Reviewerly/exHarmony huggingface dataset:
Novel multiple images encryption algorithm using CML system and DNA encoding

In this study, the authors advance a novel multiple images encryption algorithm using the coupled map lattices (CML) system and DNA encoding. The algorithm adopts a permutation-diffusion structure. First, the initial values and parameters of the CML system are determined by given values and SHA hash key of original images. Secondly, several plain images are grouped and permuted among groups. Next, different groups are combined into eight blocks of the same size, each of which is independently coded and scrambled. Thirdly, in the diffusion process, a DNA-level multiplication operation is redefined, and required key matrices are resulted from a small key connected with original images. Finally, dynamic DNA coding/decoding is adopted, and the coding and decoding rules are determined by original images. The evaluation of the simulation experiment shows that the proposed algorithm is safe and feasible, and has good encryption effect.
Blind Modulation Classification for OFDM in the Presence of Timing, Frequency, and Phase Offsets

This paper proposes a blind modulation classification (MC) algorithm for linearly modulated signals of orthogonal frequency division multiplexing (OFDM) system. The proposed MC algorithm works with unknown frequency, timing, and phase offsets and without the prior requirement of channel statistics. In this research, a larger pool of modulation formats, i.e., binary phase-shift keying (BPSK), quadrature PSK (QPSK), offset QPSK (OQPSK), minimum shift keying (MSK), and 16-quadrature amplitude modulation (16-QAM) for OFDM signal has been classified. Classification takes place in two stages. First, we compute the discrete Fourier transform (DFT) of the received OFDM signal and then a normalized fourth-order cumulant is used in frequency domain to classify OQPSK, MSK, and 16-QAM modulation formats. At the second stage, the normalized fourth-order cumulant is used on the DFT of the square of the received OFDM signal to classify BPSK and QPSK modulation formats. The success rate and computation of the proposed MC algorithm are evaluated and compared with the previous methods.
Bézier Curve Based Continuous and Smooth Motion Planning for Self-Learning Industrial Robots

The concept of reinforcement learning enables an agent to learn a task based on trial and error. Especially for the automation of industrial processes, this approach promises significant advantages in terms of flexibility and adaptability when compared to rule-based solutions. While previous works have uncovered the potential of reinforcement learning and the applicability to real-world scenarios was shown, the algorithm relies on a discretization of time, where every time step comprises a self-contained sequence of observation, execution and feedback. However, this design poses a major obstacle for tasks, which do not allow for a distinct separation of steps. A prominent example is motion planning for industrial robotics, where reinforcement-learning solutions to date result in non-fluent trajectories. In this work, we address this shortcoming of reinforcement learning by presenting an asynchronous update strategy, which enables the agent to plan its next trajectory while executing the previous one. We use Bézier curves as actions due to the ability to characterize complex trajectories with relatively few parameters. We show that our modifications further improve the smoothness of the robot's motion and allow for a smoother velocity profile without a drop in performance when compared to previous solutions.
Authors' Writing Styles Based Authorship Identification System Using the Text Representation Vector

Text mining is one of the main and typical tasks of machine learning (ML). Authorship identification (AI) is a standard research subject in text mining and natural language processing (NLP) that has undergone a remarkable evolution these last years. We need to identify/determine the actual author of anonymous texts given on the basis of a set of writing samples. Standard text classification often focuses on many handcrafted features such as dictionaries, knowledge bases, and different stylometric characteristics, which often leads to remarkable dimensionality. Unlike traditional approaches, this paper suggests an authorship identification approach based on automatic feature engineering using word2vec word embeddings, taking into account each author's writing style. This system includes two learning phases, the first stage aims to generate the semantic representation of each author by using word2vec to learn and extract the most relevant characteristics of the raw document. The second stage is to apply the multilayer perceptron (MLP) classifier to fix the classification rules using the backpropagation learning algorithm. Experiments show that MLP classifier with word2vec model earns an accuracy of 95.83% for an English corpus, suggesting that the word2vec word embedding model can evidently enhance the identification accuracy compared to other classical models such as n-gram frequencies and bag of words.
Representation Learning: A Statistical Perspective

Learning representations of data is an important problem in statistics and machine learning. While the origin of learning representations can be traced back to factor analysis and multidimensional scaling in statistics, it has become a central theme in deep learning with important applications in computer vision and computational neuroscience. In this article, we review recent advances in learning representations from a statistical perspective. In particular, we review the following two themes: ( a) unsupervised learning of vector representations and ( b) learning of both vector and matrix representations.
Transformation of Business Model in Finance Sector with Artificial Intelligence and Robotic Process Automation

Organizations operating in this fast pace era must have a dynamic structure to be competitive in a volatile business environment both inside and outside. Automation and data are driving fundamental changes in our daily lives and in the way of doing business. In this respect transforming business processes call upon the technological advancements of two rising technologies of today: artificial intelligence and robotic process operations in finance sector is analysed in terms of their ability of business models in digital age. These two emerging technologies will lead to a transformation in the customer service model and internal operation processes in finance sector with current and future potential impacts. The institutions should prepare their business models and employees for this future in order to turn this development into an opportunity. In this study, it is evaluated that how financial institutions should change their business models in order to benefit from these two developments and a use-case of a bank has been shared.
Hybrid CNN-GRU model for high efficient handwritten digit recognition

Recognition of handwritten digits is a challenging research topic in Optical Character Recognition (OCR) in recent years. In this paper, a hybrid model combining convolutional neural network (CNN) and gate recurrent units (GRU) is proposed, in which GRU is used to replace the CNN fully connected layer part to achieve high recognition accuracy with lower running time. In this model, the features of the original image are firstly extracted by the CNN, and then they are dynamically classified by the GRU. Experiment performed on MNIST handwritten digit dataset suggests that the recognition accuracy of 99.21% while the training time and testing time is only 57.91s and 3.54s, respectively.
Group Recommendation via Self-Attention and Collaborative Metric Learning Model

Group recommendation has attracted wide attention owing to its significance in real applications.One of the big challenges for group recommendation systems is how to integrate individual preferences of each group member and attain overall preferences for the group.Most of the traditional group recommendation solutions regard group members as equal participants and assign a same weight to each member.As a result, performance of this type of recommendation methods is not as good as expected.To improve the performance of group recommendation, a novel group recommendation model via Self-Attention and Collaborative Metric Learning (SACML) is presented in this paper.With the employment of Self-Attention mechanism, the SACML model can learn the similarity interactions between group members and services and decide a different weight for different group member.Based on these weights, group preferences for services can be generated by the aggregation of group members' preferences and the group's own preference.Similar metric space between group and services is obtained via collaborative metric learning with the group preferences and positive and negative services' features.Group recommendation is finally implemented based on the obtained metric space.Simulation has been conducted on CAMRa2011 and Meetup datasets, and experimental results show that the proposed SACML model has better performance in comparison with those baseline methods.
Privacy-preserving collaborative filtering: A new approach based on variable-group-size microaggregation

The amount of available information is growing steadily and, as a result, Internet users benefit from recommender systems, which help them find information, services and products that best fit their needs. A common technique used in recommender systems is Collaborative Filtering, which is based on users’ collaboration to make recommendations. However, users are getting more concerned about their privacy and can be reluctant to disclose their interests and other personal information. Hence, with the aim to foster users collaboration, the development of privacy-aware collaborative filtering methods has become a hot topic in the field. In this article we recall the concept of Privacy-Preserving Collaborative Filtering (PPCF) and introduce a novel approach based on variable-group-size microaggregation, which provides k-anonymity to the users. Also, we introduce several new metrics based on users’ behaviour that overcome the drawbacks of traditional metrics. Extensive experiments show that our approach can provide more accurate recommendations than well-known methods while, at the same time, preserving users’ privacy.
Practical Access Pattern Privacy by Combining PIR and Oblivious Shuffle

We consider the following secure data retrieval problem: a client outsources encrypted data blocks to a semi-trusted cloud server and later retrieves blocks without disclosing access patterns. Existing PIR and ORAM solutions suffer from serious performance bottlenecks in terms of communication or computation costs. To help eliminate this void, we introduce "access pattern unlinkability'' that separates access pattern privacy into short-term privacy at individual query level and long-term privacy at query distribution level. This new security definition provides tunable trade-offs between privacy and query performance. We present an efficient construction, called SBR protocol, using PIR and Oblivious Shuffling to enable secure data retrieval while satisfying access pattern unlinkability. Both analytical and empirical analysis show that SBR exhibits flexibility and usability in practice.
More than tweets

Abstract The application of machine translation (MT) in crisis settings is of increasing interest to humanitarian practitioners. We collaborated with industry and non-profit partners: (1) to develop and test the utility of an MT system trained specifically on crisis-related content in an under-resourced language combination (French-to-Swahili); and (2) to evaluate the extent to which speakers of both French and Swahili without post-editing experience could be mobilized to post-edit the output of this system effectively. Our small study carried out in Kenya found that our system performed well, provided useful output, and was positively evaluated by inexperienced post-editors. We use the study to discuss the feasibility of MT use in crisis settings for low-resource language combinations and make recommendations on data selection and domain consideration for future crisis-related MT development.
Comparison of multiple feature extractors on Faster RCNN for breast tumor detection

The Deep learning algorithm shows is strong capability in pattern recognition tasks such as object detection and speech recognition. Comparing with the typical machine learning methods which require extraction of manual features, deep learning algorithm has a more powerful feature learning ability. In this paper, the deep learning associated object detection method is applied to developed to locate and classify lesions for the detection of medical breast masses. At the same time, transfer learning based on the network of Faster RCNN is also introduced. Furthermore, five feature extractors of the network, which are ResNet101, inception V2, inception V3, Mobilenet, and inception ResNet V2, are investigated for exploring the impact for the model. Digital Database for Screening Mammography(DDSM) dataset is used in the investigation, and the performances of the models associated with the five feature extractors are compared separately in detecting benign and malignant breasts, based on the ROC trade-off curves. The simulation results demonstrate that the classification model with Inception ResNet V2 feature extractor exhibit the best performance, compared with the other four feature extractors.
Entanglement-Assisted Quantum Codes from Cyclic Codes

Entanglement-assisted quantum-error-correcting (EAQEC) codes are quantum codes which use entanglement as a resource. These codes can provide better error correction than the (entanglement unassisted) codes derived from the traditional stabilizer formalism. In this paper, we provide a general method to construct EAQEC codes from cyclic codes. Afterwards, the method is applied to Reed-Solomon codes, BCH codes, and general cyclic codes. We use the Euclidean and Hermitian construction of EAQEC codes. Three families have been created: two families of EAQEC codes are maximal distance separable (MDS), and one is almost MDS or almost near MDS. The comparison of the codes in this paper is mostly based on the quantum Singleton bound.
Evaluation of ASTER TIR data-based lithological indices in Malanjkhand Copper Mines of Madhya Pradesh, India

This study focuses on the Malanjkhand Copper Mines area of Madhya Pradesh, India and compares different types of quartz, feldspar and mafic remote sensing indices used for the detection of quartz, feldspar and mafic minerals using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal infrared (TIR) bands. Our data indicate that these lithological indices are useful for delineating quartz, feldspar or mafic minerals. Correlation matrices generated for various lithological indices indicate that both Guha's (GMI) and Ninomiya's (NMI) mafic indices yield nearly identical results. In addition, Guha's quartz index (GQI) yields significantly better results than Ninomiya's quartz index (NQI) during the identification of the quartz content. This study also shows that GQI is comparable with the Rockwall and Hofstra's quartz index (RHQI) for the identification of the quartz content. Hence, ASTER TIR data-based remote sensing indices play a key role in lithological mapping.
A Socio-Technical and Co-evolutionary Framework for Reducing Human-Related Risks in Cyber Security and Cybercrime Ecosystems

The focus on cyber security as an interaction between technical elements and humans has typically confined consideration of the latter to practical issues of implementation, conventionally those of 'human performance factors' of vigilance etc., 'raising awareness' and/or 'incentivization' of people and organizations to participate and adapt their behavior. But this is far too narrow a view that seriously constrains the ability of cyber security as a whole to adapt and evolve to keep up with adaptive, innovative attackers in a rapidly-changing technological, business and social landscape, in which personal preferences of users are also dynamically evolving. While there is isolated research across different research areas, we noticed the lack of a holistic framework combining a range of applicable theoretical concepts (e.g., cultural co-evolution such as technological arms races, opportunity management, behavioral and business models) and technological solutions on reducing human-related risks in the cyber security and cybercrime ecosystems, which involve multiple groups of human actors including offenders, victims, preventers and promoters. This paper reports our ongoing work in developing such a socio-technical framework (1) to allow a more comprehensive understanding of human-related risks within cyber security and cybercrime ecosystems and (2) to support the design of more effective approaches to engaging individuals and organizations in the reduction of such risks. We are in the process of instantiating this framework to encourage behavioral changes in two use cases that capture diverse and complicated socio-technical interactions in cyber-physical systems.
SJTU-NICT at MRP 2019: Multi-Task Learning for End-to-End Uniform Semantic Graph Parsing

This paper describes our SJTU-NICT's system for participating in the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2019 Conference for Computational Language Learning (CoNLL).Our system uses a graph-based approach to model a variety of semantic graph parsing tasks.Our main contributions in the submitted system are summarized as follows: 1.Our model is fully end-to-end and is capable of being trained only on the given training set which does not rely on any other extra training source including the companion data provided by the organizer; 2. We extend our graph pruning algorithm to a variety of semantic graphs, solving the problem of excessive semantic graph search space; 3. We introduce multitask learning for multiple objectives within the same framework.The evaluation results show that our system achieved second place in the overall F 1 score and achieved the best F 1 score on the DM framework.
F-PAD: Private Attribute Disclosure Risk Estimation in Online Social Networks

In online social networks, users always expect to share some information for benefits (e.g., personalized services) while hiding the others for privacy. Unfortunately, the hidden information is likely to be predicted by various powerful inference attacks with the rapid advances in machine learning. Then, what is the risk that a user's private information could be disclosed? What countermeasures can be taken to fight against the privacy violation for the user? To tackle these issues, this article proposes a general Framework for Private Attribute Disclosure estimation (F-PAD) including three steps: 1) private attribute prediction; 2) disclosure model training; 3) disclosure risk estimation. Not like most prior risk estimation studies focusing on one specific attack model and private attribute, F-PAD can estimate disclosure risk for individual users in terms of disclosure probability and risk level within a high confidence given a basket of potential inference attack models; furthermore, F-PAD can adapt to various attributes (e.g., gender, age) and offer countermeasures to help users lower the risk. Extensive experiment studies on two real social network datasets, Facebook and Book-Crossing, have verified the effectiveness of F-PAD in `current city', `gender' and `age' disclosure risk estimation.
Wearable Wristworn Gesture Recognition Using Echo State Network

This paper presents a novel gesture sensing system for prosthetic limb control based on a pressure sensor array embedded in a wristband. The tendon movement which produces pressure change around the wrist can be detected by pressure sensors. A microcontroller is used to gather the data from the sensors, followed by transmitting the data into a computer. A user interface is developed in LabVIEW, which presents the value of each sensor and display the waveform in real-time. Moreover, the data pattern of each gesture varies from different users due to the non-uniform subtle tendon movement. To overcome this challenge, Echo State Network (ESN), a supervised learning network, is applied to the data for calibrating different users. The results of gesture recognition show that the ESN has a good performance in multiple dimensional classifications. For experimental data collected from six participants, the proposed system classifies five gestures with an accuracy of 87.3%.
Improved Knowledge Distillation for Training Fast Low Resolution Face Recognition Model

Low resolution (LR) face recognition (FR) is a challenging, yet common problem for FR task, especially for surveillance scenario. The issue addressed here is not just to build a LR-FR model, more importantly to make it run fast. Here, the knowledge distillation method is adopted for our task, where the teacher's knowledge can be 'distilled' into a small student model by guiding its training process. For LRFR task, the original knowledge distillation scheme would update the teacher's weights first by tuning it using LR augmented train set, and then the student model is trained using same train set under updated teacher's guidance. The problem of this method is that the weights tuning of large teacher model is time-consuming, especially for large-scale dataset. In this paper, we proposed an improved scheme to enable us to avoid the teacher retraining and still be able to train the small model for LR-FR task. Here, different from the original scheme, the train sets for teacher and student model become different, where the train set for teacher model keeps unchanged and the one student is LR augmented. Therefore, it becomes unnecessary to update teacher model any more since the train set is the unchanged. Only the small student model needs to be trained under the original teacher's guidance. This can speed up the whole training process, especially for large-scale dataset. The different train sets for teacher and student will increase the data distribution discrepancy. To solve this problem, we constrained the multikernel maximum mean discrepancy between outputs to reduce this influence. Experimental results show our method can accelerate the training process by about 5 times, while preserving the accuracy. Our student model has same level coresponding author: wangmengjiao@cn.fujitsu.com with respect to state-of-art accuracy on LFW and SCFace. It can achieve 3x acceleration comparing to teacher model and only takes 35ms to run on a CPU.
Reaching Hard-To-Reach Populations

This work examined the survey distribution methods used in a past study. The goal was to identify the most effective methods to reach marginalized voices to participate in technological research and thus, create more inclusive technologies. Initial analyses identified in-person onsite recruitment as one of the better methods for reaching hard-to-reach populations compared to M-Turk, social media, newsletters, mail, and text messages. Our results call for continued efforts to use more inclusive research methods in the field of HCI.