Reviewerly/exHarmony huggingface dataset:
Analysis of Nagari Management Information System Evaluation (SIMNAG) Using PIECES and UAT Methods

SIMNag (Nagari Management Information System) Kamang Mudiak is an information system in the nagari Kamang Mudiak. The SIMNag includes the Andalas University Service Team's web-based nagari management information system. The SIMNag menu includes a Nagari Profile, which includes information about the nagari symbol, vision and mission, organizational structure, government history, nagari history, maps of the nagari, nagari achievements, legal products, and nagari potential wealth. Furthermore, information about public information is provided, specifically nagari news related to the 11 pillars of Nagari Tageh, which include tags for health, education, food, economy, faith, bundo kanduang tags, mudo paga nagari tags, and information-based tags Digital bills, disaster bills, security and social disease bills, and law bills are all examples of bills. The developer of this application's distinctive feature, in addition to the nagari tageh feature, is the convenience feature for nagari residents to input or update their own personal and family data online. As a result, data on nagari Kamang Mudiak residents will always be updated without the need to wait for the nagari apparatus to do so. Furthermore, residents at this SIMNag can file complaints with the nagari government about anything that occurs in the vicinity of the population. It is necessary to assess the SIMNag. It is necessary to evaluate the SIMNag that has been built in order to determine the feasibility of the SIMNag application in facilitating community services. This research employs two approaches: PIECES (Performance, Information/Data, Economy, Control, Efficiency, and Security) and UAT (User Acceptance Testing). The research results in an evaluation of the system that was built, namely the nagari information system (SIMNag) in the Nagari Kamang Mudiak.
Fast UAV path planning in urban environments based on three-step experience buffer sampling DDPG

The path planning of Unmanned Aerial Vehicle (UAV) is a critical issue in emergency communication and rescue operations, especially in adversarial urban environments. Due to the continuity of the flying space, complex building obstacles, and the aircraft's high dynamics, traditional algorithms cannot find the optimal collision-free flying path between the UAV station and the destination. Accordingly, in this paper, we study the fast UAV path planning problem in a 3D urban environment from a source point to a target point and propose a three-step experience buffer deep deterministic policy gradient algorithm (TSEB-DDPG). We first build the 3D model of a complex urban environment with buildings and project the 3D building surface into many 2D geometric shapes. After transformation, we propose the hierarchical learning particle swarm optimization (HL-PSO) to obtain the empirical path. Then, to ensure the accuracy of the obtained paths, the empirical path, the collision information and fast transition information are stored in the three experience buffers of the TSEB-DDPG algorithm as dynamic guidance information. The sampling ratio of each buffer is dynamically adapted to the training stages. Moreover, we designed a reward mechanism to improve the convergence speed of the DDPG algorithm for UAV path planning. The proposed TSEB-DDPG algorithm has also been compared to three widely used competitors experimentally, and the results show that the TSEB-DDPG algorithm can archive the fastest convergence speed and the highest accuracy. We also conduct experiments in real scenarios and compare the real path planning obtained by the HL-PSO algorithm, DDPG algorithm, and TSEB-DDPG algorithm. The results show that the TSEB-DDPG algorithm can archive almost the best in terms of accuracy, the average time of actual path planning, and the success rate.
Efficient inner product arguments and their applications in range proofs

Abstract Inner product arguments allow a prover to prove that the inner product of two committed vectors equals a public scalar. They are used to reduce the complexity of many cryptographic primitives, such as range proofs. Range proofs are deployed in numerous applications to prove that a committed value lies in a certain range. As core building blocks, their complexity largely determines the performance of corresponding applications. In this paper, we have optimised the inner product argument with statement including two vector commitments (IPA tvc ) and range proof of Daza et al. (PKC’20), the inner product argument with statement including only one vector commitment (IPA ovc ) of Bünz et al. (S&amp;P′18). For IPA tvc , we reduce the concrete communication complexity by 2 log 2 n field elements, where n is the vector dimension. For range proofs, we reduce the concrete communication and prover complexities by about 2 log 2 m field elements and 11 m field multiplications, respectively, where m is the bit length of range. For IPA ovc , we exponentially reduce the asymptotic verifier complexity from linear to logarithmic. Due to the asymptotic characteristics, our protocols are highly competitive when the vector dimension or bit length of range is large.
Automatic Image Generation Pipeline for Instance Segmentation of Deformable Linear Objects

Robust detection of deformable linear objects (DLOs) is a crucial challenge for the automation of handling and assembly of cables and hoses. The lack of training data is a limiting factor for deep-learning-based detection of DLOs. In this context, we propose an automatic image generation pipeline for instance segmentation of DLOs. In this pipeline, a user can set boundary conditions to generate training data for industrial applications automatically. A comparison of different replication types of DLOs shows that modeling DLOs as rigid bodies with versatile deformations is most effective. Further, reference scenarios for the arrangement of DLOs are defined to generate scenes in a simulation automatically. This allows the pipelines to be quickly transferred to new applications. The validation of models trained with synthetic images and tested on real-world images shows the feasibility of the proposed data generation approach for segmentation of DLOs. Finally, we show that the pipeline yields results comparable to the state of the art but has advantages in reduced manual effort and transferability to new use cases.
Hyperspectral compressive wavefront sensing

Abstract Presented is a novel way to combine snapshot compressive imaging and lateral shearing interferometry in order to capture the spatio-spectral phase of an ultrashort laser pulse in a single shot. A deep unrolling algorithm is utilized for snapshot compressive imaging reconstruction due to its parameter efficiency and superior speed relative to other methods, potentially allowing for online reconstruction. The algorithm’s regularization term is represented using a neural network with 3D convolutional layers to exploit the spatio-spectral correlations that exist in laser wavefronts. Compressed sensing is not typically applied to modulated signals, but we demonstrate its success here. Furthermore, we train a neural network to predict the wavefronts from a lateral shearing interferogram in terms of Zernike polynomials, which again increases the speed of our technique without sacrificing fidelity. This method is supported with simulation-based results. While applied to the example of lateral shearing interferometry, the methods presented here are generally applicable to a wide range of signals, including Shack–Hartmann-type sensors. The results may be of interest beyond the context of laser wavefront characterization, including within quantitative phase imaging.
CaQR: A Compiler-Assisted Approach for Qubit Reuse through Dynamic Circuit

Quantum measurement is important to quantum computing as it extracts out the outcome of the circuit at the end of the computation. Previously, all measurements have to be done at the end of the circuit. Otherwise, it will incur significant errors. But it is not the case now. Recently IBM starts supporting dynamic circuit through hardware (instead of software by simulator). With mid-circuit hardware measurement, we can improve circuit efficacy and fidelity from three aspects: (a) reduced qubit usage, (b) reduced swap insertion, and (c) improved fidelity. We demonstrate this using real-world applications Bernstein Verizani on real hardware and show that circuit resource usage can be improved by 60%, and circuit fidelity can be improved by 15%. We design a compiler-assisted tool that can find and exploit the tradeoff between qubit reuse, fidelity, gate count, and circuit duration. We also developed a method for identifying whether qubit reuse will be beneficial for a given application. We evaluated our method on a representative set of important applications. We can reduce resource usage by up to 80% and improve circuit fidelity by up to 20%.
A Systematic Approach to Machine Learning for Cancer Classification

Cancer is a lethal disease that is frequently brought on by the accumulation of hereditary disorders and many pathological alterations. Cancerous cells are aberrant, life-threatening growths that can appear anywhere on the human body. To determine what might be helpful for its treatment, cancer, also known as a tumour, should be promptly and accurately discovered in the early stages. Even while each approach has its own unique considerations, some of the major causes of mortality include difficult histories, inadequate diagnoses, and inadequate treatment. The study’s objective is to review, categorise, and discuss the most recent advances in machine learning for the identification of leukaemia, breast, brain, lung, and other human body cancers. Clinical practice, “translational medicine”, and the biological study of various diseases, such as cancer, have all been included in the medical applications of AI. “Current AI systems”, which are solely based on ML methodologies, have been used to improve various facets of “clinical practise”, including the “interpretation of genomic data” for the identification of genetic variants based on “high-throughput sequencing technologies” in many medical specialities, such as “pathology, radiology, ophthalmology, and dermatology”. A secondary method of data collection is considered for this research to gather relevant and factual data related to ML approaches use for cancer classification.
Distributed and deep vertical federated learning with big data

Summary In recent years, data are typically distributed in multiple organizations while the data security is becoming increasingly important. Federated learning (FL), which enables multiple parties to collaboratively train a model without exchanging the raw data, has attracted more and more attention. Based on the distribution of data, FL can be realized in three scenarios, that is, horizontal, vertical, and hybrid. In this article, we propose to combine distributed machine learning techniques with vertical FL and propose a distributed vertical federated learning (DVFL) approach. The DVFL approach exploits a fully distributed architecture within each party in order to accelerate the training process. In addition, we exploit homomorphic encryption to protect the data against honest‐but‐curious participants. We conduct extensive experimentation in a large‐scale cluster environment and a cloud environment in order to show the efficiency and scalability of our proposed approach. The experiments demonstrate the good scalability of our approach and the significant efficiency advantage (up to 6.8 times with a single server and 15.1 times with multiple servers in terms of the training time) compared with baseline frameworks.
Blockchain technology for corporate governance and IT governance: A financial perspectiv

The development of information technology (IT) and the adoption of blockchain have affected the world of finance today. Research on these two matters is still lacking, especially from a financial perspective. This study aims to review the latest research regarding changes in corporate governance with the adoption of IT governance and blockchain. A computerized multi-database literature search was conducted in January–March 2022, using the ScienceDirect and Emerald search engines. The terms “corporate governance”, “IT governance”, and “blockchain” were entered in the descriptor fields, with “language” limited to English and “source” limited to peer-reviewed journal articles. The implementation of good corporate governance will reduce the company's risk and protect investors. Technological advances can be used to develop better IT governance by making information transparent and adopting technological advances to support the implementation of good corporate governance. Under a blockchain framework, corporate governance might evolve in a variety of ways. There are several advantages to issuing and trading corporate securities on blockchains, but there are also certain drawbacks connected to increased ownership transparency. Businesses would seek out board members and outside advisors with various skill sets, and crucial issues like managerial incentives would probably change to account for the shifting character of corporate securities.
Mathematical modeling and simulation of biophysics systems using neural network

Many of the real-life problems including dynamical structures can be modeled in the shape of differential equations. A number of analytic and numerical methods are being proposed for the solution of these differential equations but for some instances, we may come across a few limitations attached to them. However, theoretically strong and computationally favorable tools such as artificial neural networks can be utilized to approximate the solutions of these differential equations. In this work, we developed mathematical models for some biophysics systems on the basis of their dynamical behavior and opted a neural network having single hidden layer of 50 neurons and Broyden–Fletcher–Goldfarb–Shanno algorithm as an optimizer to simulate the results for population of micro-organisms. The graphical representations of the results obtained both from the neural network and analytic methods are compared for different parameters and we got almost the same results.
Predicting and understanding human action decisions during skillful joint-action using supervised machine learning and explainable-AI

Abstract This study investigated the utility of supervised machine learning (SML) and explainable artificial intelligence (AI) techniques for modeling and understanding human decision-making during multiagent task performance. Long short-term memory (LSTM) networks were trained to predict the target selection decisions of expert and novice players completing a multiagent herding task. The results revealed that the trained LSTM models could not only accurately predict the target selection decisions of expert and novice players but that these predictions could be made at timescales that preceded a player’s conscious intent. Importantly, the models were also expertise specific, in that models trained to predict the target selection decisions of experts could not accurately predict the target selection decisions of novices (and vice versa). To understand what differentiated expert and novice target selection decisions, we employed the explainable-AI technique, SHapley Additive explanation (SHAP), to identify what informational features (variables) most influenced modelpredictions. The SHAP analysis revealed that experts were more reliant on information about target direction of heading and the location of coherders (i.e., other players) compared to novices. The implications and assumptions underlying the use of SML and explainable-AI techniques for investigating and understanding human decision-making are discussed.
Enabling Goal-Focused Exploration of Podcasts in Interactive Recommender Systems

Content recommender systems often rely on modeling users' past behavioral data to provide personalized recommendations - a practice that works well for suggesting more of the same and for media that require little time investment from users, such as music tracks. However, this approach can be further optimized for media where the user investment is higher, such as podcasts, because there is a broader space of user goals that might not be captured by the implicit signals of their past behavior. Allowing users to directly specify their goals might help narrow the space of possible recommendations. Thus, in this paper, we explore how we can enable goal-focused exploration in recommender systems by leveraging explicit input from users about their personal goals. Using podcast consumption as an example use-case, and informed by a large-scale survey (N=68k), we developed GoalPods, an interactive prototype that allows users to set personal goals and build playlists of podcast episode recommendations to meet those goals. We evaluated GoalPods with 14 participants where participants set a goal and spent a week listening to the episode playlist created for that goal. From the study, we identified two types of user goals: low-involvement (e.g. "combat boredom") and high-involvement (e.g. "learn something new") goals. Users found it easy to identify relevant recommendations for low-involvement goals, but they needed more structure and support to set high-involvement goals. By anchoring users on their personal goals to explore recommendations, GoalPods (and goal-focused podcast consumption) led to insightful content discovery outside the users' filter bubbles. Based on our findings, we discuss opportunities for designing recommender systems that guide exploration via interactive goal-setting as well as implications for providing better recommendations by accounting for users' personal goals.
Probing the Jaynes-Cummings Ladder with Spin Circuit Quantum Electrodynamics

We report observations of transitions between excited states in the Jaynes-Cummings ladder of circuit quantum electrodynamics with electron spins (spin circuit QED). We show that unexplained features in recent experimental work correspond to such transitions and present an input-output framework that includes these effects. In new experiments, we first reproduce previous observations and then reveal both excited-state transitions and multiphoton transitions by increasing the probe power and using two-tone spectroscopy. This ability to probe the Jaynes-Cummings ladder is enabled by improvements in the coupling-to-decoherence ratio, and shows an increase in the maturity of spin circuit QED as an interesting platform for studying quantum phenomena.
Attack detection analysis in software-defined networks using various machine learning method

The Software-Defined Network (SDN) provides a more flexible and effectively managed network design for next-generation networking. Network managers can easily manage and regulate the entire network using its programmable central controller architecture. This central controller serves as the focal point for numerous attack vectors due to its centralized structure. However, Distributed Denial of Service (DDoS) attacks against the SDN is the most prominent. The goal of this project is to use a machine learning method to categorize SDN traffic as either attack or normal traffic. Next, the Feature Selection method, such as the Filter-based Fisher score method, Wrapper-based method, and analysis of variables (ANOVA) f-test, is used for finely-granulated detection. Then, a rule-based detection method using the Renyi joint entropy algorithm is employed to detect DDoS attacks on SDN controllers. We manage a public "DDoS attack SDN Dataset" with 23 attributes overall. The dataset includes normal and attack traffic for the Internet Control Message Protocol (ICMP), User Datagram Protocol (UDP), and Transmission Control Protocol (TCP). Except for attributes that specify the target and source machines, the dataset, which contains more than 100,000 recordings, has statistical features such as byte count, duration sec, packet rate, and packet per flow. In the classification process, many classifiers such as Artificial Neural Network (ANN), XGBoost (XGB), Support Vector Machine (SVM), and k-Nearest Neighbor (k-NN) were used. The test results demonstrated the efficacy and efficiency of the suggested strategy using the analysis of variables (ANOVA), which performed better than competing methods across a range of evaluation parameters.
Precision geochemical (ICP-MS) analysis of Precambrian rocks: the method and accuracy estimation

The article discusses the results of measuring the composition of Early Precambrian mafic rocks and granitoids of the eastern part of the Fennoscandian Shield by inductively coupled plasma mass spectrometric analysis (ICP-MS). It is demonstrated that the bul kprobe sample composition can be determined both by acid digestion of samples in an open system and by autoclave digestion. The accuracy and reproducibility of the analysis were monitored by analyzing the international and Russian standards and the in-house reference sample: BHVO-2, SHD-2A, GSP-2, C-1412
FedBranched: Leveraging Federated Learning for Anomaly-Aware Load Forecasting in Energy Networks

Increased demand for fast edge computation and privacy concerns have shifted researchers’ focus towards a type of distributed learning known as federated learning (FL). Recently, much research has been carried out on FL; however, a major challenge is the need to tackle the high diversity in different clients. Our research shows that using highly diverse data sets in FL can lead to low accuracy of some local models, which can be categorised as anomalous behaviour. In this paper, we present FedBranched, a clustering-based framework that uses probabilistic methods to create branches of clients and assigns their respective global models. Branching is performed using hidden Markov model clustering (HMM), and a round of branching depends on the diversity of the data. Clustering is performed on Euclidean distances of mean absolute percentage errors (MAPE) obtained from each client at the end of pre-defined communication rounds. The proposed framework was implemented on substation-level energy data with nine clients for short-term load forecasting using an artificial neural network (ANN). FedBranched took two clustering rounds and resulted in two different branches having individual global models. The results show a substantial increase in the average MAPE of all clients; the biggest improvement of 11.36% was observed in one client.
The Rogue Scholar is now open for business

The Rogue Scholar science blog archive launched with limited functionality on April 3rd. Interested science blogs can go to the sign-up page, provide some basic information via the sign-up form, and then will be added to the Rogue Scholar archive within two business days. To be included in the service, your blog needs to:be about science or scholarship and written in English or German (more languages will follow later, reach out to me if you can help),make the full-text content available via...