Reviewerly/exHarmony huggingface dataset:
p-adic estimates of the number of permutation representations

Let p be a prime, and let G be a finite abelian p-group of type λ=(λ1,λ2,…) with λ1≥λ2≥⋯ and ∑λi=s. Set u=max⁡{λ1,[(s+1)/2]} and v=s−u. For each nonnegative integer n, let hn(G) be the number of homomorphisms from G to the symmetric group Sn on n letters. Except for the case where p=2 and u+δv0≤v+1, δ the Kronecker delta, or p=3 and u=v≥1, there exist p-adic analytic functions fr(X) for r=0,1,…,pu+1−1 and a polynomial η(X) with integer coefficients such that for any nonnegative integer y, hpu+1y+r(G)=p{∑j=1upj−(u−v)}yfr(y)∏j=1yη(j) and ordp(hpu+1y+r(G))={∑j=1upj−(u−v)}y+ordp(fr(y)). If p=2, λ3=0, and u=v≥1 or if p=3 and u=v≥1, then hn(G) has analogous properties. Under the assumption that λ3=0, some results for the number of permutation representations of G in the wreath product of a cyclic group of order p with Sn are also presented.
Advanced Professional Training for Specialists for Innovative Socio-Economic Development of Regions

Modern Russian society has experienced constant and signifi cant changes in recent years creating the need to reform the system of general and secondary vocational education and highlighting the problem of continuous vocational training. The article substantiates the idea of developing the theoretical and methodological foundations for advanced vocational training. It also shows the importance of this training for ensuring a sustainable innovative social and economic development of Russian regions. The article presents the goals and objectives of the study, describes the mechanisms of their implementation through The Centers for advanced training and the creation of appropriate conditions ensuring the integrity of the educational and production process. It also specifi es the development and testing of multi-level textbooks and teaching materials, the use of innovative technologies and various forms of interaction, including the creation of educational and industrial clusters on the basis of vocational education institutions. The authors of this study employ diff erent approaches and general scientifi c research, empirical, dialectical, experimental and specifi c sociological methods such as conversation, questioning, testing and pedagogical observation.
Multimodal Speaker Adaptation of Acoustic Model and Language Model for Asr Using Speaker Face Embedding

We present an investigation into the adaptation of the acoustic model and the language model for automatic speech recognition (ASR) using speaker face for transcription of a multimedia dataset. We begin by overviewing relevant previous work on the integration of visual signals into ASR systems. Our experimental investigation shows a small improvement in word error rate (WER) for the transcription of a collection of instruction videos using adaptation of the acoustic model and the language model with fixed-length face embedding vectors. We also present potential approaches to integrating human facial information, and body gestures into ASR as further directions for research on this topic.
Research and Implementation of Edge Computing in Web AR

Cloud computing, which includes a wide variety of cloud services, has experienced explosive growth in recent years and cloud servers are replacing traditional physical servers on a global scale. However, some emerging businesses such as AR (Augmented Reality) have made the traditional centralized cloud computing expose some shortcomings. For example, the actual physical or network distance between clients and clouds is too large and the growth rate of bandwidth resources in the cloud is far behind the growth rate of data, which makes cloud computing unadaptable for the requirements of AR computing that has high bandwidth occupancy as well as low latency. To handle these problems, this paper introduces and implements a novel idea of edge computing in the field of Web AR. Meanwhile, experiments demonstrate that edge servers have the capability of real-time complex computing with high efficiency and stability.
Analysis of Wireless Signature Feature Sets for Commercial IoT Devices : Invited Presentation

Advances in technology have led to an increase the number and type of electronic devices interconnected through the internet, or Internet of Things (IoT) devices. These devices are available commercially and are often used for applications in home environments, office buildings, medical facilities, and others. A common protocol used in IoT devices is the Institute of Electrical and Electronics Engineers (IEEE) 802.15.4 protocol. The vulnerabilities of the standard have led to numerous attacks on devices that follow this protocol. RF fingerprinting is a technique used to authenticate and verify devices in an environment to determine if they are either authorized or rogue to add a level of security at the physical layer. The fingerprints are comprised of statistical features, such as variance, skewness, and kurtosis, extracted from instantaneous RF signal characteristics. Previous RF fingerprinting work obtained features from generic ZigBee modules. This work aims to examine signal features of commercial IoT devices that adhere to the ZigBee protocol. The analysis will highlight correlations, if any, and differences between device vendor and IoT device type.
The Plurality of Goal-setting in Pedagogical Activity: Integration of Mathematics on a Chessboard

The article reveals the potential for the integration of mathematics education and gaming activities in the formation of professional competencies of a future teacher. The disclosure of a wider range of possibilities for the interdisciplinary integration of mathematical knowledge and chess skills in the context of setting and selecting goals for solving mathematical problems on a chessboard seems to be very relevant for teaching students. A modern professional is constantly faced with the need to change and select goals, search for the optimal way out in conditions of pedagogical uncertainty and pedagogical risk. An important condition for its self-realization in pedagogical activity is goal-setting. Goal-setting as a choice or resolution of uncertainty in a plurality of alternatives involves understanding by learners of their own existential essence with subsequent creative actions. Knowledge of the mechanisms of goal-setting is necessary both for assessing by a teacher his/her own activities (level of universal competencies – critical thinking, self-organization and selfdevelopment), and the activities of students (level of general professional competencies – monitoring and evaluation of educational results). Therefore, the authors propose to study the features of goal-setting with a plurality of alternatives during the process of training bachelors and masters majoring in pedagogy within the disciplines of the methodical module. The goal of the article is to theoretically substantiate, develop and implement the technology of integrative teaching mathematics on the basis of chess game with the actualization of the phenomenon of a plurality of goal-setting as an effective mechanism of content modernization in training programs of future math teachers. The main result of the research we consider the developed integrative technology of teaching mathematics on the basis of solving problems on a chessboard with the actualization of the phenomenon of goal setting plurality. To implement the technology, a holistic, hierarchical complex of multi-stage math problems on the chessboard has been composed, which encourages students to master not only various methods (combinatorial, probabilistic, graph and set theory, mathematical and computer modeling), but also to develop the basic qualities of personality, such as creativity, reflection of one’s own choice, creative independence, motivation. The materials of the article are of scientific and practical value for researches in the field of math teaching methods, psychology, and pedagogy and accounting for them in the adjustment of programs and curricula in pedagogical universities.
Challenges in collecting primary data for environmental research purposes: a case study in Parangtritis sand dune, Yogyakarta

Gumuk Pasir (Sand dune) Parangtritis, which is located in Bantul Regency, Yogyakarta Special Province, is unique to compared with several sand dunes located in Southeast Asia. This site faces many environmental problems as the result of anthropogenic interference. Landuse change practices have been done for several years now which change the general presentation of this area. Other than that, violation of the zonation also severe. Some of the core zones has been altered to tourism spots.Some area has also been altered into agricultural land. The impact of the changes needs to be monitored by installing several measuring staffs. Socialization of the research and administrative permit has been cleared before the installation. This research was a part of sand sedimentation research in Parangtritis Sand Dune. This research was conducted as the result of several disturbances in our staffs. The aim of this research was to understand the challenge in collecting primary data, notably in collecting sand sedimentation rate data in Parangtritis Sand Dune. During the research, by interviewing the local people, we can conclude that many were still unaware of the research which were resulted in the missing of several staffs. Information and identity of the researcher have been applied in each staff which was ineffective in preventing disturbance to the staff. Based on that, several efforts have to be done in order to minimize the disturbance in the future. So that, reliable data could be achieved for decision support mechanism in solving environmental problem.
Nearby Product Recommendation System Based on Users Rating

The recommendation system is very popular nowadays. Recommendation system emerged over the last decade for better findings of things over the internet. Most websites use a recommendation system for tracking and finding items by the user's behavior and preferences. Netflix, Amazon, LinkedIn, Pandora etc. platform gets 60%-70% views results from recommendation. The purpose of this paper is to introduce a recommendation system for local stores where the user gets a nearby relevant recommended item based on the rating of other local users. There are various types of recommendation systems one is User-based collaborative filtering by which the system built upon and uses user's past behavior like ratings and gives similar results made by another user. In collaborative filtering uses Euclidean distance algorithm is used to find the user's rate score to make relations with other users and Euclidean distance similarity score distinguish similarity between users. K-nearest neighbor algorithm is used to implement and find the number of users like new user where K is several similar users. Integrate with map interface to find shortest distances among stores whose product are recommended. The dataset of JSON is used to parse through the algorithm. The result shows a better approach towards the recommendation of products among local stores within a region.
Sample Complexity of Joint Structure Learning

This paper considers the problem of jointly recovering the structures of two graphical models with unknown edge structures. It is assumed that both graphs have the same number of nodes and a known subset of nodes have identical structures in both graphs. The classes of Ising models and Gaussian models are considered. For Ising models, the objective is to recover the connectivity of both graphs under an approximate recovery criterion. For Gaussian models, the objectives of edge structure recovery and inverse covariance estimation are considered. Information-theoretic bounds on the sample complexity for bounded probability of error under the aforementioned criteria are established and compared with the corresponding bounds on the sample complexity for recovering the graphs independently.
Interference of billing and scheduling strategies for energy and cost savings in modern data centers

The high energy consumption of HPC systems is an obstacle for ever-growing systems. Unfortunately, energy consumption does not decrease linearly with reduced workload; therefore, energy conservation techniques have been deployed on various levels which steer the overall system. While the overall saving of energy is useful, the price of energy is not necessarily proportional to the consumption. Particularly with renewable energies, there are occasions in which the price is significantly lower. The potential of saving energy costs when using smart contracts with energy providers is lacking research. In this paper, we conduct an analysis of the potential savings when applying cost-aware schedulers to data center workloads while considering power contracts that allow for dynamic (hourly) pricing. The contributions of this paper are twofold: (1) the theoretic assessment of cost savings; (2) the development of a simulator to replay batch scheduler traces which supports flexible energy cost models and various cost-aware scheduling algorithms. This allows to approximate the energy costs savings of data centers for various scenarios including off-peak and hourly budgeted energy prices as provided by the energy spot market. An evaluation is conducted with four annual job traces from the German Climate Computing Center (DKRZ) and Leibniz Supercomputing Centre (LRZ). The theoretic analysis indicates a cost savings for 4–8% when shutting down unused client nodes, and 6–20% with hourly cost models and optimal scheduling. The experimental validation of a practicable scheduler increases the accuracy against the theoretical best case analysis. As expected, a cost-efficient scheduling algorithm that is fed with the information about future energy costs shifts the jobs to the timeslots where the job execution is cheaper and reduces the energy expenditure, yet increases the waiting times of pending jobs. However, the expected savings for this effort are not justifiable compared to the simple strategy of turning off the unused nodes. Additionally, we compare the cost savings to the total costs of ownership showing that smaller systems with on-demand provisioning yield better cost efficiency.
From Old Fashioned “One Size Fits All” to Tailor Made Online Training

Nowadays, post-editing of machine translation output represents a significant element in the translation market and industry. Subsequently, the preparation of future translators must cover not only all routine methods but must be cost-effective, efficient and in accordance with human resources available. That is the reason we use Internet-based technologies more and more. New emerging technologies are very often driven by the marketing power of companies developing and selling applications. Each of us experienced dozens of fantastic features available in teaching software and applications. The core skill of the online educator is to find a balance between our needs and ability to use technology. Since translation demand keeps growing every day, a large number of translators use various technical tools including translation memories, terminology management tools or Machine Translation (MT) technologies and thus increase their productivity and meet this high demand. The post-editing of MT should be only done by a person who is familiar with this method and knows exactly what, how and how much needs to be edited in the text. Otherwise, the sense of post-editing is losing importance, as the work of post-editor would not be more effective as a translator’s, who translates the text traditional way “from scratch”. The contribution of the paper is to create an online educational system tailored to translators’ needs; an online system in which students translate and revise a text, post-edit machine translation output and also assess the quality of the translation.
Adapting a Conversational Text Generator for Online Chatbot Messaging

Conversational interfaces and chatbots have a long history, but have only recently been hyped as a disruptive technology ready to replace mobile apps and Web sites. Many online messaging platforms have introduced support to third-party chatbots, which can be procedurally programmed, but usually rely on a retrieval-based specification language (such as AIML), natural language processing to detect the user’s intent, or on machine learning. In this work we present a work-in-progress integration of a widely-used system for story generation, the Tracery grammar, a conversational agent design tool, the Bottery system, and online messaging platforms. The proposed system provides a complete and easy-to-use system that allows the creation of chatbots with a graph-based dialogue structure, a contextual memory, pattern-based text matching, and advanced text generation capabilities, that aims for being well-suited for experts and technically unskilled authors alike. Features of the system and future additions are discussed and compared to existing solutions.
A Survey of Fall Prevention Systems Implemented on Smart Walkers

A large number of people die around the world in consequence of a fall. The costs related to fatal and nonfatal falls have an enormous impact on society and have been growing over the years. There are several risk factors that increase the probability of falling, such as poor balance and lower extremity weakness. Patients with balance impairment, in order to overcome these problems, use walkers. The aim of this work is to do an analysis of the fall-related strategies already implemented in a smart walker. Therefore, an online search was performed based on the literature through Scopus and Web of Science databases. A study was also conducted on a commercial level on Google, as well as a patent review on Espacenet and United States Trademark Office. It was possible to conclude that exist a concern related to the development of an approach to prevent the fall event. However, the only implemented strategy that was found throughout this research consists in stopping the walker when a near fall is detected.
QR Code Based Signage to Support Automated Driving Systems on Rural Area Roads

Automated Driving Systems (ADS) have significant potential to improve road safety and efficiency. The performance and the market adoption of ADS depend on the availability of the infrastructure that can support the operation of autonomous or semi-autonomous vehicles throughout the road networks. Rural and remote areas pose significant problems because they often lack power and communication network required for ADS functionality. There is a need for low-cost machine-readable static signage that can fill the gaps in the infrastructure. As one of the early steps to achieve that goal, this paper explores the feasibility of using Quick Response (QR) codes as static road side signage that can inform and alert machine-based onboard systems about the road conditions ahead. It specifically focuses on the applications involving the relay of data pertaining to the high accuracy roadway geometric information of intersections.
Deep Convolutional Feature Histograms for Visual Object Tracking

Visual Object Tracking remains an open and challenging task in the Computer Vision field, requiring tracking algorithms to achieve a feeble balance between precision and speed performance. In this work, inspired by the classic Mean Shift algorithm for object tracking using histograms, as well as the recent advances of deep Convolutional Neural Networks (CNNs), we propose a novel tracker that incorporates elements from both worlds. Our tracker uses a deep CNN as the feature extraction backbone, which is capable of extracting semantically meaningful features from the target and its background, as well as a fully learnable Bag-of-Features mechanism which extracts histograms from those features. The tracker operates in a fully-convolutional fashion, allowing for the direct and efficient evaluation of multiple possible target locations. Extensive experimental results demonstrate the efficiency and effectiveness of the proposed tracker, allowing it to run at high speeds even on systems with lower computational capacity.
Network Threat Analysis

This chapter will share some key results from the SAINT project starting with an overview of the people who form the cybercrime network including attackers, victims and the organisations who are attempting to protect them. Then we will elaborate on the role of information from the point of view of various relevant stakeholders (cybercriminals, potential cybercrime victims, cyber-defence services providers, cybersecurity regulatory and law enforcement authorities, researchers). Finally, we will discuss the future of cybersecurity in the context of IoT and present both the underlying opportunities and threats.
Remote State Preparation for Multiple Parties

Remote state preparation (RSP) is a technique to transmit quantum states with classical communication and previously shared entanglement. In this paper, we consider RSP in a multiparty setting. A simple yet nontrivial case is studied, where there is one sender and two receivers. We put forth a broadcasting method for developing multiparty RSP protocols. We show that this method can remotely prepare arbitrary states by consuming classical and quantum resources. For preparing highly entangled states, the proposed method achieves the lower bound for the amount of consumed resources asymptotically.
Online Clustering on Uncertain Data Stream

Importance of distributed systems nowadays cannot be overstated due to of enormously fast grown of the data to process. This data of different types comes from many devises in continuous flow, usually specified as data stream. In most cases data comes with part of uncertainty, because of the inaccuracy of hardware, unstable network, delays etc. Distribution and uncertainty create challenges for implementing data mining techniques for knowledge discovery. There are modern open source technologies and tools, for instance, products of Apache foundation like Storm, Samza, Spark Streaming and Flink, which allow work with data streams without concerns of technical boundaries, but they are not provide built-in methods for data mining of inaccurate data. Thereby, this work mainly focuses on research of online part of one of data mining technique, such as clustering, for uncertain data stream in distributed systems.
OrthoMaps: an efficient convolutional neural network with orthogonal feature maps for tiny image classification

In image processing domain of deep learning, the big size and complexity of the visual data require a large number of learnable variables. Subsequently, the training process consumes enormous computation and memory resources. Based on residual modules, the authors developed a new model architecture that has a minimal number of parameters and layers that enabled us to classify tiny images using much less computation and memory costs. Also, the summation of correlations between pairs of feature maps as an additive penalty in the objective function was used. This technique encourages the kernels to be learned in a way that elicit uncorrelated representations from the input images. Also, employing Fractional pooling helped to have deeper networks that consequently resulted in more informative representation. Moreover, employing periodic learning rate curves, multiple machines are trained with a less total cost. In the training phase, a random augmentation to the input data that prevent the model from being overfitted was applied. Applying MNIST and CIFAR-10 datasets to the proposed model resulted in the classification accuracy of 99.72 and 93.98, respectively.