Reviewerly/exHarmony huggingface dataset:
Entity Pair Recognition using Semantic Enrichment and Adversarial Training for Chinese Drug Knowledge Extraction

Existing knowledge extraction methods in pharmacy often use natural language processing tools and deep learning model to identify drug entities and extract their relationships from drug instructions, thus obtaining drug-drug or drug-disease knowledge. However, sentences in drug instructions may contain multiple drug-related entities, and existing methods lack the capability of identifying valid the "drug-drug" or "drug-disease" entity pairs. This will introduce significant noise data in the subsequent tasks such as entity relationship extraction and knowledge graph construction. Meanwhile, some mentions in the sentence can have hierarchical relations even if they do not form valid entity pairs, such information is also crucial to knowledge extraction. To solve these two problems, this paper proposes an entity pair verification model based on entity semantic enhancement and adversarial training. Through the experiment on more than 2000 kinds of drug instructions data, the experimental results show that the F1 value of the model for entity pair verification is up to 98.65%, which is up to 9.37% compared with the existing methods.
Semantic Similarity and Sentiment Analysis of Short Texts in Serbian

This paper presents an overview of the open access datasets in Serbian that have been manually annotated for the tasks of semantic textual similarity and short-text sentiment classification. In addition, it describes several kinds of statistical models that have been trained and evaluated on these datasets and discusses their results.
An approach for predicting multiple-type overflow vulnerabilities based on combination features and a time series neural network algorithm

Overflow vulnerability is a common and dangerous software vulnerability that can lead to information theft, resource control, system collapse and other hazards. However, recent studies on predicting software overflow vulnerability have failed to specifically analyze factors and features that can lead to each type of overflow vulnerability and have only focused on binary classification problems rather than multiclassification problems, which are inefficient and time-consuming. Therefore, this paper proposes a multiple-type overflow vulnerability prediction method based on a combination of features and a time series neural network algorithm. First, by analyzing software overflow vulnerability features, a method is proposed to extract the internal vulnerability features of program source code. Then, an IFS set of internal vulnerability features of software overflow vulnerability is constructed. Second, an EFS set of external vulnerability features of software overflow vulnerability is extracted using a source code static analysis tool. A software overflow vulnerability feature library is constructed based on the IFS set and the EFS set. Finally, a multiple-type overflow vulnerability prediction method is constructed based on a time series bidirectional recurrent neural network after the symbol transformation and vector transformation of software overflow vulnerability features. Experiments show that the proposed method offers a higher precision, accuracy, recall rate, and F1 value. Moreover, this method can accurately detect the overflow vulnerability in actual software vulnerability predictions.
Intelligent Event Finder and Management System

An event can be a social or life cycle event which includes, engagement, wedding or an education or career based events like workshops, seminars and so on. Events are everywhere, either conducted in physical mode or often in virtual mode. Finding a perfect event is always important as there will be multiple events scheduled within the scope of a single topic. This can be challenging at times as there are no perfect platforms which can give you the perfect selection for an event. If n number of events are available for the user there is no way to select the right event. So in this paper intelligent event finder management system helps people to find the proper event according to their convenience. There are multiple stages the event organizers needs to go on, such as registration, ticketing and so on. Doing all these processes manually takes a lot of time and effort and often times people will be tempted to cancel the entire event just because of these tedious tasks. And hence, the system will give all the event organizers a platform where they can create and their events, accept registrations, payments, on boarding and so on. Every process gets automated through the platform and the organizers can concentrate on the event agenda rather than the concentrating on all other time consuming tasks.
ICT-Based Enhancement of Employment Schemes: A Case Study in Rural Uttarakhand, India

70% of India’s population resides in rural areas, where unemployment is a major concern. MGNREGA scheme was launched by the Indian government to tackle this issue. Implementation of this initiative on a massive scale covering over 120 million people has been hampered by challenges and drawbacks. This study explores the propriety of technological innovations toward tiding over the deficiencies and expansion of the program. This investigation entails a mixed method approach in a village of Uttarakhand, in North India, exploiting ethnography and Human Centered Design triangulated with statistical data. This deep and grounded understanding enables to propose an ICT-based solution design in the form of a work tracking system using GPS and a specific architecture to address the pain point in the implementation of MGNREGA in remote rural areas.
CJE-TIG: Zero-shot cross-lingual text-to-image generation by Corpora-based Joint Encoding

Recently, text-to-Image (T2I) generation has been well developed by improving synthesis authenticity, text-consistency and generation diversity. However, large amount of pairwise image–text data required restricts generalization of synthesis models only to its pre-trained language. In this paper, a cross-lingual pre-training method is proposed to adapt target low-resource language to pre-trained generative models. As far as we known, this is the first time that arbitrary input languages could access T2I generation. This joint encoding scheme fulfills both universal and visual semantic alignment. With any prepared GAN-based T2I framework, pre-trained source encoder model could be easily fine-tuned to construct target encoder model and hence entirely enable transfer of T2I synthesis ability between languages. After that, a semantic-level alignment independent of source T2I structure is established to guarantee optimal text consistency and detail generation. Different from monolingual T2I methods that apply discriminator to enhance generation quality, we use an adversarial training scheme that optimizes the sentence-level alignment along with the word-level alignment with a self-attention mechanism. Considering of training for low-resource languages lack of parallel texts in practice, target input embedding is designed available for zero-shot learning. Experimental results prove robustness of the proposed cross-lingual T2I pre-training on multiple downstream generative models and target languages applied.
Dual-det : a fast detector for oriented object detection in aerial images

Fast and accurate object detection in aerial images remains a challenging task. Usually, to better describe an object, oriented bounding boxes (OBBs) can better fit objects. Due to high background complexity and large object scale variation, single-angle anchor-based two-stage detectors are widely adopted, which offer better accuracy. However, the single-angle prediction has a small error tolerance for objects with a large aspect ratio, and the hyperparameters of the anchor-based network are difficult to adjust, and the number of hyperparameters is extremely large. Furthermore, the two-stage detection inference speed is slow, and it is difficult to achieve real-time detection. In this paper, we propose Dual-Det, a keypoint-based oriented object detector. We firstly propose a dual-angle with a short-side and ratio regression strategy (DASR), which uses the object centre and the length and angles of two diagonals to represent an object. A short side guided (SSG) loss is further added to guide the direction of the diagonal regression box. To improve the detection performance for dense and tiny objects, a lightweight supervised pixel attention learner is finally proposed. The experiment results show that Dual-Det achieves 90.23% mAP at 46FPS on HRSC2016, 90.83% mAP at 46FPS on UCAS-AOD and 72.00% mAP at 0.018 s per image in the inference phase on DOTA. The code will be open source on https://github.com/gqy4166000/ijrs_dasr.
Masked Face Recognition: Progress, Dataset, and Dataset Generation

Face recognition is one of the oldest and famous research in computer vision and has improved over the years. However, most research of this topic are designed to recognize full human face. Ever since COVID-19 pandemic, most people must wear face mask when going outside. This create new challenges for existing face recognition as it is proven to be ineffective recognizing people wearing mask. This paper focuses on discussing previous work of Masked Face Recognition, masked face recognition dataset that can be used for training, and dataset generation tools to generate new masked face dataset using existing face recognition dataset. Analysis suggests that cropping based approach is popular among researchers to solve masked face recognition combined with other method like triplet loss implemented in ResNet-50.
A Survey and Future Studies of Virtual Machine Placement Approaches in Cloud Computing Environment

Making effective usage of resources is the major purpose of Clouds Computing (CC). The progression on the cloud paves the way aimed at enormous research. Higher computational facilities are possessed by the Cloud Services Providers (CSP) that is named Data Centers (DC) at their disposal. Services are provided by CSPs to users via Virtual Machines (VM). The virtualization of computational resources permits the system to employ the resources effectively. For the effectual utilization of computing resources, a VM's placement upon the Physical Machines (PM) is a hard issue in virtualization. Better VM consolidation ratios will be produced by an effectual Virtual Machines Placements (VMP) solution that assures superior resource utilization. Therefore, the energy savings are more. An entire survey and the existent VMP scheme's analysis proposed on the literature aimed at CC and DC are offered here. The sort of VMP algorithms is also examined (such as energy-aware, resource-aware, et cetera) together with their capabilities, along with objectives are assessed. Furthermore, it also renders the VMP scheme's properties, advantages, together with its disadvantages. The concluding comments and future research guidelines are finally given.
Near Candidate-Less Apriori With Tidlists and Other Apriori Implementations

In this study we implemented four different versions of Apriori, namely, basic and basic multi-threaded, bloom filter, trie, and count-min sketch, and proposed a new algorithm – NCLAT (Near Candidate-Less Apriori with Tidlists). We compared the runtimes and max memory usages of our implementations among each other as well as with the runtime of Borgelt’s Apriori implementation in some of the cases. NCLAT implementation is more efficient than the other Apriori implementations that we know of in terms of the number of times the database is scanned, and the number of candidates generated. Unlike the original Apriori algorithm which scans the database for every level and creates all of the candidates in advance for each level, NCLAT scans the database only once and creates candidate itemsets only for level one but not afterwards. Thus the number of candidates created is equal to the number of unique items in the database.
Evolution of conditional-GANs for the synthesis of chest x-ray images

Deep learning (DL) is now widely used to perform tasks involving the analysis of biomedical imaging. However, the small amounts available of annotated examples of these types of images make it difficult to use DL-based systems, since large amounts of data are required for adequate generalization and performance. For this reason, in recent years, Generative Adversarial Networks (GANs) have been used to obtain synthetic images that artificially increase the amount available. Despite this, the usual training instability in GANs, in addition to their empirical design, does not always allow for high-quality results. Through the neuroevolution of GANs it has been possible to reduce these problems, but many of these works use benchmark datasets with thousands of images, a scenario that does not reflect the real conditions of cases in which it is necessary to increase the data due to the limited amount available. In this work, cDCGAN-PSO is presented, an algorithm for the neuroevolution of GANs that adapts the concepts of the DCGAN-PSO to a conditional-DCGAN that allows the synthesis of three classes of chest X-ray images and that is trained with only 600 images of each class. The synthetic images obtained from evolved GANs show good similarity with real chest X-ray images.
Treating adverse effects of blockbuster bias on beyond-accuracy quality of personalized recommendations

Collaborative filtering recommendation algorithms are vulnerable against the popularity bias, including the most popular items repeatedly into the produced ranked lists. However, the research on popularity bias focuses solely on the number of times items are rated rather than the magnitude of the provided ratings when scrutinizing the adverse effects of such bias. This paper introduces a metric describing the blockbuster items that are popular and highly rated simultaneously and investigates the potential biases of collaborative recommendation algorithms towards such items comprehensively. Then, we develop an algorithmic post-processing debiasing approach for potential blockbuster bias in recommendations. Specifically, this method aims to penalize blockbuster items in produced ranked lists by re-sorting items based on the artificial ranking scores, estimated by considering both the blockbuster degree of the items and the generated predictions for them simultaneously. The experiments conducted on three benchmark real-world datasets demonstrate that four prominent collaborative filtering algorithms lead to an undesirable bias in their recommendations towards blockbuster items. The empirical outcomes also indicate that our mitigation method helps treat the adverse effects of the blockbuster bias in terms of beyond-accuracy evaluations such as catalog coverage, diversity, and novelty, with negligible losses in ranking accuracy.
Public Auditing To Provide Privacy Preservation Of Cloud Data Using Ring Signatures

According to the present situation, an effective public auditing protocol that improves the restriction of the previous auditing system needs to be designed. Here a system is proposed to develop and evaluate the precision of the cloud data by a third-party auditor (TPA), repeatedly or on a request without recovering the whole data or without start the extra online load for cloud users and servers. It satisfies that throughout the auditing process no data content will escape from PA. The proper storage of data, confidentiality, and integrity of saved data is continued. This system is made up of three structures: user, cloud provider, and TPA. The user splits the file into blocks and encrypts the AES algorithms, creates the MD-5 hash value for each, connects the hash, and produces an AES signature on the file. The cloud server is utilized to supply the encrypted file block. When the user or data owner requests for public TPA auditing, it directly demands encrypted files. It uses the same MD-5 algorithm which is referred by the user. Next, it connects the hash value and produces an AES signature for that file. In the validation process, the signature produced by TPA and responsible for the data user is related to the TPA. If they are related to one another, it indicates that the data is perfect, and the data is not stolen by attackers or outsiders. If this does not relate, then it refers that the integrity of the data was affected or stolen. The data integrity verifies output is sponsored to the user.
Latent Attention Augmentation for Robust Autonomous Driving Policies

Model-free reinforcement learning has become a viable approach for vision-based robot control. However, sample complexity and adaptability to domain shifts remain persistent challenges when operating in high-dimensional observation spaces (images, LiDAR), such as those that are involved in autonomous driving. In this paper, we propose a flexible framework by which a policy’s observations are augmented with robust attention representations in the latent space to guide the agent’s attention during training. Our method encodes local and global descriptors of the augmented state representations into a compact latent vector, and scene dynamics are approximated by a recurrent network that processes the latent vectors in sequence. We outline two approaches for constructing attention maps; a supervised pipeline leveraging semantic segmentation networks, and an unsupervised pipeline relying only on classical image processing techniques. We conduct our experiments in simulation and test the learned policy against varying seasonal effects and weather conditions. Our design decisions are supported in a series of ablation studies. The results demonstrate that our state augmentation method both improves learning efficiency and encourages robust domain adaptation when compared to common end-to-end frameworks and methods that learn directly from intermediate representations.
Research on High Robust Multimedia Image Encryption Based on Gyrator Transform Domain Model

The encryption and privacy protection of multimedia image resources are of great value in the information age. The utilization of the gyrator transform domain model in multimedia image encryption can select parameters more accurately, so it has a wider scope of utilization and further ameliorates the stability of the whole system. On account of this, this paper first analyzes the concept and connotation of gyrator transform, then studies the image encryption algorithm on account of gyrator transform, and verifies the robustness of the gyrator transform algorithm under the influence of noise interference, shear attack, and other factors through the high robust multimedia image encryption and result analysis of gyrator transform.
Simplified isogeny formulas on twisted Jacobi quartic curves

Isogenies between elliptic curves play a very important role in elliptic curve related cryptosystems and cryptanalysis. It is widely known that different models of elliptic curves would induce different computational costs of elliptic curve arithmetic, and several works have been devoted to accelerate the isogeny computation on various curve models. This paper studies the case of the Jacobi quartic model, which is a classic form of elliptic curves. A new w-coordinate system on extended Jacobi quartic curves is introduced for Montgomery-like group arithmetic. Explicit formulas for 2-isogenies and odd ℓ-isogenies on the specific curves are presented, and based on the w-coordinate system, the computation of such isogenies could be further simplified.
Usable Security and Aesthetics: Designing for engaging online security warnings and cautions to optimise user security whilst affording ease of use

As of January 2021 there are 4.66 billion active internet users worldwide [39]. Moreover, during the COVID-19 lock downs, people's online traffic increased significantly, to the extent that many are still doing their shopping, banking, learning, working, exercise, entertainment and even socialising online. In this increasingly digital existence, the security issue cannot be ignored especially internet security. As more of users daily tasks are performed online, people's mobile phone, laptop, and tablet interfaces have become extensions of themselves and it is crucial that these afford and support a user in remaining secure. To ensure this, the research proposes that the everyday user needs to be able to engage and use the security driven mechanisms on these devices and applications (i.e. warnings and cautions). However, in reality many users often ignore and discard online warning and caution signs. This paper will introduce plans for some future research that will explore aesthetics for the design of a new body of online security warning and cautions. Like in the physical world, the aim is to ensure that people are equipped with effective warning and caution signs to protect them as they go about their day to day online tasks. Making future design suggestions for not just usable and secure but also engaging online warnings, the research proposes to open up an entirely new avenue of study around usable security aesthetics.
Monocular spherical depth estimation with explicitly connected weak layout cues

Spherical cameras capture scenes in a holistic manner and have been used for room layout estimation. Recently, with the availability of appropriate datasets, there has also been progress in depth estimation from a single omnidirectional image. While these two tasks are complementary, few works have been able to explore them in parallel to advance indoor geometric perception, and those that have done so either relied on synthetic data, or used small scale datasets, as few options are available that include both layout annotations and dense depth maps in real scenes. This is partly due to the necessity of manual annotations for room layouts. In this work, we move beyond this limitation and generate a 360° geometric vision (360V) dataset that includes multiple modalities, multi-view stereo data and automatically generated weak layout cues. We also explore an explicit coupling between the two tasks to integrate them into a single-shot trained model. We rely on depth-based layout reconstruction and layout-based depth attention, demonstrating increased performance across both tasks. By using single 360° cameras to scan rooms, the opportunity for facile and quick building-scale 3D scanning arises. The project page is available at https://vcl3d.github.io/ExplicitLayoutDepth/.
Architecture of a Knowledge Base in Smart Crop Production

The article presents an architecture of a knowledge base in the field of intelligent plant agriculture. The proposed architecture includes three layers – an ontology layer to represent common knowledge in the domain, a database layer for dynamic data from various measurements of evaluation characteristics and plant influencing factors, and an application layer consisting of intelligent components for connection between the two layers of knowledge.
Pathways to Data: From Plans to Datasets

What is the relationship between Data Management Plans (DMPs), DMP guidance documents, and the reality of end-of-project data preservation and access? In this short paper we report on some preliminary findings of a 3-year investigation into the impact of DMPs on federally funded science in the United States. We investigated a small sample of publicly accessible DMPs (N=14) published using DMPTool. We found that while DMPs followed the National Science Foundation's guidelines, the pathways to the resulting research data are often obscure, vague, or not obvious. We define two “data pathways” as the search tactics and strategies deployed in order to find datasets.