Reviewerly/exHarmony huggingface dataset:
Enhancing Video Recommendation Using Multimedia Content

Abstract Video recordings are complex media types. When we watch a movie, we can effortlessly register a lot of details conveyed to us (by the author) through different multimedia channels, in particular, the audio and visual modalities. To date, majority of movie recommender systems use collaborative filtering (CF) models or content-based filtering (CBF) relying on metadata (e.g., editorial such as genre or wisdom of the crowd such as user-generated tags) at their core since they are human-generated and are assumed to cover the ‘content semantics’ of movies by a great degree. The information obtained from multimedia content and learning from muli-modal sources (e.g., audio, visual and metadata) on the other hand, offers the possibility of uncovering relationships between modalities and obtaining an in-depth understanding of natural phenomena occurring in a video. These discerning characteristics of heterogeneous feature sets meet users’ differing information needs. In the context of this Ph.D. thesis [9], which is briefly summarized in the current extended abstract, approaches to automated extraction of multimedia information from videos and their integration with video recommender systems have been elaborated, implemented, and analyzed. Variety of tasks related to movie recommendation using multimedia content have been studied. The results of this thesis can motivate the fact that recommender system research can benefit from knowledge in multimedia signal processing and machine learning established over the last decades for solving various recommendation tasks.
The Snippets Taxonomy in Web Search Engines

In this paper authors analyzed 50 000 keywords results collected from localized Polish Google search engine. We proposed a taxonomy for snippets displayed in search results as regular, rich, news, featured and entity types snippets. We observed some correlations between overlapping snippets in the same keywords. Results show that commercial keywords do not cause results having rich or entity types snippets, whereas keywords resulting with snippets are not commercial nature. We found that significant number of snippets are scholarly articles and rich cards carousel. We conclude our findings with conclusion and research limitations.
Inferring Behaviors via Encrypted Video Surveillance Traffic by Machine Learning

In order to protect the safety of people, smart cameras are widely used in many places of life: homes, offices, subways and many other ways. However, those smart cameras could lead to personal information leakage. Considering the role of smart cameras in peoples life, the information leakage may result in serious security problems. In this paper, we reveal the relationship between user behavior and encrypted video surveillance traffic. We show that different user behaviors have different traffic patterns. We propose some new features for encrypted video surveillance traffic, and we can infer the basic life behavior of users from these features. We exploit eight basic activities of daily living: watching TV, reading books, styling hair, open or close door, sweeping the ground, dressing, drinking and moving, which can cover the basic behavior of human beings. First, we show how attackers carry out an attack. We demonstrate it is easy for attackers to steal users' privacy by encrypted video surveillance traffic. Then, we extract the features of network traffic rate distribution and network traffic rate changes in the time domain and the frequency domain. Finally, we explore the validity of the features. And we utilize six typical machine learning algorithms to score these features. The experiment shows that we can identify the user's behavior with 94% accuracy.
The impact of transformational leadership on promoting academic research in higher educational system in Vietnam

University educational system plays an important role for the development of any country.Most industries normally rely on highly educated people with different skills and the universities are also responsible for training leading edge technology skills.During the past few years, there have been tremendous efforts to empower Vietnamese universities and to put more efforts to increase their ranking internationally.The purpose of this study is to determine the appropriate leadership style on higher education institutions and the role of the leader in promoting academic research at Vietnamese universities.Excellent and effective leadership is needed to develop employees as a sustainable competitive advantage.The leaders of organizations normally have difficulty in speeding up the performance of their followers.Individual performance is studied and evaluated in both scholarly and practical literature.Employee engagement is not only a moderator concept established in organizational theory, but it also offers a variety of views and theories.This paper designs a questionnaire and distributes it among 232 students to measure the effect of transformational leadership in higher educational organizations.Regression analysis is used to measure the effects of different components of transformational leadership on the performance of organizations.The survey results indicate that the Transformational leadership could actually be effective when they are allowed autonomy in organizations, especially on the budget.In summary, the results of the investigation clarify the role and importance of transformational leadership in organizations.
On the Convergence of Sigmoidal Fuzzy Grey Cognitive Maps

Abstract Fuzzy cognitive maps (FCMs) are recurrent neural networks applied for modelling complex systems using weighted causal relations. In FCM-based decision-making, the inference about the modelled system is provided by the behaviour of an iteration. Fuzzy grey cognitive maps (FGCMs) are extensions of fuzzy cognitive maps, applying uncertain weights between the concepts. This uncertainty is expressed by the so-called grey numbers. Similarly as in FCMs, the inference is determined by an iteration process which may converge to an equilibrium point, but limit cycles or chaotic behaviour may also turn up. In this paper, based on the grey connections between the concepts and the parameters of the sigmoid threshold function, we give sufficient conditions for the existence and uniqueness of fixed points of sigmoid FGCMs.
NIR Reflection Augmentation for DeepLearning-Based NIR Face Recognition

Face recognition using a near-infrared (NIR) sensor is widely applied to practical applications such as mobile unlocking or access control. However, unlike RGB sensors, few deep learning approaches have studied NIR face recognition. We conducted comparative experiments for the application of deep learning to NIR face recognition. To accomplish this, we gathered five public databases and trained two deep learning architectures. In our experiments, we found that simple architecture could have a competitive performance on the NIR face databases that are mostly composed of frontal face images. Furthermore, we propose a data augmentation method to train the architectures to improve recognition of users who wear glasses. With this augmented training set, the recognition rate for users who wear glasses increased by up to 16%. This result implies that the recognition of those who wear glasses can be overcome using this simple method without constructing an additional training set. Furthermore, the model that uses augmented data has symmetry with those trained with real glasses-wearing data regarding the recognition of people who wear glasses.
Bearings Fault Diagnosis Based on Improved Deep Belief Network by Self-individual Adaptive Learning Rate

Abstract：The health monitoring of key component of mechanical equipment such as bearings is entering the era of big data and intelligence.The traditional method of bearing fault diagnosis depends on manual extraction, which relies on complex signal processing methods and abundant professional experience.As a new machine learning method which can learn deep features from data, the deep learning method is introduced into the field of mechanical fault diagnosis, and the operation efficiency and fault recognition accuracy of mechanical fault diagnosis are improved, which will further improve the practicability of deep learning method in fault diagnosis field.A novel self-adaptive deep belief network with Nesterov momentum (NM-based ADDBN) is proposed.Nesterov momentum is used to replace the traditional momentum method to predict the next position of the parameters, and control parameters' speed towards the optimal values, avoiding missing the optimal values caused by the traditional momentum method.Self-individual adaptive learning rate is used to adaptively select decrease step length for the gradient update, and to speed up the model training and improve the generalization ability of the model.Experimental results show that compared with support vector machine and standard depth belief network, the proposed method obtain the best precision performance for bearing fault recognition under different load conditions.For operational efficiency, the optimization model can effectively and steadily speed up the model training, improve generalization ability of deep belief network, and realize the rolling bearing fault diagnosis effectively compared with current optimization methods.
Review of the machine learning methods in the classification of phishing attack

The development of computer networks today has increased rapidly. This can be seen based on the trend of computer users around the world, whereby they need to connect their computer to the Internet. This shows that the use of Internet networks is very important, whether for work purposes or access to social media accounts. However, in widely using this computer network, the privacy of computer users is in danger, especially for computer users who do not install security systems in their computer. This problem will allow hackers to hack and commit network attacks. This is very dangerous, especially for Internet users because hackers can steal confidential information such as bank login account or social media login account. The attacks that can be made include phishing attacks. The goal of this study is to review the types of phishing attacks and current methods used in preventing them. Based on the literature, the machine learning method is widely used to prevent phishing attacks. There are several algorithms that can be used in the machine learning method to prevent these attacks. This study focused on an algorithm that was thoroughly made and the methods in implementing this algorithm are discussed in detail.
Quantification of the Leakage in Federated Learning

With the growing emphasis on users' privacy, federated learning has become more and more popular. Many architectures have been raised for a better security. Most architecture work on the assumption that data's gradient could not leak information. However, some work, recently, has shown such gradients may lead to leakage of the training data. In this paper, we discuss the leakage based on a federated approximated logistic regression model and show that such gradient's leakage could leak the complete training data if all elements of the inputs are either 0 or 1.
Evaluation Framework for Real-Time Adaptive 360-Degree Video Streaming over 5G Networks

The end-to-end distribution of real-time 360-degree video needs to be evaluated in a wholesome manner, considering all aspects from video capture to encoding, delivery, and playback, as well as timely and appropriate analytics, with a focus on end-user Quality of Experience (QoE). This requires a measurement framework which allows for the collection of metrics from multiple dimensions at the same time. In this work, we propose such a framework for evaluating real-time adaptive 360\degree video streaming over an experimental Fifth Generation (5G) network, which allows for the investigation of different aspects of the end-to-end video delivery chain.
Image Segmentation Using Background Subtraction On Colored Images

This paper on object detection by image processing to ensure the people suffering from various vision disorders to detect and to perceive the real image before them. The image perceived by the patients are rendered and developed so that the closest similar object is detected. The input to the program is a image which is fed to the set of processes which in turn gives a image that can be compared to the set of data to give the accuracy in (percentage) to which extend the image is similar to an object or animal.
Multi-agent Knowing How via Multi-step Plans: A Dynamic Epistemic Planning Based Approach

There are currently two approaches to the logic of knowing how: the planning-based one and the coalition-based one. However, the first is single-agent, and the second is based on single-step joint actions. In this paper, to overcome both limitations, we propose a multi-agent framework for the logic of knowing how, based on multi-step dynamic epistemic planning studied in the literature. We obtain a sound and complete axiomatization and show that the logic is decidable, although the corresponding multi-agent epistemic planning problem is undecidable.
Automated chat transcript analysis using topic modeling for library reference services

ABSTRACT Chat reference service has been used in academic libraries to more efficiently serve patrons in the digital age. Identifying question topics on chat can help librarians understand patrons' needs and improve reference services. Researchers have used qualitative methods to understand question types in chat records; however, these methods are inefficient to analyze large chat datasets. Here, we conducted a novel research using Latent Dirichlet Allocation (LDA) topic modeling to automatically extract topics from chat transcripts generated in 5 years from a large university library. With little human intervention, the model identified major topics based on statistical distributions of terms‐document relationships in chat transcripts. We also applied VOSviewer to analyze the same dataset and found consistent results. From these results, we found that the most prominent chat topics were about accessing various library resources. This finding can help libraries allocate resources, design educational materials, and provide trainings for future librarians.
Simulating Development of Science in a Country with the Use of the Cognitive Approach

The study proposes a methodological approach to justifying the directions for reforming the scientific sphere of a country which is based on the cognitive approach. Using the proposed approach, the groups of the main problems in the development of science in Ukraine are determined and indicators for their evaluation are singled out; the cognitive model of the development of science in Ukraine is constructed. On the basis of the constructed cognitive model, impulse simulation of the influence of the main factors on development of science in a country is carried out and, according to the hypotheses on the nature of their influence, four scenarios for the development of the process under study are constructed. The analysis of the developed scenarios allowed to justify the most effective measures to stimulate the development of science in Ukraine. The practical evaluation of the proposed methodological approach has proved the feasibility of using cognitive models for analyzing and simulating development of science in a country, which allows to quickly and accurately assess a situation and identify promising measures to support it.
Approximate inference for constructing astronomical catalogs from images

We present a new, fully generative model for constructing astronomical catalogs from optical telescope image sets.Each pixel intensity is treated as a random variable with parameters that depend on the latent properties of stars and galaxies.These latent properties are themselves modeled as random.We compare two procedures for posterior inference.One procedure is based on Markov chain Monte Carlo (MCMC) while the other is based on variational inference (VI).The MCMC procedure excels at quantifying uncertainty, while the VI procedure is 1000 times faster.On a supercomputer, the VI procedure efficiently uses 665,000 CPU cores to construct an astronomical catalog from 50 terabytes of images in 14.6 minutes, demonstrating the scaling characteristics necessary to construct catalogs for upcoming astronomical surveys.
Factors of Success of Initial Coin Offering. Empirical Evidence from 2016–2019

Since 2013, Initial Coin Offerings (ICO) have allowed companies to attract financing with the help of cryptocurrencies. Statistics of ICO shows that the ICO market is increasing and demand for funds continues to grow with claims of over $ 15 billion raised in the first half of 2018. The increasing volumes of investment in ICO projects as an alternative method to venture capital or IPO are caused by, for example, the possibility of reselling the received tokens at a higher price after the launch of the project or obtaining the company’s services at lower prices. While the importance of the topic is growing, there is the absence of fundamental works emphasizing the determinants of an ICO’s success. The scientific novelty of the forthcoming research consists in the formation of the model evaluation of ICO success. Using econometric analysis based on data for 1392 projects, we show that the volatility of the main cryptocurrencies has a significant impact on the success of ICO. The constraints of the platform for Smart Contacts (ERC-20) and dependence on the Ethereum volatility overcome all other factors. Our data contributes to existing literature and shows the insignificance e of the sector of the project, almost all location region and fl of infl e of quality of the team. This result may be explained by the uncertainty of the investor about the project (weak signals), absence of the regulation and legal framework. This result is beneficial for owners of companies since it is an argument for decreasing costs for marketing.
On the Visuomotor Behavior of Amputees and Able-Bodied People During Grasping

Visual attention is often predictive for future actions in humans. In manipulation tasks, the eyes tend to fixate an object of interest even before the reach-to-grasp is initiated. Some recent studies have proposed to exploit this anticipatory gaze behavior to improve the control of dexterous upper limb prostheses. This requires a detailed understanding of visuomotor coordination to determine in which temporal window gaze may provide helpful information. In this paper, we verify and quantify the gaze and motor behavior of 14 transradial amputees who were asked to grasp and manipulate common household objects with their missing limb. For comparison, we also include data from 30 able-bodied subjects who executed the same protocol with their right arm. The dataset contains gaze, first person video, angular velocities of the head, and electromyography and accelerometry of the forearm. To analyze the large amount of video, we developed a procedure based on recent deep learning methods to automatically detect and segment all objects of interest. This allowed us to accurately determine the pixel distances between the gaze point, the target object, and the limb in each individual frame. Our analysis shows a clear coordination between the eyes and the limb in the reach-to-grasp phase, confirming that both intact and amputated subjects precede the grasp with their eyes by more than 500 ms. Furthermore, we note that the gaze behavior of amputees was remarkably similar to that of the able-bodied control group, despite their inability to physically manipulate the objects.
XL-Editor: Post-editing Sentences with XLNet

While neural sequence generation models achieve initial success for many NLP applications, the canonical decoding procedure with left-to-right generation order (i.e., autoregressive) in one-pass can not reflect the true nature of human revising a sentence to obtain a refined result. In this work, we propose XL-Editor, a novel training framework that enables state-of-the-art generalized autoregressive pretraining methods, XLNet specifically, to revise a given sentence by the variable-length insertion probability. Concretely, XL-Editor can (1) estimate the probability of inserting a variable-length sequence into a specific position of a given sentence; (2) execute post-editing operations such as insertion, deletion, and replacement based on the estimated variable-length insertion probability; (3) complement existing sequence-to-sequence models to refine the generated sequences. Empirically, we first demonstrate better post-editing capabilities of XL-Editor over XLNet on the text insertion and deletion tasks, which validates the effectiveness of our proposed framework. Furthermore, we extend XL-Editor to the unpaired text style transfer task, where transferring the target style onto a given sentence can be naturally viewed as post-editing the sentence into the target style. XL-Editor achieves significant improvement in style transfer accuracy and also maintains coherent semantic of the original sentence, showing the broad applicability of our method.
Inducing the Lyndon Array

In this paper we propose a variant of the induced suffix sorting algorithm by Nong (TOIS, 2013) that computes simultaneously the Lyndon array and the suffix array of a text in $O(n)$ time using $\sigma + O(1)$ words of working space, where $n$ is the length of the text and $\sigma$ is the alphabet size. Our result improves the previous best space requirement for linear time computation of the Lyndon array. In fact, all the known linear algorithms for Lyndon array computation use suffix sorting as a preprocessing step and use $O(n)$ words of working space in addition to the Lyndon array and suffix array. Experimental results with real and synthetic datasets show that our algorithm is not only space-efficient but also fast in practice.
Pay the PIED Piper: Guidelines to Visualize Large Geochemical Datasets on Piper Diagrams

Abstract The Piper diagram has increased in popularity since its 1944 introduction and is now one of the most familiar and effective tools in the hydrogeologist's toolbox. Within the Piper diagram, three points on three related plots fully display the major ionic species of a water sample. Recently the size and availability of datasets have increased as additional field measurements and modeling results are shared more effectively in online databases. This growth presents opportunities and challenges for data analysis and conveyance—larger and longer datasets increase the potential to identify trends and patterns, but traditional Piper diagrams are quickly overwhelmed by large datasets as dense points overlap and become obscured. We present guidelines for effectively displaying large geochemical datasets on traditional Piper diagrams and new code that adds novel functionality for following these generic guidelines. This code, plotting interesting environmental data with Piper diagrams (PIED Piper), can be run within the Matlab environment or through a stand‐alone graphical user interface, and is the first Matlab code to generate Piper diagrams. The illustrative examples herein demonstrate (1) how limitations in displays of large datasets may be overcome with translucent symbology, contours, and heatmaps to identify trends and patterns, (2) how clusters of similar points can be identified and differentiated with convex hulls, and (3) how temporal‐and‐spatial patterns may be visually diagnosed with image groups and movies. The guidelines discussed in these examples will aid PIED Piper users to achieve the two goals of effective big data visualization: analysis and communication.