Reviewerly/exHarmony huggingface dataset:
Research on Face Image Noise Processing Technology Based on Computer Vision Processing

This paper contrasts and analyzes the image enhancement processing algorithm, median filtering and histogram equalization in the face recognition preprocessing process. The advantages and use methods of the OpenCV machine vision library are analyzed. The analyzed algorithm is implemented in the development environment of VC ++ 6.0 and MATLAB, and experiments are conducted on the ORL face library. The experimental results show that the algorithm used in this paper has obvious effect on processing face samples, has strong pertinence, and is suitable for preprocessing face images, and the OpenCV machine vision library can be used as a development tool for face recognition systems.
FuRong

Automated testing has been widely used to ensure the quality of Android applications. However, incomprehensible testing results make it difficult for developers to understand and fix potential bugs. This paper proposes FuRong, a novel tool, to fuse bug reports of high-readability and strong-guiding-ability via analyzing the automated testing results on multi-devices. FuRong builds a bug model with complete context information, such as screenshots, operation sequences, and logs from multi-devices, and then leverages pre-trained Decision Tree classifier (with 18 bug category labels) to classify bugs. FuRong deduplicates the classified bugs via Levenshtein distance and finally generates the easy-to-understand report, not only context information of bugs, where possible causes and fix suggestions for each bug category are also provided. An empirical study of 8 open-source Android applications with automated testing on 20 devices has been conducted, the results show the effectiveness of FuRong, which has a bug classification precision of 93.4% and a bug classification accuracy of 87.9%. Video URL: https://youtu.be/LUkFTc32B6k
Cryptanalysis of a round optimal lattice-based multisignature scheme

Kansal and Dutta recently proposed a multisignature scheme at AFRICACRYPT 2020. This is the first lattice-based multisignature scheme that generates a multisignature in only a single round of interaction and supports public key aggregation. In this letter, we provide a cryptanalysis of this multisignature scheme and demonstrate that the scheme does not satisfy unforgeability requirements. We present an attack strategy to demonstrate that if an adversary obtains a sufficient number of signatures from a signer, he/she can recover the private key of the signer in polynomial time. We also uncover the root cause of the attack and provide a possible solution for this attack to aid future designs of secure multisignature schemes.
Fusion of Saliency Map and Deep Feature-Based Correlation Filter for Enhancing Tracking Performances

This paper proposes the fusion of a saliency map and a deep feature-based correlation filter to enhance tracking accuracy by reflecting spatial attention and foreground information in the tracking process. The saliency map enables the tracker to focus on a salient object region. The target foreground region is roughly segmented from the background region using a pixel-wise likelihood map derived from the color model and the shape model. Given that only visual information in the foreground region is used, the model is prevented from learning background, and the tracker is made robust to background change. The proposed saliency map can be easily combined with any tracking methods by providing the map as the weight value to the target response map. The saliency map is combined with a correlation filter-based tracker, and we prove that the saliency map successfully improves tracking performance. Experiments are conducted to validate the proposed method on public benchmark datasets. The proposed method achieves remarkable results compared with existing state-of-the-art trackers and successfully improves the tracking performance combined with two version of correlation filter-based methods.
Multi-type Feature Mining and Fusion Model for Temporal Prediction

Mining user behavior features is an essential part of recommender systems. As user preferences change dynamically, long-term interest features are very useful for predicting users' future behavior. Hence, the accuracy of temporal prediction will be greatly improved, if we can combine user behavior regularities with the item features. In this paper, we propose a long short-term memory (LSTM) variant, named Multi-type Feature Mining and Fusion (MFMF) model, to mine the long-term dynamic user behavior pattern and the potential relationships between items. We combine these features with user and item attributes to predict which items users will click next. Experiments on two real-world datasets, LastFM dataset and movie dataset, demonstrate the effectiveness of the proposed approach comparing to both traditional and state-of-the- art methods.
Early Detection of Flaws in System Architecture Model by means of Model Simulation

Abstract Architecture definition models in system engineering usually include different views and sets of concepts, more or less correlated to each other, such as activities or functions, time‐ordered sequences, state machines. Ensuring these descriptions are coherent is in no means trivial, usually requiring either formal checking or behavioral simulation, at the expense of extra modeling work. This paper addresses coherence verification at a first level, needing little effort, to detect flaws in in the model as early as possible, before entering more in depth and more costly simulation or formal proofing. To make this verification possible, we define a model execution semantic based on a mix of model exploration and dynamic execution techniques.
Structured Data Encoder for Neural Networks Based on Gradient Boosting Decision Tree

Features are very important for machine learning tasks, therefore, feature engineering has been widely adopted to obtain effective handcrafted features, which is, however, labor-intensive and in need of expert knowledge. Therefore, feature learning using neural networks has been used to obviate the need of manual feature engineering and achieved great successes in the image and sequential data processing. However, its performance in processing structured data is usually unsatisfactory. In order to tackle this problem and learn good feature representations for structured data, in this work, we propose a structured data encoder (SDE) based on Gradient Boost Decision Tree (GBDT) to learn feature representations from structured data both effectively and efficiently. Then, PCA is further employed to extract the most useful information and to reduce the dimensionality for the following classification or regression tasks. Extensive experimental studies have been conducted to show the superior performances of the proposed SDE solution in learning representations of structured data.
Two-stage Strategy for Small-footprint Wake-up-word Speech Recognition System

In this paper, we propose a small-footprint wake- up-word speech recognition (WUWSR) system with two stages to recognize a two-syllable wake-up word. In the first stage, convolution neural network (CNN) is trained to predict the posterior probability of context-dependent state. Thus a wake-up-word is detected according to the confidence score obtained by dynamic programming. In the second stage, we cascade bidirectional long short-term memory network (LSTM), convolutional modules and deep feed-forward network (BLCDNN) successively to verify the detection. The first stage quickly filters out speech without wake-up word, and the second stage refines the detection. In addition, without the intervention of any decoding modules, the proposed system can guarantee low latency. The experimental results demonstrate the effectiveness of this method. Our system, named CNN-BLCDNN, reaches high accuracy and maintains low false alarm rate.
Underactuated Waypoint Trajectory Optimization for Light Painting Photography

Despite their abundance in robotics and nature, underactuated systems remain a challenge for control engineering. Trajectory optimization provides a generally applicable solution, however its efficiency strongly depends on the skill of the engineer to frame the problem in an optimizer-friendly way. This paper proposes a procedure that automates such problem reformulation for a class of tasks in which the desired trajectory is specified by a sequence of waypoints. The approach is based on introducing auxiliary optimization variables that represent waypoint activations. To validate the proposed method, a letter drawing task is set up where shapes traced by the tip of a rotary inverted pendulum are visualized using long exposure photography.
Sequence and word frequency

Abstract It is well-established that the linear ordering of words in a sentence is influenced by a variety of factors that are typically labelled as grammatical, discourse or cognitive constraints. The aim of the present study is to determine whether frequency effects are visible in the sequencing of words in a sentence. In other words, do “more frequently used units tend to be placed before less frequently used units” ( Fenk-Oczlon 2001 : 443)? Using a corpus of newspaper articles, we examine the frequency of words in different positions in sentences. That is, using data from thousands of sentences, we investigate the median value for the frequency or rank of words in first position in a sentence, compared with second position, and so on. We find that there is a frequency effect in English: the first element in a sentence has the highest frequency and last element in a sentence has the lowest frequency, with the middle of sentences having a more or less flat frequency profile. We also find that the overall shape of the frequency profile for sentences is rather consistent even when sentence length is taken into account.
Local Obstacle-Skirting Path Planning for a Fast Bi-steerable Rover using Bézier Curves

This paper focuses on local path planning for obstacle avoidance tasks dedicated to off-road mobile robots. This approach calculates a new local path for the vehicle using a set of cubic Bezier curves once the safety distance is not respected; otherwise, the vehicle follows the global reference path which is defined off-line. Two basic steps are used to determine this new path. Firstly, some significant points that should belong to the planned path are extracted on-line according to the obstacle's sizes and the current state of the vehicle, these points are approved as waypoints. Secondly, on-line cubic Bezier curves are computed to create a smooth path for these points such that the safety and lateral stability of the vehicle are ensured (i.e., preventing huge curvatures and wide-variation in steering angles). This path will be used as a reference to be performed by the vehicle using a constrained model predictive control. The validation of our navigation strategy is performed via numerical simulations and experiments using a fast double-steering rover.
Deep Representation of Hierarchical Semantic Attributes for Zero-shot Learning

On account of a large scale of dataset need to be annotated to fit for specific tasks, Zero-Shot Learning(ZSL) has invoked so much attention and got significant progress in recent research due to the prevalence of deep neural networks. At present, ZSL is mainly solved through the utilization of auxiliary information, such as semantic attributes and text descriptions. And then, we can employ the mapping method to bridge the gap between visual and semantic space. However, due to the lack of effective use of auxiliary information, this problem has not been solved well. Inspired by previous work, we consider that visual space can be used as the embedding space to get a stronger ability to express the precise characteristics of semantic information. Meanwhile, we take into account that there are some noise attributes in the annotated information of public datasets that need to be processed. Based on these considerations, we propose an end-to-end method with convolutional architecture, instead of conventionally linear projection, to provide a deep representation for semantic information to solve ZSL. Semantic features would express more detailed and precise information after being feed into our method. Besides, we use word embedding to generate some superclasses for original classes and propose a new loss function for these superclasses to assist in training. Experiments show that our method can get decent improvements for ZSL and Generalized Zero-Shot Learning(GZSL) on several public datasets.
Soil Geomorphology of Nevada

This chapter focuses on soil geomorphology of each of the 10 Major Land Resource Areas in Nevada. Block diagrams are used to show the relations among soils, landforms, and geologic materials.
Cascaded Mixed-Precision Networks

There has been a vast literature on Neural Network Compression, either by quantizing network variables to low precision numbers or pruning redundant connections from the network architecture. However, these techniques experience performance degradation when the compression ratio is increased to an extreme extent. In this paper, we propose Cascaded Mixed-precision Networks (CMNs), which are compact yet efficient neural networks without incurring performance drop. CMN is designed as a cascade framework by concatenating a group of neural networks with sequentially increased bitwidth. The execution flow of CMN is conditional on the difficulty of input samples, i.e., easy examples will be correctly classified by going through extremely low-bitwidth networks, and hard examples will be handled by high-bitwidth networks, so that the average compute is reduced. In addition, weight pruning is incorporated into the cascaded framework and jointly optimized with the mixed-precision quantization. To validate this method, we implemented a 2-stage CMN consisting of a binary neural network and a multi-bit (e.g. 8 bits) neural network. Empirical results on CIFAR-100 and ImageNet demonstrate that CMN performs better than state-of-the-art methods, in terms of accuracy and compute.
A Comparative Analysis of State-of-the-Art Recommendation Techniques in the Movie Domain

Recommender systems (RSs) represent one of the manifold applications in which Machine Learning can unfold its potential. Nowadays, most of the major online sites selling products and services provide users with RSs that can assist them in their online experience. In recent years, therefore, we have witnessed an impressive series of proposals for novel recommendation techniques that claim to ensure significative improvements compared to classic techniques. In this work, we analyze some of them from a theoretical and experimental point of view and verify whether they can deliver tangible real improvements in terms of performance. Among others, we have experimented with traditional model-based and memory-based collaborative filtering, up to the most recent recommendation techniques based on deep learning. We have chosen the movie domain as an application scenario, and a version of the classic MovieLens as a dataset for training and testing our models.
Optimised enhancement scheme for low contrast underwater images

Abstract Images acquired in underwater environments are usually affected by light absorption and scattering. These are the two phenomena that reduce the clarity of images that are captured in these environments. These factors cause low contrast and anamorphic colour diffusion. To tackle these issues, we propose an optimized low contrast enhancement scheme. The main thrust of this paper borders on enhancement of underwater image contrast by preserving the brightness level. The approach is termed Fuzzy-Histogram Equalisation Optimised for Brightness Preservation (FHEOBP) technique, where a combination of fuzzy and classical histogram equalisation techniques is employed towards the enhancement of the contrast of images from underwater scene. The scheme is optimized using teaching-learning-based optimisation technique that is built into the algorithm. The proposed FHEOBP filter shows improved performance over Local Histogram Equalisation (LHE) and Global Histogram Equalisation (GHE) as it has a higher luminance distortion index value than those of LHE and GHE. This translates into a better image details preservation. In fact, the computed luminance distortion indices for optimised FHEOBP are 16.4%, 28.3% and 20.1%, respectively higher than those of the corresponding GHE, in the same test images utilised for performance evaluation. Between the optimised and non-optimised FHEOBP, luminance distortion figures of optimised FHEOBP are 8%, 6.8% and 9.8% higher than those of the equivalent non-optimised FHEOBP in the test image data set.
Temporal Action Proposal Generation Via Deep Feature Enhancement

Temporal action proposal generation (TAPG) is a challenging problem for analyzing video content. It aims to localize the video segments which are likely to contain actions or events. Intuitively, making a satisfying prediction of these video segments is directly relies on their representation quality. A typical representation of a video segment is applying a two-stream feature, which comprises appearance and motion information. Rather than directly concatenating the two-stream features as the previous methods, we illustrate a feature-aggregation network (FA-Net) concerning the feature-relation among neighboring video segments for obtaining the high-quality representation that better characterizing the actions or events. Further, we design a feature-expansion network (FE-Net) to extract multi-granularity features for retrieving the proposals of high action-instance covering confidence. We evaluate our approach on two challenging datasets: ActivityNet-1.3 and THUMOS-14. The experiments showed that the proposed approach consistently outperforms the existing state-of-the-art TAPG methods.
Evolutionary hot-spots in software systems

We propose a methodology to study and visualize the evolution of the modular structure of a network of functional dependencies in a software system. Our method identifies periods of significant refactoring activities, also known as the evolutionary hot spots in software systems. Our approach is based on clustering design structure matrices of functional dependencies and Kleinberg's method of identifying evolutionary hot-spots in dynamic networks. As a case study, we characterize the evolution of the modular structure of Octave over its entire life cycle.
Development of Gamification Mobile Application for Students

Learning activities, especially in universities that are accepted by students, become critical aspects. The development of students in the current generation who have entered generations Y and Z will undoubtedly affect the university's learning process. The learning process that is not interesting will cause students not to get maximum knowledge. Gamification in the learning process at a university is one method that can be applied so that the learning process becomes more exciting and useful. Triggered by these needs, this research focuses on developing mobile applications based on gamification. The application will feature Create Quiz, Create Interactive Study Case, Create Badge, Create Rank, View Scoreboard, Video-based forums, and View Achievement Report to increase knowledge and interest learning. System Development Life Cycle was the primary method to develop this mobile application.
PLMSys: A Cloud Monitoring System Based on Cluster Performance and Container Logs

Docker, a kind of lightweight virtualization technology which has the characteristics of resource isolation, rapid deployment and low cost, is widely used in the construction of the cloud services. Docker-based containers has become the important basis of core cloud businesses. In order to manage the large-scale cloud cluster and enforce the quality of cloud services for consumers, monitoring mechanism for the container-based clouds are indispensable. In this paper, we design and implement a cloud monitoring system - PLMSys based on cluster performance and container logs. It provides the following functions: i) Multi-dimensional resources monitoring. PLMSys can monitor the running states of the cluster hosts and containers, including the utilization of CPU, memory, disk and other resources. ii) Container log collection. PLMSys can centrally collect the logs generated by all containers of the cluster. iii) Rule-based exception alerts. PLMSys allows users to define the abnormal state of the hosts and containers by creating rules, and provides multiple alerting methods. iv) Workload analysis and prediction. PLMSys extracts the descriptive statistics from the cluster workloads and uses the time series models to predict the future workloads. v) Data monitoring visualization. The system uses rich visual charts to reflect the running states of cluster hosts and containers. By using PLMSys, users can better manage cluster hosts and containers.
Relevance Feedback Based Citation Recommendation for Scholarly Publications

Scholarly article recommendation systems are an essential tool for effective research work. It plays a major role in retrieving relevant scientific papers in the era of big scholarly data. When researchers start working on a research problem, they are not always sure which papers to refer to learn the state-of-the-art or which papers are the most appropriate for their work. Numerous methods for generating recommendations have been proposed in the past decades. Very often, these are generalized systems, not specifically designed for scholarly articles. Moreover, they fail to capture a researcher's preferences for year, authorship, publication venue and so on. In this paper, we present an alternative approach to implementing a recommendation system based on relevance feedback to resolve these concerns. Extensive experiments have been performed on a real-world Microsoft Academic Graph (MAG) dataset to demonstrate that the proposed algorithm produces more accurate recommendations as compared to the baseline methods. Finally, the evaluation has been performed against a few search engines like Google scholar and CiteSeer to demonstrate the effectiveness and the scalability of proposed recommender system.
Photorealistic Object Reconstruction Using Perturbation Functions and Features of Passive Stereo Projection

An effective approach to the calculation of volumetric models based on images of silhouettes in the object space is described. A method of forming the contours of a real object from a sequence of images is proposed. The construction of a bounding pyramid using the projection of the center and silhouette is shown. The intersection of all bounding pyramids determines the geometric representation of the object. Methods of obtaining information about the depth of the scene are proposed. A method of binocular stereo reconstruction of three-dimensional objects based on perturbation functions is proposed. In the method, interference is linear and requires linear compensation methods that are performed using the GPU by means of standard OpenGL. Defines the portion of the frame buffer (channel) in the GPU in which the image is output. Passive stereo uses two channels that process images intended for the left and right eye. Both channels handle a single main scene graph representing a user-defined model and attached to a root node (scene-root). Channels differ in the settings of the projection transformation (view-transform), which is determined by known parameters, such as the size and distance to the screen, the position of the user's point of view, stereo base and others, which are specified in the calibration file. The paper shows the advantages of the proposed method over the known approaches of reconstruction of three-dimensional models.