Reviewerly/exHarmony huggingface dataset:
Utilizing Latent Posting Style for Authorship Attribution on Short Texts

Character n-grams and word n-grams are the most widely used features for authorship attribution on short texts. In this paper, we propose a new method which exploits latent posting styles estimated from authors' short texts. The new posting style features characterize each user's posting style through sentiment orientation and post length. Concise hidden posting styles are captured by Latent Dirichlet Allocation (LDA), where we consider two types of LDA models. Then the vectors of latent posting styles are concatenated with averaged word embeddings of character n-grams and word n-grams, to be used to train a support vector machine. Our results show that combining latent posting styles with the traditional features can improve the accuracy of authorship attribution up to 5.2%.
Named-Entity Recognition for Norwegian.

NER is the task of recognizing and demarcating the segments of a document that are part of a name and which type of name it is. We use 4 different categories of names: Locations (LOC), miscellaneous (MISC), organizations (ORG), and persons (PER). Even though we employ state of the art methods—including sub-word embeddings—that work well for English, we are unable to reproduce the same success for the Norwegian written forms. However, our model performs better than any previous research on Norwegian text. The study also presents the first NER for Nynorsk. Lastly, we find that by combining Nynorsk and Bokmal into one training corpus we improve the performance of our model on both languages.
A graphical heuristic for reduction and partitioning of large datasets for scalable supervised training

Abstract A scalable graphical method is presented for selecting and partitioning datasets for the training phase of a classification task. For the heuristic, a clustering algorithm is required to get its computation cost in a reasonable proportion to the task itself. This step is succeeded by construction of an information graph of the underlying classification patterns using approximate nearest neighbor methods. The presented method consists of two approaches, one for reducing a given training set, and another for partitioning the selected/reduced set. The heuristic targets large datasets, since the primary goal is a significant reduction in training computation run-time without compromising prediction accuracy. Test results show that both approaches significantly speed-up the training task when compared against that of state-of-the-art shrinking heuristics available in LIBSVM. Furthermore, the approaches closely follow or even outperform in prediction accuracy. A network design is also presented for a partitioning based distributed training formulation. Added speed-up in training run-time is observed when compared to that of serial implementation of the approaches.
Interference of Tissue-Marking Dyes With Fluorescence In Situ Hybridization Assays

To the Editor.—Tissue-marking dyes (TMDs) are widely used in pathology for identifying tissue margins in surgical excisions and as an additional step to prevent tissue mix-up in biopsies. Although the College of American Pathologists (CAP) does not require TMDs to be used during grossing of biopsies, many laboratories use various colored inks on biopsy tissues including prostate and breast. There is limited literature evaluating TMD endurance and color fidelity in identifying margins and validating use of these inks with immunohistochemical stains.1,2 However, TMDs have not been validated for fluorescence in situ hybridization (FISH) assays. The number of FISH tests we offer at our national reference laboratory has steadily increased over the years as more probes have become available, not only for diagnostic but also for predictive tests for targeted therapies. In our laboratory, we have occasionally seen orange ink used on breast biopsy tissues submitted for HER2 FISH to autofluoresce, interfering with red signal counting. More recently, the number of other tissue types, such as primary or metastatic lung cancers, that are inked with different colors and submitted for FISH testing has been increasing. We tested 10 colors (orange, yellow, green, black, gold, purple, blue, red, magenta, and teal) from Cancer Diagnostics Incorporated (Morrisville, North Carolina), 8 colors (orange, yellow, lime, red, purple, green, blue, and black) from Davidson Marketing System (Bloomington, Minnesota), and 9 colors (orange, yellow, gold, red, magenta, green, blue, teal, and black) from Royal Marker (Fort Lauderdale, Florida) to evaluate autofluorescence. The inks were used to mark all surfaces of excisional tissue samples. Autofluorescence was evaluated with an Olympus BX53 microscope using orange (excitation wavelength, 540/20 nm; emission wavelength, 575/40 nm), green (excitation wavelength, 475/30 nm; emission wavelength, 530/40 nm), Texas red (excitation wavelength, 560/40 nm; emission wavelength, 630/75 nm), and dual orange/green (excitation wavelengths, 470/30 and 575/20 nm; emission wavelengths, 530/30 and 610/20 nm) filters. The results are shown in the Table.An example of metastatic lung adenocarcinoma to the sacrum that failed testing with multiple probes because of autofluorescence of orange ink is shown in Figure 1, A and B. We do not know which brand of ink was used by the referring laboratory, but in the image shown, orange autofluorescence interfered with the detection and enumeration of ALK signal.Most FISH tests use locus-specific or centromeric probes that are labeled with orange or green fluorescent dyes. Orange, lime, and some yellow colored inks may show orange or green autofluorescence, interfering with interpretation of commonly used clinical FISH probes. Uninterpretable FISH tests may, in turn, lead to repeat testing attempts and/or repeat biopsies, leading to increased expense and/or patient morbidity.We strongly recommend that CAP issue a warning and/or discourage inking of tissue biopsies with these colors unless validated for FISH.
Long-term integrity protection of genomic data

Genomic data is crucial in the understanding of many diseases and for the guidance of medical treatments. Pharmacogenomics and cancer genomics are just two areas in precision medicine of rapidly growing utilization. At the same time, whole-genome sequencing costs are plummeting below $ 1000, meaning that a rapid growth in full-genome data storage requirements is foreseeable. While privacy protection of genomic data is receiving growing attention, integrity protection of this long-lived and highly sensitive data much less so.We consider a scenario inspired by future pharmacogenomics, in which a patient’s genome data is stored over a long time period while random parts of it are periodically accessed by authorized parties such as doctors and clinicians. A protection scheme is described that preserves integrity of the genomic data in that scenario over a time horizon of 100 years. During such a long time period, cryptographic schemes will potentially break and therefore our scheme allows to update the integrity protection. Furthermore, integrity of parts of the genomic data can be verified without compromising the privacy of the remaining data. Finally, a performance evaluation and cost projection shows that privacy-preserving long-term integrity protection of genomic data is resource demanding, but in reach of current and future hardware technology and has negligible costs of storage.
Dynamic Telecytopathology-Guided Rapid On-Site Assessment of Percutaneous Image-Guided Fine-Needle Aspiration of Hepatic Lesions: An Institutional Review of 178 Cases

Background:Rapid on-site evaluation (ROSE) of image-guided fine-needle aspiration (IGFNA) of hepatic lesions is necessary to ensure adequate sample collection and render preliminary diagnosis. We review our experience with utilization of telecytopathology for ROSE of IGFNA of liver lesions. Materials and Methods:Cytotechnologists at a remote site transmitted real-time images of Diff-Quik-stained cytology smears of hepatic lesions obtained using an Olympus DP70 digital camera via Ethernet to a pathologist. All live communications were conducted using the Vocera voice communication system. Preliminary telecytopathology ROSE diagnosis was compared with the final diagnosis for accuracy. Results:ROSE using telecytopathology of IGFNAs of hepatic lesion was performed on a total of 178 cases. Preliminary diagnosis of unsatisfactory, benign, suspicious for malignancy, and positive for malignancy was 2%, 28%, 15%, and 55%, respectively, for telecytopathology. The overall concordance between the preliminary and final cytology diagnoses was 94.4%. Nine of the cases were upgraded to suspicious/malignant final diagnosis contributing to an upgrade rate of 5%. One case with preliminary evaluation of positive for malignancy was downgraded to benign cytology on final diagnosis resulting in a downgrade rate of 0.1%. Conclusion:Utilization of telecytopathology is highly effective and accurate for ROSE of IGFNAs of hepatic lesions and may serve as a substitute for conventional microscopy.
MATHEMATICAL METHODS IN CIBERNETIC SECURITY: GRAPHS AND THEIR APPLICATION IN INFORMATION AND CYBERNETIC SECURITY

This article is devoted to the problem of applying graph theory in cybersecurity systems and is an overview. Widespread penetration of mathematical methods in the development of information technology characterizes the current stage of our society. Among the mathematical methods used in information and cyber security, a large niche is graph technology. A streamlined system of special terms and symbols of graph theory allows you to easily and easily describe complex and subtle things both geometrically and algebraically. A graph is a mathematical model of a wide variety of objects, phenomena, and the relationships between them. This justifies the choice and relevance of this study. The article outlines the main elements of graph theory, the wide scope of their implementation and provides a historical perspective on the development of this theory. The analysis of scientific works allowed to determine the main directions of application of properties, characteristics of graphs and graph algorithms in information and cyber security. Among them are studies related to the use of graphs in information systems and programming; with modeling, analysis and application of attack graphs; with cryptographic transformations; with the construction of a decision tree in decision-making tasks in conditions of risk and uncertainty. It is proved that the ability to operate with the methods of graph technologies contributes to the development of software and hardware for information protection. The considered approaches to the application of graph theory in information and cyber security can be implemented during the study of the discipline "Special methods in security systems: discrete mathematics" for students majoring in 125 Cybersecurity, as well as in training in research or course work or thesis. By increasing the professional orientation of training, future cybersecurity workers gain a thorough knowledge of fundamental disciplines.
Multiplayer AlphaZero

The AlphaZero algorithm has achieved superhuman performance in two-player, deterministic, zero-sum games where perfect information of the game state is available. This success has been demonstrated in Chess, Shogi, and Go where learning occurs solely through self-play. Many real-world applications (e.g., equity trading) require the consideration of a multiplayer environment. In this work, we suggest novel modifications of the AlphaZero algorithm to support multiplayer environments, and evaluate the approach in two simple 3-player games. Our experiments show that multiplayer AlphaZero learns successfully and consistently outperforms a competing approach: Monte Carlo tree search. These results suggest that our modified AlphaZero can learn effective strategies in multiplayer game scenarios. Our work supports the use of AlphaZero in multiplayer games and suggests future research for more complex environments.
Learning Weighted Video Segments for Temporal Action Localization

This paper proposes a novel approach of learning weighted video segments via supervised temporal attention for action localization in untrimmed videos. The learned segment weights represent informativeness of video segments to recognize actions and benefit inferring the boundaries to temporally localize actions. We build a Supervised Temporal Attention Network (STAN) to dynamically learn the weights of video segments, and generate descriptive and discriminative video representations. We use a proposal generator and a classifier to estimate the boundaries of actions and classify the classes of actions, respectively. Extensive experiments are conducted on two public benchmarks THUMOS2014 and ActivityNet1.3. The results demonstrate that our approach achieves substantially better performance than the state-of-the-art methods, verifying the effectiveness of learning weighted video segments.
Balancing Multi-level Interactions for Session-based Recommendation

Predicting user actions based on anonymous sessions is a challenge to general recommendation systems because the lack of user profiles heavily limits data-driven models. Recently, session-based recommendation methods have achieved remarkable results in dealing with this task. However, the upper bound of performance can still be boosted through the innovative exploration of limited data. In this paper, we propose a novel method, namely Intra-and Inter-session Interaction-aware Graph-enhanced Network, to take inter-session item-level interactions into account. Different from existing intra-session item-level interactions and session-level collaborative information, our introduced data represents complex item-level interactions between different sessions. For mining the new data without breaking the equilibrium of the model between different interactions, we construct an intra-session graph and an inter-session graph for the current session. The former focuses on item-level interactions within a single session and the latter models those between items among neighborhood sessions. Then different approaches are employed to encode the information of two graphs according to different structures, and the generated latent vectors are combined to balance the model across different scopes. Experiments on real-world datasets verify that our method outperforms other state-of-the-art methods.
Acompañamiento a profesores de Infantil para integrar la robótica en el aula: experiencia realizada en cuatro escuelas en Cataluña.

four schools in Catalonia.
Intelligent Data Analysis of Interactions and Relationships among Technological Pedagogical Content Knowledge Constructs via Rough Set Analysis

This study focuses on the relationship among Content Knowledge (CK), Pedagogic Knowledge (PK), and Technological Knowledge (TK) using Technological Pedagogical Content Knowledge (TPACK). The aim of the study is to use the determined relationship to provide mathematical clarity using the Rough Set Theory, which is commonly used in areas such as Artificial Intelligence, Data Reduction, Determination of Dependencies, Estimation of Data Importance and the establishment of Decision (control) Algorithms. Accordingly, TPACK scale was applied to 340 preservice teachers who, at the time of conducting this study, were continuing their teaching at elementary (grade 5-8) and secondary (grade 9-12) Mathematics Teaching Department. The gathered data was broken into three different groups - low, medium and high. The data grouping allowed for applying of the Rough Set Analysis. This will enable TPACK constructs to assign prospective teachers to any of the three identified groups. Analysis has put forth that the CK, PK and TK components explain TPACK with a dependency degree of 0.105 and that even though the levels of significance of each component is low by itself, it cannot be removed from the data set. Lastly, decision rules have been established between CK, PK and TK with TPACK.
Macroscopic instructions vs microscopic operations in quantum circuits

In many experiments on microscopic quantum systems, it is implicitly assumed that when a macroscopic procedure or “instruction” is repeated many times – perhaps in different contexts – each application results in the same microscopic quantum operation. But in practice, the microscopic effect of a single macroscopic instruction can easily depend on its context. If undetected, this can lead to unexpected behavior and unreliable results. Here, we design and analyze several tests to detect context-dependence. They are based on invariants of matrix products, and while they can be as data intensive as quantum process tomography, they do not require tomographic reconstruction, and are insensitive to imperfect knowledge about the experiments. We also construct a measure of how unitary (reversible) an operation is, and show how to estimate the volume of physical states accessible by a quantum operation.
Exploration with Multiple Random ε-Buffers in Off-Policy Deep Reinforcement Learning

In terms of deep reinforcement learning (RL), exploration is highly significant in achieving better generalization. In benchmark studies, ε-greedy random actions have been used to encourage exploration and prevent over-fitting, thereby improving generalization. Deep RL with random ε-greedy policies, such as deep Q-networks (DQNs), can demonstrate efficient exploration behavior. A random ε-greedy policy exploits additional replay buffers in an environment of sparse and binary rewards, such as in the real-time online detection of network securities by verifying whether the network is “normal or anomalous.” Prior studies have illustrated that a prioritized replay memory attributed to a complex temporal difference error provides superior theoretical results. However, another implementation illustrated that in certain environments, the prioritized replay memory is not superior to the randomly-selected buffers of random ε-greedy policy. Moreover, a key challenge of hindsight experience replay inspires our objective by using additional buffers corresponding to each different goal. Therefore, we attempt to exploit multiple random ε-greedy buffers to enhance explorations for a more near-perfect generalization with one original goal in off-policy RL. We demonstrate the benefit of off-policy learning from our method through an experimental comparison of DQN and a deep deterministic policy gradient in terms of discrete action, as well as continuous control for complete symmetric environments.
Performance analysis of Modified Shuffled Frog leaping Algorithm for Multi-document Summarization Problem

Due to massive growth of Web information, handling useful information has become a challenging issue in now-a-days. In the past few decades, text summarization is considered as one of the solution to obtained relevant information from extensive collection of information. In this paper, a novel approach using modified shuffled frog leaping algorithm (MSFLA) to extract the important sentence from multiple documents is presented. The effectiveness of MSFLA algorithm for summarization model is evaluated by comparing the ROUGE score and statistical analysis of the model with respect to results of other summarization models. The models are demonstrated by the simulation results over DUC datasets. In the present work, it elucidates that MSFLA based model improves the results and find advisable solution for summary extraction
The Potential of Using Google Expeditions and Google Lens Tools under STEM-education in Ukraine

Shapovalov Yе.B., Bilyk Zh.I., Atamas A.I., Shapovalov V.V. and Uchytel A.D. The Potential of Using Google Expeditions and Google Lens Tools under STEM-education in Ukraine.&#x0D; The expediency of using the augmented reality in the case of using of STEM-education in Ukraine is shown. The features of the augmented reality and its classification are described. The possibilities of using the Google Expeditions and Google Lens as platforms of the augmented reality is analyzed. A comparison, analysis, synthesis, induction and deduction was carried out to study the potential of using augmented reality platforms in the educational process. Main characteristics of Google Expeditions and Google Lens are described. There determined that augmented reality tools can improve students motivation to learn and correspond to trends of STEM-education. However, there problems of using of augmented reality platforms, such as the lack of awareness of this system by teachers, the lack of guidance, the absence ofthe Ukrainian-language interface and responding of educational programs of the Ministry of Education and Science of Ukraine. There proposed to involve methodical and pedagogical specialists to development of methodical provision of the tools of augmented reality.
Bicriteria Data Compression

Since the seminal work by Shannon, theoreticians have focused on designing compressors targeted at minimizing the output size without sacrificing much of the compression/decompression efficiency.On the other hand, software engineers have deployed several heuristics to implement compressors aimed at trading compressed space versus compression/decompression efficiency in order to match their application needs.In this paper we fill this gap by introducing the bicriteria datacompression problem that seeks to determine the shortest compressed file that can be decompressed in a given time bound.Then, inspired by modern data-storage applications, we instantiate the problem onto the family of Lempel--Ziv-based compressors (such as Snappy and LZ4) and solve it by combining in a novel and efficient way optimization techniques, string-matching data structures, and shortest path algorithms over properly (bi-)weighted graphs derived from the data-compression problem at hand.An extensive set of experiments complements our theoretical achievements by showing that the proposed algorithmic solution is very competitive with respect to state-of-the-art highly engineered compressors.
Spelling-Aware Construction of Macaronic Texts for Teaching Foreign-Language Vocabulary

We present a machine foreign-language teacher that modifies text in a student's native language (L1) by replacing some word tokens with glosses in a foreign language (L2), in such a way that the student can acquire L2 vocabulary simply by reading the resulting macaronic text.The machine teacher uses no supervised data from human students.Instead, to guide the machine teacher's choice of which words to replace, we equip a cloze language model with a training procedure that can incrementally learn representations for novel words, and use this model as a proxy for the word guessing and learning ability of real human students.We use Mechanical Turk to evaluate two variants of the student model: (i) one that generates a representation for a novel word using only surrounding context and (ii) an extension that also uses the spelling of the novel word.
Axiological Characteristics of Digitalized Education

The relevance of the problem of digital transformation of education is mainly connected with digital technologies and their focus on learning, however educational and development potential of this process and its axiological component are frequently ignored. If cultural and national values are continued to be disregarded, there is a risk of replacing them with a surrogate of a cultivated by the government model of a citizen. This article draws attention to the necessity of a purposeful formation of value beliefs and a national code in the process of digital transformation of the education system, of identifying and presenting the risks of this process, as well as means to solve possible problems. The following methods were used: analysis of academic sources on the issue, method of independent evaluations, monitoring, modelling. The main approach of the study is axiological.