Reviewerly/exHarmony huggingface dataset:
An External Definition of the One-Hot Constraint and Fast QUBO Generation for High-Performance Combinatorial Clustering

Recently, a clustering method using a combinatorial optimization problem, called combinatorial clustering, has been drawing attention due to the rapid spreads of quantum annealing and simulated annealing. Combinatorial clustering is performed by minimizing an objective function under a condition to satisfy a one-hot constraint. The objective function and the constraint function are generally formulated to a unified objective function of a QUBO (Quadratic Unconstrained Binary Optimization) problem using the method of the Lagrange multiplier. The coefficients of the QUBO function can be represented by a square matrix, which is called the QUBO matrix. Although the Lagrange multiplier needs to be large enough to avoid violating the constraint, it is usually hard to be set appropriately due to the limitation of the bit precision. For example, the latest quantum annealer can handle values represented by only six or fewer bits. Even conventional computing systems cannot control the larger value of the Lagrange multiplier as the number of data points increases. Besides, the execution time for combinatorial clustering increases exponentially as the problem size increases. This is because the time for the QUBO matrix generation is long and a dominant factor of the total execution time when the problem size is large. To solve these problems, this paper proposes combinatorial clustering that overcomes the limitation of the method of the Lagrange multiplier. The proposed method uses a QUBO solver that can externally define the one-hot constraint independent from the objective function, and the externally-defined constraint is satisfied by the operations with multiple bit-flips. As the QUBO function contains only the objective function, the method of the Lagrange multiplier is not necessary. The proposed method can optimize the objective function sufficiently even when the problem size is large. Since the constraint function is not included in the QUBO function, the proposed method also reduces the time for the QUBO matrix generation. The experimental results obtained using the artificial and real data show that the proposed method can improve the quality of annealing-based clustering results in all data sets. The experimental results also clarify that the quality of the proposed method is almost equal to or better than that of quasi-optimal clustering methods such as K-means. The evaluation using the multiple traveling salesman problem shows that the proposed method can obtain shorter tour lengths than the conventional annealing-based clustering in all 13 cases and K-means++ in 6 out of 12 cases with a significant difference. Furthermore, the proposed method can accelerate the execution time for combinatorial clustering because there is no need to calculate the coefficients of the constraint function.
Seasonal environmental variability drives microdiversity within a coastal Synechococcus population

Marine microbes often show a high degree of physiological or ecological diversity below the species level. This microdiversity raises questions about the processes that drive diversification and permit coexistence of diverse yet closely related marine microbes, especially given the theoretical efficiency of competitive exclusion. Here, we provide insight with an 8-year time series of diversity within Synechococcus, a widespread and important marine picophytoplankter. The population of Synechococcus on the Northeast U.S. Shelf is comprised of six main types, each of which displays a distinct and consistent seasonal pattern. With compositional data analysis, we show that these patterns can be reproduced with a simple model that couples differential responses to temperature and light with the seasonal cycle of the physical environment. These observations support the hypothesis that temporal variability in environmental factors can maintain microdiversity in marine microbial populations. We also identify how seasonal diversity patterns directly determine overarching Synechococcus population abundance features.
‘Thinking Problematically’ as a Resource for AI Design in Politicised Contexts

When designing artificial intelligence (AI) in politicised contexts, such as the public sector, optimistic promises of what AI can achieve often shape decisions around which problems AI should address. Different epistemological views carry different understandings of what is considered the problem at hand, and, as we show in this paper, ethnographic perspectives often fail to match the politicised promises of AI. This paper reflects on personal experiences from an interdisciplinary research project that aimed to take a responsible approach to research and design AI for public services in Denmark. Seeking alternatives to the inflexible algorithms [3, 38] often used to automate or augment specific decision-making tasks in these contexts [1, 2, 35], our research project took a flexible approach to research and design and included ethnographic workplace studies to explore whether AI could both leverage the increasing powers of computing and retain the discretion of the user [23]. Following Mesman [33], we present three empirical moments that were particularly challenging for us as ethnographic researchers and influenced our project in important ways regarding the problems for AI to solve. Problematising [6] them, enabled us to surface how ‘readiness’, emerging from the politicised context of AI in Denmark, had confounded our efforts at interdisciplinary collaboration. Problematisation, then, allowed us to come to a new understanding of the problem at hand and open up a space to collaboratively re-imagine the problems for AI to solve. This paper is in the spirit of serving as a bridge between our initial and revised understanding, pointing to the ongoing discussion in HCI about ‘bridging the gap’ between ethnography and design. Our contribution is a discussion of how researchers and designers might engage with problematisation at the frontiers of HCI to develop an open-ended approach to collaborative AI design in politicised contexts.
Content-aware convolutional neural networks

Convolutional Neural Networks (CNNs) have achieved great success due to the powerful feature learning ability of convolution layers. Specifically, the standard convolution traverses the input images/features using a sliding window scheme to extract features. However, not all the windows contribute equally to the prediction results of CNNs. In practice, the convolutional operation on some of the windows (e.g., smooth windows that contain very similar pixels) can be very redundant and may introduce noises into the computation. Such redundancy may not only deteriorate the performance but also incur the unnecessary computational cost. Thus, it is important to reduce the computational redundancy of convolution to improve the performance. To this end, we propose a Content-aware Convolution (CAC) that automatically detects the smooth windows and applies a 1 ×1 convolutional kernel to replace the original large kernel. In this sense, we are able to effectively avoid the redundant computation on similar pixels. By replacing the standard convolution in CNNs with our CAC, the resultant models yield significantly better performance and lower computational cost than the baseline models with the standard convolution. More critically, we are able to dynamically allocate suitable computation resources according to the data smoothness of different images, making it possible for content-aware computation. Extensive experiments on various computer vision tasks demonstrate the superiority of our method over existing methods.
Multi-scale network (MsSG-CNN) for joint image and saliency map learning-based compression

Lossy image compression is likely to produce blurred images which leads to erroneous image-level understanding. The human visual system (HVS) is focused on the area of interest present in the image. Motivated by this fact, we propose a compression-decompression algorithm focusing on the important content present in different parts of the image. Using the concept of the Guided Grad-CAM (Gradient-weighted Class Activation Mapping) technique to produce heat maps with the help of a heat map generator trained on ResNet-50, a saliency-guided encoding–decoding algorithm is developed. A wider multi-scale saliency guided convolutional neural network (MsSG-CNN) is designed, in which the notion of convolution with different size filters helps to obtain unique but different features. The feature extraction followed by multilevel fusion of features helps deep neural network (DNN) to capture contextual information and obtain high quality, good resolution, visually pleasing images with fine details. The proposed algorithm is tested on the Kodak benchmark dataset, CLIC 2019 challenging dataset, and FDDB facial images dataset. At low bit rates, the MS-SSIM of the proposed algorithm is found to be superior to JPEG, JPEG2000, BPG, WebP, and Minnen’s approaches with approximately up to 60%, 24.80%, 11.43%, 23.08% & 75% gains respectively, which is quite a significant improvement, when tested on the Kodak dataset. Similarly, at high bit rates, the improvement in MS-SSIM is approximately up to 41.67%, 37.30%, 23.90% 34.21% & 13.33% when compared with JPEG, JPEG2000, BPG, WebP, and Minnen’s approaches respectively. The improvement in PSNR at low and high bit rates is approximately up to 11.32%, 5%, 5.26% and 5.6%, 10.29%, 8.7% as compared to JPEG, Balle’s, and Lee’s algorithms respectively. The PSNR-HVS has been improved by approximately up to 27.27%, 19.15%, and 28.33%, 28% as compared to JPEG and Toderici’s algorithms respectively at low and high bit-rates. A similar type of improvement is obtained with FDDB and CLIC 2019 datasets also, which is discussed in the paper.
Using 3D Convolutional Neural Networks for Real-time Detection of Soccer Events

Developing systems for the automatic detection of events in video is a task which has gained attention in many areas including sports. More specifically, event detection for soccer videos has been studied widely in the literature. However, there are still a number of shortcomings in the state-of-the-art such as high latency, making it challenging to operate at the live edge. In this paper, we present an algorithm to detect events in soccer videos in real time, using 3D convolutional neural networks. We test our algorithm on three different datasets from SoccerNet, the Swedish Allsvenskan, and the Norwegian Eliteserien. Overall, the results show that we can detect events with high recall, low latency, and accurate time estimation. The trade-off is a slightly lower precision compared to the current state-of-the-art, which has higher latency and performs better when a less accurate time estimation can be accepted. In addition to the presented algorithm, we perform an extensive ablation study on how the different parts of the training pipeline affect the final results.
A communication-channel-based method for detecting deeply camouflaged malicious traffic

We present a novel method for detecting malicious TLS traffic based on communication channels that can detect deeply camouflaged malicious traffic. Moreover, we designed and extracted three types of channel features, namely, distribution features, consistency features of the Transport Layer Security (TLS) handshake field, and statistical features. Simultaneously, an efficacy feature selection method comprising a genetic algorithm is presented to obtain a global optimal feature subset, which reduces feature dimensions by 64% and increases accuracy by 1.5%. Comparison experiment results show that the proposed method possesses a more stable detection efficacy on different datasets with an accuracy of 97.65% and a much higher F1-score compared with other state-of-the-art classification methods.
RAT: Reinforcement-Learning-Driven and Adaptive Testing for Vulnerability Discovery in Web Application Firewalls

Due to the increasing sophistication of web attacks, Web Application Firewalls (WAFs) have to be tested and updated regularly to resist the relentless flow of web attacks. In practice, using a brute-force attack to discover vulnerabilities is infeasible due to the wide variety of attack patterns. Thus, various black-box testing techniques have been proposed in the literature. However, these techniques suffer from low efficiency. This paper presents Reinforcement-Learning-Driven and Adaptive Testing (RAT), an automated black-box testing strategy to discover injection vulnerabilities in WAFs. In particular, we focus on SQL injection and Cross-site Scripting, which have been among the top ten vulnerabilities over the past decade. More specifically, RAT clusters similar attack samples together. It then utilizes a reinforcement learning technique combined with a novel adaptive search algorithm to discover almost all bypassing attack patterns efficiently. We compare RAT with three state-of-the-art methods considering their objectives. The experiments show that RAT performs 33.53% and 63.16% on average better than its counterparts in discovering the most possible bypassing payloads and reducing the number of attempts before finding the first bypassing payload when testing well-configured WAFs, respectively.
LRGAN: Visual anomaly detection using GAN with locality-preferred recoding

Deep neural networks, including deep auto-encoder (DAE) and generative adversarial networks (GAN), have been extensively applied for visual anomaly detection. These models generally assume that reconstruction errors should be lower for normal samples but higher for anomalies. However, it has been found that DAE based models can sometimes reconstruct anomalies very well and thus result in false alarms or misdetections. To address this problem, we propose a model using GAN with locality-preferred recoding, named LRGAN. LRGAN is inspired by the observation that both normal and abnormal samples are not completely scattered throughout the latent space but clustered separately at some local regions. Therefore, a locality-preferred recoding (LR) module is designed to compulsively represent the latent vectors of anomalies by normal ones. As a result, reconstructions of anomalies will approximate to normal samples and corresponding residuals can thus be enlarged. To partly avoid latent vectors of normal samples being recoded, we further present an improved model using GAN with an adaptive LR (ALR) module, named LRGAN+. ALR applies the clustering algorithm to generate a more compact codebook; more importantly, it helps LRGAN + automatically skip the LR module for possible normal samples with a threshold strategy. Our proposed method is evaluated on two public datasets (i.e., MNIST and CIFAR-10) and one real-world industrial dataset (i.e., Fasteners), considering both one-class and multi-class anomaly detection protocols. Experimental results demonstrate that LRGAN is comparable with state-of-the-art methods and LRGAN + outperforms these methods on all datasets.
Human Interaction Understanding With Joint Graph Decomposition and Node Labeling

The task of human interaction understanding involves both recognizing the action of each individual in the scene and decoding the interaction relationship among people, which is useful to a series of vision applications such as camera surveillance, video-based sports analysis and event retrieval. This paper divides the task into two problems including grouping people into clusters and assigning labels to each of them, and presents an approach to solving these problems in a joint manner. Our method does not assume the number of groups is known beforehand as this will substantially restrict its application. With the observation that the two challenges are highly correlated, the key idea is to model the pairwise interacting relations among people via a complete graph and its associated energy function such that the labeling and grouping problems are translated into the minimization of the energy function. We implement this joint framework by fusing both deep features and rich contextual cues, and learn the fusion parameters from data. An alternating search algorithm is developed in order to efficiently solve the associated inference problem. By combining the grouping and labeling results obtained with our method, we are able to achieve the semantic-level understanding of human interactions. Extensive experiments are performed to qualitatively and quantitatively evaluate the effectiveness of our approach, which outperforms state-of-the-art methods on several important benchmarks. An ablation study is also performed to verify the effectiveness of different modules within our approach.
Using Machine Learning to Map Western Australian Landscapes for Mineral Exploration

Landscapes evolve due to climatic conditions, tectonic activity, geological features, biological activity, and sedimentary dynamics. Geological processes at depth ultimately control and are linked to the resulting surface features. Large regions in Australia, West Africa, India, and China are blanketed by cover (intensely weathered surface material and/or later sediment deposition, both up to hundreds of metres thick). Mineral exploration through cover poses a significant technological challenge worldwide. Classifying and understanding landscape types and their variability is of key importance for mineral exploration in covered regions. Landscape variability expresses how near-surface geochemistry is linked to underlying lithologies. Therefore, landscape variability mapping should inform surface geochemical sampling strategies for mineral exploration. Advances in satellite imaging and computing power have enabled the creation of large geospatial data sets, the sheer size of which necessitates automated processing. In this study, we describe a methodology to enable the automated mapping of landscape pattern domains using machine learning (ML) algorithms. From a freely available digital elevation model, derived data, and sample landclass boundaries provided by domain experts, our algorithm produces a dense map of the model region in Western Australia. Both random forest and support vector machine classification achieve approximately 98% classification accuracy with a reasonable runtime of 48 minutes on a single Intel® Core™ i7-8550U CPU core. We discuss computational resources and study the effect of grid resolution. Larger tiles result in a more contiguous map, whereas smaller tiles result in a more detailed and, at some point, noisy map. Diversity and distribution of landscapes mapped in this study support previous results. In addition, our results are consistent with the geological trends and main basement features in the region. Mapping landscape variability at a large scale can be used globally as a fundamental tool for guiding more efficient mineral exploration programs in regions under cover.
SimS: A Simplification of SiGamal

At Asiacrypt 2020, Moriya et al. introduced two new IND-CPA secure supersingular isogeny based Public Key Encryption (PKE) protocols: SiGamal and C-SiGamal. Unlike the PKEs canonically derived from SIDH and CSIDH, the new protocols provide IND-CPA security without the use of hash functions. SiGamal and C-SiGamal are however not IND-CCA secure. Moriya et al. suggested a variant of SiGamal that could be IND-CCA secure, but left its study as an open problem. In this paper, we revisit the protocols introduced by Moriya et al. First, we show that the SiGamal variant suggested by Moriya et al. for IND-CCA security is, in fact, not IND-CCA secure. Secondly, we propose a new isogeny-based PKE protocol named SimS, obtained by simplifying SiGamal. SimS has smaller public keys and ciphertexts than (C-)SiGamal and it is more efficient. We prove that SimS is IND-CCA secure under CSIDH security assumptions and one Knowledge of Exponent-type assumption we introduce. Interestingly, SimS is also much closer to the CSIDH protocol, facilitating a comparison between SiGamal and CSIDH.
Budget RNNs: Multi-Capacity Neural Networks to Improve In-Sensor Inference Under Energy Budgets

Recurrent neural networks (RNNs) are well-suited to the sequential inference tasks often found in embedded sensing systems. While RNNs have displayed high accuracy on many tasks, they are poorly equipped for inference under energy budgets that are unknown at design time. Existing RNNs meet energy constraints in sensor environments by training models to subsample input sequences. The tight coupling between the sampling strategy and the RNN prevents these systems from generalizing to new energy budgets at runtime. To address this problem, we present a novel RNN architecture called the Budget RNN. Budget RNNs use a leveled architecture to decouple the sampling strategy from the RNN model, allowing a single Budget RNN to change its subsampling behavior at runtime. We further propose a runtime feedback controller to optimize the model's accuracy for a given energy budget. Across a set of budgets, the Budget RNN inference system achieves a mean accuracy of roughly 3 points higher than standard RNNs. Alternatively, Budget RNNs can achieve comparable accuracy to existing RNNs while under 20% smaller budgets.
On Digital Ethics for Artificial Intelligence and Information Fusion in the Defense Domain

“For knowledge itself is power...”--- Francis Bacon's statement on achieving power as the meaning of all knowledge marks the beginning of the modern project. At the latest since the advent of AI in the defense domain, however, technology meant for the benefit of humanity may turn against humanity. This specific type of instrumental knowledge makes the modern crisis as visible as in spotlight. Ethical knowledge of man and his nature must complement Bacon's knowledge. There is an “ecology of man”: He does not make himself; he is responsible for himself and others. How can the data fusion community technically support responsible use of the power we are harvesting from AI? To argue more specifically, we consider documents of the German Bundeswehr, founded in the 1950s when the term AI was coined. Since these armed forces have learned lessons from “total war” and tyranny, they seem conceptually prepared for mastering the digital challenge. There exist parallels to ongoing IEEE P7000 standardization activities.