Miracle-dz/UniFAR-FTUs huggingface dataset:
Fully-Featured Attribute Transfer

Image attribute transfer aims to change an input image to a target one with expected attributes, which has received significant attention in recent years. However, most of the existing methods lack the ability to de-correlate the target attributes and irrelevant information, i.e., the other attributes and background information, thus often suffering from blurs and artifacts. To address these issues, we propose a novel Attribute Manifold Encoding GAN (AME-GAN) for fully-featured attribute transfer, which can modify and adjust every detail in the images. Specifically, our method divides the input image into image attribute part and image background part on manifolds, which are controlled by attribute latent variables and background latent variables respectively. Through enforcing attribute latent variables to Gaussian distributions and background latent variables to uniform distributions respectively, the attribute transfer procedure becomes controllable and image generation is more photo-realistic. Furthermore, we adopt a conditional multi-scale discriminator to render accurate and high-quality target attribute images. Experimental results on three popular datasets demonstrate the superiority of our proposed method in both performances of the attribute transfer and image generation quality.
Private Inner Product Retrieval for Distributed Machine Learning

In this paper, we argue that in many basic algorithms for machine learning, including support vector machine (SVM) for classification, principal component analysis (PCA) for dimensionality reduction, and regression for dependency estimation, we need the inner products of the data samples, rather than the data samples themselves.Motivated by the above observation, we introduce the problem of private inner product retrieval for distributed machine learning, where we have a system including a database of some files, duplicated across some non-colluding servers. A user intends to retrieve a subset of specific size of the set of the inner product of every pair of data items in the database with minimum communication load, without revealing any information about the identity of the requested subset. For achievability, we use the algorithms for multi-message private information retrieval. For converse, we establish that as the length of the files becomes large, the set of all inner products converges to independent random variables with uniform distribution hence we find asymptotic capacity for this problem. We also derive the rate of this convergence. To prove that, we construct special dependencies among sequences of the sets of all inner products with different length, which forms a time-homogeneous irreducible Markov chain, without affecting the marginal distribution. We show that this Markov chain has a uniform distribution as its unique stationary distribution, with rate of convergence dominated by the second largest eigenvalue of the transition probability matrix. This allows us to develop a converse, which converges to a tight bound in some cases, as the size of the files becomes large.
What Makes a Good Team? A Large-scale Study on the Effect of Team Composition in Honor of Kings

Team composition is a central factor in determining the effectiveness of a team. In this paper, we present a large-scale study on the effect of team composition on multiple measures of team effectiveness. We use a dataset from the largest multiplayer online battle arena (MOBA) game, Honor of Kings, with 96 million matches involving 100 million players. We measure team effectiveness based on team performance (whether a team is going to win), team tenacity (whether a team is going to surrender), and team rapport (whether a team uses abusive language). Our results confirm the importance of team diversity with respect to player roles, and show that diversity has varying effects on team effectiveness: although diverse teams perform well and show tenacity in adversity, they are more likely to abuse when losing than less diverse teams. Our study also contributes to the situation vs. personality debate and show that abusive players tend to choose the leading role and players do not become more abusive when taking such roles.
Does Migrate a Monolithic System to Microservices Decreases the Technical Debt?

Background. The migration from monolithic systems to microservices involves deep refactoring of the systems. Therefore, the migration usually has a big economic impact and companies tend to postpone several activities during this process, mainly to speed-up the migration itself, but also because of the need to release new features. Objective. We monitored the Technical Debt of a small and medium enterprise while migrating a legacy monolithic system to an ecosystem of microservices to analyze changes in the code technical debt before and after the migration to microservices. Method. We conducted a case study analyzing more than four years of the history of a big project (280K Lines of Code) where two teams extracted five business processes from the monolithic system as microservices, by first analyzing the Technical Debt with SonarQube and then performing a qualitative study with the developers to understand the perceived quality of the system and the motivation for eventually postponed activities. Result. The development of microservices helps to reduce the Technical Debt in the long run. Despite an initial spike in the Technical Debt, due to the development of the new microservice, after a relatively short period, the Technical Debt tends to grow slower than in the monolithic system.
Asymptotic Finite Sample Information Losses in Neural Classifiers

This paper considers the subject of information losses arising from finite datasets used in the training of neural classifiers. It proves a relationship between such losses and the product of the expected total variation of the estimated neural model with the information about the feature space contained in the hidden representation of that model. It then shows that this total variation drops extremely quickly with sample size. It ultimately obtains bounds on information losses that are less sensitive to input compression and much tighter than existing bounds. This brings about a tighter relevance of information theory to the training of neural networks, so a review of techniques for information estimation and control is provided. The paper then explains some potential uses of these bounds in the field of active learning, and then uses them to explain some recent experimental findings of information compression in neural networks which cannot be explained by previous work. It then uses the bounds to justify an information regularization term in the training of neural networks for low entropy feature space problems. Finally, the paper shows that, not only are these bounds much tighter than existing ones, but that these bounds correspond with experiments as well.
Industrial Big Data Platform Based on Open Source Software

Deep integration of industrial system and information technology triggered the fourth industrial revolution-Industry 4.0 which based on cyber physical system and Industrial Big Data. Although many researchers have discussed the basic concept of industry 4.0 and Industrial Big Data, as we known no literatures about how to design and develop an Industrial Big Data platform. Business solutions are generally not open to the public, so little is known about how to achieve it. Open source big data tools have widely used in the Internet field, but it is not clear how these tools are used in the industrial field. We focus on how to use open source big data tools to build a big data platform for industrial systems and a systematically designed framework is proposed including data acquisition, transmission, processing, storage and visualization.
NaF/KF post-deposition treatments and their influence on the structure of Cu(In,Ga)Se2 absorber surfaces

To determine the influence of NaF/KF-post-deposition treatments (PDT) on the chemical and topographical surface structure of Cu(In,Ga)Se2 (CIGSe) solar cell absorbers, we have used synchrotron-based hard x-ray photoelectron spectroscopy (HAXPES) and scanning electron microscopy (SEM). Variations of the PDT parameters can be used to tune thickness and degree of surface nanopatterning; here we find that the nanopatterning is more pronounced on CIGSe surfaces having more potassium and less copper and gallium. Detailed analysis of Se 3d and In 4d photoemission spectra reveals the presence of (at least) two different species, which indicate the formation of a (nanopatterned) K-In-Se-type surface layer.
DC-Al GAN: Pseudoprogression and True Tumor Progression of Glioblastoma multiform Image Classification Based On DCGAN and Alexnet

PURPOSE Pseudoprogression (PsP) occurs in 20-30% of patients with glioblastoma multiforme (GBM) after receiving the standard treatment. PsP exhibits similarities in shape and intensity to the true tumor progression (TTP) of GBM on the follow-up magnetic resonance imaging (MRI). These similarities pose challenges to the differentiation of these types of progression and hence the selection of the appropriate clinical treatment strategy. METHODS To address this challenge, we introduced a novel feature learning method based on deep convolutional generative adversarial network (DCGAN) and AlexNet, termed DC-AL GAN, to discriminate between PsP and TTP in MRI images. Due to the adversarial relationship between the generator and the discriminator of DCGAN, high-level discriminative features of PsP and TTP can be derived for the discriminator with AlexNet. We also constructed a multi-feature selection module to concatenate features from different layers, contributing to more powerful features used for effectively discriminating between PsP and TTP. Finally, these discriminative features from the discriminator are used for classification by a support vector machine (SVM). Tenfold cross-validation (CV) and the area under the receiver operating characteristic (AUC) were applied to evaluate the performance of this developed algorithm. RESULTS The accuracy and AUC of DC-AL GAN for discriminating PsP and TTP after tenfold CV were 0.920 and 0.947. We also assessed the effects of different indicators (such as sensitivity and specificity) for features extracted from different layers to obtain a model with the best classification performance. CONCLUSIONS The proposed model DC-AL GAN is capable of learning discriminative representations from GBM datasets, and it achieves desirable PsP and TTP classification performance superior to other state-of-the-art methods. Therefore, the developed model would be useful in the diagnosis of PsP and TTP for GBM.
State transition 7-dependent phosphorylation is modulated by changing environmental 34 conditions and its absence triggers remodeling of photosynthetic protein complexes 35 36

In plants and algae, the serine/threonine kinase STN7/STT7, orthologous protein kinases in Chlamydomonas reinhardtii and Arabidopsis (Arabidopsis thaliana), respectively, is an important regulator in acclimation to changing light environments. In this work, we assessed STT7-dependent protein phosphorylation under high light in C. reinhardtii, known to fully induce the expression of light-harvesting complex stress-related protein3 (LHCSR3) and a nonphotochemical quenching mechanism, in relationship to anoxia where the activity of cyclic electron flow is stimulated. Our quantitative proteomics data revealed numerous unique STT7 protein substrates and STT7-dependent protein phosphorylation variations that were reliant on the environmental condition. These results indicate that STT7-dependent phosphorylation is modulated by the environment and point to an intricate chloroplast phosphorylation network responding in a highly sensitive and dynamic manner to environmental cues and alterations in kinase function. Functionally, the absence of the STT7 kinase triggered changes in protein expression and photoinhibition of photosystem [. ] BERGNER, Sonja Verena, et al. State transition7-dependent phosphorylation is modulated by changing environmental conditions, and its absence triggers remodeling of photosynthetic protein complexes. Plant Physiology, 2015, vol. 168, no. 2, p. 615-634 DOI : 10.1104/pp.15. 00072
FDD Massive MIMO Based on Efficient Downlink Channel Reconstruction

Massive multiple-input multiple-output systems deploying a large number of antennas at the base station considerably increase the spectrum efficiency by serving multiple users simultaneously without causing severe interference. However, the advantage relies on the availability of the downlink channel state information (CSI) of multiple users, which is still a challenge in frequency-division-duplex transmission systems. This paper aims to solve this problem by developing a full transceiver framework that includes downlink channel training (or estimation), CSI feedback, and channel reconstruction schemes. Our framework provides accurate reconstruction results for multiple users with small amounts of training and feedback overhead. Specifically, we first develop an enhanced Newtonized orthogonal matching pursuit (eNOMP) algorithm to extract the frequency-independent parameters (i.e., downtilts, azimuths, and delays) from the uplink. Then, by leveraging the information from these frequency-independent parameters, we develop an efficient downlink training scheme to estimate the downlink channel gains for multiple users. This training scheme offers an acceptable estimation error rate of the gains with a limited pilot amount. Numerical results verify the precision of the eNOMP algorithm and demonstrate that the sum-rate performance of the system using the reconstructed downlink channel can approach that of the system using perfect CSI.
A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection

Identifying the change point of a system’s health status is important. Indeed, a change point usually signifies an incipient fault under development. The One-Class Support Vector Machine (OC-SVM) is a popular machine learning model for anomaly detection that could be used for identifying change points; however, it is sometimes difficult to obtain a good OC-SVM model that can be used on sensor measurement time series to identify the change points in system health status. In this paper, we propose a novel approach for calibrating OC-SVM models. Our approach uses a heuristic search method to find a good set of input data and hyperparameters that yield a well-performing model. Our results on the C-MAPSS dataset demonstrate that OC-SVM can achieve satisfactory accuracy in detecting change point in time series with fewer training data, compared to state-of-the-art deep learning approaches. In our case study, the OC-SVM calibrated by the proposed model is shown to be useful especially in scenarios with limited amount of training data.
DSP measurement of dielectric property of polymeric materials using interdigital electrodes

Generally, dielectric property of insulating material is evaluated as loss tangent and capacitance by using thin samples. These bulk properties are measured by the application of electric field from one side to another side of the sample film surface. Three terminal parallel plane electrodes are usually used for this measurement. In this paper, a pair of inter-digital electrodes is set only on one side of sample surface, such as silicone and EPDM rubber insulators. Then, we evaluate the dielectric property by using the leakage of the electric field lines from the edge of interdigital finger electrode to the bulk of the sample. Digital signal processor (DSP) is used for data processing. We can successfully measure the degradation and recovery process of the surface of polymer insulating materials. Our DSP measuring system of dielectric materials can detect the change in dielectric properties of polymer surface during absorption and evaporation of the moisture for every one cycle of 50 Hz electric field applications. Both capacitive and loss currents of the polymer increase during aging of the sample by the adhesion of water droplets. They decrease during recovery by the evaporation of moisture. These changes of dielectric properties can measure not only from bulk direction but also from surface direction of electric field application by using a pair of interdigital electrodes on the sample surface.
Implementation of Reference Public Safety Scenarios in ns-3

During incidents and disasters it is fundamental to provide to first responders high performance and reliable communications, in order to improve their coordination capabilities and their awareness of the surrounding environment, and to allow them to promptly transmit and receive alerts on possible dangerous situations or emergencies. The accurate evaluation of the performance of different Public Safety Communication (PSC) networking and communications technologies is therefore of paramount importance, and the characterization of the scenario in which these technologies need to operate is fundamental to obtain meaningful results. In this paper, we present the implementation of three reference PSC scenarios, which are open source and made publicly available to the research community, describing the incidents, the mobility and applications of first responders, and providing examples on how a mmWave-based Radio Access Network (RAN) can support high-traffic use cases. Moreover, we present the implementation of two novel mobility models for ns-3, which can be used to enable the simulation of realistic PSC scenarios in ns-3.
Re-determinizing Information Set Monte Carlo Tree Search in Hanabi

This technical report documents the winner of the Computational Intelligence in Games(CIG) 2018 Hanabi competition. We introduce Re-determinizing IS-MCTS, a novel extension of Information Set Monte Carlo Tree Search (IS-MCTS) that prevents a leakage of hidden information into opponent models that can occur in IS-MCTS, and is particularly severe in Hanabi. Re-determinizing IS-MCTS scores higher in Hanabi for 2-4 players than previously published work at the time of the competition. Given the 40ms competition time limit per move we use a learned evaluation function to estimate leaf node values and avoid full simulations during MCTS. For the Mixed track competition, in which the identity of the other players is unknown, a simple Bayesian opponent model is used that is updated as each game proceeds.
Realize special instructions on clustering VLIW DSP: multiplication-accumulation instruction

BWDSP is a 32bit static scalar digital signal processor with VLIW and SIMD features, which is designed for high-performance computing. Associated special instructions are designed for its special architecture and application scenarios. However, the existing compilation framework doesn't meet these special instructions. Therefore, in the context of traditional Open64 compiler, proposed a special instruction algorithm. Through this algorithm implements the multiplication-accumulation operation with BWDSP structure, to improve the performance of algorithms with multiply-accumulate requirements. Experimental results show that the algorithm, which can make an maximum of 8.85 speedup on BWDSP.
Combinatorial and Algorithmic Properties of One Matrix Structure at Monotone Boolean Functions

One matrix structure in the area of monotone Boolean functions is defined here. Some of its combinatorial, algebraic and algorithmic properties are derived. On the base of these properties, three algorithms are built. First of them generates all monotone Boolean functions of $n$ variables in lexicographic order. The second one determines the first (resp. the last) lexicographically minimal true (resp. maximal false) vector of an unknown monotone function $f$ of $n$ variables. The algorithm uses at most $n$ membership queries and its running time is $\Theta(n)$. It serves the third algorithm, which identifies an unknown monotone Boolean function $f$ of $n$ variables by using membership queries only. The experimental results show that for $1\leq n\leq 6$, the algorithm determines $f$ by using at most $m.n$ queries, where $m$ is the combined size of the sets of minimal true and maximal false vectors of $f$.
Learning Topological Representation for Networks via Hierarchical Sampling

The topological information is essential for studying the relationship between nodes in a network. Recently, Network Representation Learning (NRL), which projects a network into a low-dimensional vector space, has been shown their advantages in analyzing large-scale networks. However, most existing NRL methods are designed to preserve the local topology of a network and they fail to capture the global topology. To tackle this issue, we propose a new NRL framework, named HSRL, to help existing NRL methods capture both local and global topological information of a network. Specifically, HSRL recursively compresses an input network into a series of smaller networks using a community-awareness compressing strategy. Then, an existing NRL method is used to learn node embeddings for each compressed network. Finally, the node embeddings of the input network are obtained by concatenating the node embeddings resulting from all compressed networks. Empirical studies of link prediction on five real-world datasets demonstrate the advantages of HSRL over state-of-the-art methods.
Comparison of Language Identification Techniques

Many researches that analyse huge amounts of text data, eliminate only texts in English or use the datasets of texts, which language is identified. Accordingly, the language identification task for this text data is assumed to be accomplished. The purpose of the present work is to compare the language identification approaches using the datasets of tweets for the evaluation. The parameters and classifiers with the best performance for the wordand N-gram-based approaches are determined for four different datasets of tweets, that contain 19 different languages. Moreover, the approaches with the highest results are found for each of these datasets. The lists of sentences and words from the Leipzig Corpora Collection are used as the training data. The results of the present work show that for all used datasets the frequent words approach outperforms the short words approach and works with cumulative frequency addition classifier better than with other classifiers. The frequent words approach achieved the best results using 3100-3800 most frequent words. For most of the used datasets the improved graph-based N-gram approach, that utilises the natural logarithm of the counts of the N-grams, obtains the best performance. This approach shows the best results with the N-grams of the length from 3 to 5 and is used in all comparisons with the cumulative frequency addition classifier. However, for the Non-Latin dataset it is surpassed by the frequent words approach with the cumulative addition classifier and 3100 words.