Reviewerly/exHarmony huggingface dataset:
Two-level System and Interaction with Electromagnetic Waves

This Chapter introduces the quantum bit, or qubit, which is widely appreciated as a fundamental building block of quantum computation. We start from its definition and representation using a Bloch sphere and then introduce the interaction between a qubit and a single harmonic oscillator, or Jaynes–Cummings interaction. On this basis, a governing equation of qubit dynamics subject to driving, e.g., the irradiation of electromagnetic waves is derived as a form of a Bloch equation to see how a qubit state evolves in the Bloch sphere. The relaxations are also introduced here. Basic phenomena such as Rabi oscillation and Ramsey interference are presented.
Hybrid Phishing URL Detection Using Segmented Word Embedding

Phishing is a type of cybercrime committed by attackers to steal sensitive information. This paper focuses on URL-based phishing detection, i.e., detecting phishing webpages by analyzing the URL. Previously proposed methods tackled this problem; however, insufficient word tokenization of URLs arises unknown words, which degrades the detection accuracy. To solve the unknown-word problem, we propose a new tokenization algorithm, called URL-Tokenizer, which integrates BERT and WordSegment tokenizers, besides utilizing 24 NLP features. Then, we adopt the URL-Tokenizer to the DNN-CNN hybrid model to leverage the detection accuracy. Our experiment using the Ebbu2017 dataset confirmed that our word-DNN-CNN achieves an AUC of 99.89% compared to the state-of-the-art DNN-BiLSTM with an AUC of 98.78%.
Learning Semantic Correspondence with Sparse Annotations

Finding dense semantic correspondence is a fundamental problem in computer vision, which remains challenging in complex scenes due to background clutter, extreme intra-class variation, and a severe lack of ground truth. In this paper, we aim to address the challenge of label sparsity in semantic correspondence by enriching supervision signals from sparse keypoint annotations. To this end, we first propose a teacher-student learning paradigm for generating dense pseudo-labels and then develop two novel strategies for denoising pseudo-labels. In particular, we use spatial priors around the sparse annotations to suppress the noisy pseudo-labels. In addition, we introduce a loss-driven dynamic label selection strategy for label denoising. We instantiate our paradigm with two variants of learning strategies: a single offline teacher setting, and mutual online teachers setting. Our approach achieves notable improvements on three challenging benchmarks for semantic correspondence and establishes the new state-of-the-art. Project page: https://shuaiyihuang.github.io/publications/SCorrSAN .
Production Management: Analyzing the alignment of knowledge management with production management

The development of new information in the field of project management has had a significant effect on the manufacturing industry. As a result, it is critical that service development procedures be optimized in every project. The rise in cement manufacturing production was supposed to be accomplished through the utilization of knowledge management principles and knowledge management as a mediator, which was the purpose of this research. The research was carried out across a number of different cement-producing sites in the Kurdistan area of Iraq. The researchers looked at four different aspects of knowledge management in order to determine how much production could be increased by using knowledge management. These aspects were knowledge creation, knowledge storage management, knowledge sharing, and knowledge application, with knowledge management acting as a mediator. The researchers utilized a quantitative research methodology in the form of a survey in order to carry out the current analysis that is being presented. The questionnaire was handed out in a randomized fashion to around one hundred and forty administrative staff members working for a variety of cement production enterprises in the Kurdistan area. However, the researchers did manage to gather a total of 128 completed surveys. The study employed hierarchical multiple regression analysis as well as the Sobel test in order to evaluate the research hypotheses that had been created. According to the findings, each component of knowledge management—knowledge creation, knowledge storage development, knowledge sharing, and knowledge application—had a distinct and significant positive relationship with increased production at carpet manufacturers in the Kurdistan region of Iraq. This was the case regardless of which component of knowledge management was examined. In addition, the findings indicated that all knowledge management concepts with a mediator (a project management development program) had an indirect positive and significant relationship with increased production at cement manufacturing facilities in the Kurdistan region of Iraq. The mediator in this case was the project management development program.
THE EFFECT OF USING READ PAIR SHARE (RPS) STRATEGY IN READING DESCRIPTIVE TEXT MASTERY AT THE GRADE VIII OF SMP NEGERI 1 SIPIROK

The research was conducted by the researcher at the Grade VIII SMP Negeri 1 Sipirok. The aims of this research was to find out if there a significant effect of using Read Pair Share (RPS) strategy in reading descriptive text. In order to achieve the purpose of this research, the writer carried out in quantitative method by applying experimental types. The population of this research was the Grade VIII SMP Negeri 1 Sipirok. They were 64 students, the researcher used total sampling technique to get the sample. The samples were 32 students. Reading descriptive text was applied as the instrument of this research, the writer used “t” tes formula. Based on the data that has been analyzed, it can be found that (1) the students’ achievement in reading descriptive text without using Read Pair Share (RPS) strategy was “enough” (66.8), (2) their value by using Read Pair Share (RPS) strategy can be categorized “good” (72.8), and (3) there was a significant effect the result of reading descriptive by using Read Pair Share (RPS) strategy. It meant that the hypothesis was accepted.
Knowledge control of higher school students majoring in Physics in General Physics and Profession-oriented English by means of testing

One of the important problems of the methodology of teaching any discipline is the effective identification of the objective level of students’ knowledge, acquired skills and abilities in the study of a discipline. Traditional methods of testing and assessing knowledge contain some drawbacks: excessive time spent on the exam; elements of teacher’s subjectivity that may occur; inability to clarify clear criteria for assessing knowledge, etc. Based on this, and the prospects for the reform of higher education in Ukraine, such methods of knowledge control are needed, which would allow teachers not only to establish an objective level of students’ knowledge, but also to identify (diagnose) the causes of gaps in student learning so that they could on its basis help the student to overcome the identified gaps. This method, devoid of most of the above shortcomings, is computer testing, the use of which for a long time as a means of monitoring and evaluating students’ achievements in studying the course of General Physics and Profession-oriented English at the Department of Materials Science and New Technologies (State higher educational institution "Vasyl Stefanyk Precarpathian National University"), at the Department of Physics and the Department of Western and Oriental Languages and their Teaching Methods (The State Institution“ South Ukrainian National Pedagogical University named after K. D. Ushynsky”) has shown that the use of computer testing in the study of Physics and foreign languages in the professional field should be subject to certain reservations, since this method has some advantages alongside disadvantages that can distort the results of monitoring and evaluation of students’ achievements. Based on the analysis of the authors' experience as well as the analysis of pedagogical, psychological and scientific-methodological literature devoted to this problem, an attempt is made to establish the conditions and limits of computer testing in the organization of the learning platform (courses) in the fields of General Physics and foreign languages for the students majoring in Physics at institutes of higher education. The article states that computer testing, on condition that tests meet the quality requirements, should be used only for the current (thematic) control of knowledge during each semester. This method is at the same time an express control method aimed at testing students' knowledge in the process of studying General Physics and foreign languages; it is also a “sensitive” diagnostic method which allows you to identify shortcomings of a student in time in the process of learning, giving the teacher the opportunity to timely influence its course. The learning progress control (testing in theoretical modules) can be carried out by means of computer testing but with the use of certain additional precautionary methodological measures. It is more expedient to carry out the final control in the form of a quality written-oral examination.
Praktik Pengelolaan Limbah Industri Dalam Perspektif Maqasid Syari’ah

The existence of an industry is very synonymous with the presence of waste. Waste if disposed of carelessly will cause pollution to the environment and will affect human health. Therefore, to avoid pollution, management is needed to minimize the impact of the waste. As well as the management carried out by tapioca flour producers in Kaliboto Village, Tarokan, Kediri, although management efforts have been carried out, there is still pollution found. Based on these problems, the focus of this research is how waste management practices in the tapioca flour home industry of Kaliboto Village, Tarokan District, Kediri Regency in the perspective of maqasid syari'ah. This research is a case study using a qualitative approach. The primary data source in this study is the results of observations and interviews with tapioca flour home industry managers, village governments and local communities. The result of this study is that there are several efforts that have been appropriate or not in accordance with the value of shari'ah maqasid. First, from the value of hifdz an-nafs that have been appropriate, namely maintaining health by increasing the productivity of livestock through feeding that contains high nutrition and meets the needs of life by managing waste. The management that is not yet appropriate is the lack of maximum management of liquid waste so that it can endanger yourself and others. Second, from the value of hifdz al-mal, namely obtaining property in a good way through waste management so that it can get additional income and open new businesses.
Research on the Application of Hotel Cleanliness Compliance Detection Algorithm Based on WGAN

Aiming at the problems of irregular cleaning and supervision difficulties in the cleaning process of hotel bathrooms, a target detection algorithm based on deep learning is proposed to detect the cleaning process transmitted by the sensor in real time and analyze its prescriptivity. However, the cleaning process has factors such as occlusion, light influence and insufficient data volume, resulting in inefficient detection. Therefore, this paper proposes a deep convolutional generation adversarial network (DCGAN) as the basic framework to expand the data set, improve the adaptability and robustness of the detector to different detection targets, take advantage of the fast speed and high accuracy of the YOLOv5 target detection network to detect the target, and then design a compliance detection network algorithm to detect whether the target meets the cleanliness standards. Experimental results show that the method has rapidity, practicality and high accuracy, and fully meets the engineering needs of hotel cleaning process detection and supervision.
SISTEM INFORMASI KOPERASI MULYA ABADI SENTOSA KAMPUNG WATES KECAMATAN BUMI RATU NUBAN KABUPATEN LAMPUNG TENGAH PROVINSI LAMPUNG BERBASIS WEB MENGGUNAKAN FRAMEWORK CODEIGNITER 3

Mulya Abadi Sentosa Cooperative processing data into information is still done manually where all member data along with existing transactions are recorded using books and Microsoft Excel, therefore there are often some obstacles encountered, an information system that can process all data in cooperatives is needed to become a cooperative. solutions in all existing constraints and shortcomings so that data processing can be integrated with the database so that it can solve problems. How to design and manufacture an information system that is suitable for the Mulya Abadi Sentosa Cooperative, and can assist the processing of transaction data in the cooperative. There are two admin users and members who can access the system and then later can manage member data and transactions so that the presentation of information can be managed easily. With this information system, it can help increase the speed of the reporting process in cooperatives.
Introduction to Discrete Soft Transforms

In this paper a new class of discrete transforms of discrete straight segments (DSS), called Discrete Soft Transforms is introduced. The soft transformation of a segment consists in moving a single discrete point at each step while keeping the segment property. We propose the soft rotation and soft translation of a segment and extend these results to the soft translation of a tree.
USING PPP (PRESENTATION, PRACTICE AND PRODUCTION) STRATEGY ON SPEAKING ACHIEVEMENT

This study aimed to find out whether or not there was any significant differences in speaking achievement in Recount Text between students who were taught by using PPP (Presentation, Practice and Production) Strategy and those who were not. There were 66 students of eighth grade students of SMP Negeri 2 Palembang that was involved in this study as the sample. They were divided into two groups, experimental group consisted of 33 students and control group consisted of 33 students. For the sample, the writer chose class of VIII 7 as control group and class of VIII 6 as experimental group. The method of this study was used quantitative method with quasi experimental design. The experimental group was taught by using PPP (Presentation, Practice and Production) Strategy meanwhile the control group were taught by using Discussion Strategy. The result showed that the mean score of experimental group increased from 68.182 in pretest to 81.515 in posttest. Furthermore, the result of independent t-test from experimental and control group shown that the t-obtained was 2.346 and it was higher than t-table 1.669. In conclusion, that there was a significant difference between students who were taught using PPP (Presentation, Practice and Production) strategy and students who were not.
A Prototype System for Blockchain Performance Evaluation

Due to complex blockchain programs and numerous blockchain nodes, it takes a huge amount of time and economic cost to conduct blockchain experiments. Existing open source projects do not support modifications to the underlying blockchain, and existing blockchain simulators only focus on a single blockchain system and cannot flexibly extend or replace models. Regarding the issues above, this paper proposes a prototype system for blockchain performance evaluation, including real deployment test and simulation test. In real deployment test, a five-layer architecture for building a lightweight and efficient testing system is proposed. And in simulation test, a general scheme for building blockchain simulator is proposed, which can realize the test of throughput, storage allocation and reputation management. Experiments show that the prototype system proposed in this paper can effectively improve the efficiency of blockchain performance evaluation.
Silicon-based Quantum Computing: Scaling Strategies

Quantum computers promise to solve some of the most computationally challenging tasks. To achieve that goal quantum hardware needs to be scaled up. Here, we review the state-of-the-art on spin-based quantum computing in silicon and present strategies to scale the technology using industry manufactured silicon devices.
Adversarial Attacks on Deep Neural Network based Modulation Recognition

Modulation recognition technology based on deep learning (DL) has great advantages in feature extraction and recognition. However, due to the vulnerability of deep neural network (DNN), the automatic modulation recognition model based on DNN is vulnerable to attacks. Some researchers have successfully attacked automatic modulation recognition model-s using adversarial techniques, but the resulting adversarial samples have poor attack performance on high-performance recognition models. Therefore, this paper proposes an attack method based on double loop iteration, which can update the initial conditions of each iteration with the change of the number of iterations when generating adversarial examples. Simulation results show that the proposed attack method has better attack performance than the traditional attack methods.
The Effect of Sensor Placement for Accurate Fall Detection based on Deep Learning Model

The development of inertial sensor technology and the growing utilization of wearable electronics (such as smartwatches, smart bands, and other intelligent gadgets) have facilitated the advancement of studies into automated Fall Detection Systems (FDSs). In the last decade, there has been significant scientific interest in maintaining FDSs. Focused on assessing the data acquired by wearable inertial sensors, machine learning (ML) techniques have demonstrated high efficacy in distinguishing falls from typical motions or activities of daily living (ADLs). In most research, unfortunately, the effectiveness of machine learning classifiers was constrained by feature extraction and selection processes that relied on human-made decisions. Recently, deep learning (DL) model findings s how their effectiveness for FDS. One of these effective DL models is the ResNeXt model, a deep neural network that operates based on convolutional layers with aggregated residual transformation. This study investigates the influence of sensor placement on various body locations for the fall detection issue. The ResNeXt model was assessed and compared to other baseline deep learning algorithms using the public UMAFall dataset for fall detection. Employing sensor data on waist location, the suggested model attained the most significant classification ac curacy of 97.275% when classifying falls.
Research on missing data filling method of wind power generation based on k-nearest neighbor algorithm

In the process of data collection of wind power generation, due to equipment, human and other reasons, data is often missing. Therefore, it is necessary to fill in missing data to ensure the accuracy of data analysis. In this paper, the k-nearest neighbor algorithm is used to fill in the missing data of wind power generation, and the k-nearest neighbor algorithm is optimized from three aspects: distance measurement, k value selection and choice of forecasting method, and the correctness of the method is verified by an example.
Aggressive Performance Improvement on Processing-in-Memory Devices by Adopting Hugepages

Processing-in-Memory (PIM) devices integrated into general-purpose systems demand virtual memory support. In this way, these devices can be seamlessly coupled to the software stack, while maintaining compatibility and security provided by address management via the Operating System (OS) without requiring disruptive programming efforts. Typically, PIM intends to access large volumes of data via vector operations, and thus can suffer severe penalties due to the high cost of page misses in the Translation Look-aside Buffer (TLB). Our study demonstrates the criticality of such penalties on the system's performance and that PIM must resort to large page sizes. The presented results exploit the native large pages available on the host, and they show substantial performance improvements <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(84\times)$</tex> for wide-vector PIM operations with large pages.
Parameterized Recursive Refinement Types for Automated Program Verification

Refinement types have recently been applied to program verification, where program verification problems are reduced to type checking or inference problems. For fully automated verification of programs with recursive data structures, however, previous refinement type systems have not been satisfactory: they were not expressive enough to state complex properties of data, such as the length and monotonicity of a list, or required explicit declarations of precise types by users. To address the problem above, we introduce parameterized recursive refinement types (PRRT), which are recursive datatypes parameterized by integer parameters and refinement predicates; those parameters can be used to express various properties of data structures such as the length/sortedness of a list and the depth/size of a tree. We propose an automated type inference algorithm for PRRT, by a reduction to the satisfiability problem for CHCs (Constrained Horn Clauses). We have implemented a prototype verification tool and evaluated the effectiveness of the proposed method through experiments.
Intelligent diagnosis for bearing fault using dynamic ensemble extreme learning machine

Extreme Learning Machine (ELM), as a model with simple structure, fast calculation speed, and great generalization performance, can be combined with ensemble learning algorithms in classification tasks to further improve its comprehensive performance. The calculation cost of the original AdaBoost ensemble algorithm is too high to stretch the advantage of ELM on speed. Therefore, the Dynamic Ensemble Extreme Learning Machine (DE2LM) is proposed for bearing fault intelligent diagnosis in this paper. In addition, the autocorrelation transform is used to preprocess the signals to highlight the fault features. Autocorrelation Constrained ELM (ACELM) is developed and embedded in DE2LM as the base classifier. Compared with the original AdaBoost, the proposed method dynamically obtains the training dataset for each training round and re-weights them. Therefore, some more distinguishing and continuously improved base classifiers can be obtained to integrate into a stronger classifier, that considers both the diagnosis speed and accuracy. The experimental results show that the proposed algorithm can effectively reduce the training time and accelerate the convergence process of the model in comparison with AdaBoost.M2, and meanwhile, it can improve the identification accuracy of the faulty samples and reduce the training time compared with the constrained ELM.
Physical modelling for analog tape emulation

Reel-to-reel magnetic tape recording has a unique and specific sound which is highly sought after by music recording engineers. This talk will discuss the use of physical modelling signal processing to create algorithms which can emulate the sound of any reel-to-reel tape recorder, including ones which may not actually exist in the real world. These algorithms are designed to be used in modern music production software, meaning that they must be able to process audio in real-time with minimal latency, and offer interactive controls to the end user.
Requirements Engineering for Feedback Loops in Software-Intensive Systems

A feedback loop occurs when the output of a system induces certain changes in the environment, which, in turn, influences the system through its input. Certain self-reinforcing feedback loops can inflict significant harm on the environment, by amplifying the existing bias or other undesirable effects over time. In this paper, we argue that such feedback loops are becoming more prevalent in software-intensive systems, and propose a set of requirements engineering activities and research problems for understanding, modeling, and dealing with feedback loops.