Reviewerly/exHarmony huggingface dataset:
SparseSense: Human Activity Recognition from Highly Sparse Sensor Data-streams Using Set-based Neural Networks

Batteryless or so called passive wearables are providing new and innovative methods for human activity recognition (HAR), especially in healthcare applications for older people. Passive sensors are low cost, lightweight, unobtrusive and desirably disposable; attractive attributes for healthcare applications in hospitals and nursing homes. Despite the compelling propositions for sensing applications, the data streams from these sensors are characterised by high sparsity---the time intervals between sensor readings are irregular while the number of readings per unit time are often limited. In this paper, we rigorously explore the problem of learning activity recognition models from temporally sparse data. We describe how to learn directly from sparse data using a deep learning paradigm in an end-to-end manner. We demonstrate significant classification performance improvements on real-world passive sensor datasets from older people over the state-of-the-art deep learning human activity recognition models. Further, we provide insights into the model's behaviour through complementary experiments on a benchmark dataset and visualisation of the learned activity feature spaces.
A DEEP CONVOLUTIONAL AUTO-ENCODER WITH POOLING – UNPOOLING LAYERS IN CAFFE

This paper presents the development of several models of a deep convolutional auto-encoder in the Caffe deep learning framework and their experimental evaluation on the example of MNIST dataset. We have created five models of a convolutional auto-encoder which differ architecturally by the presence or absence of pooling and unpooling layers in the auto-encoder’s encoder and decoder parts. Our results show that the developed models provide very good results in dimensionality reduction and unsupervised clustering tasks, and small classification errors when we used the learned internal code as an input of a supervised linear classifier and multi-layer perceptron. The best results were provided by a model where the encoder part contains convolutional and pooling layers, followed by an analogous decoder part with deconvolution and unpooling layers without the use of switch variables in the decoder part. The paper also discusses practical details of the creation of a deep convolutional auto-encoder in the very popular Caffe deep learning framework. We believe that our approach and results presented in this paper could help other researchers to build efficient deep neural network architectures in the future.
iTeleScope: Softwarized Network Middle-Box for Real-Time Video Telemetry and Classification

Video continues to dominate network traffic, yet operators today have poor visibility into the number, duration, and resolutions of the video streams traversing their domain. Current monitoring approaches are inaccurate, expensive, or unscalable, as they rely on statistical sampling, middle-box hardware, or packet inspection software. We present iTelescope, the first intelligent, inexpensive, and scalable softwarized network middle-box solution for identifying and classifying video flows in realtime. Our solution is novel in combining dynamic flow rules with telemetry and machine learning, and is built on commodity OpenFlow switches and open-source software. We develop a fully functional system, train it in the lab using multiple machine learning algorithms, and validate its performance to show over 95% accuracy in identifying and classifying video streams from many providers, including YouTube and Netflix. Lastly, we conduct tests to demonstrate its scalability to tens of thousands of concurrent streams, and deploy it live on a campus network serving several hundred real users. Our traffic monitoring system gives unprecedented fine-grained real-time visibility of video streaming performance to operators of enterprise and carrier networks at very low cost.
EDUKASI SADAR BENCANA MELALUI SOSIALISASI KEBENCANAAN SEBAGAI UPAYA PENINGKATAN PENGETAHUAN SISWA TERHADAP MITIGASI BENCANA

Indonesia is a country that has a high level of vulnerability to natural disasters. Based on the disaster index of risk, Central Java Province is one of the provinces that experienced the most disasters in Indonesia. Community preparedness efforts towards disasters are still considered weak, as evidenced by a large number of fatalities, property losses, and deaths. Children are classified as vulnerable to disaster management efforts and therefore require special efforts to increase knowledge about disaster mitigation. This study aims to increase students knowledge of disaster mitigation through disaster preparedness socialization activities to realize disaster resilient schools. This research is one form of community service through socialization activities with a qualitative approach. The result of the study shows that the level of students knowledge of disaster mitigation still needs for improving more. Low knowledge can increase the number of casualties resulting from disasters. The solution offered by researchers is conducting disaster awareness education activities through disaster socialization. It is expected that follow up actions such as maintenance and supervision as controls with the aim of such activities have the optimal result to increase students knowledge of disaster mitigation and the realization of disaster resilient schools.
Private Continual Release of Real-Valued Data Streams

We present a differentially private mechanism to display statistics (e.g., the moving average) of a stream of real valued observations where the bound on each observation is either too conservative or unknown in advance.This is particularly relevant to scenarios of real-time data monitoring and reporting, e.g., energy data through smart meters.Our focus is on real-world data streams whose distribution is light-tailed, meaning that the tail approaches zero at least as fast as the exponential distribution.For such data streams, individual observations are expected to be concentrated below an unknown threshold.Estimating this threshold from the data can potentially violate privacy as it would reveal particular events tied to individuals [1].On the other hand an overly conservative threshold may impact accuracy by adding more noise than necessary.We construct a utility optimizing differentially private mechanism to release this threshold based on the input stream.Our main advantage over the state-of-the-art algorithms is that the resulting noise added to each observation of the stream is scaled to the threshold instead of a possibly much larger bound; resulting in considerable gain in utility when the difference is significant.Using two real-world datasets, we demonstrate that our mechanism, on average, improves the utility by a factor of 3.5 on the first dataset, and 9 on the other.While our main focus is on continual release of statistics, our mechanism for releasing the threshold can be used in various other applications where a (privacy-preserving) measure of the scale of the input distribution is required.
Teachers 3.0: Patterns of use of five digital tools.

Nowadays, improvements in teaching are closely related to the use of technologies which have been defined as unprecedented educational tools. This study analyzes the use that 118 Spanish teachers make of five digital tools; Google, Wikis, Blogs, YouTube and WhatsApp in the educational center and the impact of the socio-labor variables of these teachers on the greater or lesser use of digital tools for teaching and evaluation. To this end, a questionnaire is designed and validated, in both its versions, printed and online, and two types of analysis are carried out, one descriptive and the other multivariate, using General Linear Model. The results show different patterns in the use of digital tools by teachers according to their age, gender, professional experience, type of school and performance of academic positions. The implications of these results are discussed and evaluated.
Multiple‐replica integrity auditing schemes for cloud data storage

Summary Cloud computing has been an essential technology for providing on‐demand computing resources as a service on the Internet. Not only enterprises but also individuals can outsource their data to the cloud without worrying about purchase and maintenance cost. The cloud storage system, however, is not fully trustable. Cloud data integrity auditing is crucial for defending against the security threats of data in the untrusted multicloud environment. Storing multiple replicas is a commonly used strategy for the availability and reliability of critical data. In this paper, we summarize and analyze the state‐of‐the‐art multiple‐replica integrity auditing schemes in cloud data storage. We present the system model and security threats of outsourcing data to the cloud with classification of ongoing developments. We also summarize the existing data integrity auditing schemes for multicloud data storage. The important open issues and potential research directions are addressed.
WAIS: Word Attention for Joint Intent Detection and Slot Filling

Attention-based recurrent neural network models for joint intent detection and slot filling have achieved a state-of-the-art performance. Most previous works exploited semantic level information to calculate the attention weights. However, few works have taken the importance of word level information into consideration. In this paper, we propose WAIS, word attention for joint intent detection and slot filling. Considering that intent detection and slot filling have a strong relationship, we further propose a fusion gate that integrates the word level information and semantic level information together for jointly training the two tasks. Extensive experiments show that the proposed model has robust superiority over its competitors and sets the state-of-the-art.
Measuring Structural Similarities in Finite MDPs

In this paper, we investigate the structural similarities within a finite Markov decision process (MDP). We view a finite MDP as a heterogeneous directed bipartite graph and propose novel measures for state similarity and action similarity in a mutual reinforcement manner. We prove that the state similarity is a metric and the action similarity is a pseudometric. We also establish the connection between the proposed similarity measures and the optimal values of the MDP. Extensive experiments show that the proposed measures are effective.
Group Reconstruction and Max-Pooling Residual Capsule Network

In capsule networks, the mapping of low-level capsules to high-level capsules is achieved by a routing-by-agreement algorithm. Since the capsule is made up of collections of neurons and the routing mechanism involves all the capsules instead of simply discarding some of the neurons like Max-Pooling, the capsule network has stronger representation ability than the traditional neural network. However, considering too much low-level capsules' information will cause its corresponding upper layer capsules to be interfered by other irrelevant information or noise capsules. Therefore, the original capsule network does not perform well on complex data structure. What's worse, computational complexity becomes a bottleneck in dealing with large data networks. In order to solve these shortcomings, this paper proposes a group reconstruction and max-pooling residual capsule network (GRMR-CapsNet). We build a block in which all capsules are divided into different groups and perform group reconstruction routing algorithm to obtain the corresponding high-level capsules. Between the lower and higher layers, Capsule Max-Pooling is adopted to prevent overfitting. We conduct experiments on CIFAR-10/100 and SVHN datasets and the results show that our method can perform better against state-of-the-arts.
PAAE: A Unified Framework for Predicting Anchor Links with Adversarial Embedding

The goal of predicting anchor links is to align accounts from multiple networks by whether they are held by the same natural person. Network structure is the key information for predicting anchor links. Exploring the intrinsic attributes of the network structure is an important way to align anchor users across social networks. Existing methods use a representation learning approach to embed network vertices into low dimension vectors space. But these methods suffer from lack of additional constraints for enhancing the robustness of the embedding vectors when aligning anchor nodes across networks with large structural differences. To offer a robust method, we propose a novel adversarial representation learning approach to align users, called PAAE(predicting anchor links with adversarial embedding), which employs an adversarial regularization to capture the robust embedding vectors and maps anchor users with an alignment autoencoders. PAAE can solve both the network embedding problem and the user alignment problem simultaneously under a unified optimization framework. Through extensive experiments on real social network datasets, we demonstrate that PAAE significantly outperforms the state-of-the-art methods.
Inter-node Hellinger Distance based Decision Tree

This paper introduces a new splitting criterion called Inter-node Hellinger Distance (iHD) and a weighted version of it (iHDw) for constructing decision trees. iHD measures the distance between the parent and each of the child nodes in a split using Hellinger distance. We prove that this ensures the mutual exclusiveness between the child nodes. The weight term in iHDw is concerned with the purity of individual child node considering the class imbalance problem. The combination of the distance and weight term in iHDw thus favors a partition where child nodes are purer and mutually exclusive, and skew insensitive. We perform an experiment over twenty balanced and twenty imbalanced datasets. The results show that decision trees based on iHD win against six other state-of-the-art methods on at least 14 balanced and 10 imbalanced datasets. We also observe that adding the weight to iHD improves the performance of decision trees on imbalanced datasets. Moreover, according to the result of the Friedman test, this improvement is statistically significant compared to other methods.
Probabilistic Strategy Logic

We introduce Probabilistic Strategy Logic, an extension of Strategy Logic for stochastic systems. The logic has probabilistic terms that allow it to express many standard solution concepts, such as Nash equilibria in randomised strategies, as well as constraints on probabilities, such as independence. We study the model-checking problem for agents with perfect- and imperfect-recall. The former is undecidable, while the latter is decidable in space exponential in the system and triple-exponential in the formula. We identify a natural fragment of the logic, in which every temporal operator is immediately preceded by a probabilistic operator, and show that it is decidable in space exponential in the system and the formula, and double-exponential in the nesting depth of the probabilistic terms. Taking a fixed nesting depth, this gives a fragment that still captures many standard solution concepts, and is decidable in exponential space.
Incorporating Context-Relevant Knowledge into Convolutional Neural Networks for Short Text Classification

Some text classification methods don’t work well on short texts due to the data sparsity. What’s more, they don’t fully exploit context-relevant knowledge. In order to tackle these problems, we propose a neural network to incorporate context-relevant knowledge into a convolutional neural network for short text classification. Our model consists of two modules. The first module utilizes two layers to extract concept and context features respectively and then employs an attention layer to extract those context-relevant concepts. The second module utilizes a convolutional neural network to extract high-level features from the word and the contextrelevant concept features. The experimental results on three datasets show that our proposed model outperforms the stateof-the-art models.
Latent Semantics Encoding for Label Distribution Learning

Label distribution learning (LDL) is a newly arisen learning paradigm to deal with label ambiguity problems, which can explore the relative importance of different labels in the description of a particular instance. Although some existing LDL algorithms have achieved better effectiveness in real applications, most of them typically emphasize on improving the learning ability by manipulating the label space, while ignoring the fact that irrelevant and redundant features exist in most practical classification learning tasks, which increase not only storage requirements but also computational overheads. Furthermore, noises in data acquisition will bring negative effects on the generalization performance of LDL algorithms. In this paper, we propose a novel algorithm, i.e., Latent Semantics Encoding for Label Distribution Learning (LSE-LDL), which learns the label distribution and implements feature selection simultaneously under the guidance of latent semantics. Specifically, to alleviate noise disturbances, we seek and encode discriminative original physical/chemical features into advanced latent semantic features, and then construct a mapping from the encoded semantic space to the label space via empirical risk minimization. Empirical studies on 15 real-world data sets validate the effectiveness of the proposed algorithm.
iDev: Enhancing Social Coding Security by Cross-platform User Identification Between GitHub and Stack Overflow

As modern social coding platforms such as GitHub and Stack Overflow become increasingly popular, their potential security risks increase as well (e.g., risky or malicious codes could be easily embedded and distributed). To enhance the social coding security, in this paper, we propose to automate cross-platform user identification between GitHub and Stack Overflow to combat the attackers who attempt to poison the modern software programming ecosystem. To solve this problem, an important insight brought by this work is to leverage social coding properties in addition to user attributes for cross-platform user identification. To depict users in GitHub and Stack Overflow (attached with attributed information), projects, questions and answers as well as the rich semantic relations among them, we first introduce an attributed heterogeneous information network (AHIN) for modeling. Then, we propose a novel AHIN representation learning model AHIN2Vec to efficiently learn node (i.e., user) representations in AHIN for cross-platform user identification. Comprehensive experiments on the data collections from GitHub and Stack Overflow are conducted to validate the effectiveness of our developed system iDev integrating our proposed method in cross-platform user identification by comparisons with other baselines.
A Systematic Review of Tablet Technology in Mathematics Education

&lt;p class="0abstract"&gt;In 2019, the mobile learning body of knowledge is extensive and much is known about the technology impacts and affordances of mobile devices in educational settings. A particular focus has now shifted toward specific technologies in specific subjects. Mathematics is one such subject and tablets are one such technology that is gaining attention. This systematic review representing the latest generation of tablet technology within the tablet-mediated learning in mathematics body of knowledge sought to derive evidence that supported questions into (a) what math sub-disciplines were covered, (b) what technology (application/hardware) was utilized, and (c) what pedagogical approaches were deployed in educational settings. This included analysis of the (d) advantages and (e) disadvantages present in those elements. Thirty-nine relevant articles were collected from various academic technology and educational databases. The results demonstrate that tablets are being predominantly deployed in various sub-disciplines such as Arithmetic, Computation, and Geometry with the iPad as the dominant choice for tablet hardware/applications. Pedagogical approaches lean heavily on game-based learning, environment interaction, and special needs support. Technological advantages include increased collaboration and mathematics engagement enabled by tablet mobility and a high potential for customization of solutions. Developers, teachers, and researchers need to be informed of potential challenges in designing content for tablet technology deployments in mathematics.&lt;/p&gt;
A Multi-Task Learning Framework for Abstractive Text Summarization

We propose a Multi-task learning approach for Abstractive Text Summarization (MATS), motivated by the fact that humans have no difficulty performing such task because they have the capabilities of multiple domains. Specifically, MATS consists of three components: (i) a text categorization model that learns rich category-specific text representations using a bi-LSTM encoder; (ii) a syntax labeling model that learns to improve the syntax-aware LSTM decoder; and (iii) an abstractive text summarization model that shares its encoder and decoder with the text categorization and the syntax labeling tasks, respectively. In particular, the abstractive text summarization model enjoys significant benefit from the additional text categorization and syntax knowledge. Our experimental results show that MATS outperforms the competitors.1
Faster Distributed Deep Net Training: Computation and Communication Decoupled Stochastic Gradient Descent

With the increase in the amount of data and the expansion of model scale, distributed parallel training becomes an important and successful technique to address the optimization challenges. Nevertheless, although distributed stochastic gradient descent (SGD) algorithms can achieve a linear iteration speedup, they are limited significantly in practice by the communication cost, making it difficult to achieve a linear time speedup. In this paper, we propose a computation and communication decoupled stochastic gradient descent (CoCoD-SGD) algorithm to run computation and communication in parallel to reduce the communication cost. We prove that CoCoD-SGD has a linear iteration speedup with respect to the total computation capability of the hardware resources. In addition, it has a lower communication complexity and better time speedup comparing with traditional distributed SGD algorithms. Experiments on deep neural network training demonstrate the significant improvements of CoCoD-SGD: when training ResNet18 and VGG16 with 16 Geforce GTX 1080Ti GPUs, CoCoD-SGD is up to 2-3 x faster than traditional synchronous SGD.
Unobserved Is Not Equal to Non-existent: Using Gaussian Processes to Infer Immediate Rewards Across Contexts

Learning optimal policies in real-world domains with delayed rewards is a major challenge in Reinforcement Learning. We address the credit assignment problem by proposing a Gaussian Process (GP)-based immediate reward approximation algorithm and evaluate its effectiveness in 4 contexts where rewards can be delayed for long trajectories. In one GridWorld game and 8 Atari games, where immediate rewards are available, our results showed that on 7 out 9 games, the proposed GP-inferred reward policy performed at least as well as the immediate reward policy and significantly outperformed the corresponding delayed reward policy. In e-learning and healthcare applications, we combined GP-inferred immediate rewards with offline Deep Q-Network (DQN) policy induction and showed that the GP-inferred reward policies outperformed the policies induced using delayed rewards in both real-world contexts.