Reviewerly/exHarmony huggingface dataset:
Design of the Landslide Multi-factor Monitoring System Based on the GNSS Technology

Based on the multi-element monitoring of landslide, this paper adopts the GNSS technology to build the three-dimensional space monitoring system to research the landslide multi-element three- dimensional space monitoring technology. Through collecting the rainfall, soil moisture content, slope, pore water pressure, stress, single point surface deformation etc, the system has many key technologies such as multi-parameter, information acquisition, data fusion analysis and real-time early warning of landslides and integrate various elements by using the professional technology. This research provides an important reference for the landslide disaster prevention.
Explanations for Dynamic Programming

We present an approach for explaining dynamic programming that is based on computing with a granular representation of values that are typically aggregated during program execution. We demonstrate how to derive more detailed and meaningful explanations of program behavior from such a representation than would otherwise be possible. To illustrate the practicality of this approach we also present a Haskell library for dynamic programming that allows programmers to specify programs by recurrence relationships from which implementations are derived that can run with granular representation and produce explanations. The explanations essentially answer questions of why one result was obtained instead of another. While usually the alternatives have to be provided by a user, we will show that with our approach such alternatives can be in some cases anticipated and that corresponding explanations can be generated automatically.
ТРЕНДОВ LIBRARY MARKETING: TRENDS ANALYSIS

Active changes are taking place in determining the place of libraries in cultural, scientific, education and information infrastructures of the society. Assuming, that with the help of marketing it is possible to form public opinion about the specific library as well as about libraries in general, and also obtain support from library stakeholders, increase the visibility of libraries and enlarge the market share, specialists are adjusting marketing to the conditions of libraries’ functioning. The purpose of this study is to justify the importance of introducing marketing policy into the libraries’ activities.
Secure Multi-receiver Communications: Models, Proofs, and Implementation

With the demand of providing message authentication and confidentiality as well as receiver anonymity in applications such as multicast communication, digital content distribution systems, and pay-per-view channels, many anonymous multi-receiver signcryption mechanisms have been put forward to offer these functions efficiently, which have the lower computational cost and communication overhead compared with the signature-then-encryption approaches. However, most certificateless-based schemes either focus on providing receiver anonymity or focus on improving signcryption efficiency. In addition, most certificateless-based schemes rely on bilinear pairing operations, which are more time consuming than modular exponentiation and scalar multiplication in finite fields. In this paper, we propose a practical anonymous multi-receiver certificateless signcryption (AMCLS) scheme that can satisfy message confidentiality, source authentication, and anonymity simultaneously and efficiently. In the proposed scheme, the sender’s signcryption cost increases linearly with the increase of the designated receivers, while the unsigncryption cost per receiver is constant. The adoption of elliptic curve scalar multiplication instead of bilinear pairing operation improves the efficiency of the proposed scheme. Both the sender and receivers’ identities are encrypted from being exposed to offer anonymity. Through security analysis, our proposal can be proved to achieve chosen-ciphertext attack (CCA) security in encryption indistinguishability and receiver anonymity in strong, commonly accepted attack models. Theoretical analyses and experimental results demonstrate that our scheme enjoys a better efficiency than other certificateless-based schemes.
NLP4REC: The WSDM 2020 Workshop on Natural Language Processing for Recommendations

Natural language processing is becoming more and more important in recommender systems. This half day workshop explores challenges and potential research directions in Recommender Systems (RSs) combining Natural Language Processing (NLP). The focus will be on stimulating discussions around how to combine natural language processing technologies with recommendation. We welcome theoretical, experimental, and methodological studies that leverage NLP technologies to advance recommender systems, as well as emphasize the applicability in practical applications. The workshop aims to bring together a diverse set of researchers and practitioners interested in investigating the interaction between NLP and RSs to develop more intelligent RSs.
Change in Educational Models for Facing Challenges to Lead Students into a New Way of Learning

Contemporary societies face challenges that come from competition, dynamism, and change, where people and students, in particular, embrace attitudes and practices that are different from those that their parents and teachers share.Change in educational models for facing those challenges implies a different know-how and also different abilities that teachers have to develop, in order for them to lead students into new way for learning.What kind of transformational and integrative leadership is needed for professors to create conditions and abilities that will help their students to face the challenges for the future in a developing country?In this chapter, we aim to provide proof of how the most important private university in Mexico, the Tecnológico de Monterrey, has changed not only an educational model but also an inspiring and transforming leadership-oriented model for teachers for change.Projects and new teaching abilities are needed, but also a sense where the professor gets a more flexible role, contributing for creating and working with scientific knowledge.
Should I Stay or Should I Go? Automated Vehicles in the Age of Climate Change

Will automated driving help or hurt our efforts to remedy climate change? The overall impact of transportation and mobility on the global ecosystem is clear: changes to that system can greatly affect climate outcomes. The design of mobility and automotive systems will influence key factors such as driving style, fuel choice, ride sharing, traffic patterns, and total mileage. However, to date, there are few research efforts that explicitly focus on these overlapping themes (automated driving & climate changes) within the HCI and AutomotiveUI communities. Our intention is to grow this community and awareness of the related problems. Specifically, in this workshop, we invite designers, researchers, and practitioners from the sustainable HCI, persuasive design, AutomotiveUI, and mobility communities to collaborate in finding ways to make future mobility more sustainable. Using embodied design improvisation and design fiction methods, we will explore the ways that systems affect behavior which then affect the environment.
Asymptotic Enumeration of Orientations of a Graph as a Function of the Out-Degree Sequence

We prove an asymptotic formula for the number of orientations with given out-degree (score) sequence for a graph $G$. The graph $G$ is assumed to have average degrees at least $n^{1/3 + \epsilon}$ for some $\epsilon &gt; 0$, and to have strong mixing properties, while the maximum imbalance (out-degree minus in-degree) of the orientation should be not too large. Our enumeration results have applications to the study of subdigraph occurrences in random orientations with given imbalance sequence. As one step of our calculation, we obtain new bounds for the maximum likelihood estimators for the Bradley-Terry model of paired comparisons.
IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems

Personal assistant systems, such as Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana, are becoming ever more widely used. Understanding user intent such as clarification questions, potential answers and user feedback in information-seeking conversations is critical for retrieving good responses. In this paper, we analyze user intent patterns in information-seeking conversations and propose an intent-aware neural response ranking model "IART", which refers to "Intent-Aware Ranking with Transformers". IART is built on top of the integration of user intent modeling and language representation learning with the Transformer architecture, which relies entirely on a self-attention mechanism instead of recurrent nets. It incorporates intent-aware utterance attention to derive an importance weighting scheme of utterances in conversation context with the aim of better conversation history understanding. We conduct extensive experiments with three information-seeking conversation data sets including both standard benchmarks and commercial data. Our proposed model outperforms all baseline methods with respect to a variety of metrics. We also perform case studies and analysis of learned user intent and its impact on response ranking in information-seeking conversations to provide interpretation of results.
A Generic View on the Unified Zero-Knowledge Protocol and Its Applications

We present a generalization of Maurer's unified zero-knowledge (UZK) protocol, namely a unified generic zero-knowledge (UGZK) construction. We prove the security of our UGZK protocol and discuss special cases. Compared to UZK, the new protocol allows to prove knowledge of a vector of secrets instead of only one secret. We also provide the reader with a hash variant of UGZK and the corresponding security analysis. Last but not least, we extend Cogliani et al.'s lightweight authentication protocol by describing a new distributed unified authentication scheme suitable for wireless sensor networks and, more generally, the Internet of Things.
Nonnegative Sparse Probabilistic Estimation for Single Sample Face Recognition

Face recognition (FR) is a hotspot in pattern recognition and image processing for its wide applications in real life. One of the most challenging problems in FR is single sample face recognition (SSFR). In this paper, we proposed a novel algorithm based on nonnegative sparse representation, collaborative presentation, and probabilistic graph estimation to address SSFR. The proposed algorithm is named as Nonnegative Sparse Probabilistic Estimation (NNSPE). To extract the variation information from the generic training set, we first select some neighbor samples from the generic training set for each sample in the gallery set and the generic training set can be partitioned into some reference subsets. To make more meaningful reconstruction, the proposed method adopts nonnegative sparse representation to reconstruct training samples, and according to the reconstruction coefficients, NNSPE computes the probabilistic label estimation for the samples of the generic training set. Then, for a given test sample, collaborative representation (CR) is used to acquire an adaptive variation subset. Finally, the NNSPE classifies the test sample with the adaptive variation subset and probabilistic label estimation. The experiments on the AR and PIE verify the effectiveness of the proposed method both in recognition rates and time cost.
PixelHop++: A Small Successive-Subspace-Learning-Based (SSL-based) Model for Image Classification

The successive subspace learning (SSL) principle was developed and used to design an interpretable learning model, known as the PixelHop method,for image classification in our prior work. Here, we propose an improved PixelHop method and call it PixelHop++. First, to make the PixelHop model size smaller, we decouple a joint spatial-spectral input tensor to multiple spatial tensors (one for each spectral component) under the spatial-spectral separability assumption and perform the Saab transform in a channel-wise manner, called the channel-wise (c/w) Saab transform.Second, by performing this operation from one hop to another successively, we construct a channel-decomposed feature tree whose leaf nodes contain features of one dimension (1D). Third, these 1D features are ranked according to their cross-entropy values, which allows us to select a subset of discriminant features for image classification. In PixelHop++, one can control the learning model size of fine-granularity,offering a flexible tradeoff between the model size and the classification performance. We demonstrate the flexibility of PixelHop++ on MNIST, Fashion MNIST, and CIFAR-10 three datasets.
Classification of diseases using a hybrid fuzzy mutual information technique with binary bat algorithm

Genetic datasets have a large number of features that may signifi cantly affect the disease classifi cation process, especially datasets related to cancer diseases.Evolutionary algorithms (EA) are used to fi nd the fastest and best way to perform these calculations, such as the bat algorithm (BA) by reducing the dimensions of the search area after changing it from continuous to discrete.In this paper, a method of gene selection was proposed two sequent stages: in the fi rst stage, the fuzzy mutual information (FMI) method is used to choose the most important genes selected through a fuzzy model that was built based on the dataset size.In the second stage, the BBA is used to reduce and determine a fi xed number of genes affecting the process of classifi cation, which came from the fi rst stage.The proposed algorithm, FMI_BBA, describes effi ciency, by obtaining a higher classifi cation accuracy and a few numbers of selected genes compared to other algorithms.
Toward a Generic Student Profile Model

Profile modeling is an important process that aims to give as complete representation as possible of all the aspects related to the user features. I n the educational field, student profile modeling can give important solutions to variant problems. It can mainly offer the most exact description of student s in order to: be able to act in case of problems such as failure, drop out…; offer students the most appropriate orientation and recommendation; define the most adaptive learning resources depending on their profiles… In this paper, we present an analytical and statistical study on student profile modeling to propose a generic student profile model based on different. Our purpose is to build a student profile model based on many important features that can be used alone or combined for decision making in different fields of the educational domain.
Simulation of Quantum Channel and Analysis of Its State Under Network Disruption

Presently the work done in the field of quantum cryptography is primarily theoretical, the purpose of this paper is to showcase its implementation in an observable manner. Thus, this paper establishes a more secure network using quantum key distribution (QKD) for data transfer between sender and receiver and also enables the quick identification of an eavesdropper in the said network. An analysis of the quantum channel traffic at the ideal state and also during network disruption (i.e. when the quantum state collapses) has been carried out. Due to the complex nature of quantum networks, a physical implementation of the same is not feasible. Hence, a simulation has been implemented via the use of NS-3 (Network Simulator Version 3).
Nonindependent Session Recommendation Based on Ordinary Differential Equation

The recommendation method based on user sessions is mainly to model sessions as sequences in the assumption that user behaviors are independent and identically distributed, and then to use deep semantic information mining through Deep Neural Networks. Nevertheless, user behaviors may be a nonindependent intention at irregular points in time. For example, users may buy painkillers, books, or clothes for different reasons at different times. However, this has not been taken seriously in previous studies. Therefore, we propose a session recommendation method based on Neural Differential Equations in an attempt to predict user behavior forward or backward from any point in time. We used Ordinary Differential Equations to train the Graph Neural Network and could predict forward or backward at any point in time to model the user's nonindependent sessions. We tested for four real datasets and found that our model achieved the expected results and was superior to the existing session-based recommendations.
Image-Based Gender Prediction Model Using Multilayer Feed-Forward Neural Networks

In this study, we develop a reliable and highperformance multi-layer feed-forward artificial neural networks (MFANNs) model for predicting gender classification.The study used features for a set of 450 images randomly chosen from the FERET dataset.We extract the only high-merit candidate parameters form the FERET dataset. A discrete cosine transformation (DCT) is employed to facilitate an image description and conversion. To reach the final gender estimation model, authors examined three artificial neural classifiers and each extremely performs deep computation processes. In addition to the MFANNs, artificial neural networks (ANNs) classifiers include support vector regression with radial-basis function (SVR-RBF) and k-Nearest Neighbor (k-NN). A 10-folds cross-validation technique (CV) is used to prove the integrity of the dataset inputs and enhance the calculation process of the model. In this model, the performance criteriafor accuracy rate and mean squared error (MSE) are carried out.Results of the MFANNs models are compared with the ones that obtained by SVR-RBF and k-NN.It is shown that the MFANNs model performs better (i.e.lowest MSE = 0.0789, and highest accuracy rate = 96.9%)than SVR-based and k-NN models.Linked the study findings with the results obtained in the literature review, we conclude that our method achieves a recommended calculation for gender prediction.
Analysing Human Activity Patterns by Chest-Mounted Wearable Devices

Internet of things (IoT) which is the invasion in the present era led to the emergence of various wearable devices which are known as smart wearable to monitor or measure different health attributes based on the activities. Today, wearable technology is constantly expanding in the market as it is highly in demand from the year 2010. Since then around 400 wearable have been developed, and 60% of them are activity trackers. Now, activity recognition is a promising research field, originated from ubiquitous, context-aware computing as well as multimedia. In recent times, recognizing daily activities is also part of the challenges for pervasive computing. Wearable devices are equipped with various sensors such as humidity sensor, gyroscope, accelerometer, and biosensors, for self-monitoring of routine physical actions. In this paper with the help of Python language, we analysed wearable devices data set which are positioned on chest in terms of wearable single chest-mounted uncalibrated accelerometer data sets collected from fifteen candidates separately performing seven different activities such as Working at Computer, Standing Up, Walking and Going up/downstairs, Standing*, Walking, Going Up/Downstairs, Walking and Talking with Someone Talking while Standing. These activities pattern analyses are helpful in measuring numerous health outcomes.
Finding Errors in Registrations of Local Variables Using Coccinelle for Accurate Garbage Collection

For accurate garbage collection (GC), all pointers belonging to the root set must be found. In a virtual machine (VM) implemented in C language, local variables of C language may contain pointers. Thus, some VMs add the values or addresses of local variables to a table that is visible to GC. However, this approach is error-prone because it requires adding local variables and removing them correctly though the entire source code of the VM. In this research, we checked if local variables are added and removed correctly by pattern matching against control flow graphs of the source code of the VM. We applied this check to the VM of a subset of JavaScript we are developing and found that it could identify many cases of missed adding and redundant adding.
Text Like Classification of Skeletal Sequences for Human Action Recognition

Human Action Recognition (HAR) has many applications in surveillance, gaming, animation and Active and Assisted Living (AAL). Several actions performed in daily life are composed of various poses arranged sequentially in time. Recognition of such actions is a difficult and challenging task. The classification approach proposed in this paper considers an analogy between actions and text, where an action is considered as a sentence and a single pose as a word. In the first stage, the poses are grouped based on their similarity and are then assigned labels. These labels are used for constructing label sequences representing motion. We propose Hierarchical Agglomerative Clustering (HAC) for clustering poses. Once the actions are modelled as the spatio-temporal evolution of key poses, we classify the actions using the Hidden Markov Model (HMM) and Hyper-dimensional Computing (HDC) classifiers. The experiments are performed on different datasets using both classifiers and the results are indicative of the effectiveness of the proposed approach in comparison with state-of-the-art methods.
The predictability between Bitcoin and US technology stock returns: Granger causality in mean, variance, and quantile

In this chapter, we test for Granger causality in mean, variance, and quantile between Bitcoin prices and various US stock indices belonging to the overall stock market and the sector of information technology and its various industries. Using daily data covering the period 18 August 2011 to 15 April 2019, results from the application of the test of Chen (2016) show the following. Unsurprisingly, there is evidence of a relationship between Bitcoin and US tech stock indices. Specifically, there is clear evidence of predictability between Bitcoin and US tech stocks, which seems to vary across quantiles. US stocks Granger cause Bitcoin return, but this is not the case for other stock indices. Generally, Granger causality from one market to another is insignificant in both mean and variance. However, when one market is in its bear state, the other become more volatile. Furthermore, there is evidence of casualties when one market is in its bear market state, while the other is in its bear or bull market state. Our findings suggest the need to use unconventional methods such as the Granger causality in moments of Chen (2016), otherwise, it might be wrongly apparent that Bitcoin and US (tech) stock indices are independent.