Reviewerly/exHarmony huggingface dataset:
Feasibility Study of Solar Power Generation System for Public Street Lighting

One of the uses of electricity that is widely used by society today is as a source of lighting. The increasing level of community mobility makes all activities require lighting. One part that is important and requires lighting is a highway or public road. PJU is a lighting lamp that is public (for the common good) and is usually installed on roads and certain places such as parks and other public places. PJU (street lighting) or road lighting is a light source that is installed as street lighting at night. Public street lighting using solar power is a cheap and economical alternative to be used as a source of lighting electricity because it uses a new and unlimited renewable energy source that comes from nature, namely solar energy. This study aims to determine the feasibility of solar street lighting in the future as a substitute for conventional public street lighting in the future by looking at the Net present cost, and Break even point assisted by HOMER software version 3.10.3. And the results obtained from the calculation of the Net present cost of conventional street lighting costs less, which is only Rp. 30,245,473 and the Net present cost of solar street lighting costs an initial investment of Rp. 128,341,312 and the BEP (Break even point) graph that has not been found to break even.
Integrated service post information system to support baby growth data reporting

The manifestation of technological advances that have played a role in various fields is information systems. Information systems are widely applied in various government agencies such as aspects of education, economy and health. The government seeks to improve services in the health sector by carrying out community empowerment activities through Integrated Service Posts. In its implementation, the Integrated Service Post still uses the manual method, namely recording participant data from the Integrated Service Post, data on measuring baby's height and weighing baby's weight, vitamins and immunizations as well as the results of health checks for pregnant women and the elderly. Considering that continuous monitoring of infant growth and development is very important, the manual system becomes less effective. Thus, the existence of an Integrated Service Post information system helps data collection on infant growth and development as well as the health status of pregnant women and the elderly to be more effective. Researchers used research methods in the form of observation, interviews and literature study. The system design uses Unified Modeling Language and its development uses Rapid Application Development. The results of this study are a website-based Integrated Service Post information system for the implementation of Integrated Service Posts.
Repeatable Random Permutation Set

Random permutation set (RPS), as a recently proposed theory, enables powerful information representation by traversing all possible permutations. However, the repetition of items is not allowed in RPS while it is quite common in real life. To address this issue, we propose repeatable random permutation set ($\rm R^2PS$) which takes the repetition of items into consideration. The right and left junctional sum combination rules are proposed and their properties including consistency, pseudo-Matthew effect and associativity are researched. Based on these properties, a decision support system application is simulated to show the effectiveness of $\rm R^2PS$.
Minimal codewords in Norm-Trace codes

In this paper, we consider the affine variety codes obtained evaluating the polynomials $by=a_kx^k+\dots+a_1x+a_0$, $b,a_i\in\mathbb{F}_{q^r}$, at the affine $\F_{q^r}$-rational points of the Norm-Trace curve. In particular, we investigate the weight distribution and the set of minimal codewords. Our approach, which uses tools of algebraic geometry, is based on the study of the absolutely irreducibility of certain algebraic varieties.
Late Fusion with Triplet Margin Objective for Multimodal Ideology Prediction and Analysis

Prior work on ideology prediction has largely focused on single modalities, i.e., text or images. In this work, we introduce the task of multimodal ideology prediction, where a model predicts binary or five-point scale ideological leanings, given a text-image pair with political content. We first collect five new large-scale datasets with English documents and images along with their ideological leanings, covering news articles from a wide range of US mainstream media and social media posts from Reddit and Twitter. We conduct in-depth analyses of news articles and reveal differences in image content and usage across the political spectrum. Furthermore, we perform extensive experiments and ablation studies, demonstrating the effectiveness of targeted pretraining objectives on different model components. Our best-performing model, a late-fusion architecture pretrained with a triplet objective over multimodal content, outperforms the state-of-the-art text-only model by almost 4% and a strong multimodal baseline with no pretraining by over 3%.
Security vulnerability analysis for an improved anonymous authentication protocol for wearable health monitoring system in an aging society

Purpose The wearable health monitoring system (WHMS) plays a significant role in medical experts collecting and using patient medical data.The WHMS is becoming more popular than in the past through mobile devices due to meaningful progress in wireless sensor networks.However, because the data about health used by the WHMS is related to privacy, it has to be protected from malicious access when wirelessly transmitted.Jiang et al. proposed a two-factor suitable for WHMSs using a fuzzy verifier.However, Jiaqing Mo et al. revealed that the protocol proposed by Jiang et al. had various security vulnerabilities and proposed an authentication protocol with improved security and guaranteed anonymity for WHMSs.In this paper, we analyse the authentication protocol proposed by Jiaqing Mo et al. and determine problems with the offline identification, password guessing attacks, operation process bit mismatch, no perfect forward secrecy, no mutual authentication and insider attacks.Method This paper analyzed the operation process of Jiang et al.'s protocol and found various vulnerability as off-line ID, PW guessing attack, operation process bit mismatch, no perfect forward secrecy, no mutual authentication and insider attack.According to Jiaqing Mo et al.'s proposed protocol, when an adversary acquires a MD, the adversary can extract information stored in the MD and then find out the user's ID and PW.The information of {Reg_i, A_i, C_i, m, n, h()} is sent to the MD through the GWN security channel.Thereafter, the MD calculates and updates A_i^*=A_i⊕h(ID_i∥r_i) and D_i=r_i⊕h(h(ID_i∥PW_i) mod m).Finally, information of {Reg_i, A_i^*, C_i, D_i, m, n, h()} is stored in the MD.Assuming that an adversary found out this through a physical analysis method, an ID and password can be derived through the formula of 〖Reg_i〗^*=h(h(ID_i∥R_i^*∥HPW_i^*) mod m).Summarizing the above formula, the adversary will be aware of the information {A i *, D i , m, h()} except for the ID and PW.The adversary repeatedly performs verification while continuing to change until the user's ID and PW are found.Ultimately, the user's exact ID and PW can be found.Results and Discussion In this paper, a security analysis was conducted after explaining the operation process of an authentication protocol with improved security and guaranteed anonymity for the WHMS proposed by Jiaqing Mo et al.The protocols proposed by Jiaqing Mo et al. have vulnerabilities in offline identification, password guessing attacks, operation process bit mismatch, no perfect forward secrecy, no mutual authentication and insider attack problems.
Tierkreis: A Dataflow Framework for Hybrid Quantum-Classical Computing

We present Tierkreis, a higher-order dataflow graph program representation and runtime designed for compositional, quantum-classical hybrid algorithms. The design of the system is motivated by the remote nature of quantum computers, the need for hybrid algorithms to involve cloud and distributed computing, and the long-running nature of these algorithms. The graph-based representation reflects how designers reason about and visualise algorithms, and allows automatic parallelism and asynchronicity. A strong, static type system and higher-order semantics allow for high expressivity and compositionality in the program. The flexible runtime protocol enables third-party developers to add functionality using any language or environment. With Tierkreis, quantum software developers can easily build, visualise, verify, test, and debug complex hybrid workflows, and immediately deploy them to the cloud or a custom distributed environment.
Minimum Latency Training of Sequence Transducers for Streaming End-to-End Speech Recognition

Sequence transducers, such as the RNN-T and the Conformer-T, are one of the most promising models of end-to-end speech recognition, especially in streaming scenarios where both latency and accuracy are important. Although various methods, such as alignment-restricted training and FastEmit, have been studied to reduce the latency, latency reduction is often accompanied with a significant degradation in accuracy. We argue that this suboptimal performance might be caused because none of the prior methods explicitly model and reduce the latency. In this paper, we propose a new training method to explicitly model and reduce the latency of sequence transducer models. First, we define the expected latency at each diagonal line on the lattice, and show that its gradient can be computed efficiently within the forward-backward algorithm. Then we augment the transducer loss with this expected latency, so that an optimal trade-off between latency and accuracy is achieved. Experimental results on the WSJ dataset show that the proposed minimum latency training reduces the latency of causal Conformer-T from 220 ms to 27 ms within a WER degradation of 0.7%, and outperforms conventional alignment-restricted training (110 ms) and FastEmit (67 ms) methods.
Domain Adaptive Video Semantic Segmentation via Cross-Domain Moving Object Mixing

The network trained for domain adaptation is prone to bias toward the easy-to-transfer classes. Since the ground truth label on the target domain is unavailable during training, the bias problem leads to skewed predictions, forgetting to predict hard-to-transfer classes. To address this problem, we propose Cross-domain Moving Object Mixing (CMOM) that cuts several objects, including hard-to-transfer classes, in the source domain video clip and pastes them into the target domain video clip. Unlike image-level domain adaptation, the temporal context should be maintained to mix moving objects in two different videos. Therefore, we design CMOM to mix with consecutive video frames, so that unrealistic movements are not occurring. We additionally propose Feature Alignment with Temporal Context (FATC) to enhance target domain feature discriminability. FATC exploits the robust source domain features, which are trained with ground truth labels, to learn discriminative target domain features in an unsupervised manner by filtering unreliable predictions with temporal consensus. We demonstrate the effectiveness of the proposed approaches through extensive experiments. In particular, our model reaches mIoU of 53.81% on VIPER to Cityscapes-Seq benchmark and mIoU of 56.31% on SYNTHIA-Seq to Cityscapes-Seq benchmark, surpassing the state-of-the-art methods by large margins. The code is available at: https://github.com/kyusik-cho/CMOM.
Dynamic Resource Allocation Method for Load Balance Scheduling over Cloud Data Center Networks

The cloud datacenter has numerous hosts as well as application requests where resources are dynamic. The demands placed on the resource allocation are diverse. These factors could lead to load imbalances, which affect scheduling efficiency and resource utilization. A scheduling method called Dynamic Resource Allocation for Load Balancing (DRALB) is proposed. The proposed solution constitutes two steps: First, the load manager analyzes the resource requirements such as CPU, Memory, Energy and Bandwidth usage and allocates an appropriate number of VMs for each application. Second, the resource information is collected and updated where resources are sorted into four queues according to the loads of resources i.e. CPU intensive, Memory intensive, Energy intensive and Bandwidth intensive. We demonstarate that SLA-aware scheduling not only facilitates the cloud consumers by resources availability and improves throughput, response time etc. but also maximizes the cloud profits with less resource utilization and SLA (Service Level Agreement) violation penalties. This method is based on diversity of clients applications and searching the optimal resources for the particular deployment. Experiments were carried out based on following parameters i.e. average response time; resource utilization, SLA violation rate and load balancing. The experimental results demonstrate that this method can reduce the wastage of resources and reduces the traffic upto 44.89 and 58.49 in the network.
How Does Adaptive Optimization Impact Local Neural Network Geometry?

Adaptive optimization methods are well known to achieve superior convergence relative to vanilla gradient methods. The traditional viewpoint in optimization, particularly in convex optimization, explains this improved performance by arguing that, unlike vanilla gradient schemes, adaptive algorithms mimic the behavior of a second-order method by adapting to the global geometry of the loss function. We argue that in the context of neural network optimization, this traditional viewpoint is insufficient. Instead, we advocate for a local trajectory analysis. For iterate trajectories produced by running a generic optimization algorithm OPT, we introduce $R^{\text{OPT}}_{\text{med}}$, a statistic that is analogous to the condition number of the loss Hessian evaluated at the iterates. Through extensive experiments, we show that adaptive methods such as Adam bias the trajectories towards regions where $R^{\text{Adam}}_{\text{med}}$ is small, where one might expect faster convergence. By contrast, vanilla gradient methods like SGD bias the trajectories towards regions where $R^{\text{SGD}}_{\text{med}}$ is comparatively large. We complement these empirical observations with a theoretical result that provably demonstrates this phenomenon in the simplified setting of a two-layer linear network. We view our findings as evidence for the need of a new explanation of the success of adaptive methods, one that is different than the conventional wisdom.
Improving Speech Prosody of Audiobook Text-to-Speech Synthesis with Acoustic and Textual Contexts

We present a multi-speaker Japanese audiobook text-to-speech (TTS) system that leverages multimodal context information of preceding acoustic context and bilateral textual context to improve the prosody of synthetic speech. Previous work either uses unilateral or single-modality context, which does not fully represent the context information. The proposed method uses an acoustic context encoder and a textual context encoder to aggregate context information and feeds it to the TTS model, which enables the model to predict context-dependent prosody. We conducted comprehensive objective and subjective evaluations on a multi-speaker Japanese audiobook dataset. Experimental results demonstrate that the proposed method significantly outperforms two previous works. Additionally, we present insights about the different choices of context - modalities, lateral information and length - for audiobook TTS that have never been discussed in the literature before.
Understanding the benefits and roles of mobile instant messaging use for the Korean elderly’s mental health: An exploratory study focusing on emotional recovery and suicide prevention

Chung (Republic of Korea), A. Lee (Republic of Korea), S.Y. Park (Republic of Korea), Hannah Lee (Republic of Korea), Hajin Lee (Republic of Korea), Soontae An (Republic of
Connecting Stochastic Optimal Control and Reinforcement Learning

In this paper the connection between stochastic optimal control and reinforcement learning is investigated. Our main motivation is to apply importance sampling to sampling rare events which can be reformulated as an optimal control problem. By using a parameterised approach the optimal control problem becomes a stochastic optimization problem which still raises some open questions regarding how to tackle the scalability to high-dimensional problems and how to deal with the intrinsic metastability of the system. To explore new methods we link the optimal control problem to reinforcement learning since both share the same underlying framework, namely a Markov Decision Process (MDP). For the optimal control problem we show how the MDP can be formulated. In addition we discuss how the stochastic optimal control problem can be interpreted in the framework of reinforcement learning. At the end of the article we present the application of two different reinforcement learning algorithms to the optimal control problem and a comparison of the advantages and disadvantages of the two algorithms.
Decorrelation with conditional normalizing flows

The sensitivity of many physics analyses can be enhanced by constructing discriminants that preferentially select signal events. Such discriminants become much more useful if they are uncorrelated with a set of protected attributes. In this paper we show that a normalizing flow conditioned on the protected attributes can be used to find a decorrelated representation for any discriminant. As a normalizing flow is invertible the separation power of the resulting discriminant will be unchanged at any fixed value of the protected attributes. We demonstrate the efficacy of our approach by building supervised jet taggers that produce almost no sculpting in the mass distribution of the background.
E-health literacy and associated factors among Korean adults: Three age group differences

Chung (Republic of Korea), A. Lee (Republic of Korea), S.Y. Park (Republic of Korea), Hannah Lee (Republic of Korea), Hajin Lee (Republic of Korea), Soontae An (Republic of
Singularity Avoidance with Application to Online Trajectory Optimization for Serial Manipulators

This work proposes a novel singularity avoidance approach for real-time trajectory optimization based on known singular configurations. The focus of this work lies on analyzing kinematically singular configurations for three robots with different kinematic structures, i.e., the Comau Racer 7-1.4, the KUKA LBR iiwa R820, and the Franka Emika Panda, and exploiting these configurations in form of tailored potential functions for singularity avoidance. Monte Carlo simulations of the proposed method and the commonly used manipulability maximization approach are performed for comparison. The numerical results show that the average computing time can be reduced and shorter trajectories in both time and path length are obtained with the proposed approach
Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion

Temporal data such as time series can be viewed as discretized measurements of the underlying function. To build a generative model for such data we have to model the stochastic process that governs it. We propose a solution by defining the denoising diffusion model in the function space which also allows us to naturally handle irregularly-sampled observations. The forward process gradually adds noise to functions, preserving their continuity, while the learned reverse process removes the noise and returns functions as new samples. To this end, we define suitable noise sources and introduce novel denoising and score-matching models. We show how our method can be used for multivariate probabilistic forecasting and imputation, and how our model can be interpreted as a neural process.
PhysioGait: Context-Aware Physiological Context Modeling for Person Re-identification Attack on Wearable Sensing

Person re-identification is a critical privacy breach in publicly shared healthcare data. We investigate the possibility of a new type of privacy threat on publicly shared privacy insensitive large scale wearable sensing data. In this paper, we investigate user specific biometric signatures in terms of two contextual biometric traits, physiological (photoplethysmography and electrodermal activity) and physical (accelerometer) contexts. In this regard, we propose PhysioGait, a context-aware physiological signal model that consists of a Multi-Modal Siamese Convolutional Neural Network (mmSNN) which learns the spatial and temporal information individually and performs sensor fusion in a Siamese cost with the objective of predicting a person's identity. We evaluated PhysioGait attack model using 4 real-time collected datasets (3-data under IRB #HP-00064387 and one publicly available data) and two combined datasets achieving 89% - 93% accuracy of re-identifying persons.
Uniform Interpolation in provability logics

We prove the uniform interpolation theorem in modal provability logics GL and Grz by a proof-theoretical method, using analytical and terminating sequent calculi for the logics. The calculus for G\"odel-L\"ob's logic GL is a variant of the standard sequent calculus, in the case of Grzegorczyk's logic Grz, the calculus implements an explicit loop-preventing mechanism inspired by work of Heuerding.
Analisis Performance Elevator Studi Kasus Di Pasar Grosir Tanah Abang (Blok A) Menggunakan Metode Dan Graphic Mechanical And Electrical Equipment (Mee) Dan Perhitungan Empiris

Tanah Abang Blok A Wholesale Market is a modern wholesale center in Jakarta that has more than 10floors and to make it easier for visitors to carry out activities that move the first floor j another floor, a verticaltransformation is needed, namely an elevator. Elevators are needed to streamline the time, mileage, and userpower to reach the first floor to other floors in a high-rise building. So in this Tugas Akhir the author takes thetitle Analisis performance elevator case study in Pasar grosir Tanah Abang block A. The method used is to usethe method of literature, observation, and interviews so that it is hoped that the author will be able to make iteasier to analyze it. In this study, the problems that will be discussed are the carrying capacity of the elevator,the waiting time of the elevator, the power of the motor, and the number of units. From this study, the followingresults were obtained for the elevator carrying capacity of 118 people for passenger elevators and 120 people forfreight elevators, waiting times for passenger elevators for 43.4 seconds and for freight elevators for 62.9 seconds,motor power for passenger elevators of 15 KW and freight elevators of 18 KW, and the number of units as manyas 4 units for passenger elevators and as many as 3 units for freight elevators.