation component to achieve \(\sim \)2.4x reduction in model-size and \(\sim \)7x reduction in runtime while maintaining similar accuracy. On a small benchmark dataset from SemEval 2017 CQA Task 3, we show that our approach provides an NDCG@1 of 83.1. On another large dataset of \(\sim \)281K instances corresponding to \(\sim \)30K queries from diverse domains, our distilled 174 MB model provides an NDCG@1 of 75.08 with a CPU runtime of mere 31 ms establishing a new state-of-the-art for FAQ answering.
Spoken Arabic dialect recognition using X-vectors

Abstract This paper describes our automatic dialect identification system for recognizing four major Arabic dialects, as well as Modern Standard Arabic. We adapted the X-vector framework, which was originally developed for speaker recognition, to the task of Arabic dialect identification (ADI). The training and development ADI VarDial 2018 and VarDial 2017 were used to train and test all of our ADI systems. In addition to the introduced X-vectors, other systems use the traditional i-vectors, bottleneck features, phonetic features, words transcriptions, and GMM-tokens. X-vectors achieved good performance (0.687) on the ADI 2018 Discriminating between Similar Languages shared task testing dataset, outperforming other systems. The performance of the X-vector system is slightly improved (0.697) when fused with i-vectors, bottleneck features, and word uni-gram features.
Improving Restore Performance for In-Line Backup System Combining Deduplication and Delta Compression

Data deduplication, though being efficient in removing duplicate chunks, introduces chunk fragmentation which decreases restore performance. Rewriting algorithms are proposed to reduce the chunk fragmentation. Delta compression is often used as a complement for data deduplication to further improve storage efficiency. We observe that delta compression introduces a new type of chunk fragmentation stemming from improper delta compression for chunks of which the base chunks are fragmented. The new type of chunk fragmentation severely decreases restore performance and cannot be addressed by existing rewriting algorithms. To address this problem, we propose SDC, a scheme performing post-deduplication delta compression only for the chunks of which the bases can be directly found in the restore cache to eliminate additional disk reads for base chunks, thus avoiding the new type of chunk fragmentation. In addition, self-referenced chunks can be fragmented, which decrease restore performance, and these fragmented chunks can serve as bases to decrease the restore performance repeatedly. We propose a hybrid rewriting scheme for SDC to rewrite such fragmented chunks. Experimental results show that SDC improves the restore performance of the approach that directly performs delta compression after data deduplication by 2.9-16.9x, and achieves more than 95 percent of its compression gains.
Complementary Incremental Hashing With Query-Adaptive Re-Ranking for Image Retrieval

Concept drift is prevalent in non-stationary data environments but is rarely researched in image retrieval. Therefore, more research is needed on image retrieval in non-stationary data environments so that highly relevant images can still be retrieved when concept drifts happen. Hashing is a key technique to allow efficient image retrieval, so incremental hashing technique emerges in recent years for image retrieval in non-stationary environments. A state-of-the-art method is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Incremental Hashing</i> (ICH). ICH trains new hash tables on new data without considering the performance of previous hash tables, so the dependency of successive hash tables is ignored. To make use of this dependency in order to improve the performance of image retrieval in non-stationary environments, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Complementary Incremental Hashing with query-adaptive Re-ranking</i> (CIHR) is proposed in this paper. CIHR trains multiple hash tables incrementally, one for each data chunk of images. A new hash table is trained on a new data chunk of images as well as those images badly hashed by previous hash tables, thus the new hash table is complementary to the previous hash tables. To use the hash tables more effectively, a query-adaptive re-ranking method is used to weight all hash functions in each hash table according to their retrieval performance with respect to a given query. Weighted Hamming distance is finally used to evaluate the similarity between the query and the images in the database, as the basis of image retrieval. Experimental results on simulated non-stationary scenarios show that the proposed CIHR method achieves higher retrieval accuracy than all methods being compared, thus setting a new state of the art in image retrieval in non-stationary data environments.