Reviewerly/exHarmony huggingface dataset:
Are complex causal models less likely to be true than simple ones? A critical comment on Trafimow (2017)

Trafimow (2017) used probabilistic reasoning to argue that more complex causal models are less likely to be true than simpler ones, and that researchers should be skeptical of causal models involving more than a handful of variables (or even a single correlation coefficient) [Trafimow, D. (2017). The probability of simple versus complex causal models in causal analyses. Behavior Research Methods, 49, 739–746]. In this comment, I point out that Trafimow’s argument is misleading, and reduces to the observation that more informative models (that make definite statements about certain causal relations) are less likely to be true than less informative models (that remain silent about those relations, by omitting some variables from consideration). This correct but trivial statement does not deliver the epistemological leverage promised in the paper. When complexity is evaluated with reasonable criteria (such as the number of nonzero effects in alternative models involving the same variables), more complex models can be more, less, or equally likely to be true compared with simpler ones. I also discuss Trafimow’s claim that, if a model is unlikely to be true a priori, researchers will seldom be able to gather evidence of sufficient quality to support it; in practice, even low-probability models can receive strong support without the need for extraordinary evidence. Researchers should evaluate the plausibility of causal models on a case-by-case basis, and be skeptical of overblown claims about the dangers of complex theories.
Self-Similar Decomposition of Digital Signals

Abstract Traditionally, the engineers analyze signals in the time domain and in the frequency domain. These signal representations discover different signal characteristics and in many cases, the exploration of a single signal presentation is not sufficient. In the present paper, a new self-similar decomposition of digital signals is proposed. Unlike some well-known approaches, the newly proposed method for signal decomposition and description does not use pre-selected templates such as sine waves, wavelets, etc. It is realized in time domain but at the same time, it contains information about frequency signal characteristics. Good multiscale characteristics of the algorithm being proposed are demonstrated in a series of examples. It can be used for compact signal presentation, restoration of distorted signals, event detection, localization, etc. The method is also suitable for description of highly repetitive continuous and digital signals.
A New Model Representation for Road Mapping in Emerging Sciences: A Case Study on Roadmap of Quantum Computing

One of the solutions for organizations to succeed in highly competitive markets is to move toward emerging sciences. These areas provide many opportunities, but, if organizations do not meet requirements of emerging sciences, they may fail and eventually, may enter a crisis. In this matter, one of the important requirements is to develop suitable roadmaps in variety fields such as strategic, capability, product, technology and etc. In this subject, it should be mentioned that there are connections among types of roadmaps, therefore, organizations need to understand these relations. According to this, we propose a model which describes relations among the roadmaps based on braid theory and appropriate methods for developing those roadmaps. The validity of developed model was conducted through the Delphi method. Additionally, we illustrate a to design a roadmap and implement it in quantum computing as an emerging science.
Video communication for teleconferencing using edge computing

This paper proposes a backbone network resource optimization algorithm for video communications of teleconferencing that use edge computing. In the current video communication architecture, the key component, the multi-point control unit (MCU), is deployed in the central cloud server, and its bandwidth consumption in the backbone network becomes huge as the video resolution and the frequency of use increase. By using edge computing, MCU can be deployed at the entrance node of the backbone network. This can reduce the bandwidth consumption of the backbone network. However, the edge deployment and routing (EDR) problem, which is classified as NP-hard, should be solved to achieve sufficient bandwidth reduction. To solve the EDR problem within a feasible time, we propose a divide and merge algorithm using the linear programming approach. We demonstrate that, with our algorithm, bandwidth consumption using edge computing is reduced by approximately 30% compared to the current architecture on a world-wide network.
Power Optimization by Detection and Monitoring of Sensor Event in Smart Home

Sensor event monitoring and detection facilitate to recognize the activities of the human lives in a smart home daily. The major aim of this technology is to attain optimal performance in activity recognition. The symbol of the daily activity recognition involves the usage of feature inputs which have a noteworthy consequence on the output. However, commonly used representations of features dependent on daily activity have limited performance on the recognition activity. Based on the dynamic nature of the sensor events caused by daily activities, this paper presents a statistical processing approach for time series of sensor events. First time, the statistical values are extracted from sensor events dependent on time series. Subsequently, different categories of statistic formulae are proposed to solve daily activity features. To evaluate the proposed approach, two distinct datasets are adopted for activity recognition based on artificial neural network (ANN), fuzzy logic (FL), and hybrid neuro-fuzzy logic (HNFL). The experimental results reveal that the proposed HNFL approach provides power optimization.
Development and Modelling of a Novel Wheelchair with Staircase Climbing Ability

This paper presents a modified form of the wheelchair with an ability of stair-climbing to help people who are unable to climb stairs or have problems in mobility independently, i.e., old people, people with physically discouraged or paralysed. The wheelchair consists of a frame, chair seat, suspensions, clustered triple-wheel, and a type of linear actuator (leadscrew). The special newly designed wheelchair with specific mechanisms helps the user to climb and descend the stairs, to move in the organized and unorganized environment, and to overcome the curbs and uneven terrains more efficiently and comfortably. This wheelchair has a compact and lightweight structure and will remain stable throughout the whole ride path.
Network of the Future

Networks are living entities. They offer services that are constantly evolving; the traffic they serve changes from instant to instant. This chapter looks at the likely structure of future networks and the services they have the potential to offer.
A Novel Watermark Method for Image Protection Based on Periodic Haar Piecewise-Linear Transform

In this paper, a novel watermark method based on Periodic Haar Piecewise-Linear (PHL) transform is proposed. This solution provides high embedding capacity with negligible degradation of image quality. The PHL transform is based on integration and differentiation of periodic Haar functions. Its major advantage is the fact that it is notably fast and easy to implement and beyond that it provides good reconstruction quality. The procedure for constructing two-dimensional PHL functions and transform is introduced.Furthermore, the analysis of embedding capacity and imperceptibility of the proposed algorithm is performed. PSNR (Peak Signal to Noise Ratio) is taken as a measure of perceptual quality. The method is compared with DCT approach in certain aspects of watermarking algorithms.
Empirical Test Design Strategies Using Natural Language Processing

With the rise in the role played by computers and their ubiquity, both developers and consumers share responsibility for improvements in the digital platform. Thus, there is a need to reduce the load on both ends with the help of automation. This proposed system, provides a simplistic interface, where users can submit issues with applications and developers are provided with test cases before writing off on the solutions to these issues. It aims to intelligently identify preexisting similar issues, if any, to reduce redundancy, identify system requirements unsatisfied by these issues as well as identifying the criticality of the issues. It also seeks to generate test cases for functional requirements. The system uses a ‘Deep Averaging Network (DAN)’ model developed by Google for sentence similarity to detect similar issues, providing an accuracy of upwards of 89% for an in-house prepared test set of more than 100 entries. It also uses sentence dependencies and a ‘Satisfiability Modulo Theories (SMT)’ solver, for identifying parts of speech and generates appropriate test cases.
Periodic Sudden Freezing and Thawing of Entanglement in Multiparty Pure-State Quantum Systems

Within the one-excitation context of two identical two-level atoms interacting with a common cavity, we are concerned with the dynamics of all bipartite one-to-other entanglements between each qubit and the remaining part of the whole system, from the perspective of resource sharing. We find a new non-analytic dynamical behavior of entanglement. Specifically, the sum of the three one-to-other entanglements of the system can be suddenly frozen at its maximal value or can be suddenly thawed from this value in a periodic manner. We calculate the onset timing of sudden freezing and sudden thawing under several different initial conditions. The phenomenon of the permanent freezing of entanglement is also found. Further analyses about freezing and thawing processes reveal quantitative and qualitative laws of resource sharing.
Correction to: The CoMirror algorithm with random constraint sampling for convex semi-infinite programming

This erratum is published as due to some technical error author names appeared reversed in the final publication.
Smart Targeting: A Relevance-driven and Configurable Targeting Framework for Advertising System

Targeting system is an essential part of computational advertising. It allows advertisers to select and reach their targeted users. Due to various advertising goals and the demand for making budget plans, advertisers have a strong will to configure the final targeting results, or they can become very cautious in spending money on advertising campaigns. Meanwhile, to guarantee the advertising performance, the targeted users should also be relevant to the ads of the advertisers. Recent targeting methods are mainly based on tags produced by the Data Management Platform (DMP) which is easy for the advertisers to configure the targeting results. However, in such methods, the relevance between the targeted users and ads is not technically evaluated and cannot be guaranteed. The biggest challenge is that it is hard for a machine learning model to both model the relevance and take account of the advertiser’s configuration demands. In this paper, we propose a novel relevance-driven and configurable targeting framework called Smart Targeting to solve the problem. Specifically, different from Tag-wise Targeting, we first use a relevance model to retrieve the most relevant users for the ads. To further enable the advertisers to configure the final results, we develop a Delay Intervention Mechanism to leverage the power of DMP. As far as we know, this is the first attempt of combining relevance modeling and advertiser intervention into a unified targeting system. We implement and evaluate our framework on JD.com platform with over 300 million users and the results show that it can bring significant improvements to the core indicators such as CTR and eCPM. The long term monitoring also demonstrates that Smart Targeting gradually becomes the most popular targeting tool after its release.
Component of Trust for Developing Crowdwork System: A Systematic Literature Review

Crowdwork is a system that brings job providers and crowdworkers in a portal.Nowadays, many companies are turning to freelancing rather than hiring full-time employees.In addition, the workers have also switched to working as freelancers.New problems also arise in the crowdwork system.Trust is one of the main issues that arise in the crowdwork system.It happens because the job providers and workers do not meet each other on the crowdwork portal.This research aims to examine the components that affect trust in a crowdwork system.The benefit is that stakeholders can be aware of how to increase trust in the crowdwork system.The method used in this research is systematic literature review, by analyzing Scopus based journals related to trust in the crowdwork system.This research produces 11 components and 38 indicators.There are three components that needs concern to stakeholders who want to take advantage of crowdwork systems and focus on trust.These components are reciprocal voting, monetary reward, and cognitive effort.The platform should enable stakeholders to have reciprocal voting, less cognitive effort, as well as clean and clear monetary reward procedure.
Dangerous Tool Detection for CCTV Systems

In this paper we present our work towards an effective solution for detection of dangerous objects, such as firearms or knives in a Closed Circuit Television System. We have gathered a large, manually annotated dataset of recordings supplemented by our original artificial sample generation method. We have used this dataset for training of a convolutional neural network. We present our approach and training results. We have also implemented and present software architecture that implements the neural network. We have shown, that the convolutional neural networks are well suited even for such complex object detection task, when provided with enough training samples.
Dedicated to the librarians of the war period

The structure and contents of the textbook published by Orel State Institute of Culture in the year of the 75-th anniversary of the Victory in the Great Patriotic War is reviewed. The learning aid is intended for education bachelors in the discipline 071900 «Library and information activities». The reviewer emphasizes the relevancy of the publication within the context of the attempts being made to revise the results and causes of the World War II. The textbook comprises vast factual material on robbing and annihilation of cultural values by the Third Reich; on saving the Soviet library book collections during the Nazi aggression; on the heroic deeds of librarians who were saving the collections. The book provides insight into the country’s book losses during the Great Patriotic War (WWII) and the restitution issues. The reviewer identifies several limitations of the book: no discussion of front line support by the libraries, of establishing library branches at hospitals and mobile libraries in bomb shelters and Moscow metro. The reviewer also emphasizes the need for publishing an expanded and updated edition of the textbook at large publishers’.
ComplexRec 2020: Workshop on Recommendation in Complex Environments

During the past decade, recommender systems have rapidly become an indispensable element of websites, apps, and other platforms that are looking to provide personalized interaction to their users. As recommendation technologies are applied to an ever-growing array of non-standard problems and scenarios, researchers and practitioners are also increasingly faced with challenges of dealing with greater variety and complexity in the inputs to those recommender systems. For example, there has been more reliance on fine-grained user signals as inputs rather than simple ratings or likes. Many applications also require more complex domain-specific constraints on inputs to the recommender systems. The outputs of recommender systems are also moving towards more complex composite items, such as package or sequence recommendations. This increasing complexity requires smarter recommender algorithms that can deal with this diversity in inputs and outputs. The ComplexRec workshop series offers an interactive venue for discussing approaches to recommendation in complex scenarios that have no simple one-size-fits-all solution.
EyeHear: Smart Glasses for the Hearing Impaired

The hearing-impaired experience the sound localization problem, defined as the insufficient ability to estimate the direction of sounds. This problem reduces spatial awareness and amplifies social difficulties such as communicating in adverse listening conditions. Current solutions, such as hearing aids and cochlear implants, fail to solve the problem for a multitude of reasons, but recent work in head-computer interfaces can be applied to deliver a robust solution. Thus, this paper develops a smart glass, named EyeHear, that shows the direction of a speech source and its transcription, all in real-time thanks to a modified speech recognition algorithm and speech recognition system. Algorithm testing proved that the device was accurate enough to be feasible in real-world talking situations. User testing affirms that the target audience favors head-mounted displays and indicates that solving the sound localization problem is beneficial. This device paves the way for future work to extend the device to non-speech sounds. EyeHear has the potential to become an assistive technology for the hearing impaired.
New Approach of Solving Congruence Equation System

Nowadays in public key cryptography, some special basic algorithms are used. Extended Euclidian Algorithm and Chinese Remainder Theorem are the most spread basic algorithms with major contribution. Along the century’s mathematicians made a lot to improve these algorithm’s execution speed. In the paper, new approach of solving congruence equation system is presented.
A Head Mouse Alternative Solution Proposal for People with Motor Impairments: Design and Usability Assessment Study

Accessibility is important to include people with disabilities in public life. People with disabilities experience problems in their lives due to products which aren’t designed for them. Addressing these problems is the responsibility of all humanity and the search for solutions should increase. Improving disabled persons’ lives is only possible by applying some rules to product design, such as, improving use-case scenarios. Software solutions must be considered as products in this case. Developing special software programs and upgrading existing solutions to improve the user experience for disabled persons is the obligation of all software developers. Even though the mouse is one of the most important tools, required to access webpages and many other software programs, using it can be a huge problem for people with disabilities, such as, paralysis patients. Therefore, throughout history, different types of methods have been presented as solution. Most of them are not suitable, due to various reasons, like, economic feasibility, mechanic stability and bad designs. In this work, a new method, with more in-built modern and simple technologies, is presented as an alternative to others. To outclass the good economic advantage of its competitors, a limit was set on a low budget webcam and low-tier computer specifications. The Camera tracks the user’s head movements and reacts according to it, using as measurement some metrics about the physiology of human body. Machine learning is used to extract face features and learn the user’s face to determine mouse movements. Machine learning was kept as simple as possible, in order to cope with the low system specification limitations. The main topic of the work is the design and development experience. The solution was tested with a small group of participants and their feedbacks were taken to plan for future improvements. In generally, the users’ comments were good, although some features, such as sensitivity, should be improved. The result of this work will be used to improve the software solution and hopefully will touch the life of people with disabilities.
Application of LSTM in Aeration of Sewage Treatment

Long Short-Term Memory (LSTM) network is a form of Recurrent Neural Networks, it is suitable for predicting events relatively long delay in term series. In this study, LSTM is used to build a model for predicting dissolved oxygen concentration in sewage tank. This model can transform the collected data into time series data and predict the next moments data based on the current moments data. The experimental results show that the prediction errors of the proposed method are better than those of BP neural network and Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms, which can make the sewage treatment plant more accurately adjust the air intake of blower to control the dissolved oxygen in the sewage pool.
High order elements in finite fields arising from recursive towers

We provide a recipe to construct towers of fields producing high order elements in $\mathrm{GF}(q,2^n)$, for odd $q$, and in $\mathrm{GF}(2,2 \cdot 3^n)$, for $n \ge 1$. These towers are obtained recursively by $x_{n}^2 + x_{n} = v(x_{n - 1})$, for odd $q$, or $x_{n}^3 + x_{n} = v(x_{n - 1})$, for $q=2$, where $v(x)$ is a polynomial of small degree over the prime field $\mathrm{GF}(q,1)$ and $x_n$ belongs to the finite field extension $\mathrm{GF}(q,2^n)$, for $q$ odd, or to $\mathrm{GF}(2,2\cdot 3^n)$. Several examples are carried out and analysed numerically. The lower bounds of the orders of the groups generated by $x_n$, or by the discriminant $\delta_n$ of the polynomial, are similar to the ones obtained in [BCG+09], but we get better numerical results in some cases.
Research on the Development of University Library Regional Alliances in China

With the development of China's social economy, higher education and the Internet, China has established a number of regional alliances (RA) of university libraries (UL).The university library regional alliances (ULRAS) are an important part of university library alliances (ULAS).The development of ULRAS in China plays an important role in promoting the construction of ULAS and improving the whole network system of Chinese university library alliances (CULAS).This paper mainly adopts the method of case analysis to study the development of ULRAS in China from the perspectives of development status, characteristics and trend.The purpose is to study the general model of the development of ULAS in China and provide reference for the construction of RA in other regions of China and other countries in the world.
A Novel Approach to Calculate TF-IDF for Assamese Language

TF-IDF is one of the earliest and most effective word vectorization techniques that provide the basis for many natural language processing (NLP) tasks. This paper provides a novel approach to create TF-IDF vectors for Assamese text. A considerable number of experiments are carried out throughout the process, and a significant result was found which is discussed here in detail.
Haar wavelet transformation and RC4 algorithm based Image Encryption

Recently the protection and privacy of information or data have become a major concern to deal with. Developed coding for secure transmission and decrypting the image are steadily more needed for specific military, corrective, country defense, and various applications. Variance sorts of security information or data increment strategies are being created as of now, cryptographic systems are one basic path. Keep the message private by translating data or information into an alternative frame, such that the message is not perceivable. This study aims to expect the security image arrangement to be upgraded by following the Haar wavelet transformation and the RC4 encryption algorithm. For simplicity, the Image compression executed by Haar wavelet transformation to compress the image and to calculate high-speed performance. For image protection process, the RC4 encryption algorithm is applied that is also for secure image transformation.