Reviewerly/exHarmony huggingface dataset:
An Enhanced Approach to Cloud-based Privacy-preserving Benchmarking

Benchmarking is an important measure for companies to investigate their performance and to increase efficiency. As companies usually are reluctant to provide their key performance indicators (KPIs) for public benchmarks, privacy-preserving benchmarking systems are required. In this paper, we present an enhanced privacy-preserving benchmarking protocol, which we implemented and evaluated based on the real-world scenario of product cost optimisation. It is based on homomorphic encryption and enables cloud-based KPI comparison, providing a variety of statistical measures. The theoretical and empirical evaluation of our benchmarking system underlines its practicability.
Optimality of feedback control for qubit purification under inefficient measurement

A quantum system may be purified, i.e., projected into a pure state, faster if one applies feedback operations during the measurement process. However, the existing results suggest that such an enhancement is only possible when the measurement efficiency exceeds 0.5, which is difficult to achieve experimentally. We address the task of finding the global optimal feedback control for purifying a single qubit in the presence of measurement inefficiency. We use the Bloch vector length, a more physical and practical quantity than purity, to assess the quality of the state, and employ a backward-iteration algorithm to find the globally optimal strategy. Our results show that a speedup is available for quantum efficiencies well below 0.5, which opens the possibility of experimental implementation in existing systems.
PKU Paraphrase Bank: A Sentence-Level Paraphrase Corpus for Chinese

One of the main challenges of conducting research on paraphrase is the lack of large-scale, high-quality corpus, which is particularly serious for non-English investigations. In this paper, we present a simple and effective unsupervised learning model that is able to automatically extract high-quality sentence-level paraphrases from multiple Chinese translations of the same source texts. By applying this new model, we obtain a large-scale paraphrase corpus, which contains 509,832 pairs of paraphrased sentences. The quality of this new corpus is manually examined. Our new model is language-independent, meaning that such paraphrase corpora for other languages can be built in the same way.
Fuzzy–rough Fuzzification in General FL Classifiers

In this paper, a three-dimensional version of fuzzy-rough fuzzification is examined for classification tasks. Similar approach based on interval fuzzy-rough fuzzification has been demonstrated to classify with three decision labels of confidence, one of which were uncertain. The method proposed here relies on the use of fuzzification of inputs with a triangular membership function describing the nature of imprecision in data. As a result, we implement in fuzzy classifiers three dimensional membership functions using the calculus of general type-2 fuzzy sets. The approach is justified when more confidence labels are expected from the decision system, especially when the classifier is embedded in a recurrent hierarchical decision system working on easily available economic, extended, and advanced expensive real data.
Predicting the Timeliness of Student Graduation Using Decision Tree C4.5 Algorithm in Universitas Advent Indonesia

The purpose of this study is to predict whether a student will graduate on time or not, using the data collected from the database of Academic Administarion Bureau of Universitas Advent Indonesia (UNAI) for the academic period of 2009–2013. There are 9 (nine) attributes that were used to predict the timeliness factors, and the method used for predicition is the C4.5 decision tree. This study also used SMOTE (Synthetic Minority Oversampling Technique) in the WEKA application to balanced data in minor class. The result shows that the attribute GPA, was the highest root that gave the highest influence. Another attributes that also has influence in predicting the student's graduation timelines are: repeating courses, study leave, and gender, while the attribute religion, is the attributes that are less influential. The accuracy on cross validation 10 Folds with SMOTE and without SMOTE the results shows that: with SMOTE 83,055% and without SMOTE 82,644%. Split Test 70:30 with SMOTE and without SMOTE, the results are 82,026% and 84,015%. The use of SMOTE increases the value of precision and recall. The value of precision for not ontime student, with SMOTE 82.5% and without SMOTE 76.4%, the value of recall with SMOTE 80.6% and without SMOTE 61.9%.
A Methodology for Neural Network Architectural Tuning Using Activation Occurrence Maps

Finding the ideal number of layers and size for each layer is a key challenge in deep neural network design. Two approaches for such networks exist: filter learning and architecture learning. While the first one starts with a given architecture and optimizes model weights, the second one aims to find the best architecture. Recently, several visual analytics (VA) techniques have been proposed to understand the behavior of a network, but few VA techniques support designers in architectural decisions. We propose a hybrid methodology based on VA to improve the architecture of a pre-trained network by reducing/increasing the size and number of layers. We introduce Activation Occurrence Maps that show how likely each image position of a convolutional kernel’s output activates for a given class, and Class Selectivity Maps, that show the selectiveness of different positions in a kernel’s output for a given label. Both maps help in the decision to drop kernels that do not significantly add to the network’s performance, increase the size of a layer having too few kernels, and add extra layers to the model. The user interacts from the first to the last layer, and the network is retrained after each layer modification. We validate our approach with experiments in models trained with two widely-known image classification datasets and show how our method helps to make design decisions to improve or to simplify the architectures of such models.
When and Why Do Software Developers Face Uncertainty?

Recently, many developers begin to notice that uncertainty is a crucial problem in software development. Unfortunately, no one knows how often uncertainty appears or what kinds of uncertainty exist in actual projects, because there are no empirical studies on uncertainty. To deal with this problem, we conduct a large-scale empirical study analyzing commit messages and revision histories of 1,444 OSS projects randomly selected from the GitHub repositories. The main findings are as follows: 1) Uncertainty exists in the ratio of 1.44% (average); 2) Uncertain program behavior, uncertain variable/value/name, and uncertain program defects are major kinds of uncertainty; and 3) Sometimes developers tend to take an action for not resolving but escaping or ignoring uncertainty. Uncertainty exists everywhere in a certain percentage and developers cannot ignore the existence of uncertainty.
Link 'em: An Open Source Link Emulation Bridge for Reproducible Networking Research

This paper presents link 'em, an open source link emulation bridge for reproducible networking research. While reproducibility is one keystone of good research, most available link emulators are lacking crucial features or are prohibitively expensive. Link 'em is a Raspberry Pi-based layer-2 bridge that runs an extended version of netem in conjunction with a trace database to achieve reproducible link emulation. By invoking our novel packet loss module, which builds upon sagemath and NetfilterQueue, more sophisticated packet loss models can be used in addition to netem's existing ones. The proposed demonstration verifies the achieved reproducibility. This is done by showing the effects of a model-based link emulation via netem and a trace-based emulation using our link 'em bridge on a video live stream, compared to a pre-captured reference stream.
DHML: Deep Heterogeneous Metric Learning for VIS-NIR Person Re-identification

Narrowing the modal gap in person re-identification between visible domain and near infrared domain (VIS-NIR Re-ID) is a challenging problem. In this paper, we propose the deep heterogeneous metric learning (DHML) for VIS-NIR Re-ID. Our method explicitly learns a specific projection transformation for each modality. Furthermore, we design a heterogeneous metric module (HeMM), and embed it in the deep neural network to complete an end-to-end training. HeMM provides supervisory information to the network, essentially eliminating the cross-modal gap in the feature extraction stage, rather than performing a post-transformation on the extracted features. We conduct a number of experiments on the SYSU-MM01 dataset, the largest existing VIS-NIR Re-ID dataset. Our method achieves state-of-the-art performance and outperforms existing approaches by a large margin.
Fraud Payment Research

Credit card payment is called CNP which stands for "card not present". Security issues about CNP are critical. We propose an empirical study research for analyzing payment transactions using K-means clustering and decision table method of data mining techniques. The data sets we used are credit card payment from government open data website and credit card fraud data set shared from Weka. In this paper, we found that EC (Electronic Commerce) payment was the majority credit card payment fraud from the first data set and got a close look about similar attributes of fraud customer payments.
Multi-Resource Fair Allocation for Cloud Federation

Since the number of tasks submitted by user changes with time, the cloud data center cannot meet all resource requests in time when the load is high, and the idle resources are not effectively utilized at low load. Cloud federation can be established to share resources between different Cloud Service Providers (CSPs), enabling load balance between clouds. CSPs in the cloud federation are independent and their individual benefit need to be guaranteed when sharing resources with others. In this paper, we establish a two-level asynchronous scheduling model within and between the clouds, and introduce high priority queues in the cloud to prioritize the tasks sent by the federation scheduler. Due to the heterogeneity of resource requirements of user tasks, we design two multi-resource fair scheduling algorithms for cloud and federation. Within the cloud, we add the dynamically adjusted weighting factor to the Dominant Resource Fairness (DRF) to increase the priority of user whose task is outsourced. We define the dominant share and dominant contribution of the cloud. The priority of the cloud is the difference between the dominant share and the dominant contribution, which ensure the incentive and fairness of the cloud. Based on the open source container management system Kubernetes, we develop a cloud federation prototype system to implement our algorithm, and conduct performance evaluations through experiments. The results show that, compared with the existing fair allocation algorithm, the multi-resource fair allocation mechanism proposed in this paper can guarantee users' QoS of each CSP.
Quantum Algorithms for Classical Probability Distributions

We study quantum algorithms working on classical probability distributions. We formulate four different models for accessing a classical probability distribution on a quantum computer, which are derived from previous work on the topic, and study their mutual relationships.Additionally, we prove that quantum query complexity of distinguishing two probability distributions is given by their inverse Hellinger distance, which gives a quadratic improvement over classical query complexity for any pair of distributions.The results are obtained by using the adversary method for state-generating input oracles and for distinguishing probability distributions on input strings.
M-ABRC (Adaptive Binary Range Coder) using Virtual Sliding Window technique and its VLSI implementation

Arithmetic coding is the data compression techniques, which encodes the data by generating the code string that represents a functional value between 0 and 1. In this paper, we propose a modified-Adaptive Binary-RC (Range Coder) or M-ABRC. Our algorithm minimizes the multiplication bit capacity through introducing the VLSI architecture, proposed algorithm uses the LUP (Look UP Table)-VSW (Virtual Sliding Window) for the probability estimation. In order to achieve the higher compression rate, our method M-ABRC has been implemented, this in terms provides the better adoption probability in encoding phase and also gives the absolute estimation of low-EBS(entropy binary sources). Moreover In order to evaluate the algorithm we have compared with the several existing technique, comparison takes place based on the two parameter i.e. device utilization and the power dissipation (static and dynamic).
Feature selection for orthogonal broad learning system based on mutual information

The broad learning system (BLS) is a recently proposed neural network model. Different from the deep neural network model, the BLS has a flatted structure. Therefore, it can be trained efficiently and enjoyed much popularity on many applications. In this paper, we investigate the BLS from the perspective of information theory. By applying the newly proposed matrix-based Rényi's -entropy, we analyze the mutual information between the feature nodes and the output, and the results indicate that the contribution of different nodes vary from each other. Therefore, we propose a feature selection algorithm for orthogonal broad learning system based on mutual information. In the proposed algorithm, the mapping weights are chosen from a orthogonal base set to insure that the extracted features are independent to each other. In addition, each node is valued based on the mutual information between the feature and the output, and a feature selection algorithm is given. We conducted extensive experiments on challenging datasets of classification and regression to demonstrate the effectiveness of the proposed algorithm.
A Spam Review Detection Method by Verifying Consistency among Multiple Review Sites

In recent years, websites that incorporate user reviews, such as Amazon, IMDB and YELP, have become exceedingly popular. As an important factor affecting users purchasing behavior, review information has been becoming increasingly important, and accordingly, the reliability of review information becomes an important issue. This paper proposes a method to more accurately detect the appearance period of spam reviews and to identify the spam reviews by verifying the consistency of review information among multiple review sites. Evaluation experiments were conducted to show the accuracy of the detection results, and compared the newly proposed method with our previously proposed method.
Endoscopic Image Colorization Using Convolutional Neural Network

Colorization of grayscale images is crucial for clinical image-based diagnosis. However, it is an ill-posed problem that requires a comprehensive understanding of image content. The present study proposes a novel convolutional neural network (CNN) for a fully automatic colorization process by first employing the pre-trained residual network to extract high-level image features and then introducing the CNN to analyze the complex nonlinear relationship between the image features and chrominance values. Luminance and the learned chrominance values are then combined to recover the color of the image, and the proposed color-perceptual loss function is used to calculate the recovered and real color image loss. Based on the experiments conducted, the proposed method was proven to be highly effective and robust in restoring endoscopic images to their true colors. The average values of the feature similarity index incorporating chromatic information (FSIMc) and the quaternion structural similarity (QSSIM) for the experimental endoscopic image datasets reached 0.9961 and 0.9739, respectively.
Visual tracking with tree‐structured appearance model for online learning

Deep learning has been widely used in many visual recognition tasks owing to its powerful representation ability. However, online learning is a bottleneck to obstruct the application of deep learning in visual tracking. Although many algorithms have discarded the process of online learning during tracking, they demonstrate poor robustness to the online adaptation to appearance changes of the target. In this study, the authors design a tree structure specifically for online learning, which enables the appearance model to be updated smoothly. Once the target appearance has changed severely, a new branch is generated to avoid the fuzzy boundary of classification. In addition, active learning technique and artificial data are employed in the update to make the best of the limited knowledge about the interesting object during the tracking process. The proposed algorithm is evaluated on OTB2013 and VOT2017 benchmark and outperforms many state-of-the-art methods.
An Enhanced Lightweight Biometric-Based Three-Factor Anonymous Authentication Protocol for Mobile Cloud Computing

In mobile cloud computing, mobile devices generally share data with cloud servers through untrusted wireless networks, which leads to security risks. Therefore, it is extremely important to design a secure and effective authentication protocol to ensure the legitimacy of data access in mobile cloud computing. However, existing authentication protocols for mobile environment have various security vulnerabilities, such as failure to resist impersonation attack, ephemeral secret leakage attack and privileged insider attack. Moreover, mobile devices are restricted in computing, communication, and storage resources. Hence, designing a security and efficiency balanced authentication protocol has become a primary challenge for mobile cloud computing. In this paper, we first put forward an enhanced lightweight biometric-based three-factor anonymous authentication protocol for mobile cloud computing, which is based on hash operations, XOR operations, and fuzzy extractor functions. Second, we adopt the automated security verification tool ProVerif to verify security features and robustness, and analyze informal security. Third, we compare the security and complexity of the proposed protocol with other relevant protocols. The results demonstrate that our protocol has lower computation and communication costs while maintaining security.
DeepKeyStego: Protecting Communication by Key-Dependent Steganography with Deep Networks

Hiding both the presence and the content of secret information against eavesdropping over public communication channels is crucial for protecting privacy sensitive communication. Steganography is the art of hiding confidential information into normal carriers. Images are the most widely used containers for steganography. However, most of the current popular image steganographic schemes are designed with prescribed human-based rules and can be effectively detected by existing steganalysis tools. Even though the steganography implemented by deep networks has a better performance against steganalysis to some extent, it is still exposed to a threat that an attacker with access to the decoding model can recover the embedded information from steganographic images. Hence, we introduce the keys to the steganography based on deep networks and design symmetric and asymmetric steganographic schemes where the embedded information will never be recovered without secret keys. We further propose DeepKeyStego, a framework training key-dependent steganographic models with novel network structures. Compared to previous methods, DeepKeyStego based on novel network structures achieves excellent invisibility and produces a steganographic algorithm without any prescribed rules or hand-crafted features, which can perform competitively remarkable undetectability. Moreover, DeepKeyStego is the first to successfully design and implement symmetric (secret key) and asymmetric (public key) steganography, which considers the usage of keys to enhance security. Finally, we simulated our trained steganography in a practical situation and proved that the decoder can successfully recover 99.8% of embedded information, showing rather effectiveness and integrity.
How Question Generation Can Help Question Answering over Knowledge Base

We study how to improve the performance of Question Answering over Knowledge Base (KBQA) by utilizing the factoid Question Generation (QG) in this paper. The task of question generation (QG) is to generate a corresponding natural language question given the input answer, while question answering (QA) is a reverse task to find a proper answer given the question. For the KBQA task, the answer could be regarded as a fact containing a predicate and two entities from the knowledge base. Training an effective KBQA system needs a lot of labeled data which are hard to acquire. And a trained KBQA system still performs poor when answering the questions corresponding with unseen predicates in the training process. To solve these challenges, we propose a unified framework to combine the QG and QA with the help of knowledge base and text corpus. The models of QA and QG are first trained jointly on the gold dataset, then the QA model is fine tuned by utilizing a supplemental dataset constructed by the QG model with the help of text evidence. We conduct experiments on two datasets SimpleQuestions and WebQSP with the Freebase knowledge base. Empirical results show that our framework improves the performance of KBQA and performs comparably with or even better than the state-of-the-arts.
Hierarchical Recurrent Attention Networks for Context-Aware Education Chatbots

We propose a hierarchical network architecture for context-aware dialogue systems, that chooses which parts of the past conversation to focus on through a two-layer attention mechanism. The model can encode the parts of the historical dialog that are relevant to the current question to reason about the required response. We first assess the performance of our model on the Dialog bAbI task that involves a restaurant reservation system, where the goal is to book a table at a restaurant. We then train our model on a new hand-crafted dialogue data set, consisting of 7500 dialogues, to inform prospective students about the Data Science master program at University of Lyon.
FaCS: Toward a Fault-Tolerant Cloud Scheduler Leveraging Long Short-Term Memory Network

Large-scale cloud datacenters often experience reduced performance and service outage. Due to the inherent complexity, heterogeneity, and multitenant architecture of these datacenters, applications (i.e., jobs and tasks) running on them are susceptible to various types of failures. In this paper, we first characterize the application failures in Google cluster trace and then propose a prediction model which can forecast the termination status of a task. Then, we introduce a task scheduler that dynamically reschedules tasks based on the predicted results. This proactive fault-tolerant scheduler improves system reliability and ensures timely execution of the applications. Simulation results show that our scheduler reduces makespan and failure rates of tasks substantially while balancing load at the same time. Moreover, early prediction along with quick scheduling adjustment improves overall resource utilization and reduces resource wastage.