Reviewerly/exHarmony huggingface dataset:
Analysing Cyberbullying Using Natural Language Processing by Understanding Jargon in Social Media

Cyberbullying is of extreme prevalence today. Online-hate comments, toxicity, and cyberbullying amongst vulnerable groups is only growing over increased access to social platforms, especially post COVID-19. It is paramount to detect and ensure safety across social platforms so that any violence or hate-crime is automatically detected and strict action is taken against it. In our work, we explore binary classification by using a combination of datasets from various social media platforms that cover a wide range of cyberbullying such as sexism, racism, abusive, and hate-speech. We experiment through multiple models such as Bi-LSTM, GloVe, state-of-the-art models like BERT, and apply a unique preprocessing technique by introducing a slang-abusive corpus, achieving a higher precision in comparison to models without slang preprocessing.
Grounding semantic transparency in context

Abstract We present the results of a large-scale corpus-based comparison of two German event nominalization patterns: deverbal nouns in -ung (e.g., die Evaluierung , ‘the evaluation’) and nominal infinitives (e.g., das Evaluieren , ‘the evaluating’). Among the many available event nominalization patterns for German, we selected these two because they are both highly productive and challenging from the semantic point of view. Both patterns are known to keep a tight relation with the event denoted by the base verb, but with different nuances. Our study targets a better understanding of the differences in their semantic import. The key notion of our comparison is that of semantic transparency, and we propose a usage-based characterization of the relationship between derived nominals and their bases. Using methods from distributional semantics, we bring to bear two concrete measures of transparency which highlight different nuances: the first one, cosine , detects nominalizations which are semantically similar to their bases; the second one, distributional inclusion , detects nominalizations which are used in a subset of the contexts of the base verb. We find that only the inclusion measure helps in characterizing the difference between the two types of nominalizations, in relation with the traditionally considered variable of relative frequency (Hay, 2001). Finally, the distributional analysis allows us to frame our comparison in the broader coordinates of the inflection vs. derivation cline.
ASU-Net: U-shape adaptive scale network for mass segmentation in mammograms

U-Net is a commonly used deep learning model for mammogram segmentation. Despite outstanding overall performance in segmenting, U-Net still faces from two aspects of challenges: (1) the skip-connections in U-Net have limitations, which may not be able to effectively extract multi-scale features for breast masses with diverse shapes and sizes. (2) U-Net only merges low-level spatial information and high-level semantic information through concatenating, which neglects interdependencies between channels. To address these two problems, we propose the U-shape adaptive scale network (ASU-Net), which contains two modules: adaptive scale module (ASM) and feature refinement module (FRM). In each level of skip-connections, ASM is used to adaptively adjust the receptive fields according to the different scales of the mass, which makes the network adaptively capture multi-scale features. Besides, FRM is employed to allows the decoder to capture channel-wise dependencies, which make the network can selectively emphasize the feature representation of useful channels. Two commonly used mammogram databases including the DDSM-BCRP database and the INbreast database are used to evaluate the segmentation performance of ASU-Net. Finally, ASU-Net obtains the Dice Index (DI) of 91.41% and 93.55% in the DDSM-BCRP database and the INbreast database, respectively.
Multi-Grained Attention Representation With ALBERT for Aspect-Level Sentiment Classification

Aspect-level sentiment classification aims to solve the problem, which is to judge the sentiment tendency of each aspect in a sentence with multiple aspects. Previous works mainly employed Long Short-Term Memory (LSTM) and Attention mechanisms to fuse information between aspects and sentences, or to improve large language models such as BERT to adapt aspect-level sentiment classification tasks. The former methods either did not integrate the interactive information of related aspects and sentences, or ignored the feature extraction of sentences. This paper proposes a novel multi-grained attention representation with ALBERT (MGAR-ALBERT). It can learn the representation that contains the relevant information of the sentence and the aspect, while integrating it into the process of sentence modeling with multi granularity, and finally get a comprehensive sentence representation. In Masked LM (MLM) task, in order to avoid the influence of aspect words being masked in the initial stage of the pre-training, the noise linear cosine decay is introduced into n-gram. We implemented a series of comparative experiments to verify the effectiveness of the method. The experimental results show that our model can achieve excellent results on Restaurant dataset with numerous number of parameters reduced, and it is not inferior to other models on Laptop dataset.
If You Can’t Beat It, Squash It: Simplify Global Optimization by Evolving Dilation Functions

Optimization problems represent a class of pervasive and complex tasks in Computer Science, aimed at identifying the global optimum of a given objective function. Optimization problems are typically noisy, multi-modal, non-convex, non-separable, and often non-differentiable. Because of these features, they mandate the use of sophisticated population-based meta-heuristics to effectively explore the search space. Additionally, computational techniques based on the manipulation of the optimization landscape, such as Dilation Functions (DFs), can be effectively exploited to either "compress" or "dilate" some target regions of the search space, in order to improve the exploration and exploitation capabilities of any meta-heuristic. The main limitation of DFs is that they must be tailored on the specific optimization problem under investigation. In this work, we propose a solution to this issue, based on the idea of evolving the DFs. Specifically, we introduce a two-layered evolutionary framework, which combines Evolutionary Computation and Swarm Intelligence to solve the meta-problem of optimizing both the structure and the parameters of DFs. We evolved optimal DFs on a variety of benchmark problems, showing that this approach yields extremely simpler versions of the original optimization problems.
Model Libraries: Historical Outline of the Project

The article presents the history of interaction between the government authorities and the library and information sector in the project of development of municipal libraries. The project resulted in the “model libraries”. The project has been operating since the early 2000s. The purpose of the article is to reveal the implementation and analyse the support at the level of federal and regional government management of the program until 2018 (the start of work within the frames of the National project “Culture”). The author presents periodization of the project, as well as quantitative characteristics and approaches to the understanding the concept of “model library”. Model library is a public library of municipal level that has standard and optimal set of material and information resources. At the same time, there is no approved definition of this phenomenon. It is assumed that the model rural library in its functions, content and equipment meets international and domestic standards and serves as a model for other institutions. The “model” indicators are reflected in the “Model Standard for Public Library Services”. The project on creation of model libraries on the basis of municipal public libraries in rural areas of Russia started in 2002 with the emergence of the all-Russian project “Creation of model public libraries in rural areas”. Since 2012, funding under the federal target program “Culture of Russia (2012—2018)” is carried out in the form of subsidies allocated on a competitive basis. The project became a “catalyst” for modernization processes in rural libraries of Russian regions. The transformation program included the following stages: repair and equipment of the library premises; acquisition of books and electronic publications; conducting training seminars for staff. The rural model library targeted the goals of ensuring equal and free access to information, creation of a comfortable library space, etc. After the approval of the “Model Standard for Public Library Services” (2014), it was decided to test its feasibility by creating so-called “pilot libraries of a new type”. According to this document, model libraries are intellectual centres equipped with high speed Internet, access to modern domestic information resources, which should provide access points to the National Electronic Library. The regional leaders in creation of model libraries are the Republic of Chuvashia, the Belgorod Region, the Republic of Bashkortostan, the Kursk Region and the Republic of Mari El. As of January 1, 2019, there were 3,310 model libraries operating in the country. The creation of model municipal libraries has become one of the departmental projects of the National project “Culture”.
Routing, Core and Wavelength Allocation in Multi-Core-Fiber-Based Quantum-Key-Distribution-Enabled Optical Networks

Quantum-key-distribution (QKD) enabled optical networks can provide secure keys for different kinds of applications to ensure the high security of communication processes.As the number of wavelength channels in a single-core fiber is limited, space division multiplexing (SDM) has been considered to be a valuable technique to provide more wavelength resources, which solves the problem of insufficient channel capacity.Due to the rapid expansion of data volume, the SDM-based QKD network has been regarded as an important paradigm.At the same time, resource allocation in multi-core optical networks is a problem worthy of studying.In this work, four routing, core, and wavelength allocation (RCWA) algorithms are proposed in the multi-core optical network for quantum service requests and classical service requests, which are referred to as RCWA without perception (RCWA-WP), RCWA with crosstalk perception (RCWA-XTP), RCWA with spectrum perception (RCWA-SP), and RCWA with core perception (RCWA-CP).Simulations are carried out to evaluate the network performance in terms of blocking probability, key utilization, and average crosstalk intensity.The comparative results indicate that the RCWA-SP algorithm can improve network performance and reduce the blocking probability, while the RCWA-XTP algorithm can reduce inter-core crosstalk during transmission.
Autonomous Runtime Composition of Sensor-Based Skills Using Concurrent Task Planning

Constraint-based robot programming allows for implementing sensor-based skills that react to disturbances on the one hand, and composable skills that dynamically create and reconfigure complex robot behaviors on the other hand. In this letter, we address a class of problems where composing appropriate skills prior to execution is inconvenient due to unpredictable events of disturbances. To this end, we propose an autonomous replanning and acting framework that computes and executes reactive composed skills at runtime. We formulate a model of constraint-based skills and present methods for executing concurrent skill compositions at runtime without interrupting the ongoing task execution. In order to autonomously compute reactive composed skills at runtime, we propose a temporal description of planning actions, corresponding to robot skills, that ensures concurrency at the task planning level. We validate our work on a dual-arm robot system that performs an industrial assembly task while reacting to obstacle and human-induced disturbances at runtime.
Fast Video Saliency Detection via Maximally Stable Region Motion and Object Repeatability

Motion information is one important cue in unsupervised video salient object detection. In order to estimate motion in videos, most of the methods adopt time-consuming algorithms such as large displacement optical flow estimation (needs more than 8-40s with 640X480 size per frame), which leads to saliency detection with only 0.01-0.1 FPS speed and limits its application. In human visual system, the motion of one object is usually considered as a whole. Therefore, we need not compute the motion of each pixel. Instead, it is desirable to estimate the probability of each pixel belonging to a well identifiable object, which is proposed as maximally stable region (MSR) in recent work, and compute object-level motion. Motivated by this intuition, we firstly propose one fast object-level video motion model based on MSR, which only needs 49 ms for 640X480 size frame. Next, we present spatial-temporal boundary connectivity (BndCon) and spatial-temporal Minimum Barrier Distance (MBD) to estimate background probability and saliency. Then, we propose the repeatability saliency which means the frequency of the object recurs in all video sequences. Besides, we propose one simple yet effective method to combine our unsupervised method and deep learning model to further boost performance. Compared with the state-of-the-art unsupervised methods, our method shows significantly better performance with 12 FPS speed on normal CPU hardware.
Protecting Indirect Branches Against Fault Attacks Using ARM Pointer Authentication

Physically exposed embedded devices used in the IoT or automotive area are frequently targeted by fault attacks. Mitigating this threat is crucial as such attacks can be used to hijack the control-flow and bypass secure boot, gain arbitrary code execution, or retrieve sensitive information. However, control-flow integrity (CFI), which aims to be an effective countermeasure thwarting fault induced control-flow hijacking attacks, do not protect addresses, allowing an attacker to still hijack the control-flow of indirect branches. To counteract unwanted bit flips, data encoding schemes are frequently used to add redundancy to these addresses. However, as software-based data encoding schemes yield large runtime overheads, encoding schemes typically require custom CPU changes, which are not feasible for off-the-shelf systems. Hence, software-based address redundancy schemes for commodity devices are needed to thwart fault attacks on indirect branches. In this paper, we utilize the ARM pointer authentication feature of recent ARM architectures to efficiently protect the target addresses of indirect calls. In addition to the address protection, we further enhance the state update function of existing CFI schemes to protect the link between indirect control-flow transfers. To demonstrate how these defense mechanisms improve the protection of state-of-the-art CFI countermeasures, we integrate our address encoding and linking strategy into a previously introduced CFI scheme. We further extend a LLVM-based toolchain to automatically thwart fault attacks on indirect branches without user interaction. Our analysis shows an negligible overhead of less than 2.34% on average for protecting target addresses of indirect branches and the link between indirect branches for SPEC2017.
Big data classification with optimization driven MapReduce framework

With the technical advances, the amount of big data is increasing day-by-day such that the traditional software tools face burden in handling them. Additionally, the presence of the imbalance data in the big data is a huge concern to the research industry. In order to assure the effective management of big data and to deal with the imbalanced data, this paper proposes a new optimization algorithm. Here, the big data classification is performed using the MapReduce framework, wherein the map and reduce functions are based on the proposed optimization algorithm. The optimization algorithm is named as Exponential Bat algorithm (E-Bat), which is the integration of the Exponential Weighted Moving Average (EWMA) and Bat Algorithm (BA). The function of map function is to select the features that are presented to the classification in the reducer module using the Neural Network (NN). Thus, the classification of big data is performed using the proposed E-Bat algorithm-based MapReduce Framework and the experimentation is performed using four standard databases, such as Breast cancer, Hepatitis, Pima Indian diabetes dataset, and Heart disease dataset. From, the experimental results, it can be shown that the proposed method acquired a maximal accuracy of 0.8829 and True Positive Rate (TPR) of 0.9090, respectively.
Assessment of Heavy Metal Contamination in the Soils of the Gulf of Aqaba (Northwestern Saudi Arabia): Integration of Geochemical, Remote Sensing, GIS, and Statistical Data

Ghrefat, H.; Zaman, H.; Batayneh, A.; El Waheidi, M.M.; Qaysi, S.; Al-Taani, A.; Jallouli, C., and Badhris, O., 2021. Assessment of heavy metal contamination in the soils of the Gulf of Aqaba (Northwestern Saudi Arabia): Integration of geochemical, remote sensing, GIS, and statistical data. Journal of Coastal Research, 37(4), 864–872. Coconut Creek (Florida), ISSN 0749-0208.Rock and soil sample geochemical analysis was conducted to investigate the extent and causes of soil contamination in the Gulf of Aqaba region in NW Saudi Arabia. The inductively coupled plasma mass spectrometry was used to determine the concentrations of Pb, Zn, Cu, Co, Cr, Mn, Fe, Hg, Mo, and Cd in 23 soil samples and 25 samples from granitic and Cenozoic marine sedimentary formations. The geochemical results have been integrated with remote sensing, GIS, and statistical analysis to assess the severity of soil pollution in the area. The concentrations of heavy metals (ppm) in the collected soil samples were as follows: Fe (2259.70), Mn (101.85), Zn (20.15), Pb (10.74), Cr (8.67), Cu (6.10), Co (1.35), Mo (0.69), Hg (0.30), and Cd (0.17). A significant variation in the mean metal concentrations was observed for the rock samples. The correlation analysis results showed that different degrees of positive and negative relationships exist among different metals in the area. Two factors (PC1 and PC2) were identified using the principal component analysis (PCA) and were responsible for about 60% of the total variance in the data. The studied metals were separated and classified into two factors based on their geochemical features and source. In contrast, the hierarchical cluster analysis grouped the identified metals into different groups based on the similarity of their characteristics. The principal component (PC2) applied to the Sentinel-2A image classified the land cover in the area into three classes: vegetation, barren rocks, and urban area. The enrichment factor shows a relatively higher percentage of enriched Mo; however, the indices of geo-accumulation and potential ecological risk generally reveal no substantial metallic contamination in the study area. The main sources of soil contamination with metals are rock-weathering processes and various agricultural works that are widely practiced in the area.
Research on hierarchical clustering method based on partially-ordered Hasse graph

In order to solve the hierarchical classification problem in traditional evaluation, a hierarchical clustering method is constructed by applying posets to the case of unknown grading standards. Firstly, the traditional evaluation is converted into partial evaluation by partial order relation, and the classification is implemented according to partially-ordered Hasse graph. Then, the partially-ordered Hasse graph is embedded with the preference of evaluators. Next, the information within the layer expresses the result of clustering, and the information between the layers reflects the difference of grades. After that, the structural property of the partially-ordered Hasse graph is proved. Although the traditional evaluation can be upgraded to partial order evaluation theoretically, the transformation mode of the two is still in the process of improvement. This paper solves the problem that traditional clustering cannot be stratified, and provides a coarse particle method that can be used to implement grading for evaluation.
Taxonomy of Challenges in Cloud Security

The world of computing has progressed from centralized (client-server rather than web-based) to distributed systems, and we are now returning to virtual centralization (Cloud Computing). In the world of computation, the location of data and processes makes all the difference. On the one hand, a person has complete control over the data and processes in their machine. The internet is used as a networking tool in cloud computing. When it comes to data protection in cloud computing, the provider must have some assurance in service level agreements (SLAs) to reassure the customer about security concerns. Organizations that use cloud computing as a service platform are keen to look at security and confidentiality concerns for their mission-critical, non-sensitive applications. This paper addresses the fundamentals of cloud computing and security concerns, risks, and solutions. The paper also goes through several open research questions about cloud protection.
Targeted Attack of Deep Hashing Via Prototype-Supervised Adversarial Networks

Due to its powerful capability of representation learning and efficient computation, deep hashing has made significant progress in large-scale image retrieval. It has been recognized that deep neural networks are vulnerable to adversarial examples, which is a practical secure problem but seldom studied in deep hashing-based retrieval field. In this paper, we propose a novel prototype-supervised adversarial network (ProS-GAN), which formulates a flexible generative architecture for efficient and effective targeted hashing attack. <i>To the best of our knowledge</i>, this is one of the first generation-based methods to attack deep hashing networks. Generally, our proposed framework consists of three parts, <i>i.e.</i>, a PrototypeNet, a Generator and a Discriminator. Specifically, the designed PrototypeNet embeds the target label into the semantic representation and learns the prototype code as the category-level representative of the target label. Moreover, the semantic representation and the original image are jointly fed into the generator for flexible targeted attack. Particularly, the prototype code is adopted to supervise the generator to construct the targeted adversarial example by minimizing the Hamming distance between the hash code of the adversarial example and the prototype code. Furthermore, the generator fools the discriminator to simultaneously encourage the adversarial examples visually realistic and the semantic representation informative. Extensive experiments demonstrate that the proposed framework can efficiently produce adversarial examples with better targeted attack performance and transferability over state-of-the-art targeted attack methods of deep hashing. The source code is available at <uri>https://github.com/xunguangwang/ProS-GAN_Trans</uri>.
The burden of persuasion in structured argumentation

In this paper we provide an account of the burden of persuasion in the context of structured argumentation. A formal model for the burden of persuasion is defined, discussed, and used to capture the role of the burden of persuasion in adjudicating conflicts between conflicting arguments and in determining the dialectical status of arguments. We consider how our model can also capture adversarial burdens of proof, namely, those cases in which failure to establish an argument for a proposition burdened with persuasion entails establishing the complementary proposition.