Reviewerly/exHarmony huggingface dataset:
Tunable quantum logic gate on photonic qubits with a ladder emitter

We describe how a ladder emitter can implement a tunable quantum logic gate on photonic qubits encoded in the frequency basis. The ground-to-first excited state of the ladder emitter interacts with the control photon, and the first-to-second excited state transition interacts with the target photon. By controlling the relative detuning between the target photon and the first-to-second excited state transition of the ladder emitter, we enable any controlled-phase operation from 0 to π. We derive analytical formulas for the performance of the gate through the S-matrix formalism as well as describe the mechanism intuitively. This gate is deterministic, does not utilize any active control, and needs only a single ladder emitter, enabling low-footprint and more efficient decomposition of quantum circuits, especially the quantum Fourier transform. We suggest multiple potential systems for physical realization of our proposal, such as lanthanide ions embedded in Purcell-enhanced cavities. We expect these results to motivate further interest in photonic quantum information processing with designer emitters.
Model architecture can transform catastrophic forgetting into positive transfer

The work of McCloskey and Cohen popularized the concept of catastrophic interference. They used a neural network that tried to learn addition using two groups of examples as two different tasks. In their case, learning the second task rapidly deteriorated the acquired knowledge about the previous one. We hypothesize that this could be a symptom of a fundamental problem: addition is an algorithmic task that should not be learned through pattern recognition. Therefore, other model architectures better suited for this task would avoid catastrophic forgetting. We use a neural network with a different architecture that can be trained to recover the correct algorithm for the addition of binary numbers. This neural network includes conditional clauses that are naturally treated within the back-propagation algorithm. We test it in the setting proposed by McCloskey and Cohen and training on random additions one by one. The neural network not only does not suffer from catastrophic forgetting but it improves its predictive power on unseen pairs of numbers as training progresses. We also show that this is a robust effect, also present when averaging many simulations. This work emphasizes the importance that neural network architecture has for the emergence of catastrophic forgetting and introduces a neural network that is able to learn an algorithm.
Scalable Multi-Party Privacy-Preserving Gradient Tree Boosting over Vertically Partitioned Dataset with Outsourced Computations

Due to privacy concerns, multi-party gradient tree boosting algorithms have become widely popular amongst machine learning researchers and practitioners. However, limited existing works have focused on vertically partitioned datasets, and the few existing works are either not scalable or tend to leak information. Thus, in this work, we propose SSXGB, which is a scalable and acceptably secure multi-party gradient tree boosting framework for vertically partitioned datasets with partially outsourced computations. Specifically, we employ an additive homomorphic encryption (HE) scheme for security. We design two sub-protocols based on the HE scheme to perform non-linear operations associated with gradient tree boosting algorithms. Next, we propose secure training and prediction algorithms under the SSXGB framework. Then, we provide theoretical security and communication analysis for the proposed framework. Finally, we evaluate the performance of the framework with experiments using two real-world datasets.
Comparative Study of Various Crowd Detection and Classification Methods for Safety Control System

A crowd is a distinct collection of people or anything that is involved in community or society. The phenomenon of a crowd is fairly well known in a wide range of academic fields, including sociology, civil engineering, and physics, amongst others. At this point in time, it has developed into the most active-oriented research and fashionable issue in the field of computer vision. Pre-processing, object detection, and event or behavior identification are the three stages of processing that are traditionally included in crowd analysis. These stages are pre-processing, object detection, and event recognition. Pre-processing, object detection, and event or behaviour identification are the three stages of processing that are traditionally included in crowd analysis. These stages are pre-processing, object detection, and event recognition. This study gives a model of crowd analysis as well as a taxonomy of the most prevalent method to crowd analysis. It may be helpful to researchers and would serve as a good introduction connected to the area of work that has been conducted.
A new fast inverted file-based algorithm for approximate nearest neighbor search without accuracy reduction

The inverted file (IVF) is one of the most efficient non-exhaustive search algorithms for approximate nearest neighbor (ANN) search. In this paper, we propose a new three-step fast search algorithm in the framework of IVF named fast IVF (fIVF) to accelerate the ANN search. Different from the traditional IVF algorithm that conducts a full search at the coarse quantization stage, we introduce (1) cell-level rejection, (2) vector-level rejection, and (3) subspace-based pyramid model with multi-resolution to speed up this high time-consuming stage. Since our proposed fIVF concentrates on finding the exact w nearest coarse codewords at the coarse quantization stage and follows the same product quantization stage, it can achieve the same search accuracy as the traditional IVF. Experimental results on different datasets demonstrate that our proposed fIVF can significantly speed up the search process while keeping the same search accuracy as the traditional IVF.
Model Rapid Application Development Dalam Perancangan Sistem Informasi Kepegawaian

This research aims to produce a web-based personnel information system on the population Service and civil registration of the city of Pagaralam (DUKCAPIL). Where at the employment section of DUKCAPIL still use Microsoft Word or still in the form of a simple system that has not been integrated. Therefore, in the search of employee data still takes a long time and does not fit what is expected and needed by employees, such as data of all employees and data of group officers, offices and education. In making this information system researchers use methods of system development using RAD (Rapid Application Development) There are 3 (three) stages namely, Requirements Planning, RAD Design Workshop and implementation. By using the RAD method This method is suitable for a project type with a short time in the research process. System design by using Unified Modeling Language (UML), creating a system with PHP and MySql programming. System test results with Black Box Testing based on input and system output functions are declared successful as expected. From the results of the test obtained research results in the form of web-based personnel system that can be implemented by implementing the RAD method.
Optimization of Interval Type-2 Fuzzy Logic Controller Using Real-Coded Quantum Clonal Selection Algorithm

In recent years, quantum computing has gained immense popularity with the production of real quantum computers. Researchers have developed quantum-inspired evolutionary algorithms (QIEAs) to solve combinatorial optimization problems and have obtained successful results. As a special case of QIEAs, real-coded quantum evolutionary algorithm (RCQEA) is used in the optimization of high-dimensional complex problems. In this study, a novel mechanism of the quantum rotation gate (QRG) that is used to determine the rotation angle of the qubit in the RCQEA is introduced and implemented to accelerate the evolutionary process and increase the possibility of finding the optimal solution. Moreover, the skeleton of RCQEA is modified by using a clonal selection mechanism, and the real-coded quantum clonal selection algorithm (RCQCSA) is developed. Our proposed QRG accelerates the convergence speed of the algorithm. The main purpose of this study is to present a more effective algorithm that inspires quantum computing principles for optimizing the interval type-2 fuzzy logic controller (IT2FLC) membership functions (MFs). In this study, four different comparisons are made with these two different algorithms that have the original version of QRG and our proposed QRG. Optimized IT2FLC provides stabilization of the inverted pendulum system. The results show that the RCQCSA having our proposed QRG outperforms RCQEA in stabilizing the inverted pendulum system by optimizing the IT2FLC parameters.
Enhanced photon squeezing in two-photon Dicke model

We explore the phenomena of quadrature squeezing of photons in the two-photon Dicke model under the mean-field approximation in the thermodynamic limit. An effective squeezing of the photonic quadratures beyond the standard quantum limit is observed in the superradiant phase. The magnitude of squeezing is enhanced, and it persists longer as one moves away from the phase boundary and approaches the boundary between the superradiant phase and the unbounded region. This unbounded region is encountered where the coupling strength is of the same order of magnitude as one of the fundamental frequencies of the system. We find that the squeezing is prolonged near the said boundary for varying transition energy of the qubits. Furthermore, we explore the critical behavior of photon squeezing near the unbounded region.
Algorithms for constructing discrete A-optimal experiment designs in active identification of regression models of multifactor systems

To solve the problem of effective identification of regression models of multifactor systems, as a rule, they resort to using the concept of optimal experiment design. The synthesis of experimental designs involves the use of an a priori chosen optimality criterion. Quite a few criteria have been proposed. Most often, criteria are used that are associated with the accuracy of estimating the parameters of regression models. We can name such well-known criteria as: the D-optimality criterion, the A-optimality, criterion and the E-optimality criterion. It should be noted that most of the theoretical and applied research is associated with the use of the D-optimality criterion. It is noted in the paper that often plans built according to the A-optimality criterion show good performance for a number of other optimality criteria. At the same time, the criterion itself characterizes an average variance of estimates of the parameters of the regression model and, for A-optimal designs the dispersion ellipsoid has the smallest overall dimensions. The use of the D-optimality criterion makes it possible to obtain an ellipsoid of dispersion of parameter estimates of the smallest volume, which does not exclude the possibility of obtaining an ellipsoid elongated along one or more principal axes. The paper proposes and describes two algorithms for the synthesis of discrete A-optimal designs. The first of them is based on the concept of the consistent completion of the experiment design to the required volume developed by the author. It can be successfully used in a situation where the researcher needs to increase the number of experiments to achieve the required accuracy of the resulting model. The second algorithm, which makes it possible to build plans for a given number of observations, consists of iterations in which points are added and removed from the plan according to certain rules.
Effect of Trust Supplier on Firm Performance through Information Sharing and Collaboration in Manufacturing Companies

Information technology enables companies to build good communication and coordination with external partners. The information system owned by the company can provide fast information for management in determining the company's direction. This research proposed a model to investigate the effect of trust suppliers on firm performance through the mediation of information sharing and collaboration. For this purpose, this study postulates nine hypotheses to be examined. Eighty-five (85) manufacturing companies have been surveyed as the sample in this study. Data collection used a questionnaire designed with a five-point Likert scale. Data analysis used smartPLS software to examine the hypotheses. The result indicated that all hypotheses were supported as follows. Trust supplier, information sharing, and collaboration directly affect firm performance. Interestingly, information sharing and collaboration mediate the influence of trust in suppliers on firm performance. These findings provide a managerial implication on how to improve the firm performance by establishing trust in the supplier, sharing information, and developing collaboration between partners. This study also contributes to the current research in supply chain management.
Chemostratigraphic Architecture of Sandstone Facies Exposed along Auchi-Ighara Road, Mid-Western Nigeria

In this work, inorganic whole-rock geochemistry was applied to characterize the exposed sandstone facies along the Auchi-Ighara road Anambra basin Midwestern, Nigeria for distinction and correlation of the depositional units on the basis of the stratigraphic variation of geochemical trait. Fourteen representative samples collected from the outcrop were analyzed using Inductively Coupled Plasma-Mass Spectrometer (ICP-MS). From our analyses, ten major elements oxides, sixteen trace elements, and thirteen rare earth elements were obtained. The concentration of major elements in the samples range from 0.13 % and 53.71 % with SiO2, TiO2, Al2O3 and Fe2O3 being predominant. Other oxides found in the samples include K2O, CaO, Na2O, MgO and P2O5 indicating a recycled orogenic source of the grits from which the sandstone is derived. Trace elements concentration ranges between 0.017 ppm and 122.25 ppm; marked by the dominance of Zircon (Zr) which further asseverates orogenic recycling. The rare earth elements range in concentration from 0.01ppm and 5.53 ppm; the modal occurrence of Praseodymium (Pr) in this category is apparently indicative of deposition in an oxidizing environment. The appreciably high SiO2/Al2O3 ratio averages 11.2 in the samples is symptomatic of several cycles of sedimentation. Variation pattern of Ga/Rb and Al2O3/(CaO+MgO+K2O+Na2O) ratios connote a cooler and drier climatic condition after the sandstone deposition, which expectedly resulted to a decrease in hydrolytic weathering. Changes in sediment provenance are predicated on Cr/Al2O3, Cr/Na2O and Nb/Al2O3 ratio which indicate grits derivation from a more quartzose sedimentary and felsic sources that resulted from fluctuating paleoclimate during deposition. From the geochemical data, the paleoclimate and provenance of the sandstone facies were modelled and used for subdivision and correlation into two geochemical packages and five geochemical units. Keywords: Chemostratigraphy; Paleoclimate; Provenance; Weathering; Whole rock geochemistry.
Controlled text style transfer via noise enhancement of deep learning transformer

Text style transfer is one of the controllable text generation tasks, which can convert text style attributes. The mainstream method is to separate the content and style of the text, and then combine the content vectors with other style vectors to generate. However, implicit expression cannot completely separate meaning and style, separation and recombination may also lead to a decrease in the naturalness and fluency of the text. Therefore, we propose a new idea, which first encodes the text into a latent representation, and iteratively optimizes the latent representation with the smallest changes to achieve style transfer. Introducing noise enhancement before generating results improves the robustness of the generated system and reduces the occurrence of individual results with large errors. Experiments show that our optimization method based on noise enhancement performs well on two public datasets, Yelp and Amazon. Result has excellent performance in three indicators: content preservation, transfer strength, and fluency.
Network Effects on Dual Machine Learning Models Predicting Smart Home Sensor Measurements

Supportive smart home systems for older adults are growing in popularity. However, as an example of sensor networks, such systems can generate large amounts of small information packets that are repetitive and contain no new information. This has led to research projects focused on data reduction through dual machine learning models that reduce the network traffic between a home and cloud processing. This report focuses on the sensor-derived measurements that need to be efficiently communicated to the cloud. The report highlights the effects of network latency (or clock drift), jitter, and packet loss on overall performance for dual machine learning, as this has received minimal consideration previously. The current work presents a dual Autoregression machine learning model focused on predicting future sensor measurements of a smart home system. The work emulates a smart home system environment that includes cloud processing and a simulated imperfect network to reflect a real-life implementation. The results show how network effects such as the introduction of latency, jitter, or packet loss have caused as much as 50% of the cloud recording data samples being incorrect, compared to a perfect replication of data when network effects did not exist.
Research on application of space rapid response launch system based on data link

The application purpose of space rapid response launch system based on data link refers to integrate the command information system, sensor system, the launch platform, and platform-mounted command and control system, test and launch control system, measurement and control system and other elements, to realize end-to-end information interconnection and application interoperability, so as to expand the situational awareness scope, command and control efficiency and action coordination ability of a single launch unit. This paper has carried out the research on the launch efficiency improvement methods based on data link application system, distributed decision making and other key technologies, the application work modes and workflows for the typical launch mission scenario, so as to improve the effectiveness of joint command and control at the campaign level and operational coordination at the tactical level.
Entia Non Sunt Multiplicanda … Shall I look for clusters in my cognitive data?

Unsupervised clustering methods are increasingly being applied in psychology. Researchers may use such methods on multivariate data to reveal previously undetected sub-populations of individuals within a larger population. Realistic research scenarios in the cognitive science may not be ideally suited for a successful use of these methods, however, as they are characterized by modest effect sizes, limited sample sizes, and non-orthogonal indicators. This combination of characteristics even presents a high risk of detecting non-existing clusters. A systematic review showed that, among 191 studies published in 2016-2020 that used different clustering methods to classify human participants, the median sample size was only 322, and a median of 3 latent classes/clusters were detected. None of them concluded in favor of a one-cluster solution, potentially giving rise to an extreme publication bias. Dimensionality reduction techniques are almost never used before clustering. In a subsequent simulation study, we examined the performance of popular clustering techniques, including Gaussian mixture model, a partitioning, and a hierarchical agglomerative algorithm. We focused on their ability to detect the correct number of clusters, and on their classification accuracy. Under a reasoned set of scenarios that we considered plausible for the cognitive research, none of the methods adequately discriminates between one vs two true clusters. In addition, non-orthogonal indicators lead to a high risk of incorrectly detecting multiple clusters where none existed, even in the presence of only modest correlation (a frequent case in psychology). In conclusion, it is hard for researchers to be in a condition to achieve a valid unsupervised clustering for inferential purposes with a view to classifying individuals.
A Systematic Review on Improving Health Literacy in Rural Africa Using Mobile Serious Games

Driven by an increase in the availability of cheap low-cost mobile phones and a jump in the number of telecom subscribers, the African gaming world is booming. Most importantly, it has opened an opportunity for rural communities to have an almost identical mobile phone experience than people living in urban areas. It has also opened an opportunity to leverage this high penetration of mobile devices to design mobile-based applications such as mobile serious games. The latter assists individuals living in these communities to modify, change or shape their behaviors and attitudes desirably. This paper reviews mobile serious games in healthcare education, especially those intended to improve health literacy in rural Africa. The challenges and issues encountered in the design and use of persuasive mobile games as a tool can promote behavior change among people living in the rural African communities.
High capacity video hiding based on multi-resolution stationary wavelet transform and hybrid-matrix decomposition techniqueset Transform and hybrid-matrix decomposition techniques

Internet simplified digital data transferring. This data needs to be secured; so securing digital data becomes an important concern. Steganography provides security for data by inserting it into a cover and concealing it. In this paper, a steganography algorithm was introduced. This algorithm used the stationary wavelet transform (SWT) and اhybrid-matrix decomposition techniques, singular value decomposition (SVD) and QR factorization to conceal a video in another video. The algorithm performance was measured with reference to the peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) for the cover and the secret videos. The algorithm successfully hid a video in another cover video and both are of the same size; the hiding capacity was 100%. The algorithm achieved a SSIM that reached 0.97 and a high PSNR value that reached 68.8 proving that the imperceptibility of the proposed algorithm is very high. The comparative analysis shows that the suggested algorithm achieved higher imperceptibility than the other state-of-the-art algorithms regarding the average PSNR. The enhanced version of the proposed method was more robust against different types of attacks.