Reviewerly/exHarmony huggingface dataset:
DeepJS

Job scheduling is a key building block of a cloud data center. Hand-crafted heuristics cannot automatically adapt to the change of the environment and optimize for specific workloads. We present the DeepJS, a job scheduling algorithm based on deep reinforcement learning under the framework of the bin packing problem. DeepJS can automatically obtain a fitness calculation method which will minimize the makespan (maximize the throughput) of a set of jobs directly from experience. Through a trace-driven simulation, we demonstrate the convergence and generalization of DeepJS and the essence of DeepJS learning. The results prove that DeepJS outperforms the heuristic-based job scheduling algorithms.
Few Shot Learning in Histopathological Images:Reducing the Need of Labeled Data on Biological Datasets

Although deep learning pathology diagnostic algorithms are proving comparable results with human experts in a wide variety of tasks, they still require a huge amount of well annotated data for training. Generating such extensive and well labelled datasets is time consuming and is not feasible for certain tasks and so, most of the medical datasets available are scarce in images and therefore, not enough for training. In this work we validate that the use of few shot learning techniques can transfer knowledge from a well defined source domain from Colon tissue into a more generic domain composed by Colon, Lung and Breast tissue by using very few training images. Our results show that our few-shot approach is able to obtain a balanced accuracy (BAC) of 90% with just 60 training images, even for the Lung and Breast tissues that were not present on the training set. This outperforms the finetune transfer learning approach that obtains 73% BAC with 60 images and requires 600 images to get up to 81% BAC.
Detecting Credit Card Fraud Using Selected Machine Learning Algorithms

Due to the immense growth of e-commerce and increased online based payment possibilities, credit card fraud has become deeply relevant global issue. Recently, there has been major interest for applying machine learning algorithms as data mining technique for credit card fraud detection. However, number of challenges appear, such as lack of publicly available data sets, highly imbalanced class sizes, variant fraudulent behavior etc. In this paper we compare performance of three machine learning algorithms: Random Forest, Support Vector Machine and Logistic Regression in detecting fraud on real-life data containing credit card transactions. To mitigate imbalanced class sizes, we use SMOTE sampling method. The problem of ever-changing fraud patterns is considered with employing incremental learning of selected ML algorithms in experiments. The performance of the techniques is evaluated based on commonly accepted metric: precision and recall.
Escape from the Skinner Box: The case for contemporary intelligent learning environments

Abstract Intelligent Tutoring systems (ITSs) and Intelligent Learning Environments (ILEs) have been developed and evaluated over the last 40 years. Recent meta‐analyses show that they perform well enough to act as effective classroom assistants under the guidance of a human teacher. Despite this success, they have been criticised as embodying a retrograde behaviourist technology. They have also been caught up in broader controversies about the role of Artificial Intelligence in society and about the entry of big data companies into the education market and the harvesting of learner data. This paper concentrates on rebutting the criticisms of the pedagogy of ITSs and ILEs. It offers examples of how a much wider range of pedagogies are available than their critics claim. These wider pedagogies operate at both the screen level of individual systems, as well as at the classroom level within which the systems are orchestrated by the teacher. It argues that there are many ways that such systems can be integrated by the teacher into the overall experience of a class. Taken together, the screen‐level and orchestration‐level dramatically enlarge the range of pedagogies beyond what was possible with the “Skinner Box.”
Efficient high average-utility itemset mining using novel vertical weak upper-bounds

Discovering high average utility itemsets (HAUIs) in a quantitative database is a popular data mining task, which aims at identifying sets of products (items) purchased together that have a high importance or yield a high profit. However, a key challenge of HAUI mining is that the downward-closure property (DCP) does not hold for the average-utility measure. Many studies have designed average-utility upper-bounds (UBs), which satisfy the DCP, to overestimate the average-utilities of itemsets. Several data structures and algorithms were proposed based on those UBs to discover all HAUIs. However, the proposed UBs are still loose, and the designed structures are still inefficient for computing UBs. To address this issue, this paper proposes four novel weak upper-bounds (WUBs) on the average-utility based on a vertical database representation, two new efficient pruning and tightening strategies to eliminate unpromising candidates early, and a novel list structure named NVUV-list to represent the search space and calculate the proposed WUBs quickly. The novel WUBs are tighter than well-known UBs and WUBs used in state-of-the-art algorithms such as MHAI, TUB-HAUPM and dHAUIM for mining HAUIs. Moreover, the NVUV-list structure is shown to be more efficient for computing vertical WUBs than the IDUL structure employed by dHAUIM. Based on the novel WUBs and NVUV-list structure, a novel algorithm named VMHAUI is proposed for HAUI mining. Experimental results show that it outperforms the state-of-the-art MHAI, TUB-HAUPM and dHAUIM algorithms by one to two orders of magnitudes, and that VMHAUI consumes less memory and performs much less join operations.
Hyperparameter optimization in black-box image processing using differentiable proxies

Nearly every commodity imaging system we directly interact with, or indirectly rely on, leverages power efficient, application-adjustable black-box hardware image signal processing (ISPs) units, running either in dedicated hardware blocks, or as proprietary software modules on programmable hardware. The configuration parameters of these black-box ISPs often have complex interactions with the output image, and must be adjusted prior to deployment according to application-specific quality and performance metrics. Today, this search is commonly performed manually by "golden eye" experts or algorithm developers leveraging domain expertise. We present a fully automatic system to optimize the parameters of black-box hardware and software image processing pipelines according to any arbitrary (i.e., application-specific) metric. We leverage a differentiable mapping between the configuration space and evaluation metrics, parameterized by a convolutional neural network that we train in an end-to-end fashion with imaging hardware in-the-loop. Unlike prior art, our differentiable proxies allow for high-dimension parameter search with stochastic first-order optimizers, without explicitly modeling any lower-level image processing transformations. As such, we can efficiently optimize black-box image processing pipelines for a variety of imaging applications, reducing application-specific configuration times from months to hours. Our optimization method is fully automatic, even with black-box hardware in the loop. We validate our method on experimental data for real-time display applications, object detection, and extreme low-light imaging. The proposed approach outperforms manual search qualitatively and quantitatively for all domain-specific applications tested. When applied to traditional denoisers, we demonstrate that---just by changing hyperparameters---traditional algorithms can outperform recent deep learning methods by a substantial margin on recent benchmarks.
Two Stream 3D Semantic Scene Completion

Inferring the 3D geometry and the semantic meaning of surfaces, which are occluded, is a very challenging task. Recently, a first end-to-end learning approach has been proposed that completes a scene from a single depth image. The approach voxelizes the scene and predicts for each voxel if it is occupied and, if it is occupied, the semantic class label. In this work, we propose a two stream approach that leverages depth information and semantic information, which is inferred from the RGB image, for this task. The approach constructs an incomplete 3D semantic tensor, which uses a compact three-channel encoding for the inferred semantic information, and uses a 3D CNN to infer the complete 3D semantic tensor. In our experimental evaluation, we show that the proposed two stream approach substantially outperforms the state-of-the-art for semantic scene completion.
Automatically Dismantling Online Dating Fraud

Online romance scams are a prevalent form of mass-marketing fraud in the West, and yet few studies have addressed the technical or data-driven responses to this problem.In this type of scam, fraudsters craft fake profiles and manually interact with their victims.Because of the characteristics of this type of fraud and of how dating sites operate, traditional detection methods (e.g., those used in spam filtering) are ineffective.In this paper, we present the results of a multi-pronged investigation into the archetype of online dating profiles used in this form of fraud, including their use of demographics, profile descriptions, and images, shedding light on both the strategies deployed by scammers to appeal to victims and the traits of victims themselves.Further, in response to the severe financial and psychological harm caused by dating fraud, we develop a system to detect romance scammers on online dating platforms.Our work presents the first system for automatically detecting this fraud.Our aim is to provide an early detection system to stop romance scammers as they create fraudulent profiles or before they engage with potential victims.Previous research has indicated that the victims of romance scams score highly on scales for idealized romantic beliefs.We combine a range of structured, unstructured, and deep-learned features that capture these beliefs.No prior work has fully analyzed whether these notions of romance introduce traits that could be leveraged to build a detection system.Our ensemble machine-learning approach is robust to the omission of profile details and performs at high accuracy (97%).The system enables development of automated tools for dating site providers and individual users.
Gray-box adversarial testing for control systems with machine learning components

Neural Networks (NN) have been proposed in the past as an effective means for both modeling and control of systems with very complex dynamics. However, despite the extensive research, NN-based controllers have not been adopted by the industry for safety critical systems. The primary reason is that systems with learning based controllers are notoriously hard to test and verify. Even harder is the analysis of such systems against system-level specifications. In this paper, we provide a gradient based method for searching the input space of a closed-loop control system in order to find adversarial samples against some system-level requirements. Our experimental results show that combined with randomized search, our method outperforms Simulated Annealing optimization.
Pixel Level Data Augmentation for Semantic Image Segmentation Using Generative Adversarial Networks

Semantic segmentation is one of the basic topics in computer vision, it aims to assign semantic labels to every pixel of an image. Unbalanced semantic label distribution could have a negative influence on segmentation accuracy. In this paper, we investigate using data augmentation approach to balance the semantic label distribution in order to improve segmentation performance. We propose using generative adversarial networks (GANs) to generate realistic images for improving the performance of semantic segmentation networks. Experimental results show that the proposed method can not only improve segmentation performance on those classes with low accuracy, but also obtain 1.3% to 2.1% increase in average segmentation accuracy. It shows that this augmentation method can boost the accuracy and be easily applicable to any other segmentation models.
Multi-Task Learning of Hierarchical Vision-Language Representation

It is still challenging to build an AI system that can perform tasks that involve vision and language at human level. So far, researchers have singled out individual tasks separately, for each of which they have designed networks and trained them on its dedicated datasets. Although this approach has seen a certain degree of success, it comes with difficulties of understanding relations among different tasks and transferring the knowledge learned for a task to others. We propose a multi-task learning approach that enables to learn vision-language representation that is shared by many tasks from their diverse datasets. The representation is hierarchical, and prediction for each task is computed from the representation at its corresponding level of the hierarchy. We show through experiments that our method consistently outperforms previous single-task-learning methods on image caption retrieval, visual question answering, and visual grounding. We also analyze the learned hierarchical representation by visualizing attention maps generated in our network.
Effect of Data Reduction on Sequence-to-sequence Neural TTS

Recent speech synthesis systems based on sampling from autoregressive neural network models can generate speech almost indistinguishable from human recordings. However, these models require large amounts of data. This paper shows that the lack of data from one speaker can be compensated with data from other speakers. The naturalness of Tacotron2-like models trained on a blend of 5k utterances from 7 speakers is better than or equivalent to that of speaker dependent models trained on 15k utterances. Additionally, in terms of stability multi-speaker models are always more stable. We also demonstrate that models mixing only 1250 utterances from a target speaker with 5k utterances from another 6 speakers can produce significantly better quality than state-of-the-art DNN-guided unit selection systems trained on more than 10 times the data from the target speaker.
A systematic literature review of software effort prediction using machine learning methods

Abstract Machine learning (ML) techniques have been widely investigated for building prediction models, able to estimate software development effort as well as to improve the accuracy of other estimation techniques. The objective of this paper is to systematically review the recent studies which used and discussed the software effort estimation models built using ML techniques. The performed literature review is based on the empirical studies published in the time period of January 1991 to December 2017, by employing widely used guidelines. The review has selected a total of 75 primary studies after the careful filtering of inclusion/exclusion and quality assessment criteria. The performed analysis reveals that artificial neural network (ANN) as ML model, NASA as dataset, and mean magnitude of relative error (MMRE) as accuracy measure are widely used in the selected studies. ANN and support vector machine (SVM) are the two techniques which have outperformed other ML techniques in more studies. Regression techniques are the mostly used among the non‐ML techniques, which outperformed other ML techniques in about 19 studies. Moreover, SVM and regression techniques in combination are characterized by better predictions when compared with other ML and non‐ML techniques.
Faceboxes: A CPU real-time and accurate unconstrained face detector

Although tremendous strides have been made in face detection, one of the remaining open issues is to achieve CPU real-time speed as well as maintain high performance, since effective models for face detection tend to be computationally prohibitive. To address this issue, we propose a novel face detector, named FaceBoxes, with superior performance on both speed and accuracy. Specifically, the proposed method has a lightweight yet powerful network that consists of the Rapidly Digested Convolution Layers (RDCL) and the Multiple Scale Convolution Layers (MSCL). The former is designed to enable FaceBoxes to achieve CPU real-time speed, while the latter aims to enrich the features and discretize anchors over different layers to handle faces of various scales. Besides, we propose a new anchor densification strategy to make different types of anchors have the same density on the image, which significantly improves the recall rate of small faces. Finally, we present a Divide and Conquer Head (DCH) to boost the prediction ability of the detection layer using above strategy. As a consequence, the proposed detector runs at 28 FPS on the CPU and 254 FPS using a GPU for VGA-resolution images. Moreover, the speed of FaceBoxes is invariant to the number of faces. We evaluate the proposed method on several face detection benchmarks including AFW, PASCAL face, FDDB, WIDER FACE and achieve state-of-the-art performance among CPU real-time methods.
Mental health in the global south

Mental illness is rapidly gaining recognition as a serious global challenge. Recent human-computer interaction (HCI) research has investigated mental health as a domain of concern, but is yet to venture into the Global South, where the problem exhibits a more complex, intersectional nature. In this paper, we review work on mental health in the Global South and present a case for HCI for Development (HCI4D) to look at mental health-both because it is an inarguably important area of concern in itself, and also because it impacts the efficacy of HCI4D interventions in other domains. We consider the role of cultural and resource-based interactions towards accessibility challenges and continuing stigma around mental health. We also identify participants' mental health as a constant consideration for HCI4D and present best practices for measuring and incorporating it. As an example, we demonstrate how both the process and the lens of aspirations-based design, a recently proposed approach for HCI4D research and design, may benefit from the consideration of mental health concerns. Our paper thus recommends a path forward for considering mental health in HCI4D, potentially leading to new research directions in addition to enriching existing ones.
Informative gene selection for microarray classification via adaptive elastic net with conditional mutual information

Due to the advantage of achieving a better performance under weak regularization, elastic net has attracted wide attention in statistics, machine learning, bioinformatics, and other fields. In particular, a variation of the elastic net, adaptive elastic net (AEN), integrates the adaptive grouping effect. In this paper, we aim to develop a new algorithm: Adaptive Elastic Net with Conditional Mutual Information (AEN-CMI) that further improves AEN by incorporating conditional mutual information into the gene selection process. We apply this new algorithm to screen significant genes for two kinds of cancers: colon cancer and leukemia. Compared with other algorithms including Support Vector Machine, Classic Elastic Net, Adaptive Lasso and Adaptive Elastic Net, the proposed algorithm, AEN-CMI, obtains the best classification performance using the least number of genes.
Global and Local Consistent Wavelet-Domain Age Synthesis

Age synthesis is a challenging task due to the complicated and non-linear transformation in the human aging process. Aging information is usually reflected in local facial parts, such as wrinkles at the eye corners. However, these local facial parts contribute less in previous GAN-based methods for age synthesis. To address this issue, we propose a wavelet-domain global and local consistent age generative adversarial network (WaveletGLCA-GAN), in which one global specific network and three local specific networks are integrated together to capture both global topology information and local texture details of human faces. Different from the most existing methods that modeling age synthesis in image domain, we adopt wavelet transform to depict the textual information in frequency domain. Moreover, five types of losses are adopted: 1) adversarial loss aims to generate realistic wavelets; 2) identity preserving loss aims to better preserve identity information; 3) age preserving loss aims to enhance the accuracy of age synthesis; 4) pixel-wise loss aims to preserve the background information of the input face; and 5) the total variation regularization aims to remove ghosting artifacts. Our method is evaluated on three face aging datasets, including CACD2000, Morph, and FG-NET. Qualitative and quantitative experiments show the superiority of the proposed method over other state-of-the-arts.
Private selection from private candidates

Differentially Private algorithms often need to select the best amongst many candidate options. Classical works on this selection problem require that the candidates’ goodness, measured as a real-valued score function, does not change by much when one person’s data changes. In many applications such as hyperparameter optimization, this stability assumption is much too strong. In this work, we consider the selection problem under a much weaker stability assumption on the candidates, namely that the score functions are differentially private. Under this assumption, we present algorithms that are near-optimal along the three relevant dimensions: privacy, utility and computational efficiency.
Notes on Computational Hardness of Hypothesis Testing: Predictions Using the Low-Degree Likelihood Ratio

These notes survey and explore an emerging method, which we call the low-degree method, for understanding statistical-versus-computational tradeoffs in high-dimensional inference problems. In short, the method posits that a certain quantity—the second moment of the low-degree likelihood ratio—gives insight into how much computational time is required to solve a given hypothesis testing problem, which can in turn be used to predict the computational hardness of a variety of statistical inference tasks. While this method originated in the study of the sum-of-squares (SoS) hierarchy of convex programs, we present a self-contained introduction that does not require knowledge of SoS. In addition to showing how to carry out predictions using the method, we include a discussion investigating both rigorous and conjectural consequences of these predictions. These notes include some new results, simplified proofs, and refined conjectures. For instance, we point out a formal connection between spectral methods and the low-degree likelihood ratio, and we give a sharp low-degree lower bound against subexponential-time algorithms for tensor PCA.