Reviewerly/exHarmony huggingface dataset:
A GPU memory efficient speed-up scheme for training ultra-deep neural networks

Ultra-deep neural network(UDNN) tends to yield higher-quality model but its training process is often difficult to handle. Scarce GPU DRAM capacity is the primary bottleneck that limits the depth of neural network and the range of trainable minibatch size. In this paper, we present a scheme that dedicates to make the utmost use of finite GPU memory resource to speed up the training process for UDNN. Firstly, a performance-model guided dynamic swap out/in strategy between GPU and host memory is carefully orchestrated to tackle the out-of-memory problem without introducing performance penalty. Then, a hyperparameter (minibatch size, learning rate) tuning policy is designed to explore the optimal configuration after applying the swap strategy from the perspectives of training time and final accuracy simultaneously. Finally, we verify the effectiveness of our scheme in both single and distributed GPU mode.
Lempel–Ziv compressed structures for document retrieval

Document retrieval structures index a collection of string documents, to retrieve those that are relevant to query strings p : document listing retrieves all documents where p appears; top-k retrieval retrieves the k most relevant of those. Classical structures use too much space in practice. Most current research uses compressed suffix arrays, but fast indices still use 17–21 bpc (bits per character), whereas small ones take milliseconds per returned answer. We present the first document retrieval structures based on Lempel–Ziv compression, precisely LZ78. Our structures use 7–10 bpc and dominate a large part of the space/time tradeoffs. They also enable more efficient partial or approximate answers: our document listing outputs the first 75%–80% of the answers at a rate of one per microsecond; for top- k retrieval we return a result of 90% quality at the same rate and using just 4–6 bpc. This outperforms current indices by a wide margin.
The MOSAICS Model of Educational Approaches for Teaching the Practice of Software Project Management

Maybe you heard the line “managing programmers is like herding cats”, and if you consider there is some truth behind this, then you should, perhaps, think how it is to teach people to perform this job. As we know from the research literature, there is no such thing as a consensus about the most suitable teaching method of a software project management course targeted to information technology students. Moreover, the majority of publications focus on the theoretical aspects of the course, thus leaving little details about the application of the theory, or how to experience the practical side. The paper at hand proposes an abstract model of educational approaches, suggestively named MOSAICS, which may be used in teaching the practical side of a software project management course.
Measurement and Early Detection of Third-Party Application Abuse on Twitter

Third-party applications present a convenient way for attackers to orchestrate a large number of fake and compromised accounts on popular online social networks. Despite recent high-profile reports of third-party application abuse on popular online social networks, prior work lacks automated approaches for accurate and early detection of abusive applications. In this paper, we perform a longitudinal study of abusive third-party applications on Twitter that perform a variety of malicious and spam activities in violation of Twitter's Terms of Service (ToS). Our measurements spanning over a period of 16 months demonstrate an ongoing arms race between attackers continuously registering and abusing new applications and Twitter trying to detect them. We find that hundreds of thousands of abusive applications remain undetected by Twitter for several months while posting tens of millions of tweets. We propose a machine learning approach for accurate and early detection of abusive Twitter applications by analyzing their first few tweets. The evaluation shows that our machine learning approach can accurately detect abusive application with 92.7% precision and 87.0% recall by analyzing their first seven tweets. The deployment of our machine learning approach in the wild shows that attackers continue to abuse third-party applications despite Twitter's recent countermeasures targeting third-party applications.
Effects of higher-order dispersion on photon-pair generation by four-wave mixing

We present analytical results revealing the impact of higher-order dispersion on four-wave mixing in quantum applications. We derive fundamental relations between input and output modes in dispersive four-wave mixing processes and apply the theory to photon-pair generation. We analyze specific examples of four-wave-mixing configurations and find that, in some cases, spectral correlations are severely affected by dispersion while other cases are strongly unaffected. For the considered examples, we derive analytic conditions for dispersion cancellation, resulting in highly factorable two-photon states.
Hardware-Assisted, Low-Cost Video Transcoding Solution in Wireless Networks

Wireless video streaming has become an extremely popular application in recent years. Internet video streaming to mobile devices, however, faces several challenges, e.g., unstable wireless connections, long latency, high jitter and etc. Bitrate adaptive streaming and video transcoding solutions are widely used to address the above-mentioned issues, however, there are still several shortcomings of these approaches. Such challenges hinder providing satisfactory quality of video streaming service to the mobile users. We propose a hardware-assisted, real-time video transcoding solution implemented on a commercial off-the-shelf device, Raspberry Pi. We employ the software and hardware coupled architecture in order to improve the performance/quality of video streaming and enhance the user satisfactions in wireless network. Our video transcoding solution can be applied to both the downlink and uplink streaming: for downlink stream, it can provide agile bitrate adaptation to sudden network dynamics and enhance video quality by running our transcoding solution at the wireless edge. It can be used to uplink stream for broadcasting live streams in real-time. We present the design and implementation of our video transcoding system in both cases with practical scenarios. The evaluation results reveal that our transcoding solution enhances the performance of video streaming compared with other adaptive bitrate streamings and it provides higher video quality without causing rebuffering or video stall. We bridge the gap between the wireless channel capacity and the video quality while providing a better streaming experience to end user.
Understanding Reader Backtracking Behavior in Online News Articles

Rich engagement data can shed light on how people interact with online content and how such interactions may be determined by the content of the page. In this work, we investigate a specific type of interaction, backtracking, which refers to the action of scrolling back in a browser while reading an online news article. We leverage a dataset of close to 700K instances of more than 15K readers interacting with online news articles, in order to characterize and predict backtracking behavior. We first define different types of backtracking actions. We then show that “full” backtracks, where the readers eventually return to the spot at which they left the text, can be predicted by using features that were previously shown to relate to text readability. This finding highlights the relationship between backtracking and readability and suggests that backtracking could help assess readability of content at scale.
High-Dimensional Limited-Sample Biomedical Data Classification Using Variational Autoencoder

Biomedical prediction is vital to the modern scientific view of life, but it is a challenging task due to high-dimensionality, limited-sample size (also known as HDLSS problem), non-linearity, and data types tend are complex. A large number of dimensionality reduction techniques developed, but, unfortunately, not efficient with small-sample (observation) size dataset. To overcome the pitfalls of the sample-size and dimensionality this study employed variational autoencoder (VAE), which is a powerful framework for unsupervised learning in recent years. The aim of this study is to investigate a reliable biomedical diagnosis method for HDLSS dataset with minimal error. Hence, to evaluate the strength of the proposed model six genomic microarray datasets from Kent Ridge Repository were applied. In the experiment, several choices of dimensions were selected for data preprocessing. Moreover, to find a stable and suitable classifier, different popular classifiers were applied. The experimental results found that the VAE can provide superior performance compared to the traditional methods such as PCA, fastICA, FA, NMF, and LDA.
Multimodal Dense Stereo Matching

In this paper, we propose a new approach for dense depth estimation based on multimodal stereo images. Our approach employs a combined cost function utilizing robust metrics and a transformation to an illumination independent representation. Additionally, we present a confidence based weighting scheme which allows a pixel-wise weight adjustment within the cost function. We demonstrate the capabilities of our approach using RGB- and thermal images. The resulting depth maps are evaluated by comparing them to depth measurements of a Velodyne HDL-64E LiDAR sensor. We show that our method outperforms current state of the art dense matching methods regarding depth estimation based on multimodal input images.
Unsupervised Orthogonal Facial Representation Extraction via image reconstruction with correlation minimization

The identity and expression are two orthogonal facial properties which are hard to disentangle from facial images. The identity representation and emotional representation can be used for face verification and facial expression recognition. From the view of Multi-Task Learning, jointly learning both representations will generally benefit the performance. However, it is rarely considered in recent studies. In this paper, the orthogonal facial properties are modeled in a unified framework. A deep Convolutional–Deconvolutional neural network (Conv–Deconv) is proposed for extracting identity representations and emotional representations from aligned faces. A reconstruction loss, a classification loss, and a correlation loss are employed to train this network. The classification loss is used to learn emotional representations. The correlation loss ensures the independence of the identity representation and the emotional representation. The reconstruction loss confirms the information completeness of the combination of the identity representation and the emotional representation. The proof of the correctness of the proposed losses is also given in detail. The network learns both identity representation and emotional representations with only emotional labels. Compared to the existing method, the need of identity labels is eliminated, and the applicable scope is extended. Thus, we name it Unsupervised Orthogonal Facial Representation Extraction. Experiments are carried out on the synthesized LSFED dataset and the constrained RaFD dataset. The 2D t-SNE visualization and a deviation based score are used for validating the orthogonality of the representation. Furthermore, to quantitatively evaluate the learned representation, the Euclidean distance between the identity representations is used for face verification. The performance of our unsupervised face verification is comparable to the existing supervised methods.
Potential Data Sources for Sentiment Analysis Tools for Municipal Management Based on Empirical Research

The paper addresses the issue of the possibility of using sentiment analysis tools to support the management of local government entities. The author team carried out research in this field in several dozen municipalities in the northern part of the Silesian Voivodship. Among the analyzed issues, the information resources which are or should be collected for the purpose of managing municipalities were analyzed. The issue of municipal management was divided into three aspects: management of municipal offices, management of commune assets and resources, and community management. The paper presents sentiment analysis tools and a description of potential information resources that are or should be collected. The conclusions indicated fundamental barriers to the use of sentiment analysis tools for municipal management purpose.
BOLT-K: Bootstrapping Ontology Learning via Transfer of Knowledge

Dynamically extracting and representing continually evolving knowledge entities is an essential scaffold for grounded intelligence and decision making. Creating knowledge schemas for newly emerging, unfamiliar, domain-specific ideas or events poses the following challenges: (i) detecting relevant, often previously unknown concepts associated with the new domain; and (ii) learning ontological, semantically accurate relationships among the new concepts, despite having severely limited annotated data. To this end, we propose a novel LSTM-based framework with attentive pooling, BOLT-K, to learn an ontology for a target subject or domain. We bootstrap our ontology learning approach by adapting and transferring knowledge from an existing, functionally related source domain. We also augment the inadequate labeled data available for the target domain with various strategies to minimize human expertise during model development and training. BOLT-K first employs semantic and graphical features to recognize the entity or concept pairs likely to be related to each other, and filters out spurious concept combinations. It is then jointly trained on knowledge from the target and source domains to learn relationships among the target concepts. The target concepts and their corresponding relationships are subsequently used to construct an ontology. We extensively evaluate our framework on several, real-world bio-medical and commercial product domain ontologies. We obtain significant improvements of 5-25% F1-score points over state-of-the-art baselines. We also examine the potential of BOLT-K in detecting the presence of novel kinds of relationships that were unseen during training.
Deep Learning Based Handwritten Chinese Character and Text Recognition

This chapter introduces recent advances on using deep learning methods for handwritten Chinese character recognition (HCCR) and handwritten Chinese text recognition (HCTR). In HCCR, we integrate the traditional normalization-cooperated direction-decomposed feature map (directMap) with the deep convolutional neural network, and under this framework, we can eliminate the needs for data augmentation and model ensemble, which are widely used in other systems to achieve their best results. Although the baseline accuracy is very high, we show that writer adaptation with style transfer mapping (STM) in this case is still effective for further boosting the performance. In HCTR, we use an effective approach based on over-segmentation and path search integrating multiple contexts, wherein the language model (LM) and character shape models play important roles. Instead of using traditional back-off n-gram LMs (BLMs), two types of character-level neural network LMs (NNLMs), namely, feedforward neural network LMs (FNNLMs) and recurrent neural network LMs (RNNLMs) are applied. Both FNNLMs and RNNLMs are combined with BLMs to construct hybrid LMs. To further improve the performance of HCTR, we also replace the baseline character classifier, over-segmentation, and geometric context models with convolutional neural network based models. By integrating deep learning methods with traditional approaches, we are able to achieve state-of-the-art performance for both HCCR and HCTR.
Comparing Emotional Reactions to Terrorism Events on Twitter

Over the last years, terrorism attempts have threatened the global population safety, impacting people in a complex emotional way. In this paper, we apply deep learning techniques to classify emotions of terrorism events, and develop a comparative analysis about emotional reactions on four events based on the demographics of tweeters, particularly gender, age and location. Our research questions involve comparing these events in terms of emotional shift, emotions according to age and gender, emotional reaction according to the closeness of the event and number/type of victims, as well as the terms used to express emotional reactions. The main conclusions were: fear, anger and sadness are the most expressed emotions; the emotions can be related to gender (e.g. fear for women, and anger for men); emotions seem to be not related to the closeness of the events, but seem to be affected by the casualties (number of kills/injuries); tweeters expressing fear and sadness tend to share words of affection and support, while tweeters expressing anger tend to use intense words of hate, intolerance and anger.
Credulous and skeptical acceptability in probabilistic abstract argumentation: complexity results

Probabilistic abstract argumentation combines Dung’s abstract argumentation framework with probability theory in order to model uncertainty in argumentation. In this setting, we address the fundamental problem of computing the probability that an argument is credulously or skeptically acceptable ac cording to a given semantics. Specifically, we focus on the most popular semantics (i.e., admissible, stable, semi-stable, complete, grounded, preferred, ideal, ideal-set), and show that computing the probability that an argument is credulously or skeptically accepted is FP# P-complete independently from the adopted semantics, in the cases when computing it is not trivial (i.e., when skeptical acceptance is assumed under the admissible and ideal-set semantics).
Selective Transfer Classification Learning With Classification-Error-Based Consensus Regularization

Transfer learning methods are conventionally conducted by utilizing abundant labeled data in the source domain to build an accurate classifier for the target domain with scarce labeled data. However, most current transfer learning methods assume that all the source data are relevant to target domain, which may induce negative learning effect when the assumption becomes invalid as in many practical scenarios. To tackle this issue, the key is to accurately and quickly select the correlated source data and the corresponding weights. In this paper, we make use of the least square-support vector machine (LS-SVM) framework for identifying the correlated data and their weights from source domain. By keeping the consistency between the distributions of the classification errors of both the source and target domains, we first propose the classification-error-based consensus regularization (CCR), which can guarantee the performance improvement of the target classifier. Based on this approach, a novel CCR-based selective transfer classification learning method (CSTL) is then developed to autonomously and quickly choose the correlated source data and their weights to exploit the transferred knowledge by solving the LS-SVM based objective function. This method minimizes the leave-one-out cross-validation error despite scarce target training data. The advantages of the CSTL are demonstrated by evaluating its performance on public image and text datasets and comparing it with that of the state-of-the-art transfer learning methods.
A Markov Decision Process for the Interaction between Autonomous Collision Avoidance and Delayed Pilot Commands

We propose a Markov Decision Process (MDP) approach to optimizing decision making of an Unmanned Aerial Vehicle (UAV) in the presence of communication latency for collision avoidance scenarios. The UAV can be controlled by a remote human pilot or an onboard collision avoidance system (CAS). Due to communication latency, the pilot’s commands may be delayed for several seconds. If the UAV encounters a dynamic intruder, it has to make a decision on whether it should evade immediately by following the safe path generated from the CAS, or wait for the arrival of the pilot’s commands. The proposed MDP will make predictions on the state change in such a scenario. By solving the MDP with the value iteration method, sequential decisions on whether to wait can be made and an optimal waiting strategy can be obtained, which can be used to improve the human pilot’s user experience while ensuring safety.
3D local directional patterns for early diagnosis of Alzheimer's disease

We propose a three-dimensional-local directional pattern (3D-LDP) computation method in this study for early diagnosis of Alzheimer's disease. The proposed 3D-LDP is defined as a 1-bit decimal pattern which is calculated by computing and comparing edge response values in 18 directions. By concatenating three individual histograms extracted from three segmented sub-regions of grey matter, the accuracy, sensitivity, and specificity of the proposed 3D-LDP method were obtained as 81, 84, and 79%, respectively, comparable or even better than those of the existing 3D-LTP method (80, 85, and 78%).
Detecting Manipulated Smartphone Data on Android and iOS Devices

Ever improving technology allows smartphones to become an integral part of people’s lives. The reliance on and ubiquitous use of smartphones render these devices rich sources of data. This data becomes increasingly important when smartphones are linked to criminal or corporate investigations. To erase data and mislead digital forensic investigations, end-users can manipulate the data and change recorded events. This paper investigates the effects of manipulating smartphone data on both the Google Android and Apple iOS platforms. The deployed steps leads to the formulation of a generic process for smartphone data manipulation. To assist digital forensic professionals with the detection of such manipulated smartphone data, this paper introduces an evaluation framework for smartphone data. The framework uses key traces left behind as a result of the manipulation of smartphone data to construct techniques to detect the changed data. The outcome of this research study successfully demonstrates the manipulation of smartphone data and presents preliminary evidence that the suggested framework can assist with the detection of manipulated smartphone data.
Multimodal Dialog for Browsing Large Visual Catalogs using Exploration-Exploitation Paradigm in a Joint Embedding Space

We present a multimodal dialog (MMD) system to assist online customers in visually browsing through large catalogs. Visual browsing allows customers to explore products beyond exact search results. We focus on a slightly asymmetric version of a complete MMD system, in that our agent can understand both text and image queries, but responds only in images. We formulate our problem of "showing the k best images to a user'', based on the dialog context so far, as sampling from a Gaussian Mixture Model (GMM) in a high dimensional joint multimodal embedding space. The joint embedding space is learned by Common Representation Learning and embeds both the text and the image queries. Our system remembers the context of the dialog, and uses an exploration-exploitation paradigm to assist in visual browsing. We train and evaluate the system on an MMD dataset that we synthesize from large catalog data. Our experiments and preliminary human evaluation show that the system is capable of learning and displaying relevant products with an average cosine similarity of 0.85 to the ground truth results, and is capable of engaging human users.