Reviewerly/exHarmony huggingface dataset:
Towards explainable prediction of essay cohesion in Portuguese and English

Textual cohesion is an essential aspect of a formally written text, related to linguistic mechanisms that connect elements such as words, sentences, and paragraphs. Several studies have proposed approaches to estimate textual cohesion in essays automatically. There is limited research that aims to study the extent to which the use of machine learning approaches can predict the textual cohesion of essays written in different languages (not just English). This paper reports on the findings of a study that aimed to propose and evaluate approaches that automatically estimate the cohesion of essays in Portuguese and English. The study proposed regression-based models grounded in conventional feature-based machine learning methods and deep learning-based pre-trained language models. The study also examined the explainability of automated approaches to scrutinize their predictions. We analyzed two datasets composed of 4,570 (Portuguese) and 7,101 (English) essays. The results demonstrate that a deep learning-based model achieved the best performance on both datasets with a moderate Pearson correlation with human-rated cohesion scores. However, the explainability of the automatic cohesion estimations based on conventional machine learning models offered a stronger potential than that of the deep learning model.
Working Students: Educational Success and Subjective Well-being

Recently, there has been an increase in the number of students who combine study with work. This requires special attention of universities to such students and updates the study of educational success factors. The study is relevant as considers working students as subjects of learning and life, from the standpoint of personal and professional development experience acquired at the university, involvement in the educational process, satisfaction with learning, and well-being. The purpose of the empirical study is to identify the features of educational experience, subjective well-being and self-change potential among working students. The study involves 290 students of bachelor and specialist programs from five universities. The sample includes students who work for more than half a year (N=95), work from time to time (N=90) and study only (N=105). The results showed that the educational experience is most formed among those students who combine their studies with work in the specialty which is being received at the university. Long-term and occasionally working students differ from non-working students by a higher desire to participate in developmental activities, confidence in their ability to successfully solve the problems of learning and social interaction, self-directed learning skills, tendency to analyze and self-control their learning. Also, working students are superior to those who are just studying in the self-change potential and the subjective well-being level. It is concluded that the success and well-being of working students are largely determined by their resources of subjectness and aspiration for development. This allows them to function successfully, combining educational and labor activities as two areas of experience accumulation. Directions for improving university practice in terms of creating conditions for the educational success of working students are proposed.
QoS-aware energy-efficient workload routing and server speed control policy in data centers: A robust queueing theoretic approach

Operating cloud service infrastructures requires high energy efficiency while ensuring a satisfactory service level. Motivated by data centers, we consider a workload routing and server speed control policy applicable to the system operating under fluctuating demands. Dynamic control algorithms are generally more energy-efficient than static ones. However, they often require frequent information exchanges between routers and servers, making the data centers' management hesitate to deploy these algorithms. This study presents a static routing and server speed control policy that could achieve energy efficiency similar to a dynamic algorithm and eliminate the necessity of frequent communication among resources. We take a robust queueing theoretic approach to response time constraints for the quality of service (QoS) conditions. Each server is modeled as a G/G/1 processor sharing queue, and the concept of uncertainty sets defines the domain of stochastic primitives. We derive an approximative upper bound of sojourn times from uncertainty sets and develop an approximative sojourn time quantile estimation method for QoS. Numerical experiments confirm the proposed static policy offers competitive solutions compared with the dynamic algorithm.
Data embodiment: approaching the body as a choreographic medium for performing abstract data

The motivation to make information more accessible in data communication – physically to the senses and culturally to different audiences – has led designers to experiment, for example, in data physicalization and data sonification. Experimentation overlapping data communication and the performance arts suggests another practice is emerging. Calling it data embodiment, I define the term as a communicative and artistic method that approaches the body as a choreographic medium to physicalize and perform abstract data. The intent of this paper is to sufficiently delineate the space where bodily movement and abstract data have begun to intermingle across several milieu under the concept. The paper begins by contextualizing data embodiment in design, then continues by observing how choreographers have previously approached dance as data, and finishes with a case study where an environmental migration dataset is choreographed. A brief section on human-centered datasets and the surveillant technologies that produce them peppers the overarching discussion on bodily movement and abstract data. By reconstituting data in time and space, especially in the context of performance, otherwise sterile datasets are shown to become more physically and culturally accessible as well as aesthetically expressive.
Research on map emotional semantics using deep learning approach

ABSTRACTABSTRACTThe main purpose of the research on map emotional semantics is to describe and express the emotional responses caused by people observing images through computer technology. Nowadays, map application scenarios tend to be diversified, and the increasing demand for emotional information of map users bring new challenges for cartography. However, the lack of evaluation of emotions in the traditional map drawing process makes it difficult for the resulting maps to reach emotional resonance with map users. The core of solving this problem is to quantify the emotional semantics of maps, it can help mapmakers to better understand map emotions and improve user satisfaction. This paper aims to perform the quantification of map emotional semantics by applying transfer learning methods and the efficient computational power of convolutional neural networks (CNN) to establish the correspondence between visual features and emotions. The main contributions of this paper are as follows: (1) a Map Sentiment Dataset containing five discrete emotion categories; (2) three different CNNs (VGG16, VGG19, and InceptionV3) are applied for map sentiment classification task and evaluated by accuracy performance; (3) six different parameter combinations to conduct experiments that would determine the best combination of learning rate and batch size; and (4) the analysis of visual variables that affect the sentiment of a map according to the chart and visualization results. The experimental results reveal that the proposed method has good accuracy performance (around 88%) and that the emotional semantics of maps have some general rules.Key policy highlights A Map Sentiment Dataset with five discrete emotions is constructedMap emotional semantics are classified by deep learning approachesVisual variables Influencing map sentiment are analyzed.KEYWORDS: Image semanticsconvolutional neural networkscartographyfeature extractionimage classification AcknowledgementsThe authors would like to thank every volunteer who participated in the experiment. We are grateful to the anonymous reviewers and the editors of the journal for their important contributions in improving the article for publication.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are openly available at https://doi.org/10.6084/m9.figshare.19766017.v2URI: https://figshare.com/articles/online_resource/MapSentimentData/19766017Supplementary dataSupplemental data for this article can be accessed online at https://doi.org/10.1080/15230406.2023.2172081.Additional informationFundingThis research was financially supported by the National Natural Science Foundation of China; under grant [number 42171438]
Pareto 80/20 Rule: A New Principle for Serial Subscription

The purpose of the study is to test the application of the Pareto Principle on the research productivity of journals. Oncology was selected as the subject of study and data were extracted from the “Web of Science.” A series of keywords specifying Oncology and sub-fields have been derived from the Medical Subject Headings (MeSH). A string of 15 search terms (Lead, Related, and Narrow) was connected using the Boolean operator “OR” to retrieve results limiting the scope to journal articles of India and Iran consecutively. The results weren’t strictly as per Pareto’s Principle of 80/20 rule, but almost close (i.e., 75/25 in the case of India and 65/35 in the case of Iran). The results derived provide strong evidence that the Pareto principle fits the research productivity of journals to a great extend. The study could help libraries to improve the efficiency of collection development and financial management policies. The results will be highly applicable for the acquisition of scholarly journals for libraries, especially for library consortia. This law will be highly useful for cost-benefit analysis (CBA) of serial publications and will help in “subscribing the maximum collection at the least cost.”
The preliminary study of artificial intelligence based on convolutional neural network as a corrosion detection tool on ship structures

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Nurhadi Siswantoro, Trika Pitana, Taufik Reza Nurdiansyah, Muhammad Badrus Zaman, Dwi Priyanta, Hari Prastowo, Wolfgang Busse, Ede Mehta Wardhana; The preliminary study of artificial intelligence based on convolutional neural network as a corrosion detection tool on ship structures. AIP Conf. Proc. 21 February 2023; 2482 (1): 130003. https://doi.org/10.1063/5.0111346 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Dynamic learning rates for continual unsupervised learning

The dilemma between stability and plasticity is crucial in machine learning, especially when non-stationary input distributions are considered. This issue can be addressed by continual learning in order to alleviate catastrophic forgetting. This strategy has been previously proposed for supervised and reinforcement learning models. However, little attention has been devoted to unsupervised learning. This work presents a dynamic learning rate framework for unsupervised neural networks that can handle non-stationary distributions. In order for the model to adapt to the input as it changes its characteristics, a varying learning rate that does not merely depend on the training step but on the reconstruction error has been proposed. In the experiments, different configurations for classical competitive neural networks, self-organizing maps and growing neural gas with either per-neuron or per-network dynamic learning rate have been tested. Experimental results on document clustering tasks demonstrate the suitability of the proposal for real-world problems.
Implicit spatio-temporal based hybrid recommendation model to discover malicious wireless access points

Recent technological advancements have enabled users to conduct more sophisticated business transactions via Wi-Fi enabled networks. Typically, a compromised access point (CAP) can handle all traffic between a user and an Internet server, thus becoming a serious security hazard. In addition, an attacker can easily control the entire network using the CAP remotely and compromise as many victims as possible to form a botnet. This paper presents a hybrid recommendation prediction model for forecasting CAP attacks based on network traffic in a private network. This model combines various prediction techniques likethe time-series model, the kNN model and cross association algorithm for attack prediction. This hybrid blacklisting recommendation system effectively improves the prediction rate significantly as well as the robustness against poisoning attacks.
Invariance of success probability in Grover's quantum search under local noise with memory

We analyze the robustness of Grover's quantum search algorithm performed by a quantum register under a possibly time-correlated noise acting locally on the qubits. We model the noise as originating from an arbitrary but fixed unitary evolution, $U$, of some noisy qubits. The noise can occur with some probability in the interval between any pair of consecutive noiseless Grover evolutions. Although each run of the algorithm is a unitary process, the noise model leads to decoherence when all possible runs are considered. We derive a set of unitary $U$'s, called the 'good noises,' for which the success probability of the algorithm at any given time remains unchanged with varying the non-trivial total number ($m$) of noisy qubits in the register. The result holds irrespective of the presence of any time-correlations in the noise. We show that only when $U$ is either of the Pauli matrices $\sigma_x$ and $\sigma_z$ (which give rise to $m$-qubit bit-flip and phase-damping channels respectively in the time-correlation-less case), the algorithm's success probability stays unchanged when increasing or decreasing $m$. In contrast, when $U$ is the Pauli matrix $\sigma_y$ (giving rise to $m$-qubit bit-phase flip channel in the time-correlation-less case), the success probability at all times stays unaltered as long as the parity (even or odd) of the total number $m$ remains the same. This asymmetry between the Pauli operators stems from the inherent symmetry-breaking existing within the Grover circuit. We further show that the positions of the noisy sites are irrelevant in case of any of the Pauli noises. The results are illustrated in the cases of time-correlated and time-correlation-less noise. We find that the former case leads to a better performance of the noisy algorithm. We also discuss physical scenarios where our chosen noise model is of relevance.
Association Coefficient Measure of Intuitionistic Fuzzy Graphs with Application in Selecting Best Electric Scooter for Marketing Executives

Group decision-making is a technique wherein professionals rank and select the most acceptable ones based on recognised criteria. The objective of the present study was to establish a strategy for solving issues with Laplacian energy and association coefficient measures of intuitionistic fuzzy graphs through group decision-making. Initially, making use of Laplacian energy, the load of each criterion is determined, and the entire criterion load vector is then computed by averaging the determined loads. The substitutes are then ranked using the association coefficient measure linked to every criterion. Finally, we used the proposed technique wherein professionals rank and select the most acceptable based on recognised criteria with real-time application.
Computer vision-based framework for pedestrian movement direction recognition

Pedestrians are the most critical and vulnerable moving objects on roads and public areas. Learning pedestrian movement in these areas can be helpful for their safety. To improve pedestrian safety and enable driver assistance in autonomous driver assistance systems, recognition of the pedestrian direction of motion plays an important role. Pedestrian movement direction recognition in real world monitoring and ADAS systems are challenging due to the unavailability of large annotated data. Even if labeled data is available, partial occlusion, body pose, illumination and the untrimmed nature of videos poses another problem. In this paper, we propose a framework that considers the origin and end point of the pedestrian trajectory named origin-end-point incremental clustering (OEIC). The proposed framework searches for strong spatial linkage by finding neighboring lines for every OE (origin-end) lines around the circular area of the end points. It adopts entropy and Qmeasure for parameter selection of radius and minimum lines for clustering. To obtain origin and end point coordinates, we perform pedestrian detection using the deep learning technique YOLOv5, followed by tracking the detected pedestrian across the frame using our proposed pedestrian tracking algorithm. We test our framework on the publicly available pedestrian movement direction recognition dataset and compare it with DBSCAN and Trajectory clustering model for its efficacy. The results show that the OEIC framework provides efficient clusters with optimal radius and minlines.
Two-photon magneto-optical transitions in quantum rings

We study the effect of the magnetic and Aharonov–Bohm (AB) flux fields on the two-photon absorption (2PA) in a quantum ring (QR) system. The expression of 2PA coefficient is expressed through the two-photon transition probability including both intraband and interband transitions. The effect of the polarization orientation of the incident light breaks the symmetry from the contribution between the AB flux field and the azimuthal quantum number. Magnetic and AB flux fields have a significant effect on the electronic properties of the QR. The 2PA spectra for intraband transitions are in the THz range and have a much higher magnitude compared to those for interband transitions, which are located in the near-infrared range. The increase in the magnetic field results in a blue-shift of the 2PA spectra for both intraband and interband transitions, whereas changes in the azimuthal quantum number only affect the spectra for interband transitions.
Cache Me If You Can

Differential privacy (DP) allows data analysts to query databases that contain users' sensitive information while providing a quantifiable privacy guarantee to users. Recent interactive DP systems such as APEx provide accuracy guarantees over the query responses, but fail to support a large number of queries with a limited total privacy budget, as they process incoming queries independently from past queries. We present an interactive, accuracy-aware DP query engine, CacheDP , which utilizes a differentially private cache of past responses, to answer the current workload at a lower privacy budget, while meeting strict accuracy guarantees. We integrate complex DP mechanisms with our structured cache, through novel cache-aware DP cost optimization. Our thorough evaluation illustrates that CacheDP can accurately answer various workload sequences, while lowering the privacy loss as compared to related work.
MQH

Point-to-hyperplane nearest neighbor search (P2HNNS) is a fundamental problem which has many applications in data mining and machine learning. In this paper, we propose a provable Locality-Sensitive-Hashing (LSH) scheme based on multi-level quantization errors to solve this problem. In the indexing phase, for each data point, we compute the hash values of its residual vectors generated by a stepwise quantization process. In the query phase, for each processed point, we first determine its suitable level for hashing and then determine the size of hash bucket based on its quantization error in that level. We theoretically show that this treatment not only yields a probability guarantee on query results, but also makes the generated hash functions much more efficient to prune those false points. Experimental results on five real datasets show that the proposed approach generally runs 2X-10X faster than the state-of-the-art LSH-based approaches.
Multi-Analyst Differential Privacy for Online Query Answering

Most differentially private mechanisms are designed for the use of a single analyst. In reality, however, there are often multiple stakeholders with different and possibly conflicting priorities that must share the same privacy loss budget. This motivates the problem of equitable budget-sharing for multi-analyst differential privacy. Our previous work defined desiderata that any mechanism in this space should satisfy and introduced methods for budget-sharing in the offline case where queries are known in advance. We extend our previous work on multi-analyst differentially private query answering to the case of online query answering, where queries come in one at a time and must be answered without knowledge of the following queries. We demonstrate that the unknown ordering of queries in the online case results in a fundamental limit in the number of queries that can be answered while satisfying the desiderata. In response, we develop two mechanisms, one which satisfies the desiderata in all cases but is subject to the fundamental limitations, and another that randomizes the input order ensuring that existing online query answering mechanisms can satisfy the desiderata.
Web Record Extraction with Invariants

Web records are structured data on a Web page that embeds records retrieved from an underlying database according to some templates. Mining data records on the Web enables the integration of data from multiple Web sites for providing value-added services. Most existing works on Web record extraction make two key assumptions: (1) records are retrieved from databases with uniform schemas and (2) records are displayed in a linear structure on a Web page. These assumptions no longer hold on the modern Web. A Web page may present records of diverse entity types with different schemas and organize records hierarchically, in nested structures, to show richer relationships among records. In this paper, we revisit these assumptions and modify them to reflect Web pages on the modern Web. Based on the reformulated assumptions, we introduce the concept of invariant in Web data records and propose Miria ( Mi ning r ecord i nvari a nt), a bottom-up, recursive approach to construct the Web records from the invariants. The proposed approach is both effective and efficient, consistently outperforming the state-of-the-art Web record extraction methods on modern Web pages.