Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Multi-Receiver Certificateless Broadcast Encryption Scheme with De-Duplication

Nowadays, the widely deployed and high performance Internet of Things (IoT) facilitates the communication between its terminal nodes. To enhance data sharing among terminal devices and ensure the recipients' privacy protection, a few anonymous multi-recipient broadcast encryption (AMBE) proposals are recently given. Nevertheless, the majority of these AMBE proposals are only proven be securely against adaptively chosen plain-text attack (CPA) or selectively chosen ciphertext attack (CCA). Furthermore, all AMBE proposals are subjected to key escrow issue due to inherent characteristics of the ID-based public cryptography (ID-PKC), and cannot furnish secure de-duplication detection. However, for cloud storage, it is very important for expurgating duplicate copies of the identical message since de-duplication can save the bandwidth of network and storage space. To address the above problems, in the work, we present a privacy-preserving multi-receiver certificateless broadcast encryption scheme with de-duplication (PMCBED) in the cloud-computing setting based on certificateless cryptography and anonymous broadcast encryption. In comparison with the prior AMBE proposals, our scheme has the following three characteristics. First, it can fulfill semantic security notions of data-confidentiality and receiver identity anonymity, whereas the existing proposals only accomplish them by formalizing the weaker security models. Second, it achieves duplication detection of the ciphertext for the identical message encrypted with our broadcast encryption. Finally, it also avoids the key escrow problem of the AMBE schemes.
My Turn To Read: An Interleaved E-book Reading Tool for Developing and Struggling Readers

Literacy is crucial for functioning in modern society.It underpins everything from educational attainment and employment opportunities to health outcomes.We describe My Turn To Read, an app that uses interleaved reading to help developing and struggling readers improve reading skills while reading for meaning and pleasure.We hypothesize that the longerterm impact of the app will be to help users become better, more confident readers with an increased stamina for extended reading.We describe the technology and present preliminary evidence in support of this hypothesis.
Forget the Learning Rate, Decay Loss

In the usual deep neural network optimization process, the learning rate is the most important hyper parameter, which greatly affects the final convergence effect.The purpose of learning rate is to control the stepsize and gradually reduce the impact of noise on the network.In this paper, we will use a fixed learning rate with method of decaying loss to control the magnitude of the update.We used Image classification, Semantic segmentation, and GANs to verify this method.Experiments show that the loss decay strategy can greatly improve the performance of the model.
Denoising Ultrasonic Echo Signals with Generalized S Transform and Singular Value Decomposition

To remove the noise in the echo signals of ultrasonic pulse-echo testing, this paper puts forward a denoising algorithm based on the generalized S transform (GST) and singular value decomposition (SVD).Firstly, the ultrasonic echo signals were subjected to the GST, yielding the time-frequency matrix of the signals.Next, the matrix was taken as the Hankel matrix, and went through the SVD.The threshold for singular values to be zeroed was determined by the ratio between singular entropy increments.After zeroing the singular values representing the noise, the resulting denoised 2D time-frequency matrix was subjected to the inverse GST, generating the denoised echo signals.Our method was applied to denoise the simulated ultrasonic echo signals with different signal-to-noise ratios (SNRs), and compared with the wavelet soft thresholding (WST) method.The comparison show that our method outperformed the WST, especially in denoising the signals with low SNR.In addition, a scanning acoustic microscopy (SAM) system was designed for the experimental verification of our method.The C-scan image with our method was much better than that without our method.Hence, our method was proved feasible and effective.
Speaking Style Based Apparent Personality Recognition

In this study, we investigate the problem of apparent personality recognition using person’s voice, or more precisely, the way he or she speaks. Based on the style transfer idea in deep neural net image processing, we developed a system capable of speaking style extraction from recorded speech utterances, which then uses this information to estimate the so called Big-Five personality traits. The latent speaking style space is represented by the Gram matrix of convoluted acoustic features. We used a database with labels of personality traits perceived by other people (first impression). The experimental results showed that the proposed system achieves state of the art results for the task of audio based apparent personality recognition.
Sparse Twin Extreme Learning Machine With $\varepsilon$ -Insensitive Zone Pinball Loss

Twin extreme learning machine (TELM) based on the hinge-loss function shows great potential for pattern classification.However, the hinge loss is related to the shortest distance between sets and the corresponding classifier is hence sensitive to noise and unstable for resampling.In contrast, the ε-insensitive zone pinball loss is related to the quantile distance therefore is not sensitive to noise, and the resulting solution is sparse.To improve the performance of TELM, we propose a novel TELM learning framework by introducing ε-insensitive zone pinball loss function into TELM.Compared to TELM with hinge loss, the proposed SPTELM has the same computational complexity and is insensitive to noise, resampling stability and maintaining the sparsity of the solution.Further, we theoretically analyzed the sparsity, noise insensitivity and time complexity of SPTELM.Experimental results on multiple datasets demonstrate the noise insensitive, retains sparsity of the proposed method.
A Framework for Self-Adaptive Dispersal of Computing Services

Modern networking architectures are making it increasingly possible to disperse services not just across servers but into intermediate network devices as well. Here we introduce the Misson-oriented Adaptive Placement (MAP) architecture, which synthesizes prior work on middleware, load-balancing, constraint solving, and aggregate programming into a framework for self-adaptive management of dispersed services. We provide a first evaluation of the efficacy and resilience that can be provided through this approach: results in simulation demonstrating that MAP can autonomously change the deployment of services to adapt to changing needs and failures.
A Comparison of A* and RRT* Algorithms with Dynamic and Real Time Constraint Scenarios for Mobile Robots

There is an increasing number of mobile robot applications.The demanding of the Industry 4.0 pushes the robotic areas in the direction of the decision.The autonomous robots should actually decide the path according to the dynamic environment.In some cases, time requirements must also be attended and require fast path planning methods.This paper addresses a comparison between well-known path planning methods using a realistic simulator that handles the dynamic properties of robot models including sensors.The methodology is implemented in SimTwo that allows to compare the A* and RRT* algorithms in different scenarios with dynamic and real time constraint scenarios.
Building Effective Short Video Recommendation

How to build an effective personalized recommendation system is a challenging but highly valuable problem in social media services. This paper focuses on constructing a universal framework for short video recommendation by predicting the probability of finishing watching the entire video and pressing the 'like' button. Four novel techniques are proposed to improve the prediction accuracy. Firstly, we present an Incremental Multi-Window Scanning approach to extract the features pertaining to the users' behaviors. Also, a User Interaction Behavior Hierarchy is designed to capture a larger quantity of information and reduce the computing time. Additionally, the model transfer is capable of transferring the knowledge learned by the model on other datasets to the final model. Lastly, a rank-based ensemble approach which is suitable for tasks based on the evaluation metric of AUC is proposed. Our method long ranked first in the final stage of ICME Short Video Understanding Challenge (Track1) before the revision of competition rule.
Detecting the Onset of Machine Failure Using Anomaly Detection Methods

During the lifetime of any machine, components will at some point break down and fail due to wear and tear. In this paper we propose a data-driven approach to anomaly detection for early detection of faults for a condition-based maintenance. For the purpose of this study, a belt-driven single degree of freedom robot arm is designed. The robot arm is conditioned on the torque required to move the arm forward and backward, simulating a door opening and closing operation. Typical failures for this system are identified and simulated. Several semi-supervised algorithms are evaluated and compared in terms of their classification performance. We furthermore compare the needed time to train and test each model and their required memory usage. Our results show that the majority of the tested algorithms can achieve a F1-score of more than 0.9. Successfully detecting failures as they begin to occur promises to address key issues in maintenance like safety and cost effectiveness.
Comparison of MGBEKF and UKF Algorithms for Bearings-Only Tracking

Inactive target tracking using bearings-only measurements is a crucial issue of underwater tracking.Target Motion Analysis (TMA) process is highly non-linear so the non linear algorithms like Modified Gain Bearings-only Extended Kalman Filter (MGBEKF) and Unscented Kalman Filter (UKF) are implemented and their performance is evaluated based on their solution convergence times.It is presumed that the target is moving in straight line path with constant speed.The algorithms are simulated for several scenarios which are close to reality using MATLAB.Monte-Carlo runs are performed to evaluate the capability of the algorithms.
The Impact of Culture and Gender on the Production of Online Speech Acts among Jordanian Facebook Users

This study of speech acts offers deep insights into the social structure and gender differences of any speech community. Most relevant research on online speech acts has shed much light on Western speech communities, neglecting the speech act behavior of Arabic speaking communities. This study aimed to examine the influence of gender differences and the Jordanian cultural norms on the use of speech acts in Facebook Status Updates (FSUs). A total of 1718 FSUs were collected over a period of 2 months. Then, they were categorized according to Searle’s speech acts taxonomy. Results showed that women made an average of 6 updates to their Facebook status, while men averaged four. In addition, the results revealed that females and males differ in their linguistic repertoires. Male participants were inclined to use more assertive speech acts, whereas females were more likely to use expressive speech acts. Islam and tribalism are factors that were found to play an important role in defining the cultural norms of the Jordanian speech community. .
A complete classification of partial MDS (maximally recoverable) codes with one global parity

We generalize the definition of partial MDS codes to locality blocks of various length and show that these codes are maximally recoverable. Then we focus on partial MDS codes with exactly one global parity. We derive a general construction for such codes by describing a suitable parity check matrix. Then we give a construction of generator matrices of such codes. Afterwards we show that all partial MDS codes with one global parity have a generator matrix (or parity check matrix) of this form. This gives a complete classification and hence also a sufficient and necessary condition on the underlying field size for the existence of such codes. This condition is related to the classical MDS conjecture. Moreover, we investigate the decoding of such codes and give some comments on partial MDS codes with more than one global parity.
Recursive Image Dehazing via Perceptually Optimized Generative Adversarial Network (POGAN)

Existing approaches towards single image dehazing including both model-based and learning-based heavily rely on the estimation of so-called transmission maps. Despite its conceptual simplicity, using transmission maps as an intermediate step often makes it more difficult to optimize the perceptual quality of reconstructed images. To overcome this weakness, we propose a direct deep learning approach toward image dehazing bypassing the step of transmission map estimation and facilitating end-to-end perceptual optimization. Our technical contributions are mainly three-fold. First, based on the analogy between dehazing and denoising, we propose to directly learn a nonlinear mapping from the space of degraded images to that of haze-free ones via recursive deep residual learning; Second, inspired by the success of generative adversarial networks (GAN), we propose to optimize the perceptual quality of dehazed images by introducing a discriminator and a loss function adaptive to hazy conditions; Third, we propose to remove notorious halo-like artifacts at large scene depth discontinuities by a novel application of guided filtering. Extensive experimental results have shown that the subjective qualities of dehazed images by the proposed perceptually optimized GAN (POGAN) are often more favorable than those by existing state-of-the-art approaches especially when hazy condition varies.
A Novel Social Situation Analytics-Based Recommendation Algorithm for Multimedia Social Networks

Due to the increasing popularity of multimedia social networks (MSNs), the ability to mine users' interests in different contexts on such networks is crucial in recommendation systems. It is, however, challenging to mine users' current preferences based on session on MSNs. In this study, we propose a novel recommendation algorithm based on both social situation analytics and collaborative filtering for session-based recommendation. Specifically, the algorithm predicts the rating for target users based on their nearest neighbors and historical behaviors. First, for the purpose of mining users' current intentions, the session-level behavioral sequences of target user are analyzed based on SocialSitu (t). Then, the recommended contents are generated for target users based on their behaviors and perceived session-based intentions and identity. We evaluate the performance of the proposed algorithm using real-world social media dataset from Shareteches. Findings demonstrate that our algorithm outperforms two classical algorithms and a state-of-the-art method.
Practical decoy-state quantum digital signature with optimized parameters

Quantum digital signature (QDS) can guarantee the authenticity and transferability of messages through quantum theory, and is becoming an interesting topic in the field of quantum information. Various protocols have been put forward to make QDS more practical and efficient. However, in previous QDS works, only fixed parameters have been utilized due to the high time complexity of optimizing parameters. In this paper, we solve this technical problem by developing a framework of minimizing the number of quantum pulses given a certain security level, and for the first time implement the parameter optimization method onto practical decoy-state QDS systems. Simulation results indicate that decoy-state QDS with optimized parameters can present improved performance both in the signature rate and transmission distance than previous works with fixed parameters, making it very promising in possible digital signature scenarios.
Analisa Clustering K-Means Pada Data Informasi Kemiskinan Di Jawa Barat Tahun 2018

Abstract— Poverty is a condition of life that is understaffed by a person or household so that it is unable to meet the minimum or proper needs for his or her life. The poverty Data in each region will differ. It is influenced by many of its supporting indicators. By determining and measuring the indicators of poverty, it will facilitate and recognize the poverty level of the region. Grouping characteristics of a region based on poverty indicators, so that the government can precisely and quickly take policies to mitigate poverty in a region. The method used in this study uses the K-Means Clustering method. The Clustering method is selected because this method has the ability to classify large amounts of data with faster process times efficiently. The object in this study used data published by the BPS (Badan Pusat Statistik) on poverty Data and information in the Regency/city in 2018. Based on the results of this study, the results of the characteristic mapping of each group formed based on the highest and lowest value of poverty indicator of West Java province year 2018. With the characteristics found in each region, it will certainly be a solid foundation for government organizers to provide the right and quick policy/approach to overcome the poverty that is found in the region.
DAML

A discussion between Shaul Kfir and Camille Fournier.
Software mediators as first-class entities of systems-of-systems software architectures

In contrast to traditional software systems that are mostly created from scratch, current software systems are engineered as a junction of systems already in operation. Examples can be found in domains, such as smart cities, crisis and emergency, IoT, big data, industry 4.0, and connected health systems. Most of them can be considered systems-of-systems (SoS), since they refer to alliances of operational and managerial independent software-intensive systems, which are sometimes distributed over different environments. Therefore, SoS software architectures must be dynamic, evolve over time, and support the execution of emergent behaviors to accomplish SoS missions. They must be also designed to enable the connection of heterogeneous systems, making possible their interoperation, communication, coordination, cooperation, and collaboration, most of the times, in a seamless way. Similar challenges have been addressed by using software mediators as architectural entities. However, the application of mediators in SoS has not been properly explored. This article introduces MediArch, a layered architecture that considers mediators as first-class software entities to be used in the construction of SoS architectures. Our objective is to present evidence about how MediArch’s can support the construction of SoS architectures. The following four steps were conducted to define MediArch: (1) identification of mediation requirements to allow SoS properties; (2) establishment and categorization of twelve types of mediators, for enabling capabilities of communication and control of constituent systems interactions and conversion of heterogeneous messages exchanged through a mediation infrastructure; (3) specification of duties, behaviors, assumptions, and guarantees of mediators; and (4) organization of MediArch in three layers, namely, the constituents & consumer systems layer; the communication, conversion, & coordination layer; and the control layer. This architecture was used as the backbone for the software architectures of two SoS in different domains, namely, flood monitoring system-of-systems (FMSoS), and health-care supportive home system-of-systems (HSH-SoS), for providing evidence on how MediArch supports the architecting process of SoS. MediArch (1) supports the integration of independent constituent systems, (2) provides strategies to manage emergent behaviors, (3) defines different schemes of control authorities, (4) offers elements to support SoS evolution, and (5) promotes the resilience and adaptability of SoS architectures. Although MediArch contributes to the establishment of SoS architectures, some challenges related to performance, resource consumption, security, safety, and non-disruptive reconfigurations must still be overcome.
Towards a Combinatorial Proof Theory

The main part of a classical combinatorial proof is a skew fibration, which precisely captures the behavior of weakening and contraction. Relaxing the presence of these two rules leads to certain substructural logics and substructural proof theory. In this paper we investigate what happens if we replace the skew fibration by other kinds of graph homomorphism. This leads us to new logics and proof systems that we call combinatorial.
Epigenetic Information-Body Interaction and Information-Assisted Evolution from the Perspective of the Informational Model of Consciousness

Introduction: the objective of this investigation is to analyses the advances in understanding in the epigenetic processes and to extract conclusions concerning the information-based evolution from the perspective of the Informational Model of Consciousness (IMC).
Self-supervised blur detection from synthetically blurred scenes

Blur detection aims at segmenting the blurred areas of a given image. Recent deep learning-based methods approach this problem by learning an end-to-end mapping between the blurred input and a binary mask representing the localization of its blurred areas. Nevertheless, the effectiveness of such deep models is limited due to the scarcity of datasets annotated in terms of blur segmentation, as blur annotation is labor intensive. In this work, we bypass the need for such annotated datasets for end-to-end learning, and instead rely on object proposals and a model for blur generation in order to produce a dataset of synthetically blurred images. This allows us to perform self-supervised learning over the generated image and ground truth blur mask pairs using CNNs, defining a framework that can be employed in purely self-supervised, weakly supervised or semi-supervised configurations. Interestingly, experimental results of such setups over the largest blur segmentation datasets available show that this approach achieves state of the art results in blur segmentation, even without ever observing any real blurred image.
StopGuessing: Using Guessed Passwords to Thwart Online Guessing

Practitioners who seek to defend password-protected resources from online guessing attacks will find a shortage of tooling and techniques to help them. Little research suggests anything beyond blocking or throttling traffic from IP addresses sending suspicious traffic; counting failed authentication requests, or some variant, is often the sole feature used to determine suspicion. In this paper we show that several other features can greatly help distinguishing benign and attack traffic. First, we increase the penalties for clients responsible for fail events involving passwords frequently-guessed by attackers. Second, we reduce the threshold (and thus protect better) for accounts with weak passwords. Third, we detect, and are more forgiving of, login failures caused by users mistyping their passwords. Most importantly, we achieve all of these goals without needing any marker that indicates weak accounts, changing the format in which passwords are stored (i.e. we do not store passwords plaintext or in any recoverable form), or storing any information that might be harmful if leaked. We present an open-source implementation of this system and demonstrate its improvement over simpler blocking strategies in various simulated scenarios.