Reviewerly/exHarmony huggingface dataset:
Location-Aware Deep Interaction Forest for Web Service QoS Prediction

Authors:
raw_author_name: Jiaman Ding
raw_affiliation_string: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China;

Abstract: With the rapid development of the web service market, the number of web services shows explosive growth. QoS is an important factor in the recommendation scene; how to accurately recommend a high-quality service for users among the massive number of web services has become a tough problem. Previous methods usually acquired feature interaction information by network structures like DNN to improve the QoS prediction accuracy, but this generates unnecessary computations. Aiming at addressing the above problem, inspired by the multigrained scanning mechanism in a deep forest, we propose a location-aware deep interaction forest approach for web service QoS prediction (LDIF). This approach offers the following innovations: The model fuses the location similarity of users and services as a latent feature representation of them. In addition, we designed a scanning interaction structure (SIS), which obtains multiple local feature combinations from the interaction between user and service features, uses interactive computing to extract feature interaction information, and concatenates the feature interaction information with original features, which aims to enhance the dimension of the features. Equipped with these, we compose a layer-by-layer cascade by using SIS to fuse low- and high-order feature interaction information, and the early-stop mechanism controls the cascade depth to avoid unnecessary computation. The experiments demonstrate that our model outperforms eight other state-of-the-art methods on MAE and RMSE common metrics on real public datasets.
Federated Freeze BERT for text classification

Authors:
raw_author_name: Omar Galal
raw_affiliation_string: Computer Engineering Department, Cairo University, Gamaa Street, Giza, 12613, Egypt
raw_author_name: Ahmed H. Abdel-Gawad
raw_affiliation_string: Computer Engineering Department, Cairo University, Gamaa Street, Giza, 12613, Egypt
raw_author_name: Mona Farouk
raw_affiliation_string: Computer Engineering Department, Cairo University, Gamaa Street, Giza, 12613, Egypt

Abstract: Abstract Pre-trained BERT models have demonstrated exceptional performance in the context of text classification tasks. Certain problem domains necessitate data distribution without data sharing. Federated Learning (FL) allows multiple clients to collectively train a global model by sharing learned models rather than raw data. However, the adoption of BERT, a large model, within a Federated Learning framework incurs substantial communication costs. To address this challenge, we propose a novel framework, FedFreezeBERT, for BERT-based text classification. FedFreezeBERT works by adding an aggregation architecture on top of BERT to obtain better sentence embedding for classification while freezing BERT parameters. Keeping the model parameters frozen, FedFreezeBERT reduces the communication costs by a large factor compared to other state-of-the-art methods. FedFreezeBERT is implemented in a distributed version where the aggregation architecture only is being transferred and aggregated by FL algorithms such as FedAvg or FedProx. FedFreezeBERT is also implemented in a centralized version where the data embeddings extracted by BERT are sent to the central server to train the aggregation architecture. The experiments show that FedFreezeBERT achieves new state-of-the-art performance on Arabic sentiment analysis on the ArSarcasm-v2 dataset with a 12.9% and 1.2% improvement over FedAvg/FedProx and the previous SOTA respectively. FedFreezeBERT also reduces the communication cost by 5 $$\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> compared to the previous SOTA.
Social construction of XAI: Do we need one definition to rule them all?

Authors:
raw_author_name: Upol Ehsan
raw_affiliation_string: Georgia Institute of Technology, Atlanta GA, USA
raw_author_name: Mark O. Riedl
raw_affiliation_string: Georgia Institute of Technology, Atlanta GA, USA

Abstract: <h2>Summary</h2> In this opinion, Upol Ehsan and Mark Riedl argue why a singular monolithic definition of explainable AI (XAI) is neither feasible nor desirable at this stage of XAI's development.
Stability of a quantum skyrmion: Projective measurements and the quantum Zeno effect

Authors:
raw_author_name: Mikhail I. Katsnelson
raw_affiliation_string: Institute for Molecules and Materials, Radboud University, Heijendaalseweg 135, 6525 AJ Nijmegen, Netherlands
raw_author_name: Andrey A. Bagrov
raw_affiliation_string: Institute for Molecules and Materials, Radboud University, Heijendaalseweg 135, 6525 AJ Nijmegen, Netherlands
raw_author_name: Tom Westerhout
raw_affiliation_string: Institute for Molecules and Materials, Radboud University, Heijendaalseweg 135, 6525 AJ Nijmegen, Netherlands

Abstract: Magnetic skyrmions are vortexlike quasiparticles characterized by long lifetime and remarkable topological properties. That makes them a promising candidate for the role of information carriers in magnetic information storage and processing devices. Although considerable progress has been made in studying skyrmions in classical systems, little is known about the quantum case: quantum skyrmions cannot be directly observed by probing the local magnetization of the system, and the notion of topological protection is elusive in the quantum realm. Here we explore the potential robustness of quantum skyrmions in comparison to their classical counterparts. We theoretically analyze the dynamics of a quantum skyrmion subject to local projective measurements and demonstrate that the properties of the skyrmionic quantum state change very little upon external perturbations. We further show that by performing repetitive measurements on a quantum skyrmion, it can be completely stabilized through an analog of the quantum Zeno effect.
Quantum state tomography of undetected photons

Authors:
raw_author_name: Jorge Fuenzalida
raw_affiliation_string: Institute for Quantum Optics and Quantum Information, Austrian Academy of Sciences, Boltzmanngasse 3, Vienna 1090, Austria; Vienna Center for Quantum Science and Technology, Faculty of Physics, Boltzmanngasse 5, University of Vienna, Vienna 1090, Austria
raw_author_name: Jaroslav Kysela
raw_affiliation_string: Institute for Quantum Optics and Quantum Information, Austrian Academy of Sciences, Boltzmanngasse 3, Vienna 1090, Austria; Vienna Center for Quantum Science and Technology, Faculty of Physics, Boltzmanngasse 5, University of Vienna, Vienna 1090, Austria
raw_author_name: Krishna Dovzhik
raw_affiliation_string: Institute for Quantum Optics and Quantum Information, Austrian Academy of Sciences, Boltzmanngasse 3, Vienna 1090, Austria; Vienna Center for Quantum Science and Technology, Faculty of Physics, Boltzmanngasse 5, University of Vienna, Vienna 1090, Austria
raw_author_name: Gabriela Barreto Lemos
raw_affiliation_string: Instituto de Física, Universidade Federal do Rio de Janeiro, Avenida Athos da Silveira Ramos 149, Rio de Janeiro, Rio de Janeiro 68528, Brazil
raw_author_name: Armin Hochrainer
raw_affiliation_string: Institute for Quantum Optics and Quantum Information, Austrian Academy of Sciences, Boltzmanngasse 3, Vienna 1090, Austria; Vienna Center for Quantum Science and Technology, Faculty of Physics, Boltzmanngasse 5, University of Vienna, Vienna 1090, Austria
raw_author_name: Mayukh Lahiri
raw_affiliation_string: Department of Physics, Oklahoma State University, Stillwater, Oklahoma 74078, USA
raw_author_name: Anton Zeilinger
raw_affiliation_string: Institute for Quantum Optics and Quantum Information, Austrian Academy of Sciences, Boltzmanngasse 3, Vienna 1090, Austria; Vienna Center for Quantum Science and Technology, Faculty of Physics, Boltzmanngasse 5, University of Vienna, Vienna 1090, Austria

Abstract: The measurement of quantum states is one of the most important problems in quantum mechanics. We introduce a quantum state tomography technique in which the state of a qubit is reconstructed, while the qubit remains undetected. The key ingredients are (i) employing an additional qubit, (ii) aligning the undetected qubit with a known reference state by using path identity, and (iii) measuring the additional qubit to reconstruct the undetected qubit state. We establish theoretically and demonstrate experimentally the method with photonic polarization states. The principle underlying our method could also be applied to quantum entities other than photons.
Enhancing Pedestrian Tracking in Autonomous Vehicles by Using Advanced Deep Learning Techniques

Authors:
raw_author_name: Majdi Sukkar
raw_affiliation_string: Department of Computer Engineering, Marwadi University, Rajkot 360003, Gujarat, India
raw_author_name: Madhu Shukla
raw_affiliation_string: Department of Computer Engineering-AI and BDA, Marwadi University, Rajkot 360003, Gujarat, India
raw_author_name: Dinesh Kumar
raw_affiliation_string: School of Computer Science Engineering and Technology, Bennett University, Greater Noida 201310, Uttar Pradesh, India
raw_author_name: Vassilis C. Gerogiannis
raw_affiliation_string: Department of Digital Systems, University of Thessaly, 41500 Larissa, Greece
raw_author_name: Andreas Kanavos
raw_affiliation_string: Department of Informatics, Ionian University, 49100 Corfu, Greece
raw_author_name: Biswaranjan Acharya
raw_affiliation_string: Department of Computer Engineering-AI and BDA, Marwadi University, Rajkot 360003, Gujarat, India

Abstract: Effective collision risk reduction in autonomous vehicles relies on robust and straightforward pedestrian tracking. Challenges posed by occlusion and switching scenarios significantly impede the reliability of pedestrian tracking. In the current study, we strive to enhance the reliability and also the efficacy of pedestrian tracking in complex scenarios. Particularly, we introduce a new pedestrian tracking algorithm that leverages both the YOLOv8 (You Only Look Once) object detector technique and the StrongSORT algorithm, which is an advanced deep learning multi-object tracking (MOT) method. Our findings demonstrate that StrongSORT, an enhanced version of the DeepSORT MOT algorithm, substantially improves tracking accuracy through meticulous hyperparameter tuning. Overall, the experimental results reveal that the proposed algorithm is an effective and efficient method for pedestrian tracking, particularly in complex scenarios encountered in the MOT16 and MOT17 datasets. The combined use of Yolov8 and StrongSORT contributes to enhanced tracking results, emphasizing the synergistic relationship between detection and tracking modules.
Text-independent speaker identification system using discrete wavelet transform with linear prediction coding

Authors:
raw_author_name: Othman Alrusaini
raw_affiliation_string: Department of Engineering and Applied Sciences, Applied College, Umm Al-Qura University, 24382, Makkah, Saudi Arabia
raw_author_name: Khaled Daqrouq
raw_affiliation_string: Department of Electrical and Computer Engineering, King Abdulaziz University, 21589, Jeddah, Saudi Arabia

Abstract: Abstract One of the key problems of the modern day is the presentation of an identity verification system that can perform sufficient accuracy in identity verification, is resilient to assaults and noises, and can be recorded in the simplest possible method. In this study, a new speaker feature extraction which based on discrete wavelet transform (DWT) and linear prediction coding (LPC) algorithm (WLPCA) are investigated. This paper's primary objective is to evidence the performance of the new method for speaker identification by a Gaussian mixture model (GMM). The proposed method improves the recognition rate over the Mel-frequency cepstral coefficient (MFCC). Experimental evaluation of the process performance is performed on two speech databases; our recorded database and the publicly available TIMIT database. We show that the speech features derived by the newly proposed method are more suitable for GMM (91.53%), in terms of the time-consumed, by requiring less Gaussian mixtures than MFCC (85.77%). For testing the presented method in a noisy environment, Additive white Gaussian noise (AWGN) was added to the TIMIT database, where a slight improvement in the performance of the presented method (60.02%) over the MFCC (59.89%) was observed.
Relative Norm Alignment for Tackling Domain Shift in Deep Multi-modal Classification

Authors:
raw_author_name: Mirco Planamente
raw_affiliation_string: DAUIN, Politecnico di Torino, Torino, Italy
raw_author_name: Chiara Plizzari
raw_affiliation_string: DAUIN, Politecnico di Torino, Torino, Italy
raw_author_name: Barbara Caputo
raw_affiliation_string: DAUIN, Politecnico di Torino, Torino, Italy
raw_author_name: Andrea Bottino
raw_affiliation_string: DAUIN, Politecnico di Torino, Torino, Italy

Abstract: Abstract Multi-modal learning has gained significant attention due to its ability to enhance machine learning algorithms. However, it brings challenges related to modality heterogeneity and domain shift. In this work, we address these challenges by proposing a new approach called Relative Norm Alignment (RNA) loss. RNA loss exploits the observation that variations in marginal distributions between modalities manifest as discrepancies in their mean feature norms, and rebalances feature norms across domains, modalities, and classes. This rebalancing improves the accuracy of models on test data from unseen (“target”) distributions. In the context of Unsupervised Domain Adaptation (UDA), we use unlabeled target data to enhance feature transferability. We achieve this by combining RNA loss with an adversarial domain loss and an Information Maximization term that regularizes predictions on target data. We present a comprehensive analysis and ablation of our method for both Domain Generalization and UDA settings, testing our approach on different modalities for tasks such as first and third person action recognition, object recognition, and fatigue detection. Experimental results show that our approach achieves competitive or state-of-the-art performance on the proposed benchmarks, showing the versatility and effectiveness of our method in a wide range of applications.
Revisiting Bag of Words Document Representations for Efficient Ranking with Transformers

Authors:
raw_author_name: David Rau
raw_affiliation_string: University of Amsterdam, Amsterdam, The Netherlands
raw_author_name: Mostafa Dehghani
raw_affiliation_string: Google DeepMind, San Francisco, United States
raw_author_name: Jaap Kamps
raw_affiliation_string: University of Amsterdam, Amsterdam, The Netherlands

Abstract: Modern transformer-based information retrieval models achieve state-of-the-art performance across various benchmarks. The self-attention of the transformer models is a powerful mechanism to contextualize terms over the whole input but quickly becomes prohibitively expensive for long input as required in document retrieval. Instead of focusing on the model itself to improve efficiency, this paper explores different bag of words document representations that encode full documents by only a fraction of their characteristic terms, allowing us to control and reduce the input length. We experiment with various models for document retrieval on MS MARCO data, as well as zero-shot document retrieval on Robust04, and show large gains in efficiency while retaining reasonable effectiveness. Inference time efficiency gains are both lowering the time and memory complexity in a controllable way, allowing for further trading off memory footprint and query latency. More generally, this line of research connects traditional IR models with neural “NLP” models and offers novel ways to explore the space between (efficient, but less effective) traditional rankers and (effective, but less efficient) neural rankers elegantly.
Grid-like entorhinal representation of an abstract value space during prospective decision making

Authors:
raw_author_name: Alexander Nitsch
raw_affiliation_string: Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
raw_author_name: Mona M. Garvert
raw_affiliation_string: Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
raw_author_name: Nicolas W. Schuck
raw_affiliation_string: Max Planck Research Group NeuroCode, Max Planck Institute for Human Development, Berlin, Germany
raw_author_name: Christian F. Doeller
raw_affiliation_string: Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

Abstract: Abstract How valuable a choice option is often changes over time, making the prediction of value changes an important challenge for decision making. Prior studies identified a cognitive map in the hippocampal-entorhinal system that encodes relationships between states and enables prediction of future states, but does not inherently convey value during prospective decision making. In this fMRI study, participants predicted changing values of choice options in a sequence, forming a trajectory through an abstract two-dimensional value space. During this task, the entorhinal cortex exhibited a grid-like representation with an orientation aligned to the axis through the value space most informative for choices. A network of brain regions, including ventromedial prefrontal cortex, tracked the prospective value difference between options. These findings suggest that the entorhinal grid system supports the prediction of future values by representing a cognitive map, which might be used to generate lower-dimensional value signals to guide prospective decision making.
TransMI: a transfer-learning method for generalized map information evaluation

Authors:
raw_author_name: Chenglong Wang
raw_affiliation_string: School of Resource and Environmental Sciences, Wuhan University, China; School of Urban Planning and Design, Peking University Shenzhen Graduate School, China
raw_author_name: Mengjun Kang
raw_affiliation_string: School of Resource and Environmental Sciences, Wuhan University, China
raw_author_name: Shiliang Su
raw_affiliation_string: School of Resource and Environmental Sciences, Wuhan University, China

Abstract: Information Theory of Cartography plays an important role in guiding map design, generalization, and evaluation, and measurement of map information is the most basic topic of this theory. However, there are many problems in current measurement methods, and there is a long way to go to form a theoretically rigorous algorithm that can effectively depict spatial information and comprehensively consider the feeling of map readers. Luckily, we can now propose an evaluation metric that exhibits a certain correlation with map information based on deep learning to benefit actual cartography, and we term it as generalized map information evaluation to demonstrate differentiation. Specifically, this paper first constructs a subjective data set to support the supervised learning paradigm. Also, considering the difficulty of large-scale subjective data set collection, this paper proposes a Transfer-learning method for generalized Map Information evaluation (TransMI). Technically, a Siamese Network is pre-trained to explicitly acquire prior knowledge about the reasons for changes to mapped information. On this basis, one branch of the network is extracted and fine-tuned on the subjective data set to achieve the goal of predicting the quality of generalized map information. The results and the analysis of ablation studies prove the feasibility of our method.
Joint controllers in large research consortia: a funnel model to distinguish controllers in the sense of the GDPR from other partners in the consortium

Authors:
raw_author_name: Evert-Ben Van Veen
raw_author_name: Martin Boeckhout
raw_author_name: Irene Schlünder
raw_author_name: Jan Willem Boiten
raw_author_name: Vasco Dias

Abstract: <ns3:p>Large European research consortia in the health sciences face challenges regarding the governance of personal data collected, generated and/or shared during their collective research. A controller in the sense of the GDPR is the entity which decides about purposes and means of the data processing. Case law of the Court of Justice of the European Union (CJEU) and Guidelines of the European Data Protection Board (EDPB) indicate that all partners in the consortium would be joint controllers. This paper summarises the case law, the Guidelines and literature on joint controllership, gives a brief account of a webinar organised on the issue by Lygature and the MLC Foundation. Participants at the webinar agreed in large majority that it would be extreme if all partners in the consortium would become joint controllers. There was less agreement how to disentangle partners who are controllers of a study from those who are not. In order to disentangle responsibilities, we propose a funnel model with consecutive steps acting as sieves in the funnel. It differentiates between two types of partners: all partners who are involved in shaping the project as a whole versus those specific partners who are more closely involved in a sub-study following from the DoA i or the use of the data Platform. If the role of the partner would be comparable to that of an outside advisor, that partner would not be a data controller even though the partner is part of the consortium. We propose further nuances for the disentanglement which takes place in various steps. Uncertainty about formal controllership under the GDPR can stifle collaboration in consortia due to concerns over (shared) responsibility and liability. Data subjects’ ability to exercise their right can also be affected by this. The funnel model proposes a way out of this conundrum.</ns3:p>
Fast Model Selection and Hyperparameter Tuning for Generative Models

Authors:
raw_author_name: Luming Chen
raw_affiliation_string: Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA
raw_author_name: Sujit K. Ghosh
raw_affiliation_string: Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA

Abstract: Generative models have gained significant attention in recent years. They are increasingly used to estimate the underlying structure of high-dimensional data and artificially generate various kinds of data similar to those from the real world. The performance of generative models depends critically on a good set of hyperparameters. Yet, finding the right hyperparameter configuration can be an extremely time-consuming task. In this paper, we focus on speeding up the hyperparameter search through adaptive resource allocation, early stopping underperforming candidates quickly and allocating more computational resources to promising ones by comparing their intermediate performance. The hyperparameter search is formulated as a non-stochastic best-arm identification problem where resources like iterations or training time constrained by some predetermined budget are allocated to different hyperparameter configurations. A procedure which uses hypothesis testing coupled with Successive Halving is proposed to make the resource allocation and early stopping decisions and compares the intermediate performance of generative models by their exponentially weighted Maximum Means Discrepancy (MMD). The experimental results show that the proposed method selects hyperparameter configurations that lead to a significant improvement in the model performance compared to Successive Halving for a wide range of budgets across several real-world applications.