Reviewerly/exHarmony huggingface dataset:
Short Text Computing Based on Lexical Similarity Model

Short text similarity deals with determining the closeness of two text mean the same thing by lexical or semantic. Various short text similarity approaches have been proposed which are based on lexical matching, semantic knowledge background or combining models. Lexical based model does not capture the actual meaning behind the words. However, semantic approach are relying on knowledge background or corpus which cannot be assumed to be available in handling such huge new word of data sparseness and noise in short text. This work are focusing on lexical-based similarity models for analysing the unstructured short text. The term-based and edit distance model are used in comparing the applicability of these model to compute the similarity value of short text. The experimental results shows that each model have their key strengths and limitations in computing similarity value of short text.
Fusing Syntax and Word Embedding Knowledge for Measuring Semantic Similarity

The explosive growth of information makes it an important issue to effectively mine useful information from massive information. Text is an important carrier of information, so the processing and analysis of text has become one of the hot spots of data mining and information retrieval. Sentence similarity is the basis of most text-related tasks. The majority of current approaches leverage pairwise similarity characteristics to represent text pairs. Unlike the current approaches, we propose a new method to analyze and quantify the semantic textual similarity between sentences by encoding semantic knowledge based on word embedding into the syntax tree of sentences. We use SemEval-2012 task to test our approach and evaluate the performance with two widely used benchmarks:the Spearman and Pearson correlations, the experimental results show that compared with the best systems of semantic textual similarity (STS) task, our method can effectively improve the accuracy of sentence similarity judgment.
Revisiting the content of professional competencies of teacher education

The article presents the results of a theoretical study of the requirements of the Professional standard «Teacher» and the FSES in the field of training «Teacher education». The author considers the content complementarity of normative documents requirements and presents the content of mandatory professional competencies and recommended professional competencies in the profile of «life safety».
Metadata-Based Ontological Framework for Semantic Query in Multilingual Databases

Translation of a natural language query into a semantically sound and semantically meaningful structured query and mapping it onto a target database is a challenge for the interactive application designers. The realization of such a complex translation scenario requires a systematic approach with an end-to-end capability. The proposed deep learning-based ontological framework for semantic query in multilingual databases (DLOMLD) receives a natural language query from the user and maps that onto a semantically meaningful structured query using generative adversarial network and fires the query on an identified database. The DLOMLD possesses salient features such as the language translation capabilities, extraction of metadata from underlying relational databases and utilization of established ontological predicates. The functional merits of the DLOMLD are demonstrated while taking education domain as a case study, in a top-down manner elaborating the negotiations between two consecutive layers.
Security for Cross-Tenant Access Control in Cloud Computing

Cloud computing is becoming a de facto standard for most of the emerging technology solutions. In a typical cloud environment, various tenants purchase the compute, storage resource, and would be sharing the resource with other tenants. Sharing of the resources among various tenants is not popular due to the security concerns. There are few solutions that try to solve the security problem of resource sharing among tenants. Having a trusted mediator between multiple tenants is one of the methods. Few research papers have been written, and this chapter attempts to enhance one of the published solutions: Cross-tenant access control model for cloud computing. Most of the existing research papers explore the theoretical way to solve the problem. This project develops a working prototype and proves how resource sharing can be achieved. This research develops the concept of resource sharing activation, where the resource can be shared with multiple cloud tenant and the deactivation where the shared resources can be removed from the shared resource pool.
Gesture-Based Auto-Completion of Handwritten Text

Auto-completion is a major feature in all keyboard based mobile devices. However, not a lot of work has been done to extend this feature into handwritten text based input. Generally, auto-complete options are ranked on the basis of previous context and word probabilities. However, this leads to missing out on the user intended word simply because there are more frequently used words with the same prefix, in the same context. In this paper, we propose a gesture-based solution to recognize and complete partially-written words, where the user indicates to the system the missing text length by a stroke gesture, thus ruling out a huge subset of possibilities. We also propose a length dependent hybrid score ranking system, to improve the prediction accuracy and speed. Our results show that using a gesture stroke length based searching method not only reduced the processing time by 27% but also showed an accuracy increase of 10%, when the top 3 candidates are considered.
Low-Cost Method for Recognizing Table Tennis Activity

This study designs a low-cost portable device that functions as a coaching assistant system which can support table tennis practice. Although deep learning technology is a promising solution to realizing human activity recognition, we propose using cosine similarity in making inferences. Our experiments show that the cosine similarity based inference can be a good alternative to the deep learning based inference for the assistant system when resources are limited.
Availability-Aware Multi-Objective Virtual Cluster Allocation Optimization in Energy-Efficient Datacenters

With a great number of cloud applications being deployed in virtual clusters (VCs) and running in datacenters, the energy consumption of datacenters is experiencing rapidly cumulative growth. Thus, it is a critical issue that how to allocate virtual machines (VMs) of the VC in physical machines (PMs) for energy savings as much as possible. Considering each PM/switch has a certain failure rate, VMs may not be executed when they meet with any PM/switch fault. So, a compact allocation scheme may benefit in low energy consumption but increase the risk of violating the availability of the VC. In this paper, we consider four optimization objectives about the VC and the datacenter, i.e., availability, energy consumption, average resource utilization, and resource load balance. Then we propose a multi-objective optimization model and raise an evolution algorithm to trade-off among these four optimization objectives. Finally, experimental results show the effectiveness and efficiency of our algorithm.
Prepositions and Case Forms of the Russian Language as a Subject of Identification Linguistics

The article features Russian vocabulary from the aspect of identification linguistics, i.e. identification function on the morphological level, e.g. in various parts of speech and word forms belonging to different grammatical categories. The analysis focuses on auxiliary parts of speech, namely prepositions, related case forms, and grammatical meanings. The research is based on Internet correspondence. The article is included in the paradigm of research aimed at identifying and describing quantitative patterns in the distribution of units, properties, and relationships in texts and patterns of the stability / variability coefficient of units, properties, and relationships. The authors assume that different units have a different coefficient: some tend to be stable while others change their coefficient depending on different characteristics of the text. The research employed the method of Pearson’s statistical criterion. The applied method determines the frequency of lexemes in texts belonging to different author profiles and reveals their identification potential.
Optimal Feature Subset Determination for High-Dimensional Datasets in Manufacturing Processes

Selecting the most relevant feature subset with good predictive performances and computational speed for high-dimensional datasets is usually challenging in manufacturing processes for product quality control. In this paper, an optimal feature subset determination approach for high-dimensional datasets is proposed. The algorithm starts with ranking the importance of individual features using the normalized mutual information concepts. Then, through optimizing a new evaluation metric, an initial relevant feature subset is obtained. Finally, redundant features are eliminated to help obtain the final selection results. The proposed method was successfully used in solving a real-world high-dimensional feature selection problem in the semiconductor industry. Comparisons were made with four other representative feature selection algorithms, including Relief-F, mRMR, FCBF, and IWFS in processing a number of datasets from different applications. It is demonstrated that the proposed method can automatically determine an optimal feature subset to achieve good average predictive accuracy with less computational resources.
A Tree Computing Block Chain Protocol Crypto-Signature Model

This author has for the past decade developed a mathematical basis for product languages, at times with colleagues, applying agent crypto-signatures to authenticate business process models. This chapter is the newest application for what is at recent times called block chain. A new algebraic tree based public key cryptography techniques and algorithm for crypto-signatured block chain processing is presented. The techniques apply agent crypto-signatured algebras and block product language signatures for an agenda-based block chain protocol. An algorithm with a new public key computable trust model for signature tree block parties accomplishes the block chain goals.
Improved SMQT Algorithm and PCNN Model for Micro-calcification Clusters Detection in Mammograms

This paper proposes a novel combined method to improve micro-calcification clusters (MCs) detection accuracy in mammograms. The presented method includes three main steps: firstly, exponentiation operation and a new improved successive mean quantization transform (SMQT) algorithm are employed to enhance MCs; secondly, wavelet transform is introduced to obtain the significant MCs information; thirdly, pulse-coupled neural network (PCNN) model is used to detect MCs. In the experiment, totally 73 mammograms from MIAS database and 41 mammograms from JSMIT database are chosen to test the algorithm, and experimental results demonstrate that the algorithm presented in this paper is better than the other algorithms by yielding higher specificity of 98.0%, accuracy of 97.26%, and sensitivity of 95.65%. Besides, the method is verified on 20 mammograms from the People's Hospital of Gansu Province, and the detection results indicate that our algorithm can detect MCs correctly. Above all, the proposed method is simple and effective, and it can be considered to assist the radiologist for breast cancer diagnosis.
Face Identification Using Local Ternary Tree Pattern Based Spatial Structural Components

This paper reports a face identification system which makes use of a novel local descriptor called Local Ternary Tree Pattern (LTTP). Exploiting and extracting distinctive local descriptor from a face image plays a crucial role in face identification task in the presence of a variety of face images including constrained, unconstrained and plastic surgery images. LTTP has been used to extract robust and useful spatial features which use to describe the various structural components on a face. To extract the features, a ternary tree is formed for each pixel with its eight neighbors in each block. LTTP pattern can be generated in four forms such as LTTP–Left Depth (LTTP-LD), LTTP–Left Breadth (LTTP-LB), LTTP–Right Depth (LTTP-RD) and LTTP–Right Breadth (LTTP-RB). The encoding schemes of these patterns are very simple and efficient in terms of computational as well as time complexity. The proposed face identification system is tested on six face databases, namely, the UMIST, the JAFFE, the extended Yale face B, the Plastic Surgery, the LFW and the UFI. The experimental evaluation demonstrates the most promising results considering a variety of faces captured under different environments. The proposed LTTP based system is also compared with some local descriptors under identical conditions.
Named Entity Recognition for Chinese Management Case Texts

Extracting named entity especially organization name from Chinese management case texts is a challenging task due to the lack of labeled data and difficulty in identifying diversified forms of entity names. In this paper, a semi-supervised learning method based on bidirectional long short-term memory and conditional random field (BI-LSTM-CRF) model was proposed. This method has a bootstrapped framework which automatically learns complex text features from a small number of seed sets with the BI-LSTM-CRF model and then updates the seed sets after evaluating the recognition results according to the rule base. It stops iterating when the precision of the model comes to convergence. The experimental results show that the accuracy of the proposed method reaches 89.2%, which outperforms other semi-supervised learning models.
Temporal Logic Semantics for Teleo-Reactive Robotic Agent Programs

Teleo-Reactive (TR) robotic agent programs comprise sequences of guarded action rules clustered into named parameterised procedures. Their ancestry goes back to the first cognitive robot, Shakey. Like Shakey, a TR programmed robotic agent has a deductive Belief Store comprising constantly changing predicate logic percept facts, and knowledge facts and rules for querying the percepts. In this paper we introduce TR programming using a simple example expressed in the teleo-reactive programming language TeleoR, which is a syntactic extension of QuLog, a typed logic programming language used for the agent’s Belief Store. We give a formal definition of the regression property that rules of TeleoR procedures should satisfy, and an informal operational semantics of the evaluation of a TeleoR procedure call. We then formally express key features of the evaluation in LTL. Finally we show how this LTL formalisation can be used to prove that a procedure’s rules satisfy the regression property by proving it holds for one rule of the example TeleoR program. The proof requires us: to formally link a TeleoR agent’s percept beliefs with sensed configurations of the external environment; to link the agent’s robotic device action intentions with actual robot actions; to specify the eventual physical effects of the robot’s actions on the environment state.
Understanding How Non-experts Collect and Annotate Activity Data

Inexpensive, low-power sensors and microcontrollers are widely available along with tutorials about how to use them in systems that sense the world around them. Despite this progress, it remains difficult for non-experts to design and implement event recognizers that find events in raw sensor data streams. Such a recognizer might identify specific events, such as gestures, from accelerometer or gyroscope data and be used to build an interactive system. While it is possible to use machine learning to learn event recognizers from labeled examples in sensor data streams, non-experts find it difficult to label events using sensor data alone. We combine sensor data and video recordings of example events to create a better interface for labeling examples. Non-expert users were able to collect video and sensor data and then quickly and accurately label example events using the video and sensor data together. We include 3 example systems based on event recognizers that were trained from examples labeled using this process.
Blockchain in construction – hype, hope, or harm?

Business and public interest and investments in digital ledgers, smart contracts and virtual currencies such as bitcoin, has skyrocketed. Blockchain is indeed a hyped technology – and should therefore raise healthy skepticism. During construction production, projects and the involved companies take on a disintegrated economic flow and allocation of economic resources; these include supplies of materials and services, payments, accounting tasks, and other economic decisions, that are often treated second-hand by site management, subcontractors, transport companies, retailers and material suppliers. Within such a situation, blockchain technologies can maybe create value for stakeholders in handling this economic flow and integrating it with other information and material flows. So, in this sense, blockchain represents a hope for construction; however, this paper critically scrutinizes blockchain for construction, in trying to answer the question whether it indeed constitutes hope, or it is harmful. The paper will report from an ongoing study and development project aiming at implementing a blockchain prototype for digitalized construction logistics at a large building site. A literature review, undertaken in several iterations from May 2018 to summer 2019 and following the emergence of the hype for blockchain, reveals visions and a few prototypes of related systems for construction. Theoretically, the paper adopts a sociomaterial approach, appreciating that blockchain is as much about social interaction and development of trust, as it is about digitally facilitating economic transactions. Several critical issues threatening to jeopardize the adoption of blockchain are analyzed in the paper, such as the protection of the economic data during transactions, permission access control which only allows a few people to enter the system while still requiring a minimum of trust between those already inside the system, and possible financial speculation on the utilized cryptocurrencies. The way construction stakeholders can mitigate such issues, thus enabling blockchain to become a realized hope rather than harming the sector, is discussed. Probably the largest risk relates to unrealistic hopes of full transparency in open blockchain systems, potentially making the systems too vulnerable to external intervention and even speculation. Permissioned systems with some level of hierarchy appear to be a feasible compromise solution mitigating the potential harm.
Active Learning with Interpretable Predictor

Active learning is a method of constructing a useful prediction model with the minimum number of annotations or labeling for a response variable. It is widely used as a modern experimental design method, particularly for problems with high annotation cost. An appropriate reason for the selection of the next experimental setting is required for experiments with high annotation cost, for example, situations requiring large-scale experiments or long-term experiments such as agricultural examinations. In conventional active learning, it is only known that the samples that can improve the prediction accuracy of a prediction model are selected. This is not a satisfactory explanation for the selection of the next experimental setting for approving an experiment. In this paper, we propose a novel active learning algorithm with the following two models: a model to predict a response variable and a model to predict the amount of decrease in test loss. A new sample is selected using a model that predicts the amount of decrease in test loss. It is possible to provide a reason for sample selection by employing a model that can evaluate variable importance, e.g., using a random forest as a model of predicting the decrease in test loss. We applied the proposed method to multiple datasets and showed that the prediction performance of the proposed method is comparable to those of existing methods and the computational time is superior to those of existing methods. In addition, we demonstrated that it is possible to provide suitable reasons for selecting a sample in the process of active learning.
Fundamentals of Software Engineering

Software engineering involves the multi-person construction of multi-version programs. We discuss various software lifecycles and the phases in traditional software development. We discuss requirements gathering and specification, software design, implementation, testing, and maintenance. The lightweight Agile methodology is introduced, and it has become very popular in the industry.
Construction of Semi-automatic Generation System of Mathematical Exercise Book Unique to Department with Student Viewpoint

In this paper, we describe the approach that the systematized the process of student created mathematics exercises and answers and updating the contents of the mathematical exercise book. This effort was made to solve the problem that in general exercise book does not correspond to the learning content of the department or the level of the student. This effort led students to grasp the parts that they did not understand by making mathematics exercises and commentaries, and to review the contents learned in the past. Furthermore, based on the learning items of each subject, it can select necessary mathematics exercises and respond to the rapid change of the departmental situation such as curriculum change. As a result of this effort, the pass rate of mathematics subjects improved and the retention rate of learning contents increased.
AdaptiveNet: Toward an Efficient Face Alignment Algorithm

Face alignment, a challenging task in computer vision, has witnessed its tremendous improvement on the 300W benchmark. However, state-of-the-art algorithms are suffering from computational expense and therefore cannot apply in real-time. In this paper, we propose a time-efficient face alignment algorithm while maintain a sufficient algorithmic accuracy. Specifically, we adopt MobileNet-V2 as our backbone architecture to deal with easy samples, accompanied by a ResNet branch to handle hard examples. This combination leads to a low-latency and yet agreeable-performance design as our extensive experiment shows.
DOD's Cloud Strategy and the JEDI Cloud Procurement [Updated August 2, 2019]

Library of Congress. Congressional Research Service; United States. Department of Defense; Cloud computing