Reviewerly/exHarmony huggingface dataset:
Comments on “Provably Secure Generalized Signcryption Scheme With Public Verifiability for Secure Data Transmission Between Resource-Constrained IoT Devices”

Very recently, Karati <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> presented a new generalized CLSC (gCLSC) to provide the functions of digital signature and encryption to fulfill the authenticity and confidentiality for the resource-constrained Internet-of-Things (IoT) devices. Karati <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> claimed that the newly proposed gCLSC supports the property of public verifiability and security of an ideal signcryption under the strong Diffie–Hellman and bilinear Diffie–Hellman inversion problems without the random oracle model. Unfortunately, their scheme is insecure with respect to unforgeability. It is demonstrated that any receiver or nonprivileged user can generate a valid signature or signcrypted ciphertext on any message. Finally, this article also points out the flaw in their unforgeability proof and gives some suggestions on the construction of a concrete scheme.
Multi-user searchable encryption voice in home IoT system

With the development of home IoT voice systems that can provide interfaces, such as Amazon Alex, it is easy to disclose personal privacy when people search voice conveniently. It becomes a challenge how to search voice effectively with privacy-preserving for multi-user. In the paper, we propose a multi-user searchable encrypted voice scheme (MUSEV). The outline is as the follows. voices are divided into two parts. One is answer. The other is query, or key words of voices. Answer part will be encrypted and query part will be converted into features. The feature and encrypted answer are bound together and uploaded to the server. If the feature is matched successfully, the answer part encrypted can be acquired. First, the feature of voice query is extracted using Mel Frequency Cepstral Coefficents (MFCC). Next, the feature of voice query and voice answer encrypted by Advanced Encryption Standard (AES) are bound. Before uploading the server, we adopt obfuscation function to hide the features on the server. The features obfuscated will be matched. If the query feature is matched successfully, the encrypted voice answer associated with it will be returned to a user. Then, the user can get the plaintext of voice answer by decrypting these voice answers encrypted. To improve multi-user retrieval performance, we employ Long Short-Term Memory networks (LSTM) for voice conversion between users. Meantime, Diffie-Hellman algorithm is used to exchange parameters between users to enhance security. Theoretical analysis and experimental results prove that MUSEV is secure and effective.
CDAE: Towards Empowering Denoising in Side-Channel Analysis

Side-Channel Analysis (SCA) plays a crucial role in hardware security evaluation. However, side-channel acquisitions (a.k.a. traces) usually contain noises that often impose negative effects on key-recovery efficiency. In this paper, we propose convolutional denoising autoencoder (CDAE) for noise reduction in SCA. CDAE is composed of multiple layers of convolution operators, learning an end-to-end mapping from noisy traces to clean traces by minimizing the $$\ell _2$$ loss of noisy-clean trace pairs. The convolutional layers capture the abstraction of the traces while eliminating noises. We argue that CDAE is very suitable for profiled SCA especially when the attacker has a large amount of traces in the offline profiling phase. Once the network training is done, our denoising network can be applied to individual new noisy traces for the attacker to launch online attacks. To validate the effectiveness of our method, we train CDAE to denoise traces and then perform Template Attacks (TA) in three high noise jamming scenarios, including unprotected (GPU and FPGA based) and protected (MCU based) AES implementations. Our method can significantly outperform the state-of-the-art Singular Spectrum Analysis (SSA) denoising method on both information theoretic metrics and security metrics. Results show that CDAE achieves at least $$\sim 4\times $$ Signal-to-Noise Ratio (SNR) gain, thus TA with denoising preprocessing requires at most 50% of the traces in the attack phase.
Distant Supervised Relation Extraction via DiSAN-2CNN on a Feature Level

At present, the mainstream distant supervised relation extraction methods existed problems: the coarse granularity for coding the context feature information; the difficulty in capturing the long-term dependency in the sentence, and the difficulty in coding prior knowledge of structures are major issues. To address these problems, we propose a distant supervised relation extraction model via DiSAN-2CNN on feature level, in which multi-dimension self-attention mechanism is utilized to encode the features of the words and DiSAN-2CNN is used to encode the sentence to obtain the long-term dependency, the prior knowledge of the structure, the time sequence, and the entity dependence in the sentence. Experiments conducted on the NYT-Freebase benchmark dataset demonstrate that the proposed DiSAN-2CNN on a feature level model achieves better performance than the current two state-of-art distant supervised relation extraction models PCNN+ATT and ResCNN-9, and it has d generalization ability with the least artificial feature engineering.
Weaver: A Novel Configuration Designer for IT/NW Services in Heterogeneous Environments

A configuration of an information technology/network (IT/NW) service is composed of components (e.g., applications, servers, and switches), relationships among them, and their attributes. To run a service appropriately, its configuration should be carefully organized to satisfy customer requirements, as well as dependencies and constraints derived from the components. Designing such a service configuration is daunting when the service is deployed in a heterogeneous environment including multiple clouds, edge devices, and different types of existing servers and network nodes. Thus, the time and cost for the designing task are serious problems in providing new services. In this paper, we present Weaver, an automated service configuration designer that generates a concrete service configuration on the basis of abstract customer requirements and the environment in which the service is deployed. Weaver accepts information about such requirements and environment as input and converts it into a fully concretized service configuration that can be deployed in the designated environment. In this paper, we take an example of a video surveillance Internet of Things (IoT) service, where multiple components including cameras, video analyzers and terminals, are distributed over multiple locations. In the evaluation, we show that Weaver produces configurations for designated heterogeneous environments. We also show that design time is dramatically shorter than in traditional system integration procedures. For example, the design time is shorter than a minute when the number of components is less than 150.
Mastery Grading: Build-A-Syllabus Workshop

While you may be ready to make the change to implementing Mastery Grading, you may still wonder exactly how to do this. This paper seeks to compile recommendations on implementing mastery grading from various sources as well as examples from our own experiences. The reader should expect a detailed overview of the components involved in mastery grading as well as practical advice, such as guiding questions to consider before any implementation and a build-a-syllabus workshop to get started.
Template-Based Automatic Search of Compact Semantic Segmentation Architectures

Automatic search of neural architectures for various vision and natural language tasks is becoming a prominent tool as it allows to discover high-performing structures on any dataset of interest. Nevertheless, on more difficult domains, such as dense per-pixel classification, current automatic approaches are limited in their scope - due to their strong reliance on existing image classifiers they tend to search only for a handful of additional layers with discovered architectures still containing a large number of parameters. In contrast, in this work we propose a novel solution able to find light-weight and accurate segmentation architectures starting from only few blocks of a pre-trained classification network. To this end, we progressively build up a methodology that relies on templates of sets of operations, predicts which template and how many times should be applied at each step, while also generating the connectivity structure and downsampling factors. All these decisions are being made by a recurrent neural network that is rewarded based on the score of the emitted architecture on the holdout set and trained using reinforcement learning. One discovered architecture achieves 63.2% mean IoU on CamVid and 67.8% on CityScapes having only 270K parameters.
Mode separability-based state estimation for uncertain constrained dynamic systems

This paper addresses the state estimation problem for dynamic systems subject to uncertain constraints, i.e., all possible constraints are described by a finite set, and only one constraint is satisfied at each moment. For this typical hybrid system estimation problem with coupled discrete constraint modes and continuous states, we design a mode separability-based state estimation (MSSE) framework. Based on the measurement model over a window, the hypothesis testing is performed to detect whether the mode changes firstly. Then the maximum-likelihood criterion is used to estimate the mode change sequence once detecting the change. Next, to consider the possible impact of the decided modes on the state estimation, a metric of mode separability is proposed to evaluate the separability of the recognized modes, and two different state estimation methods are introduced. Specifically, if the recognized modes are separable from the others, the state estimates are obtained by a recursive mode-based constraint Kalman filter (MCKF) which is proved that the estimation error is bounded in mean square. Otherwise, the estimation results output the fused state estimates (FSE) of the inseparable modes. Finally, simulation results of road-constrained vehicle tracking are provided to demonstrate the effectiveness of the proposed framework.
Accelerating artificial intelligence with silicon photonics

As Moore’s law and Dennard scaling come to an end, new devices and computing architectures are being explored. The development of computing hardware designed to address the rapidly growing need for computational power to accelerate artificial intelligence applications has prompted investigations into both. While silicon photonics is typically viewed as a communications platform, we discuss its application to artificial intelligence and some outstanding challenges to be addressed.
Modern Code Review Benefits-Primary Findings of A Systematic Literature Review

Modern Code Review (MCR) an effective quality assurance technique that can ensure software quality and customer satisfaction through the identification of defects, code improvement and accelerating the development process. It is an asynchronous and lightweight review process supported with review tools, for instance, Gerrit. It is a light version of Fagan's inspection process and has developed as a practice for open-source and industrial software development. Researches have been conducted in the context of MCR utilizing various data collection methodologies such as interviews, surveys and comment analysis from review tools. Besides defect detection, other benefits have been reported concerning MCR process adoption, for instance, knowledge sharing, team awareness, collaboration, etc. However, the team members involved in MCR activities are not aware of the benefits of MCR activities as the literature is dispersed. No, systematize study available reporting benefits concerning MCR. As a consequence, there is a lack of actual awareness of the adoption of the MCR process. Therefore, the objective of the study is to systematically analyze and report the benefits of the MCR process. Systematic Literature Review has been utilized to identify MCR benefits. Thematic analysis has been performed to group the identified benefits into the relevant themes. The themes and reported benefits are validated by the experts for their relevancy. The study findings report 54 unique benefits, grouped into 9 themes. This research has implications for the software industry, engineer and researchers. The industry can incorporate the MCR process widely, whereas software engineers being aware of the real benefits of MCR can provide their participation effectively in achieving those benefits in reality. In future, the researchers can extend this study by identifying more benefits in different research settings and by quantifying the reported benefits of MCR.
Draw with me

The purpose of image restoration is to recover the original state of damaged images. To overcome the disadvantages of the traditional, manual image restoration process, like the high time consumption and required domain knowledge, automatic inpainting methods have been developed. These methods, however, can have limitations for complex images and may require a lot of input data. To mitigate those, we present "interactive Deep Image Prior", a combination of manual and automated, Deep-Image-Prior-based restoration in the form of an interactive process with the human in the loop. In this process a human can iteratively embed knowledge to provide guidance and control for the automated inpainting process. For this purpose, we extended Deep Image Prior with a user interface which we subsequently analyzed in a user study. Our key question is whether the interactivity increases the restoration quality subjectively and objectively. Secondarily, we were also interested in how such a collaborative system is perceived by users.
Author Identification from Handwritten Characters using Siamese CNN

The NIST Special Database 19 has been studied to solve character recognition tasks. We present a different study on this dataset, where we do author identification using features learned from handwritten characters. Recent studies in computer vision demonstrate that Siamese Convolutional Networks enjoyed successes in image information retrieval tasks. This technique has previously been applied in the identification of people using face image data producing start-of-the-art performance. We apply Siamese convolutional neural networks in author verification based on the handwritten characters. Employing a pairwise-loss approach, we developed a three-layer Convolutional Neural Network, with three fully connected layers, we achieved verification accuracy of 80% on average with unseen test data.
Generating Chinese Classical Landscape Paintings Based on Cycle-Consistent Adversarial Networks

Generative Adversarial Networks (GAN) has made it possible for computers to generate images autonomously. There are many approaches based on GAN, including Conditional Generative Adversarial Networks (CGAN), Deep Convolutional Generative Adversarial Networks (DCGAN) and Cycle-Consistent Generative Adversarial Networks (CycleGAN) and so on. Photos can be transformed into western oil painting styles using unpaired data based on CycleGAN. But so far, no research has been found using it to generate images with Chinese Classical Landscape Paintings' style. This paper examines the effects of different generator models and loss functions on training time and results under this framework. In order to save the training time, the Unet generator is used in CycleGAN. To get a better quality, L2 Loss is chosen. Experiments are conducted on the PyTorch platform using Python programming language. Different styles of photos are tested and satisfactory results are attained.
A Multi-Space Approach to Zero-Shot Object Detection

Object detection has been at the forefront for higher level vision tasks such as scene understanding and contextual reasoning. Therefore, solving object detection for a large number of visual categories is paramount. Zero-Shot Object Detection (ZSD) - where training data is not available for some of the target classes - provides semantic scalability to object detection and reduces dependence on large amount of annotations, thus enabling a large number of applications in real-life scenarios. In this paper, we propose a novel multi-space approach to solve ZSD where we combine predictions obtained in two different search spaces. We learn the projection of visual features of proposals to the semantic embedding space and class labels in the semantic embedding space to visual space. We predict similarity scores in the individual spaces and combine them. We present promising results on two datasets, PASCAL VOC and MS COCO. We further discuss the problem of hubness and show that our approach alleviates hubness with a performance superior to previously proposed methods.
Contingent Task and Motion Planning under Uncertainty for Human–Robot Interactions

Manipulation planning under incomplete information is a highly challenging task for mobile manipulators. Uncertainty can be resolved by robot perception modules or using human knowledge in the execution process. Human operators can also collaborate with robots for the execution of some difficult actions or as helpers in sharing the task knowledge. In this scope, a contingent-based task and motion planning is proposed taking into account robot uncertainty and human–robot interactions, resulting a tree-shaped set of geometrically feasible plans. Different sorts of geometric reasoning processes are embedded inside the planner to cope with task constraints like detecting occluding objects when a robot needs to grasp an object. The proposal has been evaluated with different challenging scenarios in simulation and a real environment.
Midoriko Chatbot: LSTM-Based Emotional 3D Avatar

In this work, we developed a new model of chat robot with facial expression interaction. Different from the voice assistant that only interacts with text and voice, we let the chat robot interact with the user with a specific role play. A text-sentiment-analysis network is added to this system to recognize the emotion of the response sentences. Moreover, we created a 3D-constructed anime character “Midoriko” to have the image of the chat robot more vivid. Our goal is to provide better experience to users. The technical parts of this work is based on neural network techniques.
Segmentation Stroke Objects based on CT Scan Image using Thresholding Method

Brain image segmentation is one of the most important parts from a clinical diagnostic tool to determine the characteristics of a particular stroke type. Find anatomical contours and the location of the stroke to characterize the type of stroke perfectly in segmentation is very difficult this research proposes an approach to Image segmentation by the process of separating objects from other objects in CT Scan images. CT scan image segmentation uses the thresholding method with the Binarization process. Implementation of the Threshold method is Global binary thresholding, and Otsu thresholding. Preprocessing images to make repairs before segmentation. The dataset is used from Surabaya Hajj General Hospital and public data. The results of this experiment is improved image evaluated using peak signal-to-noise ratio (PSNR) and mean-square error (MSE), the best results were seen in bilateral filtering with a PSNR value of 69% MSE which was the lowest 0.008%. The best stroke object segmentation results using Otsu Thresholding by determining the lower threshold with a High Value of ≤170.
Achieving User-Defined Location Privacy Preservation Using a P2P System

As location-based services become widely used in daily life, there is growing concern in preserving location privacy of users to avoid that attackers infer information about users by collecting and analyzing requests initiated by users. We argue that a good location privacy preservation scheme should have these properties. First, a user should never expose its precise location to any other entity. Second, a user should be able to specify its own requirement on the strength of privacy preservation, since a stricter preservation requirement may increase its overhead. Third, the scheme should be able to preserve as many as possible aspects of users' privacy under various attacks. With these desired properties in mind, we carefully design an encoding scheme of users' identifiers and a fully distributed architecture for our purpose and propose a privacy preservation scheme based on them. With the help of the encoding scheme and the distributed architecture, we develop a distributed negotiation algorithm to help users conduct negotiations among themselves to find their cloaked regions that satisfy their self-defined requirements without exposing their precise locations. The negotiations are completed without coordination from any central servers, and a random proxy is selected for each individual request, therefore the potential risks caused by any central server (location-based service servers or trusted-third-party servers) are mitigated as much as possible. Experiments show that our scheme can satisfy different strengths of privacy preservation required by each user even under the most severe scenarios.
An efficient SG-DACM framework for data integrity with user revocation in role based multiuser cloud environment

The contemporary world activates with a enormous data that are stored and managed in the cloud server. For every cloud environment, there will be multi-users to access the data and group managers (GM) to regulate it. The major concern that evolved out of the cloud environment is to provide data security. Unfortunately with the recurrent changes, providing access control for the multiuser membership is the challenge and ensuring the data integrity in the cloud becomes a tedious task. Especially in the role-based user control, the revocation generates high computational and communication overheads. For handling these issues, a novel Sign crypto Group Data Access Control Mechanism (SG-DACM) framework has been proposed based on the user's role. It involves three significant processes: 1. Random Bit Signcryption Generation (RBSG), that generate the digital signature to fortify the mechanism of group access and monitor the user revocation activities. 2. Factorial Prime Based Data Control (FPBDC) for preserving the data integrity through the cryptic mechanism. 3. Efficient Storage Retrieval (ESR) is performed for making the storage and retrieval of the processed data efficiently. The proposed framework is assessed for its performance metrics and related with the existing models. The outcome revealed that there was a reduction in communication and computation overheads. It is evident that the SG-DACM framework is efficient for the issues concerning the revoked users in the cloud environment.