Reviewerly/exHarmony huggingface dataset:
Comparison and Adaptation of Two Strategies for Anomaly Detection in Load Profiles Based on Methods from the Fields of Machine Learning and Statistics

The Federal Office for Economic Affairs and Export Control (BAFA) of Germany promotes digital concepts for increasing energy efficiency as part of the "Pilotprogramm Einsparzähler".Within this program, Limón GmbH is developing software solutions in cooperation with the University of Kassel to identify efficiency potentials in load profiles by means of automated anomaly detection.Therefore, in this study two strategies for anomaly detection in load profiles are evaluated.To estimate the monthly load profile, strategy 1 uses the artificial neural network LSTM (Long Short-Term Memory), with a data period of one month (1 M) or three months (3 M), and strategy 2 uses the smoothing method PEWMA (Probalistic Exponential Weighted Moving Average).By comparing with original load profile data, residuals or summed residuals of the sequence lengths of two, four, six and eight hours are identified as an anomaly by exceeding a predefined threshold.The thresholds are defined by the Z-Score test, i.e., residuals greater than 2, 2.5 or 3 standard deviations are considered anomalous.Furthermore, the ESD (Extreme Studentized Deviate) test is used to set thresholds by means of three significance level values of 0.05, 0.10 and 0.15, with a maximum of k = 40 iterations.Five load profiles are examined, which were obtained by the cluster method k-Means as a representative sample from all available data sets of the Limón GmbH.The evaluation shows that for strategy 1 a maximum F 1 -value of 0.4 (1 M) and for all examined companies an average F 1 -value of maximum 0.24 and standard deviation of 0.09 (1 M) could be achieved for the investigation on single residuals.In variant 3 M the highest F 1 -value could be achieved with an average F 1 -value of 0.21 and standard deviation of 0.06 (3 M) for summed How to cite this paper:
Continuous measurement of a qudit using dispersively coupled radiation

We analyze the continuous monitoring of a qudit coupled to a cavity using both phase-preserving and phase-sensitive amplification. The quantum trajectories of the system are described by a stochastic master equation, for which we derive the appropriate Lindblad operators. The measurement back-action causes spiraling in the state coordinates during collapse, which increases as the system levels become less distinguishable. We discuss two examples: a two-level system and an $N$-dimensional system and meter with rotational symmetry in the quadrature space. We also provide a comparison of the effects of phase-preserving and phase-sensitive detection on the master equation, and show that the average behavior is the same in both cases, but individual trajectories collapse at different rates depending on the measurement axis in the quadrature plane.
Isolated Arabic handwritten words recognition using EHD and HOG methods

&lt;span&gt;Handwriting recognition is a growing field of study in computer vision, artificial intelligence and pattern recognition technology aimed to recognizing texts and handwritings of hefty amount of produced official documents and paper works by institutes or governments. Using computer to distinguish and make these documents accessible and approachable is the goal of these efforts. Moreover, recognition of text has accomplished practically a major progress in many domains such as security sector and e-government structure and more. A system for recognition text’s handwriting was presented here relied on edge histogram descriptor (EHD), histogram of orientated gradients (HOG) features extraction and support vector machine (SVM) as a classifier is proposed in this paper. HOG and EHD give an optimal features of the Arabic hand-written text by extracting the directional properties of the text. Besides that, SVM is a most common machine learning classifier that obtaining an essential classification results within various kernel functions. The experimental evaluation is carried out for Arabic handwritten images from IESK-ArDB database using HOG, EHD features and proposed work provides 85% recognition rate.&lt;/span&gt;
Multi-scale Deep Learning Architecture for Nucleus Detection in Renal Cell Carcinoma Microscopy Image

Clear cell renal cell carcinoma (ccRCC) is one of the most common forms of intratumoral heterogeneity in the study of renal cancer. ccRCC originates from the epithelial lining of proximal convoluted renal tubules. These cells undergo abnormal mutations in the presence of Ki67 protein and create a lump-like structure through cell proliferation. Manual counting of tumor cells in the tissue-affected sections is one of the strongest prognostic markers for renal cancer. However, this procedure is time-consuming and also prone to subjectivity. These assessments are based on the physical cell appearance and suffer wide intra-observer variations. Therefore, better cell nucleus detection and counting techniques can be an important biomarker for the assessment of tumor cell proliferation in routine pathological investigations. In this paper, we introduce a deep learning-based detection model for cell classification on IHC stained histology images. These images are classified into binary classes to find the presence of Ki67 protein in cancer-affected nucleus regions. Our model maps the multi-scale pyramid features and saliency information from local bounded regions and predicts the bounding box coordinates through regression. Our method validates the impact of Ki67 expression across a cohort of four hundred histology images treated with localized ccRCC and compares our results with the existing state-of-the-art nucleus detection methods. The precision and recall scores of the proposed method are computed and compared on the clinical data sets. The experimental results demonstrate that our model improves the F1 score up to 86.3% and an average area under the Precision-Recall curve as 85.73%.
Differentiable Causal Discovery Under Unmeasured Confounding

The data drawn from biological, economic, and social systems are often confounded due to the presence of unmeasured variables. Prior work in causal discovery has focused on discrete search procedures for selecting acyclic directed mixed graphs (ADMGs), specifically ancestral ADMGs, that encode ordinary conditional independence constraints among the observed variables of the system. However, confounded systems also exhibit more general equality restrictions that cannot be represented via these graphs, placing a limit on the kinds of structures that can be learned using ancestral ADMGs. In this work, we derive differentiable algebraic constraints that fully characterize the space of ancestral ADMGs, as well as more general classes of ADMGs, arid ADMGs and bow-free ADMGs, that capture all equality restrictions on the observed variables. We use these constraints to cast causal discovery as a continuous optimization problem and design differentiable procedures to find the best fitting ADMG when the data comes from a confounded linear system of equations with correlated errors. We demonstrate the efficacy of our method through simulations and application to a protein expression dataset. Code implementing our methods is open-source and publicly available at this https URL and will be incorporated into the Ananke package.
BETTER LEARNING AND INCREASED MOTIVATION TO LEARN WITH MOBILE TECHNOLOGY (DEVICES): A PRELIMINARY STUDY

Mobile technology is now part of the everyday life of teachers and students and thus tends to become an inseparable part of the educational activities. Teachers and students are increasingly using mobile technologies in teaching and learning. Therefore, it is purposeful to responsibly integrate technologies into the educational process. However, technical and pedagogical support is necessary in order to facilitate both teacher and students’ understanding of this educational potential. Besides, it is still very little known and there is very little evidence about the effectiveness of the application of these technologies in the teaching/learning process. This research aims to explore the perceptions of Romanian and Lithuanian teachers regarding the use of mobile technologies in education. Keywords: motivation to learn, mobile technology, preliminary study, science education, science teachers.
The Effect of Risk Representation Using Colors and Symbols in Business Process Models on Operational Risk Management Performance

The operational management of risk and internal controls (RIC) makes increasing use of visual representations to support tasks such as risk assessment and control activity definition. The strengths and weaknesses of different representations are typically assessed by cognitive theories that assume an analytical and an intuitive mode of information processing. Previous research has focused mainly on the analytical risk assessment while intuitive information processing has largely been neglected. We develop a theoretical argument based on dual-process theory, which explains why RIC representational alternatives influence different levels of information processing. We test our hypotheses with the help of an online experiment using accountants and operation managers recruited via MTurk (N = 166). Our results suggest that highlighting risk and controls in business process modeling and notation (BPMN) by using color improves risk understanding, control understanding, and the identification of control improvements, which help reduce the risk in a given process. Furthermore, we do not find evidence that the inclusion of color leads to perception biases. This has implications for information systems research, which has primarily addressed the analytical processing of conceptual models. Our findings extend cognitive research on such models by adding an intuitive processing path that can improve the user’s risk management performance. For practitioners, our findings are particularly relevant because colors can be easily added as a secondary notation element without disguising the factual risk situation in processes.
Fighting Gradients with Gradients: Dynamic Defenses against Adversarial Attacks

Adversarial attacks optimize against models to defeat defenses. Existing defenses are static, and stay the same once trained, even while attacks change. We argue that models should fight back, and optimize their defenses against attacks at test time. We propose dynamic defenses, to adapt the model and input during testing, by defensive entropy minimization (dent). Dent alters testing, but not training, for compatibility with existing models and train-time defenses. Dent improves the robustness of adversarially-trained defenses and nominally-trained models against white-box, black-box, and adaptive attacks on CIFAR-10/100 and ImageNet. In particular, dent boosts state-of-the-art defenses by 20+ points absolute against AutoAttack on CIFAR-10 at $\epsilon_\infty$ = 8/255.
Research on Building Smart Campus Based on Cloud Computing Technology

With the development of computer technology, schools continue to adopt new management models to improve management efficiency, and campus construction is moving from digital to smart. Digital campus is an important stage of campus information construction. The development of China's smart campuses has achieved certain results. Many scientific and technological scholars, educators, and relevant national policies have made great contributions to China's education, and the information technology level of the country's education has been greatly improved. After entering the era of cloud computing, big data, and artificial intelligence technology, the development of new smart campuses is of great significance to China's education. This article first introduces cloud computing and related technologies, then analyzes the needs of smart campus service platform, and on this basis, designs a smart campus service platform based on cloud computing technology to promote the development of smart campuses in China.
Galton–Watson games

We address two‐player combinatorial games whose graph of positions is a directed Galton–Watson tree. We consider normal and misère rules (where a player who cannot move loses or wins, respectively), as well as an “escape game” in which one designated player loses if either player cannot move. We study phase transitions for the probability of a draw or escape under optimal play, as the offspring distribution varies. Across a range of natural cases, we find that the transitions are continuous for the normal and misère games but discontinuous for the escape game; we also exhibit examples where these properties fail to hold. We connect the nature of the phase transitions to the length of the game under optimal play. We establish inequalities between the different games. For instance, the draw probability is no smaller in the misère game than in the normal game.
Supervised Domain Adaptation using Graph Embedding

Getting deep convolutional neural networks to perform well requires a large amount of training data. When the available labelled data is small, it is often beneficial to use transfer learning to leverage a related larger dataset (source) in order to improve the performance on the small dataset (target). Among the transfer learning approaches, domain adaptation methods assume that distributions between the two domains are shifted and attempt to realign them. In this paper, we consider the domain adaptation problem from the perspective of multi-view graph embedding and dimensionality reduction. Instead of solving the generalised eigenvalue problem to perform the embedding, we formulate the graph-preserving criterion as a loss in the neural network and learn a domain-invariant feature transformation in an end-to-end fashion. We show that the proposed approach leads to a powerful Domain Adaptation framework which generalises the prior methods CCSA and d-SNE, and enables simple and effective loss designs; an LDA-inspired instantiation of the framework leads to performance on par with the state-of-the-art on the most widely used Domain Adaptation benchmarks, Office31 and MNIST to USPS datasets.
Testing complementarity on a transmon quantum processor

We propose quantum circuits to test interferometric complementarity using symmetric two-way interferometers coupled to a which-path detector. First, we consider the two-qubit setup in which the controlled transfer of path information to the detector subsystem depletes interference on the probed subspace, testing the visibility-distinguishability tradeoff via minimum-error state discrimination measurements. Next, we consider the quantum eraser setup, in which reading out path information in the right basis recovers an interference pattern. These experiments are then carried out in an IBM superconducting transmon processor. A detailed analysis of the results is provided. Despite finding good agreement with theory at a coarse level, we also identify small but persistent systematic deviations preventing the observation of full particlelike and wavelike statistics. We understand them by carefully modeling two-qubit gates, showing that even small coherent errors in their implementation preclude the observation of Bohr's strong formulation of complementarity.
CYBER SECURITY MANAGEMENT MODEL FOR CRITICAL INFRASTRUCTURE PROTECTION

Purpose – in this article, the authors propose a management model for Critical Infrastructure cybersecurity, further development of a model developed by Limba, Plėta, Agafonov, and Damkus (2017). Research methodology – methodology consists of researching the best practices in cybersecurity management for Critical Infrastructures and evaluating the best element to be included. The article offers an overview of the model, including structure and objectives, and further analysis that focuses on pre-existing CI management frameworks. Findings – main results show that, although previously published protocols and models contain valuable elements, there is still the need to implement a comprehensive model which can be applied to every type of CI. Research limitations – research might have been limited due to the lack of a unitary approach to cybersecurity management for CI, meaning the lack of possibility of reference to a similar model and approach. Practical implications – model which is presented in the article could offer a new approach to CI protection strategies and could be the beginning of a more structured approach towards their protection. Originality/Value – model was created by the authors with references to past published protocols and models, which are present in the quotation in the text as well as the bibliography.
A Hierarchical Subspace Model for Language-Attuned Acoustic Unit Discovery

In this work, we propose a hierarchical subspace model for acoustic unit discovery. In this approach, we frame the task as one of learning embeddings on a low-dimensional phonetic subspace, and simultaneously specify the subspace itself as an embedding on a hyper-subspace. We train the hyper-subspace on a set of transcribed languages and transfer it to the target language. In the target language, we infer both the language and unit embeddings in an unsupervised manner, and in so doing, we simultaneously learn a subspace of units specific to that language and the units that dwell on it. We conduct experiments on TIMIT and two low-resource languages: Mboshi and Yoruba. Results show that our model outperforms major acoustic unit discovery techniques, both in terms of clustering quality and segmentation accuracy.
Secure Path: Block-Chaining IoT Information for Continuous Authentication in Smart Spaces

The Internet of Things offers a wide range of possibilities that can be exploited more or less explicitly for user authentication, ranging from specifically designed systems including biometric devices to environmental sensors that can be opportunistically used to feed behavioural authentication systems. How to integrate all this information in a reliable way to get a continuous authentication service presents several open challenges. Among these: how to combine semi-trusted information coming from non-tamper-proof sensors, where to store such data avoiding a single point of failure, how to analyse data in a distributed way, which interface to use to provide an authentication service to a multitude of different services and applications. In this paper, we present a Blockchain-based architectural solution of a distributed system able to transform IoT interactions into useful data for an authentication system. The design includes: (i) a security procedure to certify users’ positions and identities, (ii) a secure storage to hold this information, and (iii) a service to dynamically assign a trust level to a user’s position. We call this system “Secure Path”.
The Four Invisible Enemies in the First Pandemic of the 'Datafied Society'

COVID-19 is the first global pandemic to emerge on such a wide scale and to such grave effect in an advanced phase of so-called “datafied society”. It lays bare the nuances of a society undergoing digital data transformation in nearly every field of human activity, from its most beneficial to its most disturbing aspects. Four distinct tendencies have emerged: “unquestioning positivism”, “information disorder”, “digital vigilantism” and the “normalisation of surveillance”. This article explores these tendencies and elaborates on the necessary antibodies to counteract them.
Circuit Design for K-coloring Problem and its Implementation on Near-term Quantum Devices

Nowadays in Quantum Computing, the implementation of quantum algorithm has created a stir since Noisy Intermediate-Scale Quantum (NISQ) devices are out in the market. Researchers are mostly interested in solving NP-complete problems with the help of quantum algorithms for its speedup. As per the work on computational complexity by Karp [1], if any of the NP-complete problem can be solved then any other NP-complete problem can be reduced to that problem in polynomial time. In this Paper, K-coloring problem (NP-complete problem) has been considered to solve using Grover's search. A comparator-based approach has been used to implement K-coloring problem which enables the reduction of the qubit cost compared to the state-of-the-art. An end-to-end automated framework has been proposed to implement the K-coloring problem for any unweighted and undirected graph on any available Noisy Intermediate-Scale Quantum (NISQ) devices, which helps in generalizing our approach.
The mediating role of accounting disclosure in the influence of AIS on decision-making: A structural equation model

The current study aims to investigate the influence of accounting information system (AIS) elements, including human resources, physical resources, and financial data, in delivering the organization to a suitable decision-making process through the mediating role of well-built accounting disclosure. The study applies its model to the commercial banks listed in the Jordanian market at the end of 2019. A quantitative approach was adopted and a questionnaire was applied on 171 financial managers and accounting department heads within 21 commercial banks in Jordan. Based on the results of a structural equation model, the main hypothesis was accepted referring to the positive influence of AIS elements of human resource, physical resource, and financial data on organizational decision-making that is attributed to accounting disclosure within commercial banks in Jordan. These results show the importance of AIS in decision-making and encourage companies to adopt modern AIS in order to help them improve the quality of accounting information. The study recommended focusing on increasing skills and knowledge of accounting and financial managers within banks on the multiple ways of benefitting from AIS applications, this can include holding conferences, seminars, and workshops.
Versatile Duck Traveler Optimization (VDTO) Algorithm Using Triple Segmentation Methods for Mammogram Image Segmentation to Improving Accuracy

Research about breast cancer consciousness has discovered that social and strict issues imply that ladies don’t get to health administrations, are reluctant to counsel male specialists despite the fact in their relatives particularly couldn’t discuss with husbands also. Breast tumor analysis is rarely simple; it is particularly hard when the lady is in her age of 30’s and has recently begun arranging a future for herself and her family. Hence, a new Meta heuristic Duck traveller optimization algorithm is used to segment the breast tumor to help the doctors to disease diagnosis. Methods: Selecting the best duck from given duck flock is an example of optimization. Optimizing the threshold values VDTO based multilevel thresholding (VDTO-MTH) algorithm is introduced to segmenting the cancer. Desired region of the breast is determined by using VDTO based Region Growing (VDTO-ROI) to highlighting the tumor region. To improving the segmentation results VDTO with K-Means Clustering is proposed (VDTO-K means). Results: The performance results of EDTO are evaluated by using the quality metrics accuracy, precision, recall and f-measure. Conclusion: It is assessed that high mortality because of breast tumor in India would increment throughout the long term. Hence, these calculations discover a terrible requirement for starting essential and auxiliary counteraction measures for the control of breast tumor in our country.