Reviewerly/exHarmony huggingface dataset:
Need of Private and Public Sector Information Security

In this research paper author surveys the need of data protection from intelligent systems in the private and public sectors. For this, she identifies that the Smart Information Security Intel processes needs to be the suggestive key policy for both sectors of governance either public or private. The information is very sensitive for any organization. When the government offices are concerned, information needs to be abstracted and encapsulated so that there is no information stealing. For this purposes, the art of skill set and new optimized technology needs to be stationed. Author identifies that digital bar-coded air port like security using conveyor belts and digital bar-coded conveyor boxes to scan switched ON articles like internet of things needs to be placed. As otherwise, there can potentially be data, articles or information stealing from the operational sites where access is unauthorized. Such activities shall need to be scrutinized, minutely. The biometric such as fingerprints, iris, voice and face recognition pattern updates in the virtual data tables must be taken to keep data entry-exit log up to-date. The information technicians of the sentinel systems must help catch the anomalies in the professional working time in private and public sectors if there is red flag as indicator. The author in this research paper shall discuss in detail what we shall station, how we shall station and what all measures we might need to undertake to safeguard the stealing of sensitive information from the organizations like administration buildings, government buildings, educational schools, hospitals, courts, private buildings, banks and all other offices nation-wide. The TO-BE new processes shall make the AS-IS office system more information secured, data protected and personnel security stronger.
The Big Data Feature Mining Technology Based on the Cloud Computing

The cloud computing platform has the functions of efficiently allocating the dynamic resources, generating the dynamic computing and storage according to the user requests, and providing the good platform for the big data feature analysis and mining. The big data feature mining in the cloud computing environment is an effective method for the efficient application of the massive data in the information age. In the process of the big data mining, the method of the big data feature mining based on the gradient sampling has the poor logicality. It only mines the big data features from a single-level perspective, which reduces the precision of the big data feature mining.
Human Computer Interaction Study on Fujifilm Instax Mini 8 Camera Using Evaluation Techniques

Usability are essential factors to consider when evaluating and improving the operation and interface of a system in order to enhance the interaction between users and system. Based on considering the perspective of both users and evaluators. The aim of this paper is to improve the interaction between the user and the Fujifilm instax mini 8 camera and make it more usable by making the process of dealing with the camera, taking pictures and printing them simpler and easier, by modifying the user interface and evaluating the interaction between the user and the camera interface. So, we used two methods one of them by user based on Laboratory Study and Questionnaire and the other was from expert based on cognitive walkthrough where the expert evaluates the ability of users to accomplish the right effect by asking four questions. The usability problems were specified by the evaluators and they have given some recommendations that might improve the user interface and make the process simpler and easier.
Network Traffic Classification and Control Technology Based on Decision Tree

The identification and classification of network traffic has very strong practical significance. The correct classification of network traffic is the basis of studying the upper network structure. The traditional traffic classification technology mainly relies on port information and load characteristics of data packets. However, in recent years, with the rapid growth of network bandwidth and the gradual complexity of application layer protocols, traditional traffic classification methods have been difficult to meet the needs of the current network environment, which urgently requires the introduction of new theories and technologies to achieve accurate network traffic classification. Combining with the existing network traffic identification and classification methods, this paper studies the network traffic classification method based on machine learning, gives the construction of the classification model based on decision tree, and realizes the function of traffic statistics and classification. Experiments show that the proposed algorithm has higher classification accuracy and is suitable for unbalanced traffic classification compared with many traditional algorithms.
Machine Perception MU—Visual Reasoning

In the previous chapters the shape classes, the 3D object classes and the picture classes, that are part of the hierarchical categorical structure of learned knowledge needed during solving the interpretational problems, were introduced.
AI, Robots and IPRs – An Approach to Ownership

Nowadays, Artificial Intelligence (AI) is tangible and not just the imaginings of a Sci-fi novelist.
College English Reading Teaching Based on WeChat and Reading Circle – a Case Study of Advanced English

With the rapid development of Internet technology in China, college English reading teaching is facing new opportunities and challenges. Under the background of developed network, the traditional teaching mode can not fully meet the needs of English reading teaching. This paper takes advantage of WeChat’s instant voice message and mutual sharing, combines traditional teaching with modern internet technology and constructs a new Reading Circle model in Advanced English teaching. This paper is aimed at suggesting a new approach for college English reading course teaching, thus enhancing the effectiveness of talent training.
Estimation of Solar Irradiance on a Contoured Platform for Optimized Solar Power Generation

Now-a-days, applications of flexible solar photovoltaic technologies are emerging in the fields of portable applications (solar photovoltaic integrated tent, solar canopy, solar aircraft etc.). Easy integration with any contoured surface and lightweight property makes flexible solar PV technology (Amorphous Silicon, thin-film Silicon, Dye sensitized solar cell) to be a potent solution for multipurpose conditions. However, contour surface produces variable tilt on the SPV modules which causes non-uniform illumination at the integrated SPV modules. Further, this leads to significant loss in power generation which requires optimization. This article carries out a numerical study to investigate the effect of surface contour on the incident solar irradiance and a devise a methodology for solar PV array layout strategy which can reduce the loss in power generation due to non-uniform illumination.
Completing Information Technology Projects as Scheduled and on Time

Even though Information Technology (IT) software development projects exhibit a checkered history of poor scheduling and late delivery, emerging project traits are promoting successful implementation and reduced risk of failure.IT project management, requirements gathering and management, user involvement, organizational alignment, development methodology, quality assurance and testing process maturity are integrating together to create a culture of rising success and efficient project implementation.Organization alignment, project management, business and technology architectures, and organizational change management domains are integrated into a successful software development framework.
Improvement for Boundary-Uncertainty-Based Classifier Parameter Status Selection Method

We propose an improved version of our boundary-uncertainty-based method for selecting the optimal classifier parameter status that corresponds to the optimal Bayes boundary. Our original method could accurately estimate the optimal status on various real-life tasks. However, several tasks showed improvement room for the estimation accuracy, time complexity, and stopping criterion of the method. This proposal reformalizes our original method to address these three issues. Experiments for selecting the optimal parameter status of an SVM classifier over 15 datasets show that our improved method can achieve even higher selection reliability, with a reduction of time complexity by a factor exceeding 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> over the presented datasets.
The exposed child in a qualitative study of cases at the Swedish Schools Inspectorate. An ideal or not-so-ideal victim?

In this qualitative study of text documents, 23 cases reported to the Swedish Schools Inspectorate are analysed through a theory of the ideal crime victim. The results show that expectations of the victim’s passivity are the same for the ideal victim of aggression in Swedish schools as for the ideal crime victim. Legitimacy as a victim in school conflicts, however, requires social normality, not weakness. The study highlights that Swedish school staff need awareness that hostile acts, according to the Swedish Education Act, are not necessarily diminished if they take place as part of a mutual conflict. Schools should therefore focus on matching measures with problems without searching for moral innocence in the exposed child. The study also shows that assessment strategies to clarify matters such as repeated aggression and power imbalance need to be developed.
Resource Selection in Federated Web Search

The current world values data, now more than ever. Data has become an integral part of everyone's day-to-day lives. The data which is processed, organized and structured or presented in a given context is information. Information is both being collected as well as generated at exponential rates every second. With this spike in interest in information, retrieving it is a key field of study. Information retrieval is the technique of obtaining information relevant to a query from a collection of information resources. To achieve high accuracy in providing users with information, sub- collections of data called verticals are often employed. Common verticals include news, health, academic, jobs, shopping, etc. Our primary goal is to extract information that is most relevant to the specified query. Federated web search is an information retrieval technology to scour through several search engines and databases for every query posed by the user. It then aggregates these results for presentation to the user. Our aim is to implement one of the tasks in federated web search that is resource selection. We need to select the right search engine for a given query; from various search engines available today. This is the purpose of the resource selection task.
A near lossless data compression method for the Geostationary Interferometric Infrared Sounder (GIIRS) based on the combination of decimation and quantization

Data from the Geostationary Interferometric Infrared Sounder (GIIRS) is critical in the domain of meteorological research. Due to limitations in the communications bandwidth, methods to compress the data captured by this instrument are mandatory. This paper is mainly focused on near lossless compression for interferogram data. According to the definition of it, a norm with different thresholds is put forward for qualifying the near lossless compression algorithm. Subsequently, the authors propose and describe a near lossless compression scheme based on decimation and quantization, and then employ various GIIRS data to evaluate this scheme. The experimental results show that this combination plan is able to reach compression ratios (CRs) range from 7.80 to 23.72 within the requirements of the norm.
Improved Neural Network 3D Space Obstacle Avoidance Algorithm for Mobile Robot

Path planning problems are classical optimization problems in many fields, such as computers, mathematics, transportation, robots, etc., which can be described as an optimization problem in mathematics. In this paper, the mathematical model of obstacle environment is established. The characteristics of neural network algorithm, simulated annealing algorithm and adaptive variable stepsize via linear reinforcement are studied respectively. A new neural network 3D space obstacle avoidance algorithm for mobile robot is proposed, which solves the problem of the computational duration and minimum distance of the traditional neural network obstacle avoidance algorithm in solving the optimal path. According to the characteristics of the improved neural network algorithm, it is fused with a variety of algorithms to obtain the optimal path algorithm that achieves the shortest path distance and meets the requirements of obstacle avoidance security. The simulation experiment of the algorithm is simulated by Matlab. The results show that the improved neural network spatial obstacle avoidance algorithm based on the multiple algorithms proposed in this paper can effectively accelerate the convergence speed of path planning, realize the minimum path distance, and achieve very good path planning effect.
Streamlining IBL workflows with computer vision and USD

DNEG is constantly improving its global tools and processes to make them more efficient and artist-friendly, while leveraging state-of-the-art technologies and trends in the industry. The special requirements for Sony Pictures'Venom movie were the perfect opportunity for us to improve our Image Based Lighting (IBL) workflows. In this paper we present the iblManager: a semi-automated system to allow fast and artist-friendly extraction of numerous lights from HDR images (HDRIs), using computer vision. The system uses LightCache, a USD-based implementation of light descriptions, to allow cross-DCC usage and pipeline integration.
AutoAlias: Automatic Variable-Precision Alias Analysis for Object-Oriented Programs

The aliasing question (can two reference expressions point, during an execution, to the same object?) is both one of the most critical in practice, for applications ranging from compiler optimization to programmer verification, and one of the most heavily researched, with many hundreds of publications over several decades. One might then expect that good off-the-shelf solutions are widely available, ready to be plugged into a compiler or verifier. This is not the case. In practice, efficient and precise alias analysis remains an open problem. We present a practical tool, AutoAlias, which can be used to perform automatic alias analysis for object-oriented programs. Based on the theory of “duality semantics”, an application of Abstract Interpretation ideas, it is directed at object-oriented languages and has been implemented for Eiffel as an addition to the EiffelStudio environment. It offers variable-precision analysis, controllable through the choice of a constant that governs the number of fixpoint iterations: a higher number means better precision and higher computation time. All the source code of AutoAlias, as well as detailed results of analyses reported in this article, are publicly available. Practical applications so far have covered a library of data structures and algorithms and a library for GUI creation. For the former, AutoAlias achieves a precision appropriate for practical purposes and execution times in the order of 25 s for about 8000 lines of intricate code. For the GUI library, AutoAlias produces the alias analysis in around 232 s for about 150,000 lines of intricate code.
A Percolation Algorithm to Discover Emerging Research Topics

With the fast growth of the scientific papers, it is vital to discover the implicit knowledge from the enormous information accurately and efficiently. To achieve this goal, we propose a percolation algorithm to discover emerging research topics based on SPO predications, which constructs a three-level SPO-based semantic relation network in the research area of stem cells. We perform the experiments on the scientific papers of stem cells from 2013 to 2015, and the experimental results indicate that the proposed approach can effectively and accurately discover the emerging research topics of stem cells.
Integration of Theory and Practice Teaching in Electrical Specialty for Applied University

The practical teaching system of reform automation major is explored in this paper under the background which the ministry of education proposes the transformation of local institution. We can clarify the talent training objectives of automation majors and cultivate undergraduate vocational and technical talents who have both theoretical training and certain skills. We propose the new talent training mode in order to improve the ability of graduates. We first introduced the background and then introduced the characteristics of applied undergraduate colleges to draw conclusions.
Smart education: kontekstno zavisimaia sistema-assistent

Developments in the field of smart education – trend in the world educational space. Smart education is the result of the evolution of the application of information and communication technologies (the first personal computers), e-learning (personal computers for working in the Internet), m-learning (laptops) and u-learning (smartphones) in education. Key elements to support smart education: mobile computing, smart electronic textbooks, and cloud computing. The model, architecture and scenario of context-dependent system-assistant for smart education, able to analyze the individual characteristics of the student, the environment, adapt their work when conditions change are presented in the article. The novelty of the approach lies in the formalization of the context model so that the context-dependent system-assistant «computes on the fly» a specific scenario taking into account the individual characteristics of users and the current educational situation. When building a content management system, it is proposed to use a model based on biologically plausible machines. Context-dependent smart system should be able to personalize the best learning style, help to choose the optimal educational trajectory. For this purpose the device of Bayesian networks and biologically plausible methods are used. Prospective developments aimed at expanding the research base of smart education are the creation of adaptive systems that support an individual approach to learning, content management systems that provide for the possibility of contextual use of educational resources repositories and provide mobility and deep personalization of educational services. In Russia, nowadays there are the necessary prerequisites for the successful implementation of the smart education system: the focus in training are educational, social, civil, professional competencies, forms and methods for the individualization of the educational trajectory change, focus on open world intellectual resources, the emphasis is shifted to self-control and self-assessment of students, to assessment the education quality by employers, professional communities.
How to do proofs: practically proving properties about effectful programs' results (functional pearl)

Dependently-typed languages are great for stating and proving properties of pure functions. We can reason about them modularly (state and prove their properties independently of other functions) and non-intrusively (without modifying their implementation). But what if we are interested in properties about the results of effectful computations? Ideally, we could keep on stating and proving them just as nicely.
ID-based Strong Designated Verifier Signature Scheme and its Applications in Internet of Things

Strong designated verifier signature scheme is a concept in which a user (signer) can issue a digital signature for a special receiver; i.e. signature is produced in such way that only intended verifier can check the validity of produced signature. Of course, this type of signature scheme should be such that no third party is able to validate the signature. In other words, the related designated verifier cannot assign the issued signature to another third party. This article proposes a new ID-based strong designated verifier signature scheme which has provable security in the ROM (Random Oracle Model) and BDH assumption. The proposed scheme satisfies the all security requirements of an ID-based strong designated verifier signature scheme. In addition, we propose some usage scenarios for the proposed schemes in different applications in the Internet of Things and Cloud Computing era.
Multimodal Movement Activity Recognition Using a Robot’s Proprioceptive Sensors

By recognizing patterns in streams of sensor readings, a robot can gain insight into the activities that are performed by its physical body. Research in Human Activity Recognition (HAR) has been thriving in recent years mainly because of the widespread use of wearable sensors such as smartphones and activity trackers. By introducing HAR approaches to the robotics domain, this work aims at creating agents that are capable of detecting their own body’s activities. An activity recognition pipeline is proposed that allows a robot to classify its actions by analyzing heterogeneous, asynchronous data streams provided by its inbuilt sensors. The approach is evaluated in two experiments featuring the service robot Pepper. In the first experiment, a set of base movements is recognized by analyzing data from various proprioceptive sensors. The findings indicate that a multimodal activity recognition approach can achieve more accurate classifications than single-sensor approaches. In the second experiment, a person interferes with the forward movement of the robot by pulling its base backward. This happens in a way that is not detected by Pepper’s inbuilt systems. The approach can detect the unexpected behavior and could be used to extend Pepper’s inbuilt capabilities. Through its generality, this work can be used to recognize activities of other robots with comparable sensing capabilities.
The Application of Computer in Physical Education Teaching and Its Influence on Campus Culture

With the rapid development of science and technology, all kinds of modern teaching media are emerging, especially multimedia technology has been widely used in various disciplines of school education, but in the teaching of physical education is relatively few involved, this paper on how to use multimedia technology in physical education and make it better for physical education services and its influence on campus culture to make further research.
Singular Value Decomposition Algorithm Combined with SDAE for Improving the Accuracy of Movie Recommended System

The sparseness of the rating data seriously affects the recommendation accuracy of the collaborative filtering algorithm. Since the current recommendation algorithm does not fully consider the item attribute matrix information, this paper proposes a Singular Value Decomposition (SVD) algorithm combined with Stacked Denoising Autoencoder (SDAE). The S-SVD algorithm first reduces the original user-item rating matrix by SVD. Then, we use SDAE for feature learning to calculate the similarity between the rating-based and attribute-based items on the item matrix. So, we can calculate their item the similarity between any two items which can be films, books and so on. Finally, we get the nearest neighbor set of unrated items and predict the rating to generate recommendations. Experiments on the real datasets show the S-SVD algorithm can overcome the data sparsity problem to a large extent and is superior to other traditional recommendation algorithms in performance.