Reviewerly/exHarmony huggingface dataset:
Comparison of VGG-16, VGG-19, and ResNet-101 CNN Models for the purpose of Suspicious Activity Detection

This paper compares the performance of three popular convolutional neural network (CNN) models, VGG-16, VGG-19, and ResNet-101, for the task of suspicious activity detection. The VGG networks are known for their depth and the use of small convolutional filters, while ResNet is known for its residual connections that allow for deeper networks without the issue of vanishing gradients. The study utilizes a dataset of surveillance videos for training and testing the models. The results show that the VGG-19 model outperforms the other two in terms of accuracy, specifically for the detection of suspicious activities. Overall, this study demonstrates the effectiveness of the three different models for suspicious activity detection and highlights its potential for use in real-world surveillance systems.
An Architectural Pattern for Dynamic Component Integration

The continuing increase of interest in Component-based Software Engineering (CBSE) signifies the emergence of a new development trend within the software industry. CBSE deals with the usage of reusable software components to provide complex integrated solutions at shorter development time and minimum cost. This engineering approach emphasizes the identification, selection, evaluation, procurement, integration, and evolution of reusable components for constructing complex and large-scale software solutions. Component-based development approach has a great potential for significantly reducing the cost and time to market of large-scale and complex software systems, improving system maintainability and flexibility. However the usage of components imposes some problems in the development process. Especially, the complexity of the integration process is increased due to the black box nature of the products. Integration is further influenced by problems due to architectural mismatches, component incompatibility etc. Apart from these general static integration problems, necessity for intelligent services and context aware computing paves way for the dynamic integration of components. Patterns have promising benefits in solving software design problems and their benefits have been realized in the design of component integration also. Patterns exist in different levels. Architectural patterns provide the overall structure of a system; design patterns deal with localized design choices; Idioms define patterns of usage within a particular language. Design patterns express proven techniques making it possible to reuse successful designs and provide a common vocabulary to share design descriptions. Architectural patterns cover a wider realm, specifying system-wide structural properties for an application, and impacting on subsystem architecture. This report presents the study of literature related to patterns and its application in the design of component-based systems. An Architectural pattern has been proposed in this report, for the design of a component-based system that dynamically integrates components. The dynamism is achieved using a service model.
An Examination of Inkjet and Laser Printed Documents using Stereomicroscope : A Forensic Approach

Printed documents are very frequently encountered in forensic cases as disputed or questioned documents. With increasing number of such cases, the printer inspection in has become a major requirement in questioned document examination in recent years considering the extensive use printers in document creation in comparison to handwritten papers. Also, the counterfeiting of printed documents by various printers have been recorded at a large scale in last two decades. In such cases, it is very much required by the investigators to examine and identify the type of printer used and to establish a linkage of the questioned document with the alleged printer. The current study is focused on the examination, analysis and identification of various types of printed documents on the basis of their specific characteristics including resolution, edge contrast, letter roughness and feathering formed from diverse printers to differentiate and classify them for forensic questioned document examination and to assist the forensic expert and questioned document examiner thorough such investigations. In this study 64 types of printers i.e. 32 inkjet printers and 32 printers comprise 1024 printed documents were collected for the analysis. The samples were analyzed using Olympus stereo microscopes SZX16 (4x magnification). Each printer has its uniqueness in printing documents, and this study aims to figure out that particular uniqueness at the documents after printing from the alleged printer. The observations revealed substantial differences in the letter’s characteristics. This study may be a useful to discriminate the documents printed from different types of printers.
Efficient Load Balancing of Resources for Different Cloud Service Providers in Cloud Computing

In distributed computing cloud computing is an emerging technology which provides pay per model as per user demand or requirement. Cloud has a collection of virtual machines which facilities both computational and storage requirement. Scheduling and Load balancing are the main challenges in the cloud computing on which we are emphasizing. Scheduling is the process to control the order of work going to be performed by computer system. Load balancing has an important role in the performance in cloud computing. Better load balancing will make cloud computing more efficient and will also increase user satisfaction. It provides a way to handle several inquiries residing inside cloud computing environment set. Complete balancing acquires two tasks, one is resource provisioning/resource allocation and task scheduling throughout the system. In the proposed research paper, we are presenting a hybrid algorithm created by FCFS and Round Robin algorithms. As the Round Robin is the easiest algorithm that's why it is frequently used and the first preference for implementing easy schedulers. The Round Robin algorithm only requires a list of nodes. In the proposed solution we have eliminated the drawbacks of simple Round Robin algorithm by introducing assignment of time slices to different processes depending upon priorities.
Sentiment Analysis to Predict Movies Success Rate Based on NLTK Movie Review Corpora Using Machine Learning

With the proliferation of social networks, peoples express their opinions about different things or issues on social media without any hesitation. The rapid growth of textual data on social media are required to develop algorithms and techniques for recognizing people’s opinions towards specific subject. These opinions are helpful in business plans development, marketing trends, political parties’ popularity. The film industry can be an important revenue generating industry of any country. Peoples express their opinion on movie trailer using social media. The effective sentiment analysis of opinions on social media such as Twitter can be helpful to predict movie ratings. This research work focuses on developing a technique to predict movie success rate on the basis of tweets data. We have collected tweets about different movies after their trailer released by using hash tag method. We applied Sentiment analysis approach using Machine learning. In this study we utilized four key algorithms (Naïve Bayes, SVM, Neural Networks, decision tree) on NLTK Movie review corpora.
Automated 3D Segmentation of Renal Structures for Renal Cancer Treatment

The KiPA 2022 challenge aims to develop reliable and reproducible methods for segmenting four kidney-related structures on CTA images to improve surgery-based renal cancer treatment. In this work, we describe our approach for 3D segmentation of renal vein, kidney, renal artery, and tumor from the KiPA 2022 dataset. We used the MONAI framework and the SegResNet architecture with a combination of Dice Focal loss and L2 regularization. We applied various data augmentation techniques and trained the model using the AdamW optimizer with a Cosine annealing scheduler. Our method achieved $$10^{th}$$ position in the open test leaderboard and $$6^{th}$$ position in the closed test leaderboard.
Segmentation of Intraoperative 3D Ultrasound Images Using a Pyramidal Blur-Pooled 2D U-Net

Automatic localization and segmentation of the tumor and resection cavity in intraoperative ultrasound images can assist in accurate navigation during image-guided surgery. In this study, we benchmark a pyramidal blur-pooled 2D U-Net as a baseline method to segment the tumor and resection cavity before, during, and after resection in 3D intraoperative ultrasound images. Slicing the 3D image along three transverse, sagittal, and coronal axes, we train a different model corresponding to each axis and average three predicted masks to obtain the final prediction. It is demonstrated that the averaged mask consistently achieves a Dice score greater than or equal to each individual mask predicted by only one model along one axis.
Accurate Detection of Mediastinal Lesions with nnDetection

The accurate detection of mediastinal lesions is one of the rarely explored medical object detection problems. In this work, we applied a modified version of the self-configuring method nnDetection to the Mediastinal Lesion Analysis (MELA) Challenge 2022. By incorporating automatically generated pseudo masks, training high capacity models with large patch sizes in a multi GPU setup and an adapted augmentation scheme to reduce localization errors caused by rotations, our method achieved an excellent FROC score of 0.9922 at IoU 0.10 and 0.9880 at IoU 0.3 in our cross-validation experiments. The submitted ensemble ranked third in the competition with a FROC score of 0.9897 on the MELA challenge leaderboard.
Regular Music Exposure and Vocabulary Recognition of ELT Sophomore Students

It has been broadly asserted that the getting used to the activity of listening to English songs is a recommended method to enrich students' vocabulary.However, further empirical studies are still needed to relate that with adult EFL learners.Therefore, this study examines the correlation between regular music exposure and vocabulary recognition in adult learners.The subject involved 72 university students.They are chosen randomly from the third semester students of English Teacher Education Department at an Islamic State University in Indonesia.To obtain the data concerning the student's habit and exposure to English songs, this study employs the instruments such as Aural Vocabulary Knowledge Test and a questionnaire.The statistical analysis employed Pearson Product Moment Correlation.The finding demonstrates the correlation between regular exposure to English song and students' vocabulary recognition which is significant.It implies that the habit of listening to English songs contributes to the better level of vocabulary recognition of the students as adult English learners.Furthermore, it is evident that the exposure has resulted in more language acquisition.The researchers also suggest further studies to explore the use of English songs of various genres to improve adult learners' vocabulary recognition.
Improving the Reading Comprehension of the Eight Graders by Developing Think-Pair-Share Strategy

This research is developing a Think-Pair-Share (TPS) strategy to improve the students' achievement in reading comprehension.In applying TPS, there were three sequential cooperative activities.First, the teacher gave questions related to the reading text, and the students had to think about the answer.Then in pairs, the students exchanged their responses.The pairs then shared their ideas with other pairs, teams, or the entire group or class by the representative in each group.This Classroom Action Research consists of three cycles with two meetings each.The researchers collaborated with English teachers to design the TPS strategy process and lesson plan, define the success criteria, take action, observe and reflect.Thirty students of the eight graders at an Islamic Junior High School in Kediri, East Java, Indonesia, are chosen as the subjects of this research.The data collected in this research are derived from observation sheets, field notes, quizzes, and questionnaires.The findings show that students become more engaged in class using the Think-Pair-Share method.They were motivated and excited to share their thoughts and opinions and ask and answer questions.Furthermore, students had positive feelings about the Think-Pair-Share strategy.After the Think-Pair-Share method was implemented and developed through three cycles, it was concluded that this strategy can improve or progress students' reading comprehension.
Scammers’ Identities as Represented in Emails to Indonesian Journal Editors

Numerous scam emails received by journal editors with various rhetorical techniques are considerable linguistic phenomena to examine.Certain rhetorical techniques provide information about the email senders' identity and ideology.Thus, this study employs the transitivity system of Halliday's Systemic Functional Linguistic and Chiluwa's discourse strategies to discover how scammers construe reality based on their identities and ideologies.The findings show that the highest number of data provide powerful relational discourses, whether the discourse is explicitly stated or implicitly inferred through the narrativity of the emails.Based on those findings, the represented identity of the scammers is understood to be ambiguous: whether the scammers have power or have learned to express power in their writing.The ambiguity, however, is proven ironic by the findings on the misapplication of Standard English writing, which also provides evidence that the scammers are most unlikely highly educated.Even though this study does not provide evidence of the real identity of the scammers, this study has provided confidence for the recipients to easily acknowledge that the scammers are the ones who have less power than the recipients do.
Social and Affective Meaning of Covid-Related Terms in ‘New York Times’ Online News

The news media has an essential role in providing information and perspective on the issue presented, such as Covid-19, which has become a hot conversation these days.Many media bring their perspective on the hot issues of Covid-19 and make people more selective about the content.Compiling the content makes it possible to use the semantic study to know the elements of meaning in the news.This study aims to identify the social and affective meaning that occurs in New York Times online news using the theory of meaning by Geoffrey Leech.This study was structured using descriptive qualitative methods.The data is taken from the news about Covid-19 on the Asia-Pacific page of the New York Times.In this study, researchers found 12 news containing about covid and then found as many as 58 sentences that have meaning social dan affective meanings; They are 29 social meanings that vary province (26 languages of law and three languages of science), 29 affective meanings.Sentences in noun phrases are mostly used in social meaning, while direct expressions are mostly used in affective meaning.Future researchers, can choose different objects and develop theories that have been used in this study or use more complex theories.
Hybrid Decision Fusion based Multimodal Ensemble Framework for Cervical Cancer Detection

Cervical cancer is fourth in the list of cancers that affect women. It has remained the main cause of death for women in developing nations. The cancer is spread through human papilloma virus (HPV), which is sexually transmitted. Pap smear and colposcopy image analysis remain prominent methods of diagnosis. These screening tests require skilled diagnostic experts, a scarce resource in developing countries thus restricting the effectiveness of the cancer detection process in large scale. Machine learning and deep learning are branches of artificial intelligence that are being used increasingly in cancer diagnosis. This study proposes a novel hybrid intelligent system for cervical cancer detection. A hybrid model of feature extraction and feature fusion is proposed for merging the two-state image and clinical data. Subsequently a machine learning ensemble learner is assembled to classify the features. The model performed with a satisfactory accuracy of 96.16%. Our results show that our method outperforms state of the art approaches and archives better, dependable accuracy.
A selective evolutionary heterogeneous ensemble algorithm for classifying imbalanced data

&lt;abstract&gt; &lt;p&gt;Learning from imbalanced data is a challenging task, as with this type of data, most conventional supervised learning algorithms tend to favor the majority class, which has significantly more instances than the other classes. Ensemble learning is a robust solution for addressing the imbalanced classification problem. To construct a successful ensemble classifier, the diversity of base classifiers should receive specific attention. In this paper, we present a novel ensemble learning algorithm called Selective Evolutionary Heterogeneous Ensemble (SEHE), which produces diversity by two ways, as follows: 1) adopting multiple different sampling strategies to generate diverse training subsets and 2) training multiple heterogeneous base classifiers to construct an ensemble. In addition, considering that some low-quality base classifiers may pull down the performance of an ensemble and that it is difficult to estimate the potential of each base classifier directly, we profit from the idea of a selective ensemble to adaptively select base classifiers for constructing an ensemble. In particular, an evolutionary algorithm is adopted to conduct the procedure of adaptive selection in SEHE. The experimental results on 42 imbalanced data sets show that the SEHE is significantly superior to some state-of-the-art ensemble learning algorithms which are specifically designed for addressing the class imbalance problem, indicating its effectiveness and superiority.&lt;/p&gt; &lt;/abstract&gt;
Data Processing by Neural Networks

In the contemporary DS, artificial neural networksNeural network have gained enormous popularity. This is the reason for dedicating a chapter to describing their structure and properties.
A SAD New Category of Abusive Intellectual Property Litigation

This paper describes a sophisticated but underreported system of mass-defendant intellectual property litigation called the “Schedule A Defendants Scheme” (the “SAD Scheme”), which occurs most frequently in the Northern District of Illinois as a way to target Chinese online marketplace vendors. The SAD Scheme capitalizes on weak spots in the Federal Rules of Civil Procedure, judicial deference to IP rightsowners, and online marketplaces’ desire to reduce their liability exposure. With substantial assistance from judges, rightsowners use these dynamics to extract settlements from online vendors without satisfying basic procedural safeguards like serving the complaint and establishing personal jurisdiction over defendants. This paper explains the scheme, how it bypasses standard legal safeguards, how it’s affected hundreds of thousands of defendants, and how it may have cost the federal courts a quarter-billion dollars. The paper concludes with some ideas about ways to curb the system.
Development of e-reminder system for data collection employee working time

The E-Reminder system is a system that is used on several communication tools designed to remind the user of an activity that will be carried out by the user so as to minimize delays in an activity that has been previously scheduled. The thing that encourages researchers to develop an android-based reminder system can be used by the personnel with the aim of minimizing errors or omissions in the data collection of IAIN Palopo employees or lecturers regarding the promotion schedule, periodic salary increases, Satya Lencana recipients, pensions, and so on. Researchers see that there are still many omissions that occur regarding the data collection of the personnel. For example, the delay of several employees/lecturers in determining the period of periodic salary increases, determining their promotions, or being proposed as candidates for the Satya Lencana recipient, as well as determining the entry of retirement periods and several other matters related to the period employees/lecturers at IAIN Palopo. As for the method to be used, namely the research and development method, this research starts from the design concept, which is realized in the form of a prototype or design drawing of the system construction, the development process, and the testing process of the designed system. In the application of this e-reminder system, there are things that become findings to note, among others, that the output produced is in the form of information data that is sent directly to the cellphone of the ASN concerned with regard to his periodic salary increase, the determination of his promotion, being proposed as a candidate for the Satya Lencana recipient, or the entry of retirement, which later became his concern to complete the file. The convenience provided by this system is very helpful for those who take care of matters relating to staffing because the output produce d in the form of information is not only obtained by the employee concerned, but the staffing staff who handles this can also find out employee data that must be prepared for a salary increase. periodically, determining his promotion, or being proposed as a candidate for the recipient of the Satya Lencana, or entry into the retirement period.
ANTROPIC PRINCIPLE OF COGNITION OF THE UNIVERSE

The article proposes an alternative view of the theory of the Big Bang, which served as the initial point for the formation of the Universe. The anthropic hypothesis sees our universe as a single electromagnetic entity, consistently realizing itself at seven stages of evolution.&#x0D; 1. The germ (Genome) of the universe, which is represented by a scalar value (field) in the form of a noumenal (hypothetical) electromagnetic dipole, that is in a state of cosmological singularity.&#x0D; 2. The spatial environment of “dark energy” in the form of a chaos of a volumetric web of point sources of electric charges and magnetic lines of force, which should not be associated with electrons and positrons of electricity familiar to our ear and the north and south poles of a magnet.&#x0D; 3. The spatial environment of “dark matter” (ether) in the form of a three-dimensional web of negatively and positively charged magnetic monopoles.&#x0D; 4. The physical Universe in the form of photon (electromagnetic) gratings - fluctuations, black holes and stars of galaxies that we observe, as well as objects of knowledge in our solar system and directly on Earth.&#x0D; 5. Psychic (Intellectual) Universe in the form of a single consciousness / mind continuum, which physically manifests itself&#x0D; as Ego – I.&#x0D; 6–7. Currently with our imperfect mind Ego – I we can hardly realize the subsequent stages of the evolution of the consciousness / mind continuum, but the anthropic hypothesis suggests that humanity, as well as every human being that is&#x0D; aware of himself as a creative individual, is able to foresee possible options for the development of our civilization up to the&#x0D; intuitive (holistic) perception of the single essence of the universe and the subsequent formation of the germ (Genome), which&#x0D; ensures its subsequent life cycle.