Reviewerly/exHarmony huggingface dataset:
Measuring The Effectiveness of U-Dictionary in Increasing The Interest in Learning English Language Student At Madrasah Ibtidaiyah

In the current era of disruption, the need to learn English is very important, therefore several digital applications have been created to make learning English easier, especially for students. U-Dictionary is a popular English learning application that can be used as an option, therefore this research aims to analyze the effectiveness of using the U-Dictionary application in increasing students' interest in learning at Madrasah Ibtidaiyah and what students' perceptions are regarding the use of U-Dictionary in increasing interest in learning languages. English at Madrasah Ibtidaiyah. This research uses mixed methods. The data collection technique used in this research is a questionnaire. Data analysis used the paired t-test data analysis technique with the Shapiro-Wilk normality test. Meanwhile, the qualitative analysis uses content analysis. Based on the results of data analysis, it can be seen that the U-Dictionary application is effective in increasing students' interest in learning at Madrasah Ibtidaiyah with a significance value (&lt;0.001). The results of the questionnaire regarding students' perceptions of using U-Dictionary, the majority of respondents had a fairly high level of use, a fairly high level of self-confidence, the ability to understand good English vocabulary, a fairly high level of interest in learning English, and felt that learning English is quite effective or very effective.
Deep Imitation Learning for Humanoid Loco-manipulation Through Human Teleoperation

We tackle the problem of developing humanoid loco-manipulation skills with deep imitation learning. The difficulty of collecting task demonstrations and training policies for humanoids with a high degree of freedom presents substantial challenges. We introduce TRILL, a data-efficient framework for training humanoid loco-manipulation policies from human demonstrations. In this framework, we collect human demonstration data through an intuitive Virtual Reality (VR) interface. We employ the whole-body control formulation to transform task-space commands by human operators into the robot's joint-torque actuation while stabilizing its dynamics. By employing high-level action abstractions tailored for humanoid loco-manipulation, our method can efficiently learn complex sensorimotor skills. We demonstrate the effectiveness of TRILL in simulation and on a real-world robot for performing various loco-manipulation tasks. Videos and additional materials can be found on the project page: https://ut-austin-rpl.github.io/TRILL.
Specifying Target Objects in Robot Teleoperation Using Speech and Natural Eye Gaze

Current approaches in robot teleoperation often require significant mental and physical effort. In this study, we propose a new intent detection framework to teleoperate robotic arms based on human speech and natural eye gaze. Our framework applies instance segmentation on the robot's camera image and predicts the human's intended object through matching eye-gaze data, instance masks, instance classes, and transcribed words. Our experiment results show a prediction accuracy between 90.7% and 98.6%, including cases when the target objects are duplicated or occluded. The prediction accuracy of the combination of eye gaze and speech inputs outperformed the prediction accuracy of eye gaze input only, between 79.9% and 89.2%, and speech input only, between 25.3% and 71.6%. Moreover, we observe that eye gaze input has a greater importance than speech input in improving prediction accuracy when two duplicated target objects are present in the scene. Our results from NASA TLX questionnaires show that teleoperating the robotic arms with our proposed framework requires little effort including cases when the target objects are duplicated or occluded.
Detecting Moving Objects Using a Novel Optical-Flow-Based Range-Independent Invariant

This paper focuses on a novel approach for detecting moving objects during camera motion. We present an optical-flow-based transformation that yields a consistent 2D invariant image output regardless of time instants, range of points in 3D, and the camera's speed. In other words, this transformation generates a lookup image that remains invariant despite the changing projection of the 3D scene and camera motion. In the new domain, projections of 3D points that deviate from the values of the predefined lookup image can be clearly identified as moving relative to the stationary 3D environment, making them seamlessly detectable. The method does not require prior knowledge of the camera's direction of motion or speed, nor does it necessitate 3D point range information. It is well-suited for real-time parallel processing, rendering it highly practical for implementation. We have validated the effectiveness of the new domain through simulations and experiments, demonstrating its robustness in scenarios involving rectilinear camera motion, both in simulations and with real-world data. This approach introduces new ways for moving objects detection during camera motion, and also lays the foundation for future research in the context of moving object detection during six-degrees-of-freedom camera motion.
Performance Analysis of Building Integrated Photovoltaic (BIPV): MANOVA Analysis

Global energy consumption has increased over the years due to population growth, economic expansion, and the pursuit of a higher quality of life. The building sector is a critical sector for high consumption, contributing to about 36% of the total global energy consumption in 2020. The global interest in reducing energy consumption allows different sectors to adopt renewable resources such as solar photovoltaics (PV). However, utilizing solar PV in buildings has limitations due to rooftop space constraints in newly designed buildings. Due to this limitation and continuous innovation in solar PV technologies, Building Integrated Photovoltaics (BIPV) have been introduced as a potential alternative for solar PV in buildings. As such, BIPV energy performance and panel temperatures are affected by many factors such as orientation setup and technology type. Therefore, this research aims to analyze the impact of technology type (indium gallium selenide (CIGS) and monocrystalline (MONO) & orientation on BIPV energy production & module temperature. The data used for this study is based on a project completed in Dubai for BIPV in building applications. The results indicate that BIPV type and system orientation during each session significantly affect BIPV energy production and BIPV module temperature.
Towards Traitor Tracing in Black-and-White-Box DNN Watermarking with Tardos-Based Codes

The growing popularity of Deep Neural Networks, which often require computationally expensive training and access to a vast amount of data, calls for accurate authorship verification methods to deter unlawful dissemination of the models and identify the source of the leak. In DNN watermarking the owner may have access to the full network (white-box) or only be able to extract information from its output to queries (black-box), but a watermarked model may include both approaches in order to gather sufficient evidence to then gain access to the network. Although there has been limited research in white-box watermarking that considers traitor tracing, this problem is yet to be explored in the black-box scenario. In this paper, we propose a black-and-white-box watermarking method for DNN classifiers that opens the door to collusion-resistant traitor tracing in black-box, exploiting the properties of Tardos codes, and making it possible to identify the source of the leak before access to the model is granted. While experimental results show that the method can successfully identify traitors, even when further attacks have been performed, we also discuss its limitations and open problems for traitor tracing in black-box.
Verification of Safety and Liveness on Process Management System using Spin Model Checker

Process management deals with processor mechanisms to execute the processes. Nowadays an efficient process management algorithm is necessary for the efficient load balancing of the processor. Algorithm efficiency and time complexity always depend upon its correctness. We address a problem related to ensuring the correctness of the process management system by checking safety and liveness properties and giving an algorithm which is easy to understand and ensure its correctness using model checking techniques. Model checking is used to formally verify the hardware and software system. There exist various algorithms with different approaches to give a complex and efficient process managing system. To the best of our knowledge, formal verification of algorithms using model-checking techniques, particularly with the SPIN model checker, is not done. We give solution of this problem by doing formal verification of proposed algorithm using SPIN. In this paper, we first give an algorithm for the system. After that formal verification of the algorithm takes place in such a way that first, we generate a program graph for the system. Second, we define the safety and liveness properties of the system according to requirements and then convert these properties in the form of LTL formulas After all of this we give the LTL formula and program graph as input to the SPIN the model checker. Model checker will ensure whether the program graph satisfies the LTL formula or not.
Multi-Dataset Human Activity Recognition: Leveraging Fusion for Enhanced Performance

This paper explores the domain of Human Activity Recognition (HAR) utilizing sensor-based data and machine learning models. Deeply, the paper research two data fusion methods with the goal of building efficient model for activity recognition problem. two dataset have combined for the purpose of classification. We address a common challenge in HAR research. such as, Using different smartphone models and sensor configurations. To tackle this challenge, we conducts experiments on two datasets, KU-HAR and UCI HAR, and employs data fusion techniques at both feature and decision levels. Four popular machine learning classifiers-Decision Trees, Random Forest, Gradient Boosting, and XGBoost-are evaluated using these fusion methods. The results show promising performance in classifying fundamental human activities. XGBoost demonstrated the highest accuracy, surpassing all other models with approximately 96.83%. In comparison to XGBoost's accuracy, Gradient Boosting achieved an accuracy of about 95.71 %, demonstrating strong performance. Random Forest, while slightly behind, still delivered an acceptable accuracy of around 94.28 %, making it a viable choice for Human Activity Recognition tasks. decision tree has the lowest performance with 85.69 % In terms of decision-level fusion accuracy, Stacking outperformed the other methods with an accuracy of approximately 96.70%. Majority Voting also demonstrated strong performance, closely following with an accuracy of about 95.96%. However, Bagging fell behind the rest, achieving an accuracy of approximately 93.65 %.
Echoes in Silence: A Technological Leap for Pakistan Sign Language Translation and Recognition

In an endeavor to bridge communication barriers for the Pakistani deaf community, this project presents a robust system capable of translating Pakistani Sign Language (PSL) to text and vice versa. The architecture leverages cutting-edge technologies: the textual and voice inputs are processed using Natural Language Processing (NLP) techniques, with voice-to-text translation facilitated by the WebSpeechToolkit. For the Sign-to-Text (S2T) module, images and videos undergo rigorous preprocessing using OpenCV, followed by hand landmark detection via MediaPipe, ensuring the precise capture of PSL gestures. TensorFlow's neural networks are instrumental in recognizing these gestures, translating them into textual counterparts. The project's unique feature is its flexibility in breaking down non-existent sentences into words and, if needed, further into alphabets. Despite its innovative approach, the system acknowledges limitations in its training data, encompassing a dataset of English words, numbers, Urdu alphabets, and English alphabets, yet still achieving an impressive accuracy of approximately 95 %. Hosted on a user-friendly Django website interface, this system introduces two primary modules: one for translating text to PSL signs and another for the inverse. With its amalgamation of technologies and user-centric design, this project holds promise as a pivotal tool for enhancing the accessibility and interaction of the deaf community in Pakistan.
Facilitating Investment Strategy Negotiations Through Logic

In the process of negotiating investment strategies between a fund and investors, establishing trust, transparency, traceability, and correctness among the involved parties is crucial to ensure smooth and successful outcomes. The adoption of logic- based AI, with its reliability, consistency, and explainability, can serve as a crucial catalyst to assist parties during negotiations by providing useful insights and explainable suggestions. This paper showcases how various Knowledge Representation and Reasoning (KRR) techniques can be leveraged to assist financial parties during investment negotiations. It demonstrates the use of logical definitions to represent complex financial investment strategies, allowing parties to gain a comprehensive understanding of the policies under discussion. Furthermore, automated reasoning is used to generate useful insights and actionable information enabling informed decision-making and enhancing the overall negotiation process.
A Collaborative Trajectory-Oriented Viewport Prediction for on-Demand and Live 360° VR Video Streaming

Viewport prediction is critical in delivering high-quality 360° virtual reality (VR) videos to a large audience across diverse networks. Traditional computationally expensive viewport prediction mechanisms that rely on static content analysis and individual viewing preferences have proven inadequate in adapting to dynamic content and user preferences. This paper introduces TOPVR, a novel trajectory-oriented viewport prediction approach for both on-demand and live 360° VR video streaming. TOPVR overcomes the challenges introduced by diverse user preferences by leveraging the collaborative behavior of viewers and smartly capturing the relationship between different trajectories. It incorporates a user management system that identifies potential users with similar preferences and the closest trajectory changes to the current user over time. Additionally, a collaborative viewport prediction method estimates future viewing positions for each user based on recent viewing information and similar trajectories and trend changes of other users watching the same content. We evaluate the performance of TOPVR using a real-world head movement dataset recorded using on-demand and live streaming experiments. Our experimental results demonstrate that TOPVR, with its viewer management system and collaborative trajectory prediction mechanisms, outperforms existing benchmark algorithms in terms of higher prediction performance.
PRNU-Based Source Camera Statistical Certification

The problem of detecting the presence of a PRNU pattern in a query image can be stated as a hypothesis testing problem. The test statistics that have been proposed to perform this test all suffer from the same drawback: decision thresholds need to be set empirically. This poses a major problem for source camera certification, since these methods do not provide an accu-rate false alarm rate for each detection but rather a lower bound related to the size of the dataset used to derive such thresholds. In this work, we propose an alternative approach for source camera certification that can be used together with the classic testing strategies. Our method relies on two hypothesis tests based on local correlations which do not require computing empirical distributions. The p-value of the test serves is a statistically founded confidence measure that can serve as certification. Our results show that in most cases, the PRNU true detections give almost absolute guarantees, with p-values smaller than 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-100</sup> , while most true negatives deliver p-values above 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> .
Stochastic Shadow-Cutting Machine

Recently, a new concept called shadow-cuts has recently been proposed for a fully-connected graph whose edge matrix is Hermitian with arbitrary complex numbers. Each neuron is associated with a phase and the sum of shadow cuts is defined as the sum of inter-cluster phased edges. However, the shadow-cut machine is 100% deterministic and therefore its modeling capacity is relatively limited. In this brief, we (i) extend it to stochastic domain which yields the so-called “Stochastic Shadow-Cutting Machine” (SSCM), and (ii) show that choosing the energy function of the SSCM as the sum of shadow-cuts yields similar phenomena as in those from the statistical mechanics like Ising model, xy-model, pott model, Stochastic Hopfield Networks, etc., Thus, the proposed SSCM provides a general framework to examine various phenomena like the phase changes of the SSCM as the temperature increases. Because the SSCM in low temperatures behaves as an Associative Memory system (i.e., “ferro-magnet”), it is possible to examine the critical temperatures when the SSCM cannot “recover/remember” the patterns any more (i.e. “anti-ferromagnet”), which we define as “phase change” of the SSCM.
Spatial Interpretation and LLMs

The research work delves into the Spatial Reasoning capabilities of Large Language Models (LLMs) within the framework of Visual Question Answering (VQA). By employing rigorous methodologies, we assess LLM proficiency in discerning spatial relationships from visual stimuli. We highlight the models’ strengths and pinpoint areas of improvement, emphasizing ethical dimensions encompassing bias, privacy, and transparency. Our findings provide valuable insights into LLM potential and pave the way for enhancements in Natural Language Processing (NLP) research.
Blockchain Empowering Web 3.0: Decentralized Trust and Secure Transactions for the Future Internet

The rise of Web 3.0 has created an opportunity for a more decentralized, secure, and transparent internet. Blockchain technology has emerged as a critical enabler for Web 3.0, enabling the development of decentralized applications that build trust and support secure transactions. In this study, we discuss how blockchain technology can improve Web 3.0 by leveraging properties like immutability, consensus mechanisms, and smart contracts. The decentralized nature of blockchain technology improves security and privacy, giving consumers more control over their personal data. It also provides safer and more efficient transactions and can simplify platform compatibility. By providing a transparent and secure platform, decentralized blockchain-based applications have the potential to transform a wide range of industry areas, from supply chain management to digital voting. As Web 3.0 develops, blockchain technology will play an increasingly important role in enabling new use cases and revolutionizing a wide range of businesses. We can build a better internet that is more secure, safer, and more equitable for all users by embracing blockchain technology.In this study, we will compare conventional Web 2.0 centralized systems and the decentralized nature of Web 3.0 by looking at the core ideas of Web 3.0 and the cutting-edge capabilities of blockchain.
Hybrid QKD &amp; PQC Protocols implemented in the Berlin OpenQKD testbed

This paper introduces several hybrid protocols based on Quantum Key Distribution (QKD) and Post-Quantum Cryptography (PQC) implemented in the OpenQKD testbed Berlin to improve the security of the QKD nodes. Instead of following the ETSI QKD014 standard of a key management system based on classical security, i.e., RSA authentication and ECC encryption, we implemented a quantum-secure Key Management System based on PQC Key Encapsulation and Signature algorithms. The hardware and the software components of the QKD network authenticate themselves using a PQC trust chain and Pre-Shared Secrets, wherever supported. The communication between QKD nodes, as for the encryption key forwarding process, is hardened by Post-Quantum Cryptography to prevent cryptographic key exposure on intermediate network nodes. Using the two implemented key exchange processes, based on QKD and PQC, applications are able to request any kind of encryption key from the Key Management System and combine them arbitrarily to compute more reliable cryptographic keys.
Disentangled (Un)Controllable Features

In the context of MDPs with high-dimensional states, downstream tasks are predominantly applied on a compressed, low-dimensional representation of the original input space. A variety of learning objectives have therefore been used to attain useful representations. However, these representations usually lack interpretability of the different features. We present a novel approach that is able to disentangle latent features into a controllable and an uncontrollable partition. We illustrate that the resulting partitioned representations are easily interpretable on three types of environments and show that, in a distribution of procedurally generated maze environments, it is feasible to interpretably employ a planning algorithm in the isolated controllable latent partition.
MDRNN for Next-Gen Speech Synthesis

In the evolving realm of speech-to-speech synthesis, this study probes the potential of MDRNN in generating nuanced audio outputs. Harnessing the Mel-frequency cepstral coefficients (MFCCs) as a novel encoding scheme, our model demonstrated marked fidelity to original signals while introducing genuine creativity. Auditory examinations revealed non-repetitive sequences, some bearing semblances to coherent linguistic constructs. However, challenges, particularly unstable predictions and pronounced amplitude spikes, underlined the paramount importance of hyperparameter optimization. This research underscores the vast potential of MDRNN in speech synthesis and sets the stage for further exploration in achieving impeccable machine-generated speech.
Linear Generalized Preparata Codes over ℤ<sub>2</sub>s and a SISO MAP Decoding Algorithm for Them

In this paper, we introduce a SISO MAP decoding algorithm for the duals of the generalized Kerdock codes (a class of low-rate linear block codes over the $\mathbb{Z}_{2}s$ ring), which we refer to as the generalized Preparata codes. The decoding complexity of this algorithm is $O(N^{S}\log_{2}N)$ (where N is the code length in $\mathbb{Z}_{2}s$), which is significantly lower than the exhaustive search MAP decoder or the classical ML decoder implemented as a minimum distance or maximum correlation decoder.