Reviewerly/exHarmony huggingface dataset:
A Generative Human-Robot Motion Retargeting Approach Using a Single RGBD Sensor

The goal of human-robot motion retargeting is to let a robot follow the movements performed by a human subject. Typically in previous approaches, the human poses are precomputed from a human pose tracking system, after which the explicit joint mapping strategies are specified to apply the estimated poses to a target robot. However, there is not any generic mapping strategy that we can use to map the human joint to robots with different kinds of configurations. In this paper, we present a novel motion retargeting approach that combines the human pose estimation and the motion retargeting procedure in a unified generative framework without relying on any explicit mapping. First, a 3D parametric human-robot (HUMROB) model is proposed which has the specific joint and stability configurations as the target robot while its shape conforms the source human subject. The robot configurations, including its skeleton proportions, joint limitations, and DoFs are enforced in the HUMROB model and get preserved during the tracking procedure. Using a single RGBD camera to monitor human pose, we use the raw RGB and depth sequence as input. The HUMROB model is deformed to fit the input point cloud, from which the joint angle of the model is calculated and applied to the target robots for retargeting. In this way, instead of fitted individually for each joint, we will get the joint angle of the robot fitted globally so that the surface of the deformed model is as consistent as possible to the input point cloud. In the end, no explicit or pre-defined joint mapping strategies are needed. To demonstrate its effectiveness for human-robot motion retargeting, the approach is tested under both simulations and on real robots which have a quite different skeleton configurations and joint degree of freedoms (DoFs) as compared with the source human subjects.
Learning from User Social Relation for Document Sentiment Classification

Sentiment analysis is a fundamental problem in the field of natural language processing. Existing methods incorporate both semantics of texts and user-level information into deep neural networks to perform sentiment classification of social media documents. However, they ignored the relations between users which can serve as a crucial evidence for classification. In this paper, we propose SRPNN, a deep neural network based model to take user social relations into consideration for sentiment classification. Our model is based on the observation that social relations between users with similar sentiment trends provide important signals for deciding the polarity of words and sentences in a document. To make use of such information, we develop a user trust network based random walk algorithm to capture the sequence of users that have similar sentiment orientation. We then propose a deep neural network model to jointly learn the text representation and user social interaction. Experimental results on two popular real-world datasets show that our model significantly outperforms state-of-the-art methods.
A BLSTM and WaveNet-Based Voice Conversion Method With Waveform Collapse Suppression by Post-Processing

In recent years, neural network-based voice conversion methods have been rapidly developed, and many different models and neural networks have been applied in parallel voice conversion. However, the over-smoothing of parametric methods [e.g., bidirectional long short-term memory (BLSTM)] and the waveform collapse of neural vocoders (e.g., WaveNet) still have negative impacts on the quality of the converted voices. To overcome this problem, we propose a BLSTM and WaveNet-based voice conversion method cooperated with waveform collapse suppression by post-processing. This method firstly uses BLSTM to convert the acoustic features between parallel speakers, and then synthesizes pre-converted voice with WaveNet. Subsequently, several alternative iterations of BLSTM post-processing is performed, and the final converted voice is generated by WaveNet. The proposed method can directly generate converted audio waveforms and avoid the waveform-collapsed speech caused by a single WaveNet generation effectively. The experimental results indicate that acoustic features trained by using the BLSTM network could achieve better results than conventional baselines. From our experiments on VCC2018, the usage of WaveNet could alleviate the problem of over-smoothing, which contributes to improving the similarity and naturalness of the final results of voice conversion.
Filter-Based Feedback Control for a Class of Markovian Open Quantum Systems

This letter considers target state preparation for Markovian open quantum systems subject to continuous measurement. Conditions on invariant and attractive subspaces are investigated, which ensure the stabilization of the target state/subspace. For a class of open quantum systems with time delay in the feedback loop, a bang-bang-like control law is proposed, and the stability of the feedback control strategy is proved. An example of four-level Markovian open quantum systems is presented to demonstrate the effectiveness of the proposed control strategy.
Byte.it

Byte.it is an exploration of the feasibility of using miniaturized, discreet hardware for teeth-clicking as hands-free input for wearable computing. Prior work has been able to identify teeth-clicking of different teeth groups. Byte.it expands on this work by exploring the use of a smaller and more discreetly positioned sensor suite (accelerometer and gyroscope) for detecting four different teeth-clicks for everyday human-computer interaction. Initial results show that an unobtrusive position on the lower mastoid and mandibular condyle can be used to classify teeth-clicking of four different teeth groups with an accuracy of 89%.
Detection of Dummy Trajectories Using Convolutional Neural Networks

Nowadays, privacy in trajectory is an important issue in the coming big data era. In order to provide better protection for trajectory privacy, a number of solutions have been proposed in the literature, and the dummy trajectory method has attracted great interests in both academia and industry recently due to the following advantages: <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mo stretchy="false">(</mml:mo><mml:mn fontstyle="italic">1</mml:mn><mml:mo stretchy="false">)</mml:mo></mml:math> neither a third-party server nor other parties’ cooperation is necessary; <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mo stretchy="false">(</mml:mo><mml:mn fontstyle="italic">2</mml:mn><mml:mo stretchy="false">)</mml:mo></mml:math> location-based services are not influenced; and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mo stretchy="false">(</mml:mo><mml:mn fontstyle="italic">3</mml:mn><mml:mo stretchy="false">)</mml:mo></mml:math> its algorithm is relatively simple and efficient. However, most of trajectory privacy generations usually consider the geometric shape of the trajectory; meanwhile the real human mobility feature is usually neglected. In fact, the real trajectory is not the product of random probability. In this paper, convolutional neural network (CNN) is used as the learning machine to train with lots of the real trajectory and the generated dummy trajectory sets. Then, the trained classifier is used to distinguish the dummy from the real trajectory. Experiments demonstrate that the method using CNN is very efficient, and more than 90% of dummy trajectories can be detected. Moreover, the real trajectory erroneous judgment rate is below 10% for most of real trajectories.
Social Media as an Opportunity for Public Health Interventions: The #Metoo Movement as an Exemplar

Background: Social media have been used exponentially and globally, providing a means for billions of users to connect, interact, share opinions and criticise, becoming one of the main channels of communication for users around the world. One of the most popular free social media networks is Twitter, with more than 100 million active users per day worldwide. Purpose: The aim of this study was to analyse a sample of the public conversations generated, using the hashtag #MeToo, around the topic of sexual abuse on Twitter. Methods: Using social media marketing software, the use of the #MeToo hashtag was analysed over a period of 60 days (14 September 2017 to 13 November of 2017). Results: The #MeToo conversation was mainly in English (79.3%), located in the United States (48.2% of cases), but with global repercussions. The volume of mentions of the #MeToo hashtag was far greater (97.7%), compared with other hashtags related to violence over this period of time, using mostly Twitter (96.2%). Conclusions: These results suggest that it is possible to describe different groups using the social media, and analyse their conversations to identify opportunities for successful public health interventions. If the topic is relevant for the general public, it will generate interest and conversations at the global level, supported by a universal and borderless channel such as Twitter.
Scalable Subgraph Counting: The Methods Behind The Madness

Subgraph counting is a fundamental problem in graph analysis that finds use in a wide array of applications. The basic problem is to count or approximate the occurrences of a small subgraph (the pattern) in a large graph (the dataset). Subgraph counting is a computationally challenging problem, and the last few years have seen a rich literature develop around scalable solutions for it. However, these results have thus far appeared as a disconnected set of ideas that are applied separately by different research groups. We observe that there are a few common algorithmic building blocks that most subgraph counting results build on. In this tutorial, we attempt to summarize current methods through distilling these basic algorithmic building blocks. The tutorial will also cover methods for subgraph analysis on “big data” computational models such as the streaming model and models of parallel and distributed computation.
Projections for Approximate Policy Iteration Algorithms

Approximate policy iteration is a class of reinforcement learning (RL) algorithms where the policy is encoded using a function approximator and which has been especially prominent in RL with continuous action spaces. In this class of RL algorithms, ensuring increase of the policy return during policy update often requires to constrain the change in action distribution. Several approximations exist in the literature to solve this constrained policy update problem. In this paper, we propose to improve over such solutions by introducing a set of projections that transform the constrained problem into an unconstrained one which is then solved by standard gradient descent. Using these projections, we empirically demonstrate that our approach can improve the policy update solution and the control over exploration of existing approximate policy iteration algorithms.
Energy Efficient Scheduling Algorithm for Cloud Computing Systems Based on Prediction Model

Existing cloud resource scheduling approaches have mainly concentrated on enhancing the reducing power consumption and resource utilization by enhancing the legacy heuristic algorithms.Although, different resourceintensive applications running on cloud data centers in realistic scenarios have significant results on the power consumption and cloud application performance.Furthermore, occurring peak loads may lead to a scheduling error, which can significantly effects on the energy efficiency of scheduling algorithms.At peak loads may lead to scheduling errors because there is no prediction model to predict the coming resource utilization of a data center through the data collected by the monitoring model.Effective scheduling mechanism gives an optimal solutions for complex problems while providing the Quality-of-Service (QoS) and avoiding Service Level Agreement (SLA) violations.To enhance the resource scheduling mechanism in cloud environment, predicting future workload to the each virtual machine pool in different manners like number of physical machines, number of virtual machines, number of requests and resource utilization etc., is an essential step.According to the prediction results, resource scheduling can be done in the right time, while avoiding QoS dropping and SLA violations.To achieve efficient resource scheduling, proposed approach lease advantages of prediction models.The proposed algorithm consists of a prediction model which is based on iterative fractal model and a scheduler which is based on an improved heuristic algorithms.Proposed scheduler algorithm is responsible for scheduling of resources while reducing the energy consumption and giving the guaranteeing the QoS.
NONLINEAR FUNCTIONS OF COMPLICATION FOR SYMMETRIC STREAM CIPHERS

Currently, the nonlinear Boolean functions are being investigated very actively around the world. However, many open questions remain in this area. The theory of nonlinear Boolean functions suitable for use in robust cryptographic algorithms is largely incomplete. Despite the presence of numerous publications on these topics, many issues related to the interrelation of design characteristics affecting the performance of the generator and its cryptographic characteristics are still open. The generation of a special type of sequences, called de Brain sequences, with minimal hardware and software costs, the rationale for their use as non-linear functions of the complexity of stream encryption systems, is the main theme of this work. The paper presents estimates of cryptographic indicators of nonlinear complexity functions of iterative bit sequence generators with various characteristics of the generated sequence, such as linear complexity and autocorrelation.
On Stochastic Sign Descent Methods

Various gradient compression schemes have been proposed to mitigate the communication cost in distributed training of large scale machine learning models. Sign-based methods, such as signSGD, have recently been gaining popularity because of their simple compression rule and connection to adaptive gradient methods, like ADAM. In this paper, we perform a general analysis of sign-based methods for non-convex optimization. Our analysis is built on intuitive bounds on success probabilities and does not rely on special noise distributions nor on the boundedness of the variance of stochastic gradients. Extending the theory to distributed setting within a parameter server framework, we assure exponentially fast variance reduction with respect to number of nodes, maintaining 1-bit compression in both directions and using small mini-batch sizes. We validate our theoretical findings experimentally.
Coupled VAE: Improved Accuracy and Robustness of a Variational Autoencoder

We present a coupled variational autoencoder (VAE) method, which improves the accuracy and robustness of the model representation of handwritten numeral images. The improvement is measured in both increasing the likelihood of the reconstructed images and in reducing divergence between the posterior and a prior latent distribution. The new method weighs outlier samples with a higher penalty by generalizing the original evidence lower bound function using a coupled entropy function based on the principles of nonlinear statistical coupling. We evaluated the performance of the coupled VAE model using the Modified National Institute of Standards and Technology (MNIST) dataset and its corrupted modification C-MNIST. Histograms of the likelihood that the reconstruction matches the original image show that the coupled VAE improves the reconstruction and this improvement is more substantial when seeded with corrupted images. All five corruptions evaluated showed improvement. For instance, with the Gaussian corruption seed the accuracy improves by 1014 (from 10-57.2 to 10-42.9) and robustness improves by 1022 (from 10-109.2 to 10-87.0). Furthermore, the divergence between the posterior and prior distribution of the latent distribution is reduced. Thus, in contrast to the β-VAE design, the coupled VAE algorithm improves model representation, rather than trading off the performance of the reconstruction and latent distribution divergence.
Location Privacy in Device-Dependent Location-Based Services: Challenges and Solution

With the evolution of location-based services (LBS), a new type of LBS has already gain a lot of attention and implementation, we name this kind of LBS as the Device-Dependent LBS (DLBS).In DLBS, the service provider (SP) will not only send the information according to the user's location, more significant, he also provides a service device which will be carried by the user.DLBS has been successfully practised in some of the large cities around the world, for example, the shared bicycle in Beijing and London.In this paper, we, for the first time, blow the whistle of the new location privacy challenges caused by DLBS, since the service device is enabled to perform the localization without the permission of the user.To conquer these threats, we design a service architecture along with a credit system between DLBS provider and the user.The credit system tie together the DLBS device usability with the curious behaviour upon user's location privacy, DLBS provider has to sacrifice their revenue in order to gain extra location information of their device.We make the simulation of our proposed scheme and the result convince its effectiveness.
Factors affecting job performance: the case of Jordanian hotels' kitchen staff

This paper aims at investigating the effects of work pressure, duties and responsibilities, career advancement opportunities, and the physical work environment on job performance of the hotels' kitchen staff. A structured questionnaire was used to collect information from the employees working in five-star hotels in Jordan. Multiple regression analysis was conducted to test the research hypotheses. Results of the current study revealed that there is a significant impact of work pressure and career advancement opportunities on job performance, whereas duties and responsibilities, and the physical work environment have not significant impact on it. Results of T-test and ANOVA showed that there is no significant difference in the impact of job performance that can be attributed to gender, age, educational level, personal income, marital status, job position, and position experience. The findings of this study will contribute a better understanding of the best work environment that could enhance the performance of the staff and increase guests' satisfactions.
Staying up to Date with Online Content Changes Using Reinforcement Learning for Scheduling

From traditional Web search engines to virtual assistants and Web accelerators, services that rely on online information need to continually keep track of remote content changes by explicitly requesting content updates from remote sources (e.g., web pages). We propose a novel optimization objective for this setting that has several practically desirable properties, and efficient algorithms for it with optimality guarantees even in the face of mixed content change observability and initially unknown change model parameters. Experiments on 18.5M URLs crawled daily for 14 weeks show significant advantages of this approach over prior art.
The combinatorics of the colliding bullets

The finite colliding bullets problem is the following simple problem: consider a gun, whose barrel remains in a fixed direction; let ( V i ) 1 ≤ i ≤ n be an i.i.d. family of random variables with uniform distribution on [0,1]; shoot n bullets one after another at times 1,2,…, n , where the i th bullet has speed V i . When two bullets collide, they both annihilate. We give the distribution of the number of surviving bullets, and in some generalization of this model. While the distribution is relatively simple (and we found a number of bold claims online), our proof is surprisingly intricate and mixes combinatorial and geometric arguments; we argue that any rigorous argument must very likely be rather elaborate.
Examining Usage to Ensure Utility: Co-Design of a Tool for Fall Prevention

Ambient Assisted Living (AAL) technologies can play an important role in helping elderly people achieve healthy ageing and maintain their autonomy. The balance quality tester (BQT) is a device for remote assessment of balance quality for older people at risk of falling. It has been validated both from a technical and a clinical perspective. However, for the BQT to be considered as a useful tool for long-term home monitoring of people with balance impairments, two issues are at stake: ease-of-use on a regular basis and trust in the validity of the data acquisition. To ensure this utility, a usage study has been made to understand the needs and values of different stakeholders: elders at risk of falling and their entourage, as well as health professionals. One main insight was the need to redesign the BQT, so as to fit the needs concerning ease-of-use and trust in validity of data acquisition. Using a Human-centred and Participatory Design approach, the redesigning work relates to hardware design, interaction design, interface design, and most of all to standardizing the protocol of stepping-on the BQT. This paper describes the main results, i.e. the design recommendations, and discusses the collaborative and iterative design process, which allowed the successful redesign of the BQT.
Mobile Instant Messaging as Collaborative Tool for Language Learning

In recent years, collaborative learning is commonly used in higher intuitions. One of the modes of collaborative learning is exploiting mobile phones specifically instant messaging to foster new possibilities for achieving more effective and attractive learning activities. Even though mobile phones have become valuable collaborative learning tools, but their substantial contributions to collaborative learning are still insufficient. The current study collated and correlated 40 journal articles published between 2015 and 2017 on mobile instant messaging as a collaborative tool in language learning at higher intuitions. A systematic review was conducted to investigate the effectiveness of mobile instant messaging as a collaborative tool for language learning. Numerous issues, such as exploration of the application, the research methods, participants, intervention duration, teaching method, collaborative learning strategies, impacts, and challenges were addressed. The outcomes revealed that mobile instant messaging improved student and lecturer collaboration, created effective distant learning opportunities, and increased students’ participation. Further, the results also provided insights on future research and practice such as recommendations on how to appropriately employ mobile phones specifically instant messaging in facilitating language learning. Additionally, some studies suggested the collaborative learning activities administered through mobile instant messaging should be planned carefully to guide students to experience more effective collaborative learning.
A Sample-Free Bayesian-Like Model for Indoor Environment Recognition

Visual localization of indoor environments enables an autonomous system to recognize its current location and environment using sensors such as a camera. This paper proposes a method for visual recognition of indoor environments leveraging on existing object detection, ontology, Bayesian-like framework, and speeded-up robust features (SURF) algorithms. Objects detected in such an environment are fed into a Bayesian-like framework for domain recognition. Finally, the SURF localizes the predicted environment. One of the objectives of the proposed model is to eliminate the image-based training phase encountered in traditional place recognition algorithms. The proposed model does not rely on any visual information on the environment for training. Experiments are carried out on two publicly available datasets with promising results.