Reviewerly/exHarmony huggingface dataset:
Private Secure Coded Computation

We present private secure coded computation that ensures both the master's privacy and data security against the workers, where the master aims to compute a function of its private data and a specific data in a library exclusively owned by the external workers. For privacy, the master should conceal the index of the specific data in the library against the workers. For security, the master should encrypt its private data. As an achievable scheme, we propose private secure polynomial codes and compare the proposed scheme with private polynomial codes in private coded computation and the optimal scheme of robust private information retrieval (RPIR).
How Fair Can We Go

The presence of bias in today's IR systems has raised concerns on the social responsibilities of IR. Fairness has become an increasingly important factor when building systems for information searching and content recommendations. Fairness in IR is often considered as an optimization problem where the system aims to optimize the utility, subject to a set of fairness constraints, or optimize fairness while guaranteeing a lower bound on the utility, or jointly optimize for both utility and fairness to achieve an overall satisfaction. While various optimization algorithms have been proposed along with theoretical analysis, in real world applications, the performance of different optimization algorithms often heavily depend on the data. Therefore, it is consequential to ask what is the solution space characterized by the data, what effect does introducing fairness bring to the system, and can we identify this solution space to help us trade-off different optimization policies and guide us to pick suitable algorithms and/or make adjustments on data? In this work, we propose a framework that offers a novel perspective into the optimization with fairness constraints problems. Our framework can effectively and efficiently estimate the solution space and answer such questions. It also has the advantage of simplicity, explainability, and reliability. Specifically, we derive theoretical expressions to identify the fairness and relevance bounds for data of different distributions, and apply them to both synthetic and real world datasets. We present a series of use cases to demonstrate how our framework is applied to facilitate various analyses and decision making.
Collective Affinity Learning for Partial Cross-Modal Hashing

In the past decade, various unsupervised hashing methods have been developed for cross-modal retrieval. However, in real-world applications, it is often the incomplete case that every modality of data may suffer from some missing samples. Most existing works assume that every object appears in both modalities, hence they may not work well for partial multi-modal data. To address this problem, we propose a novel Collective Affinity Learning Method (CALM), which collectively and adaptively learns an anchor graph for generating binary codes on partial multi-modal data. In CALM, we first construct modality-specific bipartite graphs collectively, and derive a probabilistic model to figure out complete data-to-anchor affinities for each modality. Theoretical analysis reveals its ability to recover missing adjacency information. Moreover, a robust model is proposed to fuse these modality-specific affinities by adaptively learning a unified anchor graph. Then, the neighborhood information from the learned anchor graph acts as feedback, which guides the previous affinity reconstruction procedure. To solve the formulated optimization problem, we further develop an effective algorithm with linear time complexity and fast convergence. Last, Anchor Graph Hashing (AGH) is conducted on the fused affinities for cross-modal retrieval. Experimental results on benchmark datasets show that our proposed CALM consistently outperforms the existing methods.
Code Failure Prediction and Pattern Extraction Using LSTM Networks

In this paper, we use a well-known Deep Learning technique called Long Short Term Memory (LSTM) recurrent neural networks to find sessions that are prone to code failure in applications that rely on telemetry data for system health monitoring. We also use LSTM networks to extract telemetry patterns that lead to a specific code failure. For code failure prediction, we treat the telemetry events, sequence of telemetry events and the outcome of each sequence as words, sentence and sentiment in the context of sentiment analysis, respectively. Our proposed method is able to process a large set of data and can automatically handle edge cases in code failure prediction. We take advantage of Bayesian optimization technique to find the optimal hyper parameters as well as the type of LSTM cells that leads to the best prediction performance. We then introduce the Contributors and Blockers concepts. In this paper, contributors are the set of events that casue a code failure, while blockers are the set of events that each of them individually prevents a code failure from happening, even in presence of one or multiple contributor(s). Once the proposed LSTM model is trained, we use a greedy approach to find the contributors and blockers. To develop and test our proposed method, we use synthetic (simulated) data in the first step. The synthetic data is generated using a number of rules for code failures, as well as a number of rules for preventing a code failure from happening. The trained LSTM model shows over 99% accuracy for detecting code failures in the synthetic data. The results from the proposed method outperform the classical learning models such as Decision Tree and Random Forest. Using the proposed greedy method, we are able to find the contributors and blockers in the synthetic data in more than 90% of the cases, with a performance better than sequential rule and pattern mining algorithms. In the next step, we train and test our proposed LSTM method on real data that we collected from sequences of activities performed by millions of Microsoft Office customers.
Feature Selection Technique for Effective Software Effort Estimation Using Multi-Layer Perceptrons

The software effort estimation is essentially required to be done effectively and accurately for delivering quality product within the budget limits on time. A feature selection technique is proposed to effectively estimate effort using non-linear model based on the multilayer perceptron architecture. The objective is to find out whether the feature selection technique improves the accuracy of the prediction model developed. A prediction model (PRED_MLP) is built using Multilayer perceptron architecture with back propagation algorithm. Accuracy of the proposed model is compared with the accuracy of another proposed model (PRED_MLP_FS) which is backed with feature selection technique based on neighborhood component analysis. The dataset from Desharnais Project are used. The accuracy of proposed models is assessed and empirical comparison is also made between the prediction powers of these two predictors using standard metrics. Both the proposed models namely PRED_MLP and PRED_MLP_FS are validated. The experimental work shows that the model PRED_MLP_FS outperforms the model PRED_MLP. The results are statistically significant and suggest that the feature selection techniques can improve the accuracy of the prediction model upto 40%. Therefore, some input parameters can be dropped without loss in estimation accuracy.
The Illusion of Animal Body Ownership and Its Potential for Virtual Reality Games

Virtual reality offers the unique possibility to experience a virtual representation as our own body. In contrast to previous research that predominantly studied this phenomenon for humanoid avatars, our work focuses on virtual animals. In this paper, we discuss different body tracking approaches to control creatures such as spiders or bats and the respective virtual body ownership effects. Our empirical results demonstrate that virtual body ownership is also applicable for nonhumanoids and can even outperform human-like avatars in certain cases. An additional survey confirms the general interest of people in creating such experiences and allows us to initiate a broad discussion regarding the applicability of animal embodiment for educational and entertainment purposes.
Movement patterns of a particle swarm in high dimensional spaces

In high dimensional problem spaces, particle swarm optimization (PSO) is prone to unwanted roaming behaviour due to initial velocity explosion. A particle swarm’s movement patterns are strongly influenced by the inertia weight and acceleration coefficients. This paper investigates whether the initial velocity explosion can be curbed by appropriate choice of the inertia weight and the acceleration coefficients, which restrict the standard deviation of particle positions. It is shown that roaming behaviour cannot be solved by reducing swarm variance directly, but that the relationship between the parameters must also be considered. Furthermore, the paper investigates different movement patterns that may be exhibited by the swarm. It is shown that optimal parameter configurations differ between low and high dimensional problems. Specifically, parameter configurations which produce very smooth particle trajectories and restrict the swarm’s movement range are advantageous in high dimensional spaces. These movement patterns correspond to high inertia weight and low acceleration coefficients (eg. w=0.9694, c1=c2=0.099381). Swarms with smooth particle trajectories exhibited significantly less unwanted roaming behaviour than swarms with chaotic or oscillating particle trajectories.
Coarse-to-Fine Localization of Temporal Action Proposals

Localizing temporal action proposals from long videos is a fundamental challenge in video analysis (e.g., action detection and recognition or dense video captioning). Most existing approaches often overlook the hierarchical granularities of actions and thus fail to discriminate fine-grained action proposals (e.g., hand washing laundry or changing a tire in vehicle repair). In this paper, we propose a novel coarse-to-fine temporal proposal (CFTP) approach to localize temporal action proposals by exploring different action granularities. Our proposed CFTP consists of three stages: a coarse proposal network (CPN) to generate long action proposals, a temporal convolutional anchor network (CAN) to localize finer proposals, and a proposal reranking network (PRN) to further identify proposals from previous stages. Specifically, CPN explores three complementary actionness curves (namely pointwise, pairwise, and recurrent curves) that represent actions at different levels for generating coarse proposals, while CAN refines these proposals by a multiscale cascaded 1D-convolutional anchor network. In contrast to existing works, our coarse-to-fine approach can progressively localize fine-grained action proposals. We conduct extensive experiments on two action benchmarks (THUMOS14 and ActivityNet v1.3) and demonstrate the superior performance of our approach when compared to the state-of-the-art techniques on various video understanding tasks.
Scale-Aware Crowd Counting via Depth-Embedded Convolutional Neural Networks

Scale variation of pedestrians in a crowd image presents a significant challenge for vision-based people counting systems. Such variations are mainly caused by perspective-related distortions due to the camera pose relative to the ground plane. Following the density-based counting paradigm, we postulate that generating density values adaptive to object scales plays a critical role in the accuracy of the final counting results. Motivated by this, we distill the underlying information from depth cues to obtain scale-aware representations that can respond to object scales considering the fact that the scale is inversely proportional to the object depth. Specifically, we propose a depth embedding module as add-ons into existing networks. This module exploits essential depth cues to spatially re-calibrate the magnitude of the original features. In this way, the objects, although in the same class, will attain distinct representations according to their scales, which directly benefits the estimation of scale-aware density values. We conduct a comprehensive analysis of the effects of the depth embedding module and validate that exploiting depth cues to perceive object scale variations in convolutional neural networks improves crowd counting performances. Our experiments demonstrate the effectiveness of the proposed approach on four popular benchmark datasets.
Transformation-gated LSTM: efficient capture of short-term mutation dependencies for multivariate time series prediction tasks

Most multivariate time series data have very complex long-term and short-term dependencies that change over time. Currently, some recurrent neural network (RNN) variants for sequence tasks enhance the learning ability of long-term dependence on time series data. However, there lack of RNN network for capturing short-term mutation information for multivariate time series. In the present work, we proposed a transformation-gated LSTM (TG-LSTM) to enhance the ability of capturing short-term mutation information. First, the transformation gate introduced a hyperbolic tangent function to the memory cell state of the previous time step and the input gate information of the current time step without losing the memory cell state information. Then, the function value range of the partial derivative corresponding to the transformation gate during the backpropagation fully reflected the gradient change, thereby obtaining a better error gradient flow. We further extended to multi-layer TG-LSTM network and compared its stability and robustness with all baseline models. The multi-layer TG-LSTM network was superior to all baseline models in terms of prediction accuracy and performance stability on two different multivariate time series tasks.
Load balancing optimization based on hybrid Heuristic-Metaheuristic techniques in cloud environment

Load balancing among virtual machines (VMs) is significant for delivering the cloud services in optimized way with minimum cost paid and total time acquired to deliver the services. In this paper, the various research gaps for load balancing optimization in the past literature have been presented, which need to be addressed for solving the load balancing problem in cloud environment. In present work, Hybrid approach based resource provisioning and load balancing framework for workflows execution has been proposed to optimize the utilization of VMs with uniform load distribution. The proposed framework is based on the hybridization of heuristic techniques with metaheuristic algorithm to achieve its optimal performance in terms of makespan and cost. Two hybrid approaches have been proposed for HDD-PLB framework-Hybrid Predict Earliest Finish Time (PEFT) Heuristic with Ant Colony Optimization (ACO) metaheuristic (HPA) and Hybrid Heterogeneous Earliest Finish Time (HEFT) heuristic with ACO (HHA). The two proposed approaches for load balancing have been analyzed and compared to determine which is superior for proposed HDD-PLB framework.
Estimating and reacting to forces and torques resulting from common aerodynamic disturbances acting on quadrotors

Quadrotors are increasingly expected to perform a wide variety of tasks that put them in close proximity to other objects and surfaces in the environment (including other quadrotors), where they are often subject to significant external forces and torques resulting from aerodynamic effects. We present an algorithm – based on an Unscented Kalman Filter – that estimates such forces and torques without making assumptions about their source, allowing us to bypass much of the complexity involved in modeling how wind currents interact with quadrotor dynamics. Furthermore, our algorithm does not rely on special sensors, making it suitable for commercial systems where payload and add-on capabilities are limited. Via experiment we show that the estimation algorithm can be used in conjunction with controls and machine learning for detecting and avoiding downwash and walls, and for tracking wind from a fan. We also show that the algorithm is sensitive enough to measure even small changes in force and torque.
Pyramid-Structured Depth MAP Super-Resolution Based on Deep Dense-Residual Network

Although deep convolutional neural networks (DCNN) show significant improvement for single depth map (SD) super-resolution (SR) over the traditional counterparts, most SDSR DCNNs do not reuse the hierarchical features for depth map SR resulting in blurred high-resolution (HR) depth maps. They always stack convolutional layers to make network deeper and wider. In addition, most SDSR networks generate HR depth maps at a single level, which is not suitable for large up-sampling factors. To solve these problems, we present pyramid-structured depth map super-resolution based on deep dense-residual network. Specially, our networks are made up of dense residual blocks that use densely connected layers and residual learning to model the mapping between high-frequency residuals and low-resolution (LR) depth map. Furthermore, based on the pyramid structure, our network can progressively generate depth maps of various levels by taking advantages of features from different levels. The proposed network adopts a deep supervision scheme to reduce the difficulty of model training and further improve the performance. The proposed method is evaluated on Middlebury datasets which shows improved performance compared with 6 state-of-the-art methods.
A Transformer-Based Variational Autoencoder for Sentence Generation

The variational autoencoder(VAE) has been proved to be a most efficient generative model, but its applications in natural language tasks have not been fully developed. A novel variational autoencoder for natural texts generation is presented in this paper. Compared to the previously introduced variational autoencoder for natural text where both the encoder and decoder are RNN-based, we propose a new transformer-based architecture and augment the decoder with an LSTM language model layer to fully exploit information of latent variables. We also propose some methods to deal with problems during training time, such as KL divergency collapsing and model degradation. In the experiment, we use random sampling and linear interpolation to test our model. Results show that the generated sentences by our approach are more meaningful and the semantics are more coherent in the latent space.
Unsupervised distance learning for extended self-organizing map and visualization of mixed-type data

The original self-organizing map (SOM) was proposed in the context of processing numeric data. In previous studies, an extended SOM incorporating data structure distance hierarchies has been proposed to facilitate handling of categorical values. The model could take into account the semantics embed ded in categorical values via distance hierarchies. In addition to manual construction by domain experts, an approach to learning distance hierarchies from datasets has been devised. However, the proposed approach in the previous study was based on supervised learning which demands presence of a class attribute in the dataset. In real-world applications, class attribute may not be available. Thus, the supervised approach can be inapplicable. In this article, we present several methods of unsupervised learning of distance hierarchies so that neither are class attribute nor domain experts required in measuring similarity degree between categorical values. We then integrate the learned distance hierarchies with the extended SOM to facilitate the application to datasets without a class attribute. We conduct experiments to verify feasibility and compare performance of the proposed unsupervised-learning methods.
Coal mine area monitoring method by machine learning and multispectral remote sensing images

Coal is currently the main energy in the world. A large amount of gangue is produced during the coal mining process, and the gangue contains a large amount of metal and sulfide, which is one of the environmental pollution sources. Therefore, how to monitor changes in coal mines in real time is an important task. With regard to this problem, we propose a new method of monitoring coal mining area. First, we collect samples of coal, gangue, and other objects from the Shenhua Baori Xile coal mine, and then measure the ground measured spectral reflectance data of these samples. For satellite data, we remove the interference information of the reflectance spectra from these images and established a relationship with the ground measured data. We propose a tree root algorithm and then combine extreme learning machine to build a classification model. Finally, the model is applied to remote sensing images, and a good extraction effect is obtained. Compared with typical monitoring methods, our method has the superiorities of high precision, low-priced, and fast speed, and it can monitor the changes in the coal mine area in real time. This is the basis for improving the environmental pollution around the coal mining area.
Future-Aware Knowledge Distillation for Neural Machine Translation

Although future context is widely regarded useful for word prediction in machine translation, it is quite difficult in practice to incorporate it into neural machine translation. In this paper, we propose a future-aware knowledge distillation framework (FKD) to address this issue. In the FKD framework, we learn to distill future knowledge from a backward neural language model (teacher) to future-aware vectors (student) during the training phase. The future-aware vector for each word position is computed in a bridge network and optimized towards the corresponding hidden state in the backward neural language model via a knowledge distillation mechanism. We further propose an algorithm to jointly train the neural machine translation model, neural language model and knowledge distillation module end-to-end. The learned future-aware vectors are incorporated into the attention layer of the decoder to provide full-range context information during the decoding phase. Experiments on the NIST Chinese-English and WMT English-German translation tasks show that the proposed method significantly improves translation quality and word alignment.
Clustering Validation of CLARA and K-Means Using Silhouette &amp; DUNN Measures on Iris Dataset

This paper is regarding the comparison of two techniques; Clustering Large Applications (CLARA) clustering and K-Means clustering using popular Iris dataset. CLARA clustering and K-Means clustering are the two techniques of “partitioning based” clustering. One considers medoids using some random sample data to form a cluster whereas the other considers centroid (means) of the dataset to form a cluster. In this paper, Cluster plot, Silhouette plot and Dunn Index on Iris dataset are shown for both the techniques. These all are used for “cluster validation”. The “Silhouette Analysis” is the measurement of an approximated average distance among the clusters. The “Silhouette plot” is the measurement of the closeness of the points in one cluster to the neighboring clusters, whereas the other internal clustering validation measure is the DUNN Index; higher the “Dunn Index” better is the clustering. All these statistical analysis is done in R programming. The final outcome attains that the CLARA clustering stands better than the K-Means clustering.