Reviewerly/exHarmony huggingface dataset:
Analisis Komponen Koleksi Berdasarkan Instrumen Akreditasi Perpustakaan Institut Bisnis dan Informatika Islam

This study aims to determine the extent to which the Indonesian Institute of Business and Informatics Library prepares for library accreditation, especially in the collection component. The instrument used in this study is the university library accreditation instrument listed in the national library regulations of the Republic of Indonesia number 10 of 2018. The library collection component has 26 key indicators with a maximum weight of 20. This research method uses a descriptive quantitative approach. The technique of collecting data is through observation and structured interviews with the drafting team for the accreditation of the Indonesian Institute of Business and Informatics Library. The results showed that the Indonesian Institute of Business and Informatics Library based on the university library accreditation instrument was estimated to get a value of 13.7 out of a maximum weight of 20 for the collection component. This means that the preparation of the Indonesian Institute of Business and Informatics Library is still not optimal and must be improved again. The library of the Indonesian Institute of Business and Informatics has the potential to increase its accreditation value derived from 16 other indicators which include the availability of collection development policies, implementation of policies, surveys of collection needs, number of printed books, titles of reference books, subscribed scientific journals, subscribed electronic journals, addition of printed book collections, number of electronic book collections, audio visual collections, number of brochures, leaflets, pamphlets, posters, subscribed databases, processing standards, enumeration, fumigation, and improvement of library materials.
DIAGNOSIS SYSTEM DESIGN OF DEPRESSION AND ANXIETY WITH NAÏVE BAYES METHOD

Depression and Anxiety are mental illness that arise in the presence of depression and anxiety from within the individual. The depression having features them level a dramatic decline to an interest, trouble sleeping and eat, the guilt or feel having no value self, and usually followed by a feeling of agitated, uneasiness is the norm reasonable like when we will do appearance in front of people. However, for the level of anxiety that must be considered is when the individual experiences an excessive intensity of anxiety unlike other normal individuals. The method in the system used to diagnose depression and anxiety is the Naïve Bayes Method. The process of using the naïve bayes method by using 50 correspondent data then taking 5 samples from the diagnostic results to prove the accuracy of the data generated after being diagnosed, this method can provide diagnosis, then provide therapy as an initial preventive step for sufferers, and as a solution to suppress therapy costs for suffers and sufferers’ choice as the first step before seeing the experts in person.
The set of distinctive properties of the stages of development of VR software

his paper presents a methodology that can be used to create immersive virtual reality applications for teaching, conceptual modeling in the field of virtual reality, and specifically virtual prototyping, in various fields of application. The main stages of virtual reality application development are analyzed: planning; visualization; programming; user interface; testing, as well as tasks for the virtual assembly of parts, solved using a set of software and hardware visualization tools. The use of virtual reality technologies in the educational process can improve academic performance and at the same time reduce the number of errors and costs. Virtual learning experiences should not be about learning, so these learning environments need to be designed with a constructivist approach to reap the full benefits of learning.
Troubleshooting Generator Sets using Expert System

PT. Zaman Teknindo (PT. ZT) is a company engaged in Mechanical and Engineering field which is registered as one PT. Telkomsel vendors. The problems that occur at PT. ZT, if the power outage and generator set (generator) does not start automatically. The corrective team on duty at that time will go to the field and find a solution to the problem. With a lack of knowledge from the corrective team, they need help from the mechanical team. The mechanical team is an external team of PT. ZT. To bring a mechanical team requires an enormous cost and a relatively long time needed to get to the location. Based on the problem above, this study proposes a forward chaining expert system that is by depth-first search using the certainty factor method. To prove whether a fact is certain or not, it must be in the metric form in generator troubleshooting. The research methodology used the Software Development Life Cycle (SDLC) starting from problem identification, analysis, design, coding, testing and maintenance. This system is web-based, so users can easily access and choose symptoms of the damage. With this system makes it easy for PT. ZT especially the corrective team in the field can easily find out the damage symptoms without having to meet with experts directly.
Blockchain Revolutionizing Marketing: Harnessing the Power of Distributed Ledgers for Transparent, Secure, and Efficient Marketing Practices

This study explores the transformative potential of blockchain technology in revolutionizing marketing practices.With the rise of digital marketing and concerns about privacy, transparency, and efficiency, blockchain offers solutions to these challenges.By leveraging the decentralized and immutable nature of distributed ledgers, marketers can establish trust, enhance security, and foster transparency throughout the advertising supply chain.This research examines various applications of blockchain in marketing, such as verifying ad impressions, combating ad fraud, ensuring data integrity, and optimizing customer reward programs.Through these use cases, blockchain demonstrates its ability to reshape marketing, paving the way for a more accountable, secure, and efficient future.
Towards robust paralinguistic assessment for real-world mobile health (mHealth) monitoring: an initial study of reverberation effects on speech

Speech is promising as an objective, convenient tool to monitor health remotely over time using mobile devices.Numerous paralinguistic features have been demonstrated to contain salient information related to an individual's health.However, mobile device specification and acoustic environments vary widely, risking the reliability of the extracted features.In an initial step towards quantifying these effects, we report the variability of 13 exemplar paralinguistic features commonly reported in the speech-health literature and extracted from the speech of 42 healthy volunteers recorded consecutively in rooms with low and high reverberation with one budget and two higher-end smartphones, and a condenser microphone.Our results show reverberation has a clear effect on several features, in particular voice quality markers.They point to new research directions investigating how best to record and process in-thewild speech for reliable longitudinal health state assessment.
Incremental Blockwise Beam Search for Simultaneous Speech Translation with Controllable Quality-Latency Tradeoff

Blockwise self-attentional encoder models have recently emerged as one promising end-to-end approach to simultaneous speech translation. These models employ a blockwise beam search with hypothesis reliability scoring to determine when to wait for more input speech before translating further. However, this method maintains multiple hypotheses until the entire speech input is consumed -- this scheme cannot directly show a single \textit{incremental} translation to users. Further, this method lacks mechanisms for \textit{controlling} the quality vs. latency tradeoff. We propose a modified incremental blockwise beam search incorporating local agreement or hold-$n$ policies for quality-latency control. We apply our framework to models trained for online or offline translation and demonstrate that both types can be effectively used in online mode. Experimental results on MuST-C show 0.6-3.6 BLEU improvement without changing latency or 0.8-1.4 s latency improvement without changing quality.
Parsing dialog turns with prosodic features in English

Parsing spoken dialogue presents challenges that parsing text does not, including a lack of clear sentence boundaries.We know from previous work that prosody helps in parsing single sentences [1], but we want to show the effect of prosody on parsing speech that isn't segmented into sentences.In experiments on the English Switchboard corpus, we find prosody helps our model both with parsing and with accurately identifying sentence boundaries.However, we find that the bestperforming parser is not necessarily the parser that produces the best sentence segmentation performance.We suggest that the best parses instead come from modelling sentence boundaries jointly with other syntactic boundaries.
Emo-StarGAN: A Semi-Supervised Any-to-Many Non-Parallel Emotion-Preserving Voice Conversion

Speech anonymisation prevents misuse of spoken data by removing any personal identifier while preserving at least linguistic content. However, emotion preservation is crucial for natural human-computer interaction. The well-known voice conversion technique StarGANv2-VC achieves anonymisation but fails to preserve emotion. This work presents an any-to-many semi-supervised StarGANv2-VC variant trained on partially emotion-labelled non-parallel data. We propose emotion-aware losses computed on the emotion embeddings and acoustic features correlated to emotion. Additionally, we use an emotion classifier to provide direct emotion supervision. Objective and subjective evaluations show that the proposed approach significantly improves emotion preservation over the vanilla StarGANv2-VC. This considerable improvement is seen over diverse datasets, emotions, target speakers, and inter-group conversions without compromising intelligibility and anonymisation.
A Wasserstein Distance-Based Cost-Sensitive Framework for Imbalanced Data Classification

Class imbalance is a prevalent problem in many real-world applications, and imbalanced data distribution can dramatically skew the performance of classifiers.In general, the higher the imbalance ratio of a dataset, the more difficult it is to classify.However, it is found that standard classifiers can still achieve good classification results on some highly imbalanced datasets.Obviously, the class imbalance is only a superficial characteristic of the data, and the underlying structural information is often the key factor affecting the classification performance.As implicit prior knowledge, structural information has been validated to be crucial for designing a good classifier.This paper proposes a Wasserstein-based cost-sensitive support vector machine (CS-WSVM) for class imbalance learning, incorporating prior structural information and a costsensitive strategy.The Wasserstein distance is introduced to model the distribution of majority and minority samples to capture the structural information, which is employed to weight the majority and minority samples.Comprehensive experiments on synthetic and real-world datasets, especially on the radar emitter signal dataset, demonstrated that CS-WSVM can achieve outstanding performance in imbalanced scenarios.
Improved Training for End-to-End Streaming Automatic Speech Recognition Model with Punctuation

Punctuated text prediction is crucial for automatic speech recognition as it enhances readability and impacts downstream natural language processing tasks.In streaming scenarios, the ability to predict punctuation in real-time is particularly desirable but presents a difficult technical challenge.In this work, we propose a method for predicting punctuated text from input speech using a chunk-based Transformer encoder trained with Connectionist Temporal Classification (CTC) loss.The acoustic model trained with long sequences by concatenating the input and target sequences can learn punctuation marks attached to the end of sentences more effectively.Additionally, by combining CTC losses on the chunks and utterances, we achieved both the improved F1 score of punctuation prediction and Word Error Rate (WER).
Dysarthric Speech Recognition, Detection and Classification using Raw Phase and Magnitude Spectra

In this paper, we explore the effectiveness of deploying the raw phase and magnitude spectra for dysarthric speech recognition, detection and classification.In particular, we scrutinise the usefulness of various raw phase-based representations along with their combinations with the raw magnitude spectrum and filterbank features.We employed single and multi-stream architectures consisting of a cascade of convolutional, recurrent and fully-connected layers for acoustic modelling.Furthermore, we investigate various configurations and fusion schemes as well as their training dynamics.In addition, the accuracies of the raw phase and magnitude based systems in the detection and classification tasks are studied and discussed.We report the performance on the UASpeech and TORGO dysarthric speech databases and for different severity levels.Our best system achieved WERs of 31.2% and 9.1% for dysarthric and typical speech on TORGO and 30.2% on UASpeech, respectively.
Leverage Weakly Annotation to Pixel-wise Annotation via Zero-shot Segment Anything Model for Molecular-empowered Learning

Precise identification of multiple cell classes in high-resolution Giga-pixel whole slide imaging (WSI) is critical for various clinical scenarios. Building an AI model for this purpose typically requires pixel-level annotations, which are often unscalable and must be done by skilled domain experts (e.g., pathologists). However, these annotations can be prone to errors, especially when distinguishing between intricate cell types (e.g., podocytes and mesangial cells) using only visual inspection. Interestingly, a recent study showed that lay annotators, when using extra immunofluorescence (IF) images for reference (referred to as molecular-empowered learning), can sometimes outperform domain experts in labeling. Despite this, the resource-intensive task of manual delineation remains a necessity during the annotation process. In this paper, we explore the potential of bypassing pixel-level delineation by employing the recent segment anything model (SAM) on weak box annotation in a zero-shot learning approach. Specifically, we harness SAM's ability to produce pixel-level annotations from box annotations and utilize these SAM-generated labels to train a segmentation model. Our findings show that the proposed SAM-assisted molecular-empowered learning (SAM-L) can diminish the labeling efforts for lay annotators by only requiring weak box annotations. This is achieved without compromising annotation accuracy or the performance of the deep learning-based segmentation. This research represents a significant advancement in democratizing the annotation process for training pathological image segmentation, relying solely on non-expert annotators.
SegDA: Maximum Separable Segment Mask with Pseudo Labels for Domain Adaptive Semantic Segmentation

Unsupervised Domain Adaptation (UDA) aims to solve the problem of label scarcity of the target domain by transferring the knowledge from the label rich source domain. Usually, the source domain consists of synthetic images for which the annotation is easily obtained using the well known computer graphics techniques. However, obtaining annotation for real world images (target domain) require lot of manual annotation effort and is very time consuming because it requires per pixel annotation. To address this problem we propose SegDA module to enhance transfer performance of UDA methods by learning the maximum separable segment representation. This resolves the problem of identifying visually similar classes like pedestrian/rider, sidewalk/road etc. We leveraged Equiangular Tight Frame (ETF) classifier inspired from Neural Collapse for maximal separation between segment classes. This causes the source domain pixel representation to collapse to a single vector forming a simplex vertices which are aligned to the maximal separable ETF classifier. We use this phenomenon to propose the novel architecture for domain adaptation of segment representation for target domain. Additionally, we proposed to estimate the noise in labelling the target domain images and update the decoder for noise correction which encourages the discovery of pixels for classes not identified in pseudo labels. We have used four UDA benchmarks simulating synthetic-to-real, daytime-to-nighttime, clear-to-adverse weather scenarios. Our proposed approach outperforms +2.2 mIoU on GTA -> Cityscapes, +2.0 mIoU on Synthia -> Cityscapes, +5.9 mIoU on Cityscapes -> DarkZurich, +2.6 mIoU on Cityscapes -> ACDC.
UFed-GAN: A Secure Federated Learning Framework with Constrained Computation and Unlabeled Data

To satisfy the broad applications and insatiable hunger for deploying low latency multimedia data classification and data privacy in a cloud-based setting, federated learning (FL) has emerged as an important learning paradigm. For the practical cases involving limited computational power and only unlabeled data in many wireless communications applications, this work investigates FL paradigm in a resource-constrained and label-missing environment. Specifically, we propose a novel framework of UFed-GAN: Unsupervised Federated Generative Adversarial Network, which can capture user-side data distribution without local classification training. We also analyze the convergence and privacy of the proposed UFed-GAN. Our experimental results demonstrate the strong potential of UFed-GAN in addressing limited computational resources and unlabeled data while preserving privacy.
Smart Data Mapping for Connecting Power System Model and Geospatial Data

Knowing the geospatial locations of power system model elements and linking load models with end users and their communities are the foundation for analyzing system resilience and vulnerability to natural hazards. However, power system models and geospatial data for power grid assets are often developed asynchronously without close coordination. Creating a direct mapping between the two is a challenging task, mainly due to heterogeneous data structures, target uses, historical legacies, and human errors. This work aims to build an automatic data mapping workflow to connect the two, and to support energy grid resilience studies for Puerto Rico. The primary steps in this workflow include constructing graphs using geospatial data, and aligning them to the transmission networks defined in the power system data. The results have been evaluated against existing manual mapping practices for part of the Puerto Rico Power Grid model to illustrate the performance of such auto-mapping solutions.
On the equivalence of Occam algorithms

Blumer et al. (1987, 1989) showed that any concept class that is learnable by Occam algorithms is PAC learnable. Board and Pitt (1990) showed a partial converse of this theorem: for concept classes that are closed under exception lists, any class that is PAC learnable is learnable by an Occam algorithm. However, their Occam algorithm outputs a hypothesis whose complexity is $\delta$-dependent, which is an important limitation. In this paper, we show that their partial converse applies to Occam algorithms with $\delta$-independent complexities as well. Thus, we provide a posteriori justification of various theoretical results and algorithm design methods which use the partial converse as a basis for their work.
INR-Arch: A Dataflow Architecture and Compiler for Arbitrary-Order Gradient Computations in Implicit Neural Representation Processing

An increasing number of researchers are finding use for nth-order gradient computations for a wide variety of applications, including graphics, meta-learning (MAML), scientific computing, and most recently, implicit neural representations (INRs). Recent work shows that the gradient of an INR can be used to edit the data it represents directly without needing to convert it back to a discrete representation. However, given a function represented as a computation graph, traditional architectures face challenges in efficiently computing its nth-order gradient due to the higher demand for computing power and higher complexity in data movement. This makes it a promising target for FPGA acceleration. In this work, we introduce INR-Arch, a framework that transforms the computation graph of an nth-order gradient into a hardware-optimized dataflow architecture. We address this problem in two phases. First, we design a dataflow architecture that uses FIFO streams and an optimized computation kernel library, ensuring high memory efficiency and parallel computation. Second, we propose a compiler that extracts and optimizes computation graphs, automatically configures hardware parameters such as latency and stream depths to optimize throughput, while ensuring deadlock-free operation, and outputs High-Level Synthesis (HLS) code for FPGA implementation. We utilize INR editing as our benchmark, presenting results that demonstrate 1.8-4.8x and 1.5-3.6x speedup compared to CPU and GPU baselines respectively. Furthermore, we obtain 3.1-8.9x and 1.7-4.3x lower memory usage, and 1.7-11.3x and 5.5-32.8x lower energy-delay product. Our framework will be made open-source and available on GitHub.
VERF: Runtime Monitoring of Pose Estimation with Neural Radiance Fields

We present VERF, a collection of two methods (VERF-PnP and VERF-Light) for providing runtime assurance on the correctness of a camera pose estimate of a monocular camera without relying on direct depth measurements. We leverage the ability of NeRF (Neural Radiance Fields) to render novel RGB perspectives of a scene. We only require as input the camera image whose pose is being estimated, an estimate of the camera pose we want to monitor, and a NeRF model containing the scene pictured by the camera. We can then predict if the pose estimate is within a desired distance from the ground truth and justify our prediction with a level of confidence. VERF-Light does this by rendering a viewpoint with NeRF at the estimated pose and estimating its relative offset to the sensor image up to scale. Since scene scale is unknown, the approach renders another auxiliary image and reasons over the consistency of the optical flows across the three images. VERF-PnP takes a different approach by rendering a stereo pair of images with NeRF and utilizing the Perspective-n-Point (PnP) algorithm. We evaluate both methods on the LLFF dataset, on data from a Unitree A1 quadruped robot, and on data collected from Blue Origin's sub-orbital New Shepard rocket to demonstrate the effectiveness of the proposed pose monitoring method across a range of scene scales. We also show monitoring can be completed in under half a second on a 3090 GPU.
Tweet Sentiment Extraction using Viterbi Algorithm with Transfer Learning

Tweet sentiment extraction extracts the most significant portion of the sentence, determining whether the sentiment is positive or negative. This research aims to identify the part of tweet sentences that strikes any emotion. To reach this objective, we continue improving the Viterbi algorithm previously modified by the author to make it able to receive pre-trained model parameters. We introduce the confidence score and vector as two indicators responsible for evaluating the model internally before assessing the final results. We then present a method to fine-tune this nonparametric model. We found that the model gets highly explainable as the confidence score vector reveals precisely where the least confidence predicted states are and if the modifications approved ameliorate the confidence score or if the tuning is going in the wrong direction.