Reviewerly/exHarmony huggingface dataset:
Are Jurors Intuitive Statisticians? Bayesian Causal Reasoning in Legal Contexts

In criminal trials, evidence often involves a degree of uncertainty and decision-making includes moving from the initial presumption of innocence to inference about guilt based on that evidence. The jurors’ ability to combine evidence and make accurate intuitive probabilistic judgments underpins this process. Previous research has shown that errors in probabilistic reasoning can be explained by a misalignment of the evidence presented with the intuitive causal models that people construct. This has been explored in abstract and context-free situations. However, less is known about how people interpret evidence in context-rich situations such as legal cases. The present study examined participants’ intuitive probabilistic reasoning in legal contexts and assessed how people’s causal models underlie the process of belief updating in the light of new evidence. The study assessed whether participants update beliefs in line with Bayesian norms and if errors in belief updating can be explained by the causal structures underpinning the evidence integration process. The study was based on a recent case in England where a couple was accused of intentionally harming their baby but was eventually exonerated because the child’s symptoms were found to be caused by a rare blood disorder. Participants were presented with a range of evidence, one piece at a time, including physical evidence and reports from experts. Participants made probability judgments about the abuse and disorder as causes of the child’s symptoms. Subjective probability judgments were compared against Bayesian norms. The causal models constructed by participants were also elicited. Results showed that overall participants revised their beliefs appropriately in the right direction based on evidence. However, this revision was done without exact Bayesian computation and errors were observed in estimating the weight of evidence. Errors in probabilistic judgments were partly accounted for, by differences in the causal models representing the evidence. Our findings suggest that understanding causal models that guide people’s judgments may help shed light on errors made in evidence integration and potentially identify ways to address accuracy in judgment.
Two-step hybrid collaborative filtering using deep variational Bayesian autoencoders

Existing recommender systems rely on user and item representations in a fixed continuous low-dimensional latent space. To predict ratings, they use only an implicit feedback matrix, whereas user and item side information is ignored. Furthermore, they use the same arbitrary priors for the user and item latent vectors, reducing the ability of the model to identify the actual latent vectors. Currently, the latent parameters should be learned directly for every user and movie. This is problematic, as it would require both model retraining and learning the latent vector representations when users or items are added to the underlying dataset. To address these issues, we propose a two-step hybrid variational Bayesian autoencoder to characterize the uncertainty of predicted ratings. An encoder is first trained to map data vectors to the latent space so that the latent representations can be dynamically computed. Subsequently, we use the generative process of users and items with their priors as side-specific information to handle matrix sparsity and better learn their latent vectors. Finally, we consider stochastic variational inference to approximate the posterior density of intractable user-item latent vectors. Experiments conducted on two real-world datasets demonstrate the effectiveness of the proposed method compared with state-of-the-art methods.
Strong Shadow Removal of Text Document Images Based on Background Estimation and Shading Scale

Shadows may bring uncomfortable perception when taking text document images. Previous work mainly focuses on weak shadow removal issue. However, strong shadow removal is still a challenging task. This paper proposes a method based on background estimation and shading scale to remove strong shadows from text document images. Firstly, background color estimation is designed by a number of iterations through neighboring pixels' propagation. Then, umbra and penumbra are separated by morphological operations and processed by a divide-and-conquer strategy. For umbra, shading scale strategy is exploited to obtain unshadowed result. For penumbra, background replace strategy is designed to remove the shadow regions. Finally, a reference background and a text binary image are combined to generate unshadowed image. Experiments conducted on some typical strong shadow images demonstrate the effectiveness of our method.
Continuous Real-time Automated Attendance System using Robust C2D-CNN

In this paper, we implement a hybrid real-time continuous face recognition automated attendance system (AAS) to capture attendance duration and attendance from CCTV footage - a missing temporal aspect of which many AAS currently lack. Our system consists of three parts. First, we extract additional features via an implementation of 3D facial reconstruction of which it learns detailed facial features from a single image and obtain an image set for additional complementary features via shape aggregation. Second, we apply MTCNN face detector to automatically detect people who enter a room. Third, we apply C2D-CNN face recognition, which combines features learned from original pixels with image representation and decision level fusion which results in a significantly more robust and accurate face recognition system suitable for CCTV H264 footage. The performance of our model is able to detect and recognize students accurately even with partial faces and low-quality images.
Time-Frequency Feature-Based Underwater Target Detection with Deep Neural Network in Shallow Sea

Abstract In this paper, we propose a time-frequency feature-based signal detection method with deep neural network and apply it for target detection in shallow sea. The short time Fourier transform is employed to capture the time-frequency feature of the target signal. We input the time-frequency diagram of the signal into the deep neural network model, train the network model through the labeled data samples, and use the test set to estimate the signal detection probability. In addition, we analyze the influence of different learning rates on the detection performance. The experimental results show that the performance of the proposed method is better than that of the energy detector, and the detection performance of different learning rates is different.
Technical Aspects of Automated Item Generation for <i>Blended Learning</i> Environments in Biology

Abstract Using two case studies from biology, the article demonstrates and analyses how domain-specific self-learning items with variable content can be generated automatically for a blended learning environment. It shows that automated item generation works well even for highly specific technical properties and that a good item quality can be produced. Evaluations are based on sample exercises from two courses in botany and genetics, each with more than 100 participants.
Design and Construction of Geographic Knowledge Graph

Since proposed by Google in 2012, knowledge graph has become the backbone of many intelligent systems, such as Apple Siri, IBM Watson and many other renowned Intelligent Natural Language Question Answering System(INLQAS). A geographic knowledge graph can be integrated into Google Maps, Baidu Maps and other similar systems to increase the intelligence of map services and achieve better performance of human-computer interaction. In this paper, The framework of a geographic knowledge graph is constructed. Based on the framework, the geographic ontology is studied first and then a geographic knowledge graph is established through processes of geographic entity and relation extraction, knowledge fusion and storage. Finally, a case study of geographic knowledge query is given to validate the effectiveness of the geographic knowledge graph.
The Need for Information Security Management for SMEs

A major part of the global economic activity is now constituting small to medium sized enterprises (SMEs) and under the current business globalization scenario Information Security Management (ISM) is crucial. As the size of an organization grows along with it multiplies the amount of sensitive information the organization is storing in their databases. It has been reported that small and medium business accounts for 80-90% of the market share, but most of the ISM effort is concentrated towards large business, as they provide around 50% of the turnover. SMEs are now becoming dependent on technology to provide better and more efficient services; this is a cause of concern as not all SMEs are taking the necessary steps to ensure information security. This paper explores the limitations faced by SMEs regarding ISM and how they can overcome it.
Technology Lending in the Time of Covid-19: Meeting Curricular Needs

Librarians from the Acquisitions, Collection Management, and Evaluation (ACME) unit of the Meriam Library at California State University, Chico, worked with departments from the College of Engineering to provide students with hands-on technology kits due to Covid-19 and virtual only instruction. This article reviews technology lending prior to Covid-19 and details the process of working with the instructional departments to process technology kits for circulation. Most of the kit processing was completed remotely. Students were able to pick up kits at the library or kits could be mailed. Considerations for taking on similar projects in the future are also discussed.
Automatic Quality Enhancement of Medical Diagnostic Scans with Deep Neural Image Super-Resolution Models

In modern healthcare, diagnostic imaging is an essential component for diagnosing ailments and delivering quality healthcare. Given the variety in medical scanning techniques, a recurring issue across different modalities is that the scan quality is often affected by artifacts introduced by hardware and software faults in the imaging equipment. Significant challenges in the 3D Imaging Techniques include low quality/low-resolution scan images or the addition of unwanted artifacts due to patient movement. Researchers have put forth solutions ranging from machine learning algorithms like Gradient Descent to more complex Deep CNN models for rectifying these faults. In this paper, we aim to benchmark deep learning models for improving the quality of diagnostic images, through Super-resolution, for enabling faster and easier detection of anomalies that may be missed otherwise. Super-resolution CNN and Deep CNN architectures were employed for up-sampling medical scans for enhancing their quality. The CNN models were trained to learn motion artifact characteristics that are a result of patient movement and negate its effects in the super-resolved output. We present comparative results of six super-resolution models on a standard dataset and metrics. During the experimental evaluation, it was observed that the ResNet SRCNN model outperformed all other models used for comparison by a large margin, with an improvement of 4.87 to 8.68% over the other state-of-the-art models.
Ontology Based Website for Job Posting and Searching

Abstract Searching for the jobs remains a tedious work. Whenever we search for job we getting some results which are not very relevant. So, we have to manually search through those results and find some suitable job for which we can apply. Many times, we miss out some job postings because these posting do not appear in the results. The same problem exists for recruiters also. When recruiters search for some specialization there are not getting the same results because the keywords used for searching are different from the keywords used in their resumes. So, to solve this problem we use ontology to post job and to search the jobs.
Public Distribution System Based on Blockchain Using Solidity

Blockchain is an emerging technology which is applied in all real-world transactions to proved security and transparency. Public distribution system is one of the key areas in which blockchain plays a major role. In this paper, an automated public food distribution system is presented using solidity language. Here, a blockchain-based system is developed that can be used to record all transactions/and log details of these events. The system uses a contract-oriented language framework that has most of the features and functionalities required to carry out any kind of transactions between the central government, state government, the district level offices, retail ration shops, and the beneficiaries/customers.
Classification of Customer Actions on Digital Money Transactions on PaySim Mobile Money Simulator using Probabilistic Neural Network (PNN) Algorithm

Development of technology have influenced all aspect, especially in financial sector in this pandemic situation, where most people tend to use digital money to conduct daily financial transactions. In one side, there is security point that need to be concern much. Like several disadvantages using credit cards by undue owners, social engineering, and transactions to commit fraud. In this paper, PaySim Mobile Money Simulator data is used with a machine learning algorithm called probabilistic neural network (PNN) to classify whether the customer's actions are normal or fraudulent actions. This PNN approach combined using binary classification to prevent fraudulent actions in transactions that have been or are being used by customers. And the result indicated that this system able to classify class 0 (as a normal class customer) and 1 (as a fraudulent class customer). Based on this result, maybe it would help many sectors that involved as a tool to classify a genuine customer. Especially in this pandemic covid-19, the fraud needs to detect often, to mitigate the fraud early.
Fusion of Multimodal Sensor Data for Effective Human Action Recognition in the Service of Medical Platforms

In what has arguably been one of the most troubling periods of recent medical history, with a global pandemic emphasising the importance of staying healthy, innovative tools that shelter patient well-being gain momentum. In that view, a framework is proposed that leverages multimodal data, namely inertial and depth sensor-originating data, can be integrated in health care-oriented platforms, and tackles the crucial task of human action recognition (HAR). To analyse person movement and consequently assess the patient's condition, an effective methodology is presented that is two-fold: initially, Kinect-based action representations are constructed from handcrafted 3DHOG depth features and the descriptive power of a Fisher encoding scheme. This is complemented by wearable sensor data analysis, using time domain features and then boosted by exploring fusion strategies of minimum expense. Finally, an extended experimental process reveals competitive results in a well-known benchmark dataset and indicates the applicability of our methodology for HAR.
Hardware-Accelerated Cryptography for Software-Defined Networks with P4

The paper presents a hardware-accelerated cryptographic solution for Field Programmable Gate Array (FPGA) based network cards that provide throughput up to 200 Gpbs. Our solution employs a Software-Defined Network (SDN) concept based on the high-level Programming Protocol-independent Packet Processors (P4) language that offers flexibility for network-oriented data processing. In order to accelerate cryptographic operations, we implement main cryptographic functions by VHSIC Hardware Description Language (VHDL) directly in FPGA, i.e., a symmetric cipher (AES-GCM-256), a digital signature scheme (EdDSA) and a hash function (SHA-3). Our solution then uses these widely-used cryptographic primitives as basic external P4 functions which can be applied in various customized security use cases. Thus, our solution allows engineers to avoid hardware development (VHDL) and offers rapid prototyping by using the high-level language (P4). Moreover, we test these cryptographic components on the UltraScale+ FPGA card and we present their hardware consumption and performance results.
MobileNet-SSD with adaptive expansion of receptive field

With its lightweight structure and acceptable accuracy loss, MobileNet-SSD has been widely used in embedded system. The defect of multi-scale detection structure in the original SSD algorithm makes it insufficient in detecting small targets. In this paper, SKBlock structure is used to adaptively expand the receptive field of SSD shallow feature map to improve its detection ability for small targets. Experiments show that it can effectively improve the target detection ability, and does not increase the computational burden.
Improved Algorithm Based on The Deep Integration of Googlenet and Residual Neural Network

Abstract In this paper, we propose a new improved algorithm based on the deep integration of GoogleNet and Residual neural network and we call it GRSN. The new improved algorithm has the new advantages of multi-size and small convolution kernel in the same layer in the network and the advantage of interlayer hop connection (bypass) to reduce information loss. The algorithm is applied to the general image data set cifair10, and the experimental results are compared with that of GoogleNet, the accuracy is improved, the convergence is accelerated, and the stability is better.
Accurate Object Detection with Relation Module on Improved R-FCN

In object detection, modeling the relation of objects is the key to achieve performance improvement. In order to improve the detection accuracy of the R-FCN(Region-based Fully Convolutional Network), a new algorithm called accurate R-FCN is proposed. By introducing the relation module to make an interaction between their appearance and geometric features, the detection and recognition accuracy can be improved. In addition, aiming at the problem of position deviation in the region of interest(RoI) quantization, PSRoI Align(Position-Sensitive Region of Interest Align) is applied with the RPN(Region Proposal Network), which generates the accurate region proposal. Experiments on the MS COCO dataset show that the accurate R-FCN achieves state-of-the-art accuracy compared with the other seven classical algorithms.
Vulnerability Analysis Using The Interactive Application Security Testing (IAST) Approach For Government X Website Applications

The security of technology, information and communication (ICT) is one of the tasks of government agencies X. The security of government ICT can be achieved by applying the principle of Security by Design. The Open Web Application Security Project (OWASP) publishes a list of potential vulnerability risks that are most common in web applications. Security tests can be carried out by performing a vulnerability assessment. The risk assessment is a series of measures to identify and analyze possible security gaps in the system of an organization or a company. Steps to look for vulnerabilities in the vulnerability assessment phase, starting with target discovery, scanning, results analysis, and reporting. The IAST approach (Interactive Application Security Testing) is used for security tests using a vulnerability assessment. When developing a vulnerability analysis system using the IAST approach, Jenkins tools, the ZAP-API, and SonarQube are used. The results of the vulnerability analysis are grouped based on the OWASP Top Ten 2017. Using the IAST approach, a total of 249 vulnerability risks were identified.
A Novel DBN-based Intention Inference Algorithm for Warship Air Combat

It is difficult to determine the dynamic Bayesian network (DBN) parameters applied to the inference of intention in the warship air defense environment. This paper considers time variables based on QMAP algorithm that uses both domain knowledge and a small number of static samples for parameter learning, and then we propose a D-QMAP algorithm suitable for DBN parameter learning, taking into account time variables, which utilizes the timing relationship between samples. The simulation is carried out on the background of warship air defense, and the results are in line with reality, verifying the rationality and effectiveness of the model and algorithm.
5G and Artificial Intelligence with Multi-Sensor Data Fusion Technology Support Intelligent Operation Monitoring of Power Grid in the Future

With the development of power grid, advanced indexes requirements of its operation such as safety, economy, convenience have been proposed. Traditional power grid were facing severe challenges. It is hard for traditional power grid to satisfy the new requirements. In order to solve the problem of the requirement such as safety, high efficiency, as well as state observable and controllable of power grid operation in the intelligence industry background, we put forward the opinion that 5G and artificial intelligence with multi sensor data fusion technology support the future intelligent power operation. In this paper, we firstly present 5G technology as the foundation of power grid communication technology. Then we put forward artificial intelligence is the main method of future power grid operation monitoring. Following that, we state the mode that it is resort to advanced big data and multi sensor data fusion technology to complete the base data collection of the process of intelligent monitoring and it is intelligent to complete the process of power grid operation monitoring with artificial intelligence and machine learning. To illustrate those points, we provided a case of 5G and artificial intelligence with multi sensor data fusion technology applying in the field of power system switching operation, including problem proposition, solution concept, solution result and application prospect. In the end, we sum up the whole paper.
A Distributed Authentication Scheme Based on Zero-knowledge Proof

As an underlying technology and infrastructure, blockchain creates great value when combining with other emerging information technologies, such as Artificial Intelligence, Big Data Analysis, Cryptography Technology. Although the blockchain system has been contunuously upgraded in recent years, current blockchain still has its deficiency in terms of node control and privacy protection. First of all, the high existence rate of malicious nodes in the chain made it difficult to well purify the network environment, meanwhile the malicious nodes cannot be easily dealt with. Secondly, the correlation between nodes can still be discovered through analysis. As a result, the privacy of users' information cannot be well protected. This paper proposes a distributed identity authentication scheme based on Zero-knowledge proof, which uses face recognition technology to ensure the uniqueness and legitimacy of identity and control the generating of malicious nodes; Zero-knowledge proof algorithm is used to encrypt and support face data to prevent user identity information from being disclosed. At the same time, the linkable ring signature algorithm is adopted to confuse the correspondence between nodes and private information to further protect users' privacy information.