Reviewerly/exHarmony huggingface dataset:
A Comprehensive Survey on Secure Outsourced Computation and Its Applications

With the ever-increasing requirement of storage and computation resources, it is unrealistic for local devices (with limited sources) to implement large-scale data processing. Therefore, individuals or corporations incline to outsource their computation requirements to the cloud. However, data outsourcing brings security and privacy concerns to users when the cloud servers are not fully trusted. Recently, extensive research works are conducted, aiming at secure outsourcing schemes for diverse computational tasks via different technologies. In this survey, we provide a technical review and comparison of existing outsourcing schemes using diverse secure computation methods. Specifically, we begin the survey by describing security threats and requirements of secure outsourcing computation. Meanwhile, we introduce four secure techniques (i.e., secure multi-party computation, pseudorandom functions, software guard extensions, and perturbation approaches) and their related works. Then, we focus on the theories and evolution of homomorphic encryption, as well as the applications of the basic operations and application-specific tasks. Finally, we discuss the security and performance of existing works and give future directions in this field.
A Context Knowledge Map Guided Coarse-to-Fine Action Recognition

Human actions involve a wide variety and a large number of categories, which leads to a big challenge in action recognition. However, according to similarities on human body poses, scenes, interactive objects, human actions can be grouped into some semantic groups, i.e sports, cooking, etc. Therefore, in this paper, we propose a novel approach which recognizes human actions from coarse to fine. Taking full advantage of contributions from high-level semantic contexts, a context knowledge map guided recognition method is designed to realize the coarse-to-fine procedure. In the approach, we define semantic contexts with interactive objects, scenes and body motions in action videos, and build a context knowledge map to automatically define coarse-grained groups. Then fine-grained classifiers are proposed to realize accurate action recognition. The coarse-to-fine procedure narrows action categories in target classifiers, so it is beneficial to improving recognition performance. We evaluate the proposed approach on the CCV, the HMDB-51, and the UCF101 database. Experiments verify its significant effectiveness, on average, improving more than 5% of recognition precisions than current approaches. Compared with the state-of-the-art, it also obtains outstanding performance. The proposed approach achieves higher accuracies of 93.1%, 95.4% and 74.5% in the CCV, the UCF-101 and the HMDB51 database, respectively.
Aircraft detection in remote sensing image based on corner clustering and deep learning

Owing to the variations of aircraft type, pose, size and complex background, it remains difficult to detect aircraft effectively in remote sensing images, which plays a great significance in civilian and military. Classical aircraft detection algorithms still produce thousands of candidate regions and extract the features of candidate regions manually, which affects the detection performance. To address these difficulties encountered, an aircraft detection scheme based on corner clustering and Convolutional Neural Network (CNN) is proposed in this paper. The scheme is divided into two main steps: region proposal and classification. First, candidate regions are generated by utilizing mean-shift clustering algorithm to the corners detected on binary images. Then, the CNN is used for the feature extraction and classification of candidate regions that possibly contain the aircraft, and the location of the aircraft is finally determined after further screening. Compared with other classical methods, such as selective search (SS) + CNN, Edgeboxes + CNN and histogram of oriented gradient (HOG) + support vector machine (SVM), the proposed approach has a high accuracy and efficiency since it can automatically learn the essential features of the object from a large amount of data and produce fewer high quality candidate regions.
Structured self-attention architecture for graph-level representation learning

Recently, graph neural networks (GNNs) have shown to be effective in learning representative graph features. However, current pooling-based strategies for graph classification lack efficient utilization of graph representation information in which each node and layer have the same contribution to the output of graph-level representation. In this paper, we develop a novel architecture for extracting an effective graph representation by introducing structured multi-head self-attention in which the attention mechanism consists of three different forms, i.e., node-focused, layer-focused and graph-focused. In order to make full use of the information of graphs, the node-focused self-attention firstly aggregates neighbor node features with a scaled dot-product manner, and then the layer-focused and graph-focused self-attention serve as readout module to measure the importance of different nodes and layers to the model's output. Moreover, it is able to improve the performance on graph classification tasks by combining these two self-attention mechanisms with base node-level GNNs. The proposed Structured Self-attention Architecture is evaluated on two kinds of graph benchmarks: bioinformatics datasets and social network datasets. Extensive experiments have demonstrated superior performance improvement to existing methods on predictive accuracy.
Analysing Neural Language Models: Contextual Decomposition Reveals Default Reasoning in Number and Gender Assignment

Extensive research has recently shown that recurrent neural language models are able to process a wide range of grammatical phenomena.How these models are able to perform these remarkable feats so well, however, is still an open question.To gain more insight into what information LSTMs base their decisions on, we propose a generalisation of Contextual Decomposition (GCD).In particular, this setup enables us to accurately distil which part of a prediction stems from semantic heuristics, which part truly emanates from syntactic cues and which part arise from the model biases themselves instead.We investigate this technique on tasks pertaining to syntactic agreement and co-reference resolution and discover that the model strongly relies on a default reasoning effect to perform these tasks.
An Exploration of Virtual Reality Use and Application Among Older Adult Populations

Despite increased research on virtual reality (VR) platforms in recent years, there are very few studies that specifically examine its application within a gerontological context. This study examines the usability, preferences, and application considerations of a mobile VR platform by individually interviewing community-dwelling older adults both before and after trying the Samsung Gear VR. Participants were asked to self-select and view short VR videos (30 s-3 min) that were filmed within the local community (e.g., walking path and art museum). Semi-structured interview questions explored participant perceptions of using the device and was followed with two focus group sessions. Thematic analysis was employed when reviewing observational notes and transcribed audio recordings. Ten adults (aged 63-89) participated and themes identified include (a) usability, (b) video subject matter preferences, and (c) application. These themes highlighted both the challenges and opportunities of VR use among a wide range of older populations and provided greater insight with its exploration and application in future studies. This included potential use among those older adults who have notable functional limitations, such as those who are immobile, or reside within a care facility.
A Dual-Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification

Recognising dialogue acts (DA) is important for many natural language processing tasks such as dialogue generation and intention recognition.In this paper, we propose a dualattention hierarchical recurrent neural network for DA classification.Our model is partially inspired by the observation that conversational utterances are normally associated with both a DA and a topic, where the former captures the social act and the latter describes the subject matter.However, such a dependency between DAs and topics has not been utilised by most existing systems for DA classification.With a novel dual task-specific attention mechanism, our model is able, for utterances, to capture information about both DAs and topics, as well as information about the interactions between them.Experimental results show that by modelling topic as an auxiliary task, our model can significantly improve DA classification, yielding better or comparable performance to the state-of-the-art method on three public datasets.
An Empirical Study of Dependency Downgrades in the npm Ecosystem

In a software ecosystem, a dependency relationship enables a <i>client</i> package to reuse a certain version of a <i>provider</i> package. Packages in a software ecosystem often release versions containing bug fixes, new functionalities, and security enhancements. Hence, updating the provider version is an important maintenance task for client packages. Despite the number of investigations about dependency updates, there is a lack of studies about dependency downgrades in software ecosystems. A downgrade indicates that the adopted version of a provider package is not suitable to the client package at a certain moment. In this paper, we investigate downgrades in the <inline-formula><tex-math notation="LaTeX">${\sf npm}$</tex-math></inline-formula> ecosystem. We address three research questions. In our first RQ, we provide a list of the reasons behind the occurrence of downgrades. Our manual analysis of the artifacts (e.g., release notes and commit messages) of a package code repository identified two categories of downgrades according to their rationale: reactive and preventive. The reasons behind reactive downgrades are defects in a specific version of a provider, unexpected feature changes in a provider, and incompatibilities. In turn, preventive downgrades are an attempt to avoid issues in future releases. In our second RQ, we investigate how the versioning of dependencies is modified when a downgrade occurs. We observe that 49 percent of the downgrades are performed by replacing a range of acceptable versions of a provider by a specific old version. This observation suggests that client packages have the tendency to become more conservative regarding the update of their providers after a downgrade. Also, 48 percent of the downgrades reduce the provider version by a minor level (e.g., from 2.1.0 to 2.0.0). This observation indicates that client packages in <inline-formula><tex-math notation="LaTeX">${\sf npm}$</tex-math></inline-formula> should be cautious when updating minor releases of the provider (e.g., by prioritizing tests). Finally, in our third RQ we observe that 50 percent of the downgrades are performed at a rate that is 2.6 times as slow as the median time-between-releases of their associated client packages. We also observe that downgrades that follow an explicit update of a provider package occur faster than downgrades that follow an implicit update. Explicit updates occur when the provider is updated by means of an explicit change to the versioning specification (i.e., the string used by client packages to define the provider version that they are willing to adopt). We conjecture that, due to the controlled nature of explicit updates, it is easier for client packages to identify the provider that is associated with the problem that motivated the downgrade.
Practical guide to training and validation for primary diagnosis with digital pathology

Numerous clinical pathology departments are deploying or planning to deploy digital pathology systems for all or part of their diagnostic output. Digital pathology is an evolving technology, and it is important that departments uphold or improve on current standards. Leeds Teaching Hospitals NHS Trust has been scanning 100% of histology slides since September 2018. In this practical paper, we will share our approach to training and validation, which has been incorporated into the Royal College of Pathologists’ guidance for digital pathology implementation. We will offer an overview of the Royal College endorsed training and validation protocol and the evidence base on which it is based. We will provide practical advice on implementation of the protocol and highlight areas of digital reporting that can prove difficult for the novice digital pathologist. In addition, we will share a detailed topographical list of types of diagnostic tasks and features which should form the basis of digital slide training sets.
Wearable Sport Activity Classification Based on Deep Convolutional Neural Network

This paper develops a wearable sport activity classification system and its associated deep learning-based sport activity classification algorithm for accurately recognizing sport activities.The proposed wearable system used two wearable inertial sensing modules worn on athletes' wrist and ankle to collect sport motion signals and utilized a deep convolutional neural network (CNN) to extract the inherent features from the spectrograms of the short-term Fourier transform (STFT) of the sport motion signals.The wearable inertial sensing module is composed of a microcontroller, a triaxial accelerometer, a triaxial gyroscope, an RF wireless transmission module, and a power supply circuit.All ten participants wore the two wearable inertial sensing modules on their wrist and ankle to collect motion signals generated by sport activities.Subsequently, we developed a deep learning-based sport activity classification algorithm composed of sport motion signal collection, signal preprocessing, sport motion segmentation, signal normalization, spectrogram generation, image mergence/resizing, and CNN-based classification to recognize ten types of sport activities.The CNN classifier consisting of two convolutional layers, two pooling layers, a fully-connected layer, and a softmax layer can be used to divide the sport activities into table tennis, tennis, badminton, golf, batting baseball, shooting basketball, volleyball, dribbling basketball, running, and bicycling, respectively.Finally, the experimental results show that the proposed wearable sport activity classification system and its deep learning-based sport activity classification algorithm can recognize 10 sport activities with the classification rate of 99.30%.
A Mutual Information Maximization Perspective of Language Representation Learning

We show state-of-the-art word representation learning methods maximize an objective function that is a lower bound on the mutual information between different parts of a word sequence (i.e., a sentence). Our formulation provides an alternative perspective that unifies classical word embedding models (e.g., Skip-gram) and modern contextual embeddings (e.g., BERT, XLNet). In addition to enhancing our theoretical understanding of these methods, our derivation leads to a principled framework that can be used to construct new self-supervised tasks. We provide an example by drawing inspirations from related methods based on mutual information maximization that have been successful in computer vision, and introduce a simple self-supervised objective that maximizes the mutual information between a global sentence representation and n-grams in the sentence. Our analysis offers a holistic view of representation learning methods to transfer knowledge and translate progress across multiple domains (e.g., natural language processing, computer vision, audio processing).
Observation of the Crossover from Photon Ordering to Delocalization in Tunably Coupled Resonators

Networks of nonlinear resonators offer intriguing perspectives as quantum simulators for nonequilibrium many-body phases of driven-dissipative systems. Here, we employ photon correlation measurements to study the radiation fields emitted from a system of two superconducting resonators in a driven-dissipative regime, coupled nonlinearly by a superconducting quantum interference device, with cross-Kerr interactions dominating over on-site Kerr interactions. We apply a parametrically modulated magnetic flux to control the linear photon hopping rate between the two resonators and its ratio with the cross-Kerr rate. When increasing the hopping rate, we observe a crossover from an ordered to a delocalized state of photons. The presented coupling scheme is intrinsically robust to frequency disorder and may therefore prove useful for realizing larger-scale resonator arrays.
Fooling Detection Alone is Not Enough: Adversarial Attack against Multiple Object Tracking

Recent work in adversarial machine learning started to focus on the visual perception in autonomous driving and studied Adversarial Examples (AEs) for object detection models. However, in such visual perception pipeline the detected objects must also be tracked, in a process called Multiple Object Tracking (MOT), to build the moving trajectories of surrounding obstacles. Since MOT is designed to be robust against errors in object detection, it poses a general challenge to existing attack techniques that blindly target objection detection: we find that a success rate of over 98% is needed for them to actually affect the tracking results, a requirement that no existing attack technique can satisfy. In this paper, we are the first to study adversarial machine learning attacks against the complete visual perception pipeline in autonomous driving, and discover a novel attack technique, tracker hijacking, that can effectively fool MOT using AEs on object detection. Using our technique, successful AEs on as few as one single frame can move an existing object in to or out of the headway of an autonomous vehicle to cause potential safety hazards. We perform evaluation using the Berkeley Deep Drive dataset and find that on average when 3 frames are attacked, our attack can have a nearly 100% success rate while attacks that blindly target object detection only have up to 25%.
Mining conditional functional dependency rules on big data

Current Conditional Functional Dependency (CFD) discovery algorithms always need a well-prepared training dataset. This condition makes them difficult to apply on large and low-quality datasets. To handle the volume issue of big data, we develop the sampling algorithms to obtain a small representative training set. We design the fault-tolerant rule discovery and conflict-resolution algorithms to address the low-quality issue of big data. We also propose parameter selection strategy to ensure the effectiveness of CFD discovery algorithms. Experimental results demonstrate that our method can discover effective CFD rules on billion-tuple data within a reasonable period.
Alternating Language Modeling for Cross-Lingual Pre-Training

Language model pre-training has achieved success in many natural language processing tasks. Existing methods for cross-lingual pre-training adopt Translation Language Model to predict masked words with the concatenation of the source sentence and its target equivalent. In this work, we introduce a novel cross-lingual pre-training method, called Alternating Language Modeling (ALM). It code-switches sentences of different languages rather than simple concatenation, hoping to capture the rich cross-lingual context of words and phrases. More specifically, we randomly substitute source phrases with target translations to create code-switched sentences. Then, we use these code-switched data to train ALM model to learn to predict words of different languages. We evaluate our pre-training ALM on the downstream tasks of machine translation and cross-lingual classification. Experiments show that ALM can outperform the previous pre-training methods on three benchmarks.1
Uncertainty-Aware Action Advising for Deep Reinforcement Learning Agents

Although Reinforcement Learning (RL) has been one of the most successful approaches for learning in sequential decision making problems, the sample-complexity of RL techniques still represents a major challenge for practical applications. To combat this challenge, whenever a competent policy (e.g., either a legacy system or a human demonstrator) is available, the agent could leverage samples from this policy (advice) to improve sample-efficiency. However, advice is normally limited, hence it should ideally be directed to states where the agent is uncertain on the best action to execute. In this work, we propose Requesting Confidence-Moderated Policy advice (RCMP), an action-advising framework where the agent asks for advice when its epistemic uncertainty is high for a certain state. RCMP takes into account that the advice is limited and might be suboptimal. We also describe a technique to estimate the agent uncertainty by performing minor modifications in standard value-function-based RL methods. Our empirical evaluations show that RCMP performs better than Importance Advising, not receiving advice, and receiving it at random states in Gridworld and Atari Pong scenarios.
PEIA: Personality and Emotion Integrated Attentive Model for Music Recommendation on Social Media Platforms

With the rapid expansion of digital music formats, it's indispensable to recommend users with their favorite music. For music recommendation, users' personality and emotion greatly affect their music preference, respectively in a long-term and short-term manner, while rich social media data provides effective feedback on these information. In this paper, aiming at music recommendation on social media platforms, we propose a Personality and Emotion Integrated Attentive model (PEIA), which fully utilizes social media data to comprehensively model users' long-term taste (personality) and short-term preference (emotion). Specifically, it takes full advantage of personality-oriented user features, emotion-oriented user features and music features of multi-faceted attributes. Hierarchical attention is employed to distinguish the important factors when incorporating the latent representations of users' personality and emotion. Extensive experiments on a large real-world dataset of 171,254 users demonstrate the effectiveness of our PEIA model which achieves an NDCG of 0.5369, outperforming the state-of-the-art methods. We also perform detailed parameter analysis and feature contribution analysis, which further verify our scheme and demonstrate the significance of co-modeling of user personality and emotion in music recommendation.
An ADMM Based Framework for AutoML Pipeline Configuration

We study the AutoML problem of automatically configuring machine learning pipelines by jointly selecting algorithms and their appropriate hyper-parameters for all steps in supervised learning pipelines. This black-box (gradient-free) optimization with mixed integer &amp; continuous variables is a challenging problem. We propose a novel AutoML scheme by leveraging the alternating direction method of multipliers (ADMM). The proposed framework is able to (i) decompose the optimization problem into easier sub-problems that have a reduced number of variables and circumvent the challenge of mixed variable categories, and (ii) incorporate black-box constraints alongside the black-box optimization objective. We empirically evaluate the flexibility (in utilizing existing AutoML techniques), effectiveness (against open source AutoML toolkits), and unique capability (of executing AutoML with practically motivated black-box constraints) of our proposed scheme on a collection of binary classification data sets from UCI ML &amp; OpenML repositories. We observe that on an average our framework provides significant gains in comparison to other AutoML frameworks (Auto-sklearn &amp; TPOT), highlighting the practical advantages of this framework.