Reviewerly/exHarmony huggingface dataset:
Research on Employment Efficiency Evaluation of financial graduates based on DEA Tobit two stage method

Under the background of national and local economic development entering into the new normal, financial graduates are facing great employment pressure, but also facing opportunities and challenges. Financial graduates are senior talents in China's economic and social development and an important part of national talent resources. The employment quality is directly related to the realization of its own value and social value. Based on this, this paper studies the evaluation method of financial graduates' Employment Efficiency Based on DEA Tobit two-stage method. It is beneficial to optimize the allocation and utilization of social human resources, to solve the employment problem of financial graduates, to improve the training quality of financial graduates, and to strengthen the management of human resources of employers. Based on the DEA Tobit two-stage model, this paper quantitatively analyzes the employment efficiency and its influencing factors of financial graduates by combining qualitative and quantitative methods. Through the construction of financial graduates employment quality evaluation index system and evaluation model, this paper studies the composition characteristics, change trend and influencing factors of financial graduates' employment quality, and puts forward targeted employment guidance suggestions for Commission Financial graduates.
Anthropology of student research activities

Abstract The purpose of the article is to identify problem areas of students’ research activities and to show ways to improve this activity. An anthropological analysis of the professional training of students shows that an imitation of research activities prevails at present. The issues of tutoring and mentoring support not only of a particular student’s research activities, but also of the entire process of his education are still relevant. The tutoring system has been adopted in Russia, but it still does not work fully: students lack basic training to conduct full-fledged research. The article analyzes the experience of basic training of outstanding scientists, a two-stage structure of immersion in knowledge.
Cryptanalysis of a Privacy Preserving Ranked Multi-keyword Search Scheme in Cloud Computing

Nowadays outsourcing data to cloud servers is a popular data management way for many users. But how to ensure the security and the usability of the outsourced data is a very challenge problem. Usually encryption of the outsourced data is a good way to protect the privacy. But after encryption, the usability always loses, such as how to search over the outsourced data. Aiming at solving this problem, recently Zhang et al. proposed a privacy preserving ranked multi-keyword search scheme for multiple data owners in cloud computing [IEEE Transactions on Computers, 65(5), 1566–1577, 2018]. However, in this short paper we find their scheme is not secure. Concrete, the adversary can forge the search trapdoor for any keyword, which invalidates the security goal of their original design.
Logic of defeasible permission and its dynamics

Abstract This paper develops a deontic logic for defeasible permission and studies norm change in various updated semantics. When one grants that $\varphi $ or $\psi $ is permitted, normally, it goes together with the conjunction of a permission of $\varphi $ and that of $\psi $. In the monotonic reasoning on this permission, a permission of $\varphi $ leads to a permission of $\varphi $ and $\psi $; however, if a prohibition of $\psi $ is introduced, we get into trouble. We face a paradox of free choice permission. Many solutions have been proposed, but a systematic account of handling norm change is still needed. This paper first introduces the notion of normality to develop a sound and complete deontic logic for defeasible permission, which can be used to analyse several notions in natural language and in game theory. Further, following Lewis’ idea of norm change, a systematic way to capture various dynamics for updating permission and obligation is proposed.
Speech Document Summarization using Neural Network

Text summarization is important to reduce content overloaded. Our aim of the automatic text summarization is extract the main content from the speech in a meeting or conference for the document purpose. It is difficult to remember all the things by human. At the same time when we write about the meeting/conference there is possibility to miss the content because of some inconvenience like noises, become lazy by the long speech, getting tired and other distraction . We have proposed an automatic system which can be help to get the summarized document. At First, we translate the recorded speech in to text document using google speech recognition API. Then summarization is done using that text document.
The Effect of Smaw Welding Currents on Mechanical Properties and Micro Structures of Low Carbon Steels

Shielded Metal Arc Welding (SMAW) is one of the most widely used types of electric arc welding. The mechanical properties of the welded joints produced are influenced by various factors. This study aims to determine the effect of the magnitude of the welding current on the mechanical properties and physical properties of the SMAW weld joints. The base metal material used in this study was SA 516 Gr. 70 with 3 strong variations of welding current, i.e.: 50A, 60A and 70A. The electrode used for root is E7016 while for filler and cap is E7018 which is 2.6 mm in diameter with climbing (vertical) welding position. From the results of the charpy impact test and the Vickers hardness test, the best is obtained in specimens with welding current strength of 70A. While the results of the microstructure analysis showed that the grain size of specimens with welding current of 50A and 60A was finer than the specimens with a 70A welding current.
Path Planning and Simulation Based on Cumulative Error Estimation

Path planning plays a significant role in robot navigation applications, as path exploration ability requires the knowledge of both the kinematics and the environments. Most of the current methods consider the planning process alone instead of combining the planning results with tracking control, which leads to a significant reduction in the availability of the path, especially in complex scenarios with missing GPS and low positioning sensor accuracy. This paper proposes a reinforcement learning-based path planning algorithm, which aims to consider the errors caused by the robot’s motion during the dead-reckoning process and effectively reduces the cumulative error within the optimization process. The simulation conclusion in the 2D scene verifies the effectiveness of the algorithm for reducing the cumulative error.
Exploiting Resiliency for Kernel-Wise CNN Approximation Enabled by Adaptive Hardware Design

Efficient low-power accelerators for Convolutional Neural Networks (CNNs) largely benefit from quantization and approximation, which are typically applied layer-wise for efficient hardware implementation. In this work, we present a novel strategy for efficient combination of these concepts at a deeper level, which is at each channel or kernel. We first apply layer-wise, low bit-width, linear quantization and truncation-based approximate multipliers to the CNN computation. Then, based on a state-of-the-art resiliency analysis, we are able to apply a kernel-wise approximation and quantization scheme with negligible accuracy losses, without further retraining. Our proposed strategy is implemented in a specialized framework for fast design space exploration. This optimization leads to a boost in estimated power savings of up to 34% in residual CNN architectures for image classification, compared to the base quantized architecture.
Local structure alignment guided domain adaptation with few source samples

Domain adaptation has received lots of attention for its high efficiency in dealing with cross-domain learning tasks. Most existing domain adaptation methods adopt the strategies relying on large amounts of source label information, which limits their applications in the real world where only a few label samples are available. We exploit the local geometric connections to tackle this problem and propose a Local Structure Alignment (LSA) guided domain adaptation method in this paper. LSA leverages the Nyström method to describe the distribution difference from the geometric perspective and then perform the distribution alignment between domains. Specifically, LSA constructs a domain-invariant Hessian matrix to locally connect the data of the two domains through minimizing the Nyström approximation error. And then it integrates the domain-invariant Hessian matrix with the semi-supervised learning and finally builds an adaptive semi-supervised model. Extensive experimental results validate that the proposed LSA outperforms the traditional domain adaptation methods especially when only sparse source label information is available.
The Use of Gaussian Mixture Model for Counting Human Object on Video

Abstract Video monitoring has been widely used in various places, for example, tourist attractions, stations, terminals, offices, supermarkets, minimarkets, and other places. The use of video monitoring has the aim to improve security aspects in a place that is considered quite helpful. The rapid development of technology, video monitoring has been implemented for purposes other than for security, such as people counting systems. People counting to find out the number of visitors in a place or building is a difficult job to do, requires a lot of time and often the data obtained is not appropriate. Gaussian Mixture Model (GMM) is a background reduction method, which is used to identify the background and foreground. Background reduction is an approach that is widely used to detect moving objects on video from static cameras. While blob detection is detecting a group of pixels connected in an image that has a different colour (white or black). Blob is used for object classification, whether the object is a person or not. From the results of system testing shows that counting people can be done well, with an average percentage of 95.83% recall, 94.5% precision and 90.88% accuracy of the entire video test data. The use of GMM in counting people in this study can also count people who carry objects properly as long as they are not drawn as in the morning test video. In addition, this system can store the calculated data.
Multi-Angle Face Detection Based on Improved RFCN Algorithm Using Multi-Scale Training

The R-FCN algorithm is mainly applied to general target detection, and is not a dedicated algorithm for face detection. In particular, most faces in real life have the characteristics of occlusion, blur, and changeable posture. The performance of the R-FCN algorithm needs to be improved. Therefore, based on the R-FCN algorithm, this paper proposes an improved R-FCN-FACE model to meet higher requirements for face detection. In the RFCN-FACE algorithm, the performance and detection effect of the face detection model are improved by introducing multi-scale training and online hard sample mining technology. A comparative test analysis is conducted on the benchmark data set Wider Face and FDDB. The experimental results show that the improved R-FCN-FACE model can show higher accuracy in multiangle face detection and improve the performance of the detection model.
Context Mining for Deep Learning Over Community Health Data for Disease Inference

Text mining techniques and information retrieval over medical data have always been an area of interest for the bio-information researchers. The researches mostly involve systematically collected health records which are rich in terms of medical terminologies and accurate information regarding symptoms, disease and their treatments, which is not the same case when comes to community data such as the healthcare forums such as webMD, wherein the users posting questions often give very vague descriptions of their symptoms and often miss out critical details regarding the medical conditions. With such inaccurate details, even excellent learning algorithms in the sequential domain such as GRU and LSTM will fail, since they end up learning the irrelevant information from the data and provide inaccurate predictions. We needed a context mining technique to get the best possible information from the available data, such that the words group up to form a medical term used by the doctors to provide better disease inference. To achieve this context mining, we use graph representation of the question–answer and find dense subgraphs to determine the medical term to represent the disease or medical condition described by the user. This does the same job as a restricted Boltzmann machine providing a strong connection between the hidden layer and input layer neurons which are the critical context for prediction and knock the neurons in hidden layers with least context. We have created a comparative study to find an accurate method between the two techniques for context mining above, namely the subgraph mining and Boltzmann machine.
Explaining the Application of Innovative Strategies in Management Accounting System from the Perspective of Cognitive Dissonance Theory

In the modern management accounting environment, models and theories can be used to design an effective operational plan based on a correct understanding of the organization's environment and the objectives of the accounting system, in accordance with the needs of the community. One of these theories is the theory of cognitive dissonance, in which there is a conflict between the cognitive elements in the individual, and its study and consideration of the psychological aspects of cognitive dissonance in management accountants can increase the use of psychology in accounting. Management leads to the correct selection of innovative aspects affecting the performance of the accounting information system by management accountants and ultimately to the improvement of the financial, social and economic structure of the organization.Therefore, this study explores the theory of cognitive dissonance and its application to knowledge management accounting system.This paper is based on a library and interpretive approach to explain the theory of cognitive dissonance and its application to the knowledge management accounting system. Therefore, the purpose of this study is to investigate the variables related to innovative strategies and the theory of cognitive dissonance in management accounting decision making.Therefore, in this paper, while explaining these concepts, the role of cognitive dissonance theory in general and how management accountants are deciding on the selection and application of the innovative strategies required by the business environment are discussed.The evidence in this paper suggests that cognitive conflict is an influential factor in the choice of management accountants for applying or not applying innovative techniques in any system.
Computational Data Analysis for First Quantization Estimation on JPEG Double Compressed Images

Multimedia Forensics experts work consists in providing answers about integrity of a specific media content and from where it comes from. Exploitation of any traces from JPEG double compressed images is often one of the main investigative path to be used for these purposes. Thus it is fundamental to have tools and algorithms able to safely estimate the first quantization matrix to further proceed with camera model identification and related tasks. In this paper, a technique based on extensive simulation is proposed, with the aim to infer the first quantization for a certain numbers of Discrete Cosine Transform (DCT) coefficients exploiting local image statistics without using any a-priori knowledge. The method provides also a reliable confidence value for the estimation which is of great importance for forensic purposes. Experimental results w.r.t. the state-of-the-art demonstrate the effectiveness of the proposed technique both in terms of precision and overall reliability.
Linguistic Approach to Semantic Correlation Rules

As communication between humans and machines in natural language still seems essential, especially for end users, Natural Language Processing (NLP) methods are used to classify and interpret this. NLP, as a technology, combines grammatical, semantical, and pragmatical analyses with statistics or machine learning to make language logically understandable by machines and to allow new interpretations of data in contrast to predefined logical structures. Some NLP methods do not go far beyond a retrieving of the indexation of content. Therefore, indexation is considered as a very simple linguistic approach. Semantic correlation rules offer the possibility to retrieve easy semantic relations without a special tool by using a set of predefined rules. Therefore, this paper aims to examine, to which extend Semantic Correlation Rules (SCRs) will be able to retrieve linguistic semantic relations and to what extend a simple NLP method can be set up to allow further interpretation of data. In order to do so, an easy linguistic model was modelled by an indexation that is enriched with semantical relations to give data more context. These semantic relations were then queried by SCRs to set up an NLP method.
Humanitarian knowledge in the context of the media landscape

The article deals with the issues of the humanitarian education system in the information society. The initial prerequisites are the spread of a technocratic approach to determining priorities in education, professional development of personnel, based on new strategies aimed at training a specialist, who has a high degree of knowledge of information technologies but does not have a general humanitarian culture. This situation can lead to negative civilisational implications over time because scientific and technological progress without considering the cultural component can cause moral and spiritual regression. The article substantiates the important role of humanitarian education in the system of fundamental education. Humanitarian education is perceived as a set of knowledge about the history, development patterns of human civilisation, showing the intellectual level of human society, its self-consciousness, the need for a holistic approach to studying human beings and the world around them. Humanitarian education should ensure the formation of moral consciousness, the worldview, and the life stance of the individual, his qualities, beliefs, and motives of activity. When preparing the article, the authors used the analysis and synthesis method, as well as a method of systematisation of the empirical and theoretical data available on the topic under consideration. The outcomes of the conducted research, their scientific novelty, consist in identifying humanitarian education priorities in the current context, its role in forming ideas about values, and the individual’s worldview. As stated in the article, the main task of humanitarian education is to create conditions for forming an individual’s worldview, moral consciousness, life stance, beliefs, motives, and to develop his qualities.
Hypersuccinct Trees - New universal tree source codes for optimal compressed tree data structures.

We present a new universal source code for unlabeled binary and ordinal trees that achieves asymptotically optimal compression for all tree sources covered by existing universal codes. At the same time, it supports answering many navigational queries on the compressed representation in constant time on the word-RAM; this is not known to be possible for any existing tree compression method. The resulting data structures, hence combine the compression achieved by the best known universal codes with the operation support of the best succinct tree data structures. Compared to prior work on succinct data structures, we do not have to tailor our data structure to specific applications; hypersuccinct trees automatically adapt to the trees at hand. We show that it simultaneously achieves the asymptotically optimal space usage for a wide range of distributions over tree shapes, including: random binary search trees (BSTs) / Cartesian trees of random arrays, random fringe-balanced BSTs, binary trees with a given number of binary/unary/leaf nodes, random binary tries generated from memoryless sources, full binary trees, unary paths, as well as uniformly chosen weight-balanced BSTs, AVL trees, and left-leaning red-black trees. Using hypersuccinct trees, we further obtain the first data structure that answers range-minimum queries on a random permutation of $n$ elements in constant time and using the optimal $1.736n + o(n)$ bits on average, solving an open problem of Davoodi et al. (2014) and Golin et al. (2016).
A New Convex Loss Function For Multiple Instance Support Vector Machines

We develop a novel multiple instance SVM based on maximizing the minimum witness rate (WR) among positive bags. We solve this nonlinear integer programming problem by the multiple instance SVM formulation with a convex loss function, where unknown integer labels of instances in positive bags are relaxed to real numbers between -1 and 1 using the tanh(·) to estimate WR of a positive bag. Our new model, WR-SVM, also can incorporates the multiple instance learning (MIL) problem into a simple deep neural network framework with no additional MIL pooling layers. WR-SVM is robust to input perturbation by eliminating the imbalance between positive instances allocated to positive bags. A better generalization ability is expected by the large margin due to the balanced allocation of positive instances to positive bags. We perform experiments on various video datasets to verify the effectiveness of our method for video classification. The results of WR-SVM outperform the state-of-the-art for MIL-based video classification models.
Error analysis on the initial state reconstruction problem

In this paper, we propose a method to estimate the initial state of a linear dynamical system from noisy observation based on Kalman Filters and Optimal Smoothing techniques. The method allows the user to have estimations in real-time, that is, to have a new estimation for each new observation. Moreover, at each step, the covariance matrix of the error is found, and is the one that minimizes the error variance after applying a linear filter. We analyze the stability of the method when there is no noise in the dynamics. This hypothesis is the one usually seen in dynamical sampling articles, and differs from the classical framework in control theory in which the covariance of the system noise plays a fundamental role. In particular, we prove that the dynamic of the state estimator error is always Lyapunov stable. Also, necessary and sufficient conditions are given to guarantee asymptotic stability for the error dynamics of a linear time-invariant dynamical system, which is itself a linear time-variant system.