Well-designed Class Research Articles

Java Collections Framework and Their Applications in Software Development

Abstract: Incorporating a variety of classes and interfaces, the Java Collections Framework provides a centralized and efficient means of managing and manipulating collections of objects within the Java programming language. In pursuit of thoroughly investigating the expansive array of data structures and algorithms housed within the Java Collections Framework, we craft this thoroughly researched paper. Accessing official records, scholarly works, and real-world instances, researchers employ a comprehensive approach. In software development, the Java Collections Framework (JCF) is a valuable resource for efficiently managing and manipulating collections of data. It offers a set of well-designed classes and interfaces that simplify the process. The research outcome suggests that the Java Collections Framework is an effective toolset for application developers to manage data with efficiency and accuracy.

E-Learning Teaching System in Augmentative and Alternative Communication Using Big Data Analytics

The key barriers to children with special needs to contact individuals are to sustain them alertly and efficiently for learning languages. As the first step for students with disabilities is language development. The report contributes to the sector by identifying the obstacles and opportunities. The research proposed an English language teaching in augmentative and alternative communication (ELT-AAC) and was performed using a qualitative approach by extracting evidence from several independent schools with particular students. The results showed the educators were faced with great difficulty in educating advanced students on the language: limited comprehension, classroom equipment, unawareness, and inappropriate language instruction. The research further showed that the problems were mitigated and language instruction improved by special education, another well-designed class, daily psychological counseling, and various viewpoints.

Instructor Presence and Student Satisfaction Across Modalities: Survey Data on Student Preferences in Online and On-Campus Courses

Post-COVID-19, many, if not most, college and university instructors teach both online and face-to-face, and, given that online courses historically have higher attrition rates, designing and facilitating effective online courses is key to student retention. Students need online and on-campus courses that are well designed and facilitated, but even well-designed classes can be ineffective if students feel lost in the course or disengaged from the instructor. We surveyed 2,007 undergraduate students at a public, metropolitan university in the United States about the best and worst classes they had taken at the university. The resulting data revealed important consistencies across modalities—such as the importance of clear instructions and instructor availability. However, students responded that instructors matter more in face-to-face courses, where they can establish personal relationships with students, whereas assignments “stand in” for instructors in online classes. These findings support the need for increased faculty professional development in online course design and facilitation focused on student experience as well as faculty expertise.

Matrix Product State-Based Quantum Classifier.

Interest in quantum computing has increased significantly. Tensor network theory has become increasingly popular and widely used to simulate strongly entangled correlated systems. Matrix product state (MPS) is a well-designed class of tensor network states that plays an important role in processing quantum information. In this letter, we show that MPS, as a one-dimensional array of tensors, can be used to classify classical and quantum data. We have performed binary classification of the classical machine learning data set Iris encoded in a quantum state. We have also investigated its performance by considering different parameters on the ibmqx4 quantum computer and proved that MPS circuits can be used to attain better accuracy. Furthermore the learning ability of an MPS quantum classifier is tested to classify evapotranspiration (ET ) for the Patiala meteorological station located in northern Punjab (India), using three years of a historical data set (Agri). We have used different performance metrics of classification to measure its capability. Finally, the results are plotted and the degree of correspondence among values of each sample is shown.

A Unified Semantic Model for Cross-Media Events Analysis in Online Social Networks

The gap between the raw data from various data sources and the diverse intelligent applications has been an obstacle in the field of events analysis in online social networks. Most existing analysis systems focus on data from a certain single online social network platform and a limited range of analysis applications. To comprehensively understand events, the sources of data usually include multiple online social network platforms and different existing corpora. Thus, it is necessary to build a bridge to handle the online social data from different sources and support various analysis application requirements. In this paper, a unified semantic model for events analysis is proposed. The model contains well-designed classes and properties to tackle the lack of unified representation, and provenance information is also taken into consideration. The reasoning is supported to check the consistency of the data and to discover hidden knowledge such as tacit classification and implicit relationships. The schema mapping and data transformation methods are provided to handle the heterogeneous data from various online social network platforms and datasets. The design of the cross-media event analysis system is also presented. The comparison shows the advantages of this paper. The case study shows the applicability and effectiveness of our model and system.

Neurocomputing approach to matrix product state using quantum dynamics

During the last three decades, quantum neural computation has received a relatively high amount of attention among researchers and academic communities since the model of quantum neural network has been proposed. Matrix product state is the well-designed class of tensor network states, which plays an important role in processing of quantum information. The area of dynamical systems help us to study the temporal behavior of systems in time. In our previous work, we have shown the relationship between quantum finite state machine and matrix product state. In this paper, we have used the proposed unitary criteria to investigate the dynamics of matrix product state with quantum weightless neural networks, where the output qubit is extracted and fed back (iterated) to input. Further, we have used Von Neumann entropy to measure possible entanglement of output quantum state. Finally, we have plotted the dynamics for each matrix product state against iterations and analyzed their results.

A Code Level Based Programmer Assessment and Selection Criterion Using Metric Tools

this study presents a code level measurement of computer programs developed by computer programmers using a Chidamber and Kemerer Java metric (CKJM) tool and the Myers Briggs Type Indicator (MBTI) tool. The identification of potential computer programmers using personality trait factors does not seem to be the best approach without a code level measurement of the quality of programs. Hence the need to evolve a metric tool which measures both personality traits of programmers and code level quality of programs developed by programmers. This is the focus of this study. In this experiment, a set of Java based programming tasks were given to 33 student programmers who could confidently use the Java programming language. The codes developed by these students were analyzed for quality using a CKJM tool. Cohesion, coupling and number of public methods (NPM) metrics were used in the study. The choice of these three metrics from the CKJM suite was because they are useful in measuring well designed codes. By examining the cohesion values of classes, high cohesion ranges [0,1] and low coupling imply well designed code. Also number of methods (NPM) in a well-designed class is always less than 5 when cohesion range is [0,1]. Results from this study show that 19 of the 33 programmers developed good and cohesive programs while 14 did not. Further analysis revealed the personality traits of programmers and the number of good programs written by them. Programmers with Introverted Sensing Thinking Judging (ISTJ) traits produced the highest number of good programs, followed by Introverted iNtuitive Thinking Perceiving (INTP), Introverted iNtuitive Feelingng Perceiving (INTP), and Extroverted Sensing Thinking Judging (ESTJ)

Identifying thresholds for object-oriented software metrics

Abstract Despite the importance of software metrics and the large number of proposed metrics, they have not been widely applied in industry yet. One reason might be that, for most metrics, the range of expected values, i.e., reference values are not known. This paper presents results of a study on the structure of a large collection of open-source programs developed in Java, of varying sizes and from different application domains. The aim of this work is the definition of thresholds for a set of object-oriented software metrics, namely: LCOM, DIT, coupling factor, afferent couplings, number of public methods, and number of public fields. We carried out an experiment to evaluate the practical use of the proposed thresholds. The results of this evaluation indicate that the proposed thresholds can support the identification of classes which violate design principles, as well as the identification of well-designed classes. The method used in this study to derive software metrics thresholds can be applied to other software metrics in order to find their reference values.