Reviewerly/exHarmony huggingface dataset:
The Queuing-First Approach for Tail Management of Interactive Services

Managing high-percentile tail latencies is key to designing user-facing cloud services. Rare system hiccups or unusual code paths make some requests take $\boldsymbol{10\;\times -100\;\times}$10×-100× longer than the average. Prior work seeks to reduce tail latency by trying to address primarily root causes of slow requests. However, often the bulk of requests comprising the tail are not these rare slow-to-execute requests. Rather, due to head-of-line blocking, most of the tail comprises requests enqueued behind slow-to-execute requests. Under high disparity service distributions, queuing effects drastically magnify the impact of rare system hiccups and can result in high tail latencies even under modest load. We demonstrate that improving the queuing behavior of a system often yields greater benefit than mitigating the individual system hiccups that increase service time tails. We suggest two general directions to improve system queuing behavior–server pooling and common-case service acceleration–and discuss circumstances where each is most beneficial.
Learning Task Relatedness in Multi-Task Learning for Images in Context

Multimedia applications often require concurrent solutions to multiple tasks. These tasks hold clues to each-others solutions, however as these relations can be complex this remains a rarely utilized property. When task relations are explicitly defined based on domain knowledge multi-task learning (MTL) offers such concurrent solutions, while exploiting relatedness between multiple tasks performed over the same dataset. In most cases however, this relatedness is not explicitly defined and the domain expert knowledge that defines it is not available. To address this issue, we introduce Selective Sharing, a method that learns the inter-task relatedness from secondary latent features while the model trains. Using this insight, we can automatically group tasks and allow them to share knowledge in a mutually beneficial way. We support our method with experiments on 5 datasets in classification, regression, and ranking tasks and compare to strong baselines and state-of-the-art approaches showing a consistent improvement in terms of accuracy and parameter counts. In addition, we perform an activation region analysis showing how Selective Sharing affects the learned representation.
Blockchain for economically sustainable wireless mesh networks

Summary Decentralization, in the form of mesh networking and blockchain, two promising technologies, is coming to the telecommunications industry. Mesh networking allows wider low‐cost Internet access with infrastructures built from routers contributed by diverse owners, whereas blockchain enables transparency and accountability for investments, revenue, or other forms of economic compensations from sharing of network traffic, content, and services. Crowdsourcing network coverage, combined with crowdfunding costs, can create economically sustainable yet decentralized Internet access. This means that every participant can invest in resources and pay or be paid for usage to recover the costs of network devices and maintenance. While mesh networks and mesh routing protocols enable self‐organized networks that expand organically, cryptocurrencies and smart contracts enable the economic coordination among network providers and consumers. We explore and evaluate two existing blockchain software stacks, Hyperledger Fabric (HLF) and Ethereum geth with Proof of Authority (PoA) intended as a local lightweight distributed ledger, deployed in a real city‐wide production mesh network and in laboratory network. We quantify the performance and bottlenecks and identify the current limitations and opportunities for improvement to serve locally the needs of wireless mesh networks, without the privacy and economic cost of relying on public blockchains.
An improved differential evolution algorithm using Archimedean spiral and neighborhood search based mutation approach for cluster analysis

This study proposes an improved Differential Evolution (DE) algorithm utilizing Archimedean Spiral, Mantegna Lévy Distribution, and Neighborhood Search (NS). The proposed algorithm is denoted as Adaptive Differential Evolution with Neighborhood Search (ADENS). The aim of the ADENS algorithm is to enhance the convergence speed and keeping a balance between exploration and exploitation of the DE algorithm. It uses a new mutation strategy to generate robust solutions by combining the Archimedean Spiral (AS) with the Mantegna Lévy flight. In order to enhance the efficiency of ADENS, a replacement method combines Lévy flight with neighborhood search to generate solutions that replace poorly performing ones. A self-adaptive strategy is applied to fine-tune one of the control parameters of DE and an initialization method is employed to initialize the algorithm. These strategies help the algorithm achieve good efficiency in terms of convergence speed and both local and global search. The performance is evaluated using twelve well-known standard data sets to show the algorithm’s superior performance, confirmed to be statistically significant. In order to test the proposed approach statistically, this paper applied Wilcoxon ranked sum test as well as Friedman test. Our results also shed light on the comparative performance of some recently published clustering heuristics. The results show that the algorithm is a robust algorithm and has a great superiority with respect to the employed algorithms. The proposed algorithm can be applied in different applications such as medical diagnosis and image segmentation according to the conducted experiments.
MobiFace: A Novel Dataset for Mobile Face Tracking in the Wild

Face tracking serves as the crucial initial step in mobile applications trying to analyse target faces over time in mobile settings. However, this problem has received little attention, mainly due to the scarcity of dedicated face tracking benchmarks. In this work, we introduce MobiFace, the first dataset for single face tracking in mobile situations. It consists of 80 unedited live-streaming mobile videos captured by 70 different smartphone users in fully unconstrained environments. Over 95K bounding boxes are manually labelled. The videos are carefully selected to cover typical smartphone usage. The videos are also annotated with 14 attributes, including 6 newly proposed attributes and 8 commonly seen in object tracking. 36 state-of-the-art trackers, including facial landmark trackers, generic object trackers and trackers that we have fine-tuned or improved, are evaluated. The results suggest that mobile face tracking cannot be solved through existing approaches. In addition, we show that fine-tuning on the MobiFace training data significantly boosts the performance of deep learning-based trackers, suggesting that MobiFace captures the unique characteristics of mobile face tracking. Our goal is to offer the community a diverse dataset to enable the design and evaluation of mobile face trackers. The dataset, annotations and the evaluation server will be on https://mobiface.github.io/.
Robust template matching with large angle localization

Template matching is an important solution for the object detection in instance-level. Many kinds of matching methods have been utilized to find the template position in the target image. But most of them ignore the matching angle, which causes the result can’t satisfy the detection accuracy when the object has a big in-plane rotation. In this paper, we propose a robust method for template matching with large angle localization. The basic idea is to iteratively search the corresponding patch-features and updating the template location with rotation transformation. The method firstly employs the intensity centroid to rectify local patches as rotation-invariant features. And then, Best-Buddies Pairs (BBPs) are extracted to find corresponding features between template and target images. To further enhance the robustness against outliers, a robust objective function is presented to register features based on the Maximum Correntropy Criterion and optimized for the transformation with translation and rotation parameters. Experimental results demonstrate the effectiveness and robustness of the proposed method.
A Low Latency On-Body Typing System through Single Vibration Sensor

Nowadays, smart wristbands have become one of the most prevailing wearable devices, as they are small and portable. However, due to the limited size of the touch screens, smart wristbands typically have poor interactive experience. There are a few works appropriating the human body as a surface to type on. Yet, by using multiple sensors at high sampling rates, they are not portable and are energy-consuming in practice. To break this stalemate, we proposed a portable, cost efficient text-entry system, termed ViType, which first leverages a single small form factor sensor to achieve a practical user input with much lower sampling rates. To enhance the input accuracy with less vibration information introduced by lower sampling rates, ViType designs a set of novel mechanisms, including a fine-grained feature extraction to process the vibration signals, and a runtime calibration and adaptation scheme to recover from the error due to temporal instability. Extensive experiments have been conducted on 30 human subjects. The results demonstrate that ViType is robust against various confounding factors. The average recognition accuracy is 95 percent with an initial training sample size of 20 for each key. The accuracy is 1.54 times higher than the state-of-the-art on-body typing system. Furthermore, when turning on the runtime calibration and adaptation system to update and enlarge the training sample size, the accuracy can reach around 98 percent on average during one month.
Partitioned Data Security on Outsourced Sensitive and Non-Sensitive Data

Despite extensive research on cryptography, secure and efficient query processing over outsourced data remains an open challenge. This paper continues along the emerging trend in secure data processing that recognizes that the entire dataset may not be sensitive, and hence, non-sensitivity of data can be exploited to overcome limitations of existing encryption-based approaches. We propose a new secure approach, entitled query binning (QB) that allows non-sensitive parts of the data to be outsourced in clear-text while guaranteeing that no information is leaked by the joint processing of non-sensitive data (in clear-text) and sensitive data (in encrypted form). QB maps a query to a set of queries over the sensitive and non-sensitive data in a way that no leakage will occur due to the joint processing over sensitive and non-sensitive data. Interestingly, in addition to improve performance, we show that QB actually strengthens the security of the underlying cryptographic technique by preventing size, frequency-count, and workload-skew attacks.
Gaussian mixture models as automated particle classifiers for fast neutron detectors

Abstract Pulse shape discrimination (PSD) is the task of classifying electronic pulse shapes for different particle types such as gamma rays and fast neutrons interacting in scintillators and read out by photo sensitive detectors. This field has been limited in its adoption of techniques found in the statistical learning community. Methods initially employed in the 1960s for analog electronic circuitry persist in the current PSD literature describing operations performed on digitized pulses, which are amenable to statistical rigor. Despite vast amounts of data collected at low energy levels, traditional PSD methods are unable to discriminate particles below a certain threshold. In this work, Gaussian mixture models (GMMs) are used as a clustering technique for fast neutron detection in the absence of labeled data. GMMs yield improvements spanning the energy spectrum in a desirably efficient, unsupervised fashion. An extension, the Dirichlet Process GMM, provides further flexibility and classification improvement.
Weighted external difference families and R-optimal AMD codes

In this paper, we provide a mathematical framework for characterizing AMD codes that are R-optimal. We introduce a new combinatorial object, the reciprocally-weighted external difference family (RWEDF), which corresponds precisely to an R-optimal weak AMD code. This definition subsumes known examples of existing optimal codes, and also encompasses combinatorial objects not covered by previous definitions in the literature. By developing structural group-theoretic characterizations, we exhibit infinite families of new RWEDFs, and new construction methods for known objects such as near-complete EDFs. Examples of RWEDFs in non-abelian groups are also discussed.
Towards learning-to-learn

In good old-fashioned artificial intelligence (GOFAI), humans specified systems that solved problems. Much of the recent progress in AI has come from replacing human insights by learning. These learning systems are usually built by humans. Yet there is no reason to believe that humans are particularly good at defining such systems: we may expect learning to be better if we learn it. Recent research in machine learning has started to realize the benefits of that strategy. We should thus expect this to be relevant for neuroscience: how could the correct learning rules be acquired? Cognitive science has long shown that humans learn-to-learn. Here we discuss ideas across machine learning, neuroscience, and cognitive science that matter for the principle of learning-to-learn.
Invertible Autoencoder for Domain Adaptation

The unsupervised image-to-image translation aims at finding a mapping between the source ( A ) and target ( B ) image domains, where in many applications aligned image pairs are not available at training. This is an ill-posed learning problem since it requires inferring the joint probability distribution from marginals. Joint learning of coupled mappings F A B : A → B and F B A : B → A is commonly used by the state-of-the-art methods, like CycleGAN to learn this translation by introducing cycle consistency requirement to the learning problem, i.e., F A B ( F B A ( B ) ) ≈ B and F B A ( F A B ( A ) ) ≈ A . Cycle consistency enforces the preservation of the mutual information between input and translated images. However, it does not explicitly enforce F B A to be an inverse operation to F A B . We propose a new deep architecture that we call invertible autoencoder (InvAuto) to explicitly enforce this relation. This is done by forcing an encoder to be an inverted version of the decoder, where corresponding layers perform opposite mappings and share parameters. The mappings are constrained to be orthonormal. The resulting architecture leads to the reduction of the number of trainable parameters (up to 2 times). We present image translation results on benchmark datasets and demonstrate state-of-the art performance of our approach. Finally, we test the proposed domain adaptation method on the task of road video conversion. We demonstrate that the videos converted with InvAuto have high quality and show that the NVIDIA neural-network-based end-to-end learning system for autonomous driving, known as PilotNet, trained on real road videos performs well when tested on the converted ones.
Analysis of Abdominal Computed Tomography Images for Automatic Liver Cancer Diagnosis Using Image Processing Algorithm

The application of image processing algorithms for medical image analysis has been found effectual in the past years. Imaging techniques provide assistance to the radiologists and physicians for the diagnosis of abnormalities in different organs.The proposed algorithm is designed for automatic computer-aided diagnosis of liver cancer from low contrast CT images. The idea expressed in this article is to classify the malignancy of the liver tumor ahead of liver segmentation and to locate HCC burden on the liver.A novel Fuzzy Linguistic Constant (FLC) is designed for image enhancement. To classify the enhanced liver image as cancerous or non-cancerous, fuzzy membership function is applied. The extracted features are assessed for malignancy and benignancy using the structural similarity index. The malignant CT image is further processed for automatic tumor segmentation and grading by applying morphological image processing techniques.The validity of the concept is verified on a dataset of 179 clinical cases which consist of 98 benign and 81 malignant liver tumors. Classification accuracy of 98.3% is achieved by Support Vector Machine (SVM). The proposed method has the ability to automatically segment the tumor with an improved detection rate of 78% and a precision value of 0.6.The algorithm design offers an efficient tool to the radiologist in classifying the malignant cases from benign cases. The CAD system allows automatic segmentation of tumor and locates tumor burden on the liver. The methodology adopted can aid medical practitioners in tumor diagnosis and surgery planning.
Muti-behavior features based knowledge tracking using decision tree improved DKVMN

Knowledge tracing is a significant research topic in student modeling. It is a task to model students' mastery level of knowledge points by mining their historical exercise performance. Literature has shown that Dynamic Key-Value Memory Networks (DKVMN) which has been proposed to handle the knowledge tracing task generally outperform traditional methods. However, through our experimentation, we have noticed a problem in the DKVMN model that it ignored behavior features collected by intelligence tutoring system (ITS) but only regard the exercise and the correctness as input. Behavior features, such as the response time, the hint request and the number of attempt, can be used to capture the student's learning behavior information and are very helpful in modeling the student's knowledge status. Therefore, the performance of the model can be improved. This work aims to improve the performance of the DKVMN model by incorporating more features to the input. More specifically, we apply decision tree classifier to preprocess the behavior features, which is an effective way to capture how the student deviates from others in the exercise. The predicted response concatenated with the exercise tag to train a DKVMN model, which can output the probability that a student will answer the exercise correctly. The experiment results show that our adapted DKVMN model, incorporating more combinations of behavior features can effectively improve accuracy. On the ASSISTments 2009 education dataset, the AUC value of our experiment is eight percent higher than the original DKVMN model.
Probabilistic Inference Based on LS-Method Modifications in Decision Making Problems

The article provides information on the modification of the Lauritzen-Spiegelhalter (LS) method for constructing a probabilistic inference in the Bayesian network. Modification of the method consists in a new way of filling tables of conditional probabilities. The method consists of two stages: the first stage – the construction of a combined tree, the second stage – the construction of the distribution algorithm. At the first stage, the construction of a combined tree is performed by clicking on the primary structure of the network and filling the vertices of this tree with tables of conditional probabilities of the network. The second stage of the LS-method is the refinement of the distribution algorithm. The modified method allows us to more accurately calculate a posteriori probabilities and build a probabilistic inference. The example of evaluation of decision-making options for the construction of solar and wind power plants is considered. The features of the application of the method are analyzed.
Improving Sentence Retrieval from Case Law for Statutory Interpretation

Statutory texts employ vague terms that are difficult to understand. Here we study and evaluate methods for retrieving useful sentences from court opinions that elaborate on the meaning of a vague statutory term. Retrieving sentences instead of whole cases may spare a user the need to review long lists of cases in search of useful explanations. We assembled a data set of 4,635 sentences that were responses to three statutory queries and labeled them in terms of their usefulness for interpretation. We have run a series of experiments on this data set, which we have made public, assessing different techniques to solve the task. These include techniques that measure the similarity between the sentence and the query, utilize the context of a sentence, expand queries, or assess the novelty of a sentence with respect to a statutory provision from which the interpreted term comes. Based on a detailed error analysis we propose a specialized sentence retrieval framework that mitigates the challenges of retrieving case law sentences for interpreting statutory terms. The results of evaluating different implementations of the framework are promising (.725 for NDGC at 10, .662 at 100).
Accurate Nuclear Segmentation with Center Vector Encoding

Nuclear segmentation is important and frequently demanded for pathology image analysis, yet is also challenging due to nuclear crowdedness and possible occlusion. In this paper, we present a novel bottom-up method for nuclear segmentation. The concepts of Center Mask and Center Vector are introduced to better depict the relationship between pixels and nuclear instances. The instance differentiation process are thus largely simplified and easier to understand. Experiments demonstrate the effectiveness of Center Vector Encoding, where our method outperforms state-of-the-arts by a clear margin.
Temporal Distance Matrices for Squat Classification

When working out, it is necessary to perform the same action many times for it to have effect. If the action, such as squats or bench pressing, is performed with poor form, it can lead to serious injuries in the long term. For this purpose, we present an action dataset of squats where different types of poor form have been annotated with a diversity of users and backgrounds, and propose a model, based on temporal distance matrices, for the classification task. We first run a 3D pose detector, then we normalize the pose and compute the distance matrix, in which each element represents the distance between two joints. This representation is invariant to differences in individuals, global translation, and global rotation, allowing for high generalization to real world data. Our classification model consists of a CNN with 1D convolutions. Results show that our method significantly outperforms existing approaches for the task.
Stein‐type shrinkage estimators in gamma regression model with application to prostate cancer data

Gamma regression is applied in several areas such as life testing, forecasting cancer incidences, genomics, rainfall prediction, experimental designs, and quality control. Gamma regression models allow for a monotone and no constant hazard in survival models. Owing to the broad applicability of gamma regression, we propose some novel and improved methods to estimate the coefficients of gamma regression model. We combine the unrestricted maximum likelihood (ML) estimators and the estimators that are restricted by linear hypothesis, and we present Stein‐type shrinkage estimators (SEs). We then develop an asymptotic theory for SEs and obtain their asymptotic quadratic risks. In addition, we conduct Monte Carlo simulations to study the performance of the estimators in terms of their simulated relative efficiencies. It is evident from our studies that the proposed SEs outperform the usual ML estimators. Furthermore, some tabular and graphical representations are given as proofs of our assertions. This study is finally ended by appraising the performance of our estimators for a real prostate cancer data.