Reviewerly/exHarmony huggingface dataset:
One-step implementation of a deterministic SWAP gate via a shortcut to adiabatic passage

The quantum logic gate is a basic operation in quantum computing. SWAP gates are quite useful as a type of two-qubit logic gate. In this paper, we propose a fast scheme to get a deterministic SWAP gate between two atoms in just one step. In this scheme the experimental parameters, such as evolution time, laser parameters and so on, do not need to be controlled precisely. This greatly simplifies the actual operation. Meanwhile, the strict numerical simulations show that decoherence arising from spontaneous emission and cavity decay may be allowed to some extent. What is more, the idea can be used to generate a SWAP gate between two spatially separate atoms. It is worth noting that our scheme simplifies the complex steps found in some pre-existing schemes.
Redundancy reduction based node classification with attribute augmentation

Node classification for attributed graphs has attracted more and more researchers, it is very useful when labeled data are expensive and hard to obtain. However, most existing methods either only focus on node attribute data or are designed for working in single mode, resulting in omitting extra information and impairing classification performance. In view of this, in this work, we propose a semi-supervised method that can effectively incorporate various available prior information to augment the attribute matrix. This approach adds a two-level learning strategy to select and find those discriminative attributes for classification, and an augmentation step to combining various information, thereby reducing the redundancy exists between attributes and strengthening the community structure. It provides a unified way that can preserve both the node and edge information of the network. Finally, we use the classical classifier SVM to partition the induced augmented graph, we compare our method with 5 state-of-the-art methods on 4 data sets, the results confirm the effectiveness of our method.
Age and Gender Estimation via Deep Dictionary Learning Regression

The paper addresses the problem of estimating age and gender from frontal photos. Most prior studies on deep learning based estimation formulate it as a convolutional neural network based classification problem. For gender it is a two class problem; for age, several age brackets are created to form classes. In this work we formulate it as a regression problem. This is a natural way to handle both gender and age. Gender can be represented as a single variable possible of taking binary values (male or female) whereas age can be represented by a single variable taking non-negative real values. We formulate regression on the newly proposed deep dictionary learning framework. Prior work on this topic, is on unsupervised representation learning; in this work we in-built regression into the deep dictionary learning framework making the formulation supervised. Testing has been done on several state-of-the-art datasets - Adience, MORPH, ChaLearn LAP, LFWA and CelebA. Our method yields age and gender estimation results better than the state-of-the-art.
Quantum Codes Derived from Quasi-Twisted Codes of Index 2 with Hermitian Inner Product

In this paper, we consider a wide family of λ-quasi-twisted (λ-QT) codes of index 2 and provide a bound on the minimum Hamming distance. Moreover, we give a sufficient condition for dual containing with respect to Hermitian inner product of these involved codes. As an application, some good stabilizer quantum codes over small finite fields 𝔽2 or 𝔽3 are obtained from the class of λ-QT codes.
RASE: Relationship Aware Social Embedding

This paper studies the problem of learning latent representations or embeddings for users in social networks, by leveraging relationship semantics associated with each link. User embeddings are low-dimensional vector-space representations designed to preserve structural proximity indicated by the pairwise relationships. In social networks, the closeness (or proximity) between pairs of users is very different w.r.t. multiple social relationships and thus cannot be represented accurately using a single embedding space. Furthermore, social networks pose a unique challenge of relationship label sparsity that precludes the application of knowledge-graph embedding techniques.In this paper, we associate each observed link with multiple relationship types through relationship weights and learn projection matrices for each relationship type to model the social distance (or proximity) between users specific to each relationship. We propose a novel two-step mutual enhancement framework to iteratively (a) learn user embeddings preserving relationship-specific proximity, and (b) link-relationship weights capturing the role of each link in multiple relationship types. The first step learns user embeddings optimizing relationship-specific proximity, while fixing the relationship weights (or roles) for each link. In the second step, the user embeddings and corresponding projection matrices are assumed to be fixed, while the link-relationship weights are learned. We demonstrate that the relationship-aware user embeddings learned through this mutual enhancement framework, are more effective in representing the users and outperform representative baseline techniques in multi-label classification and relationship prediction tasks.
IPRE: A Dataset for Inter-Personal Relationship Extraction

Inter-personal relationship is the basis of human society. In order to automatically identify the relations between persons from texts, we need annotated data for training systems. However, there is a lack of a massive amount of such data so far. To address this situation, we introduce IPRE, a new dataset for inter-personal relationship extraction which aims to facilitate information extraction and knowledge graph construction research. In total, IPRE has over 41,000 labeled sentences for 34 types of relations, including about 9,000 sentences annotated by workers. Our data is the first dataset for inter-personal relationship extraction. Additionally, we define three evaluation tasks based on IPRE and provide the baseline systems for further comparison in future work.
A Comparison of Performance between Fully and Partially Decentralized Applications

In Blockchain platforms, programs prioritize the immutability and traceability of stored data. These programs, known as DApps (Decentralized Applications), inherit Blockchain platform capabilities and they have two categories: fully DApp and partially DApp. In related work, software architectures for DApps have been applied to medical history, supply chain, loyalty programs among others. Fully and partially DApps have different advantages and limitations according to a purpose. In this paper, we propose a software architecture for a fully DApp and a partially DApp focused on simulating assets transactions to compare both DApps performance. We identify their advantages and disadvantages focused on performance for asset transactions. On average, partially DApp provides the frontend 44.82% more quickly than fully DApp when there are around 100 concurrent requests per second. Also, partially DApp processes between 23% and 26% more requests per second than fully DApp when the number of concurrent requests is above 250.
Neurocognitive Shared Visuomotor Network for End-to-end Learning of Object Identification, Localization and Grasping on a Humanoid

We present a unified visuomotor neural architecture for the robotic task of identifying, localizing, and grasping a goal object in a cluttered scene. The RetinaNet-based neural architecture enables end-to-end training of visuomotor abilities in a biological-inspired developmental approach. We demonstrate a successful development and evaluation of the method on a humanoid robot platform. The proposed architecture outperforms previous work on single object grasping as well as a modular architecture for object picking. An analysis of grasp errors suggests similarities to infant grasp learning: While the end-to-end architecture successfully learns grasp configurations, sometimes object confusions occur: when multiple objects are presented, salient objects are picked instead of the intended object.
Comparing vertical and horizontal SIMD vector processor architectures for accelerated image feature extraction

Embedded automotive Computer Vision systems for real-time motion tracking and 3D scene reconstruction demand for high image feature extraction performance and have a heavily constrained energy budget unable to be met by general-purpose CPUs and GPUs. Due to the required programming flexibility for software updates and algorithmic extensions, the use of fully dedicated hardware accelerators is not advisable in most cases. In this paper, a vertical and a horizontal SIMD vector processor architecture are implemented and compared for accelerating the Scale-Invariant Feature Transform feature extraction algorithm, exploiting inherent data-level parallelism prevalent in this application and considering different programming code strategies for the different vectorization paradigms. An evaluation for a 45 nm ASIC technology shows an overall performance gain of up to 24.8x, and up to 151.3x higher total performance-area-energy efficiency compared to a reference scalar two-issue VLIW processor. Compared to other implementations on programmable ASIP and mobile GPU platforms, the proposed vertical SIMD vector processor achieves a performance gain of up to 5.1x and up to 31.3x higher performance-energy efficiency.
Privacy-Preserving LDA Classification over Horizontally Distributed Data

This paper presents a framework for constructing Linear Discriminate Analysis (LDA) classifier, securely, over distributed data. It is assumed that data is partitioned among several parties such that for obtaining higher benefits from a greater extent of data, all participants are willing to model the LDA classifier on whole data, but for privacy concerns, they refuse to share the original datasets. To this end, we propose an algorithm based on secure computation protocols, which provides the data owners the possibility of LDA classifier construction over all datasets without revealing any sensitive information. In our experimental analysis, applying data-packing and tree communication model, has shown the reduction of computation and communication costs 35 and 15 times, respectively.
A Novel Clustering Algorithm based on Directional Propagation of Cluster Labels

Clustering is an important topic in the field of machine learning. There are abundant algorithms which are proposed for clustering, and they are mainly based on two basic physical metrics, distance and density. However, it is difficult to reflect the orientation relationship between data by distance and density alone, while the orientation relationship can be easily expressed by direction. Inspired by this, we regard direction as the main basic physical metric, and propose a clustering algorithm based on directional propagation of cluster labels, namely DBC (Direction Based Clustering). DBC doesn't need to make specific assumptions about the density of data, but uses the orientation relationship between data to help clustering. Similar to the density-based clustering algorithms DBSCAN (Density Based Spatial Clustering Of Applications with Noise) and DPC (Density Peak Clustering), DBC can also find clusters of arbitrary shapes. It requires two parameters, but its two parameters have a fixed range of empirical values. Moreover, it is less affected by uneven density distribution than DBSCAN and DPC. We compare DBC with four well-known clustering algorithms, including DPC, DBSCAN, AP (Affinity Propagation) and K-means++. Experiments on artificial data sets show that DBC performs better than DBSCAN and DPC on data sets with uneven density distribution, and can effectively identify clusters with overlapping regions. Experiments on real-world data sets show that DBC has advantages over DPC, AP and K-means++ in clustering effect.
Are All Duplicates Value-Neutral? An Empirical Analysis of Duplicate Issue Reports

In open source communities, there are numerous duplicate issue reports, considered as useless and negligible by developers. Conversely, some researches argued that duplicates deliver complementary information that could benefit issue resolving. Considering all duplicates as value-neutral will result in either overestimation or underestimation of valuable information. It is necessary to be aware of whether all duplicates are redundant or beneficial. In this paper, we investigate whether duplicates have the same impacts on issue resolving and identification cost. We divide duplicates into three categories according to the statuses of master reports when duplicates are submitted. The results show duplicates in different categories play different roles in issue-resolving, and identification cost is also significantly different. Our study reveals duplicates are different, but almost are paid equal attentions. It is promising to propose new approaches and tools to resolve the problem.
Multilevel LSTM for Action Recognition Based on Skeleton Sequence

Skeleton-based human action recognition has a broad range of applications for human-computer interaction and intelligent monitoring. Human actions can be represented by the trajectory of the skeleton joints, which contain key information about actions. Long-term short-term memory (LSTM) networks exhibit outstanding performance in 3D human action recognition because they are capable of modeling dynamics and dependencies in sequential data. In this paper, we propose a skeleton-based multilevel LSTM network for action recognition. First, the data for each joint and its parent joint is used as input to a fine-grained subnet. Then the features of the upper body joint are merged into the upper body subnet, the features of the lower body are merged into the lower body subnet. Finally the features of the upper body subnets and lower body subnets are structured and fused to achieve higher recognition accuracy. Experimental results on the public data set NTU RGB+D demonstrate the effectiveness of the proposed network.
Risk-Limiting Tallies

Many voter-verifiable, coercion-resistant schemes have been proposed, but even the most carefully designed systems necessarily leak information via the announced result. In corner cases, this may be problematic. For example, if all the votes go to one candidate then all vote privacy evaporates. The mere possibility of candidates getting no or few votes could have implications for security in practice: if a coercer demands that a voter cast a vote for such an unpopular candidate, then the voter may feel obliged to obey, even if she is confident that the voting system satisfies the standard coercion resistance definitions. With complex ballots, there may also be a danger of “Italian” style (aka “signature”) attacks: the coercer demands the voter cast a ballot with a specific, identifying pattern. Here we propose an approach to tallying end-to-end verifiable schemes that avoids revealing all the votes but still achieves whatever confidence level in the announced result is desired. Now a coerced voter can claim that the required vote must be amongst those that remained shrouded. Our approach is based on the well-established notion of Risk-Limiting Audits, but here applied to the tally rather than to the audit. We show that this approach counters coercion threats arising in extreme tallies and “Italian” attacks. We illustrate our approach by applying it to the Selene scheme, and we extend the approach to Risk-Limiting Verification, where not all vote trackers are revealed, thereby enhancing the coercion mitigation properties of Selene.
Autoregressive Models for Sequences of Graphs

This paper proposes an autoregressive (AR) model for sequences of graphs, which generalises traditional AR models. A first novelty consists in formalising the AR model for a very general family of graphs, characterised by a variable topology, and attributes associated with nodes and edges. A graph neural network (GNN) is also proposed to learn the AR function associated with the graph-generating process (GGP), and subsequently predict the next graph in a sequence. The proposed method is compared with four baselines on synthetic GGPs, denoting a significantly better performance on all considered problems.
A Face Privacy Protection Scheme Using CNN Based ROI Editing

Aiming at the deficiencies of the current face privacy protection based on image editing in terms of protection capability and image utility, a novel face privacy protection scheme using CNN based ROI (Region of interest) editing is proposed. In this paper, a novel face ROI calculation method based on CNN (convolutional neural Network) is first put forward. A set of CNN networks is selected and utilized to calculate the gradient of each face image in an image dataset, and then an average gradient image is obtained from the image gradients. After that, the sub-ROIs are determined from the average gradient image, and different sub-ROIs have different contribution to the output of the CNN. Based on face ROI editing, a privacy protection scheme is put forward, which is adaptive to the existing privacy protection methods such as blurring, pixelization, encryption and etc. Experimental results and analysis show that it can achieve the same privacy protection capability with less modification and better image quality compared with the existing methods.
Twitter breaking news detector in the 2018 Brazilian presidential election using word embeddings and convolutional neural networks

A breaking news event detector based on the time series of the number of positive, negative and neutral tweets obtained from a sentiment analysis classifier is proposed. The detector collects real-time tweets related to candidates and transforms them into word embeddings using the FastText algorithm. Using domain adaptation, the sentiment analysis classifier is trained based on a convolutional neural network (CNN) known as TextCNN. The number of positive, negative and neutral tweets in a time frame results in a time-series, which is monitored by an unsupervised time-series anomaly detector. The results show that the sentiment analysis classifier achieves an accuracy of 74% for the three classes, and the detector successfully detects significant breaking news in the 2018 Brazilian presidential election.
Anomaly Detection Sensors for a Modbus-Based Oil and Gas Well-Monitoring System

Timely detection of network traffic anomalies in oil and gas wells is critical to support operations. This paper describes a network sensor that has been specifically designed to operate within an existing well-monitoring infrastructure. Network traffic and flow features are extracted in real-time and compared against pre-set and moving averages to detect and report anomalies. A prototype has been tested using the Modbus protocol and network traffic covering several months of operations. In order to avoid potential impact on the production environment, scripts captured network packets that were then replayed on the IMUNES network emulator. Preliminary results have identified useful metrics for anomaly detection in a production environment.
Fast Algorithm for Video Shot Boundary Detection Using SURF features

This paper presents an efficient and fast algorithm to detect abrupt shot transition automatically. The proposed algorithm consists of three - steps: feature extraction, features matching, and similarity calculation. The Speeded-Up-Robust-Features (SURF) features are extracted from half number of frames of video file to reduce the execution time. Next, features matching is performed between two features vectors to determine which is the nearest neighbors between features vectors using the distance function. The similarity is computed using a number of matching features and then compared to a predefined global threshold for the abrupt shot detection. The results demonstrate that the proposed algorithm achieves 0.9984 recall, 0.977 precision and 0.9991 F-measure. The proposed algorithm reduces the execution time while still detecting abrupt transitions at high performance rate.
Reparameterization Gradient Message Passing

In this paper we consider efficient message passing based inference in a factor graph representation of a probabilistic model. Current message passing methods, such as belief propagation, variational message passing or expectation propagation, rely on analytically pre-computed message update rules. In practical models, it is often not feasible to analytically derive all update rules for all factors in the graph and as a result, efficient message passing-based inference cannot proceed. In related research on (non-message passing-based) inference, a “reparameterization trick” has lead to a considerable extension of the class of models for which automated inference is possible. In this paper, we introduce Reparameterization Gradient Message Passing (RGMP), which is a new message passing method based on the reparameterization gradient. In most models, the large majority of messages can be analytically derived and we resort to RGMP only when necessary. We will argue that this kind of hybrid message passing leads naturally to low-variance gradients.
Weighted SMOTE-Ensemble Algorithms: Evidence from Chinese Imbalance Credit Approval Instances

The current study proposes a novel ensemble approach rooted in the weighted synthetic minority over-sampling technique (WSMOTE) algorithm being called WSMOTE-ensemble for skewed loan performance data modeling. The proposed ensemble classifier hybridizes WSMOTE and Bagging with sampling composite mixtures (SCMs) to minimize the class skewed constraints linking to the positive and negative small business instances. It increases the multiplicity of executed algorithms as different sampling composite mixtures are applied to form diverse training sets. Based on the fitted evaluation measures, finally this study recommends that the 'WSMOTE-ensemblek-NN' methodology generating from the WSMOTE-decision tree-bagging with k nearest neighbor is the best fusion sampling strategy which is a novel finding in this domain.
Empirical Evaluation on Anomaly Behavior Detection for Low-Cost Micro-Controllers Utilizing Accurate Power Analysis

Since hardware/software vendors produce their IoT products easily and inexpensively, they often outsource their designs to third-party vendors where malicious third-party vendors can have a chance to insert software Trojans as well as “hardware Trojans” into their IoT devices. How to tackle the issue becomes a serious concern these days. In this paper, we propose an anomaly behavior detection method utilizing accurate power analysis for low-cost micro-controllers. Our method accurately measures power consumption of the target device, and then classifies its waveform into the sleep-mode part, in which a micro-controller saves power, and into the active-mode part, in which a micro-controller works in a normal operation. After that, we obtain the duration time and consumed power from each active-mode period as feature values. Finally, we detect abnormal behavior based on the obtained feature values utilizing an outlier detection method. In our experiments, we empirically evaluate the proposed method utilizing two types of micro-controllers, and the experimental results demonstrate that our proposed method successfully detects abnormal behaviors.