Reviewerly/exHarmony huggingface dataset:
A new method of Figurative Rhetoric Recognition based on Automated Essay Scoring of the Oversea Chinese Students’ Instructional Composition Corpus

With the rapid development of modern science and technology and the rapid increase of new scientific and technological knowledge and information, mankind has entered the so-called era of information explosion. In terms of human reading experience, information explosion leads to the change of reading mode, which has brought negative effects and potential crises to human society. The automated essay scoring has great educational and commercial values, and provides a cost-effective and consistent alternative to human marking. The figurative rhetoric is important to the ideological content and artistic quality of the essay. Based on large-scale language text data, various natural language processing technologies have made great progress in the fields of text classification, speech recognition and so on. This paper proposed the experimental methods such as CNN, RNN, Transformer, Fast Text and Bert base to recognize the figurative rhetoric based on the oversea Chinese students' composition corpus. We find that the Bert model has achieved an accuracy of 80.93% in the identification of figurative rhetoric sentences in oversea Chinese student compositions. The experimental methods in this paper are feasible, and can promote the improvement of the automated essay scoring technology.
Themis: Fair Memory Subsystem Resource Sharing with Differentiated QoS in Public Clouds

To reduce the increasing cost of building and operating cloud data centers, cloud providers are seeking various mechanisms to achieve higher resource effectiveness. For example, cloud operators are leveraging dynamic resource management techniques to consolidate a higher density of application workloads into commodity physical servers to maximize server resource utilization. However, higher workload density is a major source of performance interference problems in multi-tenant clouds. Existing performance isolation techniques such as dedicated CPU cores for specific workloads are not enough as there are still common resource (e.g., last-level cache and memory bandwidth in memory subsystem) on the processor that are shared among all CPUs on the same NUMA node. While prior work has proposed a variety of resource partitioning techniques, it still remains unexplored to characterize the impact of memory subsystem resource partitioning for the consolidated workloads with different priorities and investigate software support to dynamically manage memory subsystem resource sharing in a real-time manner. To bridge the gap, we propose Themis, a feedback-based controller that enables a priority-aware and fairness-aware memory subsystem resource management strategy to guarantee the performance of high-priority workloads while maintaining fairness across all colocated workloads in high-density clouds. Themis is evaluated with multiple typical cloud applications in our data center environment. The results show that Themis improves the performance of various workloads by up to 3.15%, and fairness by more than 70% in memory subsystem resource allocation compared to existing state-of-the-art work.
Getting Started with Vulnerability Disclosure and Bug Bounty Programs

The raise in online platforms and services that organizations around the world offer has grown significantly, ensuring the information security of these platforms is a task of paramount importance. There are challenges in achieving this mainly due to the shortage of skilled security professionals and the growing number of online services that companies offer. This article focuses on the benefits of using Crowdsourcing security programs, such as Vulnerability Disclosure Programs (VDP) and Bug Bounty programs to complement the existing security assessments performed by organizations. These programs provide a way for organizations to better interact with the security community, gain valuable insight into the public security posture, improve the brand image by providing a mechanism in which security researchers can notify the organizations of imminent security risks.
Staying Cyer-Safe

With potentially crippling cyber attacks on the rise The Engineer spoke to an expert panel about the steps businesses should be taking to stay safe
Improving Economic Research Impact in Low-income Countries: Key Challenges and the Way Forward

According to the World Bank, low-income countries have a per capita gross national income (GNI) of less than $1,026. There are currently 21 countries in the 2022 list. Scientific research is crucial in improving living standards and addressing socio-economic problems. Low-income countries already face the challenge of scarcity of funding research. Hence, they need to spend this limited budget towards maximizing the research outcomes and their impact on society and the economy. This research identified areas of improvement, including national research policies and impact area identification, local and international collaboration, resource management, and research publicity improvement and also proposed several actions at different levels to improve the impact of scientific research.
Elda Novitasari, Natalina Asi, Maria Arina Luardini

ABSTRACTThis research aim to describe the Content Appropriateness English Textbook ForTenth Grade Students of Senior High School by Private Publisher (Intan Pariwara) in theaspect content appropriateness. This research was descriptive qualitative with contentanalysis. The textbook was evaluated by using Krippendorf’s content analysis: unitizing,sampling, coding, reducing data, inferring contextual phenomena, and narrating the result.The instrument in the research was the researcher herself through reading the textbookrepeatedly and thoroughly. This is also done under the instrument issued by Board ofNational Education Standard. The result of the research shows as follow: The completeness ofmaterials in this textbook almost met completely according to KI and KD for tenth gradestudents of senior high school (90,9%). The materials in this textbook provided 10 materialsin the three kind of texts both of functional text, interpersonal text and transactional text but1 material left or not be fulfilled. The depth of materials was mostly grouped into depthcategory for the exposure in functional text (90%), inaccurate texts (18,7%), the materials inthe textbook was up to date, but none of the texts or activities in the textbooks supportvocational skills (0%).Keywords: Textbook, The content Appropriateness, Content Analysis
Q&amp;A Sustainable Solutions

Experts from a range of different product areas discuss how their technologies are helping different sectors of industry rise to the net zero challenge
Gradca: Generalizing to Unseen Domains Via Gradient Calibration

In domain generalization (DG) problem, we hope to train a robust model from multiple source domains and generalize it to unseen domains. However, the trained model can not perform well in the unseen domain since the domain-invariant representation is hard to learn across multiple source domains. In this work, we propose a novel gradient optimization strategy named gradient calibration (GradCa) which optimizes the dominant gradients and conflicting gradients without learning extra parameters. The dominant gradients are suppressed for addressing to be inclined to the optimization directions of special domains. Then two strategies (average and sign-mask) are designed for further alleviating the conflicting gradients. These types of gradients are effectively calibrated for improving the domain-invariant representation learning. Extension experiments on four benchmark datasets have shown the competitive results and the effectiveness of improving the model generalization with GradCa.
Research on the design and application of educational perception network based on Artificial Intelligence

This paper proposes to provide different solutions and Countermeasures for students' different online learning state needs with the help of artificial intelligence network learning system, and puts forward corresponding solutions for the practical application of current artificial intelligence network learning education from different demand angles.
A hybrid neuro-fuzzy technique to overcome clustering approach issues in big data

MapReduce is regarded as one of the major enabling methods in the big data community for handling the ever-increasing demands on computer resources imposed by enormous datasets. Currently, Google's MapReduce or its open-source equivalent Hadoop are well-known solutions for these types of applications. Several techniques are developed based on Hadoop such as apriori(M), HFUPM, apriori, EFUPM. The conventional algorithms of clustering schemes suffer from many challenging issues as clustering error, and computational complexity. To overcome the challenges of existing techniques we develop a novel approach to data pre-processing to extract the significant information. After pre-processing the data is processed applying neuro fuzzy-based scheduler to obtain semantic relationship between user queries. The fuzzy logic considers query time, query length, query expiry, total queries, CPU usages, and task activity. The proposed approach is implemented using a Hadoop platform. The comparative study shows a significant improvement in MapReduce performance for huge dataset.
Quantum statistics of heterodyne detection of squeezed light

Quantum statistics of heterodyne detection of squeezed light from a degenerate parametric oscillator are discussed for arbitrary field strengths and frequencies of the local oscillator.
Universal Multi-mode Linear Optical Quantum Operation in Time Domain

We demonstrate a universal multi-mode linear optical quantum operation in the time domain using a scalable and programmable dual-loop-based optical circuit. This work holds great promise for time-multiplexed large-scale optical quantum information processing.
No Tradeoff between Coherence and Sub-Poissonianity in Heisenberg-Limited Lasers

Let <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="eq1" display="inline"> <mml:semantics id="sm1"> <mml:mi>ℭ</mml:mi> </mml:semantics> </mml:math> be the number of photons in a coherence time of a laser beam. The Heisenberg limit is <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="eq2" display="inline"> <mml:semantics id="sm2"> <mml:mi>ℭ</mml:mi> </mml:semantics> </mml:math> = O ( µ 4 ), lying beyond the standard limit of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="eq3" display="inline"> <mml:semantics id="sm3"> <mml:mi>ℭ</mml:mi> </mml:semantics> </mml:math> = O ( µ 2 ). Here, we generalise this result for sub-Poissonian beams.
ВПЛИВ СОЦІАЛЬНОЇ КОМПЕТЕНТНОСТІ НА ЖИТТЄВИЙ УСПІХ ОСОБИСТОСТІ

The author emphasized the importance of social competence to ensure the life success of an individual. Scientific works of foreign authors devoted to this issue were analyzed. On the basis of their generalization, it was concluded that there is a certain connection between interpersonal relations, academic achievements, success at a person’s workplace and his social skills.
Diffractive optical imagers can see through random unknown diffusers

We provide a comprehensive design guide for robust and generalizable diffractive imagers to all-optically see through random unknown diffusers at the speed of light, without needing any digital computation or reconstruction.
EĞİTİM ÖRGÜTLERİNDE MİZAH

Humor, which is a product of sharp intelligence, is an entertaining, thought-provoking and questioning consciousness function, which is made spontaneously with human emotions. It enables us to look at the positive and negative situations in the society we live in with a realistic eye, laughing. Why do we laugh at what this looking function actually is? It also answers the question. Humor is used in many fields. One of these areas is education. It is a tool that enables students to learn with pleasure and willingness by creating a positive atmosphere in the learning environment. It ensures the existence of a positive school climate and culture in educational organizations. The way school administrators use humor also affects the behavior of teachers and other officials working at the school. In this research, humor and humor in educational organizations were discussed. In this context, the definition of humor, the historical development of humor, the place of humor in educational organizations, the benefits of humor and the harms of humor are discussed respectively.
Sub-optimal knowledge with positive attitude and belief of frontline health workers toward the use of information technologies: A mixed-method study from rural Haryana, India

BACKGROUND: Information technology (IT) can be used by frontline health workers (FLWs) to connect and deliver care to the community. Various studies in India have assessed the beneficial impact of IT usage by FLWs, but for the long-term sustainability, the attitude and belief toward IT usage have not been adequately studied. We conducted this study to assess the knowledge and attitude and to explore the beliefs of FLWs toward the use of IT in a rural area of Haryana.MATERIALS AND METHODS: We conducted a mixed-method study (qualitative and quantitative approach) in a rural setting of Haryana, India. We included FLWs of two primary health centers (PHCs). Data were collected from October 19, 2020 to December 31, 2020. We collected data from 75 accredited social health activists (ASHAs), 37 Anganwadi workers (AWWs), 28 multi-purpose workers (MPWs), two information assistants, and two medical officers using a semi-structured interview schedule. Their knowledge about the benefits of IT use, past use of IT applications and devices, and self-rating of knowledge regarding computers or laptops were collected. We used 14 attitude statements, each with a five-point Likert scale to assess the attitude; a total score ≥35 was considered a positive attitude. We conducted eight focus group discussions (FGDs) to explore the beliefs regarding IT usage (four FGDs with ASHAs, two FGDs with AWWs, and two FGDs with MPWs). A descriptive analysis was performed for the quantitative data, and a thematic analysis was performed for qualitative data.RESULTS: Knowledge about the benefits of IT use was present among 77.8% of FLWs. Among the FLWs, 79.2% self-rated their knowledge of computers/laptops as 'do not have knowledge', 16% self-rated as 'low knowledge', and 4.8% self-rated as 'good knowledge'. The median total score for attitude statements among all the FLWs (n = 144) was 54 [inter-quartile range – 48–59]. Four themes emerged for beliefs toward IT usage, namely, positive beliefs, negative beliefs, challenges anticipated in adopting IT use, and facilitation factors. The positive beliefs were related to improvement in work efficiency and social status, less paperwork, timely report generation, and better learning. The negative beliefs were related to an increase in working hours, close monitoring, and feeling over-burdened.CONCLUSION: We found that FLWs had knowledge regarding the benefits of IT use, but they lacked knowledge regarding laptop/computer use. They had a satisfactory level of confidence in using smartphones, and most of them were using mobile applications. The majority of the FLWs had a positive attitude and beliefs toward IT use and wanted to use it in the future.
OLPGP: An Optimized Label Propagation-Based Distributed Graph Partitioning Algorithm

One of the concepts that have attracted attention since entering the big data era is graph-structured data. Distributed systems for graph analysis are widely used to process large graphs. Graph partitioning is critical in parallel and distributed graph processing systems because it can balance the computational load and reduce communication load. An efficient graph partitioning algorithm can significantly improve the performance of large-scale graph data analysis and processing. In this paper, we propose a new Optimized Label Propagation-based distributed Graph Partitioning algorithm (OLPGP). OLPGP optimizes the label propagation algorithm and considers the differences between nodes. To improve computational efficiency, we implement OLPGP on the open-source distributed graph processing framework Spark GraphX. Conducted experiments on real-world networks indicate that OLPGP is scalable and achieves higher partition quality than the state-of-the-art label propagation-based graph partitioning algorithms.
Hybrid Solutions for Autosteering

THANKS TO GPS-GUIDED AUTOSTEERING SYSTEMS, OPERATORS CAN BENEFIT FROM REDUCED FATIGUE AND ENHANCED WORKING PRECISION
Students’ Responses through the Use of Edpuzzle as an Application in Achieving Listening Comprehension

This study aims to find out students’ responses through the use of Edpuzzleas an application in achieving listening comprehension. As a foreign language in Indonesia, sometimes students will face some obstacles and difficulties in learning the English language; the obstacles that must be faced by the first-year students, where else there are some skills to learn at the same time which will worsen their learning process as it will be hard to be understood with low competence in the first place. The use of Edpuzzle here is expected to help to get through the initial problem related to the achievement of listening comprehension. Edpuzzle is a platform that helps teachers invite students to have fun learning using videos. These videos can be edited and adjusted to the goal of the teachers in the class activity on a particular subject; they can be cut, recorded new voice, and add some more questions if it is needed. In this study, the participants are first-year students in English Literature of the University of Pamulang (UNPAM) with 54 students in the subject of listening. The data were collected by using a questionnaire and the discussion in the e-learning forum of UNPAM in listening subjects. The results of the study revealed that the students were interested in learning the English language of the subject listening using Edpuzzle as it lifts their consciousness so that they can overwhelmingly enjoy the process of learning. They also found out that the subject listening is becoming entertaining to learn and can be a fun learning too even with the task that is difficult for them, alongside their thought that learning English using Edpuzzle can help them improve their listening comprehension in a fun way possible.
In Defense of Core-set: A Density-aware Core-set Selection for Active Learning

Active learning enables the efficient construction of a labeled dataset by labeling informative samples from an unlabeled dataset. In a real-world active learning scenario, considering the diversity of the selected samples is crucial because many redundant or highly similar samples exist. Core-set approach is the promising diversity-based method selecting diverse samples based on the distance between samples. However, the approach poorly performs compared to the uncertainty-based approaches that select the most difficult samples where neural models reveal low confidence. In this work, we analyze the feature space through the lens of the density and, interestingly, observe that locally sparse regions tend to have more informative samples than dense regions. Motivated by our analysis, we empower the core-set approach with the density-awareness and propose a density-aware core-set (DACS). The strategy is to estimate the density of the unlabeled samples and select diverse samples mainly from sparse regions. To reduce the computational bottlenecks in estimating the density, we also introduce a new density approximation based on locality-sensitive hashing. Experimental results clearly demonstrate the efficacy of DACS in both classification and regression tasks and specifically show that DACS can produce state-of-the-art performance in a practical scenario. Since DACS is weakly dependent on neural architectures, we present a simple yet effective combination method to show that the existing methods can be beneficially combined with DACS.
Developing ICT Competency Training for Teachers of Marginalized Community Schools and Public Sector Schools of Himalayan Region

Information and Communication Technology (ICT) has the potential to improve education and bridge the digital divide in underserved communities. However, teachers in marginalized community schools and public sector schools of the Himalayan region often face challenges in integrating technology into their teaching and learning due to a lack of ICT competency and access to resources.
New Method for Determining Distances to Remote Objects

The procedure for calculating the distances to far removed objects using the analysis of a photograph is described, in which along with the image of the object its reflection in water is presenting. To carry out the analysis, information is required about the height of the camera above the water surface, the distance to objects in the foreground and the size of these objects. The operation of the method is demonstrated.
A Framework for Overparameterized Learning

A candidate explanation of the good empirical performance of deep neural networks is the implicit regularization effect of first order optimization methods. Inspired by this, we prove a convergence theorem for nonconvex composite optimization, and apply it to a general learning problem covering many machine learning applications, including supervised learning. We then present a deep multilayer perceptron model and prove that, when sufficiently wide, it $(i)$ leads to the convergence of gradient descent to a global optimum with a linear rate, $(ii)$ benefits from the implicit regularization effect of gradient descent, $(iii)$ is subject to novel bounds on the generalization error, $(iv)$ exhibits the lazy training phenomenon and $(v)$ enjoys learning rate transfer across different widths. The corresponding coefficients, such as the convergence rate, improve as width is further increased, and depend on the even order moments of the data generating distribution up to an order depending on the number of layers. The only non-mild assumption we make is the concentration of the smallest eigenvalue of the neural tangent kernel at initialization away from zero, which has been shown to hold for a number of less general models in contemporary works. We present empirical evidence supporting this assumption as well as our theoretical claims.
Argumentative Reward Learning: Reasoning About Human Preferences

We define a novel neuro-symbolic framework, argumentative reward learning, which combines preference-based argumentation with existing approaches to reinforcement learning from human feedback. Our method improves prior work by generalising human preferences, reducing the burden on the user and increasing the robustness of the reward model. We demonstrate this with a number of experiments.
An Automatic Evaluation of the WMT22 General Machine Translation Task

This report presents an automatic evaluation of the general machine translation task of the Seventh Conference on Machine Translation (WMT22). It evaluates a total of 185 systems for 21 translation directions including high-resource to low-resource language pairs and from closely related to distant languages. This large-scale automatic evaluation highlights some of the current limits of state-of-the-art machine translation systems. It also shows how automatic metrics, namely chrF, BLEU, and COMET, can complement themselves to mitigate their own limits in terms of interpretability and accuracy.