Reviewerly/exHarmony huggingface dataset:
MoSAR: Monocular Semi-Supervised Model for Avatar Reconstruction using Differentiable Shading

Reconstructing an avatar from a portrait image has many applications in multimedia, but remains a challenging research problem. Extracting reflectance maps and geometry from one image is ill-posed: recovering geometry is a one-to-many mapping problem and reflectance and light are difficult to disentangle. Accurate geometry and reflectance can be captured under the controlled conditions of a light stage, but it is costly to acquire large datasets in this fashion. Moreover, training solely with this type of data leads to poor generalization with in-the-wild images. This motivates the introduction of MoSAR, a method for 3D avatar generation from monocular images. We propose a semi-supervised training scheme that improves generalization by learning from both light stage and in-the-wild datasets. This is achieved using a novel differentiable shading formulation. We show that our approach effectively disentangles the intrinsic face parameters, producing relightable avatars. As a result, MoSAR estimates a richer set of skin reflectance maps, and generates more realistic avatars than existing state-of-the-art methods. We also introduce a new dataset, named FFHQ-UV-Intrinsics, the first public dataset providing intrinsic face attributes at scale (diffuse, specular, ambient occlusion and translucency maps) for a total of 10k subjects. The project website and the dataset are available on the following link: https://ubisoft-laforge.github.io/character/mosar/
Investigating techniques to optimise the layout of turbines in a windfarm using a quantum computer

We study the optimal placement of wind turbines within a windfarm to maximize the power produced by mapping the system to a Quadratic Unconstrained Binary Optimisation (QUBO) problem. We investigate solving the resulting QUBO problem using the Variational Quantum Eigensolver (VQE) on a quantum computer simulator and compare the results to those from two classical optimisation methods: simulated annealing and the Gurobi solver. The maximum grid size we study is 4 $\times$ 4, which requires 16 qubits.
When Memory Mappings Attack: On the (Mis)use of the ARM Cortex-M FPB Unit

In recent years we have seen an explosion in the usage of low-cost, low-power microcontrollers (MCUs) in embedded devices around us due to the popularity of Internet of Things (IoT) devices. Although this is good from an economics perspective, it has also been detrimental for security as microcontroller-based systems are now a viable attack target. In response, researchers have developed various protection mechanisms dedicated to improve security in these resource-constrained embedded systems. We demonstrate in this paper these defenses fall short when we leverage benign memory mapped design-for-debug (DfD) structures added by MCU vendors in their products. In particular, we utilize the Flash Patch and Breakpoint (FPB) unit present in the ARM Cortex-M family to build new attack primitives which can be used to bypass common defenses for embedded devices. Our work serves as a warning and a call in balancing security and debug structures in modern microcontrollers.
Contextual Code Switching for Machine Translation using Language Models

Large language models (LLMs) have exerted a considerable impact on diverse language-related tasks in recent years. Their demonstrated state-of-the-art performance is achieved through methodologies such as zero-shot or few-shot prompting. These models undergo training on extensive datasets that encompass segments of the Internet and subsequently undergo fine-tuning tailored to specific tasks. Notably, they exhibit proficiency in tasks such as translation, summarization, question answering, and creative writing, even in the absence of explicit training for those particular tasks. While they have shown substantial improvement in the multilingual tasks their performance in the code switching, especially for machine translation remains relatively uncharted. In this paper, we present an extensive study on the code switching task specifically for the machine translation task comparing multiple LLMs. Our results indicate that despite the LLMs having promising results in the certain tasks, the models with relatively lesser complexity outperform the multilingual large language models in the machine translation task. We posit that the efficacy of multilingual large language models in contextual code switching is constrained by their training methodologies. In contrast, relatively smaller models, when trained and fine-tuned on bespoke datasets, may yield superior results in comparison to the majority of multilingual models.
StableKD: Breaking Inter-block Optimization Entanglement for Stable Knowledge Distillation

Knowledge distillation (KD) has been recognized as an effective tool to compress and accelerate models. However, current KD approaches generally suffer from an accuracy drop and/or an excruciatingly long distillation process. In this paper, we tackle the issue by first providing a new insight into a phenomenon that we call the Inter-Block Optimization Entanglement (IBOE), which makes the conventional end-to-end KD approaches unstable with noisy gradients. We then propose StableKD, a novel KD framework that breaks the IBOE and achieves more stable optimization. StableKD distinguishes itself through two operations: Decomposition and Recomposition, where the former divides a pair of teacher and student networks into several blocks for separate distillation, and the latter progressively merges them back, evolving towards end-to-end distillation. We conduct extensive experiments on CIFAR100, Imagewoof, and ImageNet datasets with various teacher-student pairs. Compared to other KD approaches, our simple yet effective StableKD greatly boosts the model accuracy by 1% ~ 18%, speeds up the convergence up to 10 times, and outperforms them with only 40% of the training data.
Milner's Lambda-Calculus with Partial Substitutions

We study Milner's lambda-calculus with partial substitutions. Particularly, we show confluence on terms and metaterms, preservation of \b{eta}-strong normalisation and characterisation of strongly normalisable terms via an intersection typing discipline. The results on terms transfer to Milner's bigraphical model of the calculus. We relate Milner's calculus to calculi with definitions, to calculi with explicit substitutions, and to MELL Proof-Nets.
Towards Separating Computational and Statistical Differential Privacy

Computational differential privacy (CDP) is a natural relaxation of the standard notion of (statistical) differential privacy (SDP) proposed by Beimel, Nissim, and Omri (CRYPTO 2008) and Mironov, Pandey, Reingold, and Vadhan (CRYPTO 2009). In contrast to SDP, CDP only requires privacy guarantees to hold against computationally-bounded adversaries rather than computationally-unbounded statistical adversaries. Despite the question being raised explicitly in several works (e.g., Bun, Chen, and Vadhan, TCC 2016), it has remained tantalizingly open whether there is any task achievable with the CDP notion but not the SDP notion. Even a candidate such task is unknown. Indeed, it is even unclear what the truth could be!In this work, we give the first construction of a task achievable with the CDP notion but not the SDP notion, under the following strong but plausible cryptographic assumptions:•Non-Interactive Witness Indistinguishable Proofs,•Laconic Collision-Resistant Keyless Hash Functions,•Differing-Inputs Obfuscation for Public-Coin Samplers.In particular, we construct a task for which there exists an $\varepsilon$-CDP mechanism with $\varepsilon=O(1)$ achieving $1-o(1)$ utility, but any $(\varepsilon, \delta)$-SDP mechanism, including computationally-unbounded ones, that achieves a constant utility must use either a super-constant $\varepsilon$ or an inverse-polynomially large $\delta$.To prove this, we introduce a new approach for showing that a mechanism satisfies CDP: first we show that a mechanism is “private” against a certain class of decision tree adversaries, and then we use cryptographic constructions to “lift” this into privacy against computationally bounded adversaries. We believe this approach could be useful to devise further tasks separating CDP from SDP.
Analysis of Blockchain Integration in the e-Healthcare Ecosystem

No one can dispute the disruptive impact of blockchain technology, which has long been considered one of the major revolutions of contemporary times.Its integration into the healthcare ecosystem has helped overcome numerous difficulties and constraints faced by healthcare systems.This has been notably demonstrated in the meticulous management of electronic health records (EHR) and their access rights, as well as in its capabilities in terms of security, scalability, flexibility, and interoperability with other systems.This article undertakes the study and analysis of the most commonly adopted approaches in healthcare data management systems using blockchain technology.An evaluation is then conducted based on a set of observed common characteristics, distinguishing one approach from the others.The results of this analysis highlight the advantages and limitations of each approach, thus facilitating the choice of the method best suited to the readers' specific case study.Furthermore, for effective implementation in the context of ehealth, we emphasize the existence of crucial challenges, such as the incomplete representation of major stakeholders in the blockchain network, the lack of regulatory flexibility to ensure legal interoperability by country, and the insufficient integration of an official regulatory authority ensuring compliance with ethical and legal standards.To address these challenges, it is necessary to establish close collaboration between regulators, technology developers, and healthcare stakeholders.
Robotic Arm Motion Planning for Obstacle Avoidance Based on the CMN-RRT * Method

With the development of the robotic arm industry, the current industrial requirements for robotic arm motion planning are gradually increasing. The article uses the KINOVA JACO GEN2 robotic arm to study the obstacle avoidance path planning algorithm based on the Central Multi-Node (CMN) RRT* algorithm. The study is mainly aimed at the problems of long calculation time and poor obstacle avoidance effect of the traditional RRT* algorithm. The main improved method is to optimize the sampling method of the traditional algorithm. The idea of connect is introduced in the path growth process to make multiple trees grow at the same time and make the obstacle avoidance process faster. Finally, the experimental results show that the CMN-RRT* algorithm improves the calculation time by 69.8% and the path quality by 6.1% compared with the traditional RRT* algorithm. It laid the foundation for the subsequent visual and tactile fusion of the robotic arm.
CTSTC: An Energy-Efficient Coverage Path Planning Algorithm for Unmanned Surface Vehicles

Marine environment investigation and patrol operations typically encompass a range of tasks involving Unmanned Surface Vehicles (USVs) and Coverage Path Planning (CPP). The assessment of CPP performance generally revolves around three key metrics: coverage time, extent of overlapping areas, and coverage rate. However, the consideration of energy consumption metrics is often overlooked. Especially for prolonged patrol missions or extensive coverage activities in larger regions, the minimization of energy consumption plays a pivotal role in enhancing the operational endurance of the USV while sustaining its hardware efficiency. We introduce a methodology aimed at minimizing the energy consumption associated with coverage tasks performed by USVs. Our approach draws on the principles of the Scanning Tree Coverage (STC) and the Dynamic Minimum Spanning Tree (DMST) algorithms. Adapting the STC model to the distinctive marine environment, we quantify the energy consumption resulting from ocean currents and the turns of the USV as path length. We present a method for creating a set of longest edges as a crucial part of the spanning tree, which involves dividing the map into multiple elongated regions. This approach serves to simplify the complexity of the problem. Subsequently, we employ the partial DMST algorithm to further generate tree branches. This dynamic approach aims to interconnect the longest edges based on the quantified energy consumption, ultimately striving to minimize energy consumption during coverage. By employing the method, we achieve a dual advantage: a reduction in the number of required turns, coupled with a comprehensive consideration of the influence of ocean currents. This combination empowers the USV to efficiently accomplish its tasks while consuming less energy.
METREE: Max-Entropy Exploration with Random Encoding for Efficient RL with Human Preferences

In recent years, reinforcement learning has achieved significant advances in practical domains such as robotics. However, conveying intricate objectives to agents in reinforcement learning (RL) remains challenging, often necessitating detailed reward function design. In this study, we introduce an innovative approach, MEETRE, which integrates max-entropy exploration strategies with random encoders. This offers a streamlined and efficient solution for human-involved preference-based RL without the need for meticulously designed reward functions. Furthermore, MEETRE sidesteps the need for additional models or representation learning, leveraging the power of randomly initialized encoders for effective exploration.
Feature Fusion Module Based on Gate Mechanism for Object Detection

In recent years, deep learning based feature fusion has drawn significant attention in the field of information integration due to its robust representational and generative capabilities. However, existing methods struggle to effectively preserve essential information. To this end, this paper proposes a gate-based fusion module for object detection to integrate the information from distinct feature layers of convolutional neural networks. The gate structure of the fusion module adaptively selects features from neighboring layers, storing valuable information in memory units and passing it to the subsequent layer. This approach facilitates the fusion of high-level semantic and low-level detailed features. Experimental validation is conducted on the public Pascal VOC dataset. Experiments results demonstrate that the addition of the gate-based fusion module to the detection task leads to an average accuracy increment of up to 5%.
Inferring Quantum Network Topology Using Local Measurements

Statistical correlations that can be generated across the nodes in a quantum network depend crucially on its topology. However, this topological information might not be known a priori, or it may need to be verified. In this paper, we propose an efficient protocol for distinguishing and inferring the topology of a quantum network. We leverage entropic quantities—namely, the von Neumann entropy and the measured mutual information—as well as measurement covariance to uniquely characterize the topology. We show that the entropic quantities are sufficient to distinguish two networks that prepare GHZ states. Moreover, if qubit measurements are available, both entropic quantities and covariance can be used to infer the network topology without state-preparation assumptions. We show that the protocol can be entirely robust to noise and can be implemented via quantum variational optimization. Numerical experiments on both classical simulators and quantum hardware show that covariance is generally more reliable for accurately and efficiently inferring the topology, whereas entropy-based methods are often better at identifying the absence of entanglement in the low-shot regime.6 MoreReceived 22 July 2023Revised 25 October 2023Accepted 28 November 2023DOI:https://doi.org/10.1103/PRXQuantum.4.040347Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasEntanglement detectionQuantum communicationQuantum computationQuantum Information, Science & Technology
Knowledge networks in the era of Covid-19

Roughly speaking, knowledge networks are explained from a neural network in which degrees of learning are established, considering the differences between the input layer, the intermediate or hidden layer and the output layer. A non-experimental, cross-sectional and exploratory study was carried out with a non-probabilistic selection of 300 students, managers and teachers from a public university in central Mexico. The results show a factorial asymmetry of one input layer unit for three output layer units, suggesting that there is a significant degree of learning around the knowledge network. However, there are areas of opportunity around the hidden layer, since its units reveal information processing that reduces the uncertainty of the input layer and amplifies the knowledge of the output layer.
Utilizing M-Technologies for AI-Driven Career Guidance in Morocco: An Innovative Mobile Approach

In today’s interconnected world, the significance of effective career guidance has been magnified. With the advent of mobile technologies, e-orientation and artificial intelligence (AI)-orientation systems offer a promising avenue for personalized career guidance. This paper delves into the potential of transitioning from traditional e-orientation to advanced AI-orientation systems in Morocco by employing large language models (LLMs) such as LLAMA2, GPT, and PaLM. These LLMs, renowned for their human-like text generation and contextual understanding, are proposed as the backbone for AI chatbots that can serve as virtual career counselors. Accessible via mobile platforms, these mobile chatbot interfaces can provide real-time insights on career paths, educational prerequisites, and job market dynamics and outlooks. Despite challenges such as Internet reliability, data privacy, and legislative regulations, the integration of AI-orientated systems into mobile platforms can revolutionize career guidance for Moroccan students. This paper presents a detailed roadmap and implementation for embedding these innovative technologies into Morocco’s educational framework.
Effectiveness of Team-Based Project Integrated E-books in Improving Student Self-Directed Learning and Creativity

Student self-directed learning and creativity are needed to support the hybrid learning currently being implemented in higher education. This research aims to develop an integrated team-based project e-book to increase students’ self-directed learning and creativity. Products are designed according to the stages of the ADDIE model. The product is then tested for feasibility by material experts and evaluated by users. The results of the feasibility test conducted by experts show that the e-book is highly feasible for use. Meanwhile, user testing is conducted through pre- and post-tests. Furthermore, in-depth interviews were conducted with several students to enhance the depth of the obtained data. The results of user trials were analyzed using inferential statistical data analysis methods, specifically the paired sample t-test. The results of the analysis show that e-books can improve learning outcomes, students’ selfdirected learning abilities, and student creativity in cultural anthropology courses. Therefore, the results of this research can be used with students to improve learning outcomes, creativity, and students’ self-directed learning abilities among students with similar characteristics.
An Intuitive Interface for Technical Documentation Based on Semantic Knowledge Graphs

Maintaining technical documentation is a challenge. Products are becoming more complex, product lifecycles are getting shorter, and the number of product variants is increasing. Manuals that guide personnel in the use and maintenance of products are critical to their efficient and safe operation. Authoring system for technical documentation therefore increasingly apply semantic models to control the cost of maintaining technical documentation. Working with formal semantic structures is challenging for technical writers who usually work with plain, written text. This paper presents an intuitive interface for a semantic knowledge graph to facilitate the adoption and use of semantic models in technical documentation. The interface allows working with unstructured text and to postpone its semantification. Users can add semantic annotations in an iterative and incremental way. The interface was developed using a user-centered design process and subjected to an evaluation with technical writers. The results indicate that technical writers could use the prototype successfully and enjoyed the underlying concepts. Further iterations will extend the system and, for example, use artificial intelligence to suggest semantic links to improve the quality of the knowledge graph.
Electro-oculographic Discrimination of Gazing Motion to a Smartphone Notification Tone

This paper describes an experiment to validate whether unconscious responses or conscious gazing motions to notification tones can be discriminated from skin conductance responses or electro-oculograms. Our goal is to solve a problem that a smartphone cannot discriminate that a user has noticed a notification from the smartphone unless the user directly operates it or speaks to it when the user noticed the notification. In our experiment, participants were presented with notification tones while they were watching a video or reading orally as a main task, and their physiological signals were recorded during the task. As the results, we found that it took approximately four seconds to discriminate the response from skin conductance responses, whereas it took only one second to discriminate the response from the electro-oculogram. Furthermore, we found that the recall was 92.5% and the precision was 96.1% for discriminating the conscious gazing motions to the notification tones from the electro-oculograms between upper and lower of an eye.
Simulation of a Wheelchair Control System Based on Computer Vision Through Head Movements for Quadriplegic People

People with quadriplegia rely on someone else to get around using a manual wheelchair. Using motorized wheelchairs gives them some independence and, simultaneously, the need of methods to control them. In the state-of-the-art, there is a great variety of these minimally invasive methods that use external devices, for example, accelerometers, which could generate discomfort. The present work proposes a non-invasive system based on artificial vision is proposed that does not require placing any device on the user. The proposed system consists of an image acquisition stage, one for face detection using Viola-Jones algorithm and another for tracking using Kanade-Lucas-Tomasi (KLT) algorithm. Additionally, a way to enter or exit the commands mode is proposed so that the user can activate/deactivate the system as required. For this, a specific movement is presented, as long as this movement is not performed, the user can move his head freely without activating the motors in the angle of focus of the camera 20o to 45o, the system correctly interprets 100% of the head moving towards or away. The novel detail is that the system allows entry and exit of the command mode by means of a special movement of the head, if the user enters the commands mode, the following options are available: tilt his head to the left, right, forward or backward.