While feminist and critical data theories have long critiqued the use of data to uphold a positivist-informed view about science, few examples offer alternative methods to display scientific constructs. In response, we present Data and Me: an exhibit informed by feminist and critical data theories, which we designed and launched at a local science museum. Data and Me introduces museum visitors to data using a [AT]:hugging_face: vibe -- a vibe that signals that data can be [hashtag]slow, [hashtag]handmade, and [hashtag]personal. We designed this vibe to be noticeably different than the [AT]Science vibe in the rest of the museum. Throughout our design process, we adapted visualization vibes as an analytic and generative tool in the context of a science museum. We present four design choices that enable the design of a vibe: visual, topical, material, and crediting. We discuss how our exhibit aligns with ongoing discussions about alternative research outcomes and calls for plurality in HCI.

Abstract The machine learning as a service (MLaaS) paradigm has been widely adopted across various applications. However, it also raises significant privacy concerns, particularly regarding the exposure of input data and trained models. Two-party computation in convolutional neural network (CNN) inference has emerged as a promising solution to address these privacy issues in MLaaS. Nevertheless, most existing privacy-preserving CNN architectures rely on computationally expensive encryption methods, resulting in prolonged inference times and increased communication overhead. In this paper, we propose F2PQNN, a fast and secure two-party inference framework for quantized CNNs. To minimize reliance on computationally intensive encryption, F2PQNN utilizes two non-colluding servers and integrates secret sharing with oblivious transfer techniques. Furthermore, F2PQNN incorporates quantization techniques, along with batching and asynchronous computation, to significantly accelerate inference predictions. We evaluate the performance of F2PQNN on the MNIST, Fashion-MNIST, CIFAR-10, and STL-10 datasets. Experimental results demonstrate that F2PQNN outperforms existing solutions, achieving a $9.14\times $ speedup and reducing communication overhead by $59.8\times $ on the MNIST dataset.

Abstract Deep learning has been widely adopted in the field of network traffic classification due to its unique advantages in handling encrypted network traffic. However, most existing deep learning models can only classify known encrypted traffic that has been sampled and labeled. In this paper, we propose CM-UTC, a cost-sensitive matrix-based method for classifying unknown encrypted traffic. CM-UTC explores the probability distribution of the DNN output layer to filter out the unknown classes and further designs a cost-sensitive matrix to address the class imbalance problem. Additionally, we propose the utilization of the Harris Hawk optimization algorithm to modify the model parameters and improve its performance. The experiments are validated on two different datasets, and the results demonstrate that CM-UTC not only outperforms existing methods in terms of overall performance but also exhibits superior capability in correctly identifying samples from the minority class.

Internet of Things (IoT) devices generate vast amounts of real-time data across diverse sectors, offering lucrative opportunities in the data trading market. This facilitates the conversion of raw data into valuable products and services, resulting in significant economic and social benefits. To issue data security problems in trading mechanisms, several solutions based on local differential privacy (LDP) provide lightweight methods for privacy preservation and efficient data exchange. However, most solutions lack effective mechanisms for longitudinal data. Moreover, LDP needs to address the data quality problem in IoT. At last, the pricing of privacy-preserving longitudinal data remains unresolved. To address these problems, we propose a privacy-preserving data trading (PPDT) scheme for IoT in this article. Specifically, to guarantee the security of longitudinal data, we utilize two perturbation techniques to accommodate data owners with varying privacy preferences. To enhance data availability, we devise a binary tree-based aggregation algorithm combined with a weighted average strategy and maximum likelihood estimation. Additionally, we derive an optimal contract that considers different levels of privacy preservation and data trading prices. In scenarios with incomplete information, the contract can provide appropriate incentives to the involved parties. Finally, we demonstrate the efficiency and effectiveness of the proposed data trading scheme through theoretical analysis and extensive experiments.

Hashing, as an efficient retrieval strategy, has been extensively studied in the image retrieval community. It aims to map the original high-dimensional features into compact binary codes, which dramatically reduces the cost of computing similarity with economic memory consumption. In general, most existing algorithms contain two stages: projecting high-dimensional descriptors into low-dimensional features, and encoding the low-dimensional features as binary strings. Although these two-stage approaches are intuitive and effective, certain beneficial information to binary encoding is inevitably discarded in the dimensionality reduction, which tends to lossy encoding. In this paper, we propose a novel hash algorithm where integrate subspace reconstruction and binary quantization into a unified framework and compact hash codes can be generated directly from high-dimensional features. More specifically, the proposed method discovers a latent subspace from the original feature space which is suitable for hash learning, thus alleviating the heavy information loss caused by traditional dimensionality reduction strategy. Meanwhile, thanks to the excellent property of rotational invariance in Euclidean space, the proposed method avoid the challenge of directly optimizing the binary matrix. Specially, our approach essentially is built on l2estimator obeying the unsupervised paradigm, without involving any labeled data. Furthermore, we design an effective iterative alternating strategy to optimize the proposed model, so as to generate compact binary codes for the subsequent image matching. Extensive experiments on public benchmarks demonstrate that our method outperforms the compared method by a significant margin.

This paper presents Partially Observable Markov model with Duration (POMD), a statistical method that addresses the challenge of understanding sophisticated user behaviors from the search log in which some user actions, such as reading and skipping search results, cannot be observed and recorded. POMD utilizes not only the positional but also the temporal information of the clicks in the log. In this work, we treat the user engagements with a search engine as a Markov process, and model the unobservable engagements as hidden states. POMD differs from the traditional hidden Markov model (HMM) in that not all the hidden state transitions emit observable events, and that the duration of staying in each state is explicitly factored into the core statistical model. To address the training and decoding issues emerged as the results of the variations, we propose an iterative two-stage training algorithm and a greedy segmental decoding algorithm respectively. We validate the proposed algorithm with two sets of experiments. First, we show that the search behavioral patterns inferred by POMD match well with those reported in the eye tracking experiments. Secondly, through a series of A/B comparison experiments, we demonstrate that POMD can distinguish the ranking qualities of different search engine configurations much better than the patterns inferred by the model proposed in the previous work. Both of the experimental results suggest that POMD can provide a statistical and quantitative way to understand the sophisticated search behaviors by simply mining the search logs.