Reviewerly/exHarmony huggingface dataset:
Evolutionary Game Theory-Based Optimal Scheduling Strategy for Heterogeneous Computing

With the development of intelligent applications, simply relying on traditional single type of computing unit cannot efficiently satisfy diverse cloud requirements. The emergence of heterogeneous computing can efficiently achieve the adaptation of these intelligent applications by using different types of processing units such as Graphics Processing Unit (GPU) and Field Programmable Gate Array (FPGA). However, the trade-off between profit and costs in the process of scheduling heterogeneous computing resources is also an issue worthy of attention. To address this challenge, this work establishes a heterogeneous computing resource scheduling model based on Stackelberg differential game, which includes three roles Computing Power Trading Platforms (CPTPs), Heterogeneous Computing Service Providers (HCSPs), and Heterogeneous Computing Application Providers (HCAPs). The objective is to maximize utility function of CPTPs and HCSPs subject to rental ratio, pricing strategy and energy consumption of resource scheduling, which has proved that there exists a Stackelberg Nash Equilibrium (NE) solution. The Support Vector Machine based on Artificial Fish (SVM-AF) is proposed to predict the access times of heterogeneous computing applications. In addition, the distributed iteration method and Cauchy distribution is adopted to optimize the computing price strategy and improve its convergence performance. The simulation results show that compared with other strategies, the proposed strategy can effectively improve computing revenue of user experience and while reducing energy consumption in the process of resource scheduling.
stohMCharts: A Modeling Framework for Quantitative Performance Evaluation of Cyber-Physical-Social Systems

Cyber-physical-social systems (CPSS) have recently gained attention from researchers due to their combination of cyber, physical, and social spaces.Modeling and Analysis of Real-Time and Embedded systems (MARTE) is a Unified Modeling Language (UML) extension profile that supports the specification, design, and verification of Real-Time Embedded Systems (RTES).While MARTE Statecharts can assist in describing CPS, it does not model the uncertainty within a CPSS environment.To enhance the accuracy of CPSS analysis, we propose the stohMCharts (stochastic hybrid MARTE statecharts) modelling framework as an extension of MARTE statecharts for modelling and analyzing stochastic hybrid systems.stohMCharts can model CPSS in a unified manner.Additionally, based on the mapping rules and algorithms, we have developed a tool to convert models built in stohMChart language into Networks Stochastic Hybrid Automata (NSHA) which can be verified by statistical model checker UPPAAL-SMC.We demonstrate the efficiency and accuracy of the framework by applying it to one autonomous driving scenarios.
Algorithm justice – moving towards equitable artificial intelligence

Research methodology The case is presented as descriptive in nature and primarily involves exploratory research. Case overview/synopsis Ashraf, a young graduate from Bangalore, India, started a chain of lifestyle shops, his family business in Khartoum, Sudan. To modernize the shops, Ashraf approached a small finance bank for financial assistance. However, after submitting the required documents and with a good credit score, he was denied a loan. The bank officials had mentioned that the loan automation software did not approve the application. Hence, the bank personnel said that they could not do anything further. Disappointed, Ashraf sought the help of his professor, John, to understand why the software rejected his application. Professor John explained to Ashraf the advantages and disadvantages of automation. In the process, Ashraf understood the significance and compelling need to address “Algorithm Bias,” a situation in which specific attributes of an algorithm cause unfair outcomes. The case place students in Ashraf’s position to help them understand the advantages and issues of applying automation through artificial intelligence. Complexity academic level The case suits graduate-level courses like business analytics, financial analytics and business intelligence. Learning objectives Through the case, the students will be able to: Understand the role of algorithms in business and society. Understand the causes, effects and methods of reducing algorithm bias. Demonstrate the ability to detect algorithm bias. Define policies to mitigate algorithm bias.
Collaborative planning algorithm for incomplete navigation graphs

In a complex environment, in order to improve the efficiency of multi-AUV collaborative work, this paper proposes a new collaborative hunting algorithm based on the Bionic Neural Wave Network (BNWN) algorithm. This algorithm integrates search, tracking and capture of targets by multi-AUV under the incomplete navigation graphs. A search mechanism with memory tabu that to avoid redundant search and improve global search efficiency is designed. Then a real-time redistribution method is embedded into this algorithm to ensure the optimal matching state during the tracking and capture process. Further, multi-AUV track and capture prey based on energy consumption models and self-learning models, and recast time-varying navigation maps through the recognition mechanisms, under the whole collaborative operations. Simulation results have proved that the number of multi-AUV path turns is reduced and the execution time is shortened by an average of 81%. In this way, the optimal matching state is maintained, the repeated search and partial self-locking problems are overcome, and the efficient capture of intelligent prey is realized.
DMFN: A disentangled multi-level fusion network for review helpfulness prediction

Predicting the online review helpfulness is of great significance to consumers due to it affects consumers’ purchase decisions to a large extent. To generate accurate helpfulness predictions for online reviews, we need to consider all multi-modal messages including texts and images. Although extensive works have acquired great advances in the Review Helpfulness Prediction (RHP) task, there remain challenges in achieving a fine-grained fusion of the two heterogeneous modalities. Therefore, we propose a novel RHP method DMFN, which exploits the multi-level information of both texts and images to enhance the fine-detail representation of multimodal data. We further employ a weighted token-wise interaction to preserve finer-grained information in a lightweight way. Architecturally, DMFN consists of three parts, a multi-granularity feature generation part, a weighted token-wise interaction part, and a gated multi-level fully fusion part. The first part is built to fully extract the visual and linguistic features from multimodal reviews. The second part is designed to decouple the multi-granularity content and then sufficiently utilize the pair-wise correlations to obtain a disentangled manifold for multimodal inputs. The last part is constructed to adaptively integrate cross-modal features, and it controls the fusion of different features at multiple scales. Extensive experiment results on our collected RHP datasets from Yelp.com and Amazon.com show that our proposed approach is significantly superior to other existing benchmark RHP methods.
Improving Quantum Computation by Optimized Qubit Routing

In this work we propose a high-quality decomposition approach for qubit routing by swap insertion. This optimization problem arises in the context of compiling quantum algorithms onto specific quantum hardware. Our approach decomposes the routing problem into an allocation subproblem and a set of token swapping problems. This allows us to tackle the allocation part and the token swapping part separately. Extracting the allocation part from the qubit routing model of Nannicini et al. (arXiv:2106.06446), we formulate the allocation subproblem as a binary program. Herein, we employ a cost function that is a lower bound on the overall routing problem objective. We strengthen the linear relaxation by novel valid inequalities. For the token swapping part we develop an exact branch-and-bound algorithm. In this context, we improve upon known lower bounds on the token swapping problem. Furthermore, we enhance an existing approximation algorithm. We present numerical results for the integrated allocation and token swapping problem. Obtained solutions may not be globally optimal due to the decomposition and the usage of an approximation algorithm. However, the solutions are obtained fast and are typically close to optimal. In addition, there is a significant reduction in the number of gates and output circuit depth when compared to state-of-the-art heuristics. Reducing these figures is crucial for minimizing noise when running quantum algorithms on near-term hardware. As a consequence, using the novel decomposition approach leads to compiled algorithms with improved quality. Indeed, when compiled with the novel routing procedure and executed on real hardware, our experimental results for quantum approximate optimization algorithms show an significant increase in solution quality in comparison to standard routing methods.
Cancer prediction from few amounts of histology samples through self-attention based multi-routines cross-domains network

Rapid and efficient analysis of cancer has become a focus of research. Artificial intelligence can use histopathological data to quickly determine the cancer situation, but still faces challenges. For example, the convolutional network is limited by the local receptive field, human histopathological information is precious and difficult to be collected in large quantities, and cross-domain data is hard to be used to learn histopathological features. In order to alleviate the above questions, we design a novel network, Self-attention based multi-routines cross-domains network (SMC-Net).Feature analysis module and decoupling analysis module designed are the core of the SMC-Net. The feature analysis module base on multi-subspace self-attention mechanism with pathological feature channel embedding. It in charge of learning the interdependence between pathological features to alleviate the problem that the classical convolution model is difficult to learn the impact of joint features on pathological examination results. The decoupling analysis module base on the designed multi-channel and multi-discriminator architecture. Its function is to decouple the features related to the target task in cross-domain samples so that the model has cross-domain learning ability.To evaluate the performance of the model more objectively, three datasets are used. Compared with other popular methods, our model achieves better performance without performance imbalance. In this work, a novel network is design. It can use domain-independent data to assist in the learning of target tasks, and can achieve acceptable histopathological diagnosis results even in the absence of data.The proposed method has higher clinical embedding potential and provides a viewpoint for the combination of deep learning and histopathological examination.
Meta-Learning Based Dynamic Adaptive Relation Learning for Few-Shot Knowledge Graph Completion

As artificial intelligence gradually steps into cognitive intelligence stage, knowledge graphs (KGs) play an increasingly important role in many natural language processing tasks. Due to the prevalence of long-tail relations in KGs, few-shot knowledge graph completion (KGC) for link prediction of long-tail relations has gradually become a hot research topic. Current few-shot KGC methods mainly focus on the static representation of surrounding entities to explore the potential semantic features of entities, while ignoring the dynamic properties among entities and the special influence of the long-tail relation on link prediction. In this paper, a new meta-learning based dynamic adaptive relation learning model (DARL) is proposed for few-shot KGC. For obtaining better semantic information of the meta knowledge, the proposed DARL model applies a dynamic neighbor encoder to incorporate neighbor relations into entity embedding. In addition, DARL builds attention mechanism based fusion strategy for different attributes of the same relation to further enhance the relation-meta learning ability. We evaluate our DARL model on two public benchmark datasets NELL-One and WIKI-One for link prediction. Extensive experimental results indicate that our DARL outperforms the state-of-the-art models with an average relative improvement about 23.37%, 32.46% in MRR and Hits@1 on NELL-One, respectively.
Deriving mixed state of light field by partial tracing pure state in higher dimension

In this paper, we show that by partial tracing the pure state in higher dimension one can deduce the mixed state of light field. In this paper the pure state in higher dimension is a three-mode squeezed state S23σS31τ000000S31−1τS23−1σ, while the mixed state's density operator is sech2σsech2τea2†a3†tanhσsech2σtanh2τa3†a30220ea2a3tanhσ, which involves both squeezing and chaotic properties of optical field. We further explain how such kind of light field can be generated by passing a two-mode squeezed light though a single-mode dissipative channel, its Wigner function is discussed by using the coherent state representation. Our work provides an important method, which can be used to theoretically find more optical fields in quantum communication and quantum optics.
Spatio-temporal propagation and reconstruction for low-light video enhancement

Different from the single low-light image enhancement, the key for video enhancement is to improve temporal stability in spatial and temporal varying manner. However, most methods fail to effectively align images or features of adjacent frames to fuse spatio-temporal information and achieve limited enhancement performance, especially in dynamic videos with scene changes and moving targets. In this paper, we propose a spatio-temporal propagation and reconstruction (STPR) network to enable video representation learning for low-light video enhancement. Specifically, we develop a recursively supervised-based pyramid residual dense structure for extracting expressive context features from video sequences, the unique structural design enlarges the receptive field in multi-scale space with limited training parameters. Moreover, to strengthen the interaction of across-frame features, a feature propagation subnet is present to achieve precise alignment and effective fusion of spatio-temporal features. After obtaining the aggregated features of adjacent frames, the spatio-temporal feature reconstruction subnet exploits temporal dependence to improve the quality of target frames and enforce temporal consistency. Experimental results on several low-light video datasets demonstrate that our network outperforms state-of-the-art methods both in quantitative and visual comparisons.
Joint feature selection and optimal bipartite graph learning for subspace clustering

Recently, there has been tremendous interest in developing graph-based subspace clustering in high-dimensional data, which does not require a priori knowledge of the number of dimensions and subspaces. The general steps of such algorithms are dictionary representation and spectral clustering. Traditional methods use the dataset itself as a dictionary when performing dictionary representation. There are some limitations that the redundant information present in the dictionary and features may make the constructed graph structure unclear and require post-processing to obtain labels. To address these problems, we propose a novel subspace clustering model that first introduces feature selection to process the input data, randomly selects some samples to construct a dictionary to remove redundant information and learns the optimal bipartite graph with K-connected components under the constraint of the (normalized) Laplacian rank. Finally, the labels are obtained directly from the graphs. The experimental results on motion segmentation and face recognition datasets demonstrate the superior effectiveness and stability of our algorithm.
Best of both, Structured and Unstructured Sparsity in Neural Networks

Besides quantization, pruning has shown to be one of the most effective methods to reduce the inference time and required energy of Deep Neural Networks (DNNs). In this work, we propose a sparsity definition that reflects the number of saved operations by pruned parameters to guide the pruning process in order to save as many operations as possible. Based on this, we show the importance of the baseline model's size and quantify the overhead of unstructured sparsity for a commercial-of-the-shelf AI Hardware Accelerator (HWA) in terms of latency reductions. Furthermore, we show that a combination of both structured and unstructured sparsity can mitigate this effect.
Towards Practical Few-shot Federated NLP

Transformer-based pre-trained models have emerged as the predominant solution for natural language processing (NLP). Fine-tuning such pre-trained models for downstream tasks often requires a considerable amount of labeled private data. In practice, private data is often distributed across heterogeneous mobile devices and may be prohibited from being uploaded. Moreover, well-curated labeled data is often scarce, presenting an additional challenge. To address these challenges, we first introduce a data generator for federated few-shot learning tasks, which encompasses the quantity and skewness of scarce labeled data in a realistic setting. Subsequently, we propose AUG-FedPrompt, a prompt-based federated learning system that exploits abundant unlabeled data for data augmentation. Our experiments indicate that AUG-FedPrompt can perform on par with full-set fine-tuning with a limited amount of labeled data. However, such competitive performance comes at a significant system cost.
Optical neural network via loose neuron array and functional learning

This research proposes a deep-learning paradigm, termed functional learning (FL), to physically train a loose neuron array, a group of non-handcrafted, non-differentiable, and loosely connected physical neurons whose connections and gradients are beyond explicit expression. The paradigm targets training non-differentiable hardware, and therefore solves many interdisciplinary challenges at once: the precise modeling and control of high-dimensional systems, the on-site calibration of multimodal hardware imperfectness, and the end-to-end training of non-differentiable and modeless physical neurons through implicit gradient propagation. It offers a methodology to build hardware without handcrafted design, strict fabrication, and precise assembling, thus forging paths for hardware design, chip manufacturing, physical neuron training, and system control. In addition, the functional learning paradigm is numerically and physically verified with an original light field neural network (LFNN). It realizes a programmable incoherent optical neural network, a well-known challenge that delivers light-speed, high-bandwidth, and power-efficient neural network inference via processing parallel visible light signals in the free space. As a promising supplement to existing power- and bandwidth-constrained digital neural networks, light field neural network has various potential applications: brain-inspired optical computation, high-bandwidth power-efficient neural network inference, and light-speed programmable lens/displays/detectors that operate in visible light.
SPDET: Edge-Aware Self-Supervised Panoramic Depth Estimation Transformer With Spherical Geometry

Panoramic depth estimation has become a hot topic in 3D reconstruction techniques with its omnidirectional spatial field of view. However, panoramic RGB-D datasets are difficult to obtain due to the lack of panoramic RGB-D cameras, thus limiting the practicality of supervised panoramic depth estimation. Self-supervised learning based on RGB stereo image pairs has the potential to overcome this limitation due to its low dependence on datasets. In this work, we propose the SPDET, an edge-aware self-supervised panoramic depth estimation network that combines the transformer with a spherical geometry feature. Specifically, we first introduce the panoramic geometry feature to construct our panoramic transformer and reconstruct high-quality depth maps. Furthermore, we introduce the pre-filtered depth-image-based rendering method to synthesize the novel view image for self-supervision. Meanwhile, we design an edge-aware loss function to improve the self-supervised depth estimation for panorama images. Finally, we demonstrate the effectiveness of our SPDET with a series of comparison and ablation experiments while achieving the state-of-the-art self-supervised monocular panoramic depth estimation. Our code and models are available at https://github.com/zcq15/SPDET.
Web-Based Village Fund Assistance Distribution Information System Using the Quota Based Method

The development of information technology has greatly influenced several aspects of human life in the implementation of daily activities. One of the benefits of the development of information technology is that it can help local governments provide information regarding the distribution of financial assistance through monitoring. Village fund assistance is one of the government programs aimed at economic recovery for people affected by the pandemic. Currently, assistance from village funds is often not supported by good governance in every village. Particularly in Tanah Merah Village, aid arrangements were still carried out by recording manually, so sometimes data input errors occurred which resulted in inaccurate data. Therefore, we need a computerized system that can overcome the limitations and problems that occur. In making this system using the Waterfall method, the stages are needs analysis, application design, system design, testing, and system maintenance. The tools used to design this system are UML (Unified Modeling Language), which consists of Use Case Diagrams, Activity Diagrams, and Class Diagrams. The results of this study are an information system for distributing village fund assistance at the Tanah Merah Village office. The application of this information system for distributing village fund assistance can help provide alternative solutions for the village government to problems in the data collection process so that the implementation process can achieve its objectives properly.
Multi-view clustering based on a multimetric matrix fusion method

Multi-view clustering(MVC) utilizes the consistency of multiple views to learn a consensus representation. However, the existing MVC methods usually use only a single metric to learn the graph matrix, which cannot fully reveal the real structure between complex samples and makes the clustering performance unsatisfactory. To solve this problem, we propose a novel method, i.e., multi-view clustering based on a multimetric matrix fusion method(MVC3MF). Specifically, we first concatenate the multi-view data into a joint representation. Then, we learn multiple kinds of distance metric matrices based on the joint representation. Third, we fuse the obtained multimetric matrices into an optimal metric matrix by using an adaptive weight method. Finally, we use the optimal metric matrix to learn the graph matrix, which is imposed by a rank constraint. To verify the superiority of our algorithm, we performed experiments on six datasets and the experimental results show that the clustering performance of MVC3MF is superior to that of some state-of-the-art MVC methods.