Reviewerly/exHarmony huggingface dataset:
An Efficient Controlled Quantum Secure Direct Communication Protocol via GHZ-like States

Based on the GHZ-like state, we proposed a new efficient three-party controlled quantum secure direct communication protocol for the sender to send secret information to the receiver with the permission and assistance of the controller. In this scheme, the controller prepares GHZ-like state and performs random unitary operation on the particles, and the sender encodes secret information onto his particles using dense coding and then transmits them though the quantum channel. The receiver can recover the secret information based on the controller's information and the results of the Bell basis measurement. The operations performed by the controller ensure that the receiver cannot obtain any part of the secret information without the permission of the controller. In addition, it is proved that the efficiency of the proposed scheme is higher than the existing protocols by comparison.
Feature Selection Using Heterogeneous Data Indexes: a data science perspective

The curse of dimensionality is the problem of keeping too many attributes in a dataset with a number of instances from data science (analytics) perspective. Most of the attributes (or features) may not be significant or even can harm the process of classification by orienting this process to a wrong direction and subsequently yielding poor classification accuracy. Therefore, it is essential to retain only meaningful features in a dataset. In this paper, we propose a novel approach for selecting a subset of features from a dataset which is supervised in nature, having labels for each of its pattern. Five methods including data indexes viz. Information Gain, Laplace Score, Variance, Cosine Similarity and Fisher Score (one index in one method) are applied to find scores and hence ranks of all features in a dataset. The set of features (with ranks arranged in descending orders) is divided in four feature subsets with size of maximum 100,75,50 and 25% respectively. Apply statistical mode approach to fill each subset from the full subset of features. Thus, we get four subsets of features. Each of the four subsets are tested using a k - nearest neighbor classifier. The results in terms of classification accuracies and processing time for all the four subsets are obtained. After extensive experiments of proposed approach on nine datasets, the encouraging results justify the significance and suggest for further implementation on datasets with high dimensionality.
Partner Approximating Learners (PAL): Simulation-Accelerated Learning with Explicit Partner Modeling in Multi-Agent Domains

Mixed cooperative-competitive control scenarios where the interacting partners exhibit individual goals are very challenging for reinforcement learning agents. An example of such scenarios is given by human-machine interaction. In order to contribute towards intuitive human-machine collaboration, this work focuses on problems in the continuous state and control domain and prohibits explicit communication. More precisely, the agents do not know the others' goals or control laws but only sense their control inputs retrospectively. The proposed framework combines a partner model learned from online data with a reinforcement learning agent that is trained in a simulated environment including the partner model. This procedure overcomes drawbacks of independent learners and benefits from a reduced amount of real world data required for reinforcement learning—an aspect that is vital in the human-machine context. Experimental results reveal that the method learns fast due to the simulated environment and adapts to the constantly changing partner due of the partner model.
Homomorphic encryption approach for exploration of sensitive information retrieval

This paper presents the core algorithms behind DB-Query-Encryption, a proposal that supports private information retrieval (PIR) explorations. DB-Query-Encryption permits users for selectively retrieve information from a cloud database whereas keeping sensitive data terms secretive. As an example use case, a medical research institute may, as part of a sensitive data exploration, requisite to look up facts about an individual person from a cloud database deprived of reveling the person’s identity. The basic idea behind DB-Query-Encryption is to uses homomorphic encryption, which allows the cloud server to fulfill this request, whereas making it infeasible for the database owner (or a hacker who might compromised) to conclude the name being explored for, either which records are retrieved. The query, which retrieved the information, still secretive even if the spectator be able to search all the data over the cloud server and all the actions as they are being executed. Within that period, the query response produced by the cloud server is considerable smaller than the whole cloud database, making it more convenient when it is not feasible or appropriate for the user to transfer the entire database.
Context-Guided Learning to Rank Entities

We propose a method for learning entity orders, for example, safety, popularity, and livability orders of countries. We train linear functions by using samples of ordered entities as training data, and attributes of entities as features. An example of such functions is f(Entity) $$= +0.5$$ (Police budget) $$-0.8$$ (Crime rate), for ordering countries in terms of safety. As the size of training data is typically small in this task, we propose a machine learning method referred to as context-guided learning (CGL) to overcome the over-fitting problem. Exploiting a large amount of contexts regarding relations between the labeling criteria (e.g. safety) and attributes, CGL guides learning in the correct direction by estimating a roughly appropriate weight for each attribute by the contexts. This idea was implemented by a regularization approach similar to support vector machines. Experiments were conducted with 158 kinds of orders in three datasets. The experimental results showed high effectiveness of the contextual guidance over existing ranking methods.
High-Dimensional Neural Feature Using Rectified Linear Unit And Random Matrix Instance

We design a ReLU-based multilayer neural network to generate a rich high-dimensional feature vector. The feature guarantees a monotonically decreasing training cost as the number of layers increases. We design the weight matrix in each layer to extend the feature vectors to a higher dimensional space while providing a richer representation in the sense of training cost. Linear projection to the target in the higher dimensional space leads to a lower training cost if a convex cost is minimized. An ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm convex constraint is used in the minimization to improve the generalization error and avoid overfitting. The regularization hyperparameters of the network are derived analytically to guarantee a monotonic decrement of the training cost and therefore, it eliminates the need for cross-validation to find the regularization hyperparameter in each layer.
A Robust, Low-Complexity Real-Time Vehicle Counting System For Automated Traffic Surveillance

This paper presents a real-time video based vehicle counting system, the key feature of which is its low computational complexity. The counting algorithm is tailored to be able to run on a low cost hardware platform like Raspberry Pi as part of a smart camera system. Background subtraction forms the basis of day time vehicle counting while night time counting is based on headlight detection and pairing. Both operations are performed only on a pre-specified virtual detection region within each frame. When tested on public traffic datasets, the method outperforms other more complex algorithms under various weather and traffic conditions.
Video Frame Interpolation Via Exceptional Motion-Aware Synthesis

In this paper, we propose a novel video frame interpolation method via exceptional motion-aware synthesis, in which accurate optical flow could be estimated even with exceptional motion patterns. Specifically, we devise two deep learning modules: exceptional motion detection and frame interpolation with refined flow. The motion detection module detects the position and intensity of exceptional motion patterns in current frame given the past frame sequence. The flow refinement module refines the pre-estimated optical flow for synthesizing the intermediate frame using the information of exceptional motion. The proposed modules improve the quality of the synthesized intermediate frame by making the optical flow robust against exceptional case of motion. Experimental results showed that the proposed method outperforms the state-of-the-art methods qualitatively and quantitatively.
A Study on the Implementation of a Ball and Plate System using LW-RCP and Machine Vision based on Odroid

In this paper, we propose the architecture of a ball-and-plate system as an educational platform that can help students comprehensively study control engineering, robotics, machine vision, and embedded systems. In addition, we describe how to implement the sub-systems and present an example of an education plan using the proposed system. In the proposed system, we use an LW-RCP(Light Weight-Rapid Control Prototyping) system, which is lab-built, to help students implement the control algorithm. An RCP system makes it easy for students who are not familiar with u-Controller or the C language to implement controllers. We also use Odroid, a low-cost SBC (single-board computer), and a single camera to calculate the position of ball using a machine vision algorithm. In this structure, the measurement subsystem is more durable compared with contact-based sensing systems and student can test their own machine vision algorithms for better measurements. We adopt the Stewart platform, a type of parallel robot, to generate roll-and-pitch actuation for the ball-and-plate system. By adopting the Stewart platform, we can handle more complicated control problems for movement that requires more than two degrees of freedom. Additionally, we implement an LQ controller, which is a typical model-based controller, and apply it to the ball-plate system. Finally, through experiments, we illustrate the usefulness of the proposed system.
A Triage Triangle Strategy for Law Enforcement to Reduce Digital Forensic Backlogs

The explosive growth of computer technologies creates many electronic data and produces much digital evidence of people's lives. As technology has improved, the volume of data for cybercrime investigation keeps growing at unprecedented rates and creating a quandary for Law Enforcement Agencies (LEAs). This study discusses the rise of digital evidence and the triage needs in digital forensic processing. It requires the sincere examination of all available data volumes at the scene or in the lab to present digital evidence in a court of law. In order to maintain the relevance, reliability, and sufficiency of digital evidence, investigators must establish a process model that can provide a quick response at the scene. This study proposes the novel triage triangle strategy of digital forensic components and illustrates TEAR phases from the viewpoint of THOR dimensions to describe the proper practices for identifying, collecting, acquiring, and preserving the digital data. It facilitates the efficiency and effectiveness of reducing digital forensic backlogs for LEAs.
Malay Cued Speech Transliterate System using Wearable Sensors: A Survey

Cued speech is a form of visual communication system. Cued speech uses the hand to represent the sound of a spoken language. Cued Speech in Bahasa Malaysia (CSBM) is an adaptation of cued speech for use in the Malay language. For CSBM, there are eight hand shapes in four different hand placements around the head. A cued speech transliterate system will capture and understand the hand shape, movement and placement and then generate the sound of each syllable onto a computer or a mobile device This paper looks into the existing researches that have been done in this area and also the sensors and methods they used. Because of the limited number of researches for cued speech, related researches such as sign language translator systems are also reviewed. This will give some idea on what sensor and method that can be used for our transliterate system. This paper will also give a general overview on how to implement the cued speech transliterate system.
A Breach into the Authentication with Built-in Camera (ABC) Protocol

In this paper, we propose a simple and effective attack on the recently introduced Smartphone Authentication with Built-in Camera Protocol, called ABC. The ABC protocol uses the photo-response non-uniformity (PRNU) as the main authentication factor in combination with anti-forgery detection systems. The ABC protocol interprets the PRNU as a fingerprint of the camera sensor built-in a smartphone device. The protocol works as follows: during the authentication process, the user is challenged with two QR codes (sent by the server) that need to be photographed with a pre-registered device. In each QR code, the server embeds a unique pattern noise (not visible to the naked eye), called probe signal, that is used to identify potential forgeries. The inserted probe signal is very similar to a genuine fingerprint. The photos of QR codes taken by the user are then sent to the server for verification. The server checks (i) if the photos contain the user’s camera fingerprint (used to authenticate the pre-registered device) and (ii) if the photos contain the embedded probe signal. If an adversary tries to remove (subtract) his own camera fingerprint and replace it with the victim’s camera fingerprint (computed from photos shared on social media), then he will implicitly remove the embedded probe signal and the attack will fail. The ABC protocol is able to detect these attacks with a false acceptance rate (FAR) of $$0.5\%$$ . However, the ABC protocol wrongly assumes that the attacker can only determine his own camera fingerprint from the photos of the presented QR codes. The attack proposed in our work is able to get past the anti-forgery detection system with a FAR of $$54.1\%$$ , simply by estimating the attacker’s camera fingerprint from a different set of photos (e.g. five photos) owned by the attacker. This set of photos can be trivially obtained before the attack, allowing the adversary to compute his camera fingerprint independently of the attack. The key to the success of our attack is that the independently computed adversary’s camera fingerprint does not contain the probe signal embedded in the QR codes. Therefore, when we subtract the adversary’s camera fingerprint and add the victim’s camera fingerprint, the embedded probe signal will remain in place. For this reason, the proposed attack can successfully pass through the anti-forgery detection system of the ABC protocol. In this paper, we also propose a potential fix based on analyzing signals from built-in motion sensors, which are not typically shared on social media.
Multi-Way Multi-View Deep Autoencoder for Image Feature Learning with Multi-Level Graph Regularization

Multi-view feature learning has garnered much attention recently since many real world data are comprised of different representations or views. How to explore the consensus structure and eliminate the inconsistency noise in different views remains a challenging problem in multi-view feature learning. In this paper, we propose a multi-way deep autoencoder for multi-view feature learning to explore the deep consensus structure and reconcile the efficiency of encoding process meanwhile. Through a multi-way encoding process, we embed the original data feature views to nonnegative representations of multiple levels which are structured hierarchically. Along the structure of embedded representations, we recover the diversity and important information layer by layer in the decoding process. The experiments on two image datasets show the superior performance of our method.
A New Texture Feature Based on GLCM and Its Application on Edge-detection

Abstract In visual interpretation and recognition, the distinction and discrimination of objects in an image depend not only on the complexity of the texture of the image, but also the intensity of the contrast of the image texture. In the commonly used texture features, entropy can better reflect the complexity of the texture, but it can not reflect the difference between the gray values. The contrast can reflect the contrast of the image texture, but since only the difference is taken into consideration, the difference in the size of the pixel value itself is not considered. In view of the above phenomenon, in order to make the texture features satisfy the higher recognition and recognition of image differentiation, this paper proposes a new texture feature based on the gray level co-occurrence matrix. The new texture feature not only reflects the sharpness of the texture, but also reflects the complexity of the texture and applies the texture feature to edge detection. The results show that the edge detection based on the new texture features proposed in this paper can effectively detect various edges and has a good inhibitory effect on noise. At the same time, the edge detection results can effectively distinguish the edges of different degrees of change.
Dynamic System Identification and Prediction Using a Self-Evolving Takagi–Sugeno–Kang-Type Fuzzy CMAC Network

This study proposes a Self-evolving Takagi-Sugeno-Kang-type Fuzzy Cerebellar Model Articulation Controller (STFCMAC) for solving identification and prediction problems. The proposed STFCMAC model uses the hypercube firing strength for generating external loops and internal feedback. A differentiable Gaussian function is used in the fuzzy hypercube cell of the proposed model, and a linear combination function of the model inputs is used as the output of the proposed model. The learning process of the STFCMAC is initiated using an empty hypercube base. Fuzzy hypercube cells are generated through structure learning, and the related parameters are adjusted by a gradient descent algorithm. The proposed STFCMAC network has some advantages that are summarized as follows: (1) the model automatically selects the parameters of the memory structure, (2) it requires few fuzzy hypercube cells, and (3) it performs identification and prediction adaptively and effectively.
Catalytic Coherence Transformation and Distillation for Special Mixed States

Abstract The coherence transformation and distillation for a class of special mixed coherent states of rank‐2 under incoherent operations (IO) is discussed. Similar to the entanglement transformation for mixed entangled states, the catalytic coherence transformation for this class of special mixed coherent states is analyzed. On the one hand, it is found that some of the mixed coherent states can be converted into other mixed coherent states under IO. But for those mixed coherent states which fail in the coherence conversion under IO, the catalytic coherence manipulation can solve this problem. In this case, a mixed coherent state cannot be converted into another under IO, while the coherence transformation can be realized with the help of coherence‐assisted incoherent operations, that is, catalytic coherence transformation. On the other hand, these special mixed coherent states can be distilled into the maximally pure coherent states or mixed states of arbitrary dimensions by strictly incoherent operations with certain probabilities. Finally, the coherence transformation of this type of mixed states can be generalized to the case of higher rank in a similar way, which is discussed at the end of this paper.
Label Reuse for Efficient Semi-Supervised Learning

In this paper, we propose a new learning strategy for semi-supervised deep learning algorithms, called label reuse, aiming to significantly reduce the expensive computational cost of pseudo label generation and the like for each unlabeled training instance since pseudo labels require to be repeatedly evaluated through the whole training process. For label reuse, we first divide the unlabeled training data into several partitions, replicate each partition in several copies, and place them consecutively in the training queue so as to reuse the pseudo labels computed at first time before invalidation. To evaluate the effectiveness of the proposed approach, we conduct extensive experiments on CIFAR-10 [1] and SVHN [2] by applying it upon the recent state-of-the-art semi-supervised deep learning approach, MixMatch [3]. The results demonstrate the proposed approach can not only significantly reduce the cost of pseudo label computation of MixMatch by a large amount but also keep comparable classification performance.
Robust Global Optimized Affine Registration Method for Microscopic Images of Biological Tissue

Affine registration can fit the non-rigid deformation of slices effectively, and it is widely used in volume reconstruction of biological tissue. But most of the existing affine registration methods are registered in a given sequence, which results in the accumulation of errors. In this paper, a global optimized affine registration method is proposed, which can be used in volume reconstruction. To eliminate the cumulative error, the affine transformation of all images is estimated simultaneously based on an energy function. A soft penalty on affine transformation is added to restrict the shearing of images. Experiments show that our method provides a more reliable registration result compared with sequential affine registration. It can solve the problems caused by the accumulation of errors. The registration result fits the deformation of slices well and preserves the rigidity of images.
Bert is Not All You Need for Commonsense Inference

This paper studies the task of commonsense inference, especially natural language inference (NLI) and causal inference (CI), requiring knowledge beyond what is stated in the input sentences. State-of-the-arts have been neural models powered with knowledge or contextual embeddings, for example BERT, as commonsenses knowledge. Our research questions are thus: Is BERT all we need for NLI and CI? If not, what is missing information and where to find such information? While many work has studied what is captured in BERT, the limitation of BERT is rather under-studied. Our contribution is observing the limitations of BERT in commonsense inference, then leveraging complementary resources containing missing information. Specifically, we model BERT and complementary resource as two heterogeneous modalities, and explore the pros and cons of multimodal integration approaches. We demonstrate that our proposed integration models achieve the state-of-the-art performance on both NLI and CI tasks.
Dynamically Modulated Deep Metric Learning for Visual Search

This paper proposes dynamically modulated metric learning (DMML) for learning a tiered similarity space to perform visual search. Existing methods often treat the training samples having different degree of information with equal importance which hinders in capturing the underlying granularities in visual similarity. Proposed DMML automatically exploits the informativeness of samples during training by leveraging correlation between image attributes and embedding that are learned jointly. The two tasks are interlinked by supervising signals where the predicted attribute vectors are used to dynamically learn the loss function. To this end, we propose a new soft-binomial deviance loss that helps to capture the feature similarity space at multiple granularities. Compared to recent ensemble and attention based methods, our DMML framework is conceptually simple yet effective, and achieves state-of-the-art performances on standard benchmark datasets; e.g. an improvement of 4% Recall@1 over the SOTA [1] on DeepFashion dataset.