Reviewerly/exHarmony huggingface dataset:
A maximum entropy classification scheme for phishing detection using parsimonious features

Over the years, electronic mail (e-mail) has been the target of several malicious attacks. Phishing is one of the most recognizable forms of manipulation aimed at e-mail users and usually, employs social engineering to trick innocent users into supplying sensitive information into an imposter website. Attacks from phishing emails can result in the exposure of confidential information, financial loss, data misuse, and others. This paper presents the implementation of a maximum entropy (ME) classification method for an efficient approach to the identification of phishing emails. Our result showed that maximum entropy with parsimonious feature space gives a better classification precision than both the Naïve Bayes and support vector machine (SVM).
Limitations of Existing Chatbot with Analytical Survey to Enhance the Functionality Using Emerging Technology

A chatbot is an artificial intelligence computer program which performs communication using audio and video system. A person can ask any questions and chatbot will answer accordingly. Nowadays a chatbot is highly popular and takes speed as a computer communication application. Chatbot system is in trend so, it is being used on many websites. With the chatbot, one doesn’t have to wait to talk to the customer helpline, they don’t even have to search for shopping through websites. A chatbot is used in many areas like order food, product suggestions, customer support, weather, personal finance assistance, scheduled a meeting, search and track flights, send money, and many more. With many advantages of a chatbot, there are some limitations where chatbot fails to answer, It lacks flow sometimes, it often fails to resolve the primary issues, It is not understanding the local language of the user, If there is some spelling mistake or some slang is used it will not give the proper answer. The main aim of this research is to find solutions to the problems mentioned and to find a better way to the problems that can be solved and the user can use the chatbot flawlessly and smoothly. The chatbot should be efficient, accurate, and quick so the users attract to the chatbot.
Random Matrix based Extended object/Group Target Tracking with Extra Extension Parameters

The extended object (EO) and group target (GT) tracking based on random matrix can estimate the kinematic state and extension jointly. In order to describe the more complicated deformation of extension, in this paper, we add extra extension parameters to the extension state and construct a generalized random matrix model. To estimate the joint state consisting of kinematic state, extension, and other extension attributes, a sequential Monte Carlo technique called Rao-Blackwellized particle filter (RBPF) is introduced to approximate the posterior density of joint state due to the complexity of the new model. Simulation results show that the proposed model and the new approach have obvious improvement in estimation performance compared with the previous approaches.
Hybrid Framework for a Robust Face Recognition System Using EVB_CNN

Recognition of the human face is becoming an ingenious technology that enhancing its strategy gradually by finding its applications in a wide variety of fields including security and surveillance. The traditional methods that are in practise for face recognition are not adequate in producing good accuracy due to two main reasons. The first one is the pictures are affected by various uncontrolled situations such as illumination, blur, and pose, and the second one is struggling in an efficient recognition when dealing with a large number of samples. There is need for an effective face recognition as a part of life in the automated environment. The traditional methods are lagging with some parameters. To overcome the aforementioned issues, a new methodology is implemented. This methodology is a hybrid frame work combined with Eigen value-based convolutional neural networks (EVB_CNN). The EVB_CNN is designed in such a way that the significant features are extracted and classified by the softmax function and fully connected layer, respectively. The experimental analysis is carried out with AR data set and ORL data set that shows enhancement in accuracy with significant reduction in computation time with images taken over specific uncontrolled environments.
Special Issue Texture and Color in Image Analysis

Texture and color are two types of visual stimuli that determine, to a great extent, the appearance of objects, materials, and scenes [...]
Research on Key Technologies of Power System Visualization Platform

At present, the power grid is still in an inflexible state in the form of massive information representation, lack of effective visualization means to efficiently display the calculation results of various applications. To provide better service for power system monitoring operation, this paper introduces the design and implementation of a general and open data mining platform. At the level of data mining, the function module adopts J2EE enterprise component to analyze and combine rough set and neural network data mining algorithms, and preprocess the original data. In the application field, combined with artificial neural network, machine learning, parallel computing and other technologies, D3 and Ecarts are used as the visualization tools in the browser, and Oracle is used as the response server in the background. After that, the data in the database is read into the data field of Ecarts to complete the display of visual charts. Finally, through the case analysis of a campus network, the operation test of certain functional modules of power grid data visualization is realized, and the feasibility and actual effect of the visualization application in this paper are verified.
Identification and Resolution for Industrial Applications Management

The identification and resolution are the key of industrial application management system. In order to improve the generality and efficiency of industrial application management system, a novel identification and resoluation method for industrial application management is proposed. This paper analyzes the requirements of industrial applications for identifier naming scheme, and establishes a functional architecture for software deployment.
Persistent Message Passing

Graph neural networks (GNNs) are a powerful inductive bias for modelling algorithmic reasoning procedures and data structures. Their prowess was mainly demonstrated on tasks featuring Markovian dynamics, where querying any associated data structure depends only on its latest state. For many tasks of interest, however, it may be highly beneficial to support efficient data structure queries dependent on previous states. This requires tracking the data structure's evolution through time, placing significant pressure on the GNN's latent representations. We introduce Persistent Message Passing (PMP), a mechanism which endows GNNs with capability of querying past state by explicitly persisting it: rather than overwriting node representations, it creates new nodes whenever required. PMP generalises out-of-distribution to more than 2x larger test inputs on dynamic temporal range queries, significantly outperforming GNNs which overwrite states.
The socializing function of the university

The article considers the role of the university as a social institution in the urban space. The article establishes the connection and interdependence among the phenomena of the city, the university, the type of rationality, and the type of communication. The transformations of urban space and the functions of the city in history affect the organizational forms and functions of the university. The research environment at the university, using the communication practices that have developed in society, creates a certain type of rationality. The nonclassical type of rationality is formed in the industrial city environment, and university education is changing towards professionalization, and commercialization, changing communication practices and values that need to be mastered for socialization into the industrial world. The post-nonclassical type of rationality coincides with the emergence of a new type of university, created within the framework of the Bologna Process. This is associated with the emergence of so-called multiple identities, formed by a variety of socialization practices. Universities are operating in the global space of global cities and megalopolises. The convergence of these phenomena results in the transformation of the value perception of the world and the axiological paradigm of society. The activity-based method of analyzing the socialization process allowed determining the need to create the idea of a university, based on a value approach. Values and value-based communication practices in the educational system of the university allow moving to a sustainable development society.
Analisis Sentimen Mahasiswa untuk Perbaikan Desain Afektif Ruang Kelas Jurusan Teknik Industri, Universitas Trisakti

Improving the affective classroom design is essential to maximize student performance and learning achievement. The comfort and performance achievement of Trisakti University Industrial Engineering students are influenced by the affective design of classrooms. This study aimed to use sentiment analysis in the classification of students’ perceptions of the affective classroom design. Student sentiment classification is done using a Support Vector Machine (SVM). The questionnaire analysis results also showed perceptions about the subjects that were considered the most difficult (statistics). The classrooms had a positive sentiment: FGTSC, a sample for the next stage of classroom design formulation. The results show what impressions and things the students consider in choosing the FGTSC class. Some examples of the dominant kansei word are “comfortable,” this shows that students really care about the comfort of a classroom in the learning process. The word kansei for the design concept was collected from students' perceptions of the “positive” label. Design elements that need to be improved include equipment that is less comfortable to use, less lighting, walls with graffiti and uncomfortable seating. The classification results using three SVM types Kernel linear, radial and polynomial obtained linear have the best accuracy value (76%). These results indicate that the classification of student sentiment has the maximum results with SVM linear kernel (dot) type. This method will be used in classifying student sentiment on the results of improving classroom design.
Multi-scene Representation Learning with Neural Radiance Fields

Abstract Getting representations of multiple objects or scenes is a raising research topic in Machine Learning (ML) community. Here, we propose a multi-scene representation model that can learn the representation of complex scenes and reconstruct them in high resolution given novel viewing directions. Our method represents a single scene with fully-connected layers. Each set of fully-connected layers are controlled by hyper-networks for multiple scenes modeling. For each scene, we take 3D coordinates ( x, y, z ) and 2D view-point orientations ( θ , ɸ) as inputs. A set of fully-connected layers output volume density and RGB values at given 3D spatial positions. Then, we render the output volume density and RGB values along the camera rays into images using volume density rendering techniques. During training process, we optimize a continuous volume scene function with a small amount of input viewing directions. By designing versatile embedding module and multi-scene representation networks, our model can render photographic images with novel viewing directions for different complex scenes. Experiment results demonstrate the neural rendering and multi-scene representation abilities of our model. Several thorough experiments show that our method outperforms previous model on both reconstruction precision and scenes generation ability from novel viewing directions.
Latent variable modeling with random features

Gaussian process-based latent variable models are flexible and theoretically grounded tools for nonlinear dimension reduction, but generalizing to non-Gaussian data likelihoods within this nonlinear framework is statistically challenging. Here, we use random features to develop a family of nonlinear dimension reduction models that are easily extensible to non-Gaussian data likelihoods; we call these random feature latent variable models (RFLVMs). By approximating a nonlinear relationship between the latent space and the observations with a function that is linear with respect to random features, we induce closed-form gradients of the posterior distribution with respect to the latent variable. This allows the RFLVM framework to support computationally tractable nonlinear latent variable models for a variety of data likelihoods in the exponential family without specialized derivations. Our generalized RFLVMs produce results comparable with other state-of-the-art dimension reduction methods on diverse types of data, including neural spike train recordings, images, and text data.
Blended Learning of Management Courses Based on Learning Behavior Analysis

The development of information and network technologies fundamentally changes the life, learning, and mindset of students. Teaching informatization is a key content of modern education, and the main direction of teaching reform. In this context, blended learning becomes the inevitable trend of teaching form development. Currently, the teaching of management courses in China faces several problems: the lack of overall planning of teaching design and organization, the low learning interest among students, and the failure of cultivating students’ comprehensive quality. To improve the teaching effect of management courses, this paper carries out a questionnaire survey on the learning behaviors of college students and their needs for blended learning. The survey results show that: the students are not accustomed to pre-class preview and after-class review; most students have the awareness and ability to solve problems with online information; most students are willing to accept blended learning, and expect teachers to provide teaching resources in the form of videos and electronic documents. On this basis, the authors developed an evaluation index system for blended learning according to the constituent factors of this teaching model, and designed a blended learning model for management courses through learning behavior analysis. The blended learning model was divided into three phases: pre-class, in-class, and after-class, and the online/offline teaching activities of teachers and students were detailed in each phase. To verify its application effect, the blended learning model was verified through comparative analysis from two aspects: learning behavior and learning effect. The results show that blended learning model achieves better learning participation, and teaching/learning effect than traditional teaching model, and helps to enhance the knowledge and comprehensive ability of the students. The research provides a reference for further research and application of blended learning in management courses, and boasts great theoretical and practical values.
Design and Implementation of the Inner Mongolia Meteorological Service Intranet APP

Abstract The new media platform based on internet, like WeChat and other mobile phone applications (APPs), can provide meteorological information to users more conveniently and efficiently than the traditional platforms, such as TV, telephone, short message, etc. Therefore, an intranet APP is developed in Inner Mongolia Meteorological Information Center to realize the rapid acquisition of meteorological information in the 70th anniversary celebration of the Inner Mongolia Autonomous Region. Based on user experience and user demands, the APP offers multiple meteorological service products covering meteorological observations and forecasts. It quickly displays the data from the critical regional automatic stations and mobile meteorological stations for relevant users. In this way, it provides seamless, all-round, refined and targeted meteorological services throughout the celebration.
Joint Asymmetric Convolution Block and Local/Global Context Optimization for Learned Image Compression

Recently, the learned image compression methods have achieved remarkable performance gains. However, existing learned methods lack the mechanism to capture global context for probability density model parameter estimation, or the ability of extracting features to capture spatial correlations where needs to be improved. To resolve these problems, a novel learned image compression framework is proposed in this paper.
PECULIARITIES OF THE IMPLEMENTATION OF THE “FLIPPED LEARNING” TECHNOLOGY IN PRIMARY SCHOOL AS A METHOD OF FORMING SELF-READING ACTIVITY OF THE EDUCATION APPLICANTS

This article emphasizes that the important element in the primary school educational process, in particular during the second cycle, is the formation and development of self-learning skills, including self-reading activity, which includes the implementation of the effective methods, techniques, technologies and forms of learning by a teacher. It is analyzed the possibilities of the implementing the "flipped learning" technology in primary school, in particular at the lessons of Ukrainian Language and Reading, or Literary Reading lessons. The notion "flipped learning" is interpreted as a form of active learning, the essence of which is to learn new material by students at home, i.e., the acquaintance with the learning material before the next lesson is going independently, and the classroom time is devoted to comprehend and apply knowledge, skills and abilities, in other words for practical tasks. It is determined that the advantages of this form of work are to ensure that each student reaches his level of development through their own pace of work, giving more study time for practical work or to clarify specific misunderstandable issues, to activate cognitive activity of the students who are always interested in working with gadgets, and, as a consequence, it increases their interest to the lessons, the ability to quickly diagnose the educational achievements of the education applicants, etc. It is proposed to start the implementation of the "flipped learning" technology at the lessons of literary reading from a fairy tale, which is the most interesting work for primary school students. The possible tasks for self-work during the study of fairy tales "Cyrilo Kozhumiaka", "About a gray crow and magic glasses", "Legend about a pasqueflower " are described here. It is offered the comparative analysis of traditional learning and "flipped learning" technologies at Reading lessons. It is proved that the advantages of the flipped learning technology at Reading lessons are the formation of self-reading activity; the improving reading skills; enrichment of social and cognitive experience; the development of critical and logical thinking and literature-creative abilities; the formation of readiness for high-quality mastering of academic disciplines in primary and secondary schools, i.e. the competences of lifelong learning.
A New Foe in GPUs: Power Side-Channel Attacks on Neural Network

GPUs have been increasingly adopted as hardware accelerators for neural networks (NN). Prior studies show that the unique execution model and many DC-DC units on GPUs make it uneasy to reverse engineer detailed information of encryption algorithms through power side-channel attacks. However, we observe that a few core parameters of NN can be acquired through power profiling when victim and attacker share a GPU. We discuss potential solutions for the power side-channel attacks on GPUs.
The Influence of Pyramid Discussion towards Students’ Speaking Ability

The objective of this study was to find out the influence of pyramid discussion towards students’ speaking ability at the eleventh grade of SMA Negeri 11 Ambon in academic year of 2016/2017. The research design was quasi-experimental within pre-tests and post-tests given to both experimental class and control class. The students in experimental class were taught by using pyramid discussion while in the control class students were taught by using group investigation.
Stochastic methods defeat regular RSA exponentiation algorithms with combined blinding methods

Abstract Extra-reductions occurring in Montgomery multiplications disclose side-channel information which can be exploited even in stringent contexts. In this article, we derive stochastic attacks to defeat Rivest-Shamir-Adleman (RSA) with Montgomery ladder regular exponentiation coupled with base blinding. Namely, we leverage on precharacterized multivariate probability mass functions of extra-reductions between pairs of (multiplication, square) in one iteration of the RSA algorithm and that of the next one(s) to build a maximum likelihood distinguisher. The efficiency of our attack (in terms of required traces) is more than double compared to the state-of-the-art. In addition to this result, we also apply our method to the case of regular exponentiation, base blinding, and modulus blinding. Quite surprisingly, modulus blinding does not make our attack impossible, and so even for large sizes of the modulus randomizing element. At the cost of larger sample sizes our attacks tolerate noisy measurements. Fortunately, effective countermeasures exist.
Improved LZ77 Compression

The state-of-the-art LZ77 compression replaces repeated occurrences of a string with references to a single copy of that string existing earlier in the uncompressed string stream. A match is encoded by a pair of numbers called a length-distance pair. To spot matches, the compressor must keep track of some amount of the most recent string, such as the last 4KB, 8KB, or 32KB. The structure in which this string is held is called a sliding window. In practice, searching for the longest string match is expedited by a hash table and a hash chain (it simply uses a hash table when only the first match is considered). In this paper, we present a novel technique to effectively improve the LZ77 compression. Specifically, it applies different sliding windows for each match length, each possibly equipped with different hashing functions. It effectively thwarts the dilemma of the original single-window, wherein a large window size renders short strings incompressible (e.g., a 2-byte match in a 16-bit window) whereas a small window size renders long strings (lying outside of window) incompressible.