Reviewerly/exHarmony huggingface dataset:
Improved deep clustering model based on semantic consistency for image clustering

Recently, contrastive learning has gained increasing attention as a research topic for image-clustering tasks. However, most contrastive learning-based clustering models focus only on the similarity of embedded features or divergence of cluster assignments, without considering the semantic distribution of instances, undermining the performance of clustering. Therefore, an improved deep clustering model based on semantic consistency (DCSC) was proposed in this study, motivated by the assumption that the semantic probability distribution of various augmentations of the same instance should be similar and that of different instances should be orthogonal. The DCSC fully exploits instance-level differentiation, cluster-level discrimination, and semantic consistency of instances to design the objective function. Compared with existing contrastive learning-based clustering models, the proposed model is more cluster-sensitive to differentiate semantic concepts owing to the incorporation of cluster structure discovering loss. Extensive experimental results on six benchmark datasets illustrate that the proposed DCSC achieves superior performance compared to the state-of-the-art clustering models, with an improved accuracy of 9.3% for CIFAR-100 and 22.1% for tiny-ImageNet. The visualization results show that the DCSC produces geometrically well-separated cluster embeddings defined by the Euclidean distance, verifying the effectiveness of the proposed DCSC.
An Intelligent Government Complaint Prediction Approach

Recent advances in machine learning (ML) bring more opportunities for greater implementation of smart government construction. However, there are many challenges in terms of government data application due to the previous nonstandard records and man-made errors. In this paper, we propose a practical intelligent government complaint prediction (IGCP) framework that helps governments quickly respond to citizens' consultations and complaints via ML technologies. In addition, we put forward an automatic label correction method and demonstrate its effectiveness on the performance improvement of intelligent government complaint prediction task. Specifically, the central server collects the interaction records from users and departments and automatically integrates them by the label correction approach which is designed to evaluate the similarity between different labels in data, and merge highly similar labels and corresponding samples into their most similar category. Based on those refined data, the central server quickly generates accurate solutions to complaints through text classification algorithms. The main innovation of our approach is that we turn the task of government complaint distribution into a text classification problem which is uniformly coordinated by the central server, and employ the label correction approach to correct redundant labels for training better models based on limited complaint records. To explore the influences of our approach, we evaluate its performance on real-world government service records provided by our collaborator. The experimental results demonstrate the prediction task which uses the label correction algorithm achieves significant improvements on almost all metrics of the classifier.
Concept Drift Monitoring and Diagnostics of Supervised Learning Models via Score Vectors

Supervised learning models are one of the most fundamental classes of models. Viewing supervised learning from a probabilistic perspective, the set of training data to which the model is fitted is usually assumed to follow a stationary distribution. However, this stationarity assumption is often violated in a phenomenon called concept drift, which refers to changes over time in the predictive relationship between covariates X and a response variable Y and can render trained models suboptimal or obsolete. We develop a comprehensive and computationally efficient framework for detecting, monitoring, and diagnosing concept drift. Specifically, we monitor the Fisher score vector, defined as the gradient of the log-likelihood for the fitted model, using a form of multivariate exponentially weighted moving average, which monitors for general changes in the mean of a random vector. In spite of the substantial performance advantages that we demonstrate over popular error-based methods, a score-based approach has not been previously considered for concept drift monitoring. Advantages of the proposed score-based framework include applicability to broad classes of parametric models, more powerful detection of changes as shown in theory and experiments, and inherent diagnostic capabilities for helping to identify the nature of the changes.
Continuous Conditional Generative Adversarial Networks: Novel Empirical Losses and Label Input Mechanisms

This article focuses on conditional generative modeling (CGM) for image data with continuous, scalar conditions (termed regression labels). We propose the first model for this task which is called continuous conditional generative adversarial network (CcGAN). Existing conditional GANs (cGANs) are mainly designed for categorical conditions (e.g., class labels). Conditioning on regression labels is mathematically distinct and raises two fundamental problems: (P1) since there may be very few (even zero) real images for some regression labels, minimizing existing empirical versions of cGAN losses (a.k.a. empirical cGAN losses) often fails in practice; and (P2) since regression labels are scalar and infinitely many, conventional label input mechanisms (e.g., combining a hidden map of the generator/discriminator with a one-hot encoded label) are not applicable. We solve these problems by: (S1) reformulating existing empirical cGAN losses to be appropriate for the continuous scenario; and (S2) proposing a naive label input (NLI) mechanism and an improved label input (ILI) mechanism to incorporate regression labels into the generator and the discriminator. The reformulation in (S1) leads to two novel empirical discriminator losses, termed the hard vicinal discriminator loss (HVDL) and the soft vicinal discriminator loss (SVDL) respectively, and a novel empirical generator loss. Hence, we propose four versions of CcGAN employing different proposed losses and label input mechanisms. The error bounds of the discriminator trained with HVDL and SVDL, respectively, are derived under mild assumptions. To evaluate the performance of CcGANs, two new benchmark datasets (RC-49 and Cell-200) are created. A novel evaluation metric (Sliding Fréchet Inception Distance) is also proposed to replace Intra-FID when Intra-FID is not applicable. Our extensive experiments on several benchmark datasets (i.e., RC-49, UTKFace, Cell-200, and Steering Angle with both low and high resolutions) support the following findings: the proposed CcGAN is able to generate diverse, high-quality samples from the image distribution conditional on a given regression label; and CcGAN substantially outperforms cGAN both visually and quantitatively.
MTP: Multi-Task Pruning for Efficient Semantic Segmentation Networks

This paper focuses on channel pruning for semantic segmentation networks. Previous methods to compress and accelerate deep neural networks in the classification task cannot be straightforwardly applied to the semantic segmentation network that involves an implicit multi-task learning problem via pre-training. To identify the redundancy in segmentation networks, we present a multi-task channel pruning approach. The importance of each convolution filter \wrt the channel of an arbitrary layer will be simultaneously determined by the classification and segmentation tasks. In addition, we develop an alternative scheme for optimizing importance scores of filters in the entire network. Experimental results on several benchmarks illustrate the superiority of the proposed algorithm over the state-of-the-art pruning methods. Notably, we can obtain an about $2\times$ FLOPs reduction on DeepLabv3 with only an about $1\%$ mIoU drop on the PASCAL VOC 2012 dataset and an about $1.3\%$ mIoU drop on Cityscapes dataset, respectively.
Cross-media video event mining based on attention graph structure learning

Cross-media association mining based on heterogeneous information network(HIN) has received widespread attention. However, video is described by only a few words, leading to the lack of association between visual and textual information. As a result, the heterogeneous graph is inevitably incomplete, which brings great challenges to event mining. Fortunately, topological relationships can infer correlations between similar nodes. In view of this, a novel framework of web video event mining based on attention graph structure learning is proposed to generate a new adjacency matrix, which reconstructs the association among nodes. First, a novel heterogeneous network is constructed, while each relation subgraph is produced separately. Then, in each relational subgraph, feature graphs are generated by feature similarity, which can capture potential relationships between nodes. Simultaneously, semantic graph is also created by learning semantic structures to describe complex heterogeneous interactions between node semantics. Next, these graphs are fused by channel attention to reconstruct the correlation among nodes. Finally, graph convolutional network(GCN) is applied for web video event mining. Experiments on web videos from YouTube demonstrate that our proposed method is more effective than the state-of-the-art methods with significant improvement.
What Is Missing from Contemporary AI? The World

In the past three years, we have witnessed the emergence of a new class of artificial intelligence systems–—so-called foundation models, which are characterised by very large machine learning models (with tens or hundreds of billions of parameters) trained using extremely large and broad data sets. Foundation models, it is argued, have competence in a broad range of tasks, which can be specialised for specific applications. Large language models, of which GPT-3 is perhaps the best known, are the most prominent example of current foundation models. While foundation models have demonstrated impressive capabilities in certain tasks—natural language generation being the most obvious example—I argue that because they are inherently disembodied, and they are limited with respect to what they have learned and what they can do. Foundation models are likely to be very useful in many applications: but they are not the end of the road in artificial intelligence.
RASHT: A Partially Reconfigurable Architecture for Efficient Implementation of CNNs

Convolutional neural networks (CNNs) are widely used in machine learning (ML) applications such as image processing. CNN requires heavy computations to provide significant accuracy for many ML tasks. Therefore, the efficient implementations of CNNs to improve performance using limited resources without accuracy reduction is a challenge for ML systems. One of the architectures for the efficient execution of CNNs is the array-based accelerator, that consists of an array of similar processing elements (PEs). The array accelerators are popular as high-performance architecture using the features of parallel computing and data reuse. These accelerators are optimized for a set of CNN layers, not for individual layers. Using the same accelerator dimension size to compute all CNN layers with varying shapes and sizes leads to the resource underutilization problem. We propose a flexible and scalable architecture for array-based accelerator that increases resource utilization by resizing PEs to better match the different shapes of CNN layers. The low-cost partial reconfiguration improves resource utilization and performance, resulting in a 23.2% reduction in computational times of GoogLeNet compared to the state-of-the-art accelerators. The proposed architecture decreases the on-chip memory access rate by 26.5% with no accuracy loss.
A binary tournament competition algorithm for solving partial differential equation constrained optimization via finite element method

Sometimes, in a tournament competition to form a single team from two teams, 50% players are selected from the union of both teams according to their performance (fitness). Considering this idea, the aim of this article is to design an algorithm based on binary tournament process using two different types of metaheuristic algorithms. Here, at first, four swarms of populations are considered. The first two swarms are updated by Gaussian quantum behaved particle swarm optimization (GQPSO) algorithm and other two swarms are updated by weighted quantum behaved particle swarm optimization (WQPSO) algorithm considering second strategy among six strategies of existing binary tournamenting process. Thereafter, a swarm is formed from the updated solutions found from GQPSO and also from WQPSO algorithms. Finally, an algorithm is selected randomly with equal probability to update the final swarm. To test the effectiveness of the proposed algorithm, it is applied on partial differential equation (PDE) constrained optimization problems transforming these into bound constrained optimization problems using finite element discretization method and the obtained results are compared with the parent algorithms, WQPSO and GQPSO numerically. Also, the results are compared with a number of existing metaheuristic algorithms. Finally, to check the significance of the results and also to draw the fruitful conclusion, nonparametric statistical tests are performed.
SLAM algorithm and Navigation for Indoor Mobile Robot Based on ROS

SLAM and path planning are indispensable technologies for studying robot navigation performance. Aiming at the problem of simultaneous localization and mapping, the mapping effects of Gmapping algorithm and Cartographer algorithm are compared in terms of external contour and mapping accuracy. Aiming at the problem of global path planning and obstacle avoidance, Dijkstra algorithm and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{A}^{\ast}$</tex> algorithm are analyzed and compared, and TEB algorithm is combined to realize autonomous navigation. Through the indoor environment testing, The Cartographer algorithm produces clearer and more accurate maps, and Dijkstra algorithm has a slight advantage over <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{A}^{\ast}$</tex> algorithm in terms of path length and smoothness in the environment with U-shaped obstacles. This paper verifies the feasibility and effectiveness of the system from two aspects, which is of great significance to the research of robot in indoor environment.
QuFI: a Quantum Fault Injector to Measure the Reliability of Qubits and Quantum Circuits

Quantum computing is an up-and-coming technology that is expected to revolutionize the computation paradigm in the next few years. Qubits, the primary computing elements of quantum circuits, exploit the quantum physics proprieties to increase the parallelism and speed of computation drastically. Unfortunately, besides being intrinsically noisy, qubits have also been shown to be highly susceptible to external sources of faults, such as ionizing radiation. The latest discoveries highlight a much higher radiation sensitivity of qubits than traditional transistors and identify a much more complex fault model than bit-flip.We propose a framework to identify the quantum circuits sensitivity to radiation-induced faults and the probability for a fault in a qubit to propagate to the output. Based on the latest studies and radiation experiments performed on real quantum machines, we model the transient faults in a qubit as a phase shift with a parametrized magnitude. Additionally, our framework can inject multiple qubit faults, tuning the phase shift magnitude based on the proximity of the qubit to the particle strike location. As we show in the paper, the proposed fault injector is highly flexible, and it can be used on both quantum circuit simulators and real quantum machines. We report the finding of more than 285, 249, 536 injections on the Qiskit simulator and 53, 248 injections on real IBM machines. We consider three quantum algorithms and identify the faults and qubits that are more likely to impact the output. We also consider the fault propagation dependence on the circuit scale, showing that the reliability profile for some quantum algorithms is scale-dependent, with increased impact from radiation-induced faults as we increase the number of qubits. Finally, we also consider multi qubits faults, showing that they are much more critical than single faults. The fault injector and the data presented in this paper are available in a public repository to allow further analysis.
An industrial perspective on web scraping characteristics and open issues

An ongoing battle has been running for more than a decade between e-commerce websites owners and web scrapers. Whenever one party finds a new technique to prevail, the other one comes up with a solution to defeat it. Based on our industrial experience, we know this problem is far from being solved. New solutions are needed to address automated threats. In this work, we will describe the actors taking part in the battle, the weapons at their disposal, and their allies on either side. We will present a real-world setup to explain how e-commerce websites operators try to defend themselves and the open problems they seek solutions for.
SIMulation: Demystifying (Insecure) Cellular Network based One-Tap Authentication Services

A recently emerged cellular network based One-Tap Authentication (OTAuth) scheme allows app users to quickly sign up or log in to their accounts conveniently: Mobile Network Operator (MNO) provided tokens instead of user passwords are used as identity credentials. After conducting a first in-depth security analysis, however, we have revealed several fundamental design flaws among popular OTAuth services, which allow an adversary to easily (1) perform unauthorized login and register new accounts as the victim, (2) illegally obtain identities of victims, and (3) interfere OTAuth services of legitimate apps. To further evaluate the impact of our identified issues, we propose a pipeline that integrates both static and dynamic analysis. We examined 1,025/894 Android/iOS apps, each app holding more than 100 million installations. We confirmed 396/398 Android/iOS apps are affected. Our research systematically reveals the threats against OTAuth services. Finally, we provide suggestions on how to mitigate these threats accordingly.
A Unified NVRAM and TRNG in Standard CMOS Technology

True Random Number Generator (TRNG) provides the different keys for device authentication and cryptography. Typically, the TRNG is implemented in a standalone module into the systems, increasing the complexity and area of the implementation. In addition, the system needs to store the key generated by the TRNG in non-volatile memory for the different applications. However, implementing a Non-Volatile Random Access Memory (NVRAM) requires additional technology features, usually unavailable or expensive. This paper presents a unified NVRAM-TRNG in a 0.18&#x03BC;m standard CMOS technology without an additional mask or fabrication steps. The unified implementation does not need additional circuits for the random number generation mode. The differential NVRAM bit cell is implemented using a high voltage transistor to resist the non-volatile memory application. The NVRAM presents times of 15-ms of programming and erasing and 20-ns of reading functions. The bit cell needs a voltage of 8.5-V for the programming and erasing functions. The TRNG implemented passes the NIST SP800-22 statistical test and NIST SP800-90B entropy test with a 0.9859 minimum entropy. The entropy and statistical test are applied with Process, Voltage, and Temperature (PVT) variations. The implementation occupies a 476-&#x03BC;m<sup>2</sup> with 14.69-10<sup>3</sup>F<sup>2</sup> of area normalized. Besides, the NVRAM bit cell in TRNG mode shows a bit rate of 50-Mbps. Finally, the implementation as TRNG reports a 49.5-&#x03BC;W power consumption with 0.99-pJ/bit energy efficiency, respectively.
Practice Makes a Solver Perfect: Data Augmentation for Math Word Problem Solvers

Existing Math Word Problem (MWP) solvers have achieved high accuracy on benchmark datasets.However, prior works have shown that such solvers do not generalize well and rely on superficial cues to achieve high performance.In this paper, we first conduct experiments to showcase that this behaviour is mainly associated with the limited size and diversity present in existing MWP datasets.Next, we propose several data augmentation techniques broadly categorized into Substitution and Paraphrasing based methods.By deploying these methods we increase the size of existing datasets by five folds.Extensive experiments on two benchmark datasets across three state-of-the-art MWP solvers shows that proposed methods increase the generalization and robustness of existing solvers.On average, proposed methods significantly increase the state-of-the-art results by over five percentage points on benchmark datasets.Further, the solvers trained on the augmented dataset performs comparatively better on the challenge test set.We also show the effectiveness of proposed techniques through ablation studies and verify the quality of augmented samples through human evaluation.Original Problem Problem: Nancy grew 8 potatoes.Sandy grew 5 potatoes.How many potatoes did they grow in total ?True Equation: X = 8+5Paraphrasing Method Problem: How many potatoes did they grow in all given that nancy grew 8 potatoes and sandy grew 5 potatoes.Equation Label: X = 8+5 Substitution Method Problem: Dwight grew 8 potatoes.Juliette grew 5 potatoes.How many potatoes did they grow together ?Equation Label: X = 8+5