Reviewerly/exHarmony huggingface dataset:
The PhotoBook Dataset: Building Common Ground through Visually-Grounded Dialogue

This paper introduces the PhotoBook dataset, a large-scale collection of visually-grounded, task-oriented dialogues in English designed to investigate shared dialogue history accumulating during conversation.Taking inspiration from seminal work on dialogue analysis, we propose a data-collection task formulated as a collaborative game prompting two online participants to refer to images utilising both their visual context as well as previously established referring expressions.We provide a detailed description of the task setup and a thorough analysis of the 2,500 dialogues collected.To further illustrate the novel features of the dataset, we propose a baseline model for reference resolution which uses a simple method to take into account shared information accumulated in a reference chain.Our results show that this information is particularly important to resolve later descriptions and underline the need to develop more sophisticated models of common ground in dialogue interaction. 1
TallyQA: Answering Complex Counting Questions

Most counting questions in visual question answering (VQA) datasets are simple and require no more than object detection. Here, we study algorithms for complex counting questions that involve relationships between objects, attribute identification, reasoning, and more. To do this, we created TallyQA, the world’s largest dataset for open-ended counting. We propose a new algorithm for counting that uses relation networks with region proposals. Our method lets relation networks be efficiently used with high-resolution imagery. It yields stateof-the-art results compared to baseline and recent systems on both TallyQA and the HowMany-QA benchmark.
Prefix-free parsing for building big BWTs

High-throughput sequencing technologies have led to explosive growth of genomic databases; one of which will soon reach hundreds of terabytes. For many applications we want to build and store indexes of these databases but constructing such indexes is a challenge. Fortunately, many of these genomic databases are highly-repetitive-a characteristic that can be exploited to ease the computation of the Burrows-Wheeler Transform (BWT), which underlies many popular indexes. In this paper, we introduce a preprocessing algorithm, referred to as prefix-free parsing, that takes a text T as input, and in one-pass generates a dictionary D and a parse P of T with the property that the BWT of T can be constructed from D and P using workspace proportional to their total size and O(|T|)-time. Our experiments show that D and P are significantly smaller than T in practice, and thus, can fit in a reasonable internal memory even when T is very large. In particular, we show that with prefix-free parsing we can build an 131-MB run-length compressed FM-index (restricted to support only counting and not locating) for 1000 copies of human chromosome 19 in 2 h using 21 GB of memory, suggesting that we can build a 6.73 GB index for 1000 complete human-genome haplotypes in approximately 102 h using about 1 TB of memory.
Sharp Attention Network via Adaptive Sampling for Person Re-Identification

In this paper, we present novel sharp attention networks by adaptively sampling feature maps from convolutional neural networks for person re-identification (re-ID) problems. Due to the introduction of sampling-based attention models, the proposed approach can adaptively generate sharper attention-aware feature masks. This greatly differs from the gating-based attention mechanism that relies on soft gating functions to select the relevant features for person re-ID. In contrast, the proposed sampling-based attention mechanism allows us to effectively trim irrelevant features by enforcing the resultant feature masks to focus on the most discriminative features. It can produce sharper attentions that is more assertive in localizing subtle features relevant to re-identifying people across cameras. For this purpose, a differentiable Gumbel-Softmax sampler is employed to approximate the Bernoulli sampling to train the sharp attention networks. Extensive experimental evaluations demonstrate the superiority of this new sharp attention model for person re-ID over other related existing, published state-of-the-art works on three challenging benchmarks, including CUHK03, Market-1501, and DukeMTMC-reID.
The OCS-SVM: An Objective-Cost-Sensitive SVM With Sample-Based Misclassification Cost Invariance

Studies on the traditional support vector machine (SVM) implicitly assume that the costs of different types of mistakes are the same and minimize the error rate.On the one hand, it is not enough for many practical applications to rely solely on the error rate, which reflects only the average classification ability of a classifier.It is also of great significance to consider the performance of classifiers from the perspective of each sample.On the other hand, many real-world problems, such as credit card fraud detection, intrusion detection, oil-spill detection and cancer diagnosis, usually involve substantially unequal misclassification costs.To solve this problem, many works on the cost-sensitive SVM (CS-SVM) have emerged.The misclassification costs for this model are generally provided by domain experts.Inspired by the concept of the CS-SVM, we propose a new SVM with sample-based misclassification cost invariance with the aim of constructing a relatively reliable classifier.The relatively reliable classifier is defined as the one with low probabilities of finding a classifier that correctly classifies each misclassified sample.Note that the cost is determined by the inherent characteristics of each sample rather than being subjectively assigned, so we denote the proposed classifier as the objective-cost-sensitive SVM (OCS-SVM).The experimental results demonstrate the superiority of the proposed method compared with nine other commonly used classifiers.
Machine-learning attacks on interference-based optical encryption: experimental demonstration

Optical techniques have boosted a new class of cryptographic systems with some remarkable advantages, and optical encryption not only has spurred practical developments but also has brought a new insight into cryptography.However, this does not mean that it is elusive for the opponents to attack optical encryption systems.In this paper, for the first time to our knowledge, we experimentally demonstrate the machine-learning attacks on interference-based optical encryption.Using machine-learning models that are trained by a series of ciphertext-plaintext pairs, an unauthorized person is capable to retrieve the unknown plaintexts from the given ciphertexts without the usage of various different optical encryption keys existing in interference-based optical encryption.In comparison with conventional cryptanalytic methods, the proposed machine-learning-based attacking method can estimate transfer function or point spread function of interference-based optical encryption systems without subsidiary conditions.Simulations and optical experiments demonstrate feasibility and effectiveness of the proposed method, and the proposed machine-learning-based attacking method provides a versatile approach to analyzing the vulnerability of interferencebased optical encryption.
Data Clustering: Algorithms and Its Applications

Data is useless if information or knowledge that can be used for further reasoning cannot be inferred from it. Cluster analysis, based on some criteria, shares data into important, practical or both categories (clusters) based on shared common characteristics. In research, clustering and classification have been used to analyze data, in the field of machine learning, bioinformatics, statistics, pattern recognition to mention a few. Different methods of clustering include Partitioning (K-means), Hierarchical (AGNES), Density-based (DBSCAN), Grid-based (STING), Soft clustering (FANNY), Model-based (SOM) and Ensemble clustering. Challenges and problems in clustering arise from large datasets, misinterpretation of results and efficiency/performance of clustering algorithms, which is necessary for choosing clustering algorithms. In this paper, application of data clustering was systematically discussed in view of the characteristics of the different clustering techniques that make them better suited or biased when applied to several types of data, such as uncertain data, multimedia data, graph data, biological data, stream data, text data, time series data, categorical data and big data. The suitability of the available clustering algorithms to different application areas was presented. Also investigated were some existing cluster validity methods used to evaluate the goodness of the clusters produced by the clustering algorithms.
DualVD: An Adaptive Dual Encoding Model for Deep Visual Understanding in Visual Dialogue

Different from Visual Question Answering task that requires to answer only one question about an image, Visual Dialogue involves multiple questions which cover a broad range of visual content that could be related to any objects, relationships or semantics. The key challenge in Visual Dialogue task is thus to learn a more comprehensive and semantic-rich image representation which may have adaptive attentions on the image for variant questions. In this research, we propose a novel model to depict an image from both visual and semantic perspectives. Specifically, the visual view helps capture the appearance-level information, including objects and their relationships, while the semantic view enables the agent to understand high-level visual semantics from the whole image to the local regions. Futhermore, on top of such multi-view image features, we propose a feature selection framework which is able to adaptively capture question-relevant information hierarchically in fine-grained level. The proposed method achieved state-of-the-art results on benchmark Visual Dialogue datasets. More importantly, we can tell which modality (visual or semantic) has more contribution in answering the current question by visualizing the gate values. It gives us insights in understanding of human cognition in Visual Dialogue.
A Machine Learning-Based Method for Automated Blockchain Transaction Signing Including Personalized Anomaly Detection

The basis of blockchain-related data, stored in distributed ledgers, are digitally signed transactions. Data can be stored on the blockchain ledger only after a digital signing process is performed by a user with a blockchain-based digital identity. However, this process is time-consuming and not user-friendly, which is one of the reasons blockchain technology is not fully accepted. In this paper, we propose a machine learning-based method, which introduces automated signing of blockchain transactions, while including also a personalized identification of anomalous transactions. In order to evaluate the proposed method, an experiment and analysis were performed on data from the Ethereum public main network. The analysis shows promising results and paves the road for a possible future integration of such a method in dedicated digital signing software for blockchain transactions.
Robustness Certificates for Sparse Adversarial Attacks by Randomized Ablation

Recently, techniques have been developed to provably guarantee the robustness of a classifier to adversarial perturbations of bounded L1 and L2 magnitudes by using randomized smoothing: the robust classification is a consensus of base classifications on randomly noised samples where the noise is additive. In this paper, we extend this technique to the L0 threat model. We propose an efficient and certifiably robust defense against sparse adversarial attacks by randomly ablating input features, rather than using additive noise. Experimentally, on MNIST, we can certify the classifications of over 50% of images to be robust to any distortion of at most 8 pixels. This is comparable to the observed empirical robustness of unprotected classifiers on MNIST to modern L0 attacks, demonstrating the tightness of the proposed robustness certificate. We also evaluate our certificate on ImageNet and CIFAR-10. Our certificates represent an improvement on those provided in a concurrent work (Lee et al. 2019) which uses random noise rather than ablation (median certificates of 8 pixels versus 4 pixels on MNIST; 16 pixels versus 1 pixel on ImageNet.) Additionally, we empirically demonstrate that our classifier is highly robust to modern sparse adversarial attacks on MNIST. Our classifications are robust, in median, to adversarial perturbations of up to 31 pixels, compared to 22 pixels reported as the state-of-the-art defense, at the cost of a slight decrease (around 2.3%) in the classification accuracy. Code and supplementary material is available at https://github.com/alevine0/randomizedAblation/.
Learned Video Compression via Joint Spatial-Temporal Correlation Exploration

Traditional video compression technologies have been developed over decades in pursuit of higher coding efficiency. Efficient temporal information representation plays a key role in video coding. Thus, in this paper, we propose to exploit the temporal correlation using both first-order optical flow and second-order flow prediction. We suggest an one-stage learning approach to encapsulate flow as quantized features from consecutive frames which is then entropy coded with adaptive contexts conditioned on joint spatial-temporal priors to exploit second-order correlations. Joint priors are embedded in autoregressive spatial neighbors, co-located hyper elements and temporal neighbors using ConvLSTM recurrently. We evaluate our approach for the low-delay scenario with High-Efficiency Video Coding (H.265/HEVC), H.264/AVC and another learned video compression method, following the common test settings. Our work offers the state-of-the-art performance, with consistent gains across all popular test sequences.
A systematic review on content-based video retrieval

Content-based video retrieval and indexing have been associated with intelligent methods in many applications such as education, medicine and agriculture. However, an extensive and replicable review of the recent literature is missing. Moreover, relevant topics that can support video retrieval, such as dimensionality reduction, have not been surveyed. This work designs and conducts a systematic review to find papers able to answer the following research question: “what segmentation, feature extraction, dimensionality reduction and machine learning approaches have been applied for content-based video indexing and retrieval?”. By applying a research protocol proposed by us, 153 papers published from 2011 to 2018 were selected. As a result, it was found that strategies for cut-based segmentation, color-based indexing, k-means based dimensionality reduction and data clustering have been the most frequent choices in recent papers. All the information extracted from these papers can be found in a publicly available spreadsheet. This work also indicates additional findings and future research directions.
A Decentralized Peer-to-Peer Remote Health Monitoring System

Within the Internet of Things (IoT) and blockchain research, there is a growing interest in decentralizing health monitoring systems, to provide improved privacy to patients, without relying on trusted third parties for handling patients’ sensitive health data. With public blockchain deployments being severely limited in their scalability, and inherently having latency in transaction processing, there is room for researching and developing new techniques to leverage the security features of blockchains within healthcare applications. This paper presents a solution for patients to share their biomedical data with their doctors without their data being handled by trusted third party entities. The solution is built on the Ethereum blockchain as a medium for negotiating and record-keeping, along with Tor for delivering data from patients to doctors. To highlight the applicability of the solution in various health monitoring scenarios, we have considered three use-cases, namely cardiac monitoring, sleep apnoea testing, and EEG following epileptic seizures. Following the discussion about the use cases, the paper outlines a security analysis performed on the proposed solution, based on multiple attack scenarios. Finally, the paper presents and discusses a performance evaluation in terms of data delivery time in comparison to existing centralized and decentralized solutions.
BDSS-FA: A Blockchain-Based Data Security Sharing Platform With Fine-Grained Access Control

Aiming at the problem of privacy leakage during data sharing in the Internet of Things, a blockchain based secure data sharing platform with fine-grained access control(BSDS-FA) is proposed. First, this paper proposes a new hierarchical attribute-based encryption algorithm, which uses hierarchical attribute structure and multi-level authorization center. The algorithm implements flexible and fine-grained access control by distributing different user attributes to different authorization centers. Then, it combined with the Fabric blockchain technology to solve the problem of huge decryption cost for users in the Internet of things. Smart contract in blockchain executes high-complexity partial decryption algorithm to reduce the users' decryption overhead. Blockchain can also realize the traceability of historical operations to meet the security requirements of data restriction open and transparent supervision. Finally, the hierarchical attribute-based encryption algorithm is proved to be CPA-safe. The theoretical analysis and experimental results show that BDSS-FA provides more secure and reliable data sharing services for users in the Internet of Things.
Understanding manager resistance to blockchain systems

Blockchain technology has received much attention in the media and there is an increasing interest amongst organizations within financial services due to the potential benefits. As blockchain-based systems are a nascent technology, the requirements of the technology need to be understood, to allow blockchain systems to be successfully integrated within financial service organizations. There are gaps in academic research in understanding how managers evaluate the value of a blockchain-based system. This study develops a model to explain manager resistance to implementing blockchain-based systems in financial services organizations. This research advances the theoretical understanding of managers' perspectives on blockchain-based systems and models their resistance to blockchain technology.
Projected fuzzy C-means clustering with locality preservation

Traditional partition-based clustering algorithms, hard or fuzzy version of C-means, could not deal with high-dimensional data sets effectively as redundant features may impact the computation of distances and local spatial structures among patterns are rarely considered. High dimensionality of space gives rise to so-called concentration effect that is detrimental. In this paper, a novel locality preserving based fuzzy C-means (LPFCM) clustering method and its optimization are presented. An orthogonally projected space, which preserves the locality of structural properties, can be generated in LPFCM, thus enhancing the capability of fuzzy C-means (FCM) for handling high-dimensional data. It is the first time to introduce projection techniques to the FCM optimization objective function directly, and the ideas of fuzzy clustering, geometric structure preservation and feature extraction are seamlessly integrated. LPFCM is also regarded as a unified model that combines two separate stages of spectral clustering. Experimental results on some benchmark data sets show the effectiveness of LPFCM in comparison with FCM and some state-of-the-art methods.
What Can We Learn from Collective Human Opinions on Natural Language Inference Data?

Despite the subjective nature of many NLP tasks, most NLU evaluations have focused on using the majority label with presumably high agreement as the ground truth.Less attention has been paid to the distribution of human opinions.We collect ChaosNLI, a dataset with a total of 464,500 annotations to study Collective HumAn OpinionS in oft-used NLI evaluation sets.This dataset is created by collecting 100 annotations per example for 3,113 examples in SNLI and MNLI and 1,532 examples in αNLI.Analysis reveals that: (1) high human disagreement exists in a noticeable amount of examples in these datasets;(2) the state-of-the-art models lack the ability to recover the distribution over human labels; (3) models achieve near-perfect accuracy on the subset of data with a high level of human agreement, whereas they can barely beat a random guess on the data with low levels of human agreement, which compose most of the common errors made by state-of-the-art models on the evaluation sets.This questions the validity of improving model performance on old metrics for the low-agreement part of evaluation datasets.Hence, we argue for a detailed examination of human agreement in future data collection efforts, and evaluating model outputs against the distribution over collective human opinions. 1
Compiler Design for Distributed Quantum Computing

In distributed quantum computing architectures, with the network and communications functionalities provided by the Quantum Internet, remote quantum processing units (QPUs) can communicate and cooperate for executing computational tasks that single NISQ devices cannot handle by themselves. To this aim, distributed quantum computing requires a new generation of quantum compilers, for mapping any quantum algorithm to any distributed quantum computing architecture. With this perspective, in this paper, we first discuss the main challenges arising with compiler design for distributed quantum computing. Then, we analytically derive an upper bound of the overhead induced by quantum compilation for distributed quantum computing. The derived bound accounts for the overhead induced by the underlying computing architecture as well as the additional overhead induced by the sub-optimal quantum compiler -- expressly designed through the paper to achieve three key features, namely, general-purpose, efficient and effective. Finally, we validate the analytical results and we confirm the validity of the compiler design through an extensive performance analysis.
Strong but Simple Baseline With Dual-Granularity Triplet Loss for Visible-Thermal Person Re-Identification

This letter presents a conceptually simple and effective dual-granularity triplet loss for visible-thermal person re-identification (VT-ReID). Generally, ReID models are always trained with the sample-based triplet loss and identification loss from the fine granularity level. Further, center-based loss could be introduced to encourage the intra-class compactness and inter-class discrimination from the coarse granularity level. Our proposed dual-granularity triplet loss well organizes the sample-based triplet loss and center-based triplet loss in a hierarchical fine to coarse granularity manner, just with some simple configurations of typical operations, such as pooling and batch normalization. Experiments on RegDB and SYSU-MM01 datasets show that with only the global features our dual-granularity triplet loss can improve the VT-ReID performance by a significant margin. It can be a strong VT-ReID baseline to boost future research with high quality.