Reviewerly/exHarmony huggingface dataset:
Recursive neural networks: recent results and applications

Neural Network’s basic principles and functions are based on the nervous system of living organisms, they aim to simulate neurons of the human brain to solve complicated real-world problems by working in a forward-only manner. A recursive Neural Network on the other hand is based on a recursive design principle over a given sequence input, to come up with a scalar assessment of the structured input. This means that is ideal for a given sequence of input data that is when processed dependent on its previous input sequence, which by default are used in various problems of our era. A common example could be devices such as Amazon Alexa, which uses speech recognition i.e., given an audio input source that receives audio signals, tries to predict logical expressions extracted from its different audio segments to form complete sentences. But RNNs do not come with no problems or difficulties. Today’s problems become more and more complex involving parameters in big data form, therefore a need for bigger and deeper RNNs is being created. This paper aims to explore these problems and ways to reduce them while also providing a description of RNN’s beneficial nature and listing different uses of the state-of-the-art RNNs and their use in different problems as those mentioned above.
DBiased-P: Dual-Biased Predicate Predictor for Unbiased Scene Graph Generation

Scene Graph Generation (SGG) is to abstract the objects and their semantic relationships within a given image. Current SGG performance is mainly limited by the biased predicate prediction caused by the long-tailed data distribution. Though many unbiased SGG methods have emerged to enhance the prediction of the tail predicates, their improvements on the tail predicates are often accompanied by the deterioration on the head ones, leading the prediction overly debiased. Toward this end, in this work, we propose a Dual-Biased Predicate Predictor (DBiased-P) to boost the unbiased SGG, which comprises a re-weighted primary classifier and an unweighted auxiliary classifier. The former classifier is tail-biased and used for the final predicate prediction, while the latter one is head-biased and designed to boost the head predicate prediction of the primary classifier by a head-oriented soft regularization. Experiments conducted on Visual Genome and Open Image datasets indicate the superiority of our DBiased-P in unbiased SGG, which significantly improves the recall@50 of the state-of-the-art unbiased SGG method DT2-ACBS from 23.3% to 55.5% as well as the mean recall@50 from 35.9% to 37.7%.
Type-I Generative Adversarial Attack

Deep neural networks are vulnerable to adversarial attacks either by examples with indistinguishable perturbations which produce incorrect predictions, or by examples with noticeable transformations that are still predicted as the original label. The latter case is known as the Type I attack which, however, has achieved limited attention in literature. We advocate that the vulnerability comes from the ambiguous distributions among different classes in the resultant feature space of the model, which is saying that the examples with different appearances may present similar features. Inspired by this, we propose a novel Type I attack method called generative adversarial attack (GAA). Specifically, GAA aims at exploiting the distribution mapping from the source domain of multiple classes to the target domain of a single class by using generative adversarial networks. A novel loss and a U-net architecture with latent modification are elaborated to ensure the stable transformation between the two domains. In this way, the generated adversarial examples have similar appearances with examples of the target domain, yet obtaining the original prediction by the model being attacked. Extensive experiments on multiple benchmarks demonstrate that the proposed method generates adversarial images that are more visually similar to the target images than the competitors, and the state-of-the-art performance is achieved.
Anomaly detection for industrial surface inspection: application in maintenance of aircraft components

Surface defects on aircraft landing gear components represent a deviation from a normal state. Visual inspection is a safety-critical, but recurring task with automation aspiration through machine vision. Various rare occurring faults make acquisition of appropriate training data cumbersome, which represents a major challenge for artificial intelligence-based optical inspection. In this paper, we apply an anomaly detection approach based on a convolutional autoencoder for defect detection during inspection to encounter the challenge of lacking and biased training data. Results indicated the potential of this approach to assist the inspector, but improvements are required for a deployment.
A Job Killer or a Job Creator? The Adoption of AI in Organizations

Organiations use Artificial Intelligence (AI) tools and solutions to acheive operational and strategic objectives. As a result, AI could replace human labor in some jobs, but could also create new jobs and positions for other labor. However, there is lack of studies about the impact of AI in replacing existing jods or creaing new ones on the organizational intention to adopt AI. Therefore, this study aimed at a better understanding about the impact of AI on organizations, and how the availablity of AI tools and associated risks could affect the organizational adoption of AI, by developing and validating a conceptual model of intention to adopt AI in organization in a developing country; the United Arab Emirates (UAE). A quantitative method was followed by gathering 266 online survey responses from IT professionals at telecom, banks and IT companies, to test the model through Partial Least Squares-Structural equation modelling approach. The findings revealed that AI availability, perceived risk controls, ‘AI ability to replace current job positions’, and ‘AI ability to create new jobs’ significantly impact the organizational intention to adopt AI technologies to support business processes in UAE. The research also provides theoretical contributions to the literature through the validating research model and practical implications to organization’s senior managers and technology officers and suggests some future research directions.
TeluguNER: Leveraging Multi-Domain Named Entity Recognition with Deep Transformers

Named Entity Recognition (NER) is a successful and well-researched problem in English due to the availability of resources.The transformer models, specifically the masked-language models (MLM), have shown remarkable performance in NER in recent times.With growing data in different online platforms, there is a need for NER in other languages too.NER remains underexplored in Indian languages due to the lack of resources and tools.Our contributions in this paper include (i) Two annotated NER datasets for the Telugu language in multiple domains: Newswire Dataset (ND) and Medical Dataset (MD), and we combined ND and MD to form a Combined Dataset (CD) (ii) Comparison of the finetuned Telugu pretrained transformer models (BERT-Te, RoBERTa-Te, and ELECTRA-Te) with other baseline models (CRF, LSTM-CRF, and BiLSTM-CRF) (iii) Further investigation of the performance of Telugu pretrained transformer models against the multilingual models mBERT (Devlin et al., 2018), XLM-R (Conneau et al., 2020), and IndicBERT (Kakwani et al., 2020).We find that pretrained Telugu language models (BERT-Te and RoBERTa) outperform the existing pretrained multilingual and baseline models in NER.On a large dataset (CD) of 38,363 sentences, the BERT-Te achieves a high F1-score of 0.80 (entity-level) and 0.75 (token-level).Further, these pretrained Telugu models have shown state-of-the-art performance on various Telugu NER datasets.We open-source our dataset, pretrained models, and code 1 .
An online scalarization multi-objective reinforcement learning algorithm: TOPSIS Q-learning

Abstract Conventional reinforcement learning focuses on problems with single objective. However, many problems have multiple objectives or criteria that may be independent, related, or contradictory. In such cases, multi-objective reinforcement learning is used to propose a compromise among the solutions to balance the objectives. TOPSIS is a multi-criteria decision method that selects the alternative with minimum distance from the positive ideal solution and the maximum distance from the negative ideal solution, so it can be used effectively in the decision-making process to select the next action. In this research a single-policy algorithm called TOPSIS Q-Learning is provided with focus on its performance in online mode. Unlike all single-policy methods, in the first version of the algorithm, there is no need for the user to specify the weights of the objectives. The user’s preferences may not be completely definite, so all weight preferences are combined together as decision criteria and a solution is generated by considering all these preferences at once and user can model the uncertainty and weight changes of objectives around their specified preferences of objectives. If the user only wants to apply the algorithm for a specific set of weights the second version of the algorithm efficiently accomplishes that.
Syntax-guided Contrastive Learning for Pre-trained Language Model

Syntactic information has been proved to be useful for transformer-based pre-trained language models.Previous studies often rely on additional syntax-guided attention components to enhance the transformer, which require more parameters and additional syntactic parsing in downstream tasks.This increase in complexity severely limits the application of syntaxenhanced language model in a wide range of scenarios.In order to inject syntactic knowledge effectively and efficiently into pre-trained language models, we propose a novel syntaxguided contrastive learning method which does not change the transformer architecture.Based on constituency and dependency structures of syntax trees, we design phrase-guided and treeguided contrastive objectives, and optimize them in the pre-training stage, so as to help the pre-trained language model to capture rich syntactic knowledge in its representations.Experimental results show that our contrastive method achieves consistent improvements in a variety of tasks, including grammatical error detection, entity tasks, structural probing and GLUE.Detailed analysis further verifies that the improvements come from the utilization of syntactic information, and the learned attention weights are more explainable in terms of linguistics.
Learn to Zoom in Single Image Super-Resolution

In this letter, we propose a novel solution to the problem of single image super-resolution at multiple scaling factors, including the extreme case of 8x, with a single network architecture. In applications where only a detail needs to be super-resolved, traditional solutions must choose to use as input either the low-resolution detail, thus losing the information about the context, or the whole low-resolution image and then crop the desired output detail, which is quite wasteful in terms of computations and storage. To address both of these issues we propose ZoomGAN, a model that takes as input the whole low-resolution image, which we call context, and a binary mask that specifies with a box which image detail in the low-resolution image to magnify. The output of ZoomGAN has the same size as the inputs so that the scaling factor is implicitly defined by the arbitrary size of the mask box. To encourage a realistic and high-quality output, we combine adversarial training with a perceptual loss. We use two discriminators: one promotes the similarity between the distributions of real and generated details and the other promotes the similarity between the distributions of real and generated (detail, context) pairs. We evaluate ZoomGAN with several experiments on several datasets and show that it achieves state of the art performance on zoomed in details in terms of the LPIPS and PI perceptual metrics, while being on par in terms of the PSNR distortion metric.
Discussion on metallogenic model and prospecting direction of Pleistocene gravel brine Potassium deposit in western Qaidam Basin

通过对柴达木盆地西部大浪滩-黑北凹地、察汗斯拉图凹地、昆特依凹地和马海凹地发现的新类型砂砾型深层卤水钾盐矿床进行卤水化学成份和微量元素系统测试和分析、相关同位素测试，经综合分析和研究后认为，砂砾型深层卤水钾盐矿床为溶滤-沉积成因类型，其形成受第四纪砂砾层和古近纪以来古岩盐层的控制.其成矿模式为：盆地西部从始新世至上新世干旱条件下不断形成的“盐谷”是物源，早更新世砂砾沉积层是成矿空间，周围高山融水径流至山前地带，长年溶滤古近纪以来沉积的岩盐层中的盐类物质，渗流至砂砾层，在相对封闭的条件下形成高矿化度卤水，富集成为砂砾型深层卤水钾盐矿.在今后柴达木盆地开展砂砾型深层卤水钾盐勘查工作时，应优先选择盆地周缘深大断裂、山前古近纪以来盐类地层和早更新世山前冲洪积扇体同时发育的地区安排布署工作.;After comprehensive analysis and research of the chemical, trace elemental and isotopic composition of the new types of graveled pore brine discovered in Dalangtan-Heibei, Chahan Slatu, Kunteyi and Mahai in the western Qaidam Basin, it is believed that this gravel-type brine bearing potassium deposit belongs to leaching sedimentary model, and its formation is controlled by the Quaternary gravel deposit and the paleo-halite formation since the Paleogene. The metallogenic model is as follows:the salt valley which was continuously formed under drought conditions in the western part of the basin from the Eocene to the Pliocene became the provenance, and then the early Pleistocene sandy-gravel deposit was forming the ore-bearing space. As the surrounding meltwater runoff to the piedmont zone, it would then percolate into the Quaternary gravel deposit with salts that had been leached from the Paleogene halite formation. Under relatively closed conditions, it transformed into high-salinity brine and finally came into gravel-type brine bearing potassium deposit. Therefore, in the future exploration in Qaidam Basin, priority should be given to areas where at the same time developed the deep faults at the periphery of the basin, the Paleogene halite formation and the early Pleistocene alluvial fan bodies.
Training of the Digital Workforce from Today’s Youth: Individualization Versus Standardization

The paper aims to determine the optimal approach to the training of digital personnel among today’s youth in Russia and Central Asia, contrasting individualization and standardization. To achieve the goal, the authors implement the economic and mathematical apparatus, particularly the methods of trend and regression analysis. The results justify that standardization is not applicable to the training of the digital workforce among today’s youth. Instead, the authors propose a new approach that relies on individualization. The authors have found a promising Pareto optimum, in which the level of digital competence in Central Asia can reach the maximum (7 points, +115.05%) with de-regulation and individualization. For this purpose, it is recommended to increase the index of economic freedom by 72.23%, the competitiveness of leading universities by 4.07%, and the proportion of female students by 2.68%. The contribution of this research lies in the clarification of a set of factors and the nature of their influence on the success of training of today’s youth for the digital economy. It is established that standardization in the aspect of rationing the number of teachers and students in universities has a beneficial effect on the training of digital personnel among young people. However, even if this factor is unchanged, the opposing factors of economic freedom and market freedom of the leading universities ensure full-scale training of the digital workforce among today’s youth.
Ultra-Lightweight and Secure Blockchain-Assisted Charging Scheduling Scheme for Vehicular Edge Networks by Utilization of NanoPi NEO

In vehicular edge computing networks, electric vehicles can get charging services from edge servers through some road-side charging stations. Several recent studies have investigated how to deal with security requirements in these communications. Nevertheless, simultaneous provision of security and lightness, which is crucial for resource-constrained vehicles, is still an open issue. More critically, the anonymity from the perspective of honest-but-curious edge nodes has not been yet addressed. Thus, this paper proposes a novel ultra-lightweight framework for the secure and anonymous communications of vehicles during their charging reception by means of blockchain. Thanks to the blockchain technology, the accountability of electric vehicle possessors guaranteed. The proposed scheme has been validated in terms of security metrics and also implemented on two ARM-based platforms, one 32-bit ARM microcontroller and one 64-bit ARM processor. Further, its blockchain part has been deployed on a Hyperledger Fabric network. The obtained results besides the comparison with well-respected similar schemes acknowledge the usefulness and practicability of the presented scheme.
A CAM-Guided Parameter-Free Attention Network for Person Re-Identification

Most existing attention mechanisms have no supervised signal during the training phase, which limits the model feature learning capability. To solve this problem, we propose a novel parameter-free attention mechanism based on class activation mapping. Attention mechanisms usually consist of spatial attention and channel attention, which indicates that &#x201C;where&#x201D; and &#x201C;what&#x201D; is more meaningful, respectively. Our attention also contains both types of attention. For Spatial Attention, we use class activation mapping as a supervision signal to guide the generation of it directly in space. Thus our spatial attention can pay more attention to the informative pedestrian parts of the scene and reduce background interference. For Channel Attention, the importance of each channel is obtained by the similarity between the aforementioned spatial attention and the feature map of each channel. In this manner, our channel attention is indirectly guided by class activation mapping. In addition, our attention is parameter-free, which reduces the risk of over-fitting. Finally, we conduct extensive evaluations on three popular benchmark datasets including Market1501, DukeMTMC-reID, and MSMT17, demonstrating the effectiveness of our approach on discriminative person representations.
A Dual Soft-Computing Based on Genetic Algorithm and Fuzzy Logic Defect Recognition for Gearbox and Motors: Attempts Toward Optimal Performance

Motor and gearbox are considered the main components in various machines related to its supplying power and transmitting motion role. Operating machines acquire vibration signal that ere continuously monitoring by sensors placing close to vibration source. This for processing and identify the mechine&#x2019; components stetus. Breekdown of the rotating machine causes significant losses and costs, so the analysis of its vibration signals proved literately avoiding these drawbacks with effective faults diagnosis. This paper proposing two models for gearbox and motor faults identification as an attempt towards finding the optimal performance: The first developed model is a fuzzy logic based model and the other is genetic algorithm based model. The intended output of both models reduce time and cost of maintenance. It also indirectly increases the mechine component&#x2019;s life. t ,ydditioneddyhe computational results showed that, in terms of execution time and accuracy effectiveness; and with Fuzzy logic meaningful and powerful representation for measurement of uncertainties; the fuzzy logic is reliable, however it presented lower classification accuracy (96% for gear box faults and 93% for motor faults) and lower generalization schema. Yet, the second proposed strategy which combines genetic algorithm and SVM recorded high performances in optimization and higher classification capabilities (97% for both gear box and motors faults). These factors illustrate the effectiveness and optimal performance of the genetic based model.
Distributed Clustering Method Based on Spatial Information

Radar pulse deinterleaving is the core part of electronic reconnaissance system, which is responsible for sorting out the pulses coming from different emitters. In the process of signal deinterleaving, parameter clustering is an important method. The features for clustering are mainly derived from the pulse parameters emitted by the radiation source transmitter. When the multiple target signals are seriously mixed in a complex environment, the accuracy of the clustering will decrease. In order to solve this problem, a distributed clustering method based on spatial information is proposed. First, a multi-node distributed system is built, one of the nodes is taken as the central node, and the pulse information transmitted by other nodes through the link is received. All pulses at the central node are jointly clustered according to the three parameters of radio frequency (RF), pulse width (PW) and time of arrival (TOA), forming an associated pulse description word (APDW). Then, the associated pulse is spatially marked based on its spatial information within the range of the target area of interest. Finally, the associated pulse is re-clustered using marked information and pulse parameters to achieve effective target separation. The simulation results show that the method can overcome the influence of parameter agility and make full use of the spatial information of the target to improve the accuracy of the pulses clustering.
Incorporating Medical Knowledge to Transformer-based Language Models for Medical Dialogue Generation

Medical dialogue systems have the potential to assist doctors in expanding access to medical care, improving the quality of patient experiences, and lowering medical expenses.The computational methods are still in their early stages and are not ready for widespread application despite their great potential.Existing transformer-based language models have shown promising results but lack domainspecific knowledge.However, to diagnose like doctors, an automatic medical diagnosis necessitates more stringent requirements for the rationality of the dialogue in the context of relevant knowledge.In this study, we propose a new method that addresses the challenges of medical dialogue generation by incorporating medical knowledge into transformer-based language models.We present a method that leverages an external medical knowledge graph and injects triples as domain knowledge into the utterances.Automatic and human evaluation on a publicly available dataset demonstrates that incorporating medical knowledge outperforms several state-of-the-art baseline methods.
ICSTASY: An Integrated Cybersecurity Training System for Military Personnel

Cyberwarfare can occur at any moment, anywhere on the planet, and it happens more often than we realize.The new form of warfare is wreaking havoc on not only the military but also on every aspect of our daily lives.Since cybersecurity has only recently established itself as a critical element of the military, the military community relies heavily on the private sector to ensure cyber mission assurance.Given the military's secrecy, such reliance may increase the danger of mission degradation or failure.To address this issue, the military has attempted to build a dedicated cybersecurity training system for the purpose of internalizing cybersecurity training.However, existing cybersecurity training systems frequently lack comprehensive support for effective and efficient cybersecurity training.In this study, we propose ICSTASY, a scenario-based, interactive, and immersive cybersecurity training platform that supports a variety of training features holistically.The primary requirements and design principles required to overcome the challenges inherent in developing a cyber training system were offered based on a review of prior work.Through the demonstration of our prototype, we have proven the feasibility of efficient and truly realistic cyber training, not only for the military environment but also for the private sector.