Reviewerly/exHarmony huggingface dataset:
A Dynamic Co-attention Network for Session-based Recommendation

Session-based recommendation is the task of recommending the next item a user might be interested in given partially known session information, e.g., part of a session or recent historical sessions. An effective session-based recommender should be able to exploit a user's evolving preferences, which we assume to be a mixture of her short- and long-term interests. Existing session-based recommendation methods often embed a user's long-term preference into a static representation, which plays a fixed role when dealing with her current short-term interests. This is problematic because long-term preferences may be more or less important for predicting the next conversion depending on the user's short-term interests. We propose a DCN-SR. DCN-SR applies a co-attention network to capture the dynamic interactions between the user's long- and short-term interaction behavior and generates co-dependent representations of the user's long- and short-term interests. For modeling a user's short-term interaction behavior, we design a CGRU network to take actions like "click'', "collect'' and "buy'' into account. Experiments on e-commerce datasets show significant improvements of DCN-SR over state-of-the-art session-based recommendation methods, with improvements of up to 2.58% on the Tmall dataset and 3.08% on the Tianchi dataset in terms of [email protected] [email protected] improvements are 3.78% and 4.05%, respectively. We also investigate the scalability and sensitivity of DCN-SR. The improvements of DCN-SR over state-of-the-art baselines are especially noticeable for short sessions and active users with many historical interactions.
Practical card-based implementations of Yao's millionaire protocol

Yao's millionaire protocol enables Alice and Bob to know whether or not Bob is richer than Alice by using a public-key cryptosystem without revealing the actual amounts of their properties. In this paper, we present simple and practical implementations of Yao's millionaire protocol using a physical deck of playing cards; we straightforwardly implement the idea behind Yao's millionaire protocol so that even non-experts can easily understand their correctness and secrecy. Our implementations are based partially on the previous card-based scheme proposed by Nakai, Tokushige, Misawa, Iwamoto, and Ohta; their scheme admits players' private actions on a sequence of cards called Private Permutation (PP), implying that a malicious player could make an active attack (for example, he/she could exchange some of the cards stealthily when doing such a private action). By contrast, our implementations rely on a familiar shuffling operation called a random cut, and hence, they can be conducted completely publicly so as to avoid any active attack. More specifically, we present two card-based implementations of Yao's millionaire protocol; one uses a two-colored deck of cards (which consists of black and red cards), and the other uses a standard deck of playing cards. Furthermore, we also provide card-based protocols that rely on a logical circuit representing the comparison.
Review of Meta-Heuristic Optimization based Artificial Neural Networks and its Applications

Abstract There are several meta-heuristic optimization algorithms developed on inspiration from nature. Artificial neural network proves to be efficient among other machine learning techniques. The efficiency of classification and prediction is improved by optimizing artificial neural network using the meta-heuristic optimization algorithms. The review of some of these hybrid artificial neural networks that are applied for benchmark datasets and to specific real-time experiments for classification and prediction are discussed. Upcoming sections cover the current trending research topics dealing with optimized artificial neural network concepts and provide some interesting insights for researchers to use in their respective applications domains of interest.
Estimation for a Class of Parameter-Controlled Tunnel Diode Circuits

This paper studies the estimation problem for a class of nonlinear tunnel diode circuits with parameter perturbation. The dynamics of the nonlinear circuit is approximated by the Takagi-Sugeno fuzzy model with linear fractional parametric uncertainties. Considering the fuzzy filter with gain uncertainties, a filtering error system can be gotten naturally. The concentration of this paper lies in deriving the design conditions for the desired resilient filter such that the filtering error system preserves asymptotic stability as well as the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> -norm of the transfer function from the external disturbance input to the filtering error output is below a specified bound. The filter design conditions for guaranteeing the prescribed peak-to-peak performance of the filtering error system are provided by a set of linear matrix inequalities. Finally, a simulation result is presented to illustrate the validity and effectiveness of the proposed filtering design for the parameter-controlled tunnel diode circuit.
A Semantics Aware Random Forest for Text Classification

The Random Forest (RF) classifiers are suitable for dealing with the high dimensional noisy data in text classification. An RF model comprises a set of decision trees each of which is trained using random subsets of features. Given an instance, the prediction by the RF is obtained via majority voting of the predictions of all the trees in the forest. However, different test instances would have different values for the features used in the trees and the trees should contribute differently to the predictions. This diverse contribution of the trees is not considered in traditional RFs. Many approaches have been proposed to model the diverse contributions by selecting a subset of trees for each instance. This paper is among these approaches. It proposes a Semantics Aware Random Forest (SARF) classifier. SARF extracts the features used by trees to generate the predictions and selects a subset of the predictions for which the features are relevant to the predicted classes. We evaluated SARF's classification performance on $30$ real-world text datasets and assessed its competitiveness with state-of-the-art ensemble selection methods. The results demonstrate the superior performance of the proposed approach in textual information retrieval and initiate a new direction of research to utilise interpretability of classifiers.
Extreme Gradient Boosting Machine Learning Algorithm For Safe Auto Insurance Operations

Vehicle safety is one of the most important research topics not only for vehicular industry but also for auto insurance companies. It is of great significance for them to avoid compensating damaged vehicles and dealing with an incredible number of correct and fraudulent claims. In fact, fraudulent claims present a huge and a costly problem for insurance companies and end up with big losses reaching over billions of Dollars yearly. These frauds also have social-economical consequences as their costs are defrayed by the policy holder through the increase of their premiums to cover the insurer loss. Several insurance companies are exploring innovative solutions not only to improve customers safety and driving experience but also to streamline fraud detection methods since traditional ones are complex, time-consuming, and usually lead to inaccurate results. In this paper, we develop an automated fraud detection approach for auto insurance companies based on extreme gradient boosting algorithm, aka XGBoost. The objective is to automatically detect fraudulent claims and classify them into different fraud types. To this end, data analysis techniques are used to clean, explore, and extract relevant features. The proposed framework aims to minimize human intervention, deliver alerts for risky claims, and reduce monetary losses in the auto insurance industry. The obtained results reveal a high performance gain achieved by XGBoost in detecting and classifying fraudulent claims compared to other machine learning algorithms.
Text Summarization: A Brief Review

Text Summarization is the process of creating a summary of a certain document that contains the most important information of the original one, the purpose of it is to get a summary of the main points of the document. Abstractive summarization of multi-documents aims to generate a concentrated version of the document while keeping the main information. Due to the massive amount of data these days, the importance of summarization arose. Finally, this paper collects the most recent and relevant research in the field of the text summarization to study and analysis for future research. It will be significant by giving a new direction to who are interested in this domain in the future.
Research on Novel Bearing Fault Diagnosis Method Based on Improved Krill Herd Algorithm and Kernel Extreme Learning Machine

In this paper, a novel bearing intelligent fault diagnosis method based on a novel krill herd algorithm (NKH) and kernel extreme learning machine (KELM) is proposed. Firstly, multiscale dispersion entropy (MDE) is used to extract fault features of bearings to obtain a set of fault feature vectors composed of dispersion entropy. Then, it is imported into the kernel extreme learning machine for fault diagnosis. But considering the kernel function parameters <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>σ</mml:mi></mml:math> and the error penalty factor <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi>C</mml:mi></mml:math> will affect the classification accuracy of the kernel extreme learning machine, this paper uses the novel krill herd algorithm (NKH) for their optimization. The opposite populations are added to the NKH in the initialization of population to improve its speed and prevent local optimum, and during the period of looking for the optimal solution, the impulse operator is introduced to ensure it has enough impulse to rush out of the local optimal once into the local optimum. Finally, in order to verify the effectiveness of the proposed method, it was applied to the bearing fault experiment of Case Western Reserve University and XJTU-SY bearing data set. The results show that the proposed method not only has good fault diagnosis performance and generalization but also has fast convergence speed and does not easily fall into the local optimum. Therefore, this paper provides a method for fault diagnosis under different loads. Meanwhile, the new method (NKH-KELM) is compared and analyzed with other mainstream intelligent bearing fault diagnosis methods to verify the effectiveness and accuracy of the proposed method.
Simple yet efficient real-time pose-based action recognition

Recognizing human actions is a core challenge for autonomous systems as they directly share the same space with humans. Systems must be able to recognize and assess human actions in real-time. To train the corresponding data-driven algorithms, a significant amount of annotated training data is required. We demonstrate a pipeline to detect humans, estimate their pose, track them over time and recognize their actions in real-time with standard monocular camera sensors. For action recognition, we transform noisy human pose estimates in an image like format we call Encoded Human Pose Image (EHPI). This encoded information can further be classified using standard methods from the computer vision community. With this simple procedure, we achieve competitive state-of-the-art performance in pose-based action detection and can ensure real-time performance. In addition, we show a use case in the context of autonomous driving to demonstrate how such a system can be trained to recognize human actions using simulation data.
A Blockchain Policy and Charging Control Framework for Roaming in Cellular Networks

As a technology foundation of cryptocurrencies, blockchain enables decentralized peer-topeer trading through consensus mechanisms without the involvement of a third party. Blockchain has been regarded as an auspicious technology for future cellular networks. It is able to provide solutions to problems related to mobile operators and user trust, embedded smart contracts, security concerns, pricing (e.g., for roaming), and so on. When applying blockchain to cellular networks, there are significant challenges in terms of deployment and application, due to resource-constrained transactions. This article begins by introducing the basic concept of blockchain and then moves on to illustrate its benefits and limitations in the roaming system. A unified roaming-based blockchain technology is offered to demonstrate its suitability for cellular networks as opposed to traditional technology. Finally, potential issues and challenges of roaming-based blockchains are addressed and evaluated using the roaming use case in the EU.
Creating Augmented Reality USDZ Files to Visualize 3D Objects on Student Phones in the Classroom

Augmented reality (AR) is a means of superimposing artificial 3D objects over the real world via a mobile device. An AR standard file format has been recently implemented on mobile devices that current students commonly own. Here we describe three relatively nontechnical methods to produce 3D AR objects for chemistry courses and demonstrate their use as both quick in-lecture activities and as part of an extended laboratory. Preliminary in-course student assessments (32 responses out of 42 students) indicated that this technology was successfully implemented on students’ devices (100%), that it generated interest/excitement (78%), and that it helped students visualize 3D objects (81%).
Exploring Learner Perception, Experience and Motivation of Using a Mobile App in L2 Vocabulary Acquisition

In recent years, the widespread growth of mobile-assisted language learning using apps has made English vocabulary learning increasingly accessible. This study investigated students' perceptions of a vocabulary learning app developed by the researcher as a tool to help them to improve their L2 receptive vocabulary. The app, containing 20 levels, works with both iOS and Android. The participants were 14 undergraduate students at an English-medium university in Hong Kong. A qualitative methodology of semi-structured interviews captured a holistic overview and revealed students' perceptions of the app. The results suggested that Hong Kong university students have a strong motivation for acquiring an L2 vocabulary. Participants also indicated a preference for mobile applications using gamified features. The study highlights directions for future research into apps for English language learning.
An attention-based deep learning model for clinical named entity recognition of Chinese electronic medical records

Clinical named entity recognition (CNER) is important for medical information mining and establishment of high-quality knowledge map. Due to the different text features from natural language and a large number of professional and uncommon clinical terms in Chinese electronic medical records (EMRs), there are still many difficulties in clinical named entity recognition of Chinese EMRs. It is of great importance to eliminate semantic interference and improve the ability of autonomous learning of internal features of the model under the small training corpus.From the perspective of deep learning, we integrated the attention mechanism into neural network, and proposed an improved clinical named entity recognition method for Chinese electronic medical records called BiLSTM-Att-CRF, which could capture more useful information of the context and avoid the problem of missing information caused by long-distance factors. In addition, medical dictionaries and part-of-speech (POS) features were also introduced to improve the performance of the model.Based on China Conference on Knowledge Graph and Semantic Computing (CCKS) 2017 and 2018 Chinese EMRs corpus, our BiLSTM-Att-CRF model finally achieved better performance than other widely-used models without additional features(F1-measure of 85.4% in CCKS 2018, F1-measure of 90.29% in CCKS 2017), and achieved the best performance with POS and dictionary features (F1-measure of 86.11% in CCKS 2018, F1-measure of 90.48% in CCKS 2017). In particular, the BiLSTM-Att-CRF model had significant effect on the improvement of Recall.Our work preliminarily confirmed the validity of attention mechanism in discovering key information and mining text features, which might provide useful ideas for future research in clinical named entity recognition of Chinese electronic medical records. In the future, we will explore the deeper application of attention mechanism in neural network.
Cascaded MultiTask 3-D Fully Convolutional Networks for Pancreas Segmentation

Automatic pancreas segmentation is crucial to the diagnostic assessment of diabetes or pancreatic cancer. However, the relatively small size of the pancreas in the upper body, as well as large variations of its location and shape in retroperitoneum, make the segmentation task challenging. To alleviate these challenges, in this article, we propose a cascaded multitask 3-D fully convolution network (FCN) to automatically segment the pancreas. Our cascaded network is composed of two parts. The first part focuses on fast locating the region of the pancreas, and the second part uses a multitask FCN with dense connections to refine the segmentation map for fine voxel-wise segmentation. In particular, our multitask FCN with dense connections is implemented to simultaneously complete tasks of the voxel-wise segmentation and skeleton extraction from the pancreas. These two tasks are complementary, that is, the extracted skeleton provides rich information about the shape and size of the pancreas in retroperitoneum, which can boost the segmentation of pancreas. The multitask FCN is also designed to share the low- and mid-level features across the tasks. A feature consistency module is further introduced to enhance the connection and fusion of different levels of feature maps. Evaluations on two pancreas datasets demonstrate the robustness of our proposed method in correctly segmenting the pancreas in various settings. Our experimental results outperform both baseline and state-of-the-art methods. Moreover, the ablation study shows that our proposed parts/modules are critical for effective multitask learning.
Modeling hierarchical category transition for next POI recommendation with uncertain check-ins

Most existing next POI recommendation studies rely on users’ certain check-ins at individual POIs (e.g., Italian restaurant). In reality, users may leave some uncertain check-ins in the places (e.g., shopping mall), which are named as collective POIs. It indicates that we cannot always access users’ precise check-ins at collective POIs, thus existing approaches fail to work well. To this end, we propose a new research problem, that aims to recommend next individual POIs with uncertain check-ins at collective POIs. It is, however, difficult to learn complete and accurate users’ check-in transition patterns with uncertain check-ins. Besides, uncertain check-ins aggravate the cold start issue, as the individual POIs inside collective POIs cannot be observed in users’ historical check-ins. To tackle these challenges, we devise a novel hierarchical category transition (HCT) framework, which exploits category transitions at different layers to model users’ preference transition patterns in different granularity. By doing so, HCT predicts users’ preferred categories inside collective POIs. As bounded to specific categories, HCT further adopts hierarchical dependencies between POIs and categories to capture the semantic relatedness of POIs, thus easing the cold start issue. Empirical studies on multiple datasets show the superiority of HCT against state-of-the-art algorithms.
Learning-based attacks for detecting the vulnerability of computer-generated hologram based optical encryption

Optical encryption has attracted wide attention for its remarkable characteristics. Inspired by the development of double random phase encoding, many researchers have developed a number of optical encryption systems for practical applications. It has also been found that computer-generated hologram (CGH) is highly promising for optical encryption, and the CGH-based optical encryption possesses remarkable advantages of simplicity and high feasibility for practical implementations. An input image, i.e., plaintext, can be iteratively or non-iteratively encoded into one or several phase-only masks via phase retrieval algorithms. Without security keys, it is impossible for unauthorized receivers to correctly extract the input image from ciphertext. However, cryptoanalysis of CGH-based optical encryption systems has not been effectively carried out before, and it is also concerned whether CGH-based optical encryption is sufficiently secure for practical applications. In this paper, learning-based attack is proposed to demonstrate the vulnerability of CGH-based optical security system without the direct retrieval of optical encryption keys for the first time to our knowledge. Many pairs of the extracted CGH patterns and their corresponding input images (i.e., ciphertext-plaintext pairs) are used to train a designed learning model. After training, it is straightforward to directly retrieve unknown plaintexts from the given ciphertexts (i.e., phase-only masks) by using the trained learning model without subsidiary conditions. Moreover, the proposed learning-based attacks are also feasible and effective for the cryptoanalysis of CGH-based optical security systems with multiple cascaded phase-only masks. The proposed learning-based attacking method paves the way for the cryptoanalysis of CGH-based optical encryption.
Review and Categorization of Digital Applications in the Energy Sector

Digitalization is a transformation process which has already affected many parts of industry and society and is expected to yet increase its transformative speed and impact. In the energy sector, many digital applications have already been implemented. However, a more drastic change is expected during the next decades. Good understanding of which digital applications are possible and what are the associated benefits as well as risks from the different perspectives of the impacted stakeholders is of high importance. On the one hand, it is the basis for a broad societal and political discussion about general targets and guidelines of digitalization. On the other hand, it is an important piece of information for companies in order to develop and sustainably implement digital applications. This article provides a structured overview of potential digital applications in the German energy (electricity) sector, including the associated benefits and the impacted stakeholders on the basis of a literature review. Furthermore, as an outlook, a methodology to holistically analyze digital applications is suggested. The intended purpose of the suggested methodology is to provide a complexity-reduced fact base as input for societal and political discussions and for the development of new digital products, services, or business models. While the methodology is outlined in this article, in a follow-up article the application of the methodology will be presented and the use of the approach reflected.