Reviewerly/exHarmony huggingface dataset:
Physics-Enhanced Bifurcation Optimisers: All You Need is a Canonical Complex Network

Many physical systems with dynamical evolution that at their steady state give a solution to optimization problems were proposed and realized as promising alternatives to conventional computing. Systems of oscillators such as coherent Ising and XY machines based on lasers, optical parametric oscillators, memristors, polariton and photon condensates are particularly promising due to their scalability, low power consumption and room temperature operation. They achieve a solution via the bifurcation of the fundamental supermode that globally minimizes either the power dissipation of the system or the system Hamiltonian. We show that the canonical Andronov-Hopf networks can capture the bifurcation behaviour of the physical optimizer. Furthermore, a continuous change of variables transforms any physical optimizer into the canonical network so that the success of the physical XY-Ising machine depends primarily on how well the parameters of the networks can be controlled. Our work, therefore, places different physical optimizers in the same mathematical framework that allows for the hybridization of ideas across disparate physical platforms.
Anomaly Detection based on 1D-CNN-LSTM Auto-Encoder for Bearing Data

The manufacturing industry is developing rapidly due to the Fourth Industrial Revolution. If a piece of bearing equipment, which is one of the essential parts of the manufacturing industry, fails, it will hinder the production of the manufacturing industry, which will lead to huge losses for the company. To prevent this, this paper implements a 1 Dimension-Convolution Neural Networks-Long Short-Term Memory (1D-CNN-LSTM) Auto-Encoder model for fault diagnosis of bearing data. The 1D-CNN-LSTM Auto-Encoder model showed high accuracy of 58 to 100 percent for eccentric bearing data that are difficult to visually diagnose as faults. In the future, we would like to extend this to a real-time failure diagnosis system that can remotely monitor the condition of the bearing equipment through real-time communication with the cloud server and test bed.
Pendukung Keputusan dalam Penilaian Pegawai Pemerintah Non Pegawai Negeri menggunakan Metode Entropy

Polytechnic of STIA LAN Bandung is one of the units under the LAN RI which has a core business in the field of education that has an obligation to run the Tri Dharma of Higher Education. In running the Tri Dharma of Higher Education, Polytechnic of STIA LAN Bandung requires several supporting resources. One of the supporting resources that most determine the quality of the implementation of the Tri Dharma of Higher Education is the competence of human resources. To facilitate the decision-making process carried out by the Staffing sub-section in assessing Non-Civil Servant Government Employees (PPNPN), a form of Decision Support System is needed. In conducting employee assessments used a method, namely Entropy. Entropy method that serves to measure the weight (level of importance) on each assessment indicator. Assessment indicators, which are used include Basic Values, Performance, Insight Tests, Warning Letters, and Attendance. The dataset used is secondary data that has been obtained from the staffing sub-section of Polytechnic of STIA LAN Bandung. The data sample consisted of five non-civil servants (PPNPN). From the results of the weight calculation using the Entropy method, it was obtained that the Attendance Indicator has the largest weight value of 0.5495, followed by the Warning Letter indicator with a weight of 0.2353. Then the third largest weight is on the Performance indicator with a weight of 0.1107, followed by the Basic Value indicator with a weight of 0.0604. The smallest weight gain is generated by the Insight Test indicator of 0.0439
A Bowtie for a Beast: Overloading, Eta Expansion, and Extensible Data Types in F⋈

The typed merge operator offers the promise of a compositional style of statically-typed programming in which solutions to the expression problem arise naturally. This approach, dubbed compositional programming , has recently been demonstrated by Zhang et al. Unfortunately, the merge operator is an unwieldy beast. Merging values from overlapping types may be ambiguous, so disjointness relations have been introduced to rule out undesired nondeterminism and obtain a well-behaved semantics. Past type systems using a disjoint merge operator rely on intersection types, but extending such systems to include union types or overloaded functions is problematic: naively adding either reintroduces ambiguity. In a nutshell: the elimination forms of unions and overloaded functions require values to be distinguishable by case analysis, but the merge operator can create exotic values that violate that requirement. This paper presents F ⋈ , a core language that demonstrates how unions, intersections, and overloading can all coexist with a tame merge operator. The key is an underlying design principle that states that any two inhabited types can support either the deterministic merging of their values, or the ability to distinguish their values, but never both. To realize this invariant, we decompose previously studied notions of disjointness into two new, dual relations that permit the operation that best suits each pair of types. This duality respects the polarization of the type structure, yielding an expressive language that we prove to be both type safe and deterministic.
Designing a Location Trace Anonymization Contest

For a better understanding of anonymization methods for location traces, we have designed and held a location trace anonymization contest that deals with a long trace (400 events per user) and fine-grained locations (1024 regions). In our contest, each team anonymizes her original traces, and then the other teams perform privacy attacks against the anonymized traces. In other words, both defense and attack compete together, which is close to what happens in real life. Prior to our contest, we show that re-identification alone is insufficient as a privacy risk and that trace inference should be added as an additional risk. Specifically, we show an example of anonymization that is perfectly secure against re-identification and is not secure against trace inference. Based on this, our contest evaluates both the re-identification risk and trace inference risk and analyzes their relationship. Through our contest, we show several findings in a situation where both defense and attack compete together. In particular, we show that an anonymization method secure against trace inference is also secure against re-identification under the presence of appropriate pseudonymization. We also report defense and attack algorithms that won first place, and analyze the utility of anonymized traces submitted by teams in various applications such as POI recommendation and geo-data analysis.
Design of intelligent tutoring system (ITS) based on augmented reality (AR) for three-dimensional geometry material

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Mohamad Yasin, Rizqi Agus Utomo; Design of intelligent tutoring system (ITS) based on augmented reality (AR) for three-dimensional geometry material. AIP Conf. Proc. 10 January 2023; 2569 (1): 040001. https://doi.org/10.1063/5.0112170 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Quantum Speed-ups for String Synchronizing Sets, Longest Common Substring, and <i>k</i>-mismatch Matching

Longest Common Substring (LCS) is an important text processing problem, which has recently been investigated in the quantum query model. The decisional version of this problem, LCS with threshold d, asks whether two length-n input strings have a common substring of length d. The two extreme cases, d = 1 and d = n, correspond respectively to Element Distinctness and Unstructured Search, two fundamental problems in quantum query complexity. However, the intermediate case 1 ≪ d ≪ n was not fully understood.We show that the complexity of LCS with threshold d smoothly interpolates between the two extreme cases up to no(1) factors:• LCS with threshold d has a quantum algorithm in n2/3 +o(1) /d1/6 query complexity and time complexity, and requires at least Ω(n2/3/d1/6) quantum query complexity.Our result improves upon previous upper bounds Õ(min{n/d1/2,n2/3}) (Le Gall and Seddighin ITCS 2022, Akmal and Jin SODA 2022), and answers an open question of Akmal and Jin.Our main technical contribution is a quantum speed-up of the powerful String Synchronizing Set technique introduced by Kempa and Kociumaka (STOC 2019). It consistently samples n/τ1−o(1) synchronizing positions in the string depending on their length-Θ(τ) contexts, and each synchronizing position can be reported by a quantum algorithm in Õ(τ1/2+o(1)) time. Our quantum string synchronizing set also yields a near-optimal LCE data structure in the quantum setting.As another application of our quantum string synchronizing set, we study the k-mismatch Matching problem, which asks if the pattern has an occurrence in the text with at most k Hamming mismatches. Using a structural result of Charalampopoulos, Kociumaka, and Wellnitz (FOCS 2020), we obtain:• k-mismatch matching has a quantum algorithm with k3/4n1/2+o(1) query complexity and Õ(kn1/2) time complexity. We also observe a non-matching quantum query lower bound of .
Stronger Privacy Amplification by Shuffling for Renyi and Approximate Differential Privacy

The shuffle model of differential privacy has gained significant interest as an intermediate trust model between the standard local and central models [18, 12]. A key result in this model is that randomly shuffling locally randomized data amplifies differential privacy guarantees. Such amplification implies substantially stronger privacy guarantees for systems in which data is contributed anonymously [8].In this work, we improve the state of the art privacy amplification by shuffling results both theoretically and numerically. Our first contribution is the first asymptotically optimal analysis of the Renyi differential privacy parameters for the shuffled outputs of LDP randomizers. Our second contribution is a new analysis of privacy amplification by shuffling. This analysis improves on the techniques of Feldman et al. [20] and leads to tighter numerical bounds in all parameter settings.* The full version of the paper can be accessed at https://arxiv.org/abs/2208.04591
RBT-HCI: A Reliable Behavior Tree Planning Method with Human-Computer Interaction

In this paper, we propose RBT-HCI, a reliable behavior tree (BT) planning method with human-computer interaction, aiming at generating an interpretable and human-acceptable BT. Compared with other BT generation methods, RBT-HCI can reliably plan a BT based on the knowledge base. When an available BT cannot be planned automatically, instead of terminating or relaxing the rules, RBT-HCI provides a new idea, which is to make decisions through human-computer interaction, thereby enhancing the reliability and robustness of the method. The effectiveness of RBT-HCI is verified by an example of robot grasping objects, showing that a reliable and robust planning result can be obtained through knowledge-based automatic planning and human-computer interaction.
Barren plateaus from learning scramblers with local cost functions

The existence of barren plateaus has recently revealed new training challenges in quantum machine learning (QML). Uncovering the mechanisms behind barren plateaus is essential in understanding the scope of problems that QML can efficiently tackle. Barren plateaus have recently been shown to exist when learning global properties of random unitaries, which is relevant when learning black hole dynamics. Establishing whether local cost functions can circumvent these barren plateaus is pertinent if we hope to apply QML to quantum many-body systems. We prove a no-go theorem showing that local cost functions encounter barren plateaus in learning random unitary properties.
Optimising Linear Key Recovery Attacks with Affine Walsh Transform Pruning

Linear cryptanalysis [25] is one of the main families of key-recovery attacks on block ciphers. Several publications [16, 19] have drawn attention towards the possibility of reducing their time complexity using the fast Walsh transform. These previous contributions ignore the structure of the key recovery rounds, which are treated as arbitrary boolean functions. In this paper, we optimise the time and memory complexities of these algorithms by exploiting zeroes in the Walsh spectra of these functions using a novel affine pruning technique for the Walsh Transform. These new optimisation strategies are then showcased with two application examples: an improved attack on the DES [1] and the first known attack on 29-round PRESENT-128 [9].
All-parameter calibration method of the on-orbit multi-view dynamic photogrammetry system

Photogrammetry (PG) can present accurate data to evaluate the functional performance of large space structures. For camera calibration and orientation, the On-orbit Multi-view Dynamic Photogrammetry System (OMDPS) lacks appropriate spatial reference data. A multi-data fusion calibration method for all parameters for this kind of system is proposed in this paper as a solution to this issue. Firstly, a multi-camera relative position model is developed to solve the reference camera position unconstrained problem in the full-parameter calibration model of the OMDPS in accordance with the imaging model of stars and scale bar targets. Subsequently, the problem of adjustment failure and inaccurate adjustment in the multi-data fusion bundle adjustment is solved using the two-norm matrix and the weight matrix to adjust the Jacobian matrix with respect to all system parameters (e.g., camera interior parameters (CIP), camera exterior parameters (CEP), and lens distortion parameters (LDP)). Finally, all system parameters can be optimized simultaneously using this algorithm. In the actual data ground-based experiment, 333 spatial targets are measured using the V-star System (VS) and OMDPS. Taking the measurement of VS as the true value, the measurement results of OMDPS indicated that the in-plane Z-direction target coordinates root-mean-square error (RMSE) is less than 0.0538 mm and the Z-direction RMSE is less than 0.0428 mm. Out-of-plane Y-direction RMSE is less than 0.1514 mm. The application potential of the PG system for on-orbit measurement tasks is demonstrated through the actual data ground-based experiment.
Real-time face mask position recognition system using YOLO models for preventing COVID-19 disease spread in public places

The COVID-19 pandemic has infected tens of millions of individuals around the world, and it is currently posing a worldwide health calamity. Wearing a face mask in public places is one of the most effective protection strategies, according to the World Health Organization (WHO). Moreover, their effectiveness has declined due to incorrect use of the face mask. In this scenario, effective recognition systems are anticipated to ensure that people's faces are covered with masks in public locations. Many people do not correctly wear the masks due to inadequate practices, undesirable behaviour, or individual vulnerabilities. As a result, there has been an increase in demand for automatic real-time face mask detection and mask position detection to substitute manual reminders. This proposed work classifies people into three categories such as with mask, without mask and mask with incorrect position. The dataset is tested using three different variants of object detection models, namely YOLOv4, Tiny YOLOv4, and YOLOv5. The experimental result shows that YOLOv5 model outperforms with the highest mAP value of 99.40% compared to the other two models.
Low-Depth Hamiltonian Simulation by an Adaptive Product Formula

Various Hamiltonian simulation algorithms have been proposed to efficiently study the dynamics of quantum systems on a quantum computer. The existing algorithms generally approximate the time evolution operators, which may need a deep quantum circuit that is beyond the capability of near-term noisy quantum devices. Here, focusing on the time evolution of a fixed input quantum state, we propose an adaptive approach to construct a low-depth time evolution circuit. By introducing a measurable quantifier that characterizes the simulation error, we use an adaptive strategy to learn the shallow quantum circuit that minimizes that error. We numerically test the adaptive method with electronic Hamiltonians of the ${\mathrm{H}}_{2}\mathrm{O}$ and ${\mathrm{H}}_{4}$ molecules, and the transverse field Ising model with random coefficients. Compared to the first-order Suzuki-Trotter product formula, our method can significantly reduce the circuit depth (specifically the number of two-qubit gates) by around two orders while maintaining the simulation accuracy. We show applications of the method in simulating many-body dynamics and solving energy spectra with the quantum Krylov algorithm. Our work sheds light on practical Hamiltonian simulation with noisy-intermediate-scale-quantum devices.
Pedestrian walking speed monitoring at street scale by an in-flight drone

The walking speed of pedestrians is not only a reflection of one’s physiological condition and health status but also a key parameter in the evaluation of the service level of urban facilities and traffic engineering applications, which is important for urban design and planning. Currently, the three main ways to obtain walking speed are based on trails, wearable devices, and images. The first two cannot be popularized in larger open areas, while the image-based approach requires multiple cameras to cooperate in order to extract the walking speed of an entire street, which is costly. In this study, a method for extracting the pedestrian walking speed at a street scale from in-flight drone video is proposed. Pedestrians are detected and tracked by You Only Look Once version 5 (YOLOv5) and Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT) algorithms in the video taken from a flying unmanned aerial vehicle (UAV). The distance that pedestrians traveled related to the ground per fixed time interval is calculated using a combined algorithm of Scale-Invariant Feature Transform (SIFT) and random sample consensus (RANSAC) followed by a geometric correction algorithm. Compared to ground truth values, it shows that 90.5% of the corrected walking speed predictions have an absolute error of less than 0.1 m/s. Overall, the method we have proposed is accurate and feasible. A particular advantage of this method is the ability to accurately predict the walking speed of pedestrians without keeping the flight speed of the UAV constant, facilitating accurate measurements by non-specialist technicians. In addition, because of the unrestricted flight range of the UAV, the method can be applied to the entire scale of the street, which assists in a better understanding of how the settings and layouts of urban affect people’s behavior.
Deep Learning Speech Synthesis Model for Word/Character-Level Recognition in the Tamil Language

As electronics and the increasing popularity of social media are widely used, a large amount of text data is created at unprecedented rates. All data created cannot be read by humans, and what they discuss in their sphere of interest may be found. Modeling of themes is a way to identify subjects in a vast number of texts. There has been a lot of study on subject-modeling in English. At the same time, millions of people worldwide speak Tamil; there is no great development in resource-scarce languages such as Tamil being spoken by millions of people worldwide. The consequences of specific deep learning models are usually difficult to interpret for the typical user. They are utilizing various visualization techniques to represent the outcomes of deep learning in a meaningful way. Then, they use metrics like similarity, correlation, perplexity, and coherence to evaluate the deep learning models.
Optimization of Trash Identification on the House Compound Using a Convolutional Neural Network (CNN) and Sensor System

This study aims to optimize the object identification process, especially identifying trash in the house compound. Most object identification methods cannot distinguish whether the object is a real image (3D) or a photographic image on paper (2D). This is a problem if the detected object is moved from one place to another. If the object is 2D, the robot gripper only clamps empty objects. In this study, the Sequential_Camera_LiDAR (SCL) method is proposed. This method combines a Convolutional Neural Network (CNN) with LiDAR (Light Detection and Ranging), with an accuracy of ±2 mm. After testing 11 types of trash on four CNN architectures (AlexNet, VGG16, GoogleNet, and ResNet18), the accuracy results are 80.5%, 95.6%, 98.3%, and 97.5%. This result is perfect for object identification. However, it needs to be optimized using a LiDAR sensor to determine the object in 3D or 2D. Trash will be ignored if the fast scanning process with the LiDAR sensor detects non-real (2D) trash. If Real (3D), the trash object will be scanned in detail to determine the robot gripper position in lifting the trash object. The time efficiency generated by fast scanning is between 13.33% to 59.26% depending on the object’s size. The larger the object, the greater the time efficiency. In conclusion, optimization using the combination of a CNN and a LiDAR sensor can identify trash objects correctly and determine whether the object is real (3D) or not (2D), so a decision may be made to move the trash object from the detection location.
Robust Online Support Vector Regression with Truncated ε-Insensitive Pinball Loss

Advances in information technology have led to the proliferation of data in the fields of finance, energy, and economics. Unforeseen elements can cause data to be contaminated by noise and outliers. In this study, a robust online support vector regression algorithm based on a non-convex asymmetric loss function is developed to handle the regression of noisy dynamic data streams. Inspired by pinball loss, a truncated ε-insensitive pinball loss (TIPL) is proposed to solve the problems caused by heavy noise and outliers. A TIPL-based online support vector regression algorithm (TIPOSVR) is constructed under the regularization framework, and the online gradient descent algorithm is implemented to execute it. Experiments are performed using synthetic datasets, UCI datasets, and real datasets. The results of the investigation show that in the majority of cases, the proposed algorithm is comparable, or even superior, to the comparison algorithms in terms of accuracy and robustness on datasets with different types of noise.
The SearchEngine: A Holistic Approach to Matching

The SearchEngine is an open source project providing an integrated framework for diverse matching activities, especially the linkage of large scale firm data by fuzzy criteria like company names and addresses. At its core, it utilizes an efficient candidate retrieval mechanism implementing a word respectively token driven heuristic. Every record in one table becomes a search term to retrieve similar candidate records in the base table according to a search strategy replacing blocking strategies of conventional matching efforts. Because similarity is inherently established by the candidate selection, it is only required to filter false positives by using the meta data export file derived from the matching heuristic to implement a machine learning approach. This paper discusses the general foundation of the heuristic and the algorithm while two detailed walkthroughs of company linkages show practical examples.