Reviewerly/exHarmony huggingface dataset:
Show, Conceive and Tell: Image Captioning with Prospective Linguistic Information

Attention based encoder-decoder models have achieved competitive performances in image captioning. However, these models usually follow the auto-regressive way during inference, meaning that only the previously generated words, namely the explored linguistic information, can be utilized for caption generation. Intuitively, enabling the model to conceive the prospective linguistic information contained in the words to be generated can be beneficial for further improving the captioning results. Consequently, we devise a novel Prospective information guided LSTM (Pro-LSTM) model, to exploit both prospective and explored information to boost captioning. For each image, we first draft a coarse caption which roughly describes the whole image contents. At each time step, we mine the prospective and explored information from the coarse caption. These two kinds of information are further utilized by a Prospective information guided Attention (ProA) module to guide our model to comprehensively utilize the visual feature from a semantically global perspective. We also propose an Attentive Attribute Detector (AAD) which refines the object features to predict the image attributes more precisely. This further improves the semantic quality of the generated caption. Thanks to the prospective information and more accurate attributes, the Pro-LSTM model achieves near state-of-the-art performances on the MSCOCO dataset with a 129.5 CIDEr-D.
Experimental Analysis Based on Binary Classification to Distinguish the Authenticity of Text with Social Network Data

Social network platforms have made it possible to collect and store large amounts of text data. In the fields of defense, public safety, and security, social text data is used for data analysis research and network model learning. However, the authenticity of the text data causes distortion of the information or degrades the performance of the trained model. To solve this problem, we presented a binary classification model that distinguishes authenticity using a pre-trained language model and conducted an experiment using tweet data. As a result, we showed 81% accuracy and 0.61 Matthews correlation coefficient value.
Performance Analysis of Various Cryptographic Techniques

Communication plays an important role in our life. When the persons communicate, their information must be in an end to end encrypted form. So, the data security comes into the picture. To provide this data security, various concepts in cryptography are used. Each cryptographic algorithm is different in its functionality. By considering this data security, the combination of different cryptographic techniques is suggested. Some of the cryptographic algorithms like elliptic curve Diffie–Hellman algorithm, RSA, IDEA, RC4, and Data Encryption Standard algorithms were studied. These algorithms are implemented and executed. When their execution times are evaluated, compared, and analyzed, their evaluation is considered for the different number of bytes in the plain text such as four, eight, and sixteen. And mostly, it is found that the Rivest-Cipher Four algorithm consumes the least time.
Data-driven Risk Management for Requirements Engineering: An Automated Approach based on Bayesian Networks.

Requirements Engineering (RE) is a means to reduce the risk of delivering a product that does not fulfill the stakeholders’ needs. Therefore, a major challenge in RE is to decide how much RE is needed and what RE methods to apply. The quality of such decisions is strongly based on the RE expert’s experience and expertise in carefully analyzing the context and current state of a project. Recent work, however, shows that lack of experience and qualification are common causes for problems in RE. We trained a series of Bayesian Networks on data from the NaPiRE survey to model relationships between RE problems, their causes, and effects in projects with different contextual characteristics. These models were used to conduct (1) a post-mortem (diagnostic) analysis, deriving probable causes of suboptimal RE performance, and (2) to conduct a preventive analysis, predicting probable issues a young project might encounter. The method was subject to a rigorous cross-validation procedure for both use cases before assessing its applicability to real-world scenarios with a case study.
Research on Anonymous credential based medical blockchain

Abstract For the current medical block chain, the access rights of electronic medical records and agent encryption are mainly controlled by permission contract. The way of protecting electronic medical records realizes the sharing of medical data, but neglects the management and protection of user identity. In this paper, this article studies the privacy protection technology of the blockchain, combined with the stealth address and zero-knowledge proof technology, and proposes a plan to weigh anonymity and security to protect the privacy of users in the medical blockchain.
Multi-party Secure Computing Financial Shared Platform Based on Lightweight Privacy Protection under FHE

Under the background of the "Internet+" era, enterprise management and production are faced with transformative intelligent transformation, which deeply affects the financial management work and puts forward higher requirements for information security technology. Based on multi-party security computing technology, this paper designs a cooperative computing scheme based on small-scale homomorphic encryption on multi-party security data platform, and innovates the operation mode of financial sharing. The overall design of operational financial management is constructed from multi-dimensional considerations in order to provide a useful reference for the digital transformation of enterprise financial intelligence.
Explore the Use of Google Cloud Computing Platform to Build a New Environment for Online Education

In this paper, we use cloud computing, but as we all know, cloud computing is a future development direction, so in the future, how to use cloud computing well is an important topic in front of us. In the future education, cloud computing is also a development direction, and integrating resources is also the future development direction. But how to combine the two is one of the directions that people should think about in the future. This paper is in such an environment to think about the combination of the two to serve us or to integrate educational resources for the future.
Protecting Big Data Sets from Unauthorized Users on Cloud

The variety of different appearance computing has modified over a segment of instance. It is changed as its commencement from objective to disseminated figure which facilitate one machine-to other machine communications. The novel methods move toward is called as internet based method wherever information to accumulate and different security examine are externalization to public suspicion in compensate per use method. The foundation following this is that present have to be transformed in the corporation in generous assessment to industry data. Ever since the data happen to a quality to institute protect every operation develop into an essential. Through the explosion of intelligence knowledge, satellites and public media present is exponential and exceptional development of data. Such information has acquire the different types such as volume, velocity, variety and value and a appearance by name big data has be present invent to indicate it. In heart, big data is the information which is huge, through dissimilar type of information, the data is incessantly increasing and it adds assessment to the industry which preserves it. By means of big data, inclusive industry intellect can be taken out to make extremely precise planned decision. The difficulty of organization and dispensation big data which is suspicious in Peta bytes is resolve with the appearance of Hadoop wherever huge group of different security methods are preserve and specified on-demand unrestricted access exclusive of resources speculation. Big data is accumulate and development through the facilitate of disseminated encoding structure like Hadoop and which supports the new encoding prototype MapReduce which can switch enormous quantity of information through thousands of product different machines in Hadoop clusters. Though, the data are apprehensive with security of their externalization data. The big data has recognized and declares different safety challenge through reverence to big data protection and protected estimation in disseminated encoding structure. Individuals confront are big data privacy, big data security, communications security and data institute moreover consistency and instantaneous security.
Increasing the Upper Bound for the EvoMan Game Competition

This paper describes a comparison between algorithms for evolving agents able to play the game Evoman. Our team took part in the “Evoman: Game-playing Competition for WCCI 2020”, and won second place; beyond finding a good agent to satisfy the requirements of the competition - which aim at a good ability to generalise -, we have surpassed the existing non-general, best-known upper-bound. We have managed to exceed this upper bound with a Proximal Policy Optimization(PPO) algorithm, by discarding the competition requirements to generalise. We also present our other exploratory attempts: Q-learning, Genetic Algorithms, Particle Swarm Optimisation, and their PPO hybridizations. Finally, we map the behaviour of our algorithm in the space of game difficulty, generating plausible extensions to the existing upper-bound.
Effect of Pregnant Ewes Fed with Bamboo Vinegar Powder on Growth Performance of Lambs Under Intelligent Monitoring System

With the rapid development of information technology, video monitoring system has become an important part of modern city security system. Especially in prisons, surveillance cameras are almost everywhere. The concept of guided wave spread spectrum digital data communication in intelligent structure is proposed. However, with the continuous expansion of the monitoring network, surveillance cameras not only bring convenience, but also produce a lot of monitoring data, which brings great challenges to storage, analysis and retrieval. China is a big animal husbandry country. The development of animal husbandry directly affects the development of national economy. It is necessary to develop a new animal husbandry monitoring system based on Internet of things technology, which can detect the temperature, humidity, carbon dioxide and other information parameters in the process of animal growth. In order to monitor the growth quickly and accurately and realize the automation and intelligent control, an intelligent real-time monitoring system based on single chip microcomputer is designed. Animal husbandry monitoring system completes the planning and management of animal growth mode by collecting, monitoring and timely alarming the information of animal growth environment.
Study on the Life Cycle Traceability Management of NEV Traction Battery Based on Industrial Internet Identifier Resolve System

With the rapid development of NEV industry in China, it is urgent to develop the safe recycling and efficient traceability management of traction battery. However, current traceability management system on the recycling of traction battery has many limitations and difficulties, such as poor timeliness, blocked information sharing, low efficient cross-link information connection, difficult cross-industry information traceability management, etc. For this purpose, this paper integrates automotive Industrial Internet identifier resolve system creatively, makes a research on the traceability management mode of traction battery life cycle based on identifier resolve system, conducts an innovative design directing on the identifier coding rules, identifier carrier, analytical field system, data access mode and identifier resolve mode, and further launches basic traction battery identifier resolve service on the identifier secondary node system that has around 3.5 million traction battery identifier data registered. In addition, this paper systematically presents the application mode of traceability management on identifier resolve of industry management departments and companies, and expounds the application benefit from the improvement of management efficiency and the data integration of cross-industries. Through developing traceability management of NEV traction battery on the basis of identifier resolve system, we could realize the query and analysis supervision on the life cycle data of traction battery, support the NEV manufacturing enterprises to implement producer responsibility extension system, effectively solve the problems of safety, environmental protection and resource waste caused by non-standard recycling decommissioned power batteries, facilitate the data sharing and integration of pan auto industry across fields and industries, build new business ecology, and prompt the coordinated development of NEV industry.
Deep Recommender Systems Utilizing Side Information

Recommendation Systems (RS) are designed to assist users in decision making by recommending the most appropriate information or products for them. Nonetheless, many RS suffer from limitations such as data sparsity and cold-start. Side information (SI) can be integrated into a recommender system to tackle these limitations. In my Ph.D. research, I seek to build on and extend the use of SI for RS. Specifically, I propose new types and representations of SI and develop new methods to integrate SI into RS to boost its performance. This paper presents the conceptual foundation and motivation of my Ph.D. research.
A Masking-Based Image Encryption Scheme Using Chaotic Map and Elliptic Curve Cryptography

The paper introduces an efficient asymmetric algorithm for encrypting images utilizing chaotic maps and elliptic curve cryptography. In the proposed image encryption method, there is a combination of elliptic curve ElGamal encryption method with masking of image through chaotic map for enhancing security and efficiency. The proposed algorithm can encrypt images of any size. Security analysis is carried through to validate efficiency, sturdiness and feasibility of the algorithm. Compare to recent related encryption schemes, this scheme has better ability to resist crypt analytical and statistical attacks.
An Exploration on Using Mobile Smart Terminals to Promote the Reform of College English Teaching and Learning

Abstract College English is a compulsory basic course for higher education in China. At the same time, handling a secondary language is a must-have under the globalization. However, the students’ English proficiency has always been unsatisfactory after so many years of teaching and learning. Meanwhile, in the era of information and data, Internet has not only changed our country’s economic model, but also brought a revolution in education. Nowadays, Every college student has a mobile smart terminal at least, and we can make full use of this device to improve the teaching methods and even implement it into classrooms. Based on this opportunity, this article explores how to use mobile smart devices to improve the traditional “teaching” and “learning” for non-English majors in universities.
Context-Based Clustering of Assamese Words using N-gram Model

The popularity of mobile devices and the availability of the Internet increase the use of various online platforms for chatting and communicating with others. Due to the use of such platforms, the use of local languages is also increasing because everyone feels comfortable with his/her mother tongue. In this research work, clustering of Assamese words is done by using N-gram models. This clustering is context-based and preserves the contextual information of the words. This Assamese word clustering will help in the development of the Assamese language in Parts of Speech tagging, spell checker, word prediction, etc. In this research work, word clusters are designed using bigram, trigram, and quadrigram models. Words that appear in a particular context are stored in the same list of clusters. A corpus of size almost 600K words is used to design the Assamese word clusters. A similarity score is calculated between two words to keep them in the same cluster. The accuracy of the clustering system is around 60%, and 733 different clusters are extracted with a maximum of 15 words in a cluster.
Joint Extraction of Entities and Relations: An Advanced BERT-based Decomposition Method

Joint extraction of entities and relations is an important task in the field of Natural Language Processing (NLP) and the basis of many NLP high-level tasks. However, most existing joint models cannot solve the problem of overlapping triples well. We propose an efficient end-to-end model for joint extraction of entities and overlapping relations in this chapter. Firstly, the BERT pre-training model is introduced to model the text more finely. Next, we decompose triples extraction into two subtasks: head entity extraction and tail entity extraction, which solves the problem of single entity overlap in the triples. Then, We divide the tail entity extraction into three parallel extraction sub-processes to solve entity pair overlap problem of triples, that is the relation overlap problem. Finally, we transform each extraction sub-process into a sequence tag task. We evaluate our model on the New York Times (NYT) dataset and achieve overwhelming results (Precise = 0.870, Recall = 0.851, and F1 = 0.860) compared with most of the current models. The experimental results show that our model is effective in dealing with triples overlap problem.
A Novel Joint Points and Silhouette-Based Method to Estimate 3D Human Pose and Shape

This paper presents a novel method for 3D human pose and shape estimation from images with sparse views, using joint points and silhouettes, based on a parametric model. Firstly, the parametric model is fitted to the joint points estimated by deep learning-based human pose estimation. Then, we extract the correspondence between the parametric model of pose fitting and silhouettes in 2D and 3D space. A novel energy function based on the correspondence is built and minimized to fit a parametric model to the silhouettes. Our approach uses comprehensive shape information because the energy function of silhouettes is built from both 2D and 3D space. This also means that our method only needs images from sparse views, which balances data used and the required prior information. Results on synthetic data and real data demonstrate the competitive performance of our approach on pose and shape estimation of the human body.
Demystifying the Tenure-Track Faculty Search in Computer Science at Primarily Undergraduate Institutions

Primarily undergraduate institutions (PUIs), including liberal arts colleges, provide a faculty career path distinct from that of faculty at research universities. Since PhD advisors often lack PUI experience, and PhD students may lack access to mentors at PUIs, this paper serves as a resource for those who want to pursue a tenure-track position at a primarily undergraduate institution. The paper includes information about the benefits and challenges of PUI careers, why departments hire, how to interpret the job advertisement, how applications are read by the search committee, the interview stages, offers, and negotiations. While this paper does not address computing education directly, it addresses the R1 to PUI pipeline which impacts undergraduate education in computer science. The advice presented in the paper is based on common attributes of the tenure-track search process at four PUIs.
User Behavior Set: Medley of User Behaviors to Eliminate Data Spatio-Temporal Sparsity

Abstract A sequential recommendation system is war-ranted, and several models have been created to meet this need. However, sparsity is a major problem in this field. In order to deal with the space-time sparsity problem in a sequential recommend-dation system and avoid the unreliability caused by manually generated data, we have established the UBS structure. We treat user behavior sequences in batches. Each batch gets the inte-grated data set UBS after K iterations through a two-step cycle of clustering and merging. The results are connected as inputs of a sequential neural model. We use three baseline deep learning algorithms –Caser/SASRec/HGN to evaluate the model. We compared the UBS model with the original model in terms of normalized discounted cumulative gain ms(NDCG) and Recall. It is found that when the UBS model is applied to a short sequence of user behaviors and the iteration number K=2, it can improve the recommendation efficiency compared with the original algorithm (represented by K=0).
Reverse Engineering UML Sequence Diagrams for Program Comprehension Activities

Program comprehension is a required activity for many software development and maintenance tasks. A common means of understanding software is though UML diagrams. UML diagrams model the design and implementation of an application, describing both its structure and behaviour. However with rapid software development life cycles, UML diagrams often become inconsistent with the implementation of an application. This limits their effectiveness in program comprehension activities. This paper presents a program analysis framework to reverse engineer sequence diagrams from application source code. Part of this framework is the ability to filter out irrelevant operations from a sequence diagram in order to simplify the representation of an application. This is achieved by identifying important data, and following its use in the application. The purpose of this framework is to aid in program comprehension activities by providing up to date representations of an application; while also enabling developers to identify the logical operation of a program without interference from irrelevant or supporting operations.
Network Security and Prevention of Accounting Information System in the Era of Big Data

In today’s era of big data, the development of accounting informatization has been promoted, providing a platform for resource sharing for accounting information system, reducing the cost of informatization and improving the efficiency of informatization. But also brought some risks, such as information to accounting information disclosure risks, risks of virus and hacker attacks, hardware and software, personnel responsibility risk, etc., have put forward suggestion to strengthen the accounting information system security modules, strengthen hardware and software development, build perfect accounting supervision system, improve the accounting information relevant laws and regulations.