Reviewerly/exHarmony huggingface dataset:
An approach based on object detection for image forensics

With the development of the Internet and advanced image editing software, the manipulation and propagation of digital image has become easy, and image forensics has become a challenging research area. In order to detect digital image forgery, various digital image forensics techniques have been proposed. Compared to active forensics methods that require embedded additional information, passive forensics methods are more popular because of their wider application scenarios. In this paper, we propose an approach on the basis of the Faster R-CNN network which has a good performance in object detection. There are two channels in our approach. One channel for extracting noise features is additionally added to find the noise inconsistency between the real area and the tampering area. The other original Faster R-CNN network also serves as a channel for extracting features from the original image to find evidence of tampering such as strong contrast differences and unnatural tampering boundaries. We then fuse the features from the two channels through the bilinear collection layer to further detect image tampering traces. Our experiments on four standard image tamper datasets show that the two-channel framework is superior to the single detection network and has better performance than alternative methods that have the flexibility of resizing and compression in the past.
Keyphrase Guided Beam Search for Neural Abstractive Text Summarization

As a recently proposed way of text summarization, abstractive text summarization features the use of new phrases to obtain a condensed version of the source text. Most approaches nowadays in this category are under the sequence-to-sequence framework, which is the bedrock of many text generation tasks. However, these approaches usually fail to get a sound representation of the source text, and they are liable to produce summaries with a low semantic relevance. In this work, we devise a novel structure of convolutional recurrent neural network-based encoder to get a better latent representation of the source text. Further, we propose to exploit keyphrases to guide the summary selection in a modified beam search process, thus contributing to a closer semantic relevance between the source text and the generated summary. With this new approach, we outperform the state-of-the-art systems on several datasets at no extra cost of training, particularly on the benchmark CNN/Daily Mail dataset where the source texts are lengthy [1].
Tuning Network I/O Processing to Achieve Performance and Energy Objectives of Latency Critical Workloads

Latency-Critical (LC) cloud applications pose three important challenges: 1) meeting tail latency Service-Level Objective (SLO), 2) attaining predictable tail latency, and 3) achieving high energy efficiency. In this paper we consider multicore end-systems (leaf nodes) and we study how the two important workload-dependent latency sources in network I/O processing, namely, interrupts and queuing, contribute to these problems. Firstly, we show that frequency-scaled centralized interrupt processing can be as energy efficient and achieve more predictable latency compared to traditional distributed interrupt processing. And secondly, we show that a controlled dynamic frequency scaling approach that adapts to socket buffer queue length can mitigate tail latency problems due to queuing. We design and implement a Runtime Engine that employs the proposed techniques through online monitoring of the workload and dynamically allocates resources to meet the tail latency performance. Finally, we present a study for six LC applications with different latency and service characteristics. The study shows that our proposed scaling approach saves up to 16% more energy compared to Linux on-demand frequency scaling governor that adapts based on the CPU utilization.
Recommendation Algorithm Fusing Implicit Similarity of Users and Trust

The goal of the recommender system is to understand the user's preferences and make appropriate recommendations for certain behaviors. Similarity is an important part of recommendation algorithm. However, most current recommendation algorithms ignore the social relation on users, and the most similarity calculations are based on the common object, which does not fully exploit the implicit information. For these challenges, firstly, an adaptive approach which takes both trust and rating into account is proposed to measure the implicit similarity between a user and his friends when the user as a truster and a trustee, respectively. Secondly, a recommendation algorithm combining trust and ratings is proposed, which also takes the biases of users and items, and the user's implicit feedback into account to enhance the accuracy of the algorithm. Finally, the social regularization recommendation algorithm is modified. The experimental results on two different datasets show that the proposed algorithm is able to not only outperform the traditional recommendation algorithm, but also effectively alleviate the data sparsity and cold start problems.
In a Spin-Wave Reservoir for Machine Learning

Reservoir computing is a computational framework which is originally based on software recurrent neural networks and recently achieved with physical systems as well. In our previous paper [Nakane et al., IEEE ACCESS vol. 6, p. 4462, 2018], we have proposed a spin-wave-based reservoir computing device with multiple input/output electrodes, and have demonstrated its high generalization ability in the estimation of input-signal parameters performed by the spin-wave-based reservoir computing. To successfully execute many types of estimation tasks with machine learning, it is necessary to investigate fundamental properties of spin-wave-based reservoir computing, particularly the relation between its input and output. From this background, the purposes of this work are to demonstrate a different estimation task with pulse input signals and to analyze the properties of spin waves which have important roles in the task. We first describe our approach to obtain spin waves with the features useful for reservoir computing, by considering the fundamental properties of spin waves and feasible device technologies. Then, we investigate detailed characteristics of locally-excited spin waves in a garnet film by micromagnetics simulation. Using the resultant spin waves, we demonstrate a pulse interval estimation task, and achieve a high diversity in the time-sequential signals generated by the spin-wave-based reservoir. The spin-wave-based device is a highly promising hardware for next-generation machine-learning electronics.
A Traffic Sign Detection Method based on Saliency Detection

As an important research direction of intelligent transportation systems (ITS), road traffic sign recognition research has become increasingly important in autopilot and traffic sign maintenance. The difficulties and challenges faced by research in this area are also increasing, such as feature extraction and regional segmentation. The detection of visual saliency has become a hot topic in the field of computer vision research in recent years. This technology simulates the human visual attention mechanism and can quickly obtain effective information from massive image data. However, in complex situations, due to the effects of changes in the target scale, the complexity of the target contour and background interference, most of the existing algorithms have difficulty achieving a better detection effect, and there is also room for improvement in real-time detection and fusion of saliency maps. Therefore, the detection of visually significant targets is still an important issue that needs to be further studied. Combining the above two points, this paper proposes a traffic sign detection method based on saliency detection. Commonly used saliency detection methods include MBD and MDC. In this paper, the two methods are combined and improved and tested and verified on a data set containing 100 traffic sign pictures. After testing, the average IOU reached 38 percent, the recall rate reached 42 percent and the accuracy rate reached 50 percent. The results show that the proposed method results in significant performance improvement.
Sensing Platforms for Prototyping and Experimenting Wearable Continuous Health Monitoring Systems

Continuous monitoring generally imposes a set of constraints including form factor, energy consumption, and mobility support in order to enable anywhere and anytime monitoring. Sensors, within this context, play an important role as the first building block of such systems and the main entry point for the monitoring process. Therefore, to experiment continuous health-monitoring systems in real-like conditions, sensing platforms need to meet a number of related requirements and constraints, especially for mobility factor. This chapter aims to present an overview of popular sensing platforms meeting appropriate constraints, allowing experimenters and researchers to start prototyping and experimentation projects for continuous health monitoring. For that, a number of requirements and constraints for continuous monitoring prototyping are firstly identified, then sensing platforms are classified into three proposed and compared categories, namely, ready-to-use, ready-to-compose kits, and do-it-yourself platforms. On that basis, a set of interesting platforms are reviewed and compared under each category.
Deep Reinforcement Learning with Dual Targeting Algorithm

Recently, deep reinforcement learning using the Deep Q-networks (DQN) algorithm has attracted attention, and extended methods continue to improve its learning performance. A multi-step DQN using an n-step TD method in the extended method contributes to faster learning. However, in the n-step TD methods, improvement in learning speed is better when using the intermediate prediction over the long-term prediction. Therefore, to further accelerate learning, methods that can use a long-term prediction effectively are required. A learning-accelerated DQN learns faster than DQN through a training neural network with bootstrap targets up to the next positive reward and 1-step bootstrap targets. It is, however, not possible for that method to use long-term prediction for tasks in which rewards are continuously observed. Furthermore, the use of two independent updates leads to instability with respect to the convergence of the neural network. We therefore propose a dual targeting algorithm that uses a single update with bootstrap targets up to the last reward in the next consecutive positive reward and 1-step bootstrap targets. The aim of the proposed method is to reduce instability in the convergence of the neural network by calculating the dual target from the same sampled experience. We apply the proposed method to a few classic control problems involving OpenAI Gym, compare it with DQN and multi-step DQN, and verify its effectiveness.
Improving the Thread Scalability and Parallelism of BWA-MEM on Intel HPC Platforms

With modern next-generation sequencing (NGS) technology, genome sequencing is able to process much more data at low cost. Burrows-Wheeler Aligner (BWA) is one of the most widely used open-source software tools to align read sequences into a reference genome and sequence alignment takes the most time in NGS data analysis. BWA-MEM is one of the most popular tools in BWA. It is a memory-bound application and in traditional view cache miss is one of the most serious problems. But with the increase in the number of cores and threads on HPC platforms, the problem of low parallelism and thread scalability becomes more serious and deeply affects the performance. Despite extensive optimization efforts, the thread scalability and parallelism of BWA-MEM are still not very efficient as the majority of the optimization works don't take advantages of hardware's architecture characters. After analyzing BWA-MEM's performance on modern Intel HPC platforms, we find that BWA-MEM has limitations in not fully supporting multi-threading mechanism. Based on this analysis, we propose some optimizations on BWA-MEM, which focus on improving the threading scalability and parallelism to take the advantages of many-core architecture computing resources. First we reorganized the pipeline of the application and give it a dynamic thread allocation strategy to make the pipeline more efficient. Furthermore, we give an optimization on sequence processing section by optimizing the memory allocation and the dynamic thread scheduler of the calculation part. With our optimization, BWA-MEM achieves ~2x faster on Xeon and ~3x faster on Xeon Phi processors than the original implementation.
Local Differentially Private Matrix Factorization For Recommendations

In recent years recommendation systems have become popular in the e-commerce industry as they can be used to provide a personalized experience to users. However, performing analytics on users' information has also raised privacy concerns. Various privacy protection mechanisms have been proposed for recommendation systems against user-side adversaries. However most of them disregards the privacy violations caused by the service providers. In this paper, we propose a local differential privacy mechanism for matrix factorization based recommendation systems. In our mechanism, users perturb their ratings locally on their devices using Laplace and randomized response mechanisms and send the perturbed ratings to the service provider. We evaluate the proposed mechanism using Movielens dataset and demonstrate that it can achieve a satisfactory tradeoff between data utility and user privacy.
An Approach to Creating an Intelligent System for Detecting and Countering Inappropriate Information on the Internet

Currently, the Internet is becoming one of the most dangerous threats to personal, public and state information security. Therefore, the task of detecting and counteracting inappropriate information in digital network content becomes of national importance. The paper offers a new approach to creating an intelligent system for detecting and counteracting inappropriate information on the Internet based on the use of machine learning methods and processing of big data and describes the architecture of such a system. Experimental evaluation of one of the most important system components, which is the component of multidimensional evaluation and categorization of information objects in single-threaded and multi-threaded modes showed high efficiency of using various classifiers included in the Python Scikit-learn and Spark MLlib libraries to solve the problem.
Touch Pointing Performance for Uncertain Touchable Sizes of 1D Targets

When users operate smartphones and desktop interfaces with their fingers, there are differences between the motor and visual widths. For example, when a user selects an item from a vertical menu, the area that is physically touched by the user is often larger than the visual width (e.g., of the label for the item selected). Therefore, the user aims for the label assuming that the label width (the visual width) means the motor width. Consequently, the user performs operations more carefully than necessary. We conducted an experiment to investigate the effect of the motor and visual widths on finger pointing. After asking participants to explore the motor width, they performed an experimental task. Our experiment shows that the users' movement time depends on the motor width and can be predicted. We also analyze existing interfaces and discuss the implications.
Sports Motion Recognition based on Foot Trajectory State Sequence Mapping

Quantitative motion analysis to evaluate the performance of athletes has been actively studied recently. Although various methods based on wearable inertial sensors have been developed for simple and repetitive movements recognition, the understanding of continuous complex movements of in-field sports is still challenging. In this paper, we propose a new motion segmentation and recognition method based on foot swing trajectory state to achieve robust and efficient recognition of motion of interest (MOI) in the lower limbs from continuous and complex movements. In order to segment complex movements in the lower limbs, a series of foot motion states are defined based on foot-ground contact status and foot trajectory during swing. The lower body motion state sequence combining the states of both feet is matched to a prior knowledge of MOI cycle sequences obtained in advance, so as to obtain a motion type candidate set. In this case, the continuous movement is segmented based on the prescreened motion types to realize adaptive time window for feature extraction. Finally, according to the prescreened motion type candidate set, corresponding trained neural network binary classifiers are used to make the classification based on the calculated kinematic features. The proposed method is verified through experiments of football movements consisting of walking, dribbling and stepover. As the result, the motion type recognition accuracy is 95%.
Many vs. Many Query Matching with Hierarchical BERT and Transformer

Query matching is a fundamental task in the Natural Language Processing community. In this paper, we focus on an informal scenario where the query may consist of multiple sentences, namely query matching with informal text. On the basis, we first construct two datasets towards different domains. Then, we propose a novel query matching approach for informal text, namely Many vs. Many Matching with hierarchical BERT and transformer. First, we employ fine-tuned BERT (bidirectional encoder representation from transformers) to capture the pair-wise sentence matching representations. Second, we adopt the transformer to accept above all matching representations, which aims to enhance the pair-wise sentence matching vector. Third, we utilize soft attention to get the importance of each matching vector for final matching prediction. Empirical studies demonstrate the effectiveness of the proposed model to query matching with informal text.
ElaX: Provisioning Resource Elastically for Containerized Online Cloud Services

To reduce the cost of online cloud services, service providers often employ the elastic approach that allows tenants to "scale out" or "scale up" their applications at runtime. However, the traditional virtual machine-based approach cannot meet the fine-grained fluctuating demand due to its slow startup time and high reconfiguration cost. To address this challenge, we present ElaX, an online service manager that minimizes the resource provisioning cost for containerized online services while guaranteeing their tail latency requirement. ElaX designs a workload-aware resource allocation mechanism for containerized online services through the collaboration of three key components: First, Workload predictor is able to precisely predict the workload in periodic scenario, through a LSTM (Long Short-Term Memory) network; Second, Resource reservation allocates the just-right amount of resource supporting the predicted workload, using the combination of both scale-up and scale-out operations; Third, Online controller guarantees the tail latency requirement during prediction failures through a feedback-based control method, and further reduces the provision cost through resource reclamation. Our experiments on the two production workloads demonstrate that ElaX can reduce the average resource over-provisioning cost by more than 32.6% while guaranteeing the tail latency requirement.
Word Sense Disambiguation (WSD) for Indonesian Homograph Word Meaning Determination by LESK Algorithm Application

Indonesian has several words which are commonly known as ambiguous words, confusing the meaning of a sentence or a statement to be less understood or even not delivered. It is different to a human perception which has linguistic ability to determine the meaning of ambiguity or more than a word meaning. Word Sense Disambiguation is one of a topic from natural language processing (NLP) which deals with ambiguity handling. Word Sense Disambiguation is a linguistic computational process which aims to identify the proper meaning of words based on the context. This current study is designed as a system to handle the ambiguous words. It is conducted by looking up and defining the meaning of ambiguous words by using LESK algorithm. The test is performed towards the functionality from a system in which the result system test is in line with test data from KBBI. The result presents accuracy level of 78.6% for one of an ambiguous word and 62.5 % for two of ambiguous words in determining the meaning appropriately.
Locality Preserving Projection via Deep Neural Network

Dimensionality reduction is an essential problem in data mining and machine learning fields. Locality Preserving Projection (LPP) is a well-known dimensionality reduction method which can preserve the neighborhood graph structure of data, and has achieved promising performance. However linear projection makes it difficult to analyze complex data with nonlinear structure. In order to deal with this issue, this paper proposes a novel nonlinear locality preserving projection method via deep neural network, termed as DNLPP, which replaces the linear projection with an appropriate deep neural network. Benefiting from the nonlinearity of neural networks and its powerful representation capability, the proposed method is more discriminative than the conventional LPP. In order to solve the new model, we propose an iterative optimization algorithm. Extensive experiments on several public datasets illustrate that the proposed method is overall superior to the other state-of-art dimensionality reduction methods.
Sparsity as the Implicit Gating Mechanism for Residual Blocks

Neural networks are the core component in the recent empirical successes of deep learning techniques in challenging tasks. Residual network (ResNet) architectures have been instrumental in improving performance in object recognition and other tasks by enabling training much deeper neural networks. Studies of residual networks reveal that they are robust to removing layers. However, it is still an open question of why residual networks behave well and how they make it feasible to train networks with many layers. In this paper, we show that sparsity of the residual blocks acts as the implicit gating mechanism. When a neuron is inactive, it behaves as a node in an information highway, allowing the information from the previous layer to pass to the next layer unchanged. As the identity function has a derivative of 1, it avoids the exploding or vanishing gradient problem that is known to contribute to the difficulty of training deep neural networks. When a neuron is active, it captures input-output relationships that are necessary to achieve good performance. By using the ReLu activation functions, residual blocks produce sparse outputs for typical inputs. We perform systematic experimental analysis on the residual blocks of trained ResNet models and show that sparsity acts as the implicit gate for deep residual networks.
Further Towards Unambiguous Edge Bundling: Investigating Power-Confluent Drawings for Network Visualization

Bach et al. [1] recently presented an algorithm for constructing confluent drawings, by leveraging power graph decomposition to generate an auxiliary routing graph. We identify two issues with their method which we call the node split and short-circuit problems, and solve both by modifying the routing graph to retain the hierarchical structure of power groups. We also classify the exact type of confluent drawings that the algorithm can produce as `power-confluent', and prove that it is a subclass of the previously studied `strict confluent' drawing. A description and source code of our implementation is also provided, which additionally includes an improved method for power graph construction.