Reviewerly/exHarmony huggingface dataset:
Memorization in Deep Neural Networks: Does the Loss Function Matter?

Deep Neural Networks, often owing to the overparameterization, are shown to be capable of exactly memorizing even randomly labelled data. Empirical studies have also shown that none of the standard regularization techniques mitigate such overfitting. We investigate whether choice of loss function can affect this memorization. We empirically show, with benchmark data sets MNIST and CIFAR-10, that a symmetric loss function as opposed to either cross entropy or squared error loss results in significant improvement in the ability of the network to resist such overfitting. We then provide a formal definition for robustness to memorization and provide theoretical explanation as to why the symmetric losses provide this robustness. Our results clearly bring out the role loss functions alone can play in this phenomenon of memorization.
Towards Inclusive Streaming: Building Multimodal Music Experiences for the Deaf and Hard of Hearing

As online streaming becomes a primary method for music consumption, the various modalities that many people with hearing loss rely on for their enjoyment need to be supported. While visual or tactile representations can be used to experience music in a live event or from a recording, DRM anti-piracy encryption restricts access to audio data needed to create these multimodal experiences for music streaming. We introduce BufferBeats, a toolkit for building multimodal music streaming experiences. To explore the flexibility of the toolkit and to exhibit its potential use cases, we introduce and reflect upon building a collection of technical demonstrations that bring previous and new multimodal music experiences to streaming. Grounding our work in critical theories on design, making, and disability, as well as experiences from a small group of community partners, we argue that support for multimodal music streaming experiences will not only be more inclusive to the deaf and hard of hearing, but it will also empower researchers and hobbyist makers to use streaming as a platform to build creative new representations of music.
SMOSA: Spider monkey optimization‐based scheduling algorithm for heterogeneous Hadoop

Summary Hadoop is a typical framework for processing big data. Task scheduling algorithms have a significant impact on the processing performance of Hadoop clusters. Existing scheduling algorithms of Hadoop fail to consider the performance differences between nodes in heterogeneous Hadoop clusters, causing problems such as uneven task allocation and low resource utilization. Aiming to solve this problem, we propose a spider monkey optimization‐based scheduling algorithm (SMOSA) for heterogeneous Hadoop. First, the cluster heartbeat mechanism is used to obtain information such as memories and CPUs of nodes to comprehensively consider the actual load capacity of each node. Then, the spider monkey optimization algorithm is adopted to find the optimal mapping relationship between tasks and resources by taking the task completion time as the objective function and updating the position of the spider monkey. Finally, we calculate the remaining rate of node hardware resources, and according to the task type, the node with the higher remaining rate of resource will give priority to the task. Data are compressed for I/O type tasks to reduce disk operations and increase the speed of task execution. Experimental results show that, compared with existing scheduling algorithms, the SMOSA can effectively reduce task execution time and can significantly improve scheduling efficiency and task execution speed especially in heterogeneous Hadoop clusters. For different types of tasks, the execution time can be reduced by up to 19%.
Datanet: Enabling Seamless, Metered and Trusted Internet Connectivity without Subscriptions

Relying on dedicated contracts with specific network operators for Internet access significantly limits connectivity options for devices. As new usecases for internet access emerge, e.g., with the Internet of Things in smart-cities, managing such individual contracts for each deployed device with varying data needs is prohibitively cumbersome and highly expensive. In this work, we enable contract-less connectivity between end-devices and access points/networks that have no a-priori trust relationship. Our core insight is that exchange of services and payments can be trustlessly enforced by distributed ledger technologies; the credentials that blockchains use for account management can also be used for TLS-based authentication in networks. However, the blockchain's ability to enforce transaction rules is limited by the extent to which the underlying exchange of services is digitally trackable, which is susceptible to manipulation in this case. Requiring blockchain-integrated trusted hardware at the access points for bandwidth metering significantly hinders adoption; even software modifications required at the access points to process blockchain-based auth and payments incur practical deployment and scalability challenges. In designing Datanet, we address these challenges and enable seamless and incentivized connectivity between unknown end-devices and APs, using existing standards that allow for interoperability with current and future networks, and without significant overhead for client devices.
A Novel Image Captioning Model Based on Morphology and Fisher Vectors

Recent image captioning models ignore the text that is present in approximately 50% of images. These texts convey a lot of useful information about the image and its surrounding. The efficiency of an image captioning model can thus be improved by including these texts together with the visual information. In this paper, a novel image captioning model that considers the text existing in an image is proposed. The paper uses the concept of morphology of a word, and thus constructs Fisher Vectors based on the morphology of a word. The proposed model is evaluated on two publically available datasets. The captions produced by the proposed model are comparable to the state-of-the art captioning methods.
Progress and Application Analyses in Blockchain Technology

The Blockchain is a new technology developed on the basis of Bitcoin system. In fact, the developing blockchain technology has been going beyond the single application scope of cryptocurrency, and becoming one of a core support to solve financial, economic and even social problems on Internet. Based on the Bitcoin system, this paper first analyzes the basic issues of implementing distributed storage, consensus negotiation and cryptographic security in the blockchain. On this basis, several representative blockchain technologies are analyzed, including Smart-contracts, Super-ledger and Stable-currency system. Finally, the trend of his applications are prospected.
POI Recommendation Algorithm for Mobile Social Network Based on User Perference Tracking

Point of Interest (POI) recommendation is to use the user check-in data in location-based Social Networks (LBSN) to predict the next Location that the user will visit. With the rapid development of LBSN, users' check-in information (rating information, geographic location, check-in sequence, social network, etc.) has become more and more accessible. How to effectively fuse and model these multi-source heterogeneous data is crucial to personalized POI recommendations. This paper presents a POI recommendation model based on improved factorization machine and Bert. By integrating social influence on the basis of factorization machine, Bert was used to extract the check-in sequence features. Experiments on real data sets show that our model performs better than the traditional POI recommended model.
Analyzing Software Effort Estimation by Applying Static, Single &amp; Multi-Variable Models

One of the indispensable parts of Software Project Management is to estimate the size of the software. The predominant advantage of doing this exercise is that it cooperates with project managers to forecast the overall effort, development time, productivity, and manpower required to complete any project; to a great extent. For this computation, assorted measures are being applied by industries. In this paper, software size estimation practices are examined on two different models as SEL model and the W-F model. Five different projects that are developed in different programming languages are taken as an input to these models. Further, computation of estimation and related analysis has been presented.
Multi-Model Inference Acceleration on Embedded Multi-Core Processors

The predominant resource efficient approaches that enable on-device inference include designing lightweight DNN architectures like MobileNets, SqueezeNets, compressing model using techniques such as network pruning, vector quantization, distillation, binarization. Recent research on using dynamic layer-wise partitioning and partial execution of CNN based model inference also make it possible to co-inference on memory and computation resource constrained devices. However, these approaches have their own bottleneck, lightweight DNN architectures and model compression usually compromise accuracy in order to deploy on resource constrained devices, dynamic model partitioning efficiency depends heavily on the network condition. This paper proposes an approach for multimodel inference acceleration on heterogeneous devices. The idea is to deploy multiple single object detection model instead of one heavy multiple object, this is because in most cases it only needs to detect one or two objects in one scenario and single object detection model weight could be lighter for the same resolution quality and require less resource. Moreover, in cloud-edge-device scenario, with the help of a scheduler policy, it is possible to gradually update models in need.
A Knowledge Graph Embedding Model Based on Node-Relation Fusion Perception

Although modeling graph structure data with graph convolution network has become a hot research topic in recent years, the research subject in most papers are simple undirected graphs while the multi-relational ones, for example, knowledge graphs, of which each edge has labels and directions associated with entity nodes, are more commonly used and more consistent with the development of the modern Internet. However, the existing methods of dealing with multi-relational graphs all have the problem of over parameterization and are limited to the representation of learning nodes,This leads to unsatisfactory knowledge graph embedding, poor reasoning perception of the model, and inadequate automatic completion of the Knowledge Graph.In this paper, a new knowledge graph model for graph convolution network modeling is proposed, in which, a combination operator is used to embed nodes and relationships into the graph, enriching the information of the neighborhood of the central node and enhancing the perceptibility of the knowledge graph embedding model. In the experimental part of the paper, the results of node classification and link prediction tasks both prove the effectiveness of the model.
Unsupervised Learning for Multi-Style Speech Synthesis with Limited Data

Existing multi-style speech synthesis methods require either style labels or large amounts of unlabeled training data, making data acquisition difficult. In this paper, we present an unsupervised multi-style speech synthesis method that can be trained with limited data. We leverage instance discriminator to guide a style encoder to learn meaningful style representations from a multi-style dataset. Furthermore, we employ information bottleneck to filter out style-irrelevant information in the representations, which can improve speech quality and style similarity. Our method is able to produce desirable speech using a fairly small dataset, where the baseline GST-Tacotron fails. ABX tests show that our model significantly outperforms GST-Tacotron in both emotional speech synthesis task and multi-speaker speech synthesis task. In addition, we demonstrate that our method is able to learn meaningful style features with only 50 training samples per style.
Context Awareness Computing System—A Survey

Context term had been sensing in various areas like pervasive computing, intelligent environment, ubiquitous computing, etc. In such areas to sense data and then adapting the moments or behavior according to environment or context information like physical context, user task, and computational context which captured the data, an implementation of such a context-aware computing system is more difficult in many ways. The prime focus of such implementation is always to provide realistic and very specific data required for understanding the actual scene being monitored. Internet of Things (IOT) has changed the overall process of gathering the data as well as processing of that data. In smaller systems like an embedded systems data collected by the sensors and processed on a computing platform to understand the process parameters or to sense actual changes occurred during the given process execution, an attempt has been made by the researcher in this paper to review various fundaments of the context-aware computing by reviewing the available literature, and a system-based approach is presented in comparative manner so as to justify the uniqueness of this in pervasive computing. So in this paper, the concept of context, context awareness, context awareness algorithms, and some techniques are discussed.
PhotoStylist: Altering the Style of Photos Based on the Connotations of Texts

The need to modify a photo to reflect the connotations of a text can arise due to multifarious reasons (e.g., a musician might modify a photo in the album cover to better reflect the connotations in her song lyrics). An interesting observation is that different styles of photos convey different feelings. In this paper, we propose the PhotoStylist scheme to effectively modify the style of an input photo to represent the connotations in an input text. Existing methods that aim to transfer emotions into photos rely on an emotion class being provided as input and modify the overall color of photos based on the input emotion class, generating unrealistic colors for many objects in the image. To address these limitations, we design PhotoStylist, a novel deep-learning-based approach, to alter the individual style of each object in the photo in a way that the connotations of the input text are naturally and effectively embedded into the modified photos. Evaluation results on the Amazon Mechanical Turk (MTurk) show that our scheme can achieve output photos significantly closer to the connotations of the input text than the output photos from the state-of-the-art baselines.
Class-Imbalanced Classifiers Using Ensembles of Gaussian Processes And Gaussian Process Latent Variable Models

Classification with imbalanced data is a common and challenging problem in many practical machine learning problems. Ensemble learning is a popular solution where the results from multiple base classifiers are synthesized to reduce the effect of a possibly skewed distribution of the training set. In this paper, binary classifiers based on Gaussian processes are chosen as bases for inferring the predictive distributions of test latent variables. We apply a Gaussian process latent variable model where the outputs of the Gaussian processes are used for making the final decision. The tests of the new method in both synthetic and real data sets show improved performance over standard approaches.
Hierarchical mining algorithm for high dimensional spatiotemporal big data based on association rules

The traditional data mining algorithm focuses too much on a single dimension of data time or space, ignoring the association between time and space, which leads to a large amount of computation and low processing efficiency of the mining algorithm and makes it difficult to guarantee the final data mining effect. In response to the above problems, a hierarchical mining algorithm based on association rules for high-dimensional spatio-temporal big data is proposed. Based on the traditional association rules, after establishing the association rules of spatio-temporal data, the data to be mined are cleaned for redundancy. After selecting the local linear embedding algorithm to reduce the dimensionality of the data, a hierarchical mining strategy is developed to realize high-dimensional spatio-temporal big data mining by searching frequent predicates to form a spatio-temporal transaction database. The simulation experiment results verify that the algorithm has high complexity and can effectively reduce the processing volume, which can improve the processing efficiency by at least 56.26% compared with other algorithms.
Cycle-Consistent Adversarial Networks and Fast Adaptive Bi-dimensional Empirical Mode Decomposition for Style Transfer

Recently, research endeavors have shown the potentiality of Cycle-Consistent Adversarial Networks (CycleGAN) in style transfer. In Cycle-Consistent Adversarial Networks, the consistency loss is introduced to measure the difference between the original images and the reconstructed in both directions, forward and backward. In this work, the combination of Cycle-Consistent Adversarial Networks with Fast and Adaptive Bidimensional Empirical Mode Decomposition (FABEMD) is proposed to perform style transfer on images. In the proposed approach the cycle-consistency loss is modified to include the differences between the extracted Intrinsic Mode Functions (BIMFs) images. Instead of an estimation of pixel-to-pixel difference between the produced and input images, the FABEMD is applied and the extracted BIMFs are involved in the computation of the total cycle loss. This method enriches the computation of the total loss in a content-to-content and style-to-style comparison by connecting the spatial information to the frequency components. The experimental results reveal that the proposed method is efficient and produces qualitative results comparable to state-of-the-art methods.
Class Aware Robust Training

Adversarial training (AT) has been one of the most effective ways to defend adversarial attack. However, existing AT variants exhibit a large imbalanced robust accuracies among different classes, which might harm the robustness of some important class(es) in some real-world applications. For instance, diseased cells are much more important than healthy ones in medical image recognition. Given a certain task, the important class is often a priori. To improve robust accuracy of the important class(es), we are the first to propose a novel adversarial training method with class imbalance taken into account. We term it Class-Aware Robust Training (CART). CART can significantly increase the robustness of the important class(es) by an optional weighted combination of original adversarial example generation and that of the important class. Extensive experiments on three benchmark datasets verify the efficacy of CART for enhancing the robust accuracy of important classes while keeping competitive average robust accuracy.
Research on the development trend of vehicle operating system in China

The development trend, market application, standards and regulations of vehicle operating system (VOS) technology at home and abroad are studied in this paper. Combined with the definition and classification of VOS, main characteristics of the most representative VOS at home and abroad are analyzed. Based on the domestic situation of VOS, the main problems existing in vehicle industry are analyzed, and suggestions of development strategy of domestic VOS are put forward.
Learning Finite Automata with Shuffle

Learning finite automata has been a popular topic. Shuffle has been applied in information systems. Since shuffle introduced into finite automata makes the membership problem NP-hard, and there are no learning algorithms for finite automata supporting shuffle so far, it is an essential work to devise effective and precise algorithms for learning finite automata supporting shuffle. In this paper, finite automata are learned from sets of positive samples. First, we define finite automata with shuffle (FA(&)s), for which both the uniform and the non-uniform membership problem are decidable in polynomial time. Then, we learn an FA(&) from a given finite sample step by step. Our algorithm can ensure that the learned FA(&) is a precise representation of the given finite sample. Experimental results demonstrate that, FA(&) is more efficient in membership checking, and our algorithm can obtain a more concise automaton.
Privacy-Accuracy Trade-Off of Inference as Service

In this paper, we propose a general framework to provide a desirable trade-off between inference accuracy and privacy protection in the inference as service scenario. Instead of sending data directly to the server, the user will preprocess the data through a privacy-preserving mapping, which will increase privacy protection but reduce inference accuracy. To properly address the trade-off between privacy protection and inference accuracy, we formulate an optimization problem to find the optimal privacy-preserving mapping. Even though the problem is non-convex in general, we characterize nice structures of the problem and develop an iterative algorithm to find the desired privacy-preserving mapping.
Learning and Practicing Logic Circuits: Development of a Mobile-based Learning Prototype

Nowadays, with the advent of electronic devices in everyday life, mobile devices can be utilized for learning purposes. When designing a mobile-based learning application, a large number of aspects should be taken into account. For the present paper, the following aspects are of special importance: first, it should be considered how to represent information; second, possible interactions between learner and system should be defined; third – and depending on the second aspect – it should be considered how real-time responses can be provided by the system. Moreover, psychological theories as for example the 4C/ID model and findings with respect to blended learning environments should be taken into account. In this paper, a mobile-based learning prototype concerning the learning topic ”logic circuit design” is presented which considers the mentioned aspects to support independent practice. The prototype includes four different representations: (i) code-based (Verilog hardware description language), (ii) graphical-based (gate-level view), (iii) Boolean function, and (iv) truth table for each gate. The proposed learning system divides the learning content into different sections to support independent practice in meaningful steps. Multiple representations are included in order to foster understanding and transfer. The resulting implications for future work are discussed.
Dynamic Multi-UAV Cooperative Reconnaissance Task Assignment Based on ICNP

Aiming at the problem of poor timeliness and large communication volume in the traditional contract network protocol (CNP) when dealing with the dynamic multi-UAV cooperative reconnaissance task assignment problem, this paper proposes an improved contract network protocol (ICNP). Firstly, the multi-base, battlefield risk and other factors are considered to establish the multi-UAV cooperative reconnaissance task assignment model; secondly, the credit mechanism and selection mechanism are introduced into the traditional CNP, which improves the bidding efficiency and optimizes the bidding scope; finally, the simulation results show that the ICNP can significantly reduce the system traffic and improve the problem solving timeliness without reducing the reconnaissance efficiency for complex dynamic assignment problem. Keywords Multi-UAV; cooperative reconnaissance; dynamic; task assignment; contract network protocol.