Reviewerly/exHarmony huggingface dataset:
Age Factor Removal Network Based on Transfer Learning and Adversarial Learning for Cross-Age Face Recognition

It is well known that expression, pose variations, and especially age factors always affect the performance of a face recognition system in practical conditions. In this paper, we propose a novel framework called age factor removal network (AFRN) for cross-age face recognition, which combines the concepts of transfer learning and adversarial learning to enhance the performance of a pretrained face recognition network and suppress the influence of attributes, such as aging, expression, and pose variations. Similar to the generative adversarial network (GAN), our model consists of two networks: an identity feature generator G and an age discriminator D. First, the D network is trained to discriminate the age information from the feature extracted by G. Second, the G network simultaneously learns two tasks: extracting the feature through transfer learning from a pretrained face recognition network and suppressing age information through adversarial learning with D. Through the optimization of the two networks, the age factor in feature extraction process of G is removed. Besides, our network only requires the attributes' label to train and preserves the identity discriminant power through transfer learning, and thus, it does not depend on multi-label face databases. The extensive experiments have been performed on the benchmark cross-age face datasets, including MORPH Album2, CACD-VS, and Cross Age LFW, which verify the effectiveness of our model. What is more, our model can be extended to other practical face recognition tasks and decreases the influence of expressions and poses, which is verified by the experimental results on CMU Multi-PIE database.
Making Sense of Sensors

This paper explores the process by which designers come to terms with an unfamiliar and ambiguous sensor material. Drawing on craft practice and material-driven interaction design, we developed a simple yet flexible sensor technology based on the movement of conductive elements within a magnetic field. Variations in materials and structure give rise to objects which produce a complex time-varying signal in response to physical interaction. Sonifying the signal yields nuanced and intuitive action-sound correspondences which nonetheless defy easy categorisation in terms of conventional types of sensors. We reflect on a craft-based exploration of the material by one of the authors, then report on two workshops with groups of designers of varying background. Through examining the objects produced and the experience of the participants, we explore the tension between tacit and explicit understanding of unfamiliar materials and the ways that material thinking can create new design opportunities.
Better OOV Translation with Bilingual Terminology Mining

Unseen words, also called out-of-vocabulary words (OOVs), are difficult for machine translation.In neural machine translation, byte-pair encoding can be used to represent OOVs, but they are still often incorrectly translated.We improve the translation of OOVs in NMT using easy-to-obtain monolingual data.We look for OOVs in the text to be translated and translate them using simple-to-construct bilingual word embeddings (BWEs).In our MT experiments we take the 5-best candidates, which is motivated by intrinsic mining experiments.Using all five of the proposed target language words as queries we mine target-language sentences.We then back-translate, forcing the back-translation of each of the five proposed target-language OOV-translation-candidates to be the original source-language OOV.We show that by using this synthetic data to finetune our system the translation of OOVs can be dramatically improved.In our experiments we use a system trained on Europarl and mine sentences containing medical terms from monolingual data.
Verifiable ranked search over encrypted data with forward and backward privacy

The more growing discovery of cloud computing capabilities, the more likely researchers are to make progress in this field. The ability to search over the encrypted data is one of the prerequisites for cloud computing. A large number of data owners all over the world are interested in outsourcing data to the cloud, being concerned about protecting the data privacy. The method presented in this paper includes several functionalities which have never been simultaneously available in any other searchable encryption scheme. The proposed scheme is a dynamic scheme in which data update does not leak information from outsourced data or search keywords. In this model, the cloud server is assumed to be malicious and in such circumstances it is possible to verify the search results by data user. In addition to searching with multiple keywords, the user receives the ranked search results as well. The performance analysis of proposed scheme shows that despite the added functionality, its efficiency is reasonable.
German solar power generation data mining and prediction with transparent open box learning network integrating weather, environmental and market variables

A compiled dataset of hourly-averaged solar power generation (MW) for Germany in 2016 integrates eight influencing weather, environmental and market price variables (8784 data records including 5012 non-zero generation periods). It provides valuable insight to solar power variations over the course of one year. The dataset is evaluated with the transparent open box (TOB) learning network for data mining and prediction purposes. This algorithm provides accurate and repeatable MW predictions and enables detailed evaluation of the key influencing variables on each hourly data record. TOB Stage 1 applies a data matching routine driven by the squared errors between the independent variables. TOB Stage 2 applies a customized memetic firefly optimizer to minimize the root mean squared error (RMSE) for MW predictions over subsets and/or the full dataset. TOB achieves high prediction accuracy (average of five cases RMSE = 1044.4 MW; R2 = 0.975) using tuning subsets of only ∼303 data records (∼6% of the full dataset). The dataset displays some significant MW prediction outliers that are readily identified and explained individually by the TOB algorithm’s data mining capabilities. A slightly filtered dataset (4918 data records excluding 94 outlier data records) improves MW prediction accuracy (average of five cases RMSE = 936.1 MW; R2 = 0.980). Whereas the prediction outliers are readily segregated as a separate subset for more detailed evaluation. The TOB algorithm’s combined machine-learning and data mining capabilities provide valuable insight to the dataset and the influences of its independent variables. The algorithm couples high-prediction accuracy and detailed evaluation of long-term and short-term time series data and for spatial scales varying from country level to individual solar farms.
Detection and Recognition of Objects in Image Caption Generator System: A Deep Learning Approach

Image Caption Generator deals with generating captions for a given image. The semantic meaning in the image is captured and converted into a natural language. The capturing mechanism involves a tedious task that collaborates both image processing and computer vision. The mechanism must detect and establish relationships between objects, people, and animals. The aim of this paper is to detect, recognize and generate worthwhile captions for a given image using deep learning. Regional Object Detector (RODe) is used for the detection, recognition and generating captions. The proposed method focuses on deep learning to further improve upon the existing image caption generator system. Experiments are conducted on the Flickr 8k dataset using python language to demonstrate the proposed method.
Sparse Matrix Classification on Imbalanced Datasets Using Convolutional Neural Networks

This paper deals with the class imbalance problem in the context of the automatic selection of the best storage format for a sparse matrix with the aim of maximizing the performance of the sparse matrix vector multiplication (SpMV) on GPUs. Our classification method uses convolutional neural networks (CNNs) and proposes several solutions to mitigate the bias toward the majority classes when the data are not balanced. First, the CNNs are trained using images that represent the sparsity pattern of the matrices, whose pixels are colored according to different matrix features. In addition, we introduce a new network called SpNet, which achieves better results than a standard network as AlexNet in terms of prediction accuracy even having a more simple architecture. Finally, sampling techniques and cost-sensitive methods have been studied to give more emphasis on minority classes. The experiments conducted show that our classifiers are able to select the best performing format 92.8% of the time, obtaining 98.3% of the maximum attainable SpMV performance. A comparison to other state-of-the-art classification methods is also provided, demonstrating the benefits of our proposal.
Fine-grained warm water cooling for improving datacenter economy

Driven by the increasing power consumption of datacenters, the industry is focusing more on water cooling for improving the energy efficiency. Using warm water to cool servers has been considered as an efficient method to reduce the cooling energy. However, warm water cooling may lead to the risk of cooling failure and its energy efficiency suffers from the thermal imbalance among servers, due to the lack of fine-grained cooling control. In this paper, we propose a hybrid cooling architecture design that incorporates thermoelectric cooler into the water cooling system, to deal with cooling mismatching in a fine-grained manner. We exploit the warm water cooling strategy and design an adaptive cooling control framework according to workload variations, to make water cooling system more economical for datacenters. We evaluate the hybrid water cooling design based on a real hardware prototype and cluster traces from Google and Alibaba. Compared with conventional water cooling system, our hybrid water cooling system can reduce the energy consumption by 58.72%~78.43% to handle the cooling mismatching.
SecDir

Directories for cache coherence have been recently shown to be vulnerable to conflict-based side-channel attacks. By forcing directory conflicts, an attacker can evict victim directory entries, which in turn trigger the eviction of victim cache lines from private caches. This evidence strongly suggests that directories need to be redesigned for security. The key to a secure directory is to block interference between processes. Sadly, in an environment with many cores, this is hard or expensive to do.
An Admissible Heuristic to Improve Convergence in Kinodynamic Planners Using Motion Primitives

This letter introduces a new heuristic function that can be incorporated in any kinodynamic planner using motion primitives, to the purpose of increasing its convergence rate. The heuristic function is proven to be admissible and, hence, the optimality properties of the planning algorithm are preserved. Notably, it can be applied to planning problems with generic agent motion models and cost criteria, since it depends only on the database of motion primitives. The proposed heuristic has been integrated into a randomized sampling-based and a deterministic kinodynamic planner, and its effectiveness has been shown in numerical examples with different agent motion models and cost criteria.
Suppressing the information losses of accelerated qubit–qutrit system

Protecting the accelerated information coherence and suppressing of its decay due to the acceleration is achieved by using different noisy channels. The behavior of three different types of information; Fisher, nonlocal and local information of qubit–qutrit system is discussed. It is shown that, the estimation rate of the acceleration parameter is maximized if both subsystems are accelerated and interacted locally with their noise channels. However, if the largest subsystem is accelerated, then the estimation rate is minimized. The rate of change of the nonlocal information depends on which subsystem is accelerated. The results display that the local information is almost unaltered during the acceleration process, that is, the information is frozen. We show that the phase-flip channel represents the weak channel, where its suppressing of the decay due to the acceleration is smaller than the dephasing and depolarize of channels.
Analysis of Factors Affecting Firm Innovation: An Empirical Investigation for Ethiopian Firms

The present study concentrates on answering following questions: (i) Do firm's internal factors such as size, age, ownership, R&D spending, and exporting significantly predict the propensity of firms to engage in innovative activities in Ethiopia? (ii) Do firm's external factors, namely, access to finance and competition, significantly predict the propensity of firms to engage in innovative activities in Ethiopia? The data source of the paper is a survey conducted by World Bank in 2015 on Ethiopian firms. Then, the factors influencing the innovativeness of Ethiopian firms are analyzed by using a logistic regression model. Results obtained through logistic regression suggest that internal factors such as the size of the firms, education level of the employees, technology adoption capability of managers, on-job training, and R&D expenditures have a significant positive impact on both types of innovations, while private and foreign ownership were only found to be significant for process innovation. Estimations also showed that access to finance has a significant (positive) impact on product innovation and the impact of the competition level is significant (negative) only in terms of process innovation. Most importantly, we found that, disregarding their statistical significance, all explanatory variables influence both process and product innovation in the same direction, probably because of the positive correlation between the two.
Idioms and the syntax/semantics interface of descriptive content vs. reference

Abstract The syntactic literature on idioms contains some proposals that are surprising from a compositional perspective. For example, there are proposals that, in the case of verb-object idioms, the verb combines directly with the noun inside its DP complement, and the determiner is introduced higher up in the syntactic structure, or is late-adjoined. This seems to violate compositionality insofar as it is generally assumed that the semantic role of the determiner is to convert a noun to the appropriate semantic type to serve as the argument to the function denoted by the verb. In this paper, we establish a connection between this line of analysis and lines of work in semantics that have developed outside of the domain of idioms, particularly work on incorporation and work that combines formal and distributional semantic modelling. This semantic work separates the composition of descriptive content from that of discourse referent introducing material; our proposal shows that this separation offers a particularly promising way to handle the compositional difficulties posed by idioms, including certain patterns of variation in intervening determiners and modifiers.
Hierarchical automatic curriculum learning: Converting a sparse reward navigation task into dense reward

Mastering the sparse reward or long-horizon task is critical but challenging in reinforcement learning. To tackle this problem, we propose a hierarchical automatic curriculum learning framework (HACL), which intrinsically motivates the agent to hierarchically and progressively explore environments. The agent is equipped with a target area during training. As the target area progressively grows, the agent learns to explore from near to far, in a curriculum fashion. The pseudo target-achieving reward converts the sparse reward into dense reward, thus the long-horizon difficulty is alleviated. The whole system makes hierarchical decisions, in which a high-level conductor travels through different targets, and a low-level executor operates in the original action space to complete the instructions given by the high-level conductor. Unlike many existing works that manually set curriculum training phases, in HACL, the total curriculum training process is automated and suits the agent’s current exploration capability. Extensive experiments on three sparse reward tasks, long-horizon stochastic chain, grid maze, and the challenging Atari game Montezuma’s Revenge, show that HACL achieves comparable or even better performance but with significantly less training frames.
CATS: Cross-Platform E-Commerce Fraud Detection

Nowadays, the popularity of e-commerce has brought huge economic benefits to factories, third-party merchants, and e-commerce service providers. Driven by such huge economic benefits, malicious merchants attempt to promote items through inserting fraudulent purchases, fake review scores, and/or feedback, into them. Mitigating this threat is challenging due to the difficulty of obtaining internal e-commerce data, the variance of e-commerce services used by malicious merchants, and the reluctance of service providers in cooperation. In this paper, we present an efficient, platform-independent, and robust e-commerce fraud detection system, CATS, to detect frauds for different large-scale e-commerce platforms. We implement the design of CATS into a prototype system and evaluate this prototype on the world's popular e-commerce platform Taobao. The evaluation result on Taobao shows that CATS can achieve a high accuracy of 91% in detecting frauds. Based on this success, we then apply CATS on another large-scale e-commerce platforms, and again CATS achieves an accuracy of 96%, suggesting that CATS is very effective on real e-commerce platforms. Based on the cross-platform evaluation results, we conduct a comprehensive analysis on the reported frauds and reveal several abnormal yet interesting behaviors of those reported frauds. Our study in this paper is expected to shed light on defending against frauds for various e-commerce platforms.
FASE: FPGA Acceleration of Secure Function Evaluation

We present FASE, an FPGA accelerator for Secure Function Evaluation (SFE) by employing the well-known cryptographic protocol named Yao's Garbled Circuit (GC). SFE allows two parties to jointly compute a function on their private data and learn the output without revealing their inputs to each other. FASE is designed to allow cloud servers to provide secure services to a large number of clients in parallel while preserving the privacy of the data from both sides. Current SFE accelerators either target specific applications, and therefore are not amenable to generic use, or have low throughput due to inefficient management of resources. In this work, we present a pipelined architecture along with an efficient scheduling scheme to ensure optimal usage of the available resources. The scheme is built around a simulator of the hardware design that schedules the workload and assigns the most suitable task to the encryption cores at each cycle. This, coupled with optimal management of the read and write cycles of the Block RAM on FPGA, results in a minimum 2 orders of magnitude improvement in terms of throughput per core for the reported benchmarks compared to the most recent generic GC accelerator. Moreover, our encryption core requires 17% less resource compared to the most recent secure GC realization.
Personalized Service Recommendation Based on User Dynamic Preferences

In order to personalize users' recommendations, it is essential to consider their personalized preferences on non-functional attributes during service recommendation. However, to increase recommendation accuracy, it is essential that recommendation systems include users' evolving preferences. It is not sufficient to only consider users' preferences at a point in time. Existing time-based recommendation systems either disregard rich and useful historical user invocation information, or rely on information from similar users, and thus, fail to thoroughly capture users' dynamic preferences for personalized recommendation. This work proposes a method to personalize users' recommendations based on their dynamic preferences on non-functional attributes. First, we compose a user's preference profile as a time series of his/her invocation preference and pre-invocation dependencies (i.e. the different services that were viewed prior to invoking the preferred service). We model a user's invocation preference as a combination of non-functional attribute values observed during service invocation, and topic distribution from WSDL of the invoked service using Hierarchical Dirichlet Process (HDP). Next, we employ long short-term memory recurrent neural networks (LSTM-RNN) to predict the user's future invocation preference. Finally, based on the predicted future invocation preference, we recommend service(s) to that user. To evaluate our proposed method, we perform experiments using real-world service dataset, WS-Dream.
Tree-based space partition and merging ensemble learning framework for imbalanced problems

Dealing with imbalanced problems is a significant challenge in machine learning, especially when the data set exhibits an irregular distribution. To this end, this paper proposes a tree-based space partition and merging ensemble learning framework known as the space partition tree (SPT), to partition the data space into two sub-spaces recursively. The partition hyperplane partitions the current space according to the maximum scatter direction of the majority set in the current space. When the partitioned sub-space satisfies the termination conditions, the sub-space is regarded as a decision space, and the decision region of the minority and majority classes is learned in this decision space. By merging the decision regions in all decision spaces, the SPT provides the entire decision region for the original problem. Thereby, the original complex problem can be divided into smaller problems with a relatively balanced and regular distribution. Moreover, the designed partition strategy offers advantages for the recognition of minority samples in the decision space. Finally, the space partition and merging exhibit superior geometric intuition and property of diversity. By introducing the biased penalties Support Vector Machine (BPSVM) into the SPT, the SPT-BPSVM demonstrates satisfactory performance and validates the effectiveness of the SPT in the experiment.
Cross-Database Micro-Expression Recognition

Cross-database micro-expression recognition (CDMER) is one of recently emerging and interesting problems in micro-expression analysis. CDMER is more challenging than the conventional micro-expression recognition (MER), because the training and testing samples in CDMER come from different micro-expression databases, resulting in inconsistency of the feature distributions between the training and testing sets. In this paper, we contribute to this topic from two aspects. First, we establish a CDMER experimental evaluation protocol and provide a standard platform for evaluating their proposed methods. Second, we conduct extensive benchmark experiments by using NINE state-of-the-art domain adaptation (DA) methods and SIX popular spatiotemporal descriptors for investigating the CDMER problem from two different perspectives and deeply analyze and discuss the experimental results. In addition, all the data and codes involving CDMER in this paper are released on our project website: http://aip.seu.edu.cn/cdmer.