Reviewerly/exHarmony huggingface dataset:
The Detection of Actors for German

In this short paper, we discuss a straight-forward approach for the identification of noun phrases denoting actors (agents). We use a multilayer perceptron applied to the word embeddings of the head nouns in order to learn a model. A list of 9,000 actors together with 11,000 non-actors generated from a newspaper corpus are used as a silver standard. An evaluation of the results seems to indicate that the model generalises well on unseen data.
Cognitive Dissonance in the Professional Activity of an Accountant

Th e article focuses on the issue of increasing the activity of members of the professional community in the field of accounting science and practice regarding the methodological component of accounting knowledge. Based on the fundamental approaches of the logic of scientifi c research and the postulates of social psychology, the author identifi es the problem and comes to the conclusion that there is a psychological background to the professional behavior of colleagues, as a result, a contradiction is revealed between the true attitude of accountants to the ongoing reformation changes and their publicly expressed position. Th e very awareness of the causes of what is happening is an indispensable condition for restoring the atmosphere of free scientifi c and practical discussions.
A Digital Record for Privacy and Security in Internet of Things

For privacy and security, a digital transactional record method plays a major role for its excellent nature of work. This digital method used to solve the check the problems like privacy and protection of data. It also had some unfit things like high bandwidth, computation complexity, latency and restricted scalability which are inadequate for internet of things. This research paper focuses on Efficient Lightweight Integrated Block chain model which is expanded to show the development of internet of things. Especially, this paper presents a model of a digital home which is attempted to prove the various applications used by internet of things. The benefits of this smart home are information transmission, activity of outgoing and incoming in every action. Efficient Lightweight Integrated Block chain model connected with digital transactional method with powerful provided sources to prove privacy and security. Algorithm, Certificate less Cryptography, Distributed Throughput Management schemes and Lightweight Consensus are used to present the Efficient Lightweight Integrated Block Chain model. Various methods are used to prove this model by using time processing, usage of energy and so on. This model saves 60% of time processing while consuming the energy of 0.08 Jm. Many parameters are used to show outcome done by this method.
Automated Multiple Face Recognition Using Deep Learning for Security and Surveillance Applications

Face recognition is some of the difficult processes due to a large number of wild datasets. Deep learning is the research concentrate on the latest years. Beacause of its best implementation, it is widely used in the area of pattern recognition initiated deep learning structure is collected of a set of complicated designed CNN. Deep learning gave valid resolution in the matter of recognition execution. In our present paper, our purpose is to consider deep learning established face recognition below atmosphere like disparate aspects of head positions, difficult clarification, faulty exterior characteristic localization, and precision using deep learning. We are using OpenCV, Haar cascade for detecting faces, eyes, and smile. LBPH face recognizer is used for training data recognition of faces. Convolution neural network (CNN) is used for facial extractions without any flaws and with more accuracy.
Accurate mining of location data in the communication field based on big data

In order to extract value from data, data mining and data software technology are widely used in the industry. This study mainly discusses the precise mining of location data in communication field based on big data. Signaling preprocessing layer mainly obtains signaling message through acquisition module, filters FISU message in signaling message, judges abnormal message frame, and stamp it with time stamp, which provides effective data source for next processing. Signaling access layer mainly completes the function of signaling link access, mainly using high resistance jumper technology, time slot convergence technology, optical access technology and 155mdxc conversion technology to access 2 m link and 155 m link respectively. The signaling collection module must collect directly or via a link through DXC in order to reach the front-end data collection machine and access the signaling collection module of the front-end machine. The Signaling Collection Module also completes some of the message processing work. The presentation layer is the window of human-computer interaction of the whole system, which presents to users with friendly interface and perfect functions. The main goal of real-time big data analysis is to obtain signaling data sent by signaling acquisition system, and screen out the effective information in signaling data according to monitoring conditions, and then analyze the final real-time monitoring results. Geographic information module provides visual map control for the regional monitoring big data analysis module. The difficulty of system development can be reduced by using the existing WebGIS map toolkit. When the call from the Customs Bureau of Unicom in different cities is called into the mobile gateway Bureau, the call is rejected by the mobile customs bureau. The call time is 0 seconds, of which the interception success rate is up to 90% within 1 s. This research is of great significance for the better development and maintenance of signaling network and monitoring system.
Noise-Tolerant Supervised Relation Extraction

Existing supervised relation extraction methods share generate flaws that they have improvable feature extractors and are uncapable of eliminating task-irrelevant words. To handle such issues, this paper proposed a relation extraction model that can avoid interference from task-irrelevant information. In this model, input texts are converted to digital sequences initially, and then these sequences are fed into Mogrifier BiGRU to encode semantic features. While in the following GateAttention module, encoded entities are used to sift task-irrelevant words out with gate mechanism and then the left ones are fused into sentence vectors using attention network. Finally, the sentence vectors are fed into the softmax function for relation classification. Experimental result on SemEval-2010 Task8 reveals that the proposed model gets an 85.24 F1 value without relying on language tools or any additional information, which indicates this model has a stronger capability of feature extraction and higher resistance to noise interference compared with other ones.
Super Resolution of Single Image Based on Multi Level Residual Self Attention Mechanism

The existing network models achieve good reconstruction effect by deepening the network depth, but most of them have problems such as insufficient feature information extraction, single scale of feature information, weak perception of valuable information and so on. In order to solve this problem, this paper proposes a single image super-resolution network based on multi-level residual self attention mechanism. Firstly, shallow features and deep features are extracted from the input low resolution image hierarchically, and then convolution operation is performed on the deep features and shallow features to obtain high resolution image. Compared with the existing comparison methods, the reconstruction effect of this method is better, and the objective evaluation indexes PSNR and SSIM are also improved.
Construction and verification of the emerging engineering talents evaluation model based on fuzzy mathematics theory

Abstract In view of the present situation of cultivating innovation and entrepreneurship ability of engineering talents in local colleges and universities, it is necessary to perfect the evaluation mechanism of emerging engineering talents cultivation, and design the evaluation standard system of supporting the conditions of innovation and entrepreneurship education for college students. Based on the evaluation standard, the Fuzzy Analytic Hierarchy Process (FAHP) theory is used to establish the mathematical model and evaluation model of the evaluation system of college students' innovation and entrepreneurship education condition support under the emerging engineering background. The results show that the established model has universality, accuracy and practicability, and provides technical support for the evaluation of emerging engineering talents, so as to form a closed loop of quality engineering, constantly improve the quality of emerging engineering talents cultivation, and provide talent support for national and regional innovation-driven development.
Fangorn

Pervasive needs for data explorations at all scales have populated modern distributed platforms with workloads of different characteristics. The growing complexities and diversities have thereafter imposed distinct challenges to execute them on shared clusters in corporate or public clouds. This paper presents Fangorn, an adaptive execution framework built on an enriched graph model. As the underlying infrastructure for core computation platforms at Alibaba, Fangorn supports various execution modes and caters to heterogeneous workloads. With the capability to orchestrate graph executions with both long-running and requested-on-demand resources at the same time, Fangorn allows exploration of tradeoffs between latency and resource efficiency, for jobs of all scales. By modeling distributed job executions as mutable graphs with pluggable components, Fangorn offers a systematic framework to adjust job executions adaptively, according to data statistics collected during run-time. Fangorn supports an array of different computation engines ranging from relational to deep learning, and is fully deployed on production clusters across Alibaba. It manages tens of millions of distributed jobs daily, with job size scaling from one to half-million.
A Domain Data Pattern Randomization based Deep Reinforcement Learning method for Sim-to-Real transfer

Transferring reinforcement learning policies trained in a physical simulator to the real world is a highly challenging problem, because the gap between the simulation and reality, usually causes the transferred model to perform poorly in the real world. Many algorithms including domain randomization, have been proposed to try to bridge the gap between simulation and reality. However, most of them are to change the value of the corresponding data by superimposing gaussian noise on robot dynamics parameters or environmental data. Such policies often fail to solve the problem of long-term/intermittent missing data patterns caused by sensor failures in the actual operation of the robot. Faced with this problem, we proposed a memory-enhanced domain data pattern randomization method. This method achieves data enhancement by randomizing the distribution pattern of data connection, at the same time, the memory mechanism based on recurrent neural network is introduced into the decision model, to alleviate the jitter of environmental distribution caused by data pattern changes, so as to improve the decision-making ability of the robot in some observable scenes triggered by the change of data pattern.
Eye Controlled Automation for Movement Disorder Patients Using IOT

Accessible to the extent of assistive technology remains essential to persons along with defects.optic could be considered as a unique organ which can support a disabled person to communicate correctly. This paper presents a unique concept to controlling the home gadget for crippled individuals who cannot reposition anything at all except their optic. Here we will control the home apparatus through eye flicker discovery. Here we are using Raspberry pi as a main microprocessor board. The proposed method right here makes use of the optic monitoring approach for optic motion acquisition.
Didactic, Technological, and Social Challenges in Portuguese Higher Education During the Pandemic Confinement

This chapter presents testimonials of 12 national experts in the field of educational technology concerning the main didactic, technological, and social challenges they faced throughout the “emergency remote teaching” that occurred during the COVID-19 pandemic confinement. All experts are professors in Portuguese universities or polytechnic institutions, and they are acquainted with the “distance education” modality. An online questionnaire was applied to these 12 professors in order to collect their opinions about the main challenges during this period. Based on a content analysis technique, the results revealed several technological (e.g., unpredictability of internet connections), didactic (e.g., the teachers' lack of technological pedagogical content knowledge), and social challenges (e.g., the lack of proper physical spaces at home). Based on the lessons learned from this worldwide pandemic emergency, and critically reflecting about it, recommendations are suggested for future action to “distance education” in higher education.
Implementation complexity of Boolean functions with a small number of ones

Abstract We consider the class F n , k consisting of n -ary Boolean functions that take the value one on exactly k input tuples. For small values of k the class F n , k is splitted into subclasses, and for every subclass we find the asymptotics of the Shannon function of circuit implementation in the basis <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mo fence="false" stretchy="false">{</m:mo> <m:mi>x</m:mi> <m:mi mathvariant="normal">&amp;</m:mi> <m:mi>y</m:mi> <m:mo>,</m:mo> <m:mover> <m:mi>x</m:mi> <m:mo accent="false">‾</m:mo> </m:mover> <m:mo fence="false" stretchy="false">}</m:mo> </m:math> $ \{x\&amp;y,\overline x\} $ (or in the basis <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mo fence="false" stretchy="false">{</m:mo> <m:mi>x</m:mi> <m:mo>∨</m:mo> <m:mi>y</m:mi> <m:mo>,</m:mo> <m:mover> <m:mi>x</m:mi> <m:mo accent="false">‾</m:mo> </m:mover> <m:mo fence="false" stretchy="false">}</m:mo> <m:mo stretchy="false">)</m:mo> </m:math> $ \{x\vee y,\overline x\}) $ ; the weights of the basic gates are arbitrary strictly positive numbers.
Domain-Specific Sentence Encoder for Intention Recognition in Large-Scale Shopping Platforms

Intelligent dialog robot is now a primary necessity in online shopping, since it can significantly improve the service quality of the online merchants with fewer customer service staffs. And the core functionality of such robots is to identify the customers’ intention in order to provide better service. In this paper, we propose a domain-specific sentence encoder (DSSE) method for real-time online intent identification that hierarchically consists of a representation module and a classification module. We construct a large corpus using the dialog text from e-commerce platforms. We train the representation module of DSSE with the dialog corpus of all kinds of goods and then fine-tune the downstream multi-layer perceptron (MLP) classification module on the corpus of a specific kind of goods. Our model can be easily extended to new kinds of goods as it only needs a little training corpus to fine-tune the small MLP module on new goods chatting dialog text. In addition, our model has quick inference speed and relatively low resource requirement, as well as a higher intent identification accuracy score than BERT. Specifically, our model achieves 85.95% accuracy score and 81.62% F1 score. The inference time per-batch (size = 350) is 33 ms with CPU and only 5.8M parameters are loaded into the RAM.
How Can We Investigate Ancient Greek Categories Without the Influence of Our Own? Exploring Kinship Terminology Using Word2Vec

Abstract Recent advances in Natural Language Processing (NLP) continue to open up new lines of enquiry for lexicography, especially for research areas concerned with semantic categorisation. The aims of this paper fall into two parts. In the first half, the NLP tool Word2Vec is introduced and discussed with respect to unsupervised category formation for Koine Greek, using kinship terms as a demonstration of the model’s utility. The latter half of the paper employs a prototype theory framework to analyse the model’s distributional information in the construction of new dictionary entries for each kinship term. Such NLP-based research highlights the importance of encyclopaedic information for the construction of lexical entries; it also significantly improves upon earlier attempts at categorisation for Koine Greek (Louw and Nida 1988), which fall victim to using the L1 categories of the lexicographer in the construction of semantic domains for the ancient language.
A machine learning model to classify the feature model maintainability

Software Product Lines (SPL) are generally specified using a Feature Model (FM), an artifact designed in the early stages of the SPL development life cycle. This artifact can quickly become too complex, which makes it challenging to maintain an SPL. Therefore, it is essential to evaluate the artifact's maintainability continuously. The literature brings some approaches that evaluate FM maintainability through the aggregation of maintainability measures. Machine Learning (ML) models can be used to create these approaches. They can aggregate the values of independent variables into a single target data, also called a dependent variable. Besides, when using white-box ML models, it is possible to interpret and explain the ML model results. This work proposes white-box ML models intending to classify the FM maintainability based on 15 measures. To build the models, we performed the following steps: (i) we compared two approaches to evaluate the FM maintainability through a human-based oracle of FM maintainability classifications; (ii) we used the best approach to pre-classify the ML training dataset; (iii) we generated three ML models and compared them against classification accuracy, precision, recall, F1 and AUC-ROC; and, (iv) we used the best model to create a mechanism capable of providing improvement indicators to domain engineers. The best model used the decision tree algorithm that obtained accuracy, precision, and recall of 0.81, F1-Score of 0.79, and AUC-ROC of 0.91. Using this model, we could reduce the number of measures needed to evaluate the FM maintainability from 15 to 9 measures.
Low-quality Video Key Frame Extraction via Affinity Propagation

With the rapid development of Internet technology and multimedia information, multimedia data, especially video data, has become one of the most important ways to obtain information in people's daily lives. However, it has become a scientific problem to efficiently and quickly extract the information required by users from massive video data. Many years of research in the field of video retrieval show that scientific and rational use of key frame extraction technology can reduce the amount of data generated during video retrieval, save time for video processing, and improve work efficiency. Traditional key frame extraction technology has high requirements for video resolution. For videos that involve privacy protection under low-quality state, most existing literatures do not have targeted solutions. This paper proposes a new key frame extraction algorithm with affinity propagation for video data in the low-quality visual mask domain.
Economic Perspectives on the Governance of Blockchains

The structure and operation of blockchains are dynamic, which means that mechanisms must exist for implementing changes. The New Institutional Economics (NIE), with its emphasis on how rules govern the performance of any complex organization or network, provides an especially useful framework to consider governance of blockchains. We consider how NIE has been applied to blockchain and future applications. Our analysis is divided into consideration of blockchain as an institutional technology, blockchain as a polycentric enterprise, and the ways to empirically research blockchain. The Institutional Analysis and Design (IAD) framework developed by Elinor Ostrom is particularly useful to develop an empirical research agenda for comparing the institutional features of blockchains and, ultimately, to comparing their performance.
Sub histogram equalization with a brightness clipping for image enhancement

Abstract To get better image enhancement effect, this paper proposed an image enhancement method: Sub Histogram Equalization with a Brightness Clipping(SHEBC). In the proposed method, we use the histogram segmentation to control the enhancement and maximum entropy, use the average brightness value to maintain the brightness. The simulation results show that the method has a good effect on contrast enhancement and brightness retention, and the local details are also well processed for image enhancement.
Crafting an Adversarial Example in the DNN Representation Space by Minimizing the Distance from the Decision Boundary

Although deep neural networks (DNNs) achieve state-of-the-art performances in a wide range of machine learning (ML) applications, they are vulnerable: when small intentional perturbations are added to inputs, the network would misclassify them with high confidence. This phenomenon attracts broad attention because it is a security issue. In this paper, we study the geometric properties of the decision boundaries in the representation space of DNNs and propose novel adversarial approaches by moving the representations of the inputs toward the decision boundaries and thus change the predictions of the DNN. Our experimental results show that the proposed algorithms are on par or better than state-of-the-art adversarial approaches in terms of the magnitude of perturbation and computation time.
A Fast and Exact Randomisation Test for Comparing Two Systems with Paired Data

The randomisation test with B trials has been used in the information retrieval (IR) field for comparing two systems with paired data (i.e., a common set of topics). It approximates the exact randomisation test whose time complexity is O(2n) for n topics. In this paper, we first show that the counting operation for obtaining the exact p-value in a randomisation test can be reduced to a subsequence sum enumeration problem that can be solved by dynamic programming. By taking advantage of this observation along with the fact that we only require a small number of significant digits in IR evaluation measure scores, we demonstrate that the time complexity of the exact randomisation test can be reduced to O(mn), where m is the maximum subsequence sum of the paired score differences. Hence, researchers can utilise our test if they want to avoid random sampling and/or normality assumptions and want a fast and reliable test. In addition, we utilise Vandermonde's convolution in order to theoretically explain a known fact, namely, that the sign test is a special case of the exact randomisation test.