Reviewerly/exHarmony huggingface dataset:
Mitigating Class-Boundary Label Uncertainty to Reduce Both Model Bias and Variance

The study of model bias and variance with respect to decision boundaries is critically important in supervised learning and artificial intelligence. There is generally a tradeoff between the two, as fine-tuning of the decision boundary of a classification model to accommodate more boundary training samples (i.e., higher model complexity) may improve training accuracy (i.e., lower bias) but hurt generalization against unseen data (i.e., higher variance). By focusing on just classification boundary fine-tuning and model complexity, it is difficult to reduce both bias and variance. To overcome this dilemma, we take a different perspective and investigate a new approach to handle inaccuracy and uncertainty in the training data labels, which are inevitable in many applications where labels are conceptual entities and labeling is performed by human annotators. The process of classification can be undermined by uncertainty in the labels of the training data; extending a boundary to accommodate an inaccurately labeled point will increase both bias and variance. Our novel method can reduce both bias and variance by estimating the pointwise label uncertainty of the training set and accordingly adjusting the training sample weights such that those samples with high uncertainty are weighted down and those with low uncertainty are weighted up. In this way, uncertain samples have a smaller contribution to the objective function of the model’s learning algorithm and exert less pull on the decision boundary. In a real-world physical activity recognition case study, the data present many labeling challenges, and we show that this new approach improves model performance and reduces model variance.
QUARC: Quaternion Multi-Modal Fusion Architecture For Hate Speech Classification

Hate speech, quite common in the age of social media, at times harmless but can also cause mental trauma to someone or even riots in communities. Image of a religious symbol with derogatory comment or video of a man abusing a particular community, all become hate speech with its every modality (such as text, image, and audio) contributing towards it. Models based on a particular modality of hate speech post on social media are not useful, rather, we need models like multimodal fusion models that consider both image and text while classifying hate speech. Text-image fusion models are heavily parameterized, hence we propose a quaternion neural network-based model having additional fusion components for each pair of modalities. The Model is tested on the MMHS150K twitter dataset for hate speech classification. The model shows an almost 75% reduction in parameters and also benefits us in terms of storage space and training time while being at par in terms of performance as compared to its real counterpart.
Extreme learning machine-based spherical harmonic for fast ionospheric delay modeling

Due to the growing real-time data of a global navigation satellite system (GNSS) applications and strict requirements for positioning users, especially under the condition of abnormal ionospheric activity, the fast and high-accuracy model is increasingly becoming important. In the past few years, the spherical harmonic (SH) model is one of the commonly-used methods, which has been widely used for global and regional modeling of ionospheric delay. In this paper, in order to further improve the accuracy of the SH model whilst without extra time consumption, we explored how the extreme learning machine (ELM) and the SH model can be combined for accurate and real-time modeling of ionospheric delay. Feasibility of our proposed method (denoted as ELM-SH model) was evaluated, where the available ionospheric data was firstly used to produce an SH model, while the ELM was deployed to compensate for the error of the SH model. Afterward, we tested the ELM-SH model based on the ionospheric data obtained from the Continuously Operating Reference Stations (CORS) in Jiangsu Province, China. Notably, experiment results demonstrate that compared with the conventional SH model, our proposed ELM-SH method can greatly improve the precision with over 37.09% on Root Mean Square Error (RMSE). The ELM-SH achieves competitive accuracy against the traditional backpropagation algorithm (BP) based method, but with a very short modeling time (within 10−2 s). This will be significant for real-time ionosphere modeling for those real-time GNSS positioning applications.
Trends in content development and licensing of electronic resources

Academic libraries face the dual challenges of decreasing budgets and increasing demand for electronic resources. Librarians at three midwestern US public research universities discuss the electronic resources environment, the direction of content development, and using future-proof as well as future-driven licensing language to best provide for the needs of today's library collections decisions. Trends are discussed in both content development areas and the negotiated license framework. This article offers pragmatic approaches to answer current questions for electronic resources librarians as they experience inflation increases and the greater Serials Crisis while still looking to build access to their digital investments.
ChangeSim: Towards End-to-End Online Scene Change Detection in Industrial Indoor Environments

We present a challenging dataset, ChangeSim, aimed at online scene change detection (SCD) and more. The data is collected in photo-realistic simulation environments with the presence of environmental non-targeted variations, such as air turbidity and light condition changes, as well as targeted object changes in industrial indoor environments. By collecting data in simulations, multi-modal sensor data and precise ground truth labels are obtainable such as the RGB image, depth image, semantic segmentation, change segmentation, camera poses, and 3D reconstructions. While the previous online SCD datasets evaluate models given well-aligned image pairs, ChangeSim also provides raw unpaired sequences that present an opportunity to develop an online SCD model in an end-to-end manner, considering both pairing and detection. Experiments show that even the latest pair-based SCD models suffer from the bottleneck of the pairing process, and it gets worse when the environment contains the non-targeted variations. Our dataset is available at https://sammica.github.io/ChangeSim/.
Digital Watermarking For Medical Images Using Dwt And Svd Technique

Abstract In the modern world with developing technologies, all the information are collected and stored digitally. In medical field also the diagnostic information are recorded digitally and transmitted. While transferring the information digitally, security and authenticity is the main aspect to be concentrated. Watermarking is the technology which ensures the authenticity and security of the medical images, information about patient and diagnostic details. In this paper, hybrid watermarking technique by the combination of Discrete Wavelet Transform and Singular Value Decomposition is implemented, because of its high security and efficiency as compared with other frequency domain watermarking. The images have been tested against attacks like salt and pepper noise, Gaussian noise, and filtering attack. Efficiency and performance of this hybrid technique are evaluated based on PSNR, SSIM and NC. From the simulation results, PSNR is above 37 dB, which ensures better imperceptibility and shows better robustness with NC values near to 1 against image attacks during transmission.
Quality and Web Software Engineering Advances

In this chapter, the main avant-garde components that favor quality on the web are disclosed, especially from the perspectives of software and design. At the same time, the deviations of these components that slow down these processes from the technical-human point of view are presented. In this dualistic perspective, the role of education is included in each of the generations of users, programmers, and publishers of digital content on the web, as well as the context in which they are immersed. A triadic vision of past, present and future is presented in each of the aspects and components, directly and indirectly related, with the development of operations, models, and methods, which converge in obtaining a high quality of the web. Finally, parallels are drawn between the formal science professions and infinite semiosis in web engineering.
Reusability affecting factors and software metrics for reusability: A systematic literature review

Abstract Measuring and estimating the reusability of software components is important towards finding reusable candidates. Researchers have shown that software metrics can be effectively used to assess software reusability. This work provides a systematic literature review to investigate the main factors that influence software reusability and how these identified factors can be quantified using software metrics. This paper also investigates tool availability of the identified software metrics. Based on the extensive study, we narrowed down 44 factors that could positively or negatively affect the reusability of software systems. In term of software metrics, we report our findings through five main families of metrics, namely coupling, cohesion, complexity, inheritance, and size. We found that most of the metrics examine reusability at the class‐level, and the availability of software tools is limited. Furthermore, not all reusability affecting factors are equally impactful to assess the reusability of software components. While existing studies often discussed the impact of complexity towards software reusability, we found that only a handful of complexity metrics were meant for assessing reusability. We have identified several open challenges and gaps in the area, in particular lack of quantifiable measurement for reusability, limited software tools, and limited metrics that directly measure reusability.
An Intrusion Resistant SCADA Framework Based on Quantum and Post-Quantum Scheme

The rapid emergence of quantum computing threatens current Supervisory Control and Data Acquisition (SCADA) security standards, mainly, American Gas Association (AGA)-12. Therefore, researchers are developing various security schemes based on either quantum or post-quantum algorithms. However, the efficiency of quantum algorithms impacts the security of the post-quantum digital signature scheme. We propose an intrusion resistant algorithm exploiting and applying quantum principles in the post-quantum signature algorithm. We use the Bennett 1992 (B92) protocol, a quantum key distribution scheme, to obtain the cipher, and the practical Stateless Hash-based Signatures (SPHINCS)-256 protocol to obtain a post-quantum signature. However, instead of Chacha-12, a well-known cryptographically secure pseudo-random number generator, we apply a quantum random number generator to obtain a truly random Hash to Obtain Random Subset (HORS) signature with Tree (HORST) secret key used in SPHINCS-256. We have implemented the design in Python with the Quantum Information Toolkit. We have validated the proposed algorithm using the Probabilistic Model Checking for Performance and Reliability Analysis (PRISM) and Scyther tools. Moreover, the National Institute of Standards and Technology (NIST) statistical tests show that the proposed algorithm key pairs have randomness of 98% and RSA and ECDSA are below 96%.
Deeper Weight Pruning Without Accuracy Loss in Deep Neural Networks: Signed-Digit Representation-Based Approach

In addition to the word-level weight pruning, which excludes the 0-value weights from the neural network inference computation, it is recently demonstrated that the bit-level weight pruning, which excludes the 0-bits in the weight value representation regardless of whether the weight values are zero or not, is very effective to further accelerate the neural network computation without accuracy loss. This work overcomes the inherent limitation of the bit-level weight pruning, that is, the maximal computation speedup is bounded by the total number of nonzero bits of the weights and the bound is invariably considered &#x201C;uncontrollable&#x201D; (i.e., constant) for the neural network to be pruned. Precisely, this work, based on the signed-digit encoding 1) proposes a transformation technique which converts the two&#x2019;s complement representation of every weight into a set of signed-digit representations of the minimal number of essential (i.e., nonzero) bits; 2) formulates the problem of selecting signed-digit representations of weights that maximize the parallelism of bit-level multiplication on the weights into a objective shortest path problem to achieve a maximal digit-index by digit-index (i.e., columnwise) compression for the weights and solves it efficiently using an approximation algorithm; 3) proposes a supporting novel acceleration architecture (<i>DWP</i>) with no additional inclusion of nontrivial hardware; and 4) proposes a variant of <i>DWP</i> to support bit-level parallel multiplication with the capability of predicting a tight worst-case latency of the parallel processing. Through experiments on several representative models using the ImageNet dataset, it is shown that our proposed approach is able to reduce the number of essential bits by 69&#x0025; on AlexNet, 74&#x0025; on VGG-16, and 68&#x0025; on ResNet-152, by which our accelerator is able to reduce the inference computation time by up to <inline-formula> <tex-math notation="LaTeX">$3.57\times $ </tex-math></inline-formula> over the conventional bit-level weight pruning.
Global-Aware Ranking Deep Metric Learning for Remote Sensing Image Retrieval

Remote sensing image retrieval (RSIR) is the process of searching and acquiring similar images to a query image in a large-scale remote sensing image database. Several recent studies encourage the discriminative feature space by developing loss functions under the deep metric learning paradigm. However, there are still some problems that have not been well resolved. The first is that for each category in the RSIR data set, the similarity among the samples within the category is different, so the samples and the number of samples that need to be learned should also be different. We propose intraclass space sample mining, which selects “misplaced” samples based on the distribution of samples, instead of artificially setting the boundary threshold of sample mining as usual. The second problem is the inconsistency between the loss reduction direction and the optimization direction. We propose the cost-sensitive loss (CSL) by penalizing the error samples in the candidate list. As a plug-in, CSL combined with other local losses can effectively improve retrieval accuracy. Finally, in view of the problem that the current loss is optimized based on local space and cannot distinguish different categories with high similarity, we propose a global-aware ranking loss (GRL) model. This is a global optimization model based on the feature space and retrieval candidate list. We have conducted comprehensive experiments on two public remote sensing data sets. Compared with the baseline, our proposed algorithm can achieve state-of-the-art performance.
On the Performance of Mean-Based Sort for Large Data Sets

Computer and communication systems and networks deal with many cases that require rearrangement of data either in descending or ascending order. This operation is called sorting, and the purpose of an efficient sorting algorithm is to reduce the computational complexity and time taken to perform the comparison, swapping, and assignment operations. In this article, we propose an efficient mean-based sorting algorithm that sorts integer/non-integer data by making approximately the same length independent quasi-sorted subarrays. It gradually finds sorted data and checks if the elements are partially sorted or have similar values. The elapsed time, the number of divisions and swaps, and the difference between the locations of the sorted and unsorted data in different samples demonstrate the superiority of the proposed algorithm to the Merge, Quick, Heap, and conventional mean-based sorts for both integer and non-integer large data sets which are random or partially/entirely sorted. Numerical analyses indicate that the mean-based pivot is appropriate for making subarrays with approximately similar lengths. Also, the complexity study shows that the proposed mean-based sorting algorithm offers a memory complexity same as the Quick-sort and a time complexity better than the Merge, Heap, and Quick sorts in the best-case. It is similar to the Merge and Heap sorts in view of the time complexity of the worst-case much better than the Quick-sort while these algorithms experience identical complexity in the average-case. In addition to finding part by part incremental (or decremental) sorted data before reaching the end, it can be implemented by parallel processing the sections running at the same time faster than the other conventional algorithms due to having independent subarrays with similar lengths.
The ReCAP Corpus: A Corpus of Complex Argument Graphs on German Education Politics

The automatic extraction of arguments from natural language texts is a highly researched area and more important than ever today, as it is nearly impossible to manually capture all arguments on a controversial topic in a reasonable amount of time. For testing different algorithms such as the retrieval of the best arguments, which are still in their infancy, gold standards must exist. An argument consists of a claim or standpoint that is supported or opposed by at least one premise. The generic term for a claim or premise is Argumentative Discourse Unit (ADU). The relationships between ADUs can be specified by argument schemes and can lead to large graphs. This paper presents a corpus of 100 argument graphs with about 2,500 ADUs in German, which is unique in its size and the utilisation of argument schemes. The corpus is built from natural language texts like party press releases and parliamentary motions on education policies in the German federal states. Each high-quality text is presented by an argument graph and created by the use of a modified version of the annotation tool OVA. The final argument graphs resulted by merging two previously independently annotated graphs based on detailed discussions.
Modeling of the ship steady turning motion based on multiblocks of fuzzy cognitive maps

Research on modeling ship motions is beneficial to promote safety in the shipping industry. However, it has always been a difficult point in the representation of this complex nonlinear system. The fuzzy cognitive map (FCM) can represent nonlinear systems effectively, and its divide-and-conquer strategy simplifies the modeling process. We propose a scheme of multiblocks of FCMs for modeling the steady turning motion of ships. First, the physical variables of the maneuvering mathematical modeling group (MMG) are employed as the conceptual nodes, and the causal links between concepts are analysed to establish the structure of FCMs. Subsequently, combined with normalized data, the loss function is optimized by least squares method, and the automatically partitioned of the training data and the parameters estimation of the FCMs in each data block are completed simultaneously provided that the designated required accuracy are satisfied. Finally, the multiblocks FCMs are integrated into an overall Ship-FCMs system after verification by the testing data. Experiments are conducted based on the simulation data of ‘Yulong’, which demonstrate the effectiveness of the proposed algorithm.
WULAI-QA: Web Understanding and Learning with AI towards Document-based Question Answering against COVID-19

With the outbreak of COVID-19, it is urgent and necessary to design a system that can access to information from COVID-19 related documents. Current methods fail to do so since the knowledge about COVID-19, an emerging disease, keeps changing and growing. In this study, we design a dynamic document-based question answering system, namely Web Understanding and Learning with AI (WULAI-QA). WULAI-QA employs feature engineering and online learning to adapt to the non-stationary environment and maintains good and steady performance. We evaluate WULAI-QA's performance on a public question answering (https://www.datafountain.cn/competitions/424) and rank first. We demonstrate that WULAI-QA can learn from user feedback and is easy to use. We believe that WULAI-QA will definitely help people understand COVID-19 and play an important role to fight against the pandemic.
On the Common and Different Expectations on Robot Service in Restaurant between Customers and Employees

Recently, the restaurant industry attempts to introduce service robot for efficient management. We conducted a survey research to investigate the expectations of restaurant robot service between customers and employees. It was found that customers and the employees share many common expectations depending on service elements that the restaurant industry usually adopts. Our results suggest that personalized service is important for the customer-oriented robot service in restaurant.
Neural vision-based semantic 3D world modeling

Scene geometry estimation and semantic segmentation using image/video data are two active machine learning/computer vision research topics. Given monocular or stereoscopic 3D images, depicted scene/object geometry in the form of depth maps can be successfully estimated, while modern Deep Neural Network (DNN) architectures can accurately predict semantic masks on an image. In several scenarios, both tasks are required at once, leading to a need for combined semantic 3D world mapping methods. In the wake of modern autonomous systems, DNNs that simultaneously handle both tasks have arisen, exploiting machine/deep learning to save up considerably on computational resources and enhance performance, as these tasks can mutually benefit from each other A great application area is 3D road scene modeling and semantic segmentation, e.g., for an autonomous car to identify and localize in 3D space visible pavement regions (marked as “road”) that are essential for autonomous car driving. Due to the significance of this field, this paper surveys the state-of-the-art DNN-based methods for scene geometry estimation, image semantic segmentation and joint inference of both.
Development of augmented reality teaching materials of chemical bonding

Chemistry is one of the fields of science that is still difficult for students to understand. This is due to the abstract and microscopic nature of chemical materials. One of the topics in chemistry is chemical bonding. Chemical bonding material that studies the structure of atoms and molecules, is difficult for students to understand because of its microscopic nature, which makes it difficult for students to imagine the form of chemical bonds. To overcome this, it takes the role of learning media that can provide an overview of the form of chemical bonds in students. Augmented Reality (AR) technology can combine three-dimensional virtual world objects into a real three-dimensional environment. So, Augmented Reality (AR) based learning media were developed for chemical bonding materials. This research is a development research, with stages starting from pre-production, production to distribution. Data collection technique uses the qualification of the percentage score feasibility test results. Based on the validity test, it states that the teaching materials of Augmented Reality (AR) Chemical Bonds in terms of material are declared very valid, do not need revision, in terms of media they are declared very valid, do not need revision and from the feasibility test students state that this teaching material is very valid, no need for revision.