Reviewerly/exHarmony huggingface dataset:
PITS: Variational Pitch Inference without Fundamental Frequency for End-to-End Pitch-controllable TTS

Previous pitch-controllable text-to-speech (TTS) models rely on directly modeling fundamental frequency, leading to low variance in synthesized speech. To address this issue, we propose PITS, an end-to-end pitch-controllable TTS model that utilizes variational inference to model pitch. Based on VITS, PITS incorporates the Yingram encoder, the Yingram decoder, and adversarial training of pitch-shifted synthesis to achieve pitch-controllability. Experiments demonstrate that PITS generates high-quality speech that is indistinguishable from ground truth speech and has high pitch-controllability without quality degradation. Code, audio samples, and demo are available at https://github.com/anonymous-pits/pits.
Graph Neural Networks with Learnable and Optimal Polynomial Bases

Polynomial filters, a kind of Graph Neural Networks, typically use a predetermined polynomial basis and learn the coefficients from the training data. It has been observed that the effectiveness of the model is highly dependent on the property of the polynomial basis. Consequently, two natural and fundamental questions arise: Can we learn a suitable polynomial basis from the training data? Can we determine the optimal polynomial basis for a given graph and node features? In this paper, we propose two spectral GNN models that provide positive answers to the questions posed above. First, inspired by Favard's Theorem, we propose the FavardGNN model, which learns a polynomial basis from the space of all possible orthonormal bases. Second, we examine the supposedly unsolvable definition of optimal polynomial basis from Wang & Zhang (2022) and propose a simple model, OptBasisGNN, which computes the optimal basis for a given graph structure and graph signal. Extensive experiments are conducted to demonstrate the effectiveness of our proposed models. Our code is available at https://github.com/yuziGuo/FarOptBasis.
Smaller public keys for MinRank-based schemes

MinRank is an NP-complete problem in linear algebra whose characteristics make it attractive to build post-quantum cryptographic primitives. Several MinRank-based digital signature schemes have been proposed. In particular, two of them, MIRA and MiRitH, have been submitted to the NIST Post-Quantum Cryptography Standardization Process. In this paper, we propose a key-generation algorithm for MinRank-based schemes that reduces the size of the public key to about 50% of the size of the public key generated by the previous best (in terms of public-key size) algorithm. Precisely, the size of the public key generated by our algorithm sits in the range of 328-676 bits for security levels of 128-256 bits. We also prove that our algorithm is as secure as the previous ones.
SGL-PT: A Strong Graph Learner with Graph Prompt Tuning

Recently, much exertion has been paid to design graph self-supervised methods to obtain generalized pre-trained models, and adapt pre-trained models onto downstream tasks through fine-tuning. However, there exists an inherent gap between pretext and downstream graph tasks, which insufficiently exerts the ability of pre-trained models and even leads to negative transfer. Meanwhile, prompt tuning has seen emerging success in natural language processing by aligning pre-training and fine-tuning with consistent training objectives. In this paper, we identify the challenges for graph prompt tuning: The first is the lack of a strong and universal pre-training task across sundry pre-training methods in graph domain. The second challenge lies in the difficulty of designing a consistent training objective for both pre-training and downstream tasks. To overcome above obstacles, we propose a novel framework named SGL-PT which follows the learning strategy ``Pre-train, Prompt, and Predict''. Specifically, we raise a strong and universal pre-training task coined as SGL that acquires the complementary merits of generative and contrastive self-supervised graph learning. And aiming for graph classification task, we unify pre-training and fine-tuning by designing a novel verbalizer-free prompting function, which reformulates the downstream task in a similar format as pretext task. Empirical results show that our method surpasses other baselines under unsupervised setting, and our prompt tuning method can greatly facilitate models on biological datasets over fine-tuning methods.
Preparing random state for quantum financing with quantum walks

In recent years, there has been an emerging trend of combining two innovations in computer science and physics to achieve better computation capability. Exploring the potential of quantum computation to achieve highly efficient performance in various tasks is a vital development in engineering and a valuable question in sciences, as it has a significant potential to provide exponential speedups for technologically complex problems that are specifically advantageous to quantum computers. However, one key issue in unleashing this potential is constructing an efficient approach to load classical data into quantum states that can be executed by quantum computers or quantum simulators on classical hardware. Therefore, the split-step quantum walks (SSQW) algorithm was proposed to address this limitation. We facilitate SSQW to design parameterized quantum circuits (PQC) that can generate probability distributions and optimize the parameters to achieve the desired distribution using a variational solver. A practical example of implementing SSQW using Qiskit has been released as open-source software. Showing its potential as a promising method for generating desired probability amplitude distributions highlights the potential application of SSQW in option pricing through quantum simulation.
SEO: Safety-Aware Energy Optimization Framework for Multi-Sensor Neural Controllers at the Edge

Runtime energy management has become quintessential for multi-sensor autonomous systems at the edge for achieving high performance given the platform constraints. Typical for such systems, however, is to have their controllers designed with formal guarantees on safety that precede in priority such optimizations, which in turn limits their application in real settings. In this paper, we propose a novel energy optimization framework that is aware of the autonomous system's safety state, and leverages it to regulate the application of energy optimization methods so that the system's formal safety properties are preserved. In particular, through the formal characterization of a system's safety state as a dynamic processing deadline, the computing workloads of the underlying models can be adapted accordingly. For our experiments, we model two popular runtime energy optimization methods, offloading and gating, and simulate an autonomous driving system (ADS) use-case in the CARLA simulation environment with performance characterizations obtained from the standard Nvidia Drive PX2 ADS platform. Our results demonstrate that through a formal awareness of the perceived risks in the test case scenario, energy efficiency gains are still achieved (reaching 89.9%) while maintaining the desired safety properties.
Model-Based Uncertainty in Value Functions

We consider the problem of quantifying uncertainty over expected cumulative rewards in model-based reinforcement learning. In particular, we focus on characterizing the variance over values induced by a distribution over MDPs. Previous work upper bounds the posterior variance over values by solving a so-called uncertainty Bellman equation, but the over-approximation may result in inefficient exploration. We propose a new uncertainty Bellman equation whose solution converges to the true posterior variance over values and explicitly characterizes the gap in previous work. Moreover, our uncertainty quantification technique is easily integrated into common exploration strategies and scales naturally beyond the tabular setting by using standard deep reinforcement learning architectures. Experiments in difficult exploration tasks, both in tabular and continuous control settings, show that our sharper uncertainty estimates improve sample-efficiency.
Weak equivalence of local independence graphs

Classical graphical modeling of multivariate random vectors uses graphs to encode conditional independence. In graphical modeling of multivariate stochastic processes, graphs may encode so-called local independence analogously. If some coordinate processes of the multivariate stochastic process are unobserved, the local independence graph of the observed coordinate processes is a directed mixed graph (DMG). Two DMGs may encode the same local independences in which case we say that they are Markov equivalent. Markov equivalence is a central notion in graphical modeling. We show that deciding Markov equivalence of DMGs is coNP-complete, even under a sparsity assumption. As a remedy, we introduce a collection of equivalence relations on DMGs that are all less granular than Markov equivalence and we say that they are weak equivalence relations. This leads to feasible algorithms for naturally occurring computational problems related to weak equivalence of DMGs. The equivalence classes of a weak equivalence relation have attractive properties. In particular, each equivalence class has a greatest element which leads to a concise representation of an equivalence class. Moreover, these equivalence relations define a hierarchy of granularity in the graphical modeling which leads to simple and interpretable connections between equivalence relations corresponding to different levels of granularity.
Sistem Pendukung Keputusan e-voting Seleksi Pemimpin menggunakan Metode Analytical Hierarchy Process

Elections for the leaders of the Student Executive Board are held every year. e-voting is one of the most important components and forms of democracy. The result of voting is always dissatisfaction from voters. To become a candidate for the Head of BEM, a selection process is required. When choosing a Student Executive Board Leader, there are several criteria that must be met by prospective candidates. The issue of selecting leaders was also discussed by Andi Rosano in choosing the Village Head. The difficulty for voters is to get a leader candidate who has advantages in different fields. Because of that, a decision support system is needed to assist voters in determining the leader of the Student Executive Board. The Decision Support System using the Analytical Hierachy Process Method can assist voters in selecting candidates for the Student Executive Board leaders. Methodology This research was carried out using a 2 (two) method approach, namely a field study conducted by means of interviews with the student affairs section that takes care of the election of the Student Executive Board Leader and literature study to collect information related to decision support systems using the Analytical Hierarchy Process method. The results of the research are in the form of a Decision Support System application using the AHP Method to assist the committee in getting the right results
Variational Linearized Laplace Approximation for Bayesian Deep Learning

The Linearized Laplace Approximation (LLA) has been recently used to perform uncertainty estimation on the predictions of pre-trained deep neural networks (DNNs). However, its widespread application is hindered by significant computational costs, particularly in scenarios with a large number of training points or DNN parameters. Consequently, additional approximations of LLA, such as Kronecker-factored or diagonal approximate GGN matrices, are utilized, potentially compromising the model's performance. To address these challenges, we propose a new method for approximating LLA using a variational sparse Gaussian Process (GP). Our method is based on the dual RKHS formulation of GPs and retains as the predictive mean the output of the original DNN. Furthermore, it allows for efficient stochastic optimization, which results in sub-linear training time in the size of the training dataset. Specifically, its training cost is independent of the number of training points. We compare our proposed method against accelerated LLA (ELLA), which relies on the Nystr\"om approximation, as well as other LLA variants employing the sample-then-optimize principle. Experimental results, both on regression and classification datasets, show that our method outperforms these already existing efficient variants of LLA, both in terms of the quality of the predictive distribution and in terms of total computational time.
Active Velocity Estimation using Light Curtains via Self-Supervised Multi-Armed Bandits

To navigate in an environment safely and autonomously, robots must accurately estimate where obstacles are and how they move. Instead of using expensive traditional 3D sensors, we explore the use of a much cheaper, faster, and higher resolution alternative: programmable light curtains. Light curtains are a controllable depth sensor that sense only along a surface that the user selects. We adapt a probabilistic method based on particle filters and occupancy grids to explicitly estimate the position and velocity of 3D points in the scene using partial measurements made by light curtains. The central challenge is to decide where to place the light curtain to accurately perform this task. We propose multiple curtain placement strategies guided by maximizing information gain and verifying predicted object locations. Then, we combine these strategies using an online learning framework. We propose a novel self-supervised reward function that evaluates the accuracy of current velocity estimates using future light curtain placements. We use a multi-armed bandit framework to intelligently switch between placement policies in real time, outperforming fixed policies. We develop a full-stack navigation system that uses position and velocity estimates from light curtains for downstream tasks such as localization, mapping, path-planning, and obstacle avoidance. This work paves the way for controllable light curtains to accurately, efficiently, and purposefully perceive and navigate complex and dynamic environments. Project website: https://siddancha.github.io/projects/active-velocity-estimation/
Analysis of the Flipped Classroom Model Using Digital Media in Improving Students' Mathematical Communication Skills

The flipped classroom model is a learning model in which activities usually carried out in class are changed to be carried out at home, such as material explanations by the teacher, and activities carried out at home are changed to be carried out at school, such as doing assignments. The research aims to describe the flipped classroom model using digital media to improve mathematical communication skills. This research is a qualitative descriptive study. The research subjects used were six junior high school students in Batu City. In the pretest and posttest, data collection techniques to determine students' mathematical communication abilities while observation to determine teacher activities in learning uses the flipped classroom model using digital media. The instruments used are test sheets and observation sheets. Data analysis techniques consist of data reduction, data presentation, and conclusion. The results showed that after using the flipped classroom model using digital media, there was an increase in mathematical communication skills. After learning according to the lesson plan, the results of observations were in the very good category. There was an increase of 53% in the indicator of the ability to write mathematical situations correctly and precisely. The indicator of the ability to write ideas or solve problems correctly and accurately increased by 33%, while the ability to write conclusions from problems using their language increased by 39%.
Object-ABN: Learning to Generate Sharp Attention Maps for Action Recognition

In this paper we propose an extension of the Attention Branch Network (ABN) by using instance segmentation for generating sharper attention maps for action recognition. Methods for visual explanation such as Grad-CAM usually generate blurry maps which are not intuitive for humans to understand, particularly in recognizing actions of people in videos. Our proposed method, Object-ABN, tackles this issue by introducing a new mask loss that makes the generated attention maps close to the instance segmentation result. Further the Prototype Conformity (PC) loss and multiple attention maps are introduced to enhance the sharpness of the maps and improve the performance of classification. Experimental results with UCF101 and SSv2 shows that the generated maps by the proposed method are much clearer qualitatively and quantitatively than those of the original ABN.
TUTORING: Instruction-Grounded Conversational Agent for Language Learners

In this paper, we propose Tutoring bot, a generative chatbot trained on a large scale of tutor-student conversations for English-language learning. To mimic a human tutor's behavior in language education, the tutor bot leverages diverse educational instructions and grounds to each instruction as additional input context for the tutor response generation. As a single instruction generally involves multiple dialogue turns to give the student sufficient speaking practice, the tutor bot is required to monitor and capture when the current instruction should be kept or switched to the next instruction. For that, the tutor bot is learned to not only generate responses but also infer its teaching action and progress on the current conversation simultaneously by a multi-task learning scheme. Our Tutoring bot is deployed under a non-commercial use license at https://tutoringai.com.
DATA MINING AND ANALYSIS APPLIED TO PUBLIC SECURITY DATA IN BELEM OF PARA, BRAZIL

Crime is a common social problem faced around the world, and it can affect a nation's quality of life, economic growth, and reputation. Thus, law enforcement officials need to take preventive measures and one of the methods that have been gaining ground in crime analysis is data mining. With this, the purpose of this paper is to apply data analysis and data mining techniques in public security databases in the city of Belém of Pará, in order to discover hidden patterns and assist security managers in developing new public policies to try to reduce crime rates, through data from police reports in the city of Belém of Pará, Brazil in the years 2019, 2020 and 2021. To guide this study, the CRISP-DM methodology was used, where it was possible through these techniques to extract knowledge to understand and analyze the crime scene in the municipality of Belém, such as the fact that a certain crime occurs at night implies that its nature is robbery in order to assist the responsible bodies in investigations and strategies for a more effective fight against crime.
Custom Deep Neural Network for Message Scoring

This paper offers customized message scoring creation of Technical Literature of Drugs using Text Analytics and Deep Learning.A customized Deep Learning driven model has beendeveloped that feeds scientific data (texts) about medicine(s) (written by medical researchers) & creates multiple sub messages that can be used for marketing research to effectively communicate benefits of drugs.Model helped in reducing manual intervention by automating the complete process and replacing Linguists.
Lithium and gold content in salt domes and saline lands of Western and Southern Kazakhstan

Purpose. To identify the lithium content of mineralized saline lands in the lower reaches of the Chu and Sarysu Rivers and the gold content of the Inder and Shalkar salt domes, and to give a predictive estimate of their industrial prospects. Methodology. The basis of the research methodology is a classic combination of prospecting and surveying: collection of precursor materials, field work, including object mapping, hydro- and lithochemical sampling, analytical studies and analysis of laboratory data. Findings. We carried out the whole complex of the planned works on the sites under study. As a result, in 76 % of the analyzed water samples lithium content was found to be higher than clarkee in seawater (0.17 mg/l) by 1.2–2.4–4.12 times. Lithium content in salts and soils is noted in significantly lower values of lithium clarkee in the Earth’s crust, in rare cases lithium values in samples are 1.5–2 times higher than clarkee. Out of 25 samples of the Shalkar salt dome area, Au was found in 9 samples with grades from 1.36 to 6.02 g/t. The percentage of significant samples is 36 %. The average Au content in the Shalkar salt dome taking into account all other “empty” samples is 1.2 g/t. According to these data, taking into account modern technologies of extraction of Au at its low content, the Shalkar salt dome may well be classified as a large-volume deposit of poor ores. The results of quantitative analysis by atomic absorption of samples from the Inder salt dome also indicate the presence of significant Au content in seven samples – from 1.6 to 3.9 g/t. The average gold content for the entire volume of samples taken would be 0.78 g/t, which can be considered commercially significant under current conditions. The given results of hydromineral raw materials research for possible extraction of lithium and noble metals, despite their preliminary character and insignificant volumes, unequivocally indicate the necessity of large-scale exploration works for final evaluation of the described objects and identification of new ones similar to them. Originality. The originality of the study is that such work has been carried out for the first time. Quantitative assessment of lithium and gold content in saline lands and salt domes of Kazakhstan showed an excess of their clarkee and the prospects of these objects for further research. Practical value. significance lies in the identification of lithium content of hydromineral raw materials and gold content of salt domes of Kazakhstan as possible cost-effective new sources of lithium and gold.
Active Membership Inference Attack under Local Differential Privacy in Federated Learning

Federated learning (FL) was originally regarded as a framework for collaborative learning among clients with data privacy protection through a coordinating server. In this paper, we propose a new active membership inference (AMI) attack carried out by a dishonest server in FL. In AMI attacks, the server crafts and embeds malicious parameters into global models to effectively infer whether a target data sample is included in a client's private training data or not. By exploiting the correlation among data features through a non-linear decision boundary, AMI attacks with a certified guarantee of success can achieve severely high success rates under rigorous local differential privacy (LDP) protection; thereby exposing clients' training data to significant privacy risk. Theoretical and experimental results on several benchmark datasets show that adding sufficient privacy-preserving noise to prevent our attack would significantly damage FL's model utility.
A NEW METHOD FOR IMAGE STEGANOGRAPHY USING THE COMBINATION OF LSB AND MSB

The process of secretly encoding information within an image is known as steganography. Using this technique, information is incorporated into a visual representation. The pixel values of an image contain information that is encoded in such a way that it is impossible for an adversary to detect where data is being hidden. In the course of this research, an original strategy for keeping information hidden has been suggested. The idea of spatial domain has been put to use by us through the utilisation of pixel values in order to conceal and retrieve information in this manner. In this research, we buried the message using the least significant bit (LSB) pixels and then retrieved it using the most significant bit (MSB). In addition, both the pros and the potential drawbacks of the proposed technique are discussed.