Reviewerly/exHarmony huggingface dataset:
Incremental Matrix Factorization for Recommender Systems

With the help of recommender systems, users can find information they do not know about, but they are interested in. These systems are a specific type of intelligent systems that track each user's behavior and discover their behavioral patterns. Among different approaches for implementing recommender systems, Matrix-Factorization (MF) based methods are so popular due to their high accuracy and scalability. However, in real world applications that new ratings are continuously coming, processing the huge amount of data is a computationally expensive task. In this paper, we present a novel incremental matrix factorization method to learn only parts of the data that have been changed or added recently. This way, there is no need to train the system from the scratch. The input data to the proposed recommender system is of two types, batch data and stream data. Batch data is the rating data that is already saved in the system and relates to the activities of users in the past. Stream data is the rating data that enters the system in each time interval. The method is evaluated on two versions of popular MovieLens dataset from GroupLens research. The experimental results confirm that our method reduces the execution time significantly while keeping the prediction error intact.
Security Analysis of QKD Protocols: Simulation &amp; Comparison

The inception of quantum key distribution (QKD) empowered the communication networks with unconditional security because of the unique characteristics of quantum mechanics. Since QKD genesis, various protocols have been proposed to augment the feasibility of its real-world implementation and to overcome the inherent vulnerabilities of communication channel. In the past several years' studies emphasize the significance of QKD in the field of secure communication, because it can counter critical attacks quite effectively. In this paper, we present in-depth security analysis of the notable QKD protocols which encompasses from their theoretical assertions to their practical implementation factors and simulation outcomes.
A Privacy-Preserving and Robust Reputation System Based on Blockchain

In the era of the Internet, agents behaving on behalf of human beings and smart devices are ubiquitously connected, and a well-designed reputation system is essential for them to exchange information efficiently. Reputation systems bear many challenging problems, such as data security, user privacy, as well as vulnerabilities against untruthful ratings (bad-mouthing and ballot-stuffing attacks), resulting in the ineffectiveness of the systems in differentiating honest users from malicious ones. Moreover, some users even hesitate to submit their truthful negative feedbacks due to the fear of the retaliation from recipient users. To address the above issues, we are to propose a privacy preserving and robust reputation system based on the blockchain. Specifically, first of all, we propose the reputation token concept and design a reputation token age based consensus mechanism to incentivize evaluators to actively participant the block mining process. Secondly, we consider the effects of the transaction price and evaluators' reputation in adjusting the weights of ratings so as to defect against ballot-stuffing and bad-mouthing attacks. Furthermore, we improve the malicious-k-shares protocol to enable the ratings to be aggregated in a privacy preserving way, where the rating privacy is not leaked in the whole process of rating storage and usage. Experiments based evaluations and discussions have demonstrated the effectiveness of the proposed reputation system.
New binary and ternary LCD codes from matrix-product codes

Linear complementary dual (LCD) codes are linear codes satisfying C∩C⊥={0}. Researchers have proved to construct linear codes over finite fields Fq, q>3, is equivalent to construct LCD codes. This means that the investigation of binary and ternary LCD codes is the only remaining open problem. In this work, we propose new constructions of binary and ternary LCD codes by using matrix-product codes and prove that binary and ternary LCD codes from matrix-product codes are asymptotically good. Then we construct some new and good binary and ternary LCD codes using matrix-product matrices.
IPSO: Improved Particle Swarm Optimization Based Task Scheduling at the Cloud Data Center

Today, cloud computing has become an advanced form of distributed computing, grid computing, utility computing, and virtualization. Efficient task scheduling algorithms help to reduce the number of virtual machines used, thus reducing costs and improving stability. To solve the problem of cloud computing task scheduling, an improved particle swarm optimization (IPSO) task scheduling method is proposed based on the traditional PSO algorithm. Firstly, this paper describes the mathematical model of cloud computing task scheduling and the basic principle of particle swarm optimization. On this basis, the random method is used to generate the initial population definition appropriateness function, the indirect coding method is used to encode the resources, and the time-varying method is used to adjust the inertia weight. In the position update, according to the inertia weight w, the individual optimal value Pbest or the group optimal value Gbest is legalized to determine the update method of the particle velocity and position, thereby increasing the degree of discretization of the PSO algorithm. The simulation test on the CloudSim platform shows that the scheduling strategy is effective and efficient. Experimental results demonstrate that the proposed method obtains better scheduling results. Thereby controlling global search and local search, try to avoid falling into local optimum.
A Detection Confidence-Regulated Path Planning (DCRPP) Algorithm for Improved Small Object Counting in Aerial Images

Computerized object counting shows potential for conservation population estimates as an alternative to manual counting, but remains unsatisfactory for minuscule objects, such as those in UAV-produced images. We improve UAV data collection by using a novel path planner which shifts altitude to maximize deep learning-based object detection confidences from a Faster Region-Convolutional Neural Network, considering energy consumption trade-offs. Using an empirical altitude confidence relationship, our adaptive path planner (”DCRPP”) adjusts UAV altitude based on confidence, yielding better quality data given energy constraints. DCRPP achieves 11.92% greater accuracy compared to fixed-height methods in our conservation-aimed simulation.
Government Cloud Computing and National Security

Purpose The purpose of this paper is to try to reach the main factors that could put national security at risk as a result of government cloud computing programs. Design/methodology/approach The paper adopts the analytical approach to first lay foundations of the relation between national security, cybersecurity and cloud computing, then it moves to analyze the main vulnerabilities that could affect national security in cases of government cloud computing usage. Findings The paper reached several findings such as the relation between cybersecurity and national security as well as a group of factors that may affect national security when governments shift to cloud computing mainly pertaining to storing data over the internet, the involvement of a third party, the lack of clear regulatory frameworks inside and between countries. Practical implications Governments are continuously working on developing their digital capacities to meet citizens’ demands. One of the most trending technologies adopted by governments is “cloud computing”, because of the tremendous advantages that the technology provides; such as huge cost-cutting, huge storage and computing capabilities. However, shifting to cloud computing raises a lot of security concerns. Originality/value The value of the paper resides in the novelty of the topic, which is a new contribution to the theoretical literature on relations between new technologies and national security. It is empirically important as well to help governments stay safe while enjoying the advantages of cloud computing.
FPGA-Based Depth Separable Convolution Neural Network

In order to enable convolution neural network (CNN) to be deployed on a Field Programmable Gate Array (FPGA), this study builds a lightweight convolutional neural network that can be separated by a depth to reduce the amount of parameters and computations stored. We replaced the standard convolution operation with a separate convolution operation, and proposed a hardware accelerator architecture that can handle differently sized depth-separable convolution operations, using parallelization to efficiently utilize hardware resources for depth separable convolution. Therefore, data can be reused to reduce number of memory accesses. This hardware accelerator can achieve 588 frames per second and 37.88M ops/sec throughput at 100MHz clock.
Improving Security Data Access Control for Multi-Authority Cloud Storage

Cloud computing have brought a lot of advantages, such as reducing the hardware cost and providing a convenient cloud storage service. More and more people choose to put their private data in the cloud. To improve data security and privacy, Wu et al. proposed a new extensive data access control scheme for multi-authority cloud storage systems (NEDAC-MACS) on the basis of the scheme of Yang et al. In this paper, we propose a new scheme that can resist the collusion of the cloud server and user to improve the security of NEDAC-MACS. The cryptographic analysis show that our scheme is still secure in the random oracle.
Moving Towards Open Set Incremental Learning: Readily Discovering New Authors

The classification of textual data often yields important information. Most classifiers work in a closed world setting where the classifier is trained on a known corpus, and then it is tested on unseen examples that belong to one of the classes seen during training. Despite the usefulness of this design, often there is a need to classify unseen examples that do not belong to any of the classes on which the classifier was trained. This paper describes the open set scenario where unseen examples from previously unseen classes are handled while testing. This further examines a process of enhanced open set classification with a deep neural network that discovers new classes by clustering the examples identified as belonging to unknown classes, followed by a process of retraining the classifier with newly recognized classes. Through this process the model moves to an incremental learning model where it continuously finds and learns from novel classes of data that have been identified automatically. This paper also develops a new metric that measures multiple attributes of clustering open set data. Multiple experiments across two author attribution data sets demonstrate the creation an incremental model that produces excellent results.
Machine Learning for Detecting Drift Fault of Sensors in Cyber-Physical Systems

Cyber-Physical System (CPS) emerges as a potential direction to improve the applications relating to object-to-object, human-to-human and human-to-object communications in both the real world and virtual world. The examples of CPSs include Smart Girds (SG), Wireless Sensor Networks (WSNs), transportation networks etc. However, the Ubiquitiy of CPSs pose extra challenges to the systems, such as intrusions from external agent and failures originated internally in nodes of the networks. These internally appeared failures are generally referred to as faults. The Drift fault, that result in increasing linearly the output of sensor from normal value, is the most commonly occured fault in sensor nodes. In this paper, a fault detection model is proposed using machine learning to identify drift fault immediately after it appears in sensor. The proposed approach uses an Auto-Encoder (AE) to extract features from the raw signal of sensor. The vector of features is forwrded to Support Vector Machine (SVM) for fault identification. We compare the performance of four different architectures of AE: primary AE, Stacked AE (SAE), Denoising AE (DAE), and Stacked Denoising AE (SDAE). The number of features extracted by these network is varied from 2 to 15. The accuracy of the SVM is used as a parameter for comparison. The results shows that the DAE outperform the counter AE, SAE, and SDAE architectures.
Deep No-Reference Tone Mapped Image Quality Assessment

The process of rendering high dynamic range (HDR) images to be viewed on conventional displays is called tone mapping. However, tone mapping introduces distortions in the final image which may lead to visual displeasure. To quantify these distortions, we introduce a novel no-reference quality assessment technique for these tone mapped images. This technique is composed of two stages. In the first stage, we employ a convolutional neural network (CNN) to generate quality aware maps (also known as distortion maps) from tone mapped images by training it with the ground truth distortion maps. In the second stage, we model the normalized image and distortion maps using an Asymmetric Generalized Gaussian Distribution (AGGD). The parameters of the AGGD model are then used to estimate the quality score using support vector regression (SVR). We show that the proposed technique delivers competitive performance relative to the state-of-the-art techniques. The novelty of this work is its ability to visualize various distortions as quality maps (distortion maps), especially in the no-reference setting, and to use these maps as features to estimate the quality score of tone mapped images.
Multispectral imaging of mineral samples by infrared quantum dot focal plane array sensors

Infrared (IR) spectral imaging is a powerful tool for component analysis and identification in the geological field. In this report, we originally use quantum dot infrared photodetector (QDIP) focal plane arrays (FPAs) with different peak-responsivity wavelengths to analyze the mineral composition of rock samples by IR spectral imaging and show the QDIP’s potential as a solution for some geological problems. After clarifying fundamental characteristics such as the blackbody responsivity, current noise density, and spectral responsivity of QDIPs, we present qualitative and quantitative analyses of mineral samples including conglomerate, sandstone, marble, and dolomite. In the qualitative analysis, we distinguish two different compositions in each rock. In the quantitative analysis, we measure the emittance of dolomite and show the emittance errors. Finally, we discuss which experimental factor determines the quantitative precision most dominantly.
Code Nano-Pattern Detection using Deep Learning

Nano-patterns are the method-level code building blocks of the code which can reveal crucial information of the code. In this paper, we present some initial results of our investigation to detect nano-patterns in a Java code using a deep learning approach. For this purpose, first, we generated a method level tagged corpus for 15 nano-patterns using nine open source Java projects. Subsequently, the tagged corpus was used to train a Long Short-Term Memory (LSTM) network to predict the nano-patterns present in the Java code. Our deep learning model gave an average accuracy of 88.3% with an average precision of 74.4% and average recall of 78.3%.
Single Image Super-Resolution Reconstruction of Enhanced Loss Function with Multi-GPU Training

According to research on super-resolution (SR), SR image reconstruction using generated anti-networks can produce images that are more realistic than using convolutional neural networks. At present, SR technology based on convolutional neural networks ignores the impact of loss function on image reconstruction; the results lack detail and accuracy. In this paper, we use SR method and combine Generative Adversarial Networks to design a super-resolution (Lapras-GAN) model of the enhanced loss function. The proposed enhancement loss function is a Mix loss function that combines the multiscale SSIM and L1 loss functions to obtain realistic images. We performed qualitative and quantitative analysis of the performance of different loss functions and demonstrated the advantages of the Mix loss function. In addition, the neural network is accelerated by multiple GPUs of multiple nodes, which can be 3-4 times faster than a single node single GPU. Experimental results show that the proposed Lapras-GAN method can generate images consistent with images produced by human perception. Further comparisons show that our Lapras-GAN has excellent performance and test time in the PIRM2018 experimental test data set. Finally, we obtained a perception index of 1.83 and a test time of 0.031s in the PIRM2018 competition test set.
Virtual Reality Becoming Part of Our Lives—Assessing Consumers’ Perceived Applicability of Virtual Reality Supported Tasks and a Critical Reflection on the Development

With virtual reality technology rapidly advancing, virtual reality glasses can be used for a growing number of tasks and thus become increasingly integrated in consumers’ lives. The present study reanalyzes an existing dataset from a quantitative study with 611 German consumers to assesses their perceived applicability of virtual reality glasses in various tasks. Further, this study identifies situations and explores the relationship of virtual reality tasks with attitudes and purchase intention of virtual reality glasses. Finally, the present paper offers a critical reflection on the increasing adoption of technology in our everyday lives from an ethical and sociological perspective and offers impulses for future research.
Impossibility Results for Lattice-Based Functional Encryption Schemes

Functional Encryption denotes a form of encryption where a master secret key-holder can control which functions a user can evaluate on encrypted data. Learning With Errors (LWE) (Regev, STOC’05) is known to be a useful cryptographic hardness assumption which implies strong primitives such as, for example, fully homomorphic encryption (Brakerski-Vaikuntanathan, FOCS’11) and lockable obfuscation (Goyal et al., Wichs et al., FOCS’17). Despite its stre ngth, however, there is just a limited number of functional encryption schemes which can be based on LWE. In fact, there are functional encryption schemes which can be achieved by using pairings but for which no secure instantiations from lattice-based assumptions are known: function-hiding inner product encryption (Lin, Baltico et al., CRYPTO’17) and compact quadratic functional encryption (Abdalla et al., CRYPTO’18). This raises the question whether there are some mathematical barriers which hinder us from realizing function-hiding and compact functional encryption schemes from lattice-based assumptions as LWE. To study this problem, we prove an impossibility result for function-hiding functional encryption schemes which meet some algebraic restrictions at ciphertext encryption and decryption. Those restrictions are met by a lot of attribute-based, identity-based and functional encryption schemes whose security stems from LWE. Therefore, we see our results as important indications why it is hard to construct new functional encryption schemes from LWE and which mathematical restrictions have to be overcome to construct secure lattice-based functional encryption schemes for new functionalities.
Port Ship Detection in Complex Environments

Ship detection plays an important role in the port monitor and management. The difficulty in ship detection is that with the change of ship traffic flow, limited with small ship imaging scales and face the foggy navigation. The traditional ship image detection method has insufficient detection performance, which cannot meet the needs of the port. In order to improve the performance of ship detection in the port, we detect ship by yolov3 detection algorithm to solve the above problems. This ship detection framework based on convolutional neural networks. The characteristics of the ship are extracted layer by layer through the DarkNet53 network, and multi-scale image pyramid features are formed to detect ships of different scales. Specifically, large ships, medium ships, small ships. And uses yolov3 algorithm to achieve ship detection. We have selected four typical port navigation scenes: (1) small traffic flow (2)foggy ship navigation (3)large traffic flow (4) small imaging scale. Experiments show that the ship detection based on yolov3 has high accuracy in the face of complex sea navigation conditions and can cope with the detection requirements of different scenes at sea.
Improved Watermarking Algorithm Based on SURF and SVD with wavelet transformation Against Geometric Attacks

The application of digital medical images occupies an important position in the diagnostic process with the promotion of computer technology in the medical field. The resulting copyright and tampering issues have also attracted increasing attention. Digital watermarking technology can provide powerful technical support for copyright protection and tamper detection of medical images. Aiming at the problems of small embedding capacity and weak resistance to geometric attacks of digital image watermarking algorithms, a digital watermarking algorithm based on accelerated robust feature algorithm (SURF), redundant discrete wavelet transform (RDWT) and singular value decomposition (SVD) was proposed. The feature points are extracted using the rotation and translation invariance of the SURF algorithm, and the RDWT and SVD transforms and inverse transforms of the carrier image and the watermark image are performed to achieve the embedding and extraction of the watermark. A random sampling consistency algorithm is introduced to optimize the feature point matching results. Experimental results show that the algorithm improves the embedding watermark capacity and effectively enhances the robustness against geometric attacks. At the same time, compared with similar algorithms, the amount of additional information in this algorithm is a fixed value, which can better meet the detection and recovery requirements of medical images.
Harnessing Uncertainty in Photoresistor Sensor for True Random Number Generation in IoT Devices

Internet of Things (IoT) has facilitated the connection of many smart devices via internet. Recent cyberattacks have shown that resource constrained IoT nodes are easy prey that lead towards compromising the secrecy of the data and vulnerabilities could be exploited remotely to take control of safety-critical systems. Photoresistor sensors have applications in IoT systems, such as smart street lighting, intelligent cameras, light activated smart consumer electronics, smart home, smart healthcare, etc. Building hardware security primitives, such as True Random Number Generator (TRNG), based on the intrinsic properties of photoresistor would be a novel direction to develop cost-savvy IoT security primitives. Therefore, this paper proposes a TRNG prototype that is devised from uncertainty presents in photoresistor sensors. The proposed TRNG prototype does not require any complex interfacing for preprocessing the weak signal, thereby reducing the unnecessary delay and the recurring hardware cost. The proposed prototype employs the novel approach of additive scrambling that aids to sample sensors at a higher rate. The proposed TRNG has an average random bit generation rate of 8 kbps that is better than the recent work in the literature. The quality of randomness was validated by 15 test batteries of NIST STS test.