Reviewerly/exHarmony huggingface dataset:
FM2u-Net: Face Morphological Multi-Branch Network for Makeup-Invariant Face Verification

It is challenging in learning a makeup-invariant face verification model, due to (1) insufficient makeup/non-makeup face training pairs, (2) the lack of diverse makeup faces, and (3) the significant appearance changes caused by cosmetics. To address these challenges, we propose a unified Face Morphological Multi-branch Network (FMMu-Net) for makeup-invariant face verification, which can simultaneously synthesize many diverse makeup faces through face morphology network (FM-Net) and effectively learn cosmetics-robust face representations using attention-based multi-branch learning network (AttM-Net). For challenges (1) and (2), FM-Net (two stacked auto-encoders) can synthesize realistic makeup face images by transferring specific regions of cosmetics via cycle consistent loss. For challenge (3), AttM-Net, consisting of one global and three local (task-driven on two eyes and mouth) branches, can effectively capture the complementary holistic and detailed information. Unlike DeepID2 which uses simple concatenation fusion, we introduce a heuristic method AttM-FM, attached to AttM-Net, to adaptively weight the features of different branches guided by the holistic information. We conduct extensive experiments on makeup face verification benchmarks (M-501, M-203, and FAM) and general face recognition datasets (LFW and IJB-A). Our framework FMMu-Net achieves state-of-the-art performances.
Zero-Shot Object Detection via Learning an Embedding from Semantic Space to Visual Space

Zero-shot object detection (ZSD) has received considerable attention from the community of computer vision in recent years. It aims to simultaneously locate and categorize previously unseen objects during inference. One crucial problem of ZSD is how to accurately predict the label of each object proposal, i.e. categorizing object proposals, when conducting ZSD for unseen categories. Previous ZSD models generally relied on learning an embedding from visual space to semantic space or learning a joint embedding between semantic description and visual representation. As the features in the learned semantic space or the joint projected space tend to suffer from the hubness problem, namely the feature vectors are likely embedded to an area of incorrect labels, and thus it will lead to lower detection precision. In this paper, instead, we propose to learn a deep embedding from the semantic space to the visual space, which enables to well alleviate the hubness problem, because, compared with semantic space or joint embedding space, the distribution in visual space has smaller variance. After learning a deep embedding model, we perform $k$ nearest neighbor search in the visual space of unseen categories to determine the category of each semantic description. Extensive experiments on two public datasets show that our approach significantly outperforms the existing methods.
Concurrent Games in Dynamic Epistemic Logic

Action models of Dynamic Epistemic Logic (DEL) represent precisely how actions are perceived by agents. DEL has recently been used to define infinite multi-player games, and it was shown that they can be solved in some cases. However, the dynamics being defined by the classic DEL update product for individual actions, only turn-based games have been considered so far. In this work we define a concurrent DEL product, propose a mechanism to resolve conflicts between actions, and define concurrent DEL games. As in the turn-based case, the obtained concurrent infinite game arenas can be finitely represented when all actions are public, or all are propositional. Thus we identify cases where the strategic epistemic logic ATL*K can be model checked on such games.
Computational neuroscience: a frontier of the 21st century

The human brain is a biological organ, weighing about three pounds or 1.4 kg, that determines our behaviors, thoughts, emotions and consciousness. Although comprising only 2% of the total body weight, the brain consumes about 20% of the oxygen entering the body. With the expensive energy demand, the brain enables us to perceive and act upon the external world, as well as reflect on our internal thoughts and feelings. The brain is actually never at ‘rest’. Brain activities continue around the clock, ranging from functions enabling human–environment interactions to housekeeping during sleep, including processes such as synaptic homeostasis and memory formation. Whereas one could argue that sciences in the last century were dominated by physics and molecular biology, in the current century one of our major challenges is to elucidate how the brain works. A full understanding of brain functions and malfunctions is likely the most demanding task we will ever have.
A Bi-level Formulation for Label Noise Learning with Spectral Cluster Discovery

Practically, we often face the dilemma that some of the examples for training a classifier are incorrectly labeled due to various subjective and objective factors. Although intensive efforts have been put to design classifiers that are robust to label noise, most of the previous methods have not fully utilized data distribution information. To address this issue, this paper introduces a bi-level learning paradigm termed “Spectral Cluster Discovery'' (SCD) for combating with noisy labels. Namely, we simultaneously learn a robust classifier (Learning stage) by discovering the low-rank approximation to the ground-truth label matrix and learn an ideal affinity graph (Clustering stage). Specifically, we use the learned classifier to assign the examples with similar label to a mutual cluster. Based on the cluster membership, we utilize the learned affinity graph to explore the noisy examples based on the cluster membership. Both stages will reinforce each other iteratively. Experimental results on typical benchmark and real-world datasets verify the superiority of SCD to other label noise learning methods.
MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization

Recently, large-scale datasets have vastly facilitated the development in nearly all domains of Natural Language Processing.However, there is currently no cross-task dataset in NLP, which hinders the development of multi-task learning.We propose MATINF, the first jointly labeled large-scale dataset for classification, question answering and summarization.MAT-INF contains 1.07 million question-answer pairs with human-labeled categories and usergenerated question descriptions.Based on such rich information, MATINF is applicable for three major NLP tasks, including classification, question answering, and summarization.We benchmark existing methods and a novel multi-task baseline over MATINF to inspire further research.Our comprehensive comparison and experiments over MATINF and other datasets demonstrate the merits held by MAT-INF.
Utilizing Automatic Query Reformulations as Genetic Operations to Improve Feature Location in Software Models

In the combination of Model-Driven Engineering (MDE) and Search-Based Software Engineering (SBSE), genetic operations are one of the key ingredients. Our work proposes a novel adaptation of automatic query reformulations as genetic operations that leverage the latent semantics of software models (the cornerstone artefact of MDE). We analyze the impact of these reformulation operations in a real-world industrial case study of feature location in models. As baselines, we use: 1) the widespread single-point crossover plus random mutation; and 2) mask crossover plus random mutation, which is the best performer for feature location in models. We also perform a statistical analysis to provide quantitative evidence of the impact of the results and to show that this impact is significant. Our reformulation operations improve the results of the best baseline by 37.73 percent in recall and 14.08 percent in precision. These results are relevant for the task of feature location in models (one of the main activities performed during software maintenance and evolution). Furthermore, given that the only requirement to apply our approach is term availability in models, our work opens a new research direction to improve more tasks in MDE such as bug location or requirements traceability.
Understanding the Power and Limitations of Teaching with Imperfect Knowledge

Machine teaching studies the interaction between a teacher and a student/learner where the teacher selects training examples for the learner to learn a specific task. The typical assumption is that the teacher has perfect knowledge of the task---this knowledge comprises knowing the desired learning target, having the exact task representation used by the learner, and knowing the parameters capturing the learning dynamics of the learner. Inspired by real-world applications of machine teaching in education, we consider the setting where teacher's knowledge is limited and noisy, and the key research question we study is the following: When does a teacher succeed or fail in effectively teaching a learner using its imperfect knowledge? We answer this question by showing connections to how imperfect knowledge affects the teacher's solution of the corresponding machine teaching problem when constructing optimal teaching sets. Our results have important implications for designing robust teaching algorithms for real-world applications.
A device-independent protocol for XOR oblivious transfer

Oblivious transfer is a cryptographic primitive where Alice has two bits and Bob wishes to learn some function of them. Ideally, Alice should not learn Bob&amp;apos;s desired function choice and Bob should not learn any more than what is logically implied by the function value. While decent quantum protocols for this task are known, many become completely insecure if an adversary were to control the quantum devices used in the implementation of the protocol. In this work we give a fully device-independent quantum protocol for XOR oblivious transfer.
Infochain: A Decentralized, Trustless and Transparent Oracle on Blockchain

Blockchain based systems allow various kinds of financial transactions to be executed in a decentralized manner. However, these systems often rely on a trusted third party (oracle) to get correct information about the real-world events, which trigger the financial transactions. In this paper, we identify two biggest challenges in building decentralized, trustless and transparent oracles. The first challenge is acquiring correct information about the real-world events without relying on a trusted information provider. We show how a peer-consistency incentive mechanism can be used to acquire truthful information from an untrusted and self-interested crowd, even when the crowd has outside incentives to provide wrong informations. The second is a system design and implementation challenge. For the first time, we show how to implement a trustless and transparent oracle in Ethereum. We discuss various non-trivial issues that arise in implementing peer-consistency mechanisms in Ethereum, suggest several optimizations to reduce gas cost and provide empirical analysis.
Exploration of Memory Access Optimization for FPGA-based 3D CNN Accelerator

Three-dimensional convolutional networks (3D CNNs) are used efficiently in various video recognition applications. Compared to traditional 2D CNNs, extra temporal dimension causes 3D CNNs more computationally intensive and to have a larger memory footprint. Therefore, the memory optimization is extremely crucial in this case. This paper presents a design space exploration of memory access optimization for FPGA-based 3D CNN accelerator. We present a non-overlapping data tiling method for contiguous off-chip memory access and explore on-chip data reuse opportunity by leveraging different loop ordering strategies. We propose a hardware architecture design which can flexibly support different loop ordering strategies for each 3D CNN layer. With the help of hardware/software co-design, we can provide the optimal configuration toward an energy-efficient and high-performance accelerator design. According to the experiments on AlexNet, VGG16, and C3D, our optimal model reduces up to 84% DRAM accesses and 55% energy consumption on C3D compared to a baseline model, and demonstrates state-of-the-art performance compared to prior FPGA implementations.
Building a multilingual ontology for education domain using monto method

Ontologies are emerging technology in building knowledge based information retrieval systems. It is used to conceptualize the information in human understandable manner. Knowledge based information retrieval are widely used in the domain like Education, Artificial Intelligence, Healthcare and so on. It is important to provide multilingual information of those domains to facilitate multi-language users. In this paper, we propose a MOnto (Multilingual Ontology) methodology to develop multilingual ontology applications for education domain. New algorithms are proposed for merging and mapping multilingual ontologies.
ITP-KNN: Encrypted Video Flow Identification Based on the Intermittent Traffic Pattern of Video and K-Nearest Neighbors Classification

As video dominates internet traffic, researchers tend to pay attention to video-related fields, such as video shaping, differentiated service, multimedia protocol tunneling detection. Some video-related fields, e.g., traffic measurement and the metrics for Quality of Experience, are based on video flow identification. However, video flow identification faces challenges. Firstly, the increasing adoption of Transport Layer Security makes payload-based methods no longer applicable. Secondly, traffic features differ when generated by different streaming protocols. This paper proposes a video flow identification method, called ITP-KNN, which utilizes the intermittent traffic pattern-related features (ITP) and the K-nearest neighbors (KNN) algorithm. The intermittent traffic pattern is caused by fragmented transmission, which is common among video streamings generated by different streaming protocols. Therefore, the intermittent traffic pattern is useful for overcoming the above challenges and then differentiating video traffic from not-video traffic. We develop a set of features to describe the intermittent traffic pattern. Preliminary results show the promise of ITP-KNN, yielding high identification recall and precision over a range of video content and encoding qualities.
Foldover Features for Dynamic Object Behaviour Description in Microscopic Videos

A behavior description helps analyze tiny objects, similar objects, objects with weak visual information, and objects with similar visual information. It plays a fundamental role in the identification and classification of dynamic objects in microscopic videos. To this end, we propose foldover features to describe the behavior of dynamic objects. Foldover is defined as: Each frame of an object's motion is superimposed on the same spatial plane in the spacetime order of the motion, the result of the superposition is the foldover of the object's motion. Foldover of an object contains temporal information, spatial information, behavior features and static features. Therefore, the features extracted based on the foldover of the object are the foldover features. In this work, we first generate foldover for each object in microscopic videos in X, Y and Z directions, respectively. Then, we extract foldover features from the X, Y and Z directions with statistical methods, respectively. The core content of this paper is to construct the foldovers and extract the foldover features. Through these two steps, the temporal information, spatial information, behavior features and static features of the object are enhanced and included in the foldover features. Furthermore, the description of the behavior of dynamic objects by the foldover features is strengthened. Finally, we use four different classifiers to test the effectiveness of the proposed foldover features. In the experiment, we use a microscopic sperm video dataset to evaluate the proposed foldover features, including three types of 1374 sperms, and obtain the highest classification accuracy of 96.5%.
Searchable Encryption with Access Control on Keywords in Multi-User Setting

Searchable encryption technology makes it convenient to search encrypted data with keywords for people. A data owner shared his data with other users on the cloud server. For security, it is necessary for him to build a fine-grained and flexible access control mechanism. The main idea of this paper is to let the owner classify his data and then authorizes others according to categories. The cloud server maintains a permission matrix, which will be used to verify whether a trapdoor is valid or not. In this way we can achieve access control and narrow the search range at the same time. We prove that our scheme can achieve index and trapdoor indistinguishability under chosen keywords attack security in the random oracles.
WOTS-S: A Quantum Secure Compact Signature Scheme for Distributed Ledger

The digital signature scheme, which underpins most of the existing distributed ledgers, is generally based on non-quantum-resilient algorithms (e.g. elliptic curve digital signature algorithm). This highlights the need for quantum-secure signature schemes in future distributed ledgers (and other products). Therefore, in this paper, we propose a novel quantum-secure digital signature scheme designed specifically for cryptocurrencies. Our proposed scheme is a hash-based signature scheme, which is a variant of Winternitz-one time signature scheme. A comparison of the proposed scheme and two other competing quantum-secure cryptocurrencies (IoTA and QRL) reveals that our scheme respectively achieves 59% and 24% reductions in signature lengths without compromising the level of security. A salient feature of the proposed approach is that, unlike the previously proposed variants of Winternitz scheme, we avoid the need for any expensive computation. In addition, we formally model the classical cryptocurrency and the proposed quantum-secure cryptocurrency using high-level Petri-nets, which allows the implementer to understand their workings in the presence of a quantum attacker. Furthermore, we also provide formal security proof in the random oracle model.
OpenGraphGym: A Parallel Reinforcement Learning Framework for Graph Optimization Problems

This paper presents an open-source, parallel AI environment (named OpenGraphGym) to facilitate the application of reinforcement learning (RL) algorithms to address combinatorial graph optimization problems. This environment incorporates a basic deep reinforcement learning method, and several graph embeddings to capture graph features, it also allows users to rapidly plug in and test new RL algorithms and graph embeddings for graph optimization problems. This new open-source RL framework is targeted at achieving both high performance and high quality of the computed graph solutions. This RL framework forms the foundation of several ongoing research directions, including 1) benchmark works on different RL algorithms and embedding methods for classic graph problems; 2) advanced parallel strategies for extreme-scale graph computations, as well as 3) performance evaluation on real-world graph solutions.
A Fast Machine Learning Model for Large-Scale Estimation of Annual Solar Irradiation on Rooftops

Rooftop-mounted solar photovoltaics have shown to be a promising technology to provide clean electricity in urban areas.Several large-scale studies have thus been conducted in different countries and cities worldwide to estimate their PV potential for the existing building stock using different methods.These methods, however, are time-consuming and computationally expensive.This paper provides a Machine Learning approach to estimate the annual solar irradiation on building roofs (in kWh/m 2 ) for large areas in a fast and computationally efficient manner by learning from existing datasets.The estimation is based on rooftop characteristics, input features extracted from digital surface models and annual horizontal irradiation.Five ML models are compared, with Random Forests exhibiting the highest model accuracy.In the presented case study, the model is trained using data of the Swiss Romandie area and is then applied to estimate annual rooftop solar irradiation in remaining Switzerland with an accuracy of 92%.
SPEED: secure, PrivatE, and efficient deep learning

We introduce a deep learning framework able to deal with strong privacy constraints. Based on collaborative learning, differential privacy and homomorphic encryption, the proposed approach advances state-of-the-art of private deep learning against a wider range of threats, in particular the honest-but-curious server assumption. We address threats from both the aggregation server, the global model and potentially colluding data holders. Building upon distributed differential privacy and a homomorphic argmax operator, our method is specifically designed to maintain low communication loads and efficiency. The proposed method is supported by carefully crafted theoretical results. We provide differential privacy guarantees from the point of view of any entity having access to the final model, including colluding data holders, as a function of the ratio of data holders who kept their noise secret. This makes our method practical to real-life scenarios where data holders do not trust any third party to process their datasets nor the other data holders. Crucially the computational burden of the approach is maintained reasonable, and, to the best of our knowledge, our framework is the first one to be efficient enough to investigate deep learning applications while addressing such a large scope of threats. To assess the practical usability of our framework, experiments have been carried out on image datasets in a classification context. We present numerical results that show that the learning procedure is both accurate and private.
Particularities Of Communication And Interpersonal Relationship In Gymnasium Students

The article “Particularities of Communication and Interpersonal Relationship in Gymnasium Students” aims to highlight the problems faced by the teachers in the educational environment, as well as the opinions and suggestions suggested by them. The research aims to analyze this topic, to investigate the incidence of desirable behaviors involved in the communication and relationship of students in the gymnasium, as well as the barriers that intervene in the process of communication and interrelation, preventing communication by expressing their interests and needs in an assertive way, without an aggressive behavior. The article aims to present the educational approaches designed by the teachers in order to develop the capacity of active listening and assertive expression of the educables in different social communication contexts, the alternative solutions managed and adopted by the educables in the conflict situations that degenerate through deviation from the norms and social principles. In this context, the teachers' concerns are to pay special attention to the training and improvement of communication and relationship skills, present as a continuous process of development, necessary for educables in the complex dynamics of active social and professional insertion, but without overlooking the different manner of engaging the students in the educational activity and the flexible character of their behavioral reactions determined by the exercise and self-monitoring of the strategies adopted to ensure the quality of the communication and relationship act.