Reviewerly/exHarmony huggingface dataset:
A latent factor model of fusing social regularization term and item regularization term

As the social media boom and evolve, the studies of recommender system are facing increasing challenges, especially in solving the sparse data issue. The “latent factor model” (LFM), as a typical recommendation algorithm, is widely used in various recommender systems. However, LFM shows low recommendation accuracy in sparse data situation. This paper proposes a modified LFM model that fuse the information from both users’ social relationship and items’ information. To illustrate the social connections among users, we introduce the social regularization term, which is calculated from the users’ social information, into the traditional LFM. To process the item information, we use the Latent Semantic Index (LSI), which effectively constrains the loss function in the matrix decomposition, to generate item regularization. The model parameters are optimized through gradient descent. In order to verify its effectiveness, we made experiments using the Last.fm dataset, KDDcup_Track1 dataset and crawling data from Douban respectively. The experimental result shows that, our approach can achieves higher accuracy than the traditional recommendation algorithm.
Global median filtering forensic method based on Pearson parameter statistics

Median filtering forensics in images is a subject under intense study nowadays. Existing median filtering detectors are developed based on hand-crafted features and convolutional neural networks (CNN). Among hand-crafted features based detectors, most of the detector's performance deteriorate for low-resolution images compressed with low-quality factors. However, CNN-based detectors are found to be more robust at the expense of large database and large training time requirement. In this study, the authors propose a robust median filtering detector by exploiting the statistics of the Pearson parameter , is defined as the polynomial ratio of skewness and kurtosis. To capture fingerprints of median filtering, is determined for the median filtered residual (MFR) of the images to construct a novel feature set of 23 dimensions. The efficacy of the proposed feature set, against existing hand-crafted features based and CNN-based detectors, is established by a series of experiments for global median filtering detection. Results reveal that the proposed feature set exhibits performance gain of 2–4% against existing hand-crafted features based detectors and an approximate gain of 4% against CNN-based detector for detection of low-resolution median filtered images compressed with low-quality factors.
Citation Context Analysis using Word-Graph

Word Graph Citation Analysis Method (WGCAM) is proposed to perform sub graph matching of citation sentences into important and not-important citation classes. Graph similarity measures used in this research are: Maximum Common Subgraph Node Similarity (MCSNS), Maximum Common Subgraph Directed Edges Similarity (MCSDES), and Maximum Common Subgraph Undirected Edges Similarity (MCSUES). Experiments were carried out on an annotated dataset of 465 citations using five distinct classifiers; Support Vector Machine SVM, Random Forest, Naive Bayes, K-Nearest Neighbors KNN and Decision Tree. Decision Tree classifier outperforms and achieves an accuracy of 0.99.
A parallel tree node splitting criterion for fuzzy decision trees

Summary Fuzzy decision trees are one of the most important extensions of decision trees for symbolic knowledge acquisition by fuzzy representation. Many fuzzy decision trees employ fuzzy information gain as a measure to construct the tree node splitting criteria. These criteria play a critical role in the construction of decision trees. However, many of the criteria can only work well on small‐scale or medium‐scale data sets, and cannot directly deal with large‐scale data sets on the account of some limiting factors such as memory capacity, execution time, and data complexity. Parallel computing is one way to overcome these problems; in particular, MapReduce is one mainstream solution of parallel computing. In this paper, we design a parallel tree node splitting criterion (MR‐NSC) based on fuzzy information gain via MapReduce, which is completed equivalent to the traditional unparallel splitting rule. The experimental studies verify the equivalency between the proposed MR‐NSC algorithm and the traditional unparallel way through 22 UCI benchmark data sets. Furthermore, the feasibility and parallelism are also studied on two large‐scale data sets.
SAIL: Simulation-Informed Active In-the-Wild Learning

Robots in real-world environments may need to adapt context-specific behaviors learned in one environment to new environments with new constraints. In many cases, copresent humans can provide the robot with information, but it may not be safe for them to provide hands-on demonstrations and there may not be a dedicated supervisor to provide constant feedback. In this work we present the SAIL (Simulation-Informed Active In-the-Wild Learning) algorithm for learning new approaches to manipulation skills starting from a single demonstration. In this three-step algorithm, the robot simulates task execution to choose new potential approaches; collects unsupervised data on task execution in the target environment; and finally, chooses informative actions to show to co-present humans and obtain labels. Our approach enables a robot to learn new ways of executing two different tasks by using success/failure labels obtained from naïve users in a public space, performing 496 manipulation actions and collecting 163 labels from users in the wild over six 45-minute to 1-hour deployments. We show that classifiers based low-level sensor data can be used to accurately distinguish between successful and unsuccessful motions in a multi-step task ( ), even when trained in the wild. We also show that using the sensor data to choose which actions to sample is more effective than choosing the least-sampled action.
A Novel KNN Approach for Session-Based Recommendation

The KNN approach, which is widely used in recommender systems because of its efficiency, robustness and interpretability, is proposed for session-based recommendation recently and outperforms recurrent neural network algorithms. It captures the most recent co-occurrence information of items by considering the interaction time. However, it neglects the co-occurrence information of items in the historical behavior which is interacted earlier than others and cannot discriminate the impact of vertices with different popularity. Due to these observations, this paper presents a novel KNN approach to address these issues for session-based recommendation. Specifically, a diffusion-based similarity method is proposed for incorporating the popularity of items, and the candidate selection method is proposed to capture more co-occurrence information of items in the same session efficiently. Comprehensive experiments are conducted to demonstrate the effectiveness of our KNN approach over the state-of-the-art KNN approach for session-based recommendation on three benchmark datasets.
Human action recognition using hierarchic body related occupancy maps

This paper introduces a novel spatial method for human action recognition that is discriminative without needing temporal information or action key poses. First, skeletal data is acquired with the Microsoft Kinect v2 sensor and undergoes a Pose Invariant Normalization (PIN) process. The PIN process translates, rotates and scales the various observed poses to eliminate body differences and positional differences between subjects. Second, the method uses a Body Related Occupancy Map (BROM), that describes in a 3D grid how the area around specific body parts is used, as a strong indicator of the particular action that is being performed. The BROM and its 2D projections are used as feature inputs for Random Forest classifiers. These classifiers are then combined in a hierarchic structure to boost the classification performance. The approach is tested on a self-captured database of 23 human actions for game-play. On this database a classification with an accuracy score of 91% is achieved for the hierarchic BROM (HiBROM) classification. On the public CAD60 dataset, the HiBROM classifier attains 87.2% accuracy which is comparable to other state-of-the-art methods.
Electro-Optical Imaging Technology Based on Microlens Array and Fiber Interferometer

To reduce the volume and weight of traditional optical telescopes effectively, this article proposes an electro-optical imaging technology based on a microlens array and fiber interferometer. Pairs of microlenses in the microlens array collect light and couple it into a fiber interferometer to form interference fringes. Then the amplitude and phase of a large number of interferometer baselines are analyzed to generate images. In this work, the principle of electro-optical imaging technology has been analyzed according to the partially coherent light theory. The microlens-array arrangement method and baseline pairing method have been optimized for arbitrary targets. From the simulation results, it was found that the imaging resolution depends on the maximum baseline length, and the imaging quality could be effectively improved by adjusting the Nyquist sampling density and baseline pairing method. This technology can provide an important reference for the miniaturization and complanation of imaging systems.
Fault Characterization and Mitigation Strategies in Desktop Cloud Systems

Desktop cloud platforms, such as UnaCloud and CernVM, run clusters of virtual machines taking advantage of idle resources on desktop computers. These platforms execute virtual machines along with the applications started by the users in those desktops. Unfortunately, although the use of computer resources is better, desktop user actions, such as turning off the computer or running certain applications may conflict with the virtual machines. Desktop clouds commonly run applications based on technologies such as Tensorflow or Hadoop that rely on master-worker architectures and are sensitive to failures in specific nodes. To support these new types of applications, it is important to understand which failures may interrupt the execution of these clusters, what faults may cause some errors and which strategies can be used to mitigate or tolerate them. Using the UnaCloud platform as a case study, this paper presents an analysis of (1) the failures that may occur in desktop clouds and (2) the mitigation strategies available to improve dependability.
Prototyping an Augmented Reality Maintenance and Repairing System for a Deep Well Vertical Turbine Pump

We propose an augmented reality (AR) prototype to support maintenance and repair tasks by assisting the operator of the vertical centrifugal deep well pump with an interactive manual. This pump is used to collect water from the deep well and send it to the drinking water treatment plant in the city of Cali. This system provides instructions using arrows, text notes and alerts and 2D and 3D virtual images, incorporating virtual content to the real environment via the display device. The system helps the operator to make the sequences of maintenance in correct order, to improve time and quality to the execution tasks, reducing operator effort and maintenance costs.
Accelerating Deep Reinforcement Learning Using Human Demonstration Data Based on Dual Replay Buffer Management and Online Frame Skipping

Human demonstration data plays an important role in the early stage of deep reinforcement learning to accelerate the training process as well as guiding a reinforcement learning agent to learn complicated policy. However, most of current reinforcement learning approaches with human demonstration data and reward assumes that there is a sufficient amount of high-quality human demonstration data and that is not true for most real-world learning cases where enough amount of experts' demonstration data is always limited. To overcome this limitation, we propose a novel deep reinforcement learning approach with a dual replay buffer management and online frame skipping for human demonstration data sampling. The dual replay buffer consists of a human replay memory, an actor replay memory, and a replay manager. And it can manage two replay buffers with independent sampling policies. We also propose an online frame skipping to fully utilize available human data. During the training period, the frame skipping is performed dynamically to human replay buffer where the all of human data is stored. Two online frame-skipping, namely, FS-ER(Frame Skipping-Experience Replay) and DFS-ER(Dynamic Frame Skipping-Experience Replay) are used to sample data from human replay buffer. We conducted empirical experiments of four popular Atari games and the results show that our proposed two online frame skipping with dual replay memory outperforms existing baselines. Specifically, DFS-ER shows the fastest score increment during the reinforcement learning procedure in three out of four experiments. FS-ER shows the best performance in the other environment that is hard to train a model because of sparse reward.
Exploiting Heterogeneity for Opportunistic Resource Scaling in Cloud-Hosted Applications

Cloud consumers have access to an increasingly diverse range of resource and contract options, but lack appropriate resource scaling solutions that can exploit this to minimize the cost of their cloud-hosted applications. Traditional approaches tend to use homogeneous resources and horizontal scaling to handle workload fluctuations and do not leverage resource and contract heterogeneity to optimize cloud costs. In this paper, we propose a novel opportunistic resource scaling approach that exploits both resource and contract heterogeneity to achieve cost-effective resource allocations. We model resource allocation as an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unbounded knapsack problem</i> , and resource scaling as an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">one-step ahead resource allocation problem</i> . Based on these models, we propose two scaling strategies: (a) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">delta capacity optimization</i> , which focuses on optimizing costs for the difference between existing resource allocation and the required capacity based on the forecast workload, and (b) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">full capacity optimization</i> , which focuses on optimizing costs for resource capacity corresponding to the forecast workload. We evaluate both strategies using two real world workload datasets, and compare them against three different scaling strategies. The results show that our proposed approach, particularly full capacity optimization, outperforms all of them and offers in excess of 70 percent cost savings compared to the traditional scaling approach.
Efficient Incremental Learning for Mobile Object Detection

Object detection models shipped with camera-equipped mobile devices cannotcover the objects of interest for every user. Therefore, the incrementallearning capability is a critical feature for a robust and personalized mobileobject detection system that many applications would rely on. In this paper, wepresent an efficient yet practical system, IMOD, to incrementally train anexisting object detection model such that it can detect new object classeswithout losing its capability to detect old classes. The key component of IMODis a novel incremental learning algorithm that trains end-to-end for one-stageobject detection deep models only using training data of new object classes.Specifically, to avoid catastrophic forgetting, the algorithm distills threetypes of knowledge from the old model to mimic the old model's behavior onobject classification, bounding box regression and feature extraction. Inaddition, since the training data for the new classes may not be available, areal-time dataset construction pipeline is designed to collect training imageson-the-fly and automatically label the images with both category and boundingbox annotations. We have implemented IMOD under both mobile-cloud andmobile-only setups. Experiment results show that the proposed system can learnto detect a new object class in just a few minutes, including both datasetconstruction and model training. In comparison, traditional fine-tuning basedmethod may take a few hours for training, and in most cases would also need atedious and costly manual dataset labeling step.
Pedestrian detection framework based on magnetic regional regression

In recent years, pedestrian detection has become one of the core issues in self-driving car and automatic alert in video surveillance. However, the efficiency and accuracy are not ideal in multi-pedestrian scenarios because of the occlusion by other pedestrian or non-human objects. Here, the authors design a pedestrian-detection framework based on region proposal network and convolutional neural network. Magnetic region regression strategy was proposed to reduce locating errors and false detection in the process of region regression. Meanwhile, semantic segmentation is integrated into authors’ framework to improve the accuracy of classification. Additionally, the authors used soft- non-maximum suppression (NMS) to reduce the impact of NMS threshold on the model. Authors’ framework has average miss rate of 6.94% on Improved Caltech-USA dataset. The experiments show that authors’ framework achieves significant advantages.
Just Because We Can, Doesn’t Mean We Should: An Argument for Simplicity and Data Privacy With Name Authority Work in the Linked Data Environment

Neutrality is a core tenet of librarianship, although it is widely accepted that cataloging is not a neutral act. In 1876, Charles Ammi Cutter outlined the model for a library catalog. That model remained largely unchanged for over 120 years; however the publication and adoption of Functional Requirements for Bibliographic Records (FRBR), Functional Requirements for Authority Data (FRAD), and Functional Requirements for Subject Authority Data (FRSAD) by the international cataloging community in the late 1990s and early 2000s ushered in new models for organizing and describing bibliographic resources. Although the "FRBR Family" of models remains true to Cutter's guiding principles at their core, they explicitly introduced specific attributes for describing bibliographic entity groups. In particular, FRAD greatly expanded the attributes to record about Persons, and these attributes were codified in the contemporary cataloging standard Resource Description and Access (RDA). As a result, catalogers now capture much more information about people in authority records than ever before. The contribution of all this new additional metadata into authority files has the potential to harm the actual people we are now cataloging by misidentifying or censoring information through cataloger bias or by capturing personally identifying information that could be used against the person. This has great ramifications in the linked data environment when the metadata is reused and can no longer be controlled by the individuals or institutions who created the original data. The risks are too great and we have yet to see the results in our discovery systems to rationalize adding so much personal information about people in library authority records. This paper argues that we should return to a simpler, pre-RDA authority record. However, the likelihood of changing RDA is slim, but we can adjust our cataloging practice to record only the most necessary information in authority records to curb catalog bias and insure personal data privacy for authors and contributors in our authority files.
Liver Tumor Segmentation Using Triplanar Convolutional Neural Network: A Pilot Study

In this work, a liver tumor segmentation approach is proposed using triplanar views, consisting of axial, sagittal and coronal planes. These three planes are integrated as the input streams for the Convolutional Neural Network (ConvNet). The main objective of including the input patches from triplanar views is to enrich the ConvNet with more context information to aid in the classification of liver tumor. The input patches are extracted from liver Computed Tomography (CT) dataset using center pixel of interests from the triplanar views. These patches are fed into the proposed Triplanar ConvNet. Pilot experiments were conducted to evaluate the efficiency of using triplanar views in comparisons with single-view from axial plane. The preliminary results achieved in this study revealed that triplanar approach yield better results than using patches only from single-view.
Low-pass Filtering as Bayesian Inference

We propose a Bayesian nonparametric method for low-pass filtering that can naturally handle unevenly-sampled and noise-corrupted observations. The proposed model is constructed as a latent-factor model for time series, where the latent factors are Gaussian processes with non-overlapping spectra. With this construction, the low-pass version of the time series can be identified as the low-frequency latent component, and therefore it can be found by means of Bayesian inference. We show that the model admits exact training and can be implemented with minimal numerical approximations. Finally, the proposed model is validated against standard linear filters on synthetic and real-world time series.
#OurChangingClimate: Building Networks of Community Resilience Through Social Media and Design

#OurChangingClimate incorporates social media tools into a public practice design project that examines climate change as a place-based and everyday experience. Based on the pilot stage of the project, an initial series of workshops with youth in the San Francisco Bay Area of California, we found that we were able to sow seeds for strengthened community resilience by creating a hybrid online–in real life (IRL) space where participants could gain new understandings about the causes and impacts of climate change and network within and outside of their community. Together we begin to envision new opportunities to mitigate against the adverse impacts of climate change and contemporary climate disruptions. Incorporating social media offered opportunities for a focus on open and accessible exchange, the unique perspectives of specific communities, and public circulation of ideas between community members and designers.
Mutual Reinforcement Learning with Robot Trainers

The researchers in this study have developed a novel approach using mutual reinforcement learning (MRL) where both the robot and human act as empathetic individuals who function as reinforcement learning agents for each other to achieve a particular task over continuous communication and feedback. This shared model not only has a collective impact but improves human cognition and helps in building a successful human-robot relationship. In our current work, we compared our learned reinforcement model with a baseline non-reinforcement and random approach in a robotics domain to identify the significance and impact of MRL. MRL contributed to improved skill transfer, and the robot was able successfully to predict which reinforcement behaviors would be most valuable to its human partners.
AntTracks TrendViz

The complexity of modern applications makes it hard to fix memory leaks and other heap-related problems without tool support. Yet, most state-of-the-art tools share problems that still need to be tackled: (1) They group heap objects only based on their types, ignoring other properties such as allocation sites or data structure compositions. (2) Analyses strongly focus on a single point in time and do not show heap evolution over time. (3) Results are displayed in tables, even though more advanced visualization techniques may ease and improve the analysis.