Reviewerly/exHarmony huggingface dataset:
End-to-end Spatial Attention Network with Feature Mimicking for Head Detection

Human head detection is a widely used task and suitable for identifying persons in practical applications. Although existing methods have achieved significant progress, the problems of false alarm and miss detection are still challenging, which arise from weak classification power of detector in the face of variability in occlusion, illumination, etc. In this paper, we present an effective end-to-end head detector called Spatial Attention Network with feature Mimicking(SANM) that can obtain better feature and enhanced classification power, through attention mechanism and a feature mimic method. The spatial-wise attention is extracted from several levels of feature and supervised by the bounding-box annotated heat map. The attention improves the quality of the features in the head and opposite area. To further improve the classification ability, we utilize the feature mimicking method to drive network learning the feature refined by a deep cascading classifier. Compared with the baseline model, our method achieves better performance and produces leading results on head detection benchmarks.
Sentiment analysis of Twitter data about blockchain technology

There is a tendency that new technologies such as cryptocur-rencies and Blockchain reduce the use of physical money. The advancement of this type of technology has led companies to rethink the possible forms of payment for their products or services, but the substitution of forms of payment must be analyzed according to the financial market dynamics to reduce the risks in the application of financial products. This paper aims to use the sentiment analysis technique, which consists of text analysis processes and natural language processing, to verify the polarity of opinions collected from the Twitter social network about Blockchain technology.
DevOps in Pakistani Software Industry

Researchers have been working to make software processes smooth and efficient. DevOps is a software development framework that fits best in the context of ever-emerging cloud technologies. DevOps has made it possible to automate each stage of software development, this is why it is gaining popularity rapidly. For the Pakistani software industry, the acceptance of DevOps is hindered by many factors. The most important of them all is the ever-widening disparity between the industry and academia. To assess the knowledge and acceptance of DevOps in the Pakistani software industry, a survey was conducted. Apart from other results, one conclusion is clear from the survey; Pakistani software engineers are completely aware of the changes taking place in the international market and they are trying to cope up with them, but the lack of theoretical knowledge is not allowing them to get to the pace they should have been. At the end of the article, some suggestions are presented to improve the situation.
Post-quantum secure multi-party private set-intersection in star network topology

In many realistic scenarios, participants wish to perform some secret set operations such as intersection, union, cardinality of intersection, etc. on their private data sets. Private Set Intersection (PSI) plays a major role in addressing such problems. PSI is one of the widely used secure multi-party computation technique that allows the participants to securely compute the intersection of their private input sets and nothing beyond that. It is generally executed between two parties. When the number of entities is more than two, it is known as multi-party PSI (MPSI). Today, the security of all the existing MPSI protocols are based on number theoretic assumptions. However, these will become insecure once large enough quantum computers are built. As a consequence, designing of quantum computer resistant MPSI becomes an interesting direction of research work. This paper addresses the issue by presenting the first post-quantum MPSI protocol in the so-called star network topology, using lattice-based public key encryption scheme. We utilize space-efficient probabilistic data structure (Bloom filter) as building blocks of our design. It attains security in standard model (without random oracles) under the decisional learning with errors (DLWE) assumption.
Dense Point Diffusion for 3D Object Detection

The backbone network adopted in state-of-the-art 3D object detectors lacks a good balance between high point resolution and large receptive field, both of which are desirable for object detection on point clouds. This work proposes Dense Point Diffusion module, a novel backbone network that solves these issues. It adopts dilated point convolution as a building block to enlarge the receptive field and retain the point resolution at the same time. Further, a number of such layers are densely connected, giving rise to large receptive field and multi-scale feature fusion, which are effective for object detection task. Comprehensive experiments verify the efficacy of our approach. The source code <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> has been released to facilitate the reproduction of the results.
Extended Information Filter under Maximum Correntropy Criterion

This study concerns the information filtering problem for nonlinear non-Gaussian systems. Under the maximum correntropy criterion (MCC), in the information filter framework, a novel information filter named maximum correntropy extended information filter (MCEIF) is proposed for nonlinear non-Gaussian systems. Similar to the classical extended information filter (ElF) under MMSE criterion, the prior state estimate and prediction information matrix are calculated. Yet the estimation information matrix and filter gain information matrix are reconstructed by utilizing the MCC, and then the posterior state estimation and filtering information matrix are updated. The final numerical simulation results demonstrate the effectiveness of MCEIF.
Adaptive Image Denoising with Block-Rotation-Based SVD Filtering and Edge Detection

Image denoising is essential in image processing. However, existing denoising methods are not optimized for the images with distinct straight edges and lines that traverse in different directions (such as images used in autonomous driving). This paper proposes an image denoising method that uses singular value decomposition (SVD) and block-rotation-based operations and has two features: the non-fixed size of block division and the rotation operations. In the method, we first propose an image division approach, which is used to divide an image into sub-blocks of different sizes, to ensure that the line(s) or edge(s) in each sub-block have roughly one main direction. Second, we decompose an image into sub-blocks according to the image division approach. Third, we rotate each sub-block to ensure that the main direction of the edge(s) is horizontal or vertical. Fourth, we perform SVD on each sub-block, and we use the low-rank approximation of SVD to obtain each denoised sub-block. Finally, we rotate the approximation of each sub-block back to the original direction of the corresponding noisy sub-block, and then we reconstruct the denoised image using these denoised sub-blocks. Experiments show the effectiveness of this method compared with the SVD-based methods.
Framework for the Restoration of Capsule Endoscopy Images Using Partial Differential Equations-based Filter

This study presents a design and experimental results of a novel noise reduction framework for capsule endoscopy (CE) images. CE images exhibit random noise, motion blur, and illumination related degradations. These degradations reduce the efficiency of the computer-aided diagnosis (CAD) system. Restoration process aims to minimize degradations as much as possible; however; measurement of the degradations is a challenge. This study proposes a partial differential equation-based filter for the restoration of CE images. The proposed system performs noise removal while retaining high-frequency components. It measures the performance of the proposed restoration framework based on metrics related to image quality. Visual and statistical results show significant improvement in CE image quality. The proposed approach is capable of noise removal while preserving a low level and a few high-level components leading to better results. Finally, it also presents the performance of a CAD system with and without restoration.
FedChain: Secure Proof-of-Stake-Based Framework for Federated-Blockchain Systems

In this paper, we propose FedChain, a novel framework for federated-blockchain systems, to enable effective transferring of tokens between different blockchain networks. Particularly, we first introduce a federated-blockchain system together with a cross-chain transfer protocol to facilitate the secure and decentralized transfer of tokens between chains. We then develop a novel PoS-based consensus mechanism for FedChain, which can satisfy strict security requirements, prevent various blockchain-specific attacks, and achieve a more desirable performance compared to those of other existing consensus mechanisms. Moreover, a Stackelberg game model is developed to examine and address the problem of centralization in the FedChain system. Furthermore, the game model can enhance the security and performance of FedChain. By analyzing interactions between the stakeholders and chain operators, we can prove the uniqueness of the Stackelberg equilibrium and find the exact formula for this equilibrium. These results are especially important for the stakeholders to determine their best investment strategies and for the chain operators to design the optimal policy to maximize their benefits and security protection for FedChain. Simulations results then clearly show that the FedChain framework can help stakeholders to maximize their profits and the chain operators to design appropriate parameters to enhance FedChain’s security and performance.
Development of WeShare As a Knowledge Sharing Platform to Realize the Freedom in Learning

Abstract Education builds a society quality to be creative, innovative. The Ministry of Education and Culture of Indonesia (Kemdikbud) program, “Freedom in Learning”, is expected to be able to create a happy learning atmosphere for the community. It also deals with the innovative Community 5.0 where the community is human-centred and based on technology to improve the human ability to open opportunities for humanity, on the other side to break the sense of stagnation, mutual respect and be able to lead a life active and fun. Therefore, an educational platform “WeShare” was built to promote culture of sharing knowledge to society with the mission of “Together Empowerment”. The research method used Research &amp; Development (RnD) with Waterfall Models such as planning, analysis &amp; design, development, testing, and maintenance programs. The aim of this program is to develop WeShare as a Knowledge Sharing Platform, and to understand the effects and benefits of WeShare program.
Feasibility Study of tabata Training in the Reform of Physical Education Teaching in Colleges and Universities

The arrival of Internet technology makes people communicate with the outside world more frequently and conveniently, but it is also because of the high efficiency and convenience of Internet technology that people chat, party, shopping and other activities on the line become more and more frequent. On the contrary, people offline exercise, travel, face-to-face communication and other activities are less and less. In recent years, college students in our country have shown the phenomena of lack of physical ability, decrease of exercise quantity and decrease of physical quality, so it is urgent to reform physical education teaching in colleges and universities. Based on the analysis of the predicament of physical education teaching in colleges and universities, this paper introduces the tabata training method in physical education teaching with high intensity intermittent training method as the focus, so as to realize the reform and development of college education and teaching. It will further improve the physical quality and health of college students.
Video Abnormal Event Detection Based on CNN and LSTM

Nowadays, people pay more and more attention to security issues. If we can warn the occurrence of abnormal events, we can greatly reduce the occurrence of public security issues. Therefore, the detection of abnormal events in videos is an important research topic. In this paper, an improved deep learning model TCNN-LSTM is obtained by combining CNN with LSTM. We carried out experiments on the standard dataset UMN and UCSD, and achieved good results, with the accuracy reaching 99.54% and 92.36% respectively, and F1 reaching 98.41% and 87.65% respectively, it can be seen that this model has strong generalization ability. At the same time, other models were compared on the UCSD dataset. The AUC of this model reached 97.00%, far higher than that of other models. Experimental results showed that the performance of our model was better than that of other models.
Sentiment Analysis of User Reviews about Hotel in Roman Urdu

In recent years, sentiment analysis has a significant role in various social media networks, electronic marketing websites, communication forums, and blogging websites. There are many issues in sentiment mining, classification, and analysis like huge lexicon, Natural language processing overhead, fake reviews, etc. Out of these issues, one major problem is that the comments and reviews can be in different languages like French, Chines, English, Urdu, Arabic, etc. To handle each language according to its syntax, semantic, and structure is a challenging task. Many researchers work on English, Urdu, and Arabic sentiment analysis, but very limited work has been done on resource constrain languages like Roman Urdu. In this paper, Python is used to execute different classification machine learning models for Roman Urdu text analysis. A total of 3000 reviews dataset has been scrapped from different hotel websites. The results show that logistic regression and SVM outperformed in terms of accuracy, recall, precision, and F-measure.
Combining Bayesian and logical-probabilistic approaches for fuzzy inference systems implementation

Abstract In the paper, an original way to implement the fuzzy inference systems is considered. The proposed fuzzy inference model is based on usage of conjunction of probabilistic logic and Bayes’ formula at inference stage. For that preconditions of all the specified fuzzy production rules are transformed to the set of probabilistic functions. As the input probabilities, the values of membership functions of input linguistic variables terms are used. At that given set of production rules is reduced in the way that number of rules will be equal to number of terms of output linguistic variables. For that logic expressions of production preconditions are transformed to orthogonal DNFs. Calculated probability values are used as conditional probabilities in determining the posterior distributions on the set of hypotheses corresponding to values of the output linguistic variables. To determine the posterior probability distribution, we use the formula based on the Bayes’ formula. These posterior distributions are used at defuzzification stage for determining final output variable values, because they are calculated as expected values of corresponding random variables. The program implementation has allowed to estimate the proposed model effectiveness and to compare its results with results of traditionally used Mamdani and Sugeno fuzzy inference algorithms.
The Cloud Resource Forecasting Model Based HMM

Abstract One of the most important goals for companies that provide cloud computing services is to maintain high availability on large computer systems. In order to accomplish such objective, it is necessary to discovery the reason of poor availability. Software aging is a main factor in cloud computing services, leading to software failures, poor performances and may result in system downtime. This paper investigates the software aging effects on the OpenStack cloud computing platform and describes a forecasting model based on Hidden Markov Models. The prediction analysis of the Hidden Markov Models on the key performance data of the system shows that the Hidden Markov Models has excellent predictive performance and is suitable for the prediction of the reliability of the cloud server system.
Constructing Bug Knowledge Graph as a Service for Bug Search

When encountering bug issues, developers tend to search the bug repository and commit repository for references. However, the links between bug reports and commits in version control systems are often missed, and the information in bug repository and commit repository can provide is simple. When developers search a bug issue, they can only get the information of bug reports or commits, which are loose and difficult for developers to refer. What’s more, many searching results are not accurate. To deal with these problems, this paper proposes an approach to deal with the bug and commit information with the topic model, and construct bug knowledge graph as a service to assist in bug search. In addition, as the amount of bug related information continuously increase, it is time-consuming to update the data. We can automatically update the bug knowledge graph with the LTM topic model (a lifelong topic model). Finally, the experiment with the bug reports from Bugzilla@Mozilla and the corresponding commits from Github was conducted. The experiment results show that our approach is effective and efficient to help developers search relevant bugs for reference by constructing the bug knowledge as a service.
A Tunable and Explainable Attributes (TEA) for Recommendation System

Recommender system in information retrieval systems recommends relevant items to consumers by inspecting the consumer's preferences and objective behaviours. We received recommendations daily regarding what to take in as food, buy, and choices about the wear trends. However, we can understand our recommendations, and even less, we can tune our preferences efficiently. One main concern is those undesired or irrelevant recommendations, not only wasting our time, but they also cost media companies millions of dollars every year. Considering this issue, we proposed a tunable and explainable recommendation system where the attributes that determined the recommendations are explainable and tunable for every individual consumer. Irrelevant recommendations waste the consumer's time and the company's money, but if we put the power of the recommendations in the consumer's hands, we have a win-win situation. The suggested method addresses recommendation algorithms issues, which creates negative feedback loops and allows consumers to be aware of the profile they are building and tune it to see the content that interests them most.
A Study of Steganography Based on Error Correction Code and Secret Sharing Scheme

In recent years, opportunities to exchange information on the Internet has increased. Under such circumstances, steganography is a technology for safely exchanging information. Previous studies have proposed some methods that combine Shamir's secret sharing method and correlation-based steganography. Correlation-based steganography embeds pseudo-random number as a secret information in DCT (discrete cosine transform) coefficient of an image and extracts the secret information by correlating the pseudo-random number and DCT coefficient. Since it is necessary to obtain a good correlation at the time of extracting, each value of the pseudorandom number sequence is increased by coefficient. This increasing of values causes image quality deterioration. In this paper, we propose a steganography with an error-correcting code. Our proposed method can improve the embedding capacity without deterioration the image quality.
An Adaptive Current Statistical Model Based on Estimation of Target Motion Parameters

Aiming at the acceleration of the maneuvering target changes uniformly, an adaptive Current Statistical (CS) model based on the estimation of the target motion parameters is proposed. Firstly, the improved CS model is derived by adding the jerk and jerk rate into the state vector, to get a more accurate estimation of the target motion state. Secondly, the distance, speed, acceleration, and jerk obtained by motion parameters estimation are also added to the model measurement. Furthermore, the changes of a jerk in adjacent cycles are compensated by adaptive updating of maneuver frequency. Finally, the filter equation of the improved CS model is introduced. The simulation results show that the proposed model can improve the accuracy of target tracking compared with the model that only measures distance and speed.
Automated matching of pixel of interest between two digital images from two different microscope imaging devices

Matching is an essential operation towards automated registration of digital images. Registration of images is basically done either by studying the characteristics of the region surrounding the object of interest or by considering image morphological features. Many automated matching algorithms have been proposed and evaluated in the literature. However, previous studies on automated matching have either used a small number of benchmarks images in their evaluation, been directed at microscopy and not digital cameras, or have been based on ad hoc evaluation criteria. In this paper, we are presenting an image independent automated algorithm for pixel-to-pixel matching between two digital images obtained from two different microscope imaging devices. Here, SURF feature points and Euclidean distance measures are used for identifying key matching member index (KM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> I). We have extensively tested our algorithm on a macro set of 1,69,000 digital images obtained from 250 pap smear samples. Experimental results prove that the proposed algorithm performs better for digital photography.
Area-Efficient Parallel Reconfigurable Stream Processor for Symmetric Cryptograph

Represented by application-specific instruction set processors (ASIPs) and array processors, existing cryptographic processors face challenges in application to mobile terminals with sensitive security requirements. Typically, ASIPs have limited computational efficiency and algorithmic adaptability. An array processor requires massive circuits to perform fully expansive computations for ciphers, and such processors are unaffordable for terminals. To overcome these issues, we propose a highly area-efficient parallel reconfigurable symmetric cipher stream processor combining the characteristics of symmetric cryptograph stream-state processing and the advantages of stream architectures. This processor has a hierarchical stream architecture with multi-dimensional parallelism. It decouples cipher computation from data transmission, facilitating parallelism between algorithm operation and data scheduling. Furthermore, the processor fully excavates vertical pipeline parallelism and horizontal block parallelism in its operation processes. Additionally, it takes multi-granular cipher units and reconfigures computation circuits to map different algorithms efficiently. These mechanisms significantly improve its area efficiency and security intensity. The processor’s prototype was verified and synthesised in a 65 nm CMOS process. Many typical ciphers were mapped onto the processor. Experimental results demonstrate excellent performance in feedback/no-feedback modes with a small area (1.26 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). Therefore, its area efficiency is clearly higher than other cipher processors, and it has better prospects for future applications.