Reviewerly/exHarmony huggingface dataset:
The concept of student cognitive culture: Definition and conditions for development

Introduction . Nowadays, knowledge is the main resource for the innovative economy. In the information society, there is the contradiction between the gradually increasing flow of new information and the inability of users to understand it. Education, including higher vocational training, should play a key role in bridging that cognitive gap. However, the current educational standards and programmes of universities, as well as the lower level of cognitive skills and students’ competencies, do not meet the requirement to solve the global problem related to the processes for changing the knowledge structure and total informatisation of human activities. In particular, against the background of the achievements of modern epistemology, there is still no scientific-based interpretation of the student cognitive culture, which formative factors have not been also identified yet. The aim of the present article is to define the concept of cognitive culture of a university student and the conditions for its development. Methodology and research methods . The methodological framework of the research is based on cognitive, informative and cultural approaches to the discussed problem. The analytical-synthetic method and the method of comparative analysis were applied in the course of the research. Results and scientific novelty . The definition of a student cognitive culture is defined on the basis of the modern principles of cognitive functioning and its subjects (unity of consciousness, unconscious and activity; co-evolution of individual, collective and social, etc.), as well as the classical model of knowledge transformation. Student involvement in all types of information activities is substantiated as the main criterion for assessing cognitive culture maturity. A technocognitive environment with integrative character is considered as the condition for development and achievement of a high level of student cognitive culture. The version of structural technocognitive environment is proposed and the content of each of the components is described. Practical significance . The present research material is primarily addressed to the specialists involved in improving the quality of higher education. The main provisions of the article and the author’s conclusions can be employed as a theoretical basis to organise the cognitive environment at the university. To further the present research, the author intends to identify the specifics of the development of student cognitive culture in various educational fields, as well as to investigate the computer component of the student internal thinking as a part of his or her cognitive culture.
Literacy Problems in the New Digital Reality (By the Example of Schoolchildren). Part I

Linear printed text and nonlinear hypertext compete for the attention of children and adults. The article will focus on the problems of reading in the new digital reality associated with the emergence of new format texts and changing reading practices. This issue is becoming relevant, as digital reading begins to prevail over traditional, which has an impact on individual readers and on the education system as a whole. This problem remains insufficiently studied in the social Sciences. The first part of the article discusses the possible social and pedagogical consequences of the transition to digital reading in education. A review of scientific research on the problem shows that children understand texts better if they read them on paper. One reason for this is the different purposes with which we read printed and digital texts. Screen reading is more often a means to communicate or to find information, with the result that the mechanism of digital reading can be defined as «read-slip» as opposed to a slower, thoughtful immersion in printed text. The modern education system should provide conditions for the formation of double literacy: traditional and new, which will allow to understand the deep and complex things presented in digital and printed format. The results of the empirical study of reading practices of fourth grade students will be presented in the second part of the article.
Bridging knowledge between craftsman and learner in Chinese intangible cultural heritage through WebAR

The purpose of this paper is to explore new perspectives to learning Intangible Cultural Heritage (ICH) through embodied interaction with focus on learning and experience with traditional Cantonese Porcelain crafting. This research developed a WebAR application where various processes are presented through the tangible interaction of virtual porcelain represented by physical objects. The learner is able to directly interact with the plate that bridges the tangible materials and making processes of ICH utilizing WebAR. Empirical studies found that the WebAR and embodied interaction can enhance student's tangible learning experience to transfer knowledge between craftsman and student.
Deep Learning Training with Simulated Approximate Multipliers

This paper presents by simulation how approximate multipliers can be utilized to enhance the training performance of convolutional neural networks (CNNs). Approximate multipliers have significantly better performance in terms of speed, power, and area compared to exact multipliers. However, approximate multipliers have an inaccuracy which is defined in terms of the Mean Relative Error (MRE). To assess the applicability of approximate multipliers in enhancing CNN training performance, a simulation for the impact of approximate multipliers error on CNN training is presented. The paper demonstrates that using approximate multipliers for CNN training can significantly enhance the performance in terms of speed, power, and area at the cost of a small negative impact on the achieved accuracy. Additionally, the paper proposes a hybrid training method which mitigates this negative impact on the accuracy. Using the proposed hybrid method, the training can start using approximate multipliers then switches to exact multipliers for the last few epochs. Using this method, the performance benefits of approximate multipliers in terms of speed, power, and area can be attained for a large portion of the training stage. On the other hand, the negative impact on the accuracy is diminished by using the exact multipliers for the last epochs of training.
3D Single-Person Concurrent Activity Detection Using Stacked Relation Network

We aim to detect real-world concurrent activities performed by a single person from a streaming 3D skeleton sequence. Different from most existing works that deal with concurrent activities performed by multiple persons that are seldom correlated, we focus on concurrent activities that are spatio-temporally or causally correlated and performed by a single person. For the sake of generalization, we propose an approach based on a decompositional design to learn a dedicated feature representation for each activity class. To address the scalability issue, we further extend the class-level decompositional design to the postural-primitive level, such that each class-wise representation does not need to be extracted by independent backbones, but through a dedicated weighted aggregation of a shared pool of postural primitives. There are multiple interdependent instances deriving from each decomposition. Thus, we propose Stacked Relation Networks (SRN), with a specialized relation network for each decomposition, so as to enhance the expressiveness of instance-wise representations via the inter-instance relationship modeling. SRN achieves state-of-the-art performance on a public dataset and a newly collected dataset. The relation weights within SRN are interpretable among the activity contexts. The new dataset and code are available at https://github.com/weiyi1991/UA_Concurrent/
Heterogeneous Defect Prediction Based on Transfer Learning to Handle Extreme Imbalance

Software systems are now ubiquitous and are used every day for automation purposes in personal and enterprise applications; they are also essential to many safety-critical and mission-critical systems, e.g., air traffic control systems, autonomous cars, and Supervisory Control And Data Acquisition (SCADA) systems. With the availability of massive storage capabilities, high speed Internet, and the advent of Internet of Things devices, modern software systems are growing in both size and complexity. Maintaining a high quality of such complex systems while manually keeping the error rate at a minimum is a challenge. This paper proposed a heterogeneous defect prediction method considering class extreme imbalance problem in real software datasets. In the first stage, Sampling with the Majority method (SWIM) based on Mahalanobis Distance is used to balance the dataset to reduce the influence of minority samples in defect data. Due to the negative impact of uncorrelated features on the classification algorithm, the second stage uses ensemble learning and joint similarity measurement to select the most relevant and representative features between the source project and the target project. The third phase realizes the transfer learning from the source project to the target project in the Grassmann manifold space. Our experiments, conducted using nine projects of three public domain software defect libraries and compared with four existing advanced methods to verify the effectiveness of the proposed method in this paper. The experimental results indicate that the proposed method is more accurate in terms of Area under curve (AUC).
Automatic XSS Detection and Automatic Anti-Anti-Virus Payload Generation

In the Web 2.0 era, user interaction makes Web application more diverse, but brings threats, among which XSS vulnerability is the common and pernicious one. In order to promote the efficiency of XSS detection, this paper investigates the parameter characteristics of malicious XSS attacks. We identify whether a parameter is malicious or not through detecting user input parameters with SVM algorithm. The original malicious XSS parameters are deformed by DQN algorithm for reinforcement learning for rule-based WAF to be anti-anti-virus. Based on this method, we can identify whether a specific WAF is secure. The above model creates a more efficient automatic XSS detection tool and a more targeted automatic anti-anti-virus payload generation tool. This paper also explores the automatic generation of XSS attack codes with RNN LSTM algorithm.
Hierarchically Clustered Representation Learning

The joint optimization of representation learning and clustering in the embedding space has experienced a breakthrough in recent years. In spite of the advance, clustering with representation learning has been limited to flat-level categories, which often involves cohesive clustering with a focus on instance relations. To overcome the limitations of flat clustering, we introduce hierarchically-clustered representation learning (HCRL), which simultaneously optimizes representation learning and hierarchical clustering in the embedding space. Compared with a few prior works, HCRL firstly attempts to consider a generation of deep embeddings from every component of the hierarchy, not just leaf components. In addition to obtaining hierarchically clustered embeddings, we can reconstruct data by the various abstraction levels, infer the intrinsic hierarchical structure, and learn the level-proportion features. We conducted evaluations with image and text domains, and our quantitative analyses showed competent likelihoods and the best accuracies compared with the baselines.
Unsupervised Few-shot Learning via Self-supervised Training

Learning from limited exemplars (few-shot learning) is a fundamental, unsolved problem that has been laboriously explored in the machine learning community. However, current few-shot learners are mostly supervised and rely heavily on a large amount of labeled examples. Unsupervised learning is a more natural procedure for cognitive mammals and has produced promising results in many machine learning tasks. In the current study, we develop a method to learn an unsupervised few-shot learner via self-supervised training (UFLST), which can effectively generalize to novel but related classes. The proposed model consists of two alternate processes, progressive clustering and episodic training. The former generates pseudo-labeled training examples for constructing episodic tasks; and the later trains the few-shot learner using the generated episodic tasks which further optimizes the feature representations of data. The two processes facilitate with each other, and eventually produce a high quality few-shot learner. Using the benchmark dataset Omniglot and Mini-ImageNet, we show that our model outperforms other unsupervised few-shot learning methods. Using the benchmark dataset Market1501, we further demonstrate the feasibility of our model to a real-world application on person re-identification.
The Effects of Sales Reports Business Intelligence on Employee Performance

Abstract&#x0D; Selling and buying activities have been done often since long ago, and this study will be discussed about the use of technological advances, especially in the field of financial management by utilizing e-commerce. When carrying out financial data collection, staff can make recapitulation by writing each transaction in the ledger, then counting the data to be the income information. However, the process is not in tune with current technological progress, because it still use a data collection process that requires a lot of time and energy. Thus, to overcome this problem, e-commerce websites are used so that it can manage finances accurately and quickly. In this study, there are 4 (four) problems that will be overcome with 2 (two) methods, and 1 (one) solution as the answer. The advantages of e-commerce website is that it has an informative viewboard regarding financial management, which can be accessed anytime and anywhere, so it is concluded that the use of e-commerce website is able to overcome the problems found in the financial management field of Raharja Internet Cafe at Perguruan Tinggi Raharja.
Do we still need CAVEs?

This paper discusses the relevance of CAVE systems in comparison with virtual and augmented reality head-mounted displays in terms of immersion experience, costs, maintenance, ease to use, interactivity, and social interaction.It is based on a comparative study of a systematic literature review comprising the works available at CumInCAD and IEEE databases in the period from 1998-2018, and empirical data from technical visits made to five CAVEs in Europe.The discussion seeks to cover the limits of each technology and questions the need for CAVEs nowadays.
Scattering-Based Geometric Shaping of Photon-Photon Interactions

We construct an effective Hamiltonian of interacting bosons, based on scattered radiation off vibrational modes of designed molecular architectures. Making use of the infinite yet countable set of spatial modes representing the scattering of light, we obtain a variable photon-photon interaction in this basis. The effective Hamiltonian hermiticity is controlled by a geometric factor set by the overlaps of spatial modes. Using this mapping, we relate intensity measurements of the light to correlation functions of the interacting bosons evolving according to the effective Hamiltonian, rendering local as well as nonlocal observables accessible. This architecture may be used to simulate the dynamics of interacting bosons, as well as designing tool for multi-qubit photonic gates in quantum computing applications. Variable hopping, interaction and confinement of the active space of the bosons are demonstrated on a model system.
A database of handwriting samples for applications in forensic statistics

Handwriting samples were collected from 90 adults for the purpose of developing statistical approaches to the evaluation of handwriting as forensic evidence. Each participant completed three data collection sessions, each at least three weeks apart. At each session, a survey was completed and three writing prompts were each transcribed three times. In total, the repository includes 2430 handwriting sample images as well as demographic and session specific information for all 90 participants. The writing samples were scanned and instructional header text was cropped out to obtain the raw writing data as image files. Survey data are provided in table format. Reliable methods for data management were incorporated through systematic document generation, QR code text embedding, and the development of an application to facilitate data entry and automated file naming and handling. The data presented in this article were collected by researchers at the Center for Statistics and Applications in Forensic Evidence (CSAFE) at Iowa State University.
End-to-End Bootstrapping Neural Network for Entity Set Expansion

Bootstrapping for entity set expansion (ESE) has long been modeled as a multi-step pipelined process. Such a paradigm, unfortunately, often suffers from two main challenges: 1) the entities are expanded in multiple separate steps, which tends to introduce noisy entities and results in the semantic drift problem; 2) it is hard to exploit the high-order entity-pattern relations for entity set expansion. In this paper, we propose an end-to-end bootstrapping neural network for entity set expansion, named BootstrapNet, which models the bootstrapping in an encoder-decoder architecture. In the encoding stage, a graph attention network is used to capture both the first- and the high-order relations between entities and patterns, and encode useful information into their representations. In the decoding stage, the entities are sequentially expanded through a recurrent neural network, which outputs entities at each stage, and its hidden state vectors, representing the target category, are updated at each expansion step. Experimental results demonstrate substantial improvement of our model over previous ESE approaches.
Adar: Adversarial Activity Recognition in Wearables

Recent advances in machine learning and deep neural networks have led to the realization of many important applications in the area of personalized medicine. Whether it is detecting activities of daily living or analyzing images for cancerous cells, machine learning algorithms have become the dominant choice for such emerging applications. In particular, the state-of-the-art algorithms used for human activity recognition (HAR) using wearable inertial sensors utilize machine learning algorithms to detect health events and to make predictions from sensor data. Currently, however, there remains a gap in research on whether or not and how activity recognition algorithms may become the subject of adversarial attacks. In this paper, we take the first strides on (1) investigating methods of generating adversarial example in the context of HAR systems; (2) studying the vulnerability of activity recognition models to adversarial examples in feature and signal domain; and (3) investigating the effects of adversarial training on HAR systems. We introduce Adar <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> Software code and experimental data for Adar are available online at https://github.com/rameshKrSah/Adar., a novel computational framework for optimization-driven creation of adversarial examples in sensor-based activity recognition systems. Through extensive analysis based on real sensor data collected with human subjects, we found that simple evasion attacks are able to decrease the accuracy of a deep neural network from 95.1% to 3.4% and from 93.1% to 16.8% in the case of a convolutional neural network. With adversarial training, the robustness of the deep neural network increased on the adversarial examples by 49.1% in the worst case while the accuracy on clean samples decreased by 13.2%.
Pupils Perception of Using YouTube and Autonomous Learning

The purpose of this study is to examine pupils' perceptions of using YouTube and autonomous learning.The population consisted of pupils of form one till form two of SMK Sultan Tajul Ariffin Manong, Perak, Malaysia.The design of the study was done using a survey methodology.A survey using questionnaires was conducted among these students to gauge their perceptions towards the use of YouTube in their learning of English and autonomous learning.SPSS was used for the statistical analyses.The primary aim is to examine pupils' perceptions in using YouTube videos to enhance their learning and autonomous learning.The respondents of this study were 50 pupils ranging from form one till form two pupils in the same school.Data were gathered using questionnaires to identify pupils perceptions of using YouTube and autonomous learning.This study analyzed the data descriptively, using percent and frequency in the procedure of data analysis.Through SPSS, the findings showed that there was a significant difference in the perception of the pupils using YouTube towards their belief in the usage of the application and autonomous learning.The study shows that the YouTube application is highly accepted among these pupils.The experiences they gained were also worthwhile to be acknowledged as a way of self-learning.
oLMpics -- On what Language Model Pre-training Captures

Recent success of pre-trained language models (LMs) has spurred widespread interest in the language capabilities that they possess. However, efforts to understand whether LM representations are useful for symbolic reasoning tasks have been limited and scattered. In this work, we propose eight reasoning tasks, which conceptually require operations such as comparison, conjunction, and composition. A fundamental challenge is to understand whether the performance of a LM on a task should be attributed to the pre-trained representations or to the process of fine-tuning on the task data. To address this, we propose an evaluation protocol that includes both zero-shot evaluation (no fine-tuning), as well as comparing the learning curve of a fine-tuned LM to the learning curve of multiple controls, which paints a rich picture of the LM capabilities. Our main findings are that: (a) different LMs exhibit qualitatively different reasoning abilities, e.g., RoBERTa succeeds in reasoning tasks where BERT fails completely; (b) LMs do not reason in an abstract manner and are context-dependent, e.g., while RoBERTa can compare ages, it can do so only when the ages are in the typical range of human ages; (c) On half of our reasoning tasks all models fail completely. Our findings and infrastructure can help future work on designing new datasets, models and objective functions for pre-training.
Quantum subspace alignment for domain adaptation

Domain adaptation (DA) is used for adaptively obtaining labels of an unprocessed data set with a given related, but different labelled data set. Subspace alignment (SA), a representative DA algorithm, attempts to find a linear transformation to align the subspaces of the two different data sets. The classifier trained on the aligned labelled data set can be transferred to the unlabelled data set to predict the target labels. In this paper, two quantum versions of the SA are proposed to implement the DA procedure on quantum devices. One method, the quantum subspace alignment algorithm (QSA), achieves quadratic speedup in the number and dimension of given samples. The other method, the variational quantum subspace alignment algorithm (VQSA), can be implemented on the near term quantum devices through a variational hybrid quantum-classical procedure. The results of the numerical experiments on different types of datasets demonstrate that the VQSA can achieve competitive performance compared with the corresponding classical algorithm.