Reviewerly/exHarmony huggingface dataset:
Non-link Preserving Network Embedding using Subspace Learning for Network Reconstruction

Learning meaningful vector representations of nodes of a network has been a subject of intense study in the past few years, with various objectives such as node or link labeling, preserving higher order structures, etc. In this paper, we focus on reconstruction of adjacency matrix, which also leads to preservation of higher order structures, through spectral distance. Methodologically, existing techniques focus on construction of various neighborhoods based on the link-structure, but do not explicitly give importance to nonlinks. Our method, called the subspace learning method (SLM), is based on a simple observation that in addition to representations of neighbors sharing a common subspace, representations of non-neighbors should lie in each others' null-space. We devise an efficient, negative sampling based algorithm for learning the node representations. Experimental results on many real world benchmark networks show surprising improvement in both reconstruction error and spectral distance over state of the art methods. Moreover, we show that the representations given by SLM perform better than state of the art representations on the tasks of community detection and link prediction.
Existential Diophantine definability of string length

We show that any existential Diophantine formula defining string length must involve at least two bound variables assuming sizes that are doubly exponential in string length.
Research of Digital Signal Processing Based on System Learning Model

Undergraduates majoring in information science usually offer compulsory or elective courses in signal processing, while digital signal processing courses are both theoretically and practically strong. According to the learning objectives of undergraduates in universities, the author introduces the digital signal processing in the background of the development of modern information field, this paper puts forward a practice learning method oriented to system of thinking and engineering mode of ability cultivation, which can improve traditional learning of theory and practice and has reference significance for agricultural colleges to enhance the professional foundation of information students.
Two Anti-quantum Attack Protocols for Secure Multiparty Computation

Millionaire problem and private set intersection problem are not only the basic issues in the secure multiparty computation, but also the building block for privacy-preserving cooperative computation. However, so far the existing solutions to the two problems cannot resist the quantum attack, and in the meanwhile are inefficient enough. Aiming at these drawbacks, in this paper we first construct two new 0–1 encoding. Subsequently, using the designed 0–1 encoding, we transform Millionaire problem into the summation problem, and further transform the set intersection problem into the product problem. Lastly, taking advantage of NTRU homomorphic encryption, we propose Protocol 1 for Millionaire problem and Protocol 2 for the secure set intersection problem, respectively. The final analyses indicate that the two protocols designed in this paper are not only secure against the quantum attack but also more efficient compared with the previous schemes, In addition, Protocol 1 has more fine-grained comparing result for any two elements in total order set than the previous; Protocol 2 has a two-fold functionality in that it is not only secure against quantum attacks but also applicable for cloud computing environment.
Tomato Automation Cultivation System: Automatize Watering and Fertilizer Based On Sensory Information

This research is to build a tomato watering and fertilizing machine for household-based agriculture. The objective is to reduce the work of planting the tomato tree, keeping the tomato tree stays healthily, and increase the interest of the people on the innovative agriculture in the household. This project aims to increase the efficiency of planting tomato, by reducing the tomato growth period and promoting the innovative way of planting. The planting of tomato tree in the household environment has a high chance of suffering diseases such as black spot disease, mould leaf disease, and yellow leaf disease, due to reasons of poorly controlling of temperature, watering, and humanity. The outcome of this automated cultivation machine can prevent the tomato trees away from the above-mentioned diseases. In conclusion, the automated cultivation machine provides an eco-farming environment that closes to the natural environment and the tomato tree not only grow healthy but also speed up the growing process in the machine. Moreover, the size of the machine is suited to household and promotes the interest of the people in household-based agriculture.
Mimic Geographic Information System

With the development of the Internet, the geographic information system gets a chance to develop rapidly. Aiming at the security problems of existing geographic information systems, a Mimic Geographic Information System, i.e. M-GIS, based on mimic defense is proposed to improve the security of geographic information systems. The system consists of heterogeneous redundancy geographic information execution bodies pool, request distributor, scheduler and arbiter. Firstly, the scheduler dynamically selects the geographic information execution bodies set for processing, and then makes a mimic decision on the processing results. The experimental results show that the mimic system is more security than traditional system.
A Study on Online Fault Diagnosis Technology for Shield Core Components

The development of micro-electronic techniques, large-scale application of integrated circuit technology, progress in computer technology and sensor technology, and increasing signal analysis means have provided favorable opportunities for the advance of shield fault diagnosis technology. This paper, on the basis of analyzing Chinese and foreign studies on fault diagnosis technology and its application status quo, explores the necessity of introducing online fault diagnosis technology in the construction process of shield equipment, analyzes the difficulty in online diagnosis technology for shield fault and proposes suggestions and countermeasures.
Multimedia analysis and design: a conceptual framework

The paper proposes a conceptual model inspired to semiotic theories, to be applied to the analysis and design of multimedia. We introduce a meta-model with four levels of semantic aggregation. There results a framework of concepts, relations and processes accounting for the multiple meanings that arise from a multimedia text. We explore the effectiveness of the model by considering four commercial clips by the brand Lancôme. Our analysis confirms that the framework is well suited to the analysis, indexing, design of narrative multimedia.
AyurTourism

AyurTourism:A recommendation system which provides a web based platform for collaborating Ayurvedic treatment and Tourism is proposed in this paper. Our proposal is that the leisure mind of people can be utilized as an opportunity to increase the revenue. The search keywords, implicit and explicit feed backs, reviews and user profiles are effectively used to determine the user preferences in our model. The personalized recommendations are generated on preferred hospitals for Ayurvedic treatment, trending tourist places, travel, food, and accommodation facility near the hospitals. Data mining and collaborative filtering are employed to implement this model.
Multivariate Sequential Analytics for Treatment Trajectory Forecasting

Chronic conditions, especially cardiovascular disease account for a large burden on modern healthcare systems. These conditions are by their nature ones that unfold over a long period of time, typically involving many healthcare events, treatments and changes of patient status. The gold standard in public health informatics for risk assessment is regression-based. While these techniques are effective in identifying factors contributing to risk, they produce reductive scores (e.g. probability of a specific class of event, like a heart attack) or binary prediction results, and moreover, they are sequence agnostic. In the area of long-term chronic disease management, multivariate sequential modeling offers an opportunity to forecast disease progression and treatment trajectory in a fine-grained manner in order to aid clinical decision making. This paper investigates the suitability of Long short-term memory, a type of recurrent neural network, in conducting multivariate sequential modeling in the healthcare domain, specifically in the task of forecasting. The eventual goal is to apply this technique to linked New Zealand health data through the Vascular Informatics using Epidemiology and the Web (VIEW) research project. This paper presents initial experiments and results for modeling patients' treatment trajectories during hospitalization using the Medical Information Mart for Intensive Care (MIMIC-III) data set.
Typology of Single-Industry Towns According to the Degree of Diversification of Secondary Vocational Education as an Information Base of Professional Retraining Projects

In the process of diversifying the economy of single-industry towns, a contradiction may appear between the homogeneous professional composition of the population and the need for personnel with diverse professional and qualification characteristics. Elimination of this risk lies in the field of diversification of vocational education.&#x0D; The task of the study was to diagnose the degree of diversification of secondary vocational education in single-industry towns with the status of a territory of advanced socio-economic development. The definition of this characteristic was considered as the definition formed to date, one of the prerequisites for the implementation of the program for the development of single-industry towns.&#x0D; The present research features the existing differences between single-industry cities with the status of territory of advanced socio-economic development in terms of the degree of diversification of secondary vocational education. The authors analyzed the mid-level training programs and programs for training skilled workers and employees that are being implemented in the 2017/18 school year. On this basis, a typology of single-industry towns was developed and proposed.&#x0D; This typology can form the basis for the development of a set of strategies for the diversification of free software for single-industry towns of different types. This, in turn, will make it possible to introduce into the development program of each single-industry town a project to transform the vocational qualification structure of the population in accordance with the needs of a diversifying economy, which will increase the level of complexity of the program for developing a single-industry town as a whole.
Information Visualisation for Project Management: Case Study of Bath Formula Student Project

This paper contributes to a better understanding and design of dashboards for monitoring of engineering projects based on the projects’ digital footprint and user-centered design approach.
Modern Technology and Women Empowerment in Pakistan

The issue of women rights violations is nothing new. However, the modes of tackling the menace has changed and gained momentum by many degrees. With advances in technology, these modes have assumed the semblance of a technological crusade for the cause of women rights. Pakistan, a patriarchal society to the core, has not been behind others in the use of smart technology for socio-economic safety and empowerment of women. Some of these efforts are directly sponsored by government; others are the initiatives by individuals and companies in private capacity. Though different in form and shape, these measures have a common goal to empower women socio-economically, give them necessary technical training, and/or stand by them when justice is impeded. The current chapter throws light on the spectrum of work being done to use technology as a vehicle to curb incidence of violation of women rights, strengthen women, and help them live honorably.
Tuning of evolutionary particle filtering approach for estimation of trajectory deviation

Abstract An autonomous vehicle launched from moving platform is subjected to flexure‐like disturbance and needs to stabilize itself before embarking on the target tracking phase. Control and guidance remains inactive during stabilization phase. Hence, estimating the deviation from the intended trajectory is important in the absence of control and guidance commands for trajectory correction. This estimation problem is here mapped to the misalignment estimation problem, with the intended trajectory data resembling mother data and the followed trajectory data resembling daughter data. The main requirement is fast convergence for early commencement of target tracking phase. Kalman filter and its variants fail to converge due to the inability to model time varying nonlinearities and flexure in the system model. Conventional particle filter converges, but computation time is high. This work explores evolutionary particle filter variants that may provide faster convergence through adaptive tuning of the range from where particle support points are selected. Such heuristics is based on the observed residual trend. Monte Carlo simulation results underline the importance of designing estimator based on adaptive tuned evolutionary particle filter algorithm.
From Big Data to Big Knowledge

Today's knowledge bases (KBs) capture facts about the world's entities, their properties, and their semantic relationships in the form of subject-predicate-object (SPO) triples. Domain-oriented KBs, such as DBpedia, Yago, Wikidata or Freebase, capture billions of facts that have been (semi-)automatically extracted from Wikipedia articles. Their commercial counterparts at Google, Bing or Baidu provide back-end support for search engines, online recommendations, and various knowledge-centric services. This invited talk provides an overview of our recent contributions—and also highlights a number of open research challenges—in the context of extracting, managing, and reasoning with large semantic KBs. Compared to domain-oriented extraction techniques, we aim to acquire facts for a much broader set of predicates. Compared to open-domain extraction methods, the SPO arguments of our facts are canonicalized, thus referring to unique entities with semantically typed predicates. A core part of our work focuses on developing scalable inference techniques for querying an uncertain KB in the form of a probabilistic database. A further, very recent research focus lies also in scaling out these techniques to a distributed setting. Here, we aim to process declarative queries, posed in either SQL or logical query languages such as Datalog, via a proprietary, asynchronous communication protocol based on the Message Passing Interface.
Study on the Automatic Classification Algorithm of Dongba Hieroglyphs

By analyzing the structural features of characters, Dongba hieroglyphs can be divided into two types: single graphemes and compound graphemes. The single graphemes can be further divided into contour type and structure type. Using the classification algorithm to automatically separate different types of Dongba hieroglyphs is helpful to the independent study of single and compound graphemes, and extracting the commonalities between the same type and the differences between the different. Therefore, by studying the structure features of Dongba hieroglyphs, we give a preprocessing and classification algorithm. The algorithm can achieve complete separation of single graphemes and compound graphemes, and even includes the separation of contour type and structure type in single graphemes. Finally, we verified the accuracy of the algorithm through experiments.
Supervisor Recommendation Tool for Computer Science Projects

In most Computer Science programmes, students are required to undertake an individual project under the guidance of a supervisor during their studies. With increasing student numbers, matching students to suitable supervisors is becoming an increasing challenge. This paper presents a software tool which assists Computer Science students in identifying the most suitable supervisor for their final year project. It does this by matching a list of keywords or a project proposal provided by the students to a list of keywords which were automatically extracted from freely available data for each potential supervisor. The tool was evaluated using both manual and user testing, with generally positive results and user feedback. 83% of respondents agree that the current implementation of the tool is accurate, with 67% saying it would be a useful tool to have when looking for a supervisor. The tool is currently being adapted for wider use in the School.
Research on the Current Situation and Development of Intelligent Precision Fertilizer Technology

Accurate preparation technique of water and fertilizer is the combination of irrigation and fertilization organically, and it is applied to the crop root system accurately to make the crop root system fully and directly absorb the water and fertilizer coupling solution. At present, there are mainly nutrient solution management method based on conductivity (EC) value, nutrient solution management method for nutrient addition and nutrient solution management method based on crop model. Based on the “integration of water and fertilizer” technology, precise fertilizer control technology put to use the automatic control technology, computer technology, sensor technology, etc., which can improve the accuracy of preparation of water and fertilizer solution concentration, and make the management of irrigation and fertilization intelligently, and also timing quantitative irrigation and fertilization for crop is guaranteed to improve the yield and quality.
Improving Classification Quality in Uncertain Graphs

In many real applications that use and analyze networked data, the links in the network graph may be erroneous or derived from probabilistic techniques. In such cases, the node classification problem can be challenging, since the unreliability of the links may affect the final results of the classification process. If the information about link reliability is not used explicitly, then the classification accuracy in the underlying network may be affected adversely. In this article, we focus on situations that require the analysis of the uncertainty that is present in the graph structure. We study the novel problem of node classification in uncertain graphs, by treating uncertainty as a first-class citizen. We propose two techniques based on a Bayes model and automatic parameter selection and show that the incorporation of uncertainty in the classification process as a first-class citizen is beneficial. We experimentally evaluate the proposed approach using different real data sets and study the behavior of the algorithms under different conditions. The results demonstrate the effectiveness and efficiency of our approach.
Where to Post

At present, question-answer (QA) sites have become one of the most important sources of information sharing. In order to ease search and categorization, QA sites create communities to discuss a specific topic or interest. As a consequence, a large number of communities have been created in the last few years. A lot of research has been conducted on community QA sites to address various problems including expert identification and tag recommendation. However, an important problem that has been neglected so far is to automatically route a question to the right community. In this paper, we propose a novel word-embedding based method to route a question to the right community. We use syntactic as well as semantic features to characterize a question and community. Although this approach of characterization performs well, it is highly computationally expensive. To deal with this problem, we use topic modeling, which effectively summarizes a community and reduces the computation time. Our experimental results reveal that usage of both syntactic and semantic features helps in question routing and leads to a better community prediction. We evaluate our methods on a well-known question answering system Stack Exchange and show the effectiveness of the proposed method.
IDEA: An Interactive Dialogue Translation Demo System Using Furhat Robots

We showcase IDEA, an Interactive DialoguE trAnslation system using Furhat robots, whose novel contributions are: (i) it is a web service-based application combining translation service, speech recognition service and speech synthesis service; (ii) it is a task-oriented hybrid machine translation system combining statistical and neural machine learning methods for domain-specific named entity (NE) recognition and translation; and (iii) it provides user-friendly interactive interface using Furhat robot with speech input, output, head movement and facial emotions. IDEA is a case-study demo which can efficiently and accurately assist customers and agents in different languages to reach an agreement in a dialogue for the hotel booking.
Criterion for the assessment of the ecological condition of bottom sediments (on the example of the Brest region)

Geochemical parameters of bottom sediments allow quick estimating the state of the environment within the catchment of watercourses, as the composition of bottom sediments inherits the geochemical features of the components of the surrounding landscape. New data on the content of trace elements, sulfates, chlorides, nitrates, petroleum products, synthetic surfactants and phenols in the bottom sediments, obtained during complex geochemical studies in the Brest region, are presented. The concentration levels of the determined ingredients for sediments of the main types of reservoirs were determined. The maximum accumulation of nitrogen compounds, chlorides, phosphates and trace elements (nickel, cobalt, vanadium, chromium, copper, zinc and manganese) was marked in the sediments of rivers, while sulfates and lead are concentrated to a greater extent in the bottom sediments of the drainage channels. The map of pollution of bottom deposits of the territory of the Brest region of scale 1:200000 was constructed. It was found that the geochemical state of bottom sediments in the reservoirs of the Brest region is mainly in satisfactory condition, the degree of pollution by heavy metals was estimated as permissible with only 3.0 % belonging to the category of dangerous pollution level.
Experiment 2: Sonority Effects

The primary goal of this chapter is to better test the interactive approach—sonority hierarchy type and sonority natural classes and the approach of pure sonority natural classes proposed in Chap. 4 (see Sect. 4.14 ).
CHEERBOT

The objective of this chapter is to discuss the authors' interaction and involvement with technology and bots, opening a whole new and wide scope of possibilities while letting bots comfort us. The prevalence of bots and automation is increasing by every passing day – Cortana, Siri have been here for a long time and have now been overtaken by Alexa and other home automation systems that provide a two-way dialogue conversations. This chapter explores the possibilities of creating bots that can cheer us up when we are sad. Analyzing the semantics of our sentences and analyzing the pitch of our voice to identify our emotional state, and then providing an n-way dialogue conversation, relevant to the then existing context, instead of the mundane two-way dialogue conversation is the lucid content of this chapter. Summing it up, this chapter examines the possibility of creating bots that can serve as an emotional support to us humans.