Reviewerly/exHarmony huggingface dataset:
Orthogonal Temporal Interpolation for Zero-Shot Video Recognition

Zero-shot video recognition (ZSVR) is a task that aims to recognize video categories that have not been seen during the model training process. Recently, vision-language models (VLMs) pre-trained on large-scale image-text pairs have demonstrated impressive transferability for ZSVR. To make VLMs applicable to the video domain, existing methods often use an additional temporal learning module after the image-level encoder to learn the temporal relationships among video frames. Unfortunately, for video from unseen categories, we observe an abnormal phenomenon where the model that uses spatial-temporal feature performs much worse than the model that removes temporal learning module and uses only spatial feature. We conjecture that improper temporal modeling on video disrupts the spatial feature of the video. To verify our hypothesis, we propose Feature Factorization to retain the orthogonal temporal feature of the video and use interpolation to construct refined spatial-temporal feature. The model using appropriately refined spatial-temporal feature performs better than the one using only spatial feature, which verifies the effectiveness of the orthogonal temporal feature for the ZSVR task. Therefore, an Orthogonal Temporal Interpolation module is designed to learn a better refined spatial-temporal video feature during training. Additionally, a Matching Loss is introduced to improve the quality of the orthogonal temporal feature. We propose a model called OTI for ZSVR by employing orthogonal temporal interpolation and the matching loss based on VLMs. The ZSVR accuracies on popular video datasets (i.e., Kinetics-600, UCF101 and HMDB51) show that OTI outperforms the previous state-of-the-art method by a clear margin.
Knowing Where to Focus: Event-aware Transformer for Video Grounding

Recent DETR-based video grounding models have made the model directly predict moment timestamps without any hand-crafted components, such as a pre-defined proposal or non-maximum suppression, by learning moment queries. However, their input-agnostic moment queries inevitably overlook an intrinsic temporal structure of a video, providing limited positional information. In this paper, we formulate an event-aware dynamic moment query to enable the model to take the input-specific content and positional information of the video into account. To this end, we present two levels of reasoning: 1) Event reasoning that captures distinctive event units constituting a given video using a slot attention mechanism; and 2) moment reasoning that fuses the moment queries with a given sentence through a gated fusion transformer layer and learns interactions between the moment queries and video-sentence representations to predict moment timestamps. Extensive experiments demonstrate the effectiveness and efficiency of the event-aware dynamic moment queries, outperforming state-of-the-art approaches on several video grounding benchmarks.
UIPC-MF: User-Item Prototype Connection Matrix Factorization for Explainable Collaborative Filtering

Recommending items to potentially interested users has been an important commercial task that faces two main challenges: accuracy and explainability. While most collaborative filtering models rely on statistical computations on a large scale of interaction data between users and items and can achieve high performance, they often lack clear explanatory power. We propose UIPC-MF, a prototype-based matrix factorization method for explainable collaborative filtering recommendations. In UIPC-MF, both users and items are associated with sets of prototypes, capturing general collaborative attributes. To enhance explainability, UIPC-MF learns connection weights that reflect the associative relations between user and item prototypes for recommendations. UIPC-MF outperforms other prototype-based baseline methods in terms of Hit Ratio and Normalized Discounted Cumulative Gain on three datasets, while also providing better transparency.
Expanding bipartite Bell inequalities for maximum multi-partite randomness

Nonlocal tests on multipartite quantum correlations form the basis of protocols that certify randomness in a device-independent (DI) way. Such correlations admit a rich structure, making the task of choosing an appropriate test difficult. For example, extremal Bell inequalities are tight witnesses of nonlocality, however achieving their maximum violation places constraints on the underlying quantum system, which can reduce the rate of randomness generation. As a result there is often a trade-off between maximum randomness and the amount of violation of a given Bell inequality. Here, we explore this trade-off for more than two parties. More precisely, we study the maximum amount of randomness that can be certified by correlations exhibiting a violation of the Mermin-Ardehali-Belinskii-Klyshko (MABK) inequality. We find that maximum quantum violation and maximum randomness are incompatible for any even number of parties, with incompatibility diminishing as the number of parties grow, and conjecture the precise trade-off. We also show that maximum MABK violation is not necessary for maximum randomness for odd numbers of parties. To obtain our results, we derive new families of Bell inequalities certifying maximum randomness from a technique for randomness certification, which we call "expanding Bell inequalities". Our technique allows one to take a bipartite Bell expression, known as the seed, and transform it into a multipartite Bell inequality tailored for randomness certification, showing how intuition learned in the bipartite case can find use in more complex scenarios.
Quantification of Entanglement and Coherence with Purity Detection

Entanglement and coherence are fundamental properties of quantum systems, promising to power the near future quantum technologies. Yet, their quantification, rather than mere detection, generally requires reconstructing the spectrum of quantum states, i.e., experimentally challenging measurement sets that increase exponentially with the system size. Here, we demonstrate quantitative bounds to operationally useful entanglement and coherence that are universally valid, analytically computable, and experimentally friendly. Specifically, our main theoretical results are lower and upper bounds to the coherent information and the relative entropy of coherence in terms of local and global purities of quantum states. To validate our proposal, we experimentally implement two purity detection methods in an optical system: shadow estimation with random measurements and collective measurements on pairs of state copies. The experiment shows that both the coherent information and the relative entropy of coherence of pure and mixed unknown quantum states can be bounded by purity functions. Our research offers an efficient means of verifying large-scale quantum information processing without spectrum reconstruction.
Relation between quantum illumination and quantum parameter estimation

Quantum illumination (QI) leverages entangled lights to detect the potential presence of low-reflective objects in a region surrounded by a thermal bath. Homologously, quantum parameter estimation utilizes non-classical probes to accurately estimate the value of the unknown parameter(s) of interest in a system. There appears to be a certain connection between these two areas. However, they are commonly studied using different figures of merit: signal-to-noise ratio and quantum Fisher information. In this study, we prove that the two measures are equivalent to QI in the limit of zero object reflectivity. We further demonstrate this equivalence by investigating QI protocols employing non-Gaussian states, which are obtained by de-Gaussifying the two-mode squeezed vacuum state with photon addition and photon subtraction. However, our analysis leads to a no-go result which demonstrates that de-Gaussification operations do not offer an advantage compared to the null case.
Algorithms for the Training of Neural Support Vector Machines

Neural support vector machines (NSVMs) allow for the incorporation of domain knowledge in the design of the model architecture. In this article we introduce a set of training algorithms for NSVMs that leverage the Pegasos algorithm and provide a proof of concept by solving a set of standard machine learning tasks.
AAFACE: Attribute-aware Attentional Network for Face Recognition

In this paper, we present a new multi-branch neural network that simultaneously performs soft biometric (SB) prediction as an auxiliary modality and face recognition (FR) as the main task. Our proposed network named AAFace utilizes SB attributes to enhance the discriminative ability of FR representation. To achieve this goal, we propose an attribute-aware attentional integration (AAI) module to perform weighted integration of FR with SB feature maps. Our proposed AAI module is not only fully context-aware but also capable of learning complex relationships between input features by means of the sequential multi-scale channel and spatial sub-modules. Experimental results verify the superiority of our proposed network compared with the state-of-the-art (SoTA) SB prediction and FR methods.
RestoreFormer++: Towards Real-World Blind Face Restoration from Undegraded Key-Value Pairs

Blind face restoration aims at recovering high-quality face images from those with unknown degradations. Current algorithms mainly introduce priors to complement high-quality details and achieve impressive progress. However, most of these algorithms ignore abundant contextual information in the face and its interplay with the priors, leading to sub-optimal performance. Moreover, they pay less attention to the gap between the synthetic and real-world scenarios, limiting the robustness and generalization to real-world applications. In this work, we propose RestoreFormer++, which on the one hand introduces fully-spatial attention mechanisms to model the contextual information and the interplay with the priors, and on the other hand, explores an extending degrading model to help generate more realistic degraded face images to alleviate the synthetic-to-real-world gap. Compared with current algorithms, RestoreFormer++ has several crucial benefits. First, instead of using a multi-head self-attention mechanism like the traditional visual transformer, we introduce multi-head cross-attention over multi-scale features to fully explore spatial interactions between corrupted information and high-quality priors. In this way, it can facilitate RestoreFormer++ to restore face images with higher realness and fidelity. Second, in contrast to the recognition-oriented dictionary, we learn a reconstruction-oriented dictionary as priors, which contains more diverse high-quality facial details and better accords with the restoration target. Third, we introduce an extending degrading model that contains more realistic degraded scenarios for training data synthesizing, and thus helps to enhance the robustness and generalization of our RestoreFormer++ model. Extensive experiments show that RestoreFormer++ outperforms state-of-the-art algorithms on both synthetic and real-world datasets.
A qubit-ADAPT Implementation for H$_2$ Molecules using an Explicitly Correlated Basis

With the recent advances in the development of devices capable of performing quantum computations, a growing interest in finding near-term applications has emerged in many areas of science. In the era of non-fault tolerant quantum devices, algorithms that only require comparably short circuits accompanied by high repetition rates are considered to be a promising approach for assisting classical machines with finding solution on computationally hard problems. The ADAPT approach previously introduced in Nat. Commun. 10, 3007 (2019) extends the class of variational quantum eigensolver (VQE) algorithms with dynamically growing ans\"atze in order to find approximations to ground and excited state energies of molecules. In this work, the ADAPT algorithm has been combined with a first-quantized formulation for the hydrogen molecule in the Born-Oppenheimer approximation, employing the explicitly correlated basis functions introduced in J. Chem. Phys. 43, 2429 (1965). By the virtue of their explicit electronic correlation properties, it is shown in classically performed simulations that relatively short circuits yield chemical accuracy ($< 1.6$ mHa) for ground and excited state potential curves that can compete with second quantized approaches such as Unitary Coupled Cluster.
A Robust Approach Towards Distinguishing Natural and Computer Generated Images using Multi-Colorspace fused and Enriched Vision Transformer

The works in literature classifying natural and computer generated images are mostly designed as binary tasks either considering natural images versus computer graphics images only or natural images versus GAN generated images only, but not natural images versus both classes of the generated images. Also, even though this forensic classification task of distinguishing natural and computer generated images gets the support of the new convolutional neural networks and transformer based architectures that can give remarkable classification accuracies, they are seen to fail over the images that have undergone some post-processing operations usually performed to deceive the forensic algorithms, such as JPEG compression, gaussian noise, etc. This work proposes a robust approach towards distinguishing natural and computer generated images including both, computer graphics and GAN generated images using a fusion of two vision transformers where each of the transformer networks operates in different color spaces, one in RGB and the other in YCbCr color space. The proposed approach achieves high performance gain when compared to a set of baselines, and also achieves higher robustness and generalizability than the baselines. The features of the proposed model when visualized are seen to obtain higher separability for the classes than the input image features and the baseline features. This work also studies the attention map visualizations of the networks of the fused model and observes that the proposed methodology can capture more image information relevant to the forensic task of classifying natural and generated images.
Platypus: Quick, Cheap, and Powerful Refinement of LLMs

We present $\textbf{Platypus}$, a family of fine-tuned and merged Large Language Models (LLMs) that achieves the strongest performance and currently stands at first place in HuggingFace's Open LLM Leaderboard as of the release date of this work. In this work we describe (1) our curated dataset $\textbf{Open-Platypus}$, that is a subset of other open datasets and which $\textit{we release to the public}$ (2) our process of fine-tuning and merging LoRA modules in order to conserve the strong prior of pretrained LLMs, while bringing specific domain knowledge to the surface (3) our efforts in checking for test data leaks and contamination in the training data, which can inform future research. Specifically, the Platypus family achieves strong performance in quantitative LLM metrics across model sizes, topping the global Open LLM leaderboard while using just a fraction of the fine-tuning data and overall compute that are required for other state-of-the-art fine-tuned LLMs. In particular, a 13B Platypus model can be trained on $\textit{a single}$ A100 GPU using 25k questions in 5 hours. This is a testament of the quality of our Open-Platypus dataset, and opens opportunities for more improvements in the field. Project page: https://platypus-llm.github.io
Leveling the Playing Field: Tackling Unfair Contractual Capacity Discrepancies in Artificial Intelligence-driven B2C Financial Transactions

Artificial Intelligence and other digital technologies have been integrated into various industries, impacting consumer rights in Business to Consumer (B2C) transactions. While these technologies have the potential to revolutionize economies and financial transactions, safeguards must be in place to minimize the risks they pose to consumers’ interests. This research considers whether existing regulations protect consumers in cases of B2C unfair practices that could impede their ability to seek justice. The study will analyze critical problems associated with artificial intelligence systems in B2C transactions and financial markets. The analytical comparative method used in this study will reveal various biases and injustices that impact consumer rights and the current legal approach to consumer protection is found to be deficient, and modernizing regulations and promoting transparency is necessary to protect consumer rights. artificial intelligence
LCAG ：A Lightweight Consensus Algorithm Based on Graph for the Internet of Things

Consensus algorithms are the core technology of blockchain and a focus in the current distributed system research. The consensus algorithms are widely used in distributed systems, it has solved the decentralization problem. The traditional consensus algorithm needs the process of node legitimacy checking, identity authentication, and primary node view change, so the time cost of reaching an agreement between nodes is still exponential. In response to the problem, a lightweight consensus algorithm based on graph (LCAG) is proposed for the Internet of Things (IoT) in this paper, which is proposes an access control table, and reaches an agreement among nodes by calculating the probability of nodes in the control table, and reduces the time overhead of the reaching an agreement in distributed systems. We have carried out simulation experiments for the new algorithm, and the experiments show that: the new algorithm needs less time overhead than the classical Byzantine algorithm need, as well as Byzantine Generals problem (BGP), practical Byzantine fault-tolerant algorithm (PBFT) and directed acyclic graph (DAG) algorithm, and so on. The new algorithm can be applied to the devices of IoT, which have limited computing power.
MyData Cloud: Secure Cloud Architecture for Strengthened Control Over Personal Data

Abstract MyData is a personal data management approach that allows data subjects to control their data and service providers to utilize it. The data subjects can decide how to use and where to provide their data. With the explicit consent of the participants, service providers can collect scattered data from data sources and offer personalized services based on the collected data. MyData services are commercialized using two main approaches: the operator and device models. In the operator model, a centralized data controller collects the personal data of all data subjects. In the device model, a data controller located on the personal device collects the personal data of only one subject. Personal data from both models are provided to the data processors with the explicit consent of the data subjects. However, in practice, tracing and controlling personal data are difficult for subjects once it is transferred to third-party processors. In this paper, we propose a cloud model in which personal data are collected and processed only in the cloud. As both the data controllers and processors are hosted on a cloud, personal data remains confined to the cloud. However, a data breach could endanger all the data. The threat analysis showed that cloud-only approaches could not reduce this risk. We propose a cloud- device cooperative architecture that minimizes data leakage by encrypting personal data using a key managed by a user device. This architecture poses a similar level of risk to the device model while allowing data subjects to easily control their data.
Personal Data Privacy vs Public Interest

Governments around the world have gathered masses of personal information on their citizens as part of the fight against the Covid pandemic. Citizens, willingly for the most part, yielded such data in order to protect the public good and safety of society. Focusing on personal data gathering, processing and protection for public good, the authors consider how far citizens are willing to accept that their personal data can be collected by governments during a public health crisis. The situation in Europe and in China shall be compared, showing how the “public interest” during Covid-19 was understood very differently in different jurisdictions.
ANALISIS KESULITAN SISWA PADA PEMBAHASAN MATERI PENGOLAAN DATA KELAS VI SDN PONDOK JAGUNG TIMUR

This study seeks to investigate the challenges that students encounter while discussing data management concepts in their sixth-grade curriculum. Data management is a substantial component of their mathematics lessons, where they delve into collecting, organizing, and assessing data. However, within this learning journey, certain students may encounter difficulties in comprehending and applying data management concepts effectively. Our study is geared towards understanding the specific hurdles faced by Class VI students when engaging with data management topics. The research methodology entails data collection through classroom observations, student interviews, and the analysis of student practice questions. The findings of the study highlight that several students encounter challenges across various aspects of comprehending data management material.
IMPORTANCE OF PERSONNEL TRAINING IN ACHIEVING ORGANIZATIONAL OBJECTIVES

The growth and success of any company in the business world are related to the development of its employees.Bearing in mind that training is considered a fundamental part of the organizational development process.This article aims to address the importance of personnel training in achieving organizational objectives, however, it is understood that the training process is important for the development of the individual in front of an organization, and his capacity to carry out his activities.From the methodological point of view, bibliographical research was used to support the information obtained in the field.With regard to the type, this research is classified as exploratory, with a qualitative focus, and as such an interview was used.The results showed that some companies in the surroundings of the city of Nampula do not have a training plan for their staff, this leads us to conclude that in these companies there is no strategic plan to achieve the organizational objectives.
Retracted: Design of an Incremental Music Teaching and Assisted Therapy System Based on Artificial Intelligence Attention Mechanism

[This retracts the article DOI: 10.1155/2022/7117986.].
Retracted: Correlation between the Quality of Talent Training and Regional Economic Development Based on Multivariate Statistical Analysis Model

[This retracts the article DOI: 10.1155/2022/2206689.].
Retracted: Research on Athlete Detection Method Based on Visual Image and Artificial Intelligence System

[This retracts the article DOI: 10.1155/2022/2418367.].
Retracted: Realization of Music-Assisted Interactive Teaching System Based on Virtual Reality Technology

[This retracts the article DOI: 10.1155/2022/1007954.].
Retracted: Teaching Mode Based on Educational Big Data Mining and Digital Twins

[This retracts the article DOI: 10.1155/2022/9071944.].
Retracted: Observation on the Effect of Intelligent Machine-Assisted Surgery and Perioperative Nursing

[This retracts the article DOI: 10.1155/2022/6264441.].
Retracted: Tech Optimization in Cybersecurity Defenses by Advanced ML Methods: The Use Case of Volleyball Industry

[This retracts the article DOI: 10.1155/2022/9907427.].
Retracted: Implementation of Computer-Aided Piano Music Automatic Notation Algorithm in Psychological Detoxification

[This retracts the article DOI: 10.1155/2022/4457167.].
Retracted: Machine-Vision-Based Enhanced Deep Genetic Algorithm for Robot Action Analysis

[This retracts the article DOI: 10.1155/2022/4047826.].