Reviewerly/exHarmony huggingface dataset:
No-reference image quality assessment based on neighborhood co-occurrence matrix

No-reference image quality assessment (NR-IQA) aims to develop models that can predict the quality of distorted image automatically and accurately in the absent of reference image. Previous NR-IQA methods based on natural scene statistics (NSS) always focus on the luminance contrast of image but attach limited attention to pixel-wise relationship. However, human visual system (HVS) is highly adaptive to extract spatial correlation according to relative position within visual field. In this paper, a new approach is proposed for NR-IQA, in which the neighborhood co-occurrence matrix (NCM) is introduced to describe spatial correlation of pixels for quality assessment. The NCM is constructed based on spatial correlation of every pixel and its neighborhood through a mapping to highlight the one-to-many pixel-wise relationship. Moreover, a series of tailored statistical metrics are designed to quantify the unnaturalness extent of NCM effectively, which is combined with others natural scene statistics to predict image quality. Extensive experiments demonstrate the proposed method has superior performance against compared methods, and achieves significant improvements on distortions associated with color or locality.
A generic testbed for simulation and physical-based testing of maritime cyber-physical system of systems

Abstract e-Navigation Systems are CPSoS, which are regarded as systems based on systems with physical and information technology-based components. SoS are usually open systems which can have emerging behaviour and are not fully defined during design time. Future maritime bridge systems, for instance, will be embedded into the vessel infrastructure and its navigation and safety subsystems receive continuous updates and additional functionalities for the ship’s command and navigation. Suitable testbeds are required to support the technological and regulatory development and certification processes. To support the development and integration of CPS, this paper introduces the testbed eMIR as an open and modular research, development and test environment. The test environment is suitable for early testing, validation and verification or demonstration of new maritime technologies in a complex simulation environment (traffic, environment and sensors) and supports a seamless transfer of these technologies from a virtual into a physical testbed. eMIR comprised various interconnected virtual and physical components via a standardised co-simulation environment based on HLA, combined with a message passing middleware as a backbone for the physical testbed elements. The testbed was effectively applied in various maritime projects for the evaluation of maritime technologies in navigation, management and surveillance or maritime (transportation) systems.
An Effective Recommender System Based on Clustering Technique for TED Talks

With the enormous amount of information circulating on the Web, it is becoming increasingly difficult to find the necessary and useful information quickly and efficiently. However, with the emergence of recommender systems in the 1990s, reducing information overload became easy. In the last few years, many recommender systems employ the collaborative filtering technology, which has been proven to be one of the most successful techniques in recommender systems. Nowadays, the latest generation of collaborative filtering methods still requires further improvements to make the recommendations more efficient and accurate. Therefore, the objective of this article is to propose a new effective recommender system for TED talks that first groups users according to their preferences, and then provides a powerful mechanism to improve the quality of recommendations for users. In this context, the authors used the Pearson Correlation Coefficient (PCC) method and TED talks to create the TED user-user matrix. Then, they used the k-means clustering method to group the same users in clusters and create a predictive model. Finally, they used this model to make relevant recommendations to other users. The experimental results on real dataset show that their approach significantly outperforms the state-of-the-art methods in terms of RMSE, precision, recall, and F1 scores.
A Privacy-Preserving Asynchronous Averaging Algorithm based on Shamir’s Secret Sharing

Average consensus is widely used in information fusion, and it requires information exchange between a set of nodes to achieve an agreement.Unfortunately, the information exchange may disclose the individual's private information, and this raises serious concerns for individual privacy in some applications.Hence, a privacy-preserving asynchronous averaging algorithm is proposed in this paper to maintain the privacy of each individual using Shamir's secret sharing scheme, as known from secure multiparty computation.The proposed algorithm is based on a lightweight cryptographic technique.It gives identical accuracy solution as the non-privacy concerned algorithm and achieves perfect security in clique-based networks without the use of a trusted third party.In each iteration of the algorithm, each individual's privacy in the selected clique is protected under a passive attack where the adversary controls some of the nodes.Finally, it also achieves robustness of up to one third transmission error.
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Low rank matrix approximation (LRMA) has drawn increasing attention in recent years, due to its wide range of applications in computer vision and machine learning. However, LRMA, achieved by nuclear norm minimization (NNM), tends to over-shrink the rank components with the same threshold and ignore the differences between rank components. To address this problem, we propose a flexible and precise model named multi-band weighted $l_p$ norm minimization (MBWPNM). The proposed MBWPNM not only gives more accurate approximation with a Schatten $p$-norm, but also considers the prior knowledge where different rank components have different importance. We analyze the solution of MBWPNM and prove that MBWPNM is equivalent to a non-convex $l_p$ norm subproblems under certain weight condition, whose global optimum can be solved by a generalized soft-thresholding algorithm. We then adopt the MBWPNM algorithm to color and multispectral image denoising. Extensive experiments on additive white Gaussian noise removal and realistic noise removal demonstrate that the proposed MBWPNM achieves a better performance than several state-of-art algorithms.
Generating Paraphrase with Topic as Prior Knowledge

Paraphrase generation can be modeled as a sequence-to-sequence (Seq2Seq) learning problem. Nonetheless, a typical Seq2Seq model is liable to convey the original meaning incorrectly, as the vectorial representation of the given sentence is sometimes inadequate in recapitulating complicated semantic. Naturally, paraphrases concern the same topic, which can serve as an auxiliary guidance to promote the preservation of source semantic. Moreover, some interesting words for restatements can be derived from the topical information. To exploit topic in paraphrase generation, we incorporate topic words into the Seq2Seq framework through a topic-aware input and a topic-biased generation distribution. Direct supervision signals are also introduced to help dealing with the topic information more accurately. Empirical studies on two benchmark datasets show that the proposed method significantly improves the basic Seq2Seq model, and it is comparable with the state-of-the-art systems.
Exploration of port intelligent AGV path tracking based on vision

In order to prove the practical application of intelligent AGV (Automated Guided Vehicle) in port, the special requirements of port AGV are analyzed, BP (Back Propagation) network algorithm is improved, AGV road sign recognition system and AGV visual system are established, and finally the recognit ion accuracy and response speed of visual system are tested through experiments. The results show that the characteristics of AGV can meet the complex port conditions, and prove that the improved algorithm has good practical application value, as well as good recognition rate and accuracy of the visual system height. This will be of positive significance to the future research on the application of port AGV system in China.
EngraveChain

A reliable log system is a prerequisite for security applications. One of the first actions a hacker takes upon penetrating a system is altering the log files. Maintaining redundant copies in a distributed manner in a Byzantine setting has always been a challenging task, however it has recently become simpler given recent advances in blockchain technologies. In this work, we present a tamper-resistant log system through the use of a blockchain. We leverage the immutable write action and distributed storage provided by the blockchain and add an additional encryption layer to develop a secure log system. We assess the security and privacy aspects of our solution. Finally, we implement our system over Hyperledger Fabric and demonstrate the system's value for several use cases.
Precise 3D Baseball Pitching Trajectory Estimation Using Multiple Unsynchronized Cameras

We developed a method for the precise estimation of the 3D trajectory of a baseball by modeling the movement of the baseball and estimating the capture delay, using multiple unsynchronized cameras.To develop the proposed algorithm, we mimicked the real-world process of capturing a baseball in simulation space, and analyzed the capture process using a multiple unsynchronized camera system.We represented the movement of the baseball using a piece-wise spline model, and predicted the position of the baseball in the subframes in a manner which is robust to position error and change in direction of movement of the baseball.This method accurately predicts the baseball position over time by modeling the movement of the baseball in a real baseball game environment, and improves the accuracy of the reconstructed 3D baseball trajectories.We defined an objective function to estimate the capture delay, and estimate the optimal capture delay parameter using non-linear optimization method.In addition, we evaluated the performance of the proposed method in simulation space and in a real-world situation.The experimental results show that the proposed method can estimate a 3D baseball trajectory precisely using a multiple unsynchronized camera system and is robust to variations in capture delay, both in the simulation space and in real-world situations.
Study on user quitting rate for adaptive bitrate video streaming

In this study, the effect of coding degradation and stalling events on the percentage of users who quit watching videos midway is studied (referred to as the user quitting rate). The results are based on three laboratory-based subjective experiments involving up to 104 participants. The results show that for the coding condition only, a Mean Opinion Score (MOS) lower than 3 on a 5-point ACR quality scale will result in users quitting the video. The quitting rate was found to increase further when the MOS falls below 2.5. When a stalling event occurs, the quitting rate was found to be dependent on the stalling position, the stalling duration, the percentage of user who already quit, and the MOS. Statistical analysis allowed the identification of interaction terms between the stalling position and percentage of users who had already quit. Finally, the results were used to establish a model of the user quitting rate due to stalling.
Worksheet-Based Learning Research to Improve Creative Thinking Skills

Abstract This study aims to develop student worksheets that can be used in research based on research so that students’ creative thinking abilities can increase. The research method used is development research that refers to a five-step cycle model, namely analysis, design, development, implementation, evaluation. The results of the study showed that the results of the study showed that from the results of the review of the three validators it was excellent and perfect.
A Novel Human-3DTV Interaction System Based on Free Hand Gestures and a Touch-Based Virtual Interface

The input method based on free-hand gestures has gradually become a hot research direction in the field of human-computer interaction.Hand gestures like sign languages, however, demand quite a lot of knowledge and practice for interaction, and air writing methods require their users to hold the arm and hand in mid-air for a period of time.These methods limit the user experience and get severer when a large number of gestures are required.To address the problem, this paper presents a novel human-3DTV interaction system based on a set of simple free-hand gestures for direct-touch interaction with a virtual interface to facilitate human-3DTV interaction.Specifically, our system projects a virtual interface in front of the user who wears the 3D shutter glass, and the user just stretches the arm and touches the virtual interface like performing on a smart phone with a touch screen, using gestures such as Click, Slide, Hold, Drag and Zoom In/Out.Our system is able to recognize the user's gesture fast and accurately, as the system only needs to search for a small region neighboring the virtual interface for a small set of gesture types.Because we adopt the key gestures using on smart phones, our free-hand gestures can be easily used by anyone with only a brief training.The users feel more comfortable than traditional gesture input methods and can effectively interact with 3DTV using our system.We report a comprehensive user study on accuracy and speed to validate the advantages of the proposed human-3DTV interaction system.
Visualization 4.0

With this article, the author would like to provide a renewed perspective on opportunities for visualization and visual analytics in business. The thoughts presented are based on his experiences as both a visual analytics scholar and practitioner. He highlights research opportunities that have received limited attention or are just emerging but will undoubtedly play a very promising role in the near future. Broadly considered, he considers opportunities along four dimensions: (1) Making Sense of the Complex, (2) Accelerating the Provision, (3) Amplifying the Experience, and (4) Ensuring Integrity. The author fully acknowledges that what is presented is not comprehensive, many other promising topics likely exist, but hopes that the discussion will serve as an exciting call for action and stimulate the visualization community to pursue them actively.
Zonal Segmentation of Prostate T2W-MRI using Atrous Convolutional Neural Network

The number of prostate cancer cases is steadily increasing especially with rising number of ageing population. It is reported that 5-year relative survival rate for man with stage 1 prostate cancer is almost 99% hence, early detection will significantly improve treatment planning and increase survival rate. Magnetic resonance imaging (MRI) technique is a common imaging modality for diagnosis of prostate cancer. MRI provide good visualization of soft tissue and enable better lesion detection and staging of prostate cancer. The main challenge of prostate whole gland segmentation is due to blurry boundary of central gland (CG) and peripheral zone (PZ) which lead to differential diagnosis. Since there is substantial difference in occurance and characteristic of cancer in both zones. So to enhance the diagnosis of prostate gland, we implemented DeeplabV3+ semantic segmentation approach to segment the prostate into zones. DeepLabV3+ achieved significant results in segmentation of prostate MRI by applying several parallel atrous convolution with different rates. The CNN-based semantic segmentation approach is trained and tested on NCI-ISBI 1.5T and 3T MRI dataset consist of 40 patients. Performance evaluation based on Dice similarity coefficient (DSC) of the Deeplab-based segmentation is compared with two other CNN-based semantic segmentation techniques: FCN and PSNet. Results shows that prostate segmentation using DeepLabV3+ can perform better than FCN and PSNet with average DSC of 70.3% in PZ and 88% in CG zone. This indicates the significant contribution made by the atrous convolution layer, in producing better prostate segmentation result.
NDP

The key goals of datacenter networks are to simultaneously provide wire-level latency for RPC-style applications and high-throughput for network-bound applications such as storage. Folded Clos networks [1, 11] are used in datacenters worldwide; such networks use many cheap commodity switches to provide the illusion of a big non-blocking switch to all hosts in a datacenter, offering many paths between any two pairs of hosts.
E-learning implementation using user experience questionnaire

Abstract Online learning system or commonly called e-Learning which is commonly applied to the world of education, is now increasingly used in companies with diverse business fields. The concept of e-Learning is a transformation of conventional learning into digital form. The concept of e-Learning is now widely known and can be accepted in the community as evidenced by the many applications in the world of education and business. The aim to be achieved in this study is to implement e-Learning to PT MRK Diagnostics and find out whether or not there are inhibiting factors in implementing e-Learning to PT MRK Diagnostics. The method used to obtain recommendations after using e-Learning uses the User Experience Questionnaire (UEQ) method to find out the impression of using e-Learning that affects employee performance. Edmodo e-Learning has positive impression values in the Attractiveness (1,950), Perspicuity (2,100), Efficiency (1,775), Dependability (1,575), Stimulation (1,125), and Novelty (0.875) groups.
Short Utterance Speaker Recognition Using Time-Delay Neural Network

In recent years, important studies on speaker recognition have been implemented. Some solutions have been proposed and high achievements have been achieved. But one of the major issues faced by speaker recognition researchers is the short utterance speaker recognition. In short utterances, the recognition performance decreases. Within the scope of this study, it is aimed to increase the speaker recognition performance in short-term utterances by using Time-Delay Neural Networks (TDNN). I-vector-based systems have been developed using conventional GMM-UBM and TDNN-UBM-based methods. In this study, error rate changes of the audio files at various durations are compared using GMM and TDNN methods. Our results demonstrate that TDNN method provides improvement in equal error rate(EER) values compared to GMM method on speaker recognition area. However, the relative improvement of TDNN decreases while the test data duration decreases.
SAMPL

Organized surveillance, especially by governments poses a major challenge to individual privacy, due to the resources governments have at their disposal, and the possibility of overreach. Given the impact of invasive monitoring, in most democratic countries, government surveillance is, in theory, monitored and subject to public oversight to guard against violations. In practice, there is a difficult fine balance between safeguarding individual's privacy rights and not diluting the efficacy of national security investigations, as exemplified by reports on government surveillance programs that have caused public controversy, and have been challenged by civil and privacy rights organizations. Surveillance is generally conducted through a mechanism where federal agencies obtain a warrant from a federal or state judge (e.g., the US FISA court, Supreme Court in Canada) to subpoena a company or service-provider (e.g., Google, Microsoft) for their customers' data. The courts provide annual statistics on the requests (accepted, rejected), while the companies provide annual transparency reports for public auditing. However, in practice, the statistical information provided by the courts and companies is at a very high level, generic, is released after-the-fact, and is inadequate for auditing the operations. Often this is attributed to the lack of scalable mechanisms for reporting and transparent auditing. In this paper, we present SAMPL, a novel auditing framework which leverages cryptographic mechanisms, such as zero knowledge proofs, Pedersen commitments, Merkle trees, and public ledgers to create a scalable mechanism for auditing electronic surveillance processes involving multiple actors. SAMPL is the first framework that can identify the actors (e.g., agencies and companies) that violate the purview of the court orders. We experimentally demonstrate the scalability for SAMPL for handling concurrent monitoring processes without undermining their secrecy and auditability.
Automatic Phase‐Picking Method for Detecting Earthquakes Based on the Signal‐to‐Noise‐Ratio Concept

Abstract The automatic phase‐picking detection of earthquakes is a challenge under the background of big data and strong noise circumstances. The short‐term average/long‐term average (STA/LTA) ratio is widely used to detect earthquake due to its simplicity and robustness. However, STA/LTA‐based methods may not perform well with noisy data. Based on the signal‐to‐noise‐ratio (SNR) concept, a short‐term power/long‐term power (STP/LTP) ratio method is proposed. The characteristic function and the detection thresholds of the STP/LTP method are given physical meanings. Through a sample analysis, the STP/LTP detection results of both the P and S phases are better than the results of the STA/LTA by means of mean deviation, standard deviations, distributions of detection results, error rate, and missed rate on different SNR levels. In general, the STP/LTP method inherits the simple characteristics of the STA/LTA method, and it is suitable for phase picking of low‐SNR seismic data.
Video-enriched worksheet based on augmented reality technology: The heat experiment is easier

Experimental learning requires media and teaching materials to support the learning process in the laboratory. This research resulted in a student worksheet model with video based on augmented reality (AR) to facilitate laboratory learning on the Heat topic. This research and development used the Dick and Carey model to produce media worksheets for student activities enriched with AR-based videos. The product of this study has been through due diligence and limited trials. The results of the feasibility test showed that student worksheets supplemented with AR-based videos obtained a percentage of material worth 95.68%, media 89.95% and learning 82%. The results of limited trials conducted on high school students obtained an average of n-gain of 0.50. Based on the results of the feasibility test and limited trials, it was concluded that student worksheets that were equipped with AR videos were suitable for use as media and teaching materials for physics experiments in the laboratory.
A model for defining project lifecycle phases: Implementation of CMMI level 2 specific practice

Carefully considering the Capability Maturity Model Integration (CMMI) Level 2 specific practice to “define project life cycle phases” (SP 1.3) is a key requirement, particularly when it comes to small and medium-sized software development organizations. This is a necessary step to help these organizations get nearer to achieving CMMI Level 2 certification. In this paper, we, therefore, report on our latest empirical study that recently explored both the perceptions and experiences of practitioners about SP 1.3 implementation. During our research, we visited three firms and carried out three in-depth interviews. We developed a SP 1.3 model using the different experiences and opinions of practitioners regarding SP 1.3 implementation. The four essential stages of this model are plan, design, review, and update/rework. Practitioners will receive advice in this model about implementing SP 1.3 effectively.