Reviewerly/exHarmony huggingface dataset:
Design of English Teaching Post Competency Evaluation System Based on K-means Clustering Algorithm

This paper designs a competency evaluation system for English teaching posts in higher vocational colleges. On the basis of the established constraint parameter index analysis model, the ability of big data information model is evaluated and the feature information of ability constraint is extracted. According to this, the teaching ability of English teaching can be evaluated by integrating the average of teaching resources and teaching ability. The experimental results show that the designed evaluation algorithm significantly improves the ability and accuracy of information fusion analysis in the process of English education post competency evaluation, and has high practical application value.
Continuous Herded Gibbs Sampling

Herding is a technique to sequentially generate deterministic samples from a probability distribution. In this work, we propose a continuous herded Gibbs sampler that combines kernel herding on continuous densities with the Gibbs sampling idea. Our algorithm allows for deterministically sampling from high-dimensional multivariate probability densities, without directly sampling from the joint density. Experiments with Gaussian mixture densities indicate that the L2 error decreases similarly to kernel herding, while the computation time is significantly lower, i.e., linear in the number of dimensions.
Flexible Knowledge Distillation with an Evolutional Network Population

Deep neural networks have continually surpassed traditional methods on a variety of computer vision tasks. Though deep neural networks are very powerful, the large number of parameters and complex structures consume considerable storage and calculation time, making it hard to deploy with limited resources. To tackle this issue, many recently proposed knowledge distillation approaches are aimed at obtaining a small student network to imitate a large teacher network. However, the student network structure is pre-defined and may be hard to train. In this paper, we propose to distill knowledge with an evolutional student network population. The population is initialized with several basic structures and each network is evaluated by the imitation ability (i.e., fitness) to the teacher network. By reusing the weights, we provide five enhancement options to strengthen the networks with high fitness and abandon the weak ones. By changing the fitness criterion, we can select networks to meet different requirements, such as balancing size and accuracy. This allows one to find a superior student network structure that better imitates the teacher model from various aspects with easier training. The experimental results demonstrate the proposed method can achieve superior performance of knowledge distillation with flexible student structures.
A Hybrid Approach for Object Localization Combining Mask R-CNN and Halcon in an Assembly Scenario

Robust and accurate object localization is a key component in the context of robotic grasping. Especially in collaborative assembly scenarios it is important that the robot is able to detect objects precisely to grasp them quickly and reliable. Therefore, classic image-based methods for object localization are not always applicable, as they may experience bad localization rates in large scenes or long runtimes at high camera resolutions. The focus of this paper is to provide a hybrid approach for the localization of objects in a scene by using a deep neural network for classification and segmentation of objects and classic shape-based matching for exact localization in the detected region of interest. We use Mask R-CNN as a deep neural network pixel based classifier and Halcon as an example for a well-known classic image processing program. Last but not least the solution is evaluated in comparison to only using Halcon by evaluating the performance regarding execution time and reliability of the object pose localization.
Pattern Prediction Using Binary Trees

In this busy world, no one has time now. Technology is being developed every day to increase the efficiency. In this front, word predictor is a small step which increases our efficiency multifold times. Word predictor has applications in various areas like texting, search engine, etc. To develop our word predictor program, this project uses the data structure Trie. Our program uses a stored file of words to predict the words which the user may think of thus helping a lot. This project has compared the implementation of word completion using binary trees to that of binary tries. The proposed method that this project has used is word prediction using binary trees as compared to already existing binary tries and has proved that implementation of binary tries takes longer time as compared to our proposed work. Auto-complete is a feature which helps the user to find out the things that one wants to search by predicting the value in the search box. This auto-complete starts predicting the searches related to the few letters or words that are being typed by the user in the search box. This feature works best when the words typed by the user are more common such as when addressing an email.
An adaptive and intelligent e-learning environment using fuzzy logic

A major issue of great concern in student modeling is the inherent uncertainty that exist due to lack of sufficient information with high degree of certainty to build this model. The proposed system presents a novel approach which is a web-based education system that performs individualized instruction on the domain of programming languages is presented. This approach is fully implemented and evaluated in an educational application module, called Fuzzy Knowledge and Preference Based E earning (Fuz KPBE). This also tracks the events and preferences of the users before finding the data. In particular, Fuz KPBE Performs user modeling by dynamically identifying and updating a student's knowledge level of all the concepts of the domain knowledge. The operation of Fuz KPBE is based on Fuzzy clustering by Local Approximation of Memberships (FLAME ) that are used to represent the dependences among the domain concepts. Fuz KPBE uses fuzzy sets to represent a student's knowledge level as a subset of the domain knowledge. Thus, it combines fuzzy theory with the overlay model. Moreover, it employs a novel inference mechanism that dynamically updates user stereotypes using fuzzy sets. It should be noted that the overlay model and stereotypes constitute two widely used methods for user modeling.
A Model to Enable the Reuse of Metadata-Based Frameworks in Adaptive Object Model Architectures

The Adaptive Object Model (AOM) is an architectural style in which domain entity types are represented as instances that can be changed at runtime. It can be used to achieve higher flexibility in domain classes. Due to AOM entities having a distinct structure, they are not compatible with most popular frameworks, especially those that use reflection and code annotations. To solve such limitations, this study aims to propose a model that enables the reuse of frameworks designed for classic object-oriented domain models in an AOM application. The proposed model uses dynamically-generated adapters for AOM entities that encapsulate them in a class with the format expected by the frameworks. A reference implementation was developed in the Esfinge AOM RoleMapper framework to evaluate the viability of the proposed model. Initially, to evaluate the solution feasibility, a case study was carried out using the Hibernate framework. Further, an experiment was conducted to assess how the participants perceived the framework functionality reuse through the proposed model. The feasibility study revealed that the solution could be applied in a complex setting for the chosen object-relational mapping frame. It raised some difficulties that can be addressed in future studies. In the experiment, the development time did not present a significant difference compared to the competing approach. Despite the considerable learning curve, most participants considered that the proposed approach has more advantages than the alternative. Based on the evaluations, we can conclude that the proposed model can be successfully employed to use AOM entities with frameworks that were not designed for AOM applications. The possibility of reusing existing frameworks can reduce the effort required to adopt an AOM architecture and, consequently, be a facilitator in implementing more flexible and adaptive approaches.
The Comparison of Secure Key Rate of BB84 Protocol Using Single Photon Detectors and Optical Homodyne Detectors

Abstract To increase the security and efficiency of information transmission, the technology of Quantum Key Distribution is developed. A simulation of secure key rate of BB84 protocol is done to give a visualized comparison of efficiency of the protocol using different light sources and detectors. The simulation shows that the Poisson light source is less efficient than single photon source and the secure key rate of using continuous variable detectors is lower than using discrete variable detectors.
Murder, She Modeled: Modeling to Support Crimino-Forensic Processes

Digital technologies are increasingly used by law enforcement in forensic processes. This opens the door to new opportunities for the use of conceptual modeling techniques to support pro-active collection and modeling of crime scene evidence—especially for the more challenging case of soft evidence. As opposed to digital evidence and other ‘hard’ evidence (such as finger prints and DNA samples), ‘soft’ evidence refers to heterogeneous meta-data on the crime scene (such as modus operandi, victim’s background, etc.), which challenges the automation and support of both collection and modeling processes for such evidence. Using homicide crime scene staging (HCSS) as a case study, we explore the challenges and opportunities that an approach applying conceptual modeling techniques offers to support crimino-forensic processes in the collection and systematic modeling of soft evidence.
A Comparative Security Analysis of the German Federal Postal Voting Process

The percentage of votes cast by postal voting increases with every election for the German federal parliament (Bundestag). However, especially compared to Internet voting, concerns regarding security, transparency, and trustworthiness of postal voting are rarely discussed. This paper outlines the established process of postal voting in Germany and evaluates it with regard to various security-relevant characteristics. For this evaluation, a methodology originally developed for Internet voting is used in order to ensure comparability. The aim is to identify weaknesses as well as potential for optimization, to compare German postal voting with selected Internet voting schemes, and to derive implications for policy and further research.
Production Line Vacuum Robot Based on Image Processing and Fuzzy PID Control

Aiming at the problems such as low efficiency of manual cleaning and easy wear of production line, a production line vacuuming robot based on image processing and fuzzy PID control was studied and designed. First of all, the dust image of the conveyor belt is collected by the camera. After the original image is preprocessed, the low-frequency image and the high-frequency image are obtained by using wavelet transform to decompose the two-dimensional dust image of the conveyor belt. The grayscale symbiosis matrix of the low-frequency subband is constructed. Then, the speed of DC motor is controlled by fuzzy PID algorithm. Through the experiment to test the reliability of the design, implementation along the conveyor belt production line robot automatic vacuuming, combining wavelet transform and gray level co-occurrence matrix algorithm described the conveyor dust image texture features, has good recognition effect, and the simulation results show that compared with the traditional PID control, fuzzy PID control dc motor speed control precision is higher, quantitative evaluation production line vacuuming robots vacuuming effect, dust collection efficiency can reach above 93.5%.
Intelligent processing of unstructured textual data in document based NoSQL databases

Many organizations view themselves as market driven in this digitalized society, where the decision making depends solely on the customer feedbacks and suggestions. This massive amount of unstructured data produced, affects query processing performance adding to the difficulty in managing or retrieving the data. Many organizations are progressively exploring ways to comprehend and analyse this huge unstructured data to infer operational insights from it. We could solve many of the storage issues of unstructured data using the document-based NoSQL databases. But applying artificial intelligence to make exact matching predictions with the text data is remaining as a challenge. With the new method proposed here, the intelligent processing of the textual data stored in document-based NoSQL databases is made possible, and it is really an added benefit to those, who opt or use NoSQL databases for unstructured data storage.
A Split Key Unique Sudoku Steganography (SKUSS)-Based Reversible High Embedded Data Hiding Technique

Confidential data transfer or secure communication is always remaining as an essential need in this era of modern communication. This research work is an implementation of a new method named Split Key Unique Sudoku Steganography (SKUSS). It is a reversible data hiding with high embedding capacity, where high embedding signifies data bits per pixels of the cover image that are more with maintaining the naturalness of the cover image. For the implementation of SKUSS first, a unique Sudoku has been generated with a user-defined 64-bit key and a 64-bit public key, then that Sudoku is used for hiding data digits (ASCII code) into cover image pixels. The data can be only recovered with that same Sudoku with the correct public and user keys. This method is based on a hybrid algorithm that applies the techniques of Sudoku and Steganography to offer different security features to the images transmitted between entities on the Internet. Based on the proposed method, the authenticity and integrity of the transmitted images can be verified either in the spatial domain or in the encrypted domain or both domains. The work is implemented on MATLAB design and simulation tools.
Feed Hungry, Feel Happy

The sharp increase within the quantity of wastage in terms of food makes the requirement for charity in terms of donation. within the current situation food is being wasted daily on an outsized basis in numerous restaurants, weddings, social functions, faculty canteens and plenty of different social events. individuals give food manually by visiting every organization range of times so as to cut back the issues of food wastage whereas their area unit websites that have taken efforts to assist individuals give food. The planned system presents, a replacement internet-based application that gives a platform for donating leftover food to all or any poverty-stricken people/organizations. The system is shown to be an efficient suggests that of donating things to organizations, etc. over the web. It shows the potential for avoiding the wastage of food. It provides data regarding the motivation to return up with such associate application, thereby describing existing donation system and the way the merchandise works for betterment of the society. this technique can produce a standard collaboration portal for hotels/restaurants and charities, charity will directly contact edifices who have food remaining and report generation which can show what quantity food is given by that restaurant and providing reward points for them .In this system Food Donor, Food receiver, Third party merchandiser, admin and premium user area unit the most modules wherever Food Donor may be any organization, institute or faculty who desires to give food and build a replacement food donation request and Food receiver may be any charity firm seeking for food. a replacement food donation request are going to be created on the portal and once the request is accepted, a notification is distributed.
Context-driven Abnormal Semantic Event Recognition for Healthcare Applications

Healthcare applications in a smart environment present an increasing need for technological support to monitor and recognize patients' activities in the hospital environment and their daily routine. Recognition systems for activities use knowledge models to define patients' routine activities, and deviations from the model are considered abnormalities. It models provide representation and reasoning, inferring implicit facts to discover high-level activities, analyzing and correlating events. We propose a knowledge-based hybrid reasoning approach to allow the recognition of abnormal semantic events. This approach consists of two stages: 1) modeling, ontology-based and probabilistic, supporting aspects with temporal and uncertainty data, 2) semantic reasoning, events are interpreted by a strategy context-driven hierarchical.
Expert Survey on Current Trends in Agile, Disciplined and Hybrid Practices for Software Development

Abstract Every software development company makes software development based on a specific approach. There are a number of approaches to software development, both disciplined and agile. Each approach includes a set of different activities. Sometimes, the specific nature of a company’s work requires a specific approach, but the need to make work more efficient, faster and better requires implementing activities of other approaches. Then hybrid software development approaches come in. The paper presents an expert survey to examine the most important software development activities, the combinations of development approaches that are used in software development processes and the way of upgrading existing approaches. The evaluated activities of software development process are classified according to their nature – whether they correspond with a team, organisation, documentation, development, and testing. The conclusions are also made on the practices that are required most – disciplined, Agile or hybrid.
Characterizing Resource Heterogeneity in Edge Devices for Deep Learning Inferences

Significant advances in hardware capabilities and the availability of enormous data sets have led to the rise and penetration of artificial intelligence (AI) and deep learning (DL) in various domains. Considerable efforts have been put forth in academia and industry to make these computationally demanding DL tasks work on resource-constrained edge devices. However, performing DL tasks on edge devices is still challenging due to the diversity of DNN (Deep Neural Networks) architectures and heterogeneity of edge devices. This study evaluates and characterizes the performance and resource heterogeneity in various edge devices for performing DL tasks. We benchmark various DNN models for image classification on a set of edge devices ranging from the widely popular and relatively less powerful Raspberry Pi to GPU-equipped high-performance edge devices like Jetson Xavier NX. We also compare and contrast the performance of three widely-used DL frameworks when used in these edge devices. We report DL inference throughput, CPU and memory usage, power consumption, and frameworks' initialization overhead, which are the most critical factors for characterizing DL tasks on edge devices. Additionally, we provide our insights and findings, which will provide a better idea of how compatible or feasible edge devices are for running DL applications.
Enterprise Information Security Awareness and Behavior as an Element of Security Culture During Remote Work

It's the first time that many users are operating their work from home. There is not only the tension of the uncertainty around the COVID-19 pandemic but also a time for adjusting people to their remote working habits considering ever-increasing cyber-attacks. When employees work in an office, there is an IT team working with them closely for their information security problems. However, it is difficult to provide sufficient information security protection that can compensate for human errors in remote working. Information security familiarity, information security awareness, and information security behavior are critical concepts to consider again during the pandemic as the new normal. In this chapter, a literature review will be conducted for information security awareness and information security familiarity concepts. Analysis of the context of these concepts is the aim of this chapter. This study can give insight to understand, evaluate, and determine the information security behavior of employees during new remote working conditions.
Discriminative Bayesian Filtering for the Semi-supervised Augmentation of Sequential Observation Data

We aim to construct a probabilistic classifier to predict a latent, time-dependent boolean label given an observed vector of measurements. Our training data consists of sequences of observations paired with a label for precisely one of the observations in each sequence. As an initial approach, we learn a baseline supervised classifier by training on the labeled observations alone, ignoring the unlabeled observations in each sequence. We then leverage this first classifier and the sequential structure of our data to build a second training set as follows: (1) we apply the first classifier to each unlabeled observation and then (2) we filter the resulting estimates to incorporate information from the labeled observations and create a much larger training set. We describe a Bayesian filtering framework that can be used to perform step 2 and show how a second classifier built using the latter, filtered training set can outperform the initial classifier. At Adobe, our motivating application entails predicting customer segment membership from readily available proprietary features. We administer surveys to collect label data for our subscribers and then generate feature data for these customers at regular intervals around the survey time. While we can train a supervised classifier using paired feature and label data from the survey time alone, the availability of nearby feature data and the relative expensive of polling drive this semi-supervised approach. We perform an ablation study comparing both a baseline classifier and a likelihood-based augmentation approach to our proposed method and show how our method best improves predictive performance for an in-house classifier.
Three Factor Authentication Novel Framework through Improve System Privacy and Data Security

Authentication, Authorization, security and confidential information are number of foremost topics part in the Digital cyber - security Area. There are multiple solutions available in this virtualize cyber world that are presented to user’s strength as per digital security. Username and password base are common factor authentication methods. The growing-up popularity with compliance of second factorized methods those driven by the improvement of privacy and security as per the requirement during this digitalization of technological trend. The recognition and success of digital security various measures are big deal to obsessed with simple convenience. Its need more and more implementation for the user privacy, data confidentiality and network security’s system. mainly focus on this research Article into dealing with and analysing the implications of security part of 3FA proposal model for additional security in way of systematic manner to improvements of digital security in web-based Application. This paper will be presenting as web-based Application. so, we can create potentially ensure systems are authorized with authentication of user's assurance without loss of convenience between user-system data communication in network.