Reviewerly/exHarmony huggingface dataset:
Improving Neural Machine Translation by Transferring Knowledge from Syntactic Constituent Alignment Learning

Statistical machine translation (SMT) models rely on word-, phrase-, and syntax-level alignments. But neural machine translation (NMT) models rarely explicitly learn the phrase- and syntax-level alignments. In this article, we propose to improve NMT by explicitly learning the bilingual syntactic constituent alignments. Specifically, we first utilize syntactic parsers to induce syntactic structures of sentences, and then we propose two ways to utilize the syntactic constituents in a perceptual (not adversarial) generator-discriminator training framework. One way is to use them to measure the alignment score of sentence-level training examples, and the other is to directly score the alignments of constituent-level examples generated with an algorithm based on word-level alignments from SMT. In our generator-discriminator framework, the discriminator is pre-trained to learn constituent alignments and distinguish the ground-truth translation from the fake ones, while the generative translation model is fine-tuned to receive the alignment knowledge and to generate translations that best approximate the true ones. Experiments and analysis show that the learned constituent alignments can help improve the translation results.
Playing with evolution

Evolution: a process we’ve all been through but have yet to fully understand. You started off as a tiny little creature who couldn’t talk, couldn’t walk, and didn’t even have feet. There were many other possibilities, too, but, somehow, nature decided you were the fittest. You were given the chance to live and do your thing in the world, while millions of others were discarded at the concept stage. Genes from your parents were selected, combined, and modified in an attempt to make you best adapted to your environment. This whole process is fascinating: how does nature decide what features give you the chance to be the best that you can be?
A Novel Semi-blind Digital Image Watermarking Using Fire Fly Algorithm

The proposed firefly optimization technique is more robust and imperceptible, while compared with particle swam optimization. Firefly is able to optimize the system fast for the proposed innovative scheme of watermarking. The insertion of watermark, discrete cosine transform (DCT) applied to the sub-bands with the coefficients to generate the watermarked image. Embedding and extraction have been done with parameters and FF algorithm to optimize the values of peak signal-to-noise ratio (PSNR), normalize cross correlation. Firefly algorithm is an objective function to get more robustness and imperceptibility. The results obtained offer a superior approach to traditional methods and are more effectively resistant to various attacks.
Blockchain-Based Methodology for Collaborative Risk Assessment

Risk assessment is an important part of risk management in construction projects as it involves the identification, evaluation, and prioritization of potential risk events. This enables project teams to properly mitigate them. With the intent of getting risk input from multiple stakeholders, different systems have been developed to support collaborative risk assessment. However, these systems often face challenges such as no data confidentiality, poor data security, lack of transparency, and absence of traceability and auditability for the team members. Blockchain is an emergent decentralized digital technology that can provide solutions to overcome such deficiencies of centralized systems. In this study, the need for a novel blockchain-based methodology for collaborative risk assessment is analyzed. Multiple traits of blockchain technology have been found through the literature review. From these multiple traits, the key functional elements for the methodology have been identified and discussed in further detail. The research points out some of the limitations at the current state so that future work can be conducted to build a methodology that can be even more beneficial for the risk management of construction projects.
Application of Image Similarity Detection based on Typical ITAI Servers

In this paper, image detection algorithms based on traditional methods and deep-learning method are migrated from Intel X86 architecture ecosystem to X86 and ARM architectures on the typical localized servers and softwares in Information Technology Application Innovation (ITAI) catalog. By solving the adaptation problems as well as considering the different processor architectures of the localized server, targeted performance optimization methods are proposed, and large-scale performance testing on massive images have been experimented on physical machines, verifying the production capacity of current ITAI servers in image detection scenario.
A deep learning-based approach for motion-induced error reduction in fringe projection profilometry

Fringe projection profilometry (FPP) is one of popular 3D measurement techniques, which can be divided into two categories: Fourier transform profilometry (FTP) and phase-shifting profilometry (PSP). Compared to FTP, PSP has been increasingly appealing to researchers due to its merits of higher accuracy and sensitivity. Although PSP works well with the assumption that the object stays quasi-static, it is sensitive to the object motion and causes the motion-induced error in dynamic measurement due to the multi-frame measurement mechanism. However, the multi-frequency phase unwrapping is always utilized to solve the problem of surface discontinuities, which limits the reduction of numbers of projected patterns. Besides, using the high-speed camera will increase the cost of the hardware. Recently, researchers have demonstrated that the phase unwrapping, fringe denoising and dynamic range can be improved with the assistance of deep learning technique. Therefore, in this paper, a deep learning-based method is proposed for motion-induced error reduction. With the aid of strong fitting capability of the neural network, the motion-induced errors can be significantly reduced even under low capture frame rate. The proposed method is experimentally verified on its applicability for dynamic 3D measurement.
Coal Mine Image Dust and Fog Clearing Algorithm Based on Deep Learning Network

The purpose of coal mine dust fog image sharpening is to recover clear content from low visibility images. In response to the problems of over-enhancement and insufficient applicability of traditional image defogging methods based on prior knowledge, this paper proposed an end-to-end parallel high-resolution deep learning network to directly restore the final clear images. In this network, we used a parallel high-resolution deep network structure to deeply integrate multi-scale features between branches to help the network obtain contextual information. We also proposed a lightweight attention module, which reduced the parameter burden of the model and paid more attention to the target area when extracting feature information. Finally, in order to evaluate the effectiveness and versatility of the clarification algorithm proposed in this paper, experiments were conducted on the public data set RESIDE and the coal mine dust and fog image data set produced by myself, and compared with the existing classic clarification algorithm GCANet. The results show that the algorithm proposed in this paper can effectively solve the over-enhancement phenomenon, and improve the clarity and visualization of coal mine images.
Towards interpreting deep neural networks via layer behavior understanding

Deep neural networks (DNNs) have achieved success in many machine learning tasks. However, how to interpret DNNs is still an open problem. In particular, how do hidden layers behave is not clearly understood. In this paper, relying on a teacher-student paradigm, we seek to understand the layer behaviors of DNNs by “monitoring” the distribution evolution for both across-layer and single-layer along the depth and training epochs, respectively. Relying on the optimal transport theory, we employ the Wasserstein distance (W-distance) to measure the divergence between the layer distribution and the target distribution. Theoretically, we prove that (i) the W-distance between the distribution of any layer and the target distribution tends to decrease along the depth; (ii) for a specific layer, the W-distance between the distribution in an iteration and the target distribution tends to decrease along training epochs; (iii) a deeper layer, however, is not always better than a shallower layer. Relying on these properties, we are able to propose an early-exit inference method to improve the performance of the multi-label classification. Moreover, our results help to analyze the stability of layer distributions and explain why auxiliary losses are helpful in training DNNs. Extensive experiments justify our theoretical findings.
Consistency Mean-Teaching for Unsupervised Domain Adaptive Person Re-identification

Unsupervised domain adaptive (UDA) person re-identification (re-ID) transfers a labeled source domain model to an unlabeled target domain. In this paper, we propose a Consistency Mean Teaching (CMT) method to improve clustering-based UDA Re-ID. Our CMT consists of two consistencies, i.e., an inter-view consistency and an intra-identity consistency. First, the inter-view consistency exploits a popular self-supervised training for UDA, which has been neglected in existing clustering-based UDA methods. Second, the intra-identity consistency imposes regulations between the teacher and student model to output consistent representations for different samples from the same identity. Third, these two consistencies are integrated into a single student-teacher framework and facilitate complementary benefits. Experimental results show that CMT brings significant improvements over the baseline and achieves competitive accuracy on four popular UDA re-ID.
Cost-Effective Integration and Deployment of Enterprise Application Using Azure Cloud Devops

Nowadays to deliver any enterprise application into the market the functionality of the application is tested over the automated environment. The application is developed using different software development life cycle approaches like waterfall and agile. The traditional development tool needs a lot of manual intervention and manpower which is a time-consuming process. In the case of agile, the trail deployment build happens once which is difficult to fix and rectify the bugs. To improve the quality and the speed of development DevOps came into a picture that can build and deploy the application at stages. To do the continuous integration and delivery the automation tools like Jenkins, bamboo, and Travis CI are available in the market. The proposed approach utilizes the azure cloud DevOps along with the feature of Jenkins for continuous integration and deployment. The application is built and can be auto-configured in a cloud platform which greatly improves the build and deployment speed. The parallel and continuous integration adopts the dynamic changes effectively using the automation tools.
Deep Guidance Decoder with Semantic Boundary Learning for Boundary-Aware Semantic Segmentation

Image semantic segmentation is ubiquitously used in consumer electronics, such as AI Camera, which require high accuracy at the boundaries between semantic classes. To improve the semantic boundary accuracy, we propose low complexity deep-guidance decoder (DGD) networks, trained with a novel semantic boundary learning (SBL) strategy. Our ablation studies on Cityscapes and the ADE20K most-frequent 31 classes, when using different encoders and feature extractors, confirm the effectiveness of our approach. We show that the proposed DGD with SBL significantly improve the mIoU by up to 10.4&#x0025; relative gain and the mean boundary F1-score (mBF) by up to 38.5&#x0025;.
Grid-connected Hybrid Solar/Thermoelectric Power System with Hybrid INC/PSO/PO MPPT System in Sharjah, United Arab Emirates

A grid-connected hybrid solar/Thermoelectric renewable power source design is proposed. The outlined hybrid power framework comprises of solar Photovoltaic (PV) panels besides thermoelectric modules. The proposed energy management system aims to obtain the maximum power using a hybrid system of three Maximum Power Point Tracking (MPPT) algorithms: Perturb and Observe (PO), Incremental Conductance (INC), and Particle Swarm Optimization (PSO). The solar power system uses real temperature and solar irradiance values in Sharjah, United Arab Emirates. The used values were obtained from weather forecasting in September 2021. The design aims to provide a maximum power of 70 kW at peak point of operation using solar system and a maximum power of 145 kW at the peak of operation using the thermoelectric modules
Contemporary issues of languaging, participation and ways-of-being

Abstract This paper introduces the theme of Languaging, Diversity and Democracy. Contemporary issues of participation and ways-of-being and positions the 12 individual papers that constitute the 2022 double special issue of Bandung: Journal of the Global South . Its interest lies in contributing to knowledge that is relevant for contemporary human challenges related to issues of mobility, digitalization, and communication in and across different geopolitical regions across the planet and across virtual-physical spaces. Raising concerns regarding universalizing tendencies of special issues (and collected volumes generally), and based on the premise that what kind of knowledge matters is tied up with the issue of whose knowledge and in what named-language this knowledge matters, this paper raises critical queries that focus on the narrators positionality and gaze, the composition of scholarly narratives, the flow of narratives, what vocabularies circulate in frontline scholarship, including the organization of special issues, etc. Drawing attention to the universalizing Euro/America-centrism that shapes what counts as knowledge, the paper draws attention to the taken-for-grantedness of what counts as international languages of publishing which eclipses alternative epistemologies, ways-of-thinking and ways-of-being. It argues that by taking such issues as inspiration in the curation and editing of this double special issue, participatory processes and ways-of-being enabled a contribution to the doing of democracy and diversity in the scholarly enterprise. Such work of democratizing academic publication work calls for unlearning to learn that is closely related to the theme explored in the double special issue. Aligning with analogue-digital languaging in contemporary existence, the paper also traces the journey of how this double special issue has come into being.
Hierarchical Classification using Neighbourhood Exploration for Sparse Text Tweets

Twitter has grown into a vast network of small informal text, and navigating it often becomes difficult for us. Here, we explore Natural Language Processing (NLP) approaches to make the topic classification of tweets easier. We do so with the use case for filtering non-profit tweets among different categories which are arranged in a hierarchy. This paper proposes an efficient pipeline for filtering relevant tweets and a novel data augmentation strategy for sparse datasets. Our data augmentation technique shows a significant leap in the training metrics and the accuracy on the test data increases by 9.52&#x0025; and the F1-score by 24.82&#x0025;.
Multi-view WiFi imaging

Data driven models outperform their model driven counterparts, and model driven models are easier to explain and extend. Bridging the gap between these approaches can benefit from the desirable features on both ends. In this respect, a data driven model proposed on the foundations of model driven methods in wireless imaging can extend the single-view imaging problem to the multi-view scenario. Accordingly, we propose two multi-view approaches based on the plenoptic model. The plenoptic model is rooted in computational imaging and captures what the observer perceives. We establish that the wireless imaging problem can be formulated as an association problem between the plenoptic function in the visible light and the radio frequency spectrum. We then develop two Deep Neural Networks based on the expressed association problem. Both models can generate three camera views from WiFi channel state information of a single receiver. These models are the first solutions to extend WiFi imaging to multi-view settings. We demonstrate our method’s performance and illustrate that it can be an effective stand-alone tool or to augment visible light frameworks.
Detection of Women Profanity in Twitter

In recent years, global users have been allowed to meet and participate in discussions on various social media platforms. In particular, online communities have evolved to become significant places for interpersonal communication. In recent times, the identification of offensive language in online content has become a topic of rising concern. It can be observed that abusive or vulgar language is being used both in the form of jokes and hate conversation. And those are humiliating and insulting words or phrases. The hate conversation may be based on gender orientation, caste, race, and sex. Due to a number of factors, the detection of abusive language is more often complicated than one assumes. This is not only a challenging work to automate but also a difficult one for individuals due to the noise in the data and the need for global awareness. In this paper, we develop a collection of comments elucidated for hate speech and propose an application that detects profanity in social media. The pre-processing of the dataset is done and then further visualization of the most commonly used terminology in twitter, and the classified hate speech is displayed using word clouds. The text length of the tweets that are considered to contain offensive words by the CF coders is shown by the histogram.
Reinforcement Learning-Based Interactive Video Search

Despite the rapid progress in text-to-video search due to the advancement of cross-modal representation learning, the existing techniques still fall short in helping users to rapidly identify the search targets. Particularly, in the situation that a system suggests a long list of similar candidates, the user needs to painstakingly inspect every search result. The experience is frustrated with repeated watching of similar clips, and more frustratingly, the search targets may be overlooked due to mental tiredness. This paper explores reinforcement learning-based (RL) searching to relieve the user from the burden of brute force inspection. Specifically, the system maintains a graph connecting shots based on their temporal and semantic relationship. Using the navigation paths outlined by the graph, an RL agent learns to seek a path that maximizes the reward based on the continuous user feedback. In each round of interaction, the system will recommend one most likely video candidate for users to inspect. In addition to RL, two incremental changes are introduced to improve VIREO search engine. First, the dual-task cross-modal representation learning has been revised to index phrases and model user query and unlikelihood relationship more effectively. Second, two more deep features extracted from SlowFast and Swin-Transformer, respectively, are involved in dual-task model training. Substantial improvement is noticed for the automatic Ad-hoc search (AVS) task on the V3C1 dataset.
Distributed Authentication and Authorization Models in Cloud Computing Systems: A Literature Review

As the functionality and services provided by cloud computing increase, control access to these services becomes more complex, and more security breaches are generated. This is mainly based on the emergence of new requirements and constraints in the open, dynamic, heterogeneous, and distributed cloud environment. Despite the importance of identifying these requirements for designing and evaluating access control models, the available studies do not provide a rigorous review of these requirements and the mechanisms that fulfill them. The purpose of this study was to conduct a literature review of the published articles that have dealt with cloud access control requirements and techniques. This paper allowed us to answer the following two research questions: What cloud access control security requirements have been presented in the published literature? What access control mechanisms are proposed to fulfill them? This review yielded 21 requirements and nine mechanisms, reported by 20 manuscripts. The identified requirements in this review will help researchers, academics and practitioners assess the effectiveness of cloud access control models and identify gaps that are not addressed in the proposed solutions. In addition, this review showed the current cloud access control mechanisms used to meet these requirements such as access control based on trust, risk, multi-tenant, and attribute encryption.
A novel automated method to detect XSS vulnerability in webpages

In this modern world, everyday there is a new technology and with every new technology there is a possibility of a new threat. Cross Site Scripting (XSS) is a type of injection attack to exploit web-page vulnerabilities. This threat becomes a risk when a website can run custom made java scripts appended into its URL or the webpage itself. Once the code executes in the victim browser then the victim&#x0027;s system can be compromised. XSS is one of the top 10 OWASP vulnerability which is very common and it is very hard to get reliable data with respectto the real-world attacks. The paper focuses on detecting XSS vulnerable websites along with its risk and severity. Moreover, this research uses a dedicated clawer along with POST and GET command analysis which can comprehend multi-threading with error and exception handling.
Meet: Rack-Level Pooling Based Load Balancing in Datacenter Networks

Datacenter networks enable multiple paths between hosts to provide large bisection bandwidth. It requires load balancers to cope with network uncertainties such as traffic dynamics and topology asymmetry. Existing edge-based load balancing schemes are usually faced with the problem of limited network visibility. This article proposes <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Meet</small> , a rack-level pooling based load-balancer deployed at the edge that can handle the aformentioned uncertainties. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Meet</small> utilizes both passive information as well as active probing to comprehensively sense the network conditions with relatively low cost. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Meet</small> dynamically reroutes flows effectively based on the visibility of the network condition. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Meet</small> has been tested with extensive flow-level simulations against state-of-the-art load balancers. It outperforms Hermes by up to 10% in the experiments, and outperforms others solutions such as DRILL by up to 50%. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Meet</small> requires no modifications to the switches and is feasible to deploy at the edge.