Reviewerly/exHarmony huggingface dataset:
Formation of future teachers’ worldview culture by means of foreign-language education

Introduction. The formation of students’ worldview culture, which manifests itself in broad erudition, orientation at humanistic principles, aspiration for knowledge, realisation of essential potential along with spiritual perfection, is an important and personality developing purpose of foreign-language education at higher educational institutions. The aim of the article is to reveal and analyse the pedagogical conditions of forming the world-outlook culture of future teachers in the process of realisation of the methodological model for development of intrinsic intellectual culture of bachelor degree students majoring in pedagogical profile by means of foreign-language education. Materials and methods. The research methodology was based on the ontological, event-based, anthropological, synergetic, hermeneutic, humanistic, axiological, culturological, personality-developing approaches. The study involved 687 students of the following profiles: humanities, pedagogical-, social-, technical-, physical-, mathematical-, natural sciences, majoring in Bachelor-, Specialist- and Master Degree programmes at Petrozavodsk State University. Eighteen students – future teachers were the participants of the main experimental training. To handle the research objectives, a set of methods was used: questioning, polls, D.A. Leontiev’s test of meaning-life orientations, quantitative analysis of paper projects, Pearson correlation coefficient. Results. The comparative analysis of the diagnostic test results revealed an improvement in the experimental group students with respect to the indicator of integrity of knowledge (85.72%) manifested in the solidity of acquired knowledge, stability of ideas on integrity of the world, self-awareness as a subject of perception and the urge towards self-reflection based on self-comprehension. Moreover, the moral orientation rates increased by 14.29%, including motivational and value-based attitude to the teacher’s profession; the social activity of future teachers increased significantly (by 42.86%) – manifested through the translation of the internal worldview into one’s vital activity. The positive dynamics of change in the extent of correlation of the number of students by level at the initial (0.614) and final stages (0.474) was identified, which is accounted for by the increased number of students with high level and absence of students with low level, as a result of the experimental training. Conclusions. The formation of worldview culture of future teachers is achieved through actualisation of cognitive, gnoseological, regulatory, communicative, educational, creative, hermeneutic, humanistic and other orientations of foreign-language education at the university.
Continual Learning through Retrieval and Imagination

Continual learning is an intellectual ability of artificial agents to learn new streaming labels from sequential data. The main impediment to continual learning is catastrophic forgetting, a severe performance degradation on previously learned tasks. Although simply replaying all previous data or continuously adding the model parameters could alleviate the issue, it is impractical in real-world applications due to the limited available resources. Inspired by the mechanism of the human brain to deepen its past impression, we propose a novel framework, Deep Retrieval and Imagination (DRI), which consists of two components: 1) an embedding network that constructs a unified embedding space without adding model parameters on the arrival of new tasks; and 2) a generative model to produce additional (imaginary) data based on the limited memory. By retrieving the past experiences and corresponding imaginary data, DRI distills knowledge and rebalances the embedding space to further mitigate forgetting. Theoretical analysis demonstrates that DRI can reduce the loss approximation error and improve the robustness through retrieval and imagination, bringing better generalizability to the network. Extensive experiments show that DRI performs significantly better than the existing state-of-the-art continual learning methods and effectively alleviates catastrophic forgetting.
Multi-Relational Graph Representation Learning with Bayesian Gaussian Process Network

Learning effective representations of entities and relations for knowledge graphs (KGs) is critical to the success of many multi-relational learning tasks. Existing methods based on graph neural networks learn a deterministic embedding function, which lacks sufficient flexibility to explore better choices when dealing with the imperfect and noisy KGs such as the scarce labeled nodes and noisy graph structure. To this end, we propose a novel multi-relational graph Gaussian Process network (GGPN), which aims to improve the flexibility of deterministic methods by simultaneously learning a family of embedding functions, i.e., a stochastic embedding function. Specifically, a Bayesian Gaussian Process (GP) is proposed to model the distribution of this stochastic function and the resulting representations are obtained by aggregating stochastic function values, i.e., messages, from neighboring entities. The two problems incurred when leveraging GP in GGPN are the proper choice of kernel function and the cubic computational complexity. To address the first problem, we further propose a novel kernel function that can explicitly take the diverse relations between each pair of entities into account and be adaptively learned in a data-driven way. We address the second problem by reformulating GP as a Bayesian linear model, resulting in a linear computational complexity. With these two solutions, our GGPN can be efficiently trained in an end-to-end manner. We evaluate our GGPN in link prediction and entity classification tasks, and the experimental results demonstrate the superiority of our method. Our code is available at https://github.com/sysu-gzchen/GGPN.
Beyond Shared Subspace: A View-Specific Fusion for Multi-View Multi-Label Learning

In multi-view multi-label learning (MVML), each instance is described by several heterogeneous feature representations and associated with multiple valid labels simultaneously. Although diverse MVML methods have been proposed over the last decade, most previous studies focus on leveraging the shared subspace across different views to represent the multi-view consensus information, while it is still an open issue whether such shared subspace representation is necessary when formulating the desired MVML model. In this paper, we propose a DeepGCN based View-Specific MVML method (D-VSM) which can bypass seeking for the shared subspace representation, and instead directly encoding the feature representation of each individual view through the deep GCN to couple with the information derived from the other views. Specifically, we first construct all instances under different feature representations into the corresponding feature graphs respectively, and then integrate them into a unified graph by integrating the different feature representations of each instance. Afterwards, the graph attention mechanism is adopted to aggregate and update all nodes on the unified graph to form structural representation for each instance, where both intra-view correlations and inter-view alignments have been jointly encoded to discover the underlying semantic relations. Finally, we derive a label confidence score for each instance by averaging the label confidence of its different feature representations with the multi-label soft margin loss. Extensive experiments have demonstrated that our proposed method significantly outperforms state-of-the-art methods.
Transferring the Contamination Factor between Anomaly Detection Domains by Shape Similarity

Anomaly detection attempts to find examples in a dataset that do not conform to the expected behavior. Algorithms for this task assign an anomaly score to each example representing its degree of anomalousness. Setting a threshold on the anomaly scores enables converting these scores into a discrete prediction for each example. Setting an appropriate threshold is challenging in practice since anomaly detection is often treated as an unsupervised problem. A common approach is to set the threshold based on the dataset's contamination factor, i.e., the proportion of anomalous examples in the data. While the contamination factor may be known based on domain knowledge, it is often necessary to estimate it by labeling data. However, many anomaly detection problems involve monitoring multiple related, yet slightly different entities (e.g., a fleet of machines). Then, estimating the contamination factor for each dataset separately by labeling data would be extremely time-consuming. Therefore, this paper introduces a method for transferring the known contamination factor from one dataset (the source domain) to a related dataset where it is unknown (the target domain). Our approach does not require labeled target data and is based on modeling the shape of the distribution of the anomaly scores in both domains. We theoretically analyze how our method behaves when the (biased) target domain anomaly score distribution converges to its true one. Empirically, our method outperforms several baselines on real-world datasets.
Summarizing Legal Regulatory Documents using Transformers

Companies invest a substantial amount of time and resources in ensuring the compliance to the existing regulations or in the form of fines when compliance cannot be proven in auditing procedures. The topic is not only relevant, but also highly complex, given the frequency of changes and amendments, the complexity of the cases and the difficulty of the juristic language. This paper aims at applying advanced extractive summarization to democratize the understanding of regulations, so that non-jurists can decide which regulations deserve further follow-up. To achieve that, we first create a corpus named EUR-LexSum EUR-LexSum containing 4595 curated European regulatory documents and their corresponding summaries. We then fine-tune transformer-based models which, applied to this corpus, yield a superior performance (in terms of ROUGE metrics) compared to a traditional extractive summarization baseline. Our experiments reveal that even with limited amounts of data such transformer-based models are effective in the field of legal document summarization.
Comparative Analysis on Front-End Frameworks for Web Applications

Abstract: An essential step in developing any program or app is choosing the appropriate front-end framework or library. Frontend Web development sounds similar to the JavaScript framework. Both of these choices are available for web development requirements. Vue, React, and Angular all fall under the umbrella of JavaScript frameworks. Due to the extensive spectrum of issues developers encounter daily, the industry offers a wide diversity. With the help of many accessible frameworks, a web application may be constructed as intended while considering all practical considerations. The advantages and disadvantages of the fundamental elements and distinctive features of frameworks are discussed in this paper. Additionally, it offers a thorough analysis of the research on front-end frameworks. This study gives an overview of the front-end frameworks discovered in the literature, outlining the essential components of these frameworks using a systematic literature review as methodology. The three most popular frameworks, Vue.js, Angular, and React, were examined for the necessary features.
Learned Incremental Representations for Parsing

We present an incremental syntactic representation that consists of assigning a single discrete label to each word in a sentence, where the label is predicted using strictly incremental processing of a prefix of the sentence, and the sequence of labels for a sentence fully determines a parse tree.Our goal is to induce a syntactic representation that commits to syntactic choices only as they are incrementally revealed by the input, in contrast with standard representations that must make output choices such as attachments speculatively and later throw out conflicting analyses.Our learned representations achieve 93.72 F1 on the Penn Treebank with as few as 5 bits per word, and at 8 bits per word they achieve 94.97 F1, which is comparable with other state of the art parsing models when using the same pre-trained embeddings.We also provide an analysis of the representations learned by our system, investigating properties such as the interpretable syntactic features captured by the system and mechanisms for deferred resolution of syntactic ambiguities.
Compact Message Permutation for a Fully Pipelined BLAKE-256/512 Accelerator

Developing a low-cost and high-performance BLAKE accelerator has recently become an attractive research trend because the BLAKE algorithm is important in widespread applications, such as cryptocurrencies, data security, and digital signatures. Unfortunately, the existing BLAKE circuits are limited in performance and hardware efficiency. Therefore, this paper introduces the first fully pipelined BLAKE-256/512 accelerator to improve throughput and hardware efficiency. Moreover, based on the rates of changed words in consecutive message inputs, a compact message permutation scheme is proposed to reduce the area and energy consumption of the fully pipelined BLAKE-256/512 accelerator. To achieve these goals, the compact message permutation scheme includes two novel optimization techniques: register optimization, reducing the number of registers used by over 80% compared to conventional message permutation in a theoretical evaluation, and XOR optimization, decreasing the number of XOR gates by 93.8%. An ASIC-based experiment shows that the proposed compact message permutation scheme helps reduce the area and power consumption by up to 11.35% and 21.10%, respectively, for the fully pipelined BLAKE-256 accelerator and by up to 9.86% and 20.32%, respectively, for the fully pipelined BLAKE-512 accelerator. The correctness of the compact message permutation scheme is verified on a real hardware platform (an Alveo U280 FPGA).
Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbation

The robustness of Text-to-SQL parsers against adversarial perturbations plays a crucial role in delivering highly reliable applications.Previous studies along this line primarily focused on perturbations in the natural language question side, neglecting the variability of tables.Motivated by this, we propose the Adversarial Table Perturbation (ATP) as a new attacking paradigm to measure the robustness of Textto-SQL models.Following this proposition, we curate ADVETA, the first robustness evaluation benchmark featuring natural and realistic ATPs.All tested state-of-the-art models experience dramatic performance drops on ADVETA, revealing models' vulnerability in real-world practices.To defend against ATP, we build a systematic adversarial training example generation framework tailored for better contextualization of tabular data.Experiments show that our approach not only brings the best robustness improvement against tableside perturbations but also substantially empowers models against NL-side perturbations.We release our benchmark and code at: https://github.com/microsoft/ContextualSP.
Power Allocation and Energy Cost Minimization in Cloud Data Centers Microgrids: A Two-Stage Optimization Approach

Data centers are becoming an important asset in today's connected and intelligent world.Indeed, with the emergence of 5G networks, the growth of Internet of Things (IoT) and cloud computing, the data centers are increasingly becoming an essential component in delivering our daily services.On the other hand, cloud data centers, are outfitted with power-hungry servers and electrical installations that require enormous quantities of energy.In addition, operating cloud data centers in microgrids can be more reliable and sustainable than having them managed directly by the smart grid operator.However, microgrids have to deal with the high energy cost that their data centers may incur.In this context, managing the power allocated to the data centers and minimizing microgrids energy cost is a challenging problem.In this paper, we model a two-stage optimization approach in a microgrid-cloud architecture.In the first stage, we consider that the cloud data centers are run by multiple providers and try to get as much power as possible from the microgrid, to store this energy and to be able to execute the maximum of users applications.Such a power behavior requires an optimal power assignment from the microgrid to mitigate the risk of blackouts and power outages.Thus, the data center's huge power demand was modeled as a non-cooperative game.The microgrid controller calculates the optimal power that will be assigned to each data center according to its Power Usage Effectiveness (PUE), number of real-time applications and its network bandwidth usage.In the second stage, the microgrids try to minimize their energy cost by optimally purchasing power from the main grid and the other migrogrids, and by selling back their stored energy.We compare our approach with three existing power minimization approaches: (i) our previously presented scheme called "Basic Game Scheme" (BGS), (ii) traditional and (iii) price-based approaches.Simulations results show that our two stage optimization approach is up to 25% more effective in terms of energy cost.In addition, our scheme promotes MGs green energy usage which significantly reduces power load rate and CO2 emission with a percentage up to 52% comparing to "BGS" and price-based scheme.
A Simple and Fast Medical Image Encryption System Using Chaos-Based Shifting Techniques

Medical images such as brain MRI, ultrasound, and X-ray images are currently used by doctors to treat many serious diseases in most hospitals and clinics. These images play a major role in medical diagnosis. Diagnostic images with sensitive patient data are recorded and exchanged owing to the advancement of public networks. As a result, maintaining the privacy of sensitive patient data is becoming increasingly important. In medical imaging, the pixels are highly correlated. To reduce pixel correlation, a variety of complex encryption methods have been used in recent years, increasing the algorithm's computational complexity. This paper proposes an image encryption method based on chaos-based shifting operations. Security, convenience, and speed are all advantages of shifting activities. This method initially executes bit-plane flipping and permutation operations, then block-based flipping and diffusion operations to obtain the cipher images. The simulation and security analysis findings show that the proposed approach performs better.
Debiasing Pre-Trained Language Models via Efficient Fine-Tuning

An explosion in the popularity of transformerbased language models (such as GPT-3, BERT, RoBERTa, and ALBERT) has opened the doors to new machine learning applications involving language modeling, text generation, and more.However, recent scrutiny reveals that these language models contain inherent biases towards certain demographics reflected in their training data.While research has tried mitigating this problem, existing approaches either fail to remove the bias completely, degrade performance ("catastrophic forgetting"), or are costly to execute.This work examines how to reduce gender bias in a GPT-2 language model by fine-tuning less than 1% of its parameters.Through quantitative benchmarks, we show that this is a viable way to reduce prejudice in pre-trained language models while remaining cost-effective at scale.
The Modification of A* Pathfinding Algorithm for Building Mechanical, Electronic and Plumbing (MEP) Path

This paper introduces a modified A* pathfinding algorithm that can be used in building Mechanical Electronic Plumbing (MEP) path design by revising nodes selection process and post-processing. The pathfinding algorithm is used when a computer calculates the optimal path in a given space by algorithmizing how humans intuitively calculate the optimal path. As construction technology is gradually advancing, buildings with large and complex internal structures are increasing, so there is a need to automatically optimize existing design methods that rely on human intuition for a more efficient design. In the case of building MEP design, it is time and money consuming to design paths since they are complexly arranged throughout the building, and designs are frequently changed in response to the nature of the construction industry, where construction errors are frequent. Therefore, an MEP path design optimization module, MEPAutoroute, was developed by implementing a modified A* pathfinding algorithm to solve these problems. Algorithm was applied to seven different exemplary structures with MEP equipment, and the results are analyzed to determine its efficiency.
Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions

Integrating artificial intelligence (AI) and machine learning (ML) methods with interactive visualization is a research area that has evolved for years. With the rise of AI applications, the combination of AI/ML and interactive visualization is elevated to new levels of sophistication and has become more widespread in many domains. Such application drive has led to a growing trend to bridge the gap between AI/ML and visualizations. This chapter summarizes the current research trend and provides foresight to future research direction in integrating AI/ML and visualization. It investigates different areas of integrating the named disciplines, starting with visualization in ML, visual analytics, visual-enabled machine learning, natural language processing, and multidimensional visualization and AI to illustrate the research trend towards visual knowledge discovery. Each section of this chapter presents the current research state along with problem statements or future directions that allow a deeper investigation of seamless integration of novel AI methods in interactive visualizations.
Multi-Image Encryption Method via Computational Integral Imaging Algorithm

Under the framework of computational integral imaging, a multi-image encryption scheme based on the DNA-chaos algorithm is proposed. In this scheme, multiple images are merged to one image by a computational integral imaging algorithm, which significantly improves the efficiency of image encryption. Meanwhile, the computational integral imaging algorithm can merge images at different depth distances, thereby the different depth distances of multiple images can also be used as keys to increase the security of the encryption method. In addition, the high randomness of the chaos algorithm is combined to address the outline effect caused by the DNA encryption algorithm. We have experimentally verified the proposed multi-image encryption scheme. The entropy value of the encrypted image is 7.6227, whereas the entropy value of the merge image with two input images is 3.2886, which greatly reduces the relevance of the image. The simulation results also confirm that the proposed encryption scheme has high key security and can protect against various attacks.