Reviewerly/exHarmony huggingface dataset:
The commercial transformation of America’s schools

A school-focused commercializing process over 100 years in the making has been turbo-charged by the rise of data-collecting digital educational platforms and a pandemic that has forced widespread use of distance learning. Alex Molnar and Faith Boninger explain how advertising to students creates what John Dewey would call “mis-educative experiences.” They describe how private companies have made inroads into public schools by offering funding and free resources, while using their interactions with schools as an opportunity both to directly market to students and to collect data on them that can be used for future marketing or other unknown purposes.
An Evaluation of Reverse Image Search Performance of Google

This study investigates reverse image search performance of Google, in terms of Average Precisions (APs) at various cut-off points, on finding out similar images by using fresh Image Queries (IQs) from the five categories "Fashion", "Computer", "Home", "Sports", and "Toys", in order to have an insight about reverse image search performance of Google and then, motivate the researchers and inform the users. Five fresh IQs with different main concepts were created for each of the five categories. These 25 IQs were run on the search engine and for each, the first 100 images retrieved were evaluated with binary relevance judgment. APs at the cut-off points 20, 40, 60, 80, and 100 were calculated for each category and for all 25 IQs. The performance range is from ~42% for Toys category at the cut-off point 100 to 71% for Home category at the cut-off point 20. When the categories are ignored, Google's performance range is from ~52% at the cut-off point 100 to ~57% at the cut-off point 20. It seems that reverse image search performance of Google needs to be improved.
Collaborative Filtering: Graph Neural Network with Attention

In recent years, graph neural networks have been widely used in natural language processing and speech recognition. However, there is relatively little exploration of graph neural networks in recommendation systems. In this work, we strive to develop neural network based technology to solve the problem of collaborative filtering recommendation based on implicit feedback. Some recent work use deep learning for recommendation, but they mainly use it for auxiliary information modeling. When modeling the key elements of collaborative filtering (the interaction between users and item features), they still use matrix factorization and apply inner products to the potential features of users and items. The collaboration signal(interaction information between users and items) will not be encoded during the embedding process. At the same time, the high-order connectivity of user-item cannot be fully utilized, making the interactive information not comprehensive enough. Therefore, the final embedding may not be enough to capture the collaborative filtering effect. By replacing the inner product with a neural architecture that can learn arbitrary functions from data, we propose a general method called "Graph Neural Network with Attention" (GNNA). GNNA captures CF signals based on neural networks and uses high-order connectivity to obtain neglected interactive information, thereby improving the embedding of users and items. By introducing attention mechanism and high-order connectivity, it can learn user vectors and item vectors on the user item interaction graph, collect neighbor interaction information for coding, and spread it on the user-item interaction graph. This can comprehensively inject user-item collaboration signals into the embedding process. We have conducted extensive experiments on two public benchmarks. Further analysis verified the importance of using attention mechanism and high-order connectivity to learn user and item representations, and proved the rationality and effectiveness of GNNA.
Research on English Text Information Filtering Algorithm Based on SVM

In terms of the current international situation, English is still the most widely used language interoperability many countries. Many people use English as a second language, and even some large international companies, who store their important documents in English, so research in the English language is needed, and one of the most significant research is the English text information filtering algorithm. As we all know, text information filtering is a kind of text information processing technology, including information classification, filtering and other technologies which is the link between text information and computer processing and filtering technology, and can be reprocessed according to the information content of the text. In the process of information processing, support vector machines (SVM) can solve many problems in the process. Generally, the problems such as high sparse dimension of text vector, high correlation between information features, and high sparsity among vectors always exist in text information filtering, while SVM can exactly solve the above problems. According to the feature of SVM for text problems, the center set region is established for positive sample clustering when filtering information, and it is difficult to judge the data and continue to use clustering decision when meeting later, so the accuracy of SVM algorithm in information filtering is improved.
Cognitive Diagnostic Models for Random Guessing Behaviors

Many test-takers do not carefully answer every test question; instead they sometimes quickly answer without thoughtful consideration (rapid guessing, RG). Researchers have not modeled RG when assessing student learning with cognitive diagnostic models (CDMs) to personalize feedback on a set of fine-grained skills (or attributes). Therefore, this study proposes to enhance cognitive diagnosis by modeling RG via an advanced CDM with item response and response time. This study tests the parameter recovery of this new CDM with a series of simulations via Markov chain Monte Carlo methods in JAGS. Also, this study tests the degree to which the standard and proposed CDMs fit the student response data for the Programme for International Student Assessment (PISA) 2015 computer-based mathematics test. This new CDM outperformed the simpler CDM that ignored RG; the new CDM showed less bias and greater precision for both item and person estimates, and greater classification accuracy of test results. Meanwhile, the empirical study showed different levels of student RG across test items and confirmed the findings in the simulations.
Hierarchical Variational Attention for Sequential Recommendation

Attention mechanisms have been successfully applied in many fields, including sequential recommendation. Existing recommendation methods often use the deterministic attention network to consider latent user preferences as fixed points in low-dimensional spaces. However, the fixed-point representation is not sufficient to characterize the uncertainty of user preferences that prevails in recommender systems. In this paper, we propose a new Hierarchical Variational Attention Model (HVAM), which employs variational inference to model the uncertainty in sequential recommendation. Specifically, the attention vector is represented as density by imposing a Gaussian distribution rather than a fixed point in the latent feature space. The variance of the attention vector measures the uncertainty associated with the user's preference representation. Furthermore, the user's long-term and short-term preferences are captured through a hierarchical variational attention network. Finally, we evaluate the proposed model HVAM using two public real-world datasets. The experimental results demonstrate the superior performance of our model comparing to the state-of-the-art methods for sequential recommendation.
Modeling the Hierarchical Fuzzy System for Suitable Crop Recommendation

To obtain successive sustainable agriculture, a farmer needs good practical and expert’s knowledge regarding the modern farming technologies. As per the personal experience and traditional knowledge, the framers recommend a suitable crop for cultivation and this prediction may go wrong several times. Therefore, fuzzy expert system designed for crop recommendation systems for archiving the efficient crop recommendation. In the traditional single-layer fuzzy system, the rule is exponentially increased when the system variables are increased and a larger rule base will affect the system performance and transparency. Therefore, the multi-layer system is developed using the fuzzy hierarchical approach. The hierarchical fuzzy model is applied in a Mamdani fuzzy inference system for a suitable crop recommendation system. The crop recommendation system has 12 input variables, and it was decomposed into six fuzzy subsystems and arranged as per the priority. The final results show that a hierarchical crop recommendation system provides better performance than traditional fuzzy crop recommendation.
Learning Scrum

The LEGO®-Scrum simulation-based training (SBT) described here shows how LEGO® bricks can help professionals learn first-hand about Scrum methodology, an Agile approach to software development projects. The chapter's objectives are 1) to present the modalities of the LEGO®-Scrum SBT, 2) to demonstrate how LEGO® bricks can help professionals learn, first-hand, about Scrum, and 3) to illustrate how this learning can be relevant and impactful for participants. Based on observations, interviews, and a data collection by questionnaire carried out with 198 participants, the proposed SBT appears to provide a significant, relevant, and valuable learning experience. In addition, four experienced Scrum masters and IT project managers, who played key roles in the SBT, argued that the LEGO®-Scrum SBT provides a realistic representation of real-world Scrum projects; that it is dynamic, complex, challenging, and motivating; and that participants' learning is evocative and relevant, since they learn by doing.
Typical opinions mining based on Douban film comments in animated movies

The film comments data contains a huge amount of mining research value, and text mining analysis of the animated film’s comments can objectively reflect the quality of the animated film presentation and the problems generally expressed by the audience. However, these film comments are often mixed. The existing well-known film reviews websites have not excavated typical reviews on the users film comment text, so neither the audience nor the animation creators can analyze and apply the comments.This paper presents a general framework for mining typical opinions of film comments and uses crawler technology to obtain network review data, extract comment keywords based on the TF-IDF algorithm, and convert comments segmentation into word vectors trained by a neural network through Word2Vec. Then, using certain extraction rules and the K-means algorithm, the typical opinions with the same semantics but different expressions are aggregated together, and the typical opinions of the animation review of “Monkey King: Hero Is Back” are excavated. From the excavated information, we find out the production problems of the animation, so as to provide a certain reference to the creation of animated movies.
Application of virtual reality and augmented reality technology in Teaching<sup>1</sup>

"Internet Plus Education" is just unfolding with the upgrading of educational philosophy and the innovation of teaching methods. The application of virtual reality and augmented reality technology in the field of education has broad prospects. This paper introduces the classification of virtual technology, puts forward the composition of teaching system and human-computer interaction system based on AR and AR technology, describes the key technologies of teaching system based on VR and AR, and points out the problems of VR and AR application in teaching.
TouristHub: User Experience and Interaction Design for Supporting Tourist Trip Planning

In this paper we present the design of the interactions and the user experience of a web platform that aims to assist travelers in planning personalized trips based on personal user preferences and constraints. The paper emphasizes the design challenges and the methods used to elicit information that led to the outline of design requirements. It also presents the design considerations and the prototyping processes from low to high fidelity prototypes of the user interfaces.
ClothFace: A Batteryless Glove-Integrated User Interface Solution based on Passive UHF RFID Technology

Due to their unique advantages, wearable user interface solutions have gained a lot of research and commercial interest. This paper introduces ClothFace technology by presenting a batteryless glove-integrated user interface. The solution is based on passive ultrahigh frequency (UHF) radio frequency identification (RFID) technology. The first prototype of this solution was fabricated from copper tape to a normal cotton-based glove. The user interface on the glove consists of three antenna parts on three different fingers of the glove, each of which has an RFID microchip with a unique ID. Further, an additional antenna part is attached to the thumb of the glove. The antennas are initially separated from each other, and none of the microchips is readable for the RFID reader. When thumb antenna touches any of the three finger antennas, the touch creates an electrical connection, and the corresponding microchip can be detected by the RFID reader. In this study, the developed glove-integrated user interface was evaluated in an office environment by three test subjects, who all received 100 orders from a specific testing software. The average success rate in this first test was 98 %. These initial results are very encouraging, especially when considering that the glove-integrated user interface, being light, flexible, and cost-effective, promises versatile interesting applications in several fields.
Identification of Fuzzy Rule-Based Models With Output Space Knowledge Guidance

In this article, we advocate that a knowledge tidbit residing in the output space could be helpful in improving the performance (accuracy) of the fuzzy rule-based model. It states that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">if two outputs are far apart from each other</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">it is advisable to place their corresponding inputs in different clusters when forming subspaces of the input space</i> . Considering this knowledge guidance mechanism, we propose two different methods to partition the input space. In the first method, input data are first partitioned with the use of the standard clustering algorithm, say fuzzy C-means; here, a constructed partition matrix is reflective of the structure present in the input space. Then, the knowledge tidbit is used to adjust the entries of the original partition matrix in such a way that those input data whose corresponding output data are far apart from each other are assigned with low values of proximity. In the second method, we propose two strategies to modify the distance between input data and a prototype (cluster center) identified in the input space. The crux of this method is that if there are many input data (which, in virtue of the knowledge tidbit, are regarded as being far-apart from the input data of interest) around a certain prototype, the distance between the input data of interest and this prototype should be penalized. Thus, the membership of these input data to the prototype is reduced. The comprehensive experimental studies carried out on both synthetic and publicly available data are used to examine the usefulness of the proposed methods.
Threshold Optimized Strategy Based on Improved Flower Pollination Algorithm for Unbalanced Data

The imbalance learning is one of the main challenges of machine learning. In recent years, more and more scholars have paid attention to the application of imbalanced algorithms in real scenes, but it is often accompanied by cost sensitivity problems, that is, the misclassification of the minority will lead to extremely high cost, so this field is difficult. Aiming at this problem, this paper proposed a decision threshold optimization strategy based on the Improved Flower Pollination Algorithm, which combined machine learning with a new metaheuristics algorithm. Through the ImFPA, the threshold is adjusted to obtain better prediction results so that makes the precision perform well. Experiments reveal that when applied to imbalanced data, the method can effectively improve the precision of minority classes significantly, and has high practical value in the field of real-world problems.
An approach to near-lossless hyperspectral data compression using deep autoencoder

In recent years, the development of sensor technology in the area of hyperspectral imaging has been continuously moving forward. The result is a significant increase in availability, applications and, consequently, data volume. Compression is required to facilitate the transmission and storage of hyperspectral data sets. The high spectral correlation between adjacent bands allows for decorrelation approaches to compress the data with minimal loss of important information. Since it is not known a priori which feature is essential, the compression of hyperspectral data is a challenging task. In this paper, we introduce an approach to compress hyperspectral data using a Deep Autoencoder. An Autoencoder is an artificial neural network that first learns the important features from the data, and subsequently reconstructs the data from the reduced encoded representation. The evaluation is done by comparing the classification performance between the original and the reconstructed data. As a classifier we use the Adaptive Coherence Estimator to compare the spectral signatures. Performance is assessed by comparing the mean classification accuracy for a fixed false alarm rate. Additionally, the Signal to Noise Ratio and the spectral angle are used as metrics for evaluating the reconstruction performance. Airborne hyperspectral data were used in combination with simulated data, representing a linear mixture with different ratios of target and background spectra. Multiple target and background materials are tested to compare the performance. The selected data provide a representative set of target and background spectra to evaluate the compression method in relation to the detection limit. The compression rate is set to 4:1 and the reconstruction accuracy is investigated. Additionally, classification of noisy data is compared to the compression results to show the impact of information loss. If both results are similar, it can be deduced that the compression process is near-lossless.
Detecting Malicious TLS Network Traffic Based on Communication Channel Features

For highly camouflaged Command and Control (C&C) communications, especially those using the Transport Security Layer (TLS) protocol, traditional classifiers which only based on statistical features or TLS handshake features gradually fail to detect such behavior. In this context, exploring features of other dimensions to build a more targeted recognition model is one of the ways to alleviate this problem. This paper proposed a new method of detecting malicious TLS traffic by using the communication channel as the detection unit, and a new set of modeling features for the communication channel was designed, including distribution features, the consistency features and statistical features of TLS communication channel. Experiments show that compared with other two types of features, the consistency features contribute most, and combining these three types of features together can train a better classifier which the precision reaches 92.57%. Comparative experiments show that the proposed method is more advantageous when faced with highly camouflaged TLS flows because the proposed method also achieved highest F1 score, and the accuracy is about 2% higher than the classifier based on the TLS handshake features, and 12% higher than the clustering model based on the statistical features of flow.
Towards Model Checking of Voting Protocols in Uppaal

The design and implementation of a trustworthy e-voting system is a challenging task. Formal analysis can be of great help here. In particular, it can lead to a better understanding of how the voting system works, and what requirements on the system are relevant. In this paper, we propose that the state-of-art model checker Uppaal provides a good environment for modelling and preliminary verification of voting protocols. To illustrate this, we demonstrate how to model a version of Prêt à Voter in Uppaal, together with some natural extensions. We also show how to verify a variant of receipt-freeness, despite the severe limitations of the property specification language in the model checker.The aim of this work is to open a new path, rather then deliver the ultimate outcome of formal analysis. A comprehensive model of Prêt à Voter, more accurate specification of requirements, and exhaustive verification are planned for the future.
Bayesian Update with Importance Sampling: Required Sample Size

Importance sampling is used to approximate Bayes’ rule in many computational approaches to Bayesian inverse problems, data assimilation and machine learning. This paper reviews and further investigates the required sample size for importance sampling in terms of the χ2-divergence between target and proposal. We illustrate through examples the roles that dimension, noise-level and other model parameters play in approximating the Bayesian update with importance sampling. Our examples also facilitate a new direct comparison of standard and optimal proposals for particle filtering.
Authenticated Quantum Sortition and Application in “Picking at Random” Problems

Sortition is one fairness-preserving decision-making method, whereby each of the involved legitimate users can obtain a completely random but mutually different message. Here, using the fantastic high-dimension multi-particle singlet states as the random lot pool, we propose the first authenticated quantum sortition protocol in theory with some desirable properties of privacy, fairness, and uniqueness, which shows particular function glamour over to the classical sortitions. The proposed protocol also can expand to solve some related problems, defined as “picking at random”. Any effective attacks from an outside eavesdropper, semi-honest designated authority, and inside malicious user will be detected by honest participants, and thus guarantee the security. This work to sortition theory helps elucidate how quantum mechanics can be harnessed for secure strategic advantage.
Automatic repair of OWASP Top 10 security vulnerabilities

Current work on automatic program repair has not focused on actually prevalent vulnerabilities in web applications, such as described in the OWASP Top 10 categories, leading to a scarcely explored field, which in turn leads to a gap between industry needs and research efforts. In order to assess the extent of this gap, we have surveyed and analyzed the literature on fully automatic source-code manipulating program repair of OWASP Top 10 vulnerabilities, as well as their corresponding test suites. We find that there is a significant gap in the coverage of the OWASP Top 10 vulnerabilities, and that the test suites used to test the analyzed approaches are highly inadequate. Few approaches cover multiple OWASP Top 10 vulnerabilities, and there is no combination of existing test suites that achieves a total coverage of OWASP Top 10.