Reviewerly/exHarmony huggingface dataset:
Statute Law Information Retrieval and Entailment

Our Yes/No statute law question answering system combines components for both statute law information retrieval and confirmation of textual entailment between statues and legal questions. We describe a statute law question answering system that exploits TF-IDF and a language model for information retrieval, and inter-paragraph entailment. We have evaluated our system using the data from the competition on legal information extraction/entailment (COLIEE-2019). The competition consists of four tasks: Tasks 1 and 2 are for the case law information extraction/entailment, and Tasks 3 and 4 are for the statute law information extraction/entailment. Here we explain our methods and evaluation results for Tasks 3 and 4. Task 3 requires the identification of civil law articles relevant to Japan legal bar exam query. For this task, we used TF-IDF and language model-based information retrieval approaches. Task 4 requires a decision on yes/no answer for previously unseen queries given relevant civil law articles. Our approach compares the approximate meanings of queries with relevant articles. Because many statute law and queries consist of more than one paragraph, we need an inter-paragraph entailment method. Our inter-paragraph entailment process exploits an analysis of statute law structure, and negation patterns to predict entailments. Using our heuristic selection of attributes, we perform two experiments which provide the basis for making a decision on the yes/no questions. One experiment uses an SVM model, and the other uses a general heuristic rule. Our experimental evaluation demonstrates the value of our method, and the results show that our method was ranked No. 1 in both of the Tasks 3 and 4 in COLIEE 2019.
H₂O-Cloud: A Resource and Quality of Service-Aware Task Scheduling Framework for Warehouse-Scale Data Centers

Cloud computing has attracted both end-users and cloud service providers (CSPs) in recent years. Improving resource utilization rate (RUtR), such as CPU and memory usages on servers, while maintaining quality of service (QoS) is one key challenge faced by CSPs with warehouse-scale datacenters. Prior works proposed various algorithms to reduce energy cost or to improve RUtR, which either lack the fine-grained task scheduling capabilities, or fail to take a comprehensive system model into consideration. This article presents H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O-Cloud, a Hierarchical and Hybrid Online task scheduling framework for warehouse-scale Cloud service providers, to improve resource usage effectiveness while maintaining QoS. H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O-Cloud is highly scalable and considers comprehensive information, such as various workload scenarios, cloud platform configurations, user request information, and dynamic pricing model. The hierarchy and hybridity of the framework, combined with its deep reinforcement learning (DRL) engines, enable H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O-Cloud to efficiently start on-the-go scheduling and learning in an unpredictable environment without pretraining. Our experiments confirm the high efficiency of the proposed H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O-Cloud when compared to baseline approaches, in terms of energy and cost while maintaining QoS. Compared with a state-of-the-art DRL-based algorithm, H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O-Cloud achieves up to 201.17% energy cost efficiency improvement, 47.88% energy efficiency improvement, and 551.76% reward rate improvement.
Learning a robust representation via a deep network on symmetric positive definite manifolds

Recent studies have shown that aggregating convolutional features of a Convolutional Neural Network (CNN) can obtain impressive performance for a variety of computer vision tasks. The Symmetric Positive Definite (SPD) matrix becomes a powerful tool due to its remarkable ability to learn an appropriate statistic representation to characterize the underlying structure of visual features. In this paper, we propose a method of aggregating deep convolutional features into a robust representation through the SPD generation and the SPD transformation under an end-to-end deep network. To this end, several new layers are introduced in our method, including a nonlinear kernel generation layer, a matrix transformation layer, and a vector transformation layer. The nonlinear kernel generation layer is employed to aggregate convolutional features into a kernel matrix which is guaranteed to be an SPD matrix. The matrix transformation layer is designed to project the original SPD representation to a more compact and discriminative SPD manifold. The vectorization and normalization operations are performed in the vector transformation layer to take the upper triangle elements of the SPD representation and carry out the power normalization and l2 normalization to reduce the redundancy and accelerate the convergence. The SPD matrix in our network can be considered as a mid-level representation bridging convolutional features and high-level semantic features. Results of extensive experiments show that our method notably outperforms state-of-the-art methods.
User intimacy model for question recommendation in community question answering

In this paper, we address the problem of automatic recommendation of new questions to suitable users in community question answering (CQA). The major challenge is the accurate selection of suitable users to answer a given question. Most approaches seek suitable users for a question by estimating their capability, interests or a blend of both. However, this ignores intimacy between the user and the asker of a question over different topics. Intimacy between askers and answerers is an important factor in question recommendation. For example, a user is likely to post an answer if interested in a question and intimate with its asker. We propose to model and learn intimacy between users over topics with social interaction in CQA for question recommendation using a novel topic model. We believe this paper is the first to estimate the intimacy between users over different topics and investigate influences on the performance of question recommendation in CQA. We propose a user intimacy model (UIM), an LDA-style model that incorporates social interaction in the generative process of a question-answer (QA) pair to model and learn intimacy between users over topics. Experiments using real-world data from Stack Overflow show that our UIM-based approach consistently and significantly improves the performance of question recommendation, demonstrating that our approach can increase question recommendation accuracy in CQA by utilizing the intimacy between users over topics and that this is an important factor in question recommendation.
DCSVM: fast multi-class classification using support vector machines

Using binary classification techniques to perform multi-class classification of data is still of great practical interest due to the robustness and simplicity of binary classifiers. These techniques produce a single multi-class classification decision based on many binary decisions. Our work relies on the simple observation that as dimensionality increases so does the data sparsity and, consequently, a single binary classifier may separate multiple classes. Therefore, we claim that the number of binary decisions can be significantly reduced. We present Divide and Conquer Support Vector Machines (DCSVM), an efficient algorithm for multi-class classification using Support Vector Machines. DCSVM is a divide and conquer algorithm which relies on data sparsity in high dimensional space and performs a smart partitioning of the whole training data set into disjoint subsets that are easily separable. A single prediction performed between two partitions eliminates at once one or more classes in one partition, leaving only a reduced number of candidate classes for subsequent steps. The algorithm continues recursively, reducing the number of classes at each step, until a final binary decision is made between the last two classes left in the competition. In the best case scenario, our algorithm makes a final decision between k classes in $$O(\log k)$$ decision steps and in the worst case scenario DCSVM makes a final decision in $$k{-}1$$ steps, which is not worse than the existent techniques.
"You give a little of yourself"

Low levels of childhood literacy in global contexts may be mitigated by educational technologies, however, these technologies often rely on parents of sufficient literacy to effectively support their children. Given low levels of adult literacy in many low-resource contexts, we investigate the nature of low-literate adult support for children's use of a literacy technology designed to foster early literacy precursors. We deployed an interactive voice response (IVR) system with 38 families in a rural village in Côte d'Ivoire using the IVR for 5 weeks in their homes. Using call log data and grounded theory analyses of IVR observations and interviews, we find evidence that families leverage complex support networks where family members support children's use of the IVR in different ways, via a collective network of intermediaries. These results suggest opportunities to scaffold low-literate family supporters for educational technologies.
End-to-End Background Subtraction via a Multi-Scale Spatio-Temporal Model

Background subtraction is an important task in computer vision.Traditional approaches usually utilize low-level visual features like color, texture, or edge to build background models.Due to the lack of deep features, they often achieve poor performance when facing complex video scenes such as illumination changes, background, or camera motions, camouflage effects and shadows.Recently, deep learning has shown to perform well in extracting deep features.To improve the robustness of background subtraction, in this paper, we propose an end-to-end multi-scale spatio-temporal (MS-ST) method which is able to extract deep features from video sequences.First, a video clip is input into a convolutional neural network for extracting multi-scale spatial features.Subsequently, to exploit the temporal information, we combine temporal sampling operations and ConvLSTM modules to extract the multi-scale temporal contextual information.Finally, the segmentation result is generated by fusing multi-scale spatio-temporal features.The experimental results on the CDnet-2014 dataset and the LASIESTA dataset demonstrate the effectiveness and superiority of the proposed method.
Localization Guided Fight Action Detection in Surveillance Videos

Automatic detection of fight behaviors in surveillance videos is an important task for surveillance systems. In this work, we propose a novel localization guided framework for detecting fight actions in surveillance videos. Specifically, we exploit optical flow maps to extract motion activation information, which indicates the location of active regions. Then, a detection guided alignment module is designed to adjust the localized active regions. This approach employs a two-stream based 3D convolution network as the backbone network with a novel motion acceleration representation on the temporal stream. While most existing methods are still evaluated on three benchmark datasets which were not originally collected from surveillance scenarios, we present a novel Fight Action Detection in Surveillance-videos (FADS) dataset for this purpose. With a total of 1,520 video clips, the FADS is the largest known dataset in terms of number of surveillance videos with fight scenes. Experimental results on both the benchmark datasets and the FADS show that our proposed localization guided method outperforms state-of-the-art techniques.
Aspect-Ratio-Preserving Multi-Patch Image Aesthetics Score Prediction

Owing to the spread of social networking services (SNS), there is an increasing demand for automatically selecting, editing or generating impressive images, which raises the importance of evaluating image aesthetics. We propose the first multi-patch method for image aesthetic score prediction with the original image aspect ratios being preserved. Our method just uses images for training and does not require external information both in training as well as prediction. In an experiment using the large-scale AVA dataset containing 250,000 images, our approach outperforms other existing methods in image aesthetic score prediction, especially reducing mean squared error (MSE) of predicted aesthetic scores by 0.061 (18%) and improving the linear correlation coefficient (LCC) by 0.056 (8.9%). Noticeably, the decrease in mean absolute error (MAE) by our method for images with an unbalanced aspect ratio is at most 7.9 times larger than the decrease in MAE for images with a typical digital camera aspect ratio. This result indicates that our multi-patch method expands the range of aspect ratios with which aesthetics scores of images can be predicted accurately.
RGB-D Scene Recognition via Spatial-Related Multi-Modal Feature Learning

RGB-D image-based scene recognition has achieved significant performance improvement with the development of deep learning methods. While convolutional neural networks can learn high-semantic level features for object recognition, these methods still have limitations for RGB-D scene classification. One limitation is that how to learn better multi-modal features for the RGB-D scene recognition is still an open problem. Another limitation is that the scene images are usually not object-centric and with great spatial variability. Thus, vanilla full-image CNN features maybe not optimal for scene recognition. Considering these problems, in this paper, we propose a compact and effective framework for RGB-D scene recognition. Specifically, we make the following contributions: 1) A novel RGB-D scene recognition framework is proposed to explicitly learn the global modal-specific and local modal-consistent features simultaneously. Different from existing approaches, local CNN features are considered for the learning of modal-consistent representations; 2) key Feature Selection (KFS) module is designed, which can adaptively select important local features from the high-semantic level CNN feature maps. It is more efficient and effective than object detection and dense patch-sampling based methods, and; 3) a triplet correlation loss and a spatial-attention similarity loss are proposed for the training of KFS module. Under the supervision of the proposed loss functions, the network can learn import local features of two modalities with no need for extra annotations. Finally, by concatenating the global and local features together, the proposed framework can achieve new state-of-the-art scene recognition performance on the SUN RGB-D dataset and NYU Depth version 2 (NYUD v2) dataset.
Alt-PoW: An Alternative Proof-of-Work Mechanism

Bitcoin is the world's most successful digital currency. It uses a public distributed ledger called the blockchain to maintain the transaction history in the network. It employs a Proof-of-Work (PoW) protocol for miners to add more blocks of transactions to the chain in order to arrive at a consensus of the state of the network. Due to the nature of the consensus mechanism used, miners are not given any sense of their progression when solving a PoW problem. The only strategy presented to them is a brute-force search where it is unknown to them how far away they or other miners are to a solution, and if the problem is worth dedicating further resources to. This results in a slow and highly energy inefficient consensus protocol. In this paper we present an alternative PoW mechanism which introduces the idea of progression when solving such puzzles. This Alt-PoW mechanism gives participants a network view of how all other miners are faring in the block finding process, so they can decide whether it is in their interest to withdraw from a block race or to continue dedicating their resources towards it. We also present a mechanism to allow for multiple interconnected chains as opposed to a single blockchain, so that block finding can be parallelized. Participants can decide which chain to dedicated their resources to, based on which has the best chance of them being successful mining on at any particular time. We show that this results in a faster and more energy efficient protocol.
Validation of discrete time‐to‐event prediction models in the presence of competing risks

Abstract Clinical prediction models play a key role in risk stratification, therapy assignment and many other fields of medical decision making. Before they can enter clinical practice, their usefulness has to be demonstrated using systematic validation. Methods to assess their predictive performance have been proposed for continuous, binary, and time‐to‐event outcomes, but the literature on validation methods for discrete time‐to‐event models with competing risks is sparse. The present paper tries to fill this gap and proposes new methodology to quantify discrimination, calibration, and prediction error (PE) for discrete time‐to‐event outcomes in the presence of competing risks. In our case study, the goal was to predict the risk of ventilator‐associated pneumonia (VAP) attributed to Pseudomonas aeruginosa in intensive care units (ICUs). Competing events are extubation, death, and VAP due to other bacteria. The aim of this application is to validate complex prediction models developed in previous work on more recently available validation data.
Some Nonprimitive BCH Codes and Related Quantum Codes

Let <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> be a prime power and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m\geq 3$ </tex-math></inline-formula> be odd. Suppose that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n=\frac {q^{2m}-1}{a}$ </tex-math></inline-formula> with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$a|(q^{m}+1)$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\leq a \leq 2(q^{2}-q+1)$ </tex-math></inline-formula> . This paper mainly determines the actual maximum designed distance of Hermitian dual-containing Bose-Chaudhuri-Hocquenghem (BCH) codes over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {F}_{q^{2}}$ </tex-math></inline-formula> of length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> . Firstly, we give the maximum designed distance <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta _{m,a}^{R}$ </tex-math></inline-formula> of narrow-sense Hermitian dual-containing BCH codes. Secondly, we show that there are also non-narrow-sense ones of designed distance up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta _{m,a}^{R}$ </tex-math></inline-formula> . It is worth mentioning that our maximum designed distance <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta _{m,a}^{R}&gt;\lceil \frac {a}{2}\rceil \delta _{m}^{A}$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta _{m}^{A}$ </tex-math></inline-formula> is given by Aly <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (IEEE Trans. Inf. Theory, vol. 53, no. 3, pp. 1183-1188, 2007). Thus, many families of Hermitian dual-containing BCH codes with relatively large designed distance are obtained. Using the Hermitian construction to them, we can subsequently construct different classes of nonprimitive quantum codes, which are new in the sense that their parameters are not covered in the literature.
AttentivU: A Wearable Pair of EEG and EOG Glasses for Real-Time Physiological Processing

Recently several research projects have explored using physiological sensors such as electroencephalography (EEG) or electrooculography (EOG) electrodes to measure the engagement of a user in different contexts and augment learning activities. However, these systems still suffer from limitations such as an absence of a socially acceptable design, or use of impractical gel-based electrodes. We present AttentivU, a device using both EEG and EOG for real-time monitoring of physiological data. The device is designed as a socially acceptable pair of glasses and employs silver electrodes as an alternative to the commonly used silver/silver chloride (Ag/AgCI) “wet” electrodes. A detailed description of the hardware design and proof of concept prototype is provided, as well as a side by side comparison of conventional wet electrodes.
More Results on Shortest Linear Programs

At the FSE conference of ToSC 2018, Kranz et al. presented their results on shortest linear programs for the linear layers of several well known block ciphers in literature. Shortest linear programs are essentially the minimum number of 2-input xor gates required to completely describe a linear system of equations. In the above paper the authors showed that the commonly used metrics like d-xor/s-xor count that are used to judge the "lightweightedness" do not represent the minimum number of xor gates required to describe a given MDS matrix. In fact they used heuristic based algorithms of Boyar/Peralta and Paar to find implementations of MDS matrices with even fewer xor gates than was previously known. They proved that the AES mixcolumn matrix can be implemented with as little as 97 xor gates. In this paper we show that the values reported in the above paper are not optimal. By suitably including random bits in the instances of the above algorithms we can achieve implementations of almost all matrices with lesser number of gates than were reported in the above paper. As a result we report an implementation of the AES mixcolumn matrix that uses only 95 xor gates. In the second part of the paper, we observe that most standard cell libraries contain both 2 and 3-input xor gates, with the silicon area of the 3-input xor gate being smaller than the sum of the areas of two 2-input xor gates. Hence when linear circuits are synthesized by logic compilers (with specific instructions to optimize for area), most of them would return a solution circuit containing both 2 and 3-input xor gates. Thus from a practical point of view, reducing circuit size in presence of these gates is no longer equivalent to solving the shortest linear program. In this paper we show that by adopting a graph based heuristic it is possible to convert a circuit constructed with 2-input xor gates to another functionally equivalent circuit that utilizes both 2 and 3-input xor gates and occupies less hardware area. As a result we obtain more lightweight implementations of all the matrices listed in the ToSC paper.