Reviewerly/exHarmony huggingface dataset:
Selecting best predictors from large software repositories for highly accurate software effort estimation

Abstract Accurate prediction of software effort is important for planning, scheduling, and allocating resources. However, software effort estimation has been a challenging task. Although numerous estimation models have been proposed, few achieve anything close to accurate prediction of software development effort. To achieve optimal results, machine learning techniques have recently been employed for predicting software development effort using relatively large software repositories. However, some issues remain unresolved, and this paper aims to address the following issues. First, feature selection methods often neglected the information rich variables present in the dataset. Second, selection of important features was done through statistical methods, which lack domain knowledge. Third, missing values in the data that significantly influence the prediction outcome was not efficiently handled. Fourth, majority of the literature neglected advanced evaluation measures, which thoroughly evaluate the ability of learning models to produce accurate results. To address the above issues, a machine learning‐based model has been proposed in this paper, which not only allows effective preprocessing of data but also provides highly accurate prediction results with minimum error rate. The purpose is to best identify attributes (predictors) from large software repositories that are most influential in the estimation of effort. In addition, we apply MMRE for better performance analysis.
New Automatic Search Method for Truncated-Differential Characteristics Application to Midori, SKINNY and CRAFT

Abstract In this paper, using Mixed-Integer Linear Programming, a new automatic search tool for truncated differential characteristic is presented. Our method models the problem of finding a maximal probability truncated differential characteristic, being able to distinguish the cipher from a pseudo-random permutation. Using this method, we analyze Midori64, SKINNY64/X and CRAFT block ciphers, for all of which the existing results are improved. In all cases, the truncated differential characteristic is much more efficient than the (upper bound of) bit-wise differential characteristic proven by the designers, for any number of rounds. More specifically, the highest possible rounds, for which an efficient differential characteristic can exist for Midori64, SKINNY64/X and CRAFT are 6, 7 and 10 rounds, respectively, for which differential characteristics with maximum probabilities of $2^{-60}$, $2^{-52}$ and $2^{-62.61}$ (may) exist. Using our new method, we introduce new truncated differential characteristics for these ciphers with respective probabilities $2^{-54}$, $2^{-4}$ and $2^{-24}$ at the same number of rounds. Moreover, the longest truncated differential characteristics found for SKINNY64/X and CRAFT have 10 and 12 rounds, respectively. This method can be used as a new tool for differential analysis of SPN block ciphers.
Image Processing based Improved Face Recognition for Mobile Devices by using Scale-Invariant Feature Transform

Face detection has become an essential biometric method used in unlocking the smart mobiles now-a-days. Face detection identifies the face of the user by calculating the facial features. In some cases, people can use the photos and face masks to hack mobile security systems. In order to avoid this eye blinking detection, which finds eyes through the proportion of human face was proposed in this paper. The Proposed method detects the movements of eyeball and the number of eye blinking to improve face recognition for screen unlock on the mobile devices. To achieve this halfway face acknowledgment approach by adjusting the test fractional face to exhibition faces utilizing the geometrical and textural data of the extricated nearby highlights was proposed in this model to obtain better results. To extend further a hearty strategy called stepwise direct discriminant examination centers around choosing the limited highlights from the face outlines and separating their class dependent on relapse esteems was done. The motivation behind an element extraction system is to remove the confined highlights from appearances that the past component extraction procedures were constrained in examining. Our essential instinct is that if the test halfway face fix and the exhibition face picture are from a similar individual, the cost capacity of our arrangement strategy ought to be limited. Experimental results proved that our method is efficient and robust for the screen unlock on the mobile devices.
Low-bit Quantization Needs Good Distribution

Low-bit quantization is challenging to maintain high performance with limited model capacity (e.g., 4-bit for both weights and activations). Naturally, the distribution of both weights and activations in deep neural network are Gaussian-like. Nevertheless, due to the limited bitwidth of low-bit model, uniform-like distributed weights and activations have been proved to be more friendly to quantization while preserving accuracy. Motivated by this, we propose Scale-Clip, a Distribution Reshaping technique that can reshape weights or activations into a uniform-like distribution in a dynamic manner. Furthermore, to increase the model capability for a low-bit model, a novel Group- based Quantization algorithm is proposed to split the filters into several groups. Different groups can learn different quantization parameters, which can be elegantly merged into batch normalization layer without extra computational cost in the inference stage. Finally, we integrate Scale-Clip technique with Group-based Quantization algorithm and propose the Group-based Distribution Reshaping Quantization (GDRQ) framework to further improve the quantization performance. Experiments on various networks (e.g. VGGNet and ResNet) and vision tasks (e.g. classification, detection, and segmentation) demonstrate that our framework achieves much better performance than state-of-the- art quantization methods. Specifically, the ResNet-50 model with 2-bit weights and 4-bit activations obtained by our framework achieves less than 1% accuracy drop on ImageNet classification task, which is a new state-of-the-art to our best knowledge.
Optimal approximations of available states and a triple uncertainty relation

We investigate the optimal convex approximation of the quantum state with respect to a set of available states. By isometric transformation, we have presented the general mathematical model and its solutions together with a triple uncertainty equality relation. Meanwhile, we show a concise inequality criterion for decomposing qubit mixed states. The new results include previous ones as special cases. Our model and method may be applied to solve similar problems in high-dimensional and multipartite scenarios.
Residual-Duet Network with Tree Dependency Representation for Chinese Question-Answering Sentiment Analysis

Question-answering sentiment analysis (QASA) is a novel but meaningful sentiment analysis task based on question-answering online reviews. Existing neural network-based models that conduct sentiment analysis of online reviews have already achieved great success. However, the syntax and implicitly semantic connection in the dependency tree have not been made full use of, especially for Chinese which has specific syntax. In this work, we propose a Residual-Duet Network leveraging textual and tree dependency information for Chinese question-answering sentiment analysis. In particular, we explore the synergies of graph embedding with structural dependency links to learn syntactic information. The transverse and longitudinal compression encoders are developed to capture sentiment evidence with disparate types of compression and different residual connections. We evaluate our model on three Chinese QASA datasets in different domains. Experimental results demonstrate the superiority of our proposed model in Chinese question-answering sentiment analysis.
REASONABLE APPLICATION OF AUGMENTED REALITY IN ENGINEERING EDUCATION

Abstract Augmented Reality (AR) is to be used extensively in today's digitized teaching in order to enable students to a more efficient learning. However, teaching content must not be only digitised, but must be communicated in a meaningful way. For this purpose, a generally valid flowchart has been developed, that allows lectures to choose the right content for AR experiences. In order to create this flowchart, empirical values from test runs were combined with pedagogically proven empirical values for good teaching.
A Hybrid Concession Mechanism for Negotiating Software Agents in Competitive Environments

This paper presents a new hybrid concession mechanism for negotiating agents. It considers both the current concession behavior of the proposing agent and the concession offered by its opponent in the last counteroffer to create a new offer. The proposed mechanism is a kind of imitating offer generation tactic. The difference is that it uses the first order difference between the two last counteroffers received from the opponent as its current reservation value which is one of the important inputs in generating a new offer. In this paper, a bilateral negotiation over a single issue is considered where agents have adverse interests over the issue such as price. Four negotiation environmental settings are used to test the proposed offer generating mechanism. The experimental results show that the proposed hybrid concession mechanism outperforms the time-dependent concession tactic in terms of utility rate while performing lower in one negotiation environment and similarly in most of negotiation environments.
Normalizing Flows With Multi-Scale Autoregressive Priors

Flow-based generative models are an important class of exact inference models that admit efficient inference and sampling for image synthesis. Owing to the efficiency constraints on the design of the flow layers, e.g. split coupling flow layers in which approximately half the pixels do not undergo further transformations, they have limited expressiveness for modeling long-range data dependencies compared to autoregressive models that rely on conditional pixel-wise generation. In this work, we improve the representational power of flow-based models by introducing channel-wise dependencies in their latent space through multi-scale autoregressive priors (mAR). Our mAR prior for models with split coupling flow layers (mAR-SCF) can better capture dependencies in complex multimodal data. The resulting model achieves state-of-the-art density estimation results on MNIST, CIFAR-10, and ImageNet. Furthermore, we show that mAR-SCF allows for improved image generation quality, with gains in FID and Inception scores compared to state-of-the-art flow-based models.
Time-dependent Decentralized Routing using Federated Learning

Recent advancements in cloud computing have driven rapid development in data-intensive smart city applications by providing near real time processing and storage scalability. This has resulted in efficient centralized route planning services such as Google Maps, upon which millions of users rely. Route planning algorithms have progressed in line with the cloud environments in which they run. Current state of the art solutions assume a shared memory model, hence deployment is limited to multiprocessing environments in data centers. By centralizing these services, latency has become the limiting parameter in the technologies of the future, such as autonomous cars. Additionally, these services require access to outside networks, raising availability concerns in disaster scenarios. Therefore, this paper provides a decentralized route planning approach for private fog networks. We leverage recent advances in federated learning to collaboratively learn shared prediction models online and investigate our approach with a simulated case study from a mid-size U.S. city.
FigExplorer: A System for Retrieval and Exploration of Figures from Collections of Research Articles

In this paper, we present FigExplorer, a novel general system that supports the retrieval and exploration of research article figures. Specifically, FigExplorer can support 1) figure retrieval using keyword queries, 2) exploration of related figures of a given figure, 3) exploration of a figure topic using the citation network, and 4) search result re-ranking using an example figure. The different functions were implemented using either classical IR models or neural network-based figure embeddings. Finally, the system was designed to facilitate the collection of user data for training and test purposes and it is flexible enough such that it can be extended to include new functions and algorithms. As an open-source system, FigExplorer can help advance the research, evaluation, and development of applications in this area.
Cross-dataset email classification

Email is one of the most popular ways of communication. Nevertheless, it is also a potential tool to deceive and fill users with unwanted publicity, which reduces productivity. To alleviate such fact, a common solution has been building machine learning models based on the content of emails to automatically separate emails (spam vs ham). In this work, a study of a set of machine learning models and content-based features for the problem of cross-dataset email classification is presented. This problem consists in training and testing the models using different datasets; considering the fact that the datasets were collected under different independent setups. This has the purpose of simulating future variable or unpredictable conditions in the emails content distributions as could happen in a real setting, where models are trained using emails from a certain period of time, group of users or accounts, but tested with emails from other users or accounts. Experiments were conducted with the models and features using different datasets and two setups, same-dataset, and cross-dataset, to show the complexity of the later. The performance was evaluated using the Area Under the ROC Curve, a common metric in email classification. The results show interesting insights for the problem.
A real-time fisheye video correction method based on Android smart-phone GPU

We propose a fisheye camera calibration algorithm, in which the center of the distortion and equivalent focal length of the fisheye camera are obtained by the contour constraint, and the distortion parameters are calculated by linear constraint and Levenberg-Marquardt (LM) optimization algorithm. According to the obtained camera parameters, the fisheye image can be corrected by cubic-box-mapping-method (CBMM). The result shows that for checkerboard images the calibration algorithm demonstrates a high-accuracy of about 0.6727 pixels in the average reprojection root-mean-square error (RMSE). Based on the calibration algorithm, the ream-time fisheye video correction is implemented on an android smart-phone. In the implementation, a direct mapping method is used to simplify the cubic box mapping method, and a heterogeneous architecture with a graphics processing unit (GPU) is built to accelerate the algorithm. Lastly, a fisheye video stream of 1280×1280 resolution is timely corrected on the smart-phone with a speed of 25 frames/sec.
Joint multislice and cooperative detection aided RFID method based on deep learning

Radio frequency fingerprint identification (RFID) provokes many promising applications of internet of things. Deep learning is considered as one of powerful tools to empower RFID techniques. Recently, several deep learning based RFID methods have been proposed. However, their deep neural networks were trained from the limited length of RF fingerprint samples due to the very high training cost. Hence, existing deep learning based RFID methods are hard to extract full features from the RF fingerprint (RFF) datasets. In order to solve this problem, we propose a joint multislice and cooperative detection aided RFID method based on deep learning. Firstly, acquisition equipment is used to collect RFF signals from seven power amplifiers, which are composed of in-phase and quadrature (IQ) samples with 200,000 sampling points. The IQ samples are sliced and turned into slices with fewer sampling points, which makes them use less training resources, and then they are input into neural network for training. The convolutional neural network (CNN) and convolutional long short-term deep neural networks (CLDNN) are used for training. Secondly, a cooperative detection algorithm is proposed to classify all slices from the same signal to determine the signal category. Finally, experiment results are given to confirm the proposed method in different scenarios. It shows that this method can greatly reduce the training resources, and at the same time maintain a high accuracy.
Analysis and evaluation of the riak cluster environment in distributed databases

Many institutions and companies undergoing technological developments have been producing large amounts of structured and unstructured data. Special databases are required to deal with such data and NoSQL databases have thus emerged. They are widely used in cloud databases and distributed systems. In the era of big data, these databases provide a scalable solution with high availability. In this context, we need new architectures in order to store more and more different kinds of data. To obtain a good structure for large and diverse data, structures must be tested and analyzed in depth with the use of different benchmark tools. In this paper, we test the Riak key-value database to measure its performance in terms of throughput and latency, where huge amounts of data are stored and retrieved in different sizes in a distributed database environment. The throughput and latency of the NoSQL database in different types of experiments and with different sizes of data are compared. As we increase the data size in the experiments, an increase in the number of threads leads to better throughput and latency factor was reduced. High performance results are obtained for only read operations for all experiments. We observed that performance advanced when there was only read operations compared with a mix of read and update operations. Moreover, our findings intensify the understanding of the distributed database and have to help future developers through the experimental results shown in this paper.
Effective footstep planning using homotopy-class guidance

Planning the motion for humanoid robots is a computationally-complex task due to the high dimensionality of the system. Thus, a common approach is to first plan in the low-dimensional space induced by the robot's feet—a task referred to as footstep planning. This low-dimensional plan is then used to guide the full motion of the robot. One approach that has proven successful in footstep planning is using search-based planners such as A* and its many variants. To do so, these search-based planners have to be endowed with effective heuristics to efficiently guide them through the search space. However, designing effective heuristics is a time-consuming task that requires the user to have good domain knowledge. Thus, our goal is to be able to effectively plan the footstep motions taken by a humanoid robot while obviating the burden on the user to carefully design local-minima free heuristics. To this end, we propose to use user-defined homotopy classes in the workspace that are intuitive to define. These homotopy classes are used to automatically generate heuristic functions that efficiently guide the footstep planner. Additionally, we present an extension to homotopy classes such that they are applicable to complex multi-level environments. We compare our approach for footstep planning with a standard approach that uses a heuristic common to footstep planning. In simple scenarios, the performance of both algorithms is comparable. However, in more complex scenarios our approach allows for a speedup in planning of several orders of magnitude when compared to the standard approach.
The forecasting of solar energy based on Machine Learning

Recently, the majority of countries are seeking to redefine their energy mix, due to the global warming and climate change concerns. As a result, the world is moving towards the use of renewable energy to produce electricity. In this sense, solar energy has become one of the main strengths of the most promising renewable resources for mass power generation. Solar energy forecasting is a very active field of research. Its objective is to reduce the uncertainty of variable renewable production. Statistical, physical, machine learning and hybrid approaches were used. However, research contributions in this area remain immature. Therefore, the search for more relevant methods, adapted to this field, will be important because it would improve accuracy, in order to provide the world with many economic and environmental benefits. In this context, this article discusses the fundamental principles of the machine learning based approaches.
Att-MoE: Attention-based Mixture of Experts for nuclear and cytoplasmic segmentation

Cell segmentation is a critical step in histology images analysis. Recently, Convolutional Neural Network (CNN) has shown outstanding performance for various segmentation problems, however, the segmentation of histology images remains challenging due to the tight arrangement of cells and their weak boundaries. This paper proposes a novel architecture called Attention-based Mixture of Experts (Att-MoE) for nuclear and cytoplasmic segmentation in fluorescent histology images, which integrates multiple Expert networks using a single Gating network. Expert networks complement each other to accomplish sub-tasks under the direction of the Gating network, which enforces the adaptive use of multiple networks to complete the segmentation task. The Att-MoE also introduces attention gates and residual blocks in the Expert networks to improve segmentation accuracy. The attention gate is used to emphasize useful features and suppress irrelevant features for segmentation in a self-adaptive manner. On the other hand, residual blocks are employed to enhance gradient flow in training and improve the stability and segmentation accuracy of the network. Experiments on fluorescent histology images of mouse liver show that Att-MoE is superior to recent segmentation methods and has the potential for cancer diagnosis based on histology images.
Deep Grouping Model for Unified Perceptual Parsing

The perceptual-based grouping process produces a hierarchical and compositional image representation that helps both human and machine vision systems recognize heterogeneous visual concepts. Examples can be found in the classical hierarchical superpixel segmentation or image parsing works. However, the grouping process is largely overlooked in modern CNN-based image segmentation networks due to many challenges, including the inherent incompatibility between the grid-shaped CNN feature map and the irregular-shaped perceptual grouping hierarchy. Overcoming these challenges, we propose a deep grouping model (DGM) that tightly marries the two types of representations and defines a bottom-up and a top-down process for feature exchanging. When evaluating the model on the recent Broden+ dataset for the unified perceptual parsing task, it achieves state-of-the-art results while having a small computational overhead compared to other contextual-based segmentation models. Furthermore, the DGM has better interpretability compared with modern CNN methods.