Reviewerly/exHarmony huggingface dataset:
Novel Fuzzy Technique for Denoising Mammogram Images Damaged By Low and High Impulse Noise Density.

Abstract A Fuzzy logic based mean filter (FLBMF) is presented for impulse noise reduction of mammogram images degraded with additive impulse noise. FLBMF removes both low and high density impulsive noise from mammogram images. FLBMF performs this in three major phases. In phase one, the detection of noisy pixels is performed and determined. In phase two, an adaptive threshold is determined by examining the neighboring pixels. In phase three, fuzzy membership functions and fuzzy rules are used to decide whether the current pixel is noise-free, or the noise pixel is in a smooth or detailed region. All these phases are based on fuzzy rules making use of membership functions. FLBMF can be applied iteratively to effectively reduce impulsive noise. In particular, the membership function’s shape is adapted according to the remaining noise level after each iteration, making use of the distribution of the homogeneity in the image. In this approach, the mammogram images are selected from mini-MIAS database and renamed as MammoB1, MammoB2, MammoB3 and MammoB4, are then deformed by varying intensities of impulse noise. The performance evaluation of various filters including FLBMF tested at low, medium and high noise densities on different standard grey scale mammogram images is then carried out. Mathematical performance parameters including Mean Square Error (MSE), Peak-signal-to-noise-ratio (PSNR), and Structural Similarity Index Measure (SSIM) are finally applied to measure the accuracy and performance of this approach. The image modalities implementation and analysis of our approach is carried out in MATLAB functions. Keywords: Impulsive Noise; FLBMF, Fuzzy membership function, Fuzzy rules, Edge preserving filtering, Fuzzy image filtering, Noise reduction
Feature Selection Method Based on Honeybee-SMOTE for Medical Data Classification

Bio-Medical data analysis has an important role in clinical practices. Usually, bio-medical data have complex issues like skeweedness, redundant and irrelevant attributes etc.. Several redundant and unrelated features frequently degrade the accuracy of the classifier while using with imbalanced datasets. The selection of features becomes critical in this situation. The key goal of feature selection is to establish a feature subspace that maintains classifier accuracy even as reducing the excessive computational learning cost and casting off noise. Appropriate feature selection approaches are highly dependent on their ability to match the issue context and uncover fundamental patterns within the data. This study’s main goal is to construct a disease detection model that uses a hybrid feature-selection strategy based on Honeybee-SMOTE and classification using the c4.5 algorithm. The empirical results establish the suggested hybrid methodology's superiority over competing methods regarding the accuracy parameter, precision-parameter, recall-parameter, f1-score parameter and G-Mean parameter. The statistical analysis of the collected findings demonstrates that the suggested hybrid method outperforms and is competitive with existing state-of-the-art algorithms.
UVA Image Registration Model Based on VGG and Multi-Branch Attention

For UVA images with different resolutions and large areas of weak texture, image feature extraction is insufficient and mis-matching is increased during image registration. To solve these problems, an unsupervised registration model based on VGG feature extraction and multi-branch attention is proposed. First of all, two feature extraction networks with shared weight parameters are used to extract the low and high level fusion features of the moving image and the reference image. The convolution neural network is used to extract the high-dimensional feature map of the image, and the key points are selected according to the conditions that meet both the channel maximum and the local maximum, and the corresponding 512-dimensional descriptor is extracted on the feature map, In the matching stage, add multi-branch attention based on residual block to filter out the wrong features. The algorithm is tested with multiple groups of images and compared with several image matching algorithms. The results show that the algorithm can extract the scale-invariant similar features of images, and has strong adaptability and robustness.
Proxy Re-Encryption based approach for Digital Evidence Management in Cybercrime Investigation - A Review

Cybercrime is on the rise as the digital world continues to expand.Gathering legitimate evidence is what digital forensics is all about.In a legal context, such proof is essential since it demonstrates the victim's crime beyond a reasonable doubt.It is crucial to keep evidence using a good evidence management system to ensure its admissibility in court during trials.We suggest a proxy re-encryption system that only works in one way for delegating power.The suggested approach will provide the safe delegation of access to electronic evidence.Proxy re-encryption as a means of incorporating access control into a secure evidence management solution is validated by the re-encryption scheme's enhanced sense of security.Within this study, we compare and contrast the many different systems that have been presented over the years.Future proposals for improved evidence handling could benefit from this investigation.
Masking Techniques for Confidential Data Protection in Privacy-Preserving Data Mining

Privacy-Preserving Data Mining (PPDM) develops algorithms for altering sensitive data.The private knowledge of a person, industry, or business organization remains private after the usage of data from the database.Data modification is one of the prominent privacy-preserving techniques used to alter confidential information available in the database and guarantees high privacy protection.In this paper, a new masking technique is proposed for hiding sensitive numerical attributes that are later analyzed using clustering algorithms, namely k-means, filtered clusters, and density-based clusters.The proposed technique is used to hide confidential numerical attributes.After modification, the proposed algorithm compares the original and the modified data and ensures that all the data items are altered or not.Experimental evaluation is illustrated using the employee dataset.The accuracy is calculated based on the comparison of the original and the modified data set in terms of data items found in the number of clusters.For every clustering technique, both the original and modified at a set are divided into two, three, four, and five, clusters.Based on the performance metrics, the k-means algorithm gives the best result compared to other algorithms.The results obtained from the proposed technique are compared with the existing approach.The experimental result indicates that the newly developed method is more efficient than the existing approaches.
Analysis of Activity Program Planning

This study aims to analyze the implementation of activity program planning at the Bureau of General Administration of Planning and Finance (AUPK) Sultan Syarif Kasim Riau State Islamic University and to find out what are the obstacles encountered in implementing the activity planning. This study used a qualitative descriptive method with 14 informants. The results of the study show that in carrying out program planning activities at the AUPK Bureau of UIN Suska Riau when viewed from the point of view of 1). Determining the goals of the planners of activities has a tendency to be misinterpreted or misperceived 2). Formulating the current situation has not been carried out properly where there are several activities that are not in accordance with the strategic plan. (3). Identifying the conveniences and obstacles so far has succeeded in gathering some of the strengths and weaknesses. 4) Develop a series of activities The AUPK Bureau of UIN Suska Riau has made every effort to direct each faculty to be able to select and develop a series of activities to be carried out for 1 fiscal year
Deixis Analysis on The Halsey’s Song Lyrics “Manic” Album

Deixis is important to help the listener or addressee distinguish between what the speaker says and what is intended. It is possible that the listener might misinterpret a reference in vague utterance. The meaning of a sentence or utterance becomes understandable if the addressee knows who, when, and where it is spoken. In this study, researcher examines the types and meaning of deixis used in Halsey's songwriting lyrics. The study involves qualitative data analysis. The data are obtained through listening to song lyrics, understanding lyrics, looking for deixis, and finally noting down the data. After obtaining the data, the data are analyzed in some ways; identifying the types of deixis in Halsey’s song lyrics “manic” album, describing the meaning of deixis words that found in that song lyrics, and concluding the result of the data analysis. The result of the study showed that there are 130 data of deixis. They are divided into 105 data of person deixis, 5 data of place deixis, 1 datum of time deixis, 17 data of discourse deixis, and 2 data of social deixis in Halsey’s song lyric album. Person deixis was investigated as the most dominant type of deixis in the lyrics. All deixis had their meanings based on the situations of the song.
OMISSION REPERCUSSIONS IN THE UTTERANCES OF ADULTS SUFFERING FROM AUTISTIC SPECTRUM DISORDER

Autism Spectrum Disorder (hereinafter referred to as ASD ) is a developmental disorder experienced by a person since childhood, which causes an inability to communicate and express feelings. This research is an extension of previous research that investigated the language development of ASD children in relation to aspects of discourse that affect speech omission (Halliday &amp; Matthiessen, 2013a). This study uses primary data derived from spontaneous speeches from 2 adults with ASD and 6 children with normal development. Children with normal development were included in the control group as a comparison to see the phenomenon of omission by using storytelling techniques with or without pictures. The second data collection technique is the storytelling technique without assistance. Researchers have prepared a general theme given to research participants. This study confirms and shows that the phenomenon of omission that is present in the speech of adults with ASD and children with normal development as a control group is not too much different quantitatively, but qualitatively has characteristics that can be used as comparisons. This special characteristic is related to the problem of autism spectrum disorders, namely the ability to communicate in responding to the interlocutor. In the end, the results of this study can contribute as a first step in diagnosing language disorders for people with ASD .
Discourse on Artificial Intelligence Design Using ITS and SDLC Methods in Building Islamic Religious Education Learning Applications

The development of increasingly sophisticated programming language technology where designed algorithms are able to think intelligently have been used for various purposes that are good for humans. This technology can also be applied to Islamic religious learning by using an intelligent tutoring system combined with artificial intelligence to: find sources of books, source modules, hadiths, verses of the Koran and their translations that are really related to learning with correct and solid data and compare Islamic teaching methods. The application program was built using two methods, namely the system design method System Development Life Cycle and the Intelligent Tutoring System method. The System Development Life Cycle method is used to build a comprehensive system that includes design tools such as use cases, activities, classes, sequences. While the Intelligent Tutoring System method is used in designing intelligent algorithms with a programming language as a machine to operate this learning Artificial Intelligence system. The result of this study is a smart application program for Islamic religious learning and is applied in high schools in the province of West Sumatra, Indonesia with an application effectiveness level of up to 96% with an error rate index of 0.23.
THE EFFECT OF INTERNET MEDIA USAGE TOWARD LEARNING INTEREST THROUGH LEARNING MOTIVATION AS A MODERATOR VARIABLE

This research aims at analyzing the significant effect of: (1) the effect of internet media usage toward learning interest; (2) the effect of learning motivation toward learning interest; (3) the effect of internet media usage toward learning motivation and (4) the impact of internet media usage toward learning interest through learning motivation as a moderator variable. The method used is a quantitative approach, with questionnaire to measure the variable. The samples consist of 199 students who are taken by random sampling at the first grade of SMA Negeri 7 Kediri, it is calculated using Slovin Pattern formula. The path analysis used to analyze the data is (1) simple linear regression and (2) multiple linear regression. The result reveal that: (1) Internet media usage has positive significant effect toward learning interest about 26,3%; (2) Learning motivation has positive significant effect toward learning interest about 42,7%; (3) Internet media usage has positive significant effect toward learning motivation about 24,4% and (4) Internet media usage has an indirect impact toward learning interest through learning motivation as a moderator variable about 25,1%, while the direct impact is about 26,1%. It shows that the result of indirect impact is greater than direct impact. It is recommended that internet media usage is very well used as a new innovation in learning that is able to foster students’ learning interest, but it must be accompanied by learning motivation that encourages students to use the internet wisely and finally it has a better effect on improving students’ academic performance.
EXPLORING REFERENCE ACCURACY ON UNDERGRADUATE THESIS OF ENGLISH LANGUAGE AND LITERATURE PROGRAM

This study specifically reports on the referencing accuracy to examine the breached APA sixth edition formatting references rules produced by English language and literature undergraduate students. This study was frame in Aronsky et.al (2005) employed a qualitative descriptive method to describe the breaches and the most breached rules. The selection of data samples was obtained from undergraduate thesis of nine students in English language and literature program in a private college in Bandung. The results showed that the students breached six formatting general rules: 1) not on a new page (3.23%), 2) incorrect formation of the section name (29.03%), 3) spacing errors (22.58%), 4) not listed alphabetically (19.35%), 5) indentation errors (16.13%), and 6) in­correct recording of the entries (9.68%). While based on the type of source, 20 rules in total were breached, which spread in six types of sources i.e., 1) periodicals, 2) books, reference books, and book chapters, 3) technical and research reports, 4) doctoral dissertation/ master’s thesis, 5) document from web site, and 6) unclear sources. Furthermore, the most breached APA sixth edition formatting references rules are incorrect formation of the section name (29.03%), incorrect formation of book, reference books, and book chapters (17.44%). The result tracks well with Aronsky et.al (2005) that formatting a paper in APA style can be tricky.
Smart Plug Hub: A Sensor Fusion-Based Approach to Non-Invasive Activities of Daily Living Estimation

Abstract This paper presents Smart Plug Hub (SPH), a non-invasive system for accurately estimating a patient's Activities of Daily Living (ADL). Traditional methods for measuring ADL include interviews, remote video systems, and behavior-tracking wearable devices. However, these approaches have limitations, such as patient memory dependency, privacy violations, and device management issues. To overcome these limitations, SPH utilizes sensor fusion to analyze time-series environmental signals and accurately estimate a patient's ADL. We also efficiently utilized computing resources by implementing "device collaboration" in SPH to receive event data and segment a portion of the time-series environmental signal. By segmenting the data into small segments, we extracted an analyzable dataset, which was processed by an edge device. We conducted several experiments with SPH and successfully classified a patient's ADL by visualizing different activities. This study proposes an effective approach to analyzing both emergency situations and ADL, which is critical for accurate clinical decision-making.
BLEMS OF STUDENTS THROUGH THE EYES OF EXPERIENCED ELEMENTARY TEACHERS: A PHENOMENOLOGICAL STUDY

Behavioral problems of students are now challenging issue for all ranked school teachers in the classroom worldwide. Nature of the behavioral problems of students varies country to country, culture to culture, gender to gender and even grade to grade level. This research study explores the coping strategies that experienced elementary teachers used to deal with behavioral problems of the students in the classrooms. Research study being qualitative in nature specifically conducted through transcendental phenomenological methodology. Data was collected through semi-structured interviews from purposefully selected 10 experienced elementary teachers within the one district of province Punjab Pakistan and analyzed using qualitative method. It was recommended based on the finding of the study that traditional and conventional coping strategies to deal behavioral problems of students would be substituted with preventive and supportive coping strategies as these are much desired and effective toward securing students’ learning and teachers’ instructional time in the classroom. Keywords: Behavioral problems of students, coping strategies, experienced elementary teachers.
Extrapolating dynamics with machine learning

Machine learning can extrapolate beyond tipping points and predict the long-term behavior of non-stationary systems like climate change.
Measuring Adversarial Robustness using a Voronoi-Epsilon Adversary

Previous studies on robustness have argued that there is a tradeoff between accuracy and adversarial accuracy. The tradeoff can be inevitable even when we neglect generalization. We argue that the tradeoff is inherent to the commonly used definition of adversarial accuracy, which uses an adversary that can construct adversarial points constrained by $\epsilon$-balls around data points. As $\epsilon$ gets large, the adversary may use real data points from other classes as adversarial examples. We propose a Voronoi-epsilon adversary which is constrained both by Voronoi cells and by $\epsilon$-balls. This adversary balances between two notions of perturbation. As a result, adversarial accuracy based on this adversary avoids a tradeoff between accuracy and adversarial accuracy on training data even when $\epsilon$ is large. Finally, we show that a nearest neighbor classifier is the maximally robust classifier against the proposed adversary on the training data.
Description the completeness of prescriptions for inpatients at the Pharmacy Installation HKBP Balige General Hospital period of July 2020

Prescription is a written request from a doctor to a pharmacist to provide drugs for patients written in the prescription. For in accordance with applicable laws and regulations to minimize irrational use of drugs and preventmedication errors for patient safety purposes. The purpose of this study was to describe Description the completeness of prescriptions for inpatients at the Pharmacy Installation HKBP Balige General Hospital period of July 2020. The design of this study was descriptive, with a sample of 186 prescriptions, using a cluster random sampling technique. The results showed that 46.2% of prescriptions at the Balige HKBP RSU for the July 2020 period were complete and 53.8% incomplete. Of the total 186 prescription sheets, the information on the doctor's name (27.4%), the doctor's initial (5.9%), the patient's name (0.5%), the patient's address (36.6%), the instructions for use (10,2%). This shows that the completeness of the recipe at RSU HKBP Balige is not complete. Incomplete prescriptions occur because many visiting patients increase the busyness of the prescriber, so doctors do not have time to write a complete prescription. It is recommended to the management of RSU HKBP Balige to put more emphasis on doctors to complete prescriptions, so that TTK does not find incomplete prescriptions anymore.
An Ising Model-Based Annealing Processor With 1024 Fully Connected Spins for Combinatorial Optimization Problems

This brief presents a novel annealing processor (AP) design with 1024 fully-connected spins based on a modified Ising model annealing algorithm for combinatorial optimization problems. By adopting the proposed Turbo code-based interleaved random sequence generator (TCSG) and multi-spin update method, the memory usage is made considerable reduction and multi-spin parallel update is supported. The prototype is implemented using FPGA with the operation frequency of 100 MHz. We tested our design on various G-set problems with an average cut accuracy of 99.19% achieved. The proposed design outperforms the CPU-based method by achieving a max speedup of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1099\times $ </tex-math></inline-formula> .
Scene Parsing via Tree Structure Enhancement Lightweight Network

Scene parsing is a hot topic in the field of computer vision communities. It has extensive applications in visual perception e.g. education system, human-object robots, etc. However, there exists a huge size difference among objects in the scene image because of the diversity of objects and the influence of observation distance and other factors. How to better solve the varying scale problem has become a challenging problem in scene parsing. Thus, a tree-structure is proposed to handle the varying scale problem, where the feature maps of different levels are gradually nested and connected, which strengthens the connection between multiple feature maps, and captures more representative information. For real-time, we propose a framework named tree structure enhancement lightweight network (TSELight), which introduces the depth-wise separable dilated convolution (DSDC) into the tree structure and decomposes the middle nodes in the tree structure along the channel direction, thus improving the efficiency. Experimental results demonstrate that our TSELight architecture outperforms state-of-the-art methods on Cityscapes dataset, and provides consistent improvements on the real-time scene parsing performance.
Semantic Analysis of Food Ordering and Tasting Reviews in Web Pages using Deep Convolution Neural Networks

Nowadays, users on the net face many issues love junk knowledge on unwanted web content, security issues, intense search time, unwanted advertisements, and so on to beat the matter of looking online pages, a replacement plan has been projected to get web pages victimization the calculated connexion worth, the connection of user representations and inputs. It is accessed by selecting the web contents weight classification and find the gap value of web pages by Markoff properties such as interpretation (G), cross validation web page (C), optimality (0). Finally, the satisfaction value for the user was calculated to rank the web pages according to the worth of the relationship.