Reviewerly/exHarmony huggingface dataset:
Conditions for local transformations between sets of quantum states

We study the problem of transforming a set of pure bipartite states into another using deterministic LOCC (local operations and classical communication). Necessary conditions for the existence of such a transformation are obtained using LOCC constraints on state transformation, entanglement, and distinguishability. These conditions are shown to be independent but not sufficient. We discuss their satisfiability and classify all possible input-output pairs of sets accordingly. We also prove that strict inclusions hold between LOCC, separable, and positive partial transpose operations for set transformation problems.
Faktor-Faktor yang Mempengaruhi Persepsi Etis Auditor Dengan Filosofi Tri Kaya Parisudha Sebagai Variabel Pemoderasi

The purpose of the study was to obtain empirical evidence regarding the effect of love of money, machiavellian nature, and ethical sensitivity on auditors' ethical perceptions with the Tri Kaya Parisudha philosophy as a moderating variable. This research was conducted at 12 Public Accounting Firms. The method of determining the sample in this study used a purposive sampling technique so that a sample of 78 auditors was obtained. Data was collected using the questionnaire method. The data analysis technique used in this research is using PLS 3.0 software. Based on the results of the study, it is known that love of money has a negative effect on the ethical perception of the auditor, while the machaivellian nature and ethical sensitivity have no effect on the ethical perception of the auditor. Based on the results of the research that has been done, it is also known that the Tri Kaya Parisudha philosophy is not able to weaken the influence of love of money on the ethical perception of the auditors, while the Tri Kaya Parisudha philosophy is able to weaken the influence of Machiavellian nature and ethical sensitivity on the auditor's ethical perception.&#x0D; Keywords: Love of Money; Machiavellian; Ethical Sensitivity; Ethical Perception; Philosophy Tri Kaya Parisudha.
Combining Counterfactuals With Shapley Values To Explain Image Models

With the widespread use of sophisticated machine learning models in sensitive applications, understanding their decision-making has become an essential task. Models trained on tabular data have witnessed significant progress in explanations of their underlying decision making processes by virtue of having a small number of discrete features. However, applying these methods to high-dimensional inputs such as images is not a trivial task. Images are composed of pixels at an atomic level and do not carry any interpretability by themselves. In this work, we seek to use annotated high-level interpretable features of images to provide explanations. We leverage the Shapley value framework from Game Theory, which has garnered wide acceptance in general XAI problems. By developing a pipeline to generate counterfactuals and subsequently using it to estimate Shapley values, we obtain contrastive and interpretable explanations with strong axiomatic guarantees.
Differentiable Rule Induction with Learned Relational Features

Rule-based decision models are attractive due to their interpretability. However, existing rule induction methods often result in long and consequently less interpretable rule models. This problem can often be attributed to the lack of appropriately expressive vocabulary, i.e., relevant predicates used as literals in the decision model. Most existing rule induction algorithms presume pre-defined literals, naturally decoupling the definition of the literals from the rule learning phase. In contrast, we propose the Relational Rule Network (R2N), a neural architecture that learns literals that represent a linear relationship among numerical input features along with the rules that use them. This approach opens the door to increasing the expressiveness of induced decision models by coupling literal learning directly with rule learning in an end-to-end differentiable fashion. On benchmark tasks, we show that these learned literals are simple enough to retain interpretability, yet improve prediction accuracy and provide sets of rules that are more concise compared to state-of-the-art rule induction algorithms.
Sentiment Analysis of Delivery Service Opinions on Twitter Documents using K-Nearest Neighbor

The global pandemic of Covid-19 in 2020 encourage a significant increase of online trading such as selling and buying around the world, especially in Indonesia. The dependency on goods delivery services to fulfill transaction process among sellers and buyers are essential as the government policy enforce a limitation of errands outside home. Since 2000, top brand awards have often conducted official surveys in terms of awarding and displaying comparisons of goods or services based on public opinion from several regions. One of the services that became the top brand survey is delivery service. However, it was found that the survey rankings were not quite accurate because the public and companies were still unable to find out in detail the success factors and drawbacks of their brands based on the ratings submitted. This research was conducted to analyze opinion sentiment of delivery service (JNE, J&amp;T, TIKI, Pos Indonesia, and DHL Indonesia) and determine the aspect of timeliness and quality. The data is retrieved from Twitter “tweets” of delivery service official account of @JNE_ID, @shippexpressid, @PosIndonesia, and @IdTiki, and search keyword of “DHL Indonesia”. K-Nearest Neighbor with parameter K=3 is adopted as the main algorithm following some text processing method, feature extraction and feature selection. The result shows DHL Indonesia is in the first position at 55.6% of Twitter users who gave positive feedback for service aspect by timeliness and at 15% for service aspect by quality. This research system has accuracy at 94.56%, precision at 62.31%, recall at 45.26%, and F-measure at 62.32%. It can be concluded that the parameters used are quite optimal.
Cinema as urban modeling

In The Architecture Machine (1970), Nicholas Negroponte comments a photographic record of a busy crossroads in a city in the following way: “In this case the simulation is the real world, the best model but the most expensive. Similar displays will soon be manageable by computers.” I interpret the moving-image camera as the “machine” that can observe the performance of an everyday environment. I posit that fiction films provide us with useful perceptual tools to grasp complex urban phenomena. In order to elicit the mechanisms that make up the projected image of city films, I have devised new analytical tools. Using examples, in this chapter I demonstrate how cinema as well as observational film techniques can usefully complement urban simulation models, highlighting the importance of arts and humanities research in mediating and humanizing often highly technical/scientific approaches.
Clustered Scheduling and Communication Pipelining For Efficient Resource Management Of Wireless Federated Learning

This paper proposes using communication pipelining to enhance the wireless spectrum utilization efficiency and convergence speed of federated learning in mobile edge computing applications. Due to limited wireless sub-channels, a subset of the total clients is scheduled in each iteration of federated learning algorithms. On the other hand, the scheduled clients wait for the slowest client to finish its computation. We propose to first cluster the clients based on the time they need per iteration to compute the local gradients of the federated learning model. Then, we schedule a mixture of clients from all clusters to send their local updates in a pipelined manner. In this way, instead of just waiting for the slower clients to finish their computation, more clients can participate in each iteration. While the time duration of a single iteration does not change, the proposed method can significantly reduce the number of required iterations to achieve a target accuracy. We provide a generic formulation for optimal client clustering under different settings, and we analytically derive an efficient algorithm for obtaining the optimal solution. We also provide numerical results to demonstrate the gains of the proposed method for different datasets and deep learning architectures.
Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions

Stochastic gradient descent (SGD) is a pillar of modern machine learning, serving as the go-to optimization algorithm for a diverse array of problems. While the empirical success of SGD is often attributed to its computational efficiency and favorable generalization behavior, neither effect is well understood and disentangling them remains an open problem. Even in the simple setting of convex quadratic problems, worst-case analyses give an asymptotic convergence rate for SGD that is no better than full-batch gradient descent (GD), and the purported implicit regularization effects of SGD lack a precise explanation. In this work, we study the dynamics of multi-pass SGD on high-dimensional convex quadratics and establish an asymptotic equivalence to a stochastic differential equation, which we call homogenized stochastic gradient descent (HSGD), whose solutions we characterize explicitly in terms of a Volterra integral equation. These results yield precise formulas for the learning and risk trajectories, which reveal a mechanism of implicit conditioning that explains the efficiency of SGD relative to GD. We also prove that the noise from SGD negatively impacts generalization performance, ruling out the possibility of any type of implicit regularization in this context. Finally, we show how to adapt the HSGD formalism to include streaming SGD, which allows us to produce an exact prediction for the excess risk of multi-pass SGD relative to that of streaming SGD (bootstrap risk).
Rethinking networked collaboration in the live coding environment Gibber

We describe a new set of affordances for networked live coding performances in the browser-based environment Gibber, and discuss their implications in the context of three different performances by three different ensembles at three universities.Each ensemble possessed differing levels of programming and musical expertise, leading to different challenges and subsequent extensions to Gibber to address them.We describe these and additional extensions that came about after shared reflection on our experiences.While our chosen design contains computational inefficiencies that pose challenges for larger ensembles, our experiences suggest that this is a reasonable tradeoff for the low barrier-to-entry that browser-based environments provide, and that the design in general supports a variety of educational goals and compositional strategies.
Keyword Extraction – Comparison of Latent Dirichlet Allocation and Latent Semantic Analysis

The main aim of the present study is to compare the keywords extracted from abstracts and full length text of scientific research papers. In addition to that, here, we compare Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) to identify better performer for keyword extraction. This comparative study is divided into three levels, In the first level, scientific research articles on topics such as Indian Economic growth, GDP, Economic Slowdown etc. were collected and abstracts and full length text was extracted from the sources and pre-processed to remove the words and characters which were not useful to obtain the semantic structures or necessary patterns to make the meaningful corpus. In the second level, the pre-processed data were converted into a bag of words and numerical statistic TF-IDF (Term Frequency – Inverse Document Frequency) is used to assess how relevant a word is to a document in a corpus. In the third level, in order to study the feasibility of the Natural Language Processing (NLP) techniques, Latent Semantic analysis (LSA) and Latent Dirichlet Allocations (LDA) methods were applied over the resultant corpus.
The Immortal Shakspeare: Transcribing a micro-calligraphic drawing

A poster-sized drawing from the 19th Century portrays Shakespeare's monument in Holy Trinity Church, Stratfordupon-Avon.The linework is composed of tiny lettering, less than 1 mm in height, running in a continuous path throughout the design.Direct transcription of the text proved to be difficult.The print was digitised at 1200 dpi and image processing techniques were tried to follow the text lines and recognise the characters, but they were ineffective because of the irregularity of the writing.A software viewer was developed to facilitate navigation of the text on screen, with functions for enlargement, translation and rotation, and for constructing and editing a trajectory line following the path of the text.The text was fully transcribed and three sources were identified from the 1840s.
Seen Description: Visualising and crafting data in mixed realities

The immense potential of the convergence of digital technologies such as, 3D scanning and photogrammetry, 3D printing, motion capture, real time performance capture and mapping, camera tracking, interactive lighting and virtual production, is yet to be fully grasped, exploited and appreciated.The creative aspects of this technology convergence offer exciting new opportunities for the visualisation, mixing and crafting of materials from the physical and digital worlds; real world objects can be 3D scanned, manipulated digitally, printed out using the 3D printing process, and painted in the real world to be used as set dressing on a virtual production set to be recorded on digital video.The real and digital states of objects have become fluidly interchangeable offering unprecedented creative control to artists to shape the world at will.This paper highlights some of the possible applicationsbeyond the mainstream usage in film and games productionthat the convergence of these technologies offer based on case studies of select student and staff projects undertaken at The National Centre for Computer Animation at Bournemouth University, UK.These applications include, but are not limited to, creative art, photography, digital heritage and preservation, and customised applications for rehabilitation of patients. Visualisation. Mixed
Computer-assisted academic methods: determining the methods of teaching to higher education students

This work aims to investigate different teaching methodologies for university students, although legislation mandates that students with autism receive instruction linked to the general education core content, there is limited research supporting the effectiveness of interventions for teaching core content to these students. The current study was conducted at private universities. A quantitative way for analyzing this paper. In order to analyze this study, a questionnaire was carried out. 180 questionnaires were distributed, 171 were received, however of the 162 questionnaires were properly filled. According to multiple regression analyzes, computer-assisted academic method had the highest value. A computer-assisted academic lecture is one of the ways that can achieve educational objectives.
Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement Learning

Offline reinforcement learning (RL) extends the paradigm of classical RL algorithms to purely learning from static datasets, without interacting with the underlying environment during the learning process. A key challenge of offline RL is the instability of policy training, caused by the mismatch between the distribution of the offline data and the undiscounted stationary state-action distribution of the learned policy. To avoid the detrimental impact of distribution mismatch, we regularize the undiscounted stationary distribution of the current policy towards the offline data during the policy optimization process. Further, we train a dynamics model to both implement this regularization and better estimate the stationary distribution of the current policy, reducing the error induced by distribution mismatch. On a wide range of continuous-control offline RL datasets, our method indicates competitive performance, which validates our algorithm. The code is publicly available.
An Efficient Gradient Sensitive Alternate Framework for VQE with Variable Ansatz

Variational quantum eigensolver (VQE), aiming at determining the ground state energy of a quantum system described by a Hamiltonian on noisy intermediate scale quantum (NISQ) devices, is among the most significant applications of variational quantum algorithms (VQAs). However, the accuracy and trainability of the current VQE algorithm are significantly influenced due to the barren plateau (BP), the non-negligible gate error and limited coherence time in NISQ devices. To tackle these issues, a gradient-sensitive alternate framework with variable ansatz is proposed in this paper to enhance the performance of the VQE. We first propose a theoretical framework for VA-VQE via alternately solving a multi-objective optimization problem and the original VQE, where the multi-objective optimization problem is defined with respect to cost function values and gradient magnitudes. Then, we propose a novel implementation method based on the double $\epsilon$-greedy strategy with the candidate tree and modified multi-objective genetic algorithm. As a result, the local optima are avoided both in ansatz and parameter perspectives, and the stability of output ansatz is enhanced. The experimental results indicate that our framework shows considerably improvement of the error of the found solution by up to 87.9% compared with the hardware-efficient ansatz. Furthermore, compared with the full-randomized VA-VQE implementation, our framework is able to obtain the improvement of the error and the stability by up to 36.0% and 58.7%, respectively, with similar quantum costs.
Influence of Nanoparticle CaCO3 Addition to the Physical and Mechanical Properties of Polypropylene-CaCO3 Composite

Influence of Nanopaticle CaCO3 Addition to the Physical and Mechanical Properties of Polypropylene-CaCO3 Composite. This research was carried out to study the effect of adding CaCO3 nanoparticle on the physical and mechanical properties of polypropylene-CaCO3 composites. It was characterized by several parameters such as tensile strength, hardness, and thermal analysis including both melting point and heat of fusion using Differential Scanning Calorimetry (DSC). Based on XRD results, the particle size of CaCO3 after 24 hours of milling was 39 nm. There are various compositions of polypropylene-CaCO3 composites (PP MF35: nano-CaCO3) made in this study, namely 40%:60%, 35%:65%, 30%:70%, and 25%:75%. The results showed that the tensile strength of the PP MF35-CaCO3 composites decreased with increasing nano-CaCO3 content. Meanwhile, the hardness of the nanocomposites increased with increasing nano-CaCO3 content, but decreased the melting point and heat of fusions (ΔHm) of the nanocomposites. The infrared spectrum showed that the interaction between PP MF35 and nano-CaCO3 was only physical interaction and there was no chemical reaction.
Text Mining

The unstructured texts which contain massive amount of information cannot simply be Used for further processing by computers. Therefore, specific processing methods and Algorithms are required in order to extract useful patterns. The process of extracting Interesting information and knowledge from unstructured text completed by using Text Mining. In this paper, we have discussed text mining, as a recent and interesting field with The detail of steps involved in the overall process. We have also discussed different Technologies that teach computers with natural language so that they may analyze, Understand, and even generate text. In addition, we briefly discuss a number of successful Applications of text mining which are used currently and in future.
Analisis Kesalahan Ejaan Pada Teks Eksposisi Siswa Kelas V Sekolah Dasar Persatuan Umat Islam Haurgeulis

The importance of using good and correct language rules needs to be trained and applied from an early age in elementary schools and madrasah Ibtidaiyah thus there are no errors or mistakes in language. Elementary school students are guided to be able to understand spelling rules following the General Guidelines for Indonesian Spelling (PUEBI) in language skills, one of which is writing skills. They can provide information in their writings correctly thus the readers can understand the message or opinion conveyed easily.Researchers focus on two scopes, namely; the mistakes of fifth graders of SD PUI Haurgeulis In using capital letters, the punctuation marks and commas, the use of words in expository essays, and their understanding of Indonesian spelling.This study is used the descriptive qualitative method. For the data collection, researchers conducted observations, interviews, and documentation. At the data analysis stage, the researchers reduce the data, encode, present, and finally find conclusions from the processed data. The result of this study indicates that students quite understand spelling in Indonesian properly and correctly. The students' errors in the use of spelling are as follows; as many as 156 capital letter errors occupy the number 67%, as many as 26 punctuation errors occupy the number 22%, then the last as many as 51 errors in word usage occupy the number 11%, and all included in the overall percentage error diagram contained in the essays of class students V SD PUI Haurgeulis.
Notice of Retraction: E2V-SDE: From Asynchronous Events to Fast and Continuous Video Reconstruction via Neural Stochastic Differential Equations

Event cameras respond to brightness changes in the scene asynchronously and independently for every pixel. Due to the properties, these cameras have distinct features: high dynamic range (HDR), high temporal resolution, and low power consumption. However, the results of event cameras should be processed into an alternative representation for computer vision tasks. Also, they are usually noisy and cause poor performance in areas with few events. In recent years, numerous researchers have attempted to reconstruct videos from events. However, they do not provide good quality videos due to a lack of temporal information from irregular and discontinuous data. To overcome these difficulties, we introduce an E2V-SDE whose dynamics are governed in a latent space by Stochastic differential equations (SDE). Therefore, E2V-SDE can rapidly reconstruct images at arbitrary time steps and make realistic predictions on unseen data. In addition, we successfully adopted a variety of image composition techniques for improving image clarity and temporal consistency. By conducting extensive experiments on simulated and real-scene datasets, we verify that our model outperforms state-of-the-art approaches under various video reconstruction settings. In terms of image quality, the LPIPS score improves by up to 12% and the reconstruction speed is 87% higher than that of ET-Net.
Post-digital sax - a digitally controlled acoustic single-reed woodwind instrument

In a search for a symbiotic relationship between the digital and physical worlds, I am developing a hybrid, digital-acoustic wind instrument - the Post-Digital Sax. As the name implies, the instrument combines the advantages and flexibility of digital control with a hands-on physical interface and a non-orthodox means of sound production, in which the airflow, supplied by the playerâs lungs, is the actual sound source. The pitch, however, is controlled digitally, allowing a wide range of musical material manipulation, bringing the possibilities of a digitally augmented performance into the realm of acoustic sound.