Reviewerly/exHarmony huggingface dataset:
Purchase Pattern Analysis with FP-Growth Algorithm for Product Sales Promotion Recommendation Strategies

The development of retail business technology is related to the need for management to meet customer demands by using technology. To help make effective sales strategic decisions, it is necessary to optimize the use of information technology on existing sales transaction data. The transaction database that has been stored as a company archive asset can be used for processing information that is useful in increasing product sales and promotions. This study aims to provide an analysis related to the product sales pattern of PT. X in Sumbawa Besar city. PT. X is a retail company that sells distributes daily consumer goods. The algorithm used is Frequent Pattern – Growth which is one of the algorithms in data mining used to find relationships in large data based on the number of occurrences of these data relationships. The Association Rule Mining method can be used in the retail business field, known as Market Basket Analysis. The application used for testing is Rapidminer 9.10. The research stages include: data collection, data preparation, FP-Growth algorithm implementation, result analysis and conclusions. The results of the tests carried out resulted in 819 rules with a total of 85 rules. The results of grouping strong rules based on the combination and number of products that produce information that is expected to be used as recommendations to promote products with discount, cross-selling, up-selling, product bundling and other types of promotions to increase product sales.
Evaluation of deep image embedders for healthcare informatics improvement using visualized performance metrics

Extraction of feature attributes is prominent in any image analytics study, and especially for health informatics, biomedical images need to be represented as feature vectors for further analytics. Deep learning model approach incurs high computational overheads with big image data. Transfer learning, however, promises a truce; hence, this study evaluates the performances of five deep image embedders on X-ray and computed tomography signals for feature extraction towards predicting Covid-19 instances. Performances of three predictive models is evaluated through statistical tools of F1 measure, receiver operating characteristics curve, and multidimensional scaling to ascertain their precision rate on Covid-19 health informatics. Experimental result from VGG16 and Painters have the highest precision weighted average of 0.997 and 0.998, respectively, with SVM and MLP models; while F1 measure returns Painters as the best on SVM and MLP. Multidimensional scaling shows that feature vector attributes extracted by Inception v3 and Painters are loosely networked, while SqueezeNet, VGG16, and VGG19-generated data points are closely knitted with relative isolated data points.
Curriculum and Feedback Measures to Improve Writing Efficacy －Focusing on the Case of K University's Writing Subject

It must be tiresome and burdensome for college freshmen to write, the Z generation, most of whom get information and communicate through hypertext, digital media. In order to conduct writing education for learners with low confidence in writing, educators should start by trying to increase their student’s writing efficacy. In the preliminary survey of K University’s writing subject, it was found that the confidence of learners in writing was low.As part of the review, this paper presents a three-stage writing curriculum operation plan based on Fisher &amp; Frey’s framework (2021:4) for effective education. In the first step, learning goals and achievement standards are set and presented clearly to learners. In the second stage, examples that meet achievement standards are presented to guide and educate, and feedback is provided to support learning. In the third step, learners must complete individual learning and instructors should present them with feedback.In the course of operating the three-stage curriculum, the feedback of the instructor is very important. Therefore, this paper examined that feedback, taking into consideration that the emotional aspects of learners is necessary to improve their writing efficacy. In addition, specific feedback procedures were presented for this approach, and cases of students enrolled in the writing curriculum of K University were also examined. As can be seen from the survey results of K University’s writing students, I think that the discussions in this paper will help improve writing skills by improving the efficacy of writing learners.
Auxiliary Diagnosis Method of Chest Pain Based on Machine Learning

Chest pain is sudden, its pathological causes are complex and various, fatal or non-fatal so that improving the diagnostic accuracy is extremely important in the emergency system of prehospital and hospitals. Therefore, we propose a method of introducing a decision tree, support vector machine, and KNN algorithm in machine learning into the auxiliary diagnosis of chest pain. First select the algorithm with better performance among decision tree, support vector machine, and KNN algorithm; Then compare the classification performance of the CART algorithm, the support vector machine using the Gaussian kernel function, and the K nearest neighbor algorithm using the Euclidean distance to select the best; Finally, through the analysis of the experimental results, the support vector machine algorithm with Gaussian kernel function is obtained. Its detection time and diagnosis accuracy rate are the best among the three algorithms, which can assist medical staff in the emergency system to carry out targeted chest pain diagnosis.
Experimental Speech Recognition From Pathological Voices

Speech recognition has been the subject of quite a few research subjects as it is the adequate means for dynamic, efficient and interaction Human communication simultaneously using the two phenomena of phonation and hearing between speakers, the applications of its searches are enormous for example one can quote: the dictation, the speech synthesis within the Windows software, the speech recognition of the Google search engine at the Smartphone level …… ..etc. all its applications depend on the conditions of use in which they are implemented, to be done and to overcome the puzzles of imperfection it is necessary to be sure to properly characterize the speech signal by extracting the most relevant characters such as: the fundamental frequency (pitch in English), timbre, tonality, to extract them many techniques are possible, the most used of which are acoustic such as: MFCC, PLP, LPC, RASTA and other in the form of combination (hybridization) namely: PLP RASTA, MFCC PLP etc. They are used in data transmission, speaker recognition and even in speech synthesis.
Asset Inventory System of Employee Cooperative’s Pusri Palembang Inc.

Employee cooperative has been widely existed in every institution in Indonesia, both at governement and non-government. This research aims at facilitating the office employees of the Pusri Employee Cooperative to manage the assets owned. To support this convenience, the author proposes an asset inventory system using VB.Net 2008 and MySQL database. The proposed system can be used to manage asset data in the form of asset data input, asset search, asset grouping, deletion, sales and asset reports owned. With the creation of an asset inventory system, the performance of office employees will be faster and more efficient and ensure the security of asset data. In this study the author uses the Waterfall research method including several stages i.e., requirements analysis, system design, program code generation, testing and maintenance. The method of data collection is done through interviews and direct observation to the object of research. The results of the research are expected to be useful for all office employees of the Pusri Palembang Employee Cooperative in managing data on assets owned by the office.
Two-Stage Filter Response Normalization Network for Real Image Denoising

In this paper, we propose a two-stage network for real image denoising with filter response normalization, named as two-stage filter response normalization network (TFRNet). In TFRNet, we propose a filter response normalization(FRN) block to extract features and accelerate the training of the network. TFRNet consists of two stages, at each stage of which we use the encoder-decoder structure based on U-Net. We also use the coordinate attention block(CAB), double channel downsampling module, double skip connection module, and convolutional (Conv) block in our TFRNet. With the help of these modules, TFRNet provides excellent results on both SIDD and DND datasets for real image denoising.
The role of n-back task and statistical learning in L2 sentence processing

This study investigates the role of working memory and statistical learning ability in L2 sentence processing. A total of 26 native-Korean-speaking L2 learners of English completed three tasks: (a) an n-back task, as a measure of working memory; (b) an artificial grammar learning task containing a nonadjacent dependency rule formed between words to test statistical learning ability; and (c) a self-paced English reading task including sentences with object relative clauses. The results show a significant relationship between participants’ working memory and their L2 sentence processing performance (r = .403), but no significant relationship between statistical learning ability and L2 sentence processing performance. The findings provide better understanding of learners’ individual differences by examining the relationships between statistical learning, working memory, and L2 sentence processing.
MT-Forward Algorithm for Prediction of Project Categories Based on Selection of Mutants

Mutation testing is an effective technique for errors. Although effective, mutation testing has the main limitation that it is awfully expensive because it requires a series of tests on each mutant. Therefore, many researchers have focused on presenting various techniques to reduce the cost of mutation testing. In this study, the MT-Forward algorithm was developed. The concept of MT-Forward is mutation testing that connects the selection and prediction methods. As a result of the selection, the mutant operator can make a more efficient selection of the number of mutants obtained. The shrinkage of the selected mutants was from 1,019 live mutants to 749 live mutants as a priority. There are 15 features used in this study. The required features are obtained from the Locmetrix Test, Coverage Test, and Mutation Testing. The accuracy value of each method is almost the same, which distinguishes this study is the selection of important operators who prioritize improvement programs without reducing the level of accuracy. Where the PMS Method uses 43 Mutant Operators, PMT Method 22 Mutant Operators, while MT-Forward uses 7 mutant operators as priority mutant operators. The method used to evaluate the performance of the algorithm is 5-fold cross-validation. The accuracy of each method is PMS 57.14%, PMT Method 57.14%, and MT-Forward is 95.00%. MT-Forward states that combining mutant selection techniques and project category prediction is important for fixing faults.
Trajectory optimization using learning from demonstration with meta-heuristic grey wolf algorithm

&lt;span&gt;Nowadays, most robotic systems perform their tasks in an environment that is generally known. Thus, robot's trajectory can be planned in advance depending on a given task. However, as a part of modern manufacturing systems which are faced with the requirements to produce high product variety, mobile robots should be flexible to adapt to changing and diverse environments and needs. In such scenarios, a modification of the task or a change in the environment, forces the operator to modify robot's trajectory. Such modification is usually expensive and time-consuming, as experienced engineers must be involved to program robot's movements. The current paper presents a solution to this problem by simplifying the process of teaching the robot a new trajectory. The proposed method generates a trajectory based on an initial raw demonstration of its shape. The new trajectory is generated in such a way that the errors between the actual and target end positions and orientations of the robot are minimized. To minimize those errors, the grey wolf optimization (GWO) algorithm is applied. The proposed approach is demonstrated for a two-wheeled mobile robot. Simulation and experimental results confirm high accuracy of generated trajectories.&lt;/span&gt;
Ransomware summit will advance US foreign policy

Headline INT: Ransomware summit will advance US foreign policy
Amping up quantum computing technologies

Presto, an all-digital microwave instrument, improves scalability of qubit systems
Efficient data replay mechanism of sensor stream data based on concurrent buffer pool

Analyzing historical time series data in the form of replay is very important. When replaying massive sensor data, long processing times predominantly occur, that significantly affects the performance of the whole system. Existing solutions do not consider the requirements of large-scale sensor data replay. Therefore, a data replay mechanism based on a concurrent buffer pool is proposed. In this mechanism, a multi-level buffer queue is designed and implemented using a priority queue to ensure the efficiency and stability during the replaying process. Then, a task queue is adopted to ensure the correct order when multiple threads fill the multi-level buffer queue. Moreover, an anomaly detection operator is integrated to reduce outliers in replay data. Using the real hydrologic dataset, the data replay mechanism is evaluated by several experiments. The results show that the data replay mechanism has better performance compare to Apache Phoenix and Apache Hive.
Comparative Study of Recommendation Algorithms on Yelp Dataset

In this work, we compare various recommendation algorithms on the Yelp dataset by exploiting multiple collaborative filtering algorithms. We use an innovative and novel method to represent the yelp dataset using a weighted bipartite graph in which the weight of the links contain a value which signifies the rating that the user has given to the business which is then used as a measure for recommendation. The nodes are represented by users and businesses. Later, we discuss different standard methods used for recommendation, we then attempt to experiment and do a comparative study on those algorithms with the Yelp dataset. The goal is to construct a customized recommender system which will exactly predict a user’s preference for a restaurant.
BTM: Boundary Trimming Module for Temporal Action Detection

Temporal action detection (TAD) aims to recognize actions as well as their corresponding time spans from an input video. While techniques exist that accurately recognize actions from manually trimmed videos, current TAD solutions often struggle to identify the precise temporal boundaries of each action, which are required in many real applications. This paper addresses this problem with a novel Boundary Trimming Module (BTM), a post-processing method that adjusts the temporal boundaries of the detected actions from existing TAD solutions. Specifically, BTM operates based on the classification of frames in the input video, aiming to detect the action more accurately by adjusting the surrounding frames of the start and end frames of the original detection results. Experimental results on the THUMOS14 benchmark data set demonstrate that the BTM significantly improves the performance of several existing TAD methods. Meanwhile, we establish a new state of the art for temporal action detection through the combination of BTM and the previous best TAD solution.
Generative Adversarial Networks (GANs): A Survey on Network Traffic Generation

Generating network traffic flows remains a critical aspect of developing cyber and network security systems.In this survey, we first consider the history of network traffic generation methods and identify the weaknesses of these.We then proceed to introduce more recent approaches based on machine learning (ML) models.In particular, we focus on Generative Adversarial Network (GAN) models, which have developed from their initial form to encompass many variants in today's ML landscape.The use of GANs for generating traffic flows that have appeared in the literature are then presented.For each instance, we present the architecture, training methods, generated results, identified limitations and prospects for further research.We thus demonstrate that GANs are key to future developments in network traffic generation and secure cyber and network systems.
Signal detection based on deep learning in MIMO-OFDM systems

The signal detection is presented based on deep learning (DL) for Multiple-in-Multiple-out Orthogonal Frequency Division Multiplexing (MIMO-OFDM) Systems. In MIMO-OFDM systems, a receiver is designed to eliminate successive interference cancellation based on DL for multiple users. The signal detection and channel estimation are carried out using deep neural network (DNN) which is trained offline depend on simulation data. And the symbols online are recovered directly. The simulation results show that Deep learning (DL) method is better than those traditional methods for channel estimation. The error propagation effects are reduced by DNN in the signal detector. The inter-symbol interference (ISI) of systems is serious, which shows that the DL approach can achieve the better performance by the DL approach than the maximum likelihood approach.
Handling Communication Skills: The Holistic Approach

The three-year study interrogated classroom attitudes toward English and communication skills, using English language learners (ELL), teachers of communication skills and teachers of other subjects.Generally, the study, which highlighted the dominant role of English Language in global relations, was also a teacher's avenue to improve upon her classroom practice.The specific objective was to help learners develop critical perspectives to strive for effective communication skills, in and out of college, for excellence.The study was guided by two research questions: How could a communications skills instructor explore the most effective classroom approaches to address her students' needs?How does she balance teaching principles and gentle motivation of students hampered by severe communication handicaps?The study explored the principles of participatory action research to investigate the reasons behind poor communication skills of the participants.It emerged that students' English grasp was too low, thus, obstructing advanced learning.Participants were persuaded to revisit their position on the subject.It was recommended, among others, that the institution should admit into professional programmes learners who possess the required language proficiency.Additionally, communication skills should be taught throughout a professional programme to help consolidate the communicative skills of speaking, writing, reading, and listening among learners.
Social Media Fake Profile Identification using novel hybrid model machine learning technique

On every social networking site, there are people using fake accounts for a variety of reasons. The many characteristics of false profiles and the strategies they use to accomplish their objectives continue to evolve with the passage of time. On the one hand, new detection methods and systems are being devised, while on the other side, adversaries adopt sophisticated tactics in order to dodge from these detection systems. In the sub-sections that follow, we will have a short discussion about some of the most significant difficulties linked with the identification of phoney profiles in OSNs. Here in our research work we are mainly focus on In-stagram. As majority of the fake profile on this platform is high as compared to others. In this research paper we have represented and implemented the novel hybrid model machine learning technique for the fake profile identification of Instagram.
Learning From Synthetic Data for Crowd Instance Segmentation in the Wild

Crowd understanding has widespread applications, including video surveillance, crowd monitoring. Unlike existing coarse-grained crowd understanding methods(e.g., counting people in images), crowd instance segmentation can provide more precise results (pixel-wise segmentation for each person in images). However, crowd instance segmentation demands a considerable amount of pixel-wise labeled data, which is very time-consuming and challenging to annotate accurate human instance masks in the crowd scene. In this paper, we propose a data generator and labeler to automatically generate synthetic crowd instance segmentation data. Then based on it, we build a large-scale synthetic crowd instance segmentation dataset called "GCIS Dataset". Besides, we demonstrate two approaches that utilize the synthetic GCIS dataset to advance the performance of crowd instance segmentation: 1)supervised crowd instance segmentation: pretrain crowd instance segmentation models on GCIS dataset, then finetune on other real data. It can remarkably boost the model’s real-world performance; 2) crowd instance segmentation via domain adaption: transfer the synthetic GCIS dataset to photo-realistic images, then train the model together with transformed data and real data, which shows better performance when tested on real-world data. Extensive experiments show the validity of the synthetic GCIS dataset for crowd instance segmentation. The dataset and source code will be released online.
DTransGAN: Deblurring Transformer Based on Generative Adversarial Network

Motion deblurring is challenging due to the fast movements of the object or the camera itself. Existing methods usually try to liberate it by training CNN model or Generative Adversarial Networks(GAN). However, their methods can’t restore the details very well. In this paper, a Deblurring Transformer based on Generative Adversarial Network(DTransGAN) is proposed to improve the deblurring performance of the vehicles under the surveillance camera scene. The proposed DTransGAN combines the low-level information and the high-level information through skip connection, which saves the original information of the image as much as possible to restore the details. Besides, we replace the convolution layer in the generator with the swin transformer block, which could pay more attention to the reconstruction of details. Finally, we create the vehicle motion blur dataset. It contains two parts, namely the clear image and the corresponding blurry image. Experiments on public datasets and the collected dataset report that DTransGAN achieves the state-of-the-art for motion deblurring task.