Reviewerly/exHarmony huggingface dataset:
Probabilistic Selective Encryption of Convolutional Neural Networks for Hierarchical Services

Model protection is vital when deploying Convolutional Neural Networks (CNNs) for commercial services, due to the massive costs of training them. In this work, we propose a selective encryption (SE) algorithm to protect CNN models from unauthorized access, with a unique feature of pro-viding hierarchical services to users. Our algorithm firstly selects important model parameters via the proposed Probabilistic Selection Strategy (PSS). It then encrypts the most important parameters with the designed encryption method called Distribution Preserving Random Mask (DPRM), so as to maximize the performance degradation by encrypting only a very small portion of model parameters. We also design a set of access permissions, using which different amount of most important model parameters can be decrypted. Hence, different levels of model performance can be naturally provided for users. Experimental results demonstrate that the proposed scheme could effectively protect the classification model VGG19 by merely encrypting 8% parameters of convolutional layers. We also implement the proposed model protection scheme in the denoising model DnCNN, showcasing the hierarchical denoising services.
Two-stage Network For Single Image Super-Resolution

The task of single-image super-resolution (SISR) is a highly inverse problem because it is very challenging to reconstruct rich details from blurred images. Most previous super-resolution (SR) methods based on the convolutional neural networks (CNN) tend to design more complex network structures to directly learn the mapping between low-resolution images and high-resolution images. However, this is not the best choice to blindly increase the network depth, because the performance improvement may not increase, but it will increase the computational cost. To solve this problem, we propose an effective method that learns high-frequency information in high-resolution images to enhance the image reconstruction. In this work, we propose a two-stage network (TSN) to recover clear SR images. The proposed TSN firstly learns the high-frequency information in high-resolution images, then learns how to transform to high-resolution images. A large number of experiments show that our TSN achieves satisfactory performance.
A Wireless Flexible Electrooculogram Monitoring System With Printed Electrodes

Electroocugraphy (EOG) is a popular method of measuring biopotentials developed across the eyes during eye activities such as eye-blinking, vertical, and horizontal eye-movements. The measured signal is called electrooculogram (EOG) and has been known to be used in behavioral studies, cognitive neuroscience and sleep monitoring. In this work a single channel wearable wireless EOG monitoring system is presented. The entire system is implemented on a double sided polymide flexible substrate. The recording silver electrodes are printed on the bottom side of the substrate whereas the EOG signal recording and transmission circuitries are implemented on the top side of the substrate with printed silver traces. The system is run by a rechargeable battery and uses a BLE 5.0 transceiver for wireless connectivity. Design considerations for the wearable EOG monitoring system are discussed in details. The system performance is validated by successfully monitoring different eye movements with it. Additionally, comparison between the EOG signals observed using the printed silver electrodes and commercial gold electrodes of same dimensions demonstrate that the printed electrodes provide similar EOG signal amplitude like the commercial gold electrodes. With a 5.2 gram mass and flexibility the system has potential for monitoring EOG signal without causing discomfort to the wearer.
Deep Metric Learning for K Nearest Neighbor Classication

KNN has gained popularity in machine learning due to its simplicity and good performance. However, kNN faces two problems with classification tasks. The first is that an appropriate distance measurement is required to compute distances between test sample and training samples. The other is the highly computational complexity due to the requirement of searching the nearest neighbors in the whole training data. In order to mitigate these two problems, we propose a novel method named KCNN to enhance the performance of kNN. KCNN uses convolutional neural networks to learn a suitable distance metric as well as prototype reduction to learn a reduced set of prototypes which can represent the original set. It has several superiorities compared with related methods. The combination of CNN and kNN empowers it to extract discriminative hierarchical features with which kNN can easily classify. KCNN learns spatial information on an image instead of considering it as a vector to learn distance metric. Moreover, KCNN simultaneously learns a reduced set of prototypes, which help improve classification efficiency and avoid noisy samples of the massive training set. The proposed method has a better robustness and convergence than CNN, especially when projecting input data into a low-dimension space.
Pixel-Guided Dual-Branch Attention Network for Joint Image Deblurring and Super-Resolution

Image deblurring and super-resolution (SR) are computer vision tasks aiming to restore image detail and spatial scale, respectively. Besides, only a few recent works of literature contribute to this task, as conventional methods deal with SR or deblurring separately. We focus on designing a novel Pixel-Guided dual-branch attention network (PDAN) that handles both tasks jointly to address this issue. Then, we propose a novel loss function better focus on large and medium range errors. Extensive experiments demonstrated that the proposed PDAN with the novel loss function not only generates remarkably clear HR images and achieves compelling results for joint image deblurring and SR tasks. In addition, our method achieves second place in NTIRE 2021 Challenge on track 1 of the Image Deblurring Challenge.
ProMIPS: Efficient High-Dimensional c-Approximate Maximum Inner Product Search with a Lightweight Index

Due to the wide applications in recommendation systems, multi-class label prediction and deep learning, the Maximum Inner Product (MIP) search problem has received extensive attention in recent years. Faced with large-scale datasets containing high-dimensional feature vectors, the state-of-the-art LSH-based methods usually require a large number of hash tables or long hash codes to ensure the searching quality, which takes up lots of index space and causes excessive disk page accesses. In this paper, we relax the guarantee of accuracy for efficiency and propose an efficient method for c-Approximate Maximum Inner Product (c-AMIP) search with a lightweight iDistance index. We project high-dimensional points to low-dimensional ones via 2-stable random projections and derive probability-guaranteed searching conditions, by which the c-AMIP results can be guaranteed in accuracy with arbitrary probabilities. To further improve the efficiency, we propose Quick-Probe for quickly determining the searching bound satisfying the derived condition in advance, avoiding the inefficient incremental searching process. Extensive experimental evaluations on four real datasets demonstrate that our method requires less pre-processing cost including index size and pre-processing time. In addition, compared to the state-of-the-art benchmark methods, it provides superior results on searching quality in terms of overall ratio and recall, and efficiency in terms of page access and running time.
High performance ant colony system based on GPU warp specialization with a static–dynamic balanced candidate set strategy

To improve the performance of parallel algorithms, it is necessary to make full utilization of computing resources and computing power of parallel hardware. However, the utilization efficiency of the computation must also be considered. Ant Colony System (ACS) has natural parallelism, and the procedure “Selecting the next element” is one of its main key computational components for combinatorial optimization problems. If all elements in the candidate set have been visited, a global search in the complete element domain needs to be performed and its computational overhead is enormous. Based on extensive experiments, we show that the results of global searches are also valuable for subsequent iterations of ACS. Therefore, an innovative static–dynamic balanced candidate set strategy, denoted by ID-CS, is proposed. ID-CS saves the result of global searches in previous iterations in order to reuse them in later iterations so that it can decrease the number of global searches. Furthermore, a novel GPU parallel ACS algorithm, ACS_GPU_WSP, is proposed based on the producer–consumer parallel model by GPU Warp Specialization. Each ant divides its computational work into private work and public work. For 11 large-scale typical TSP problems, compared with the state-of-art GPU data-level parallel implementation, it has achieved a large performance improvement.
A Universal Encoder Rate Distortion Optimization Framework for Learned Compression

Learning-based image compression has drawn increasing attention in recent years. Despite impressive progress has been made, it still lacks a universal encoder optimization method to seek efficient representation for different images. In this paper, we develop a universal rate distortion optimization framework for learning-based compression, which adaptively optimizes latents and side information together for each image. The proposed framework is independent of network architecture and can be flexibly applied to existing and potential future compression networks. Experimental results demonstrate that we can achieve 6.6% bit rate saving against the latest traditional codec, i.e., VVC, yielding the state-of-the-art compression ratio. Moreover, with the proposed optimization framework, we win the first place in CLIC validation phase for all the three different bit rates in terms of PSNR.
Structured Object Matching across Web Page Revisions

A considerable amount of useful information on the web is (semi-)structured, such as tables and lists. An extensive corpus of prior work addresses the problem of making these human-readable representations interpretable by algorithms. Most of these works focus only on the most recent snapshot of these web objects. However, their evolution over time represents valuable information that has barely been tapped, enabling various applications, including visual change exploration and trust assessment. To realize the full potential of this information, it is critical to match such objects across page revisions.In this work, we present novel techniques that match tables, infoboxes and lists within a page across page revisions. We are, thus, able to extract the evolution of structured information in various forms from a long series of web page revisions. We evaluate our approach on a representative sample of pages and measure the number of correct matches. Our approach achieves a significant improvement in object matching over baselines and over related work.
Smart Contracts in the Global South

While blockchain applications are at a nascent stage of development in developing countries (referred to as “Southern countries” in this article), they are already having significant economic, political, and social impacts.1 Writing and implementing contracts are one area that is likely to benefit from blockchain. Specifically, smart contracts, which execute automatically when certain conditions are met, can address some of the deficiencies of existing problems associated with contract laws and their enforcement in Southern countries. In many cases, the real challenge is in the interpretation of the contract rather than the breach by a party. Often, an uncertainty about the meaning of the terms in a contract presents questions of interpretation.2 Since smart contracts are enforced by software codes, many of the interpretation-related problems are not likely to occur in such platforms. Among the major benefits, smart contracts can dramatically reduce costs associated with verification and enforcement. Other advantages include higher speed, precision, efficiency, and transparency. A key challenge in this is how Southern countries can benefit from blockchain and its potentially disruptive application in smart contracts. In this article, we attempt to assess the potential of blockchain-powered smart contracts in Southern economies vis-a`-vis traditional contracting mechanisms.
Enhancing Quality Of Experience For Cloud Virtual Reality Gaming: An Object-Aware Video Encoding

Cloud gaming is an ideal solution to provide the desired GPU rendering for the Virtual Reality (VR) gaming while reducing the requirements for the weight, price, and capabilities of the head-mounted display. However, transmitting the high-resolution video requires a large amount of network bandwidth. In this paper, we present a cloud VR gaming system that significantly improves the coding efficiency by using a proposed Object-Aware Video Encoding (OAVE). Different from the video service, the game engine can offer information about the objects in the game scene. By combining this information and the eye-tracking data, the encoder compressed the rendered video in an object-dependent way that sets the quantization parameter according to the object of the user’s gaze. Through the subjective experiment conducted on the prototype of the system, above 40% to 47% bandwidth saving can be achieved compared with the conventional VR gaming system without the proposed optimization.
Profiles of Schema Evolution in Free Open Source Software Projects

In this paper, we present the findings of a large study of the evolution of the schema of 195 Free Open Source Software projects. We identify families of evolutionary behaviors, or taxa, in FOSS projects. A large percentage of the projects demonstrate very few, if any, actions of schema evolution. Two other taxa involve the evolution via focused actions, with either a single focused maintenance action, or a large percentage of evolution activity grouped in no more than a couple interventions. Schema evolution also involves moderate, and active evolution, with very different volumes of updates to the schema. To the best of our knowledge, this is the first study of this kind in the area of schema evolution, both in terms of presenting profiles of how schemata evolve, and, in terms of the dataset magnitude and the generalizability of the findings.
Conference key agreement based on continuous-variable quantum key distribution

We propose a conference key agreement protocol that satisfies future demands for secure communication, which requires that all participants contribute to the generation of the shared key and that any nontrivial subset of participants cannot determine the shared key alone. We construct a basic network topology which supports three participants communication by connecting to a distrusted intermediate station via insecure links. The participants independently decide whether to send a quantum signal pulse or a local oscillator pulse to the intermediate station at any time window i. We derive the secret key rate from our protocol, and a novel encoded strategy is adopted. The protocol is a promising step towards conference key agreement.
Simpler Certified Radius Maximization by Propagating Covariances

One strategy for adversarially training a robust model is to maximize its certified radius - the neighborhood around a given training sample for which the model's prediction remains unchanged. The scheme typically involves analyzing a "smoothed" classifier where one estimates the prediction corresponding to Gaussian samples in the neighborhood of each sample in the mini-batch, accomplished in practice by Monte Carlo sampling. In this paper, we investigate the hypothesis that this sampling bottleneck can potentially be mitigated by identifying ways to directly propagate the covariance matrix of the smoothed distribution through the network. To this end, we find that other than certain adjustments to the network, propagating the covariances must also be accompanied by additional accounting that keeps track of how the distributional moments transform and interact at each stage in the network. We show how satisfying these criteria yields an algorithm for maximizing the certified radius on datasets including Cifar-10, ImageNet, and Places365 while offering runtime savings on networks with moderate depth, with a small compromise in overall accuracy. We describe the details of the key modifications that enable practical use. Via various experiments, we evaluate when our simplifications are sensible, and what the key benefits and limitations are.
Intent Disentanglement and Feature Self-Supervision for Novel Recommendation

One key property in recommender systems is the long-tail distribution in user-item interactions where most items only have few user feedback. Improving the recommendation of tail items can promote novelty and bring positive effects to both users and providers, and thus is a desirable property of recommender systems. Current novel recommendation studies over-emphasize the importance of tail items without differentiating the degree of users' intent on popularity and often incur a sharp decline of accuracy. Moreover, none of existing methods has ever taken the extreme case of tail items, i.e., cold-start items without any interaction, into consideration. In this work, we first disclose the mechanism that drives a user's interaction towards popular or niche items by disentangling her intent into conformity influence (popularity) and personal interests (preference). We then present a unified end-to-end framework to simultaneously optimize accuracy and novelty targets based on the disentangled intent of popularity and that of preference. We further develop a new paradigm for novel recommendation of cold-start items which exploits the self-supervised learning technique to model the correlation between collaborative features and content features. We conduct extensive experimental results on three real-world datasets. The results demonstrate that our proposed model yields significant improvements over the state-of-the-art baselines in terms of accuracy, novelty, coverage, and trade-off.
Identifying Critical Projects via PageRank and Truck Factor

Recently, Google's Open Source team presented the criticality score a metric to assess "influence and importance" of a project in an ecosystem from project specific signals, e.g., number of dependents, commit frequency, etc. The community showed mixed reactions towards the score doubting if it can accurately identify critical projects. We share the community's doubts and we hypothesize, that a combination of PageRank (PR) and Truck Factor (TF) can more accurately identify critical projects than Google's current Criticality Score (CS). To verify our hypothesis, we conduct an experiment in which we compute the PR of thousands of projects from various ecosystems, such as, Maven (Java), NPM (JavaScript), PyPI (Python), etc., we compute the TFs of the projects with the highest PR in the respective ecosystems, and we compare these to the scores provided by the Google project. Unlike Google's CS, our approach identifies projects, such as, six and idna from PyPI, com.typesafe:config from Maven, or tap from NPM, as critical projects with high degree of transitive dependents (highest PR) and low amount of core developers (each of them possessing a TF of one).
Partition-Guided GANs

Despite the success of Generative Adversarial Networks (GANs), their training suffers from several well-known problems, including mode collapse and difficulties learning a disconnected set of manifolds. In this paper, we break down the challenging task of learning complex high dimensional distributions, supporting diverse data samples, to simpler sub-tasks. Our solution relies on designing a partitioner that breaks the space into smaller regions, each having a simpler distribution, and training a different generator for each partition. This is done in an unsupervised manner without requiring any labels. We formulate two desired criteria for the space partitioner that aid the training of our mixture of generators: 1) to produce connected partitions and 2) provide a proxy of distance between partitions and data samples, along with a direction for reducing that distance. These criteria are developed to avoid producing samples from places with non-existent data density, and also facilitate training by providing additional direction to the generators. We develop theoretical constraints for a space partitioner to satisfy the above criteria. Guided by our theoretical analysis, we design an effective neural architecture for the space partitioner that empirically assures these conditions. Experimental results on various standard benchmarks show that the proposed unsupervised model outperforms several recent methods.
Long term spatio-temporal modeling for action detection

Modeling person interactions with their surroundings has proven to be effective for recognizing and localizing human actions in videos. While most recent works focus on learning short term interactions, in this work, we consider long-term person interactions and jointly localize actions of multiple actors over an entire video shot. We construct a graph with nodes that correspond to keyframe actor instances and connect them with two edge types. Spatial edges connect actors within a keyframe, and temporal edges connect multiple instances of the same actor over a video shot. We propose a Graph Neural Network that explicitly models spatial and temporal states for each person instance and learns to effectively combine information from both modalities to make predictions at the same time. We conduct experiments on the AVA dataset and show that our graph-based model provides consistent improvements over several video descriptors, achieving state-of-the-art performance without any fine-tuning.
The DiscoverFramework freeware toolkit for multivariate spatio-temporal environmental data visualization and evaluation

The freeware DiscoverFramework provides new tools to build spatial and temporal data visualization applications accessible to stakeholders, policy makers, scientists, and educators. By focusing on environmental data and supporting applications accessible via laptops, tablets, and cell phones, the DiscoverFramework can be used to increase public awareness and inspire responsible use of complex environmental systems upon which human society depends. DiscoverFramework enables computer-savvy domain scientists to develop interactive applications using “Elements” and workflows defined to make visualization easy and address common problems such as spatio-temporal scales and user engagement. Two applications are used to demonstrate DiscoverFramework: DiscoverWater and DiscoverHABs. DiscoverWater uses Map, Chart, and Text Elements to relate streamflow changes to groundwater withdrawals. DiscoverHABs uses the Scenario Element to aid stakeholders, such as resource managers and users, struggling to identify when and where harmful algal blooms (HABs) are likely given that causal relations in these systems remain poorly understood. • Visualize complex spatio-temporal multivariate data sets with modular “Elements”. • Construct many environmental data applications using DiscoverFramework. • Experience using DiscoverWater and DiscoverHABs applications. • Explore important insights quickly using holistic data visualization.