Reviewerly/exHarmony huggingface dataset:
Ordering Policy Estimation for High Utility Item-Sets Considering Negative Item Values in Large Databases

Utility mining with negative item values has recently received interest in the data mining field due to its practical considerations. Previously, the values of utility item-sets have been taken into consideration as positive. However, in real-world applications an item-set may be related to negative item values. This paper presents a method for redesigning the ordering policy by including high utility item-sets with negative items. Initially, utility mining algorithm is used to find high utility item-sets. Then, ordering policy is estimated for high utility items considering defective and non-defective items. A numerical example is illustrated to validate the results
Understanding the Transaction Placement Problem in Blockchain Sharding Protocols

While many researchers adopt a sharding approach to design scaling blockchains, few works have studied the transaction placement problem incurred by sharding protocols. The widely-used hashing placement algorithm renders an overwhelming portion of transactions as cross-shard. In this paper, we analyze the high cost of cross-shard transactions and reveal that most Bitcoin transactions have simple dependencies and can become single-shard under a placement algorithm taking transaction dependencies into account. In addition, we perform a case study of OptChain, which is the state-of-the-art transaction placement algorithm for sharded blockchains, and find a shortcoming of it. A fix is proposed, and our evaluation results demonstrate that the fix helps OptChain improve the system throughput by 4x.
Virtual Reality Experience ‘The World of Hiroshige’

‘The World of Hiroshige’ is an immersive virtual reality experience that allows the participants not only to view the artworks of the Japanese artist Utagawa Hiroshige in 3 dimensions, but also to experience and interact with the Ukiyo or “floating world" that inspired the works.
Edge‐enhancement densenet for X‐ray fluoroscopy image denoising in cardiac electrophysiology procedures

Reducing X-ray dose increases safety in cardiac electrophysiology procedures but also increases image noise and artifacts which may affect the discernibility of devices and anatomical cues. Previous denoising methods based on convolutional neural networks (CNNs) have shown improvements in the quality of low-dose X-ray fluoroscopy images but may compromise clinically important details required by cardiologists.In order to obtain denoised X-ray fluoroscopy images whilst preserving details, we propose a novel deep-learning-based denoising framework, namely edge-enhancement densenet (EEDN), in which an attention-awareness edge-enhancement module is designed to increase edge sharpness. In this framework, a CNN-based denoiser is first used to generate an initial denoising result. Contours representing edge information are then extracted using an attention block and a group of interacted ultra-dense blocks for edge feature representation. Finally, the initial denoising result and enhanced edges are combined to generate the final X-ray image. The proposed denoising framework was tested on a total of 3262 clinical images taken from 100 low-dose X-ray sequences acquired from 20 patients. The performance was assessed by pairwise voting from five cardiologists as well as quantitative indicators. Furthermore, we evaluated our technique's effect on catheter detection using 416 images containing coronary sinus catheters in order to examine its influence as a pre-processing tool.The average signal-to-noise ratio of X-ray images denoised with EEDN was 24.5, which was 2.2 times higher than that of the original images. The accuracy of catheter detection from EEDN denoised sequences showed no significant difference compared with their original counterparts. Moreover, EEDN received the highest average votes in our clinician assessment when compared to our existing technique and the original images.The proposed deep learning-based framework shows promising capability for denoising interventional X-ray fluoroscopy images. The results from the catheter detection show that the network does not affect the results of such an algorithm when used as a pre-processing step. The extensive qualitative and quantitative evaluations suggest that the network may be of benefit to reduce radiation dose when applied in real time in the catheter laboratory.
Putting a Spotlight on Validators of Easy-to-Read Content

Easy-to-Read content results from applying text simplification principles to make information accessible for persons with reading and learning difficulties. While both the creation process and simplification principles have gained the interest of scholars and the general public in the past years, the role of validators is still less visible compared with that of writers or translators. This paper sought to put a spotlight on validators by answering the questions of who these professionals are, what tasks they take on, and how they have acquired the necessary knowledge and skills for the job. In doing so, it investigated a subset of the data about validators’ demographical and educational backgrounds and current activity collected in an online survey launched within the innovative framework of the Erasmus+ project Train2Validate.
GPS-Based Path Planning Algorithm for Agriculture Drones

Drone technology is advancing in rapid pace in the past decade, particularly in agriculture and its allied fields it has been extensively used. Drones make farming productive and precise. They help in optimizing agricultural operations, monitoring crops and their growth, and help in monitoring soil health with help of sensors and digital image processing. Piloting a drone is not easy and requires some level of expertise. So, a novice user cannot pilot a drone without proper training. To overcome this, an autonomous drone can be used. In any autonomous mobile robot, path planning is an important subsystem. In this work, a GPS-based path planning algorithm is proposed which can aid farmers in various agricultural activities. The system takes inputs related to farm boundary and crop planting pattern and generates path output based on the type of agriculture activities like surveying, spraying, seeding and quick monitoring. Cubic polynomial and spline function are used to calculate the path and its via points. The proposed algorithm is implemented in MATLAB.
A 1.3-Mbit Annealing System Composed of Fully-Synchronized 9-board x 9-chip x 16-kbit Annealing Processor Chips for Large-Scale Combinatorial Optimization Problems

Combinatorial optimization problems, which are categorized into NP-hard problems, are emerging in ever-growing social systems, such as logistics, traffic, and so on. A new computer architecture, called an annealing processor (AP) [1–4], has advanced to solve such difficult problems efficiently as an accelerator in the computing systems. A specific type of AP discovers the ground state (optimal combination of variables, spins) of an Ising model in a short time by highly parallelizing the spin state update process based on simulated annealing (SA) [2–4]. We have built boards with nine AP chips connected to each other [5]. To lunch the annealing systems for real usage, it is necessary to provide a larger-scale annealing system. We built a multiple-board annealing system (AS) with a simple multi-board control beyond the multi-chip AS for the increasing amount of data in the real-world optimization problems. In order to realize such system, it is necessary to adopt a method in which the host board connected to the PC controls the other slave boards, rather than controlling each board individually. Our CMOS-AS consists of three technologies: (i) a routing table to control communication between boards, (ii) a technology to synchronize the annealing process on each chip, and (iii) a compression technology for efficient propagation of spin information in inter-board communication where data transmission amount is limited. The CMOS-AS demonstrated noise reduction as an example of combinatorial optimization problems and multi-board operation of the 9x9x16k spin system with an annealing speed at least three orders of magnitude faster and higher accuracy than running the SG3 and SA on a CPU, conventional technologies to solve optimization problems.
QoE optimization for HTTP adaptive streaming: Performance evaluation of MEC-assisted and client-based methods

Seamless streaming of high quality video under unstable network condition is a big challenge. HTTP adaptive streaming (HAS) provides a solution that adapts the video quality according to the network conditions. Traditionally, HAS algorithm runs at the client side while the clients are unaware of bottlenecks in the radio channel and competing clients. The traditional adaptation strategies do not explicitly coordinate between the clients, servers, and cellular networks. The lack of coordination has been shown to lead to suboptimal user experience. As a response, multi-access edge computing (MEC)-assisted adaptation techniques emerged to take advantage of computing and content storage capabilities in mobile networks. In this study, we investigate the performance of both MEC-assisted and client-side adaptation methods in a multi-client cellular environment. Evaluation and comparison are performed in terms of not only the video rate and dynamics of the playback buffer but also the fairness and bandwidth utilization. We conduct extensive experiments to evaluate the algorithms under varying client, server, dataset, and network settings. Results demonstrate that the MEC-assisted algorithms improve fairness and bandwidth utilization compared to the client-based algorithms for most settings. They also reveal that the buffer-based algorithms achieve significant quality of experience; however, these algorithms perform poorly compared with throughput-based algorithms in protecting the playback buffer under rapidly varying bandwidth fluctuations. In addition, we observe that the preparation of the representation sets affects the performance of the algorithms, as does the playback buffer size and segment duration. Finally, we provide suggestions based on the behaviors of the algorithms in a multi-client environment.
An Anonymity and Interaction Supported Complaint Platform based on Blockchain Technology for National and Social Welfare

People face numerous severe problems in their everyday life. Extortion, corruption, threat, harassment, negligence, poor public service, etc., are some common problems people are dealing with everyday. As it may take a long time, most of the victims do not have the intention to complain anymore. As a result, these issues are getting worse rather than getting resolved. There are some ways online to contact and complain to the competent authorities. However, a complainant may be hesitant to complain in fear of insecurity because of the transparency issue of a centralized system. To resolve the issues, in this paper, we will present a blockchain-based anonymous, transparent, and decentralized platform, where people can complain anonymously and interact with the authorities to resolve their complaints. An authority can start resolving a complaint after receiving it from the platform. Those facing a similar problem or evaluating a problem to be prioritized can support/oppose the complaint. Complaints like damaged roads or pandemic situations like Covid-19 may need some financial support to be solved. In this case, authorities can start crowdfunding where both complainants and authorities can donate. Ultimately, we can claim that our proposed blockchain-based platform will be one of the most secure and reliable platforms for complainants to complain about their issues.
First steps towards Open Science in Albania

Open Science is directly linked to the present trend of Research Data Management. To have fully open research data, systems that allow free access to the data need to be present. As a result, research institutions have tried to adapt to the changes required to be deemed as Open Science supporters. Several institutions implement a Current Research Information System (CRIS) and an Open Science Policy for all their published research data.Interoperable data between different systems implemented is a key feature of Open Science. In Europe, the European Open Science Cloud provides a set of standards and guidelines that must be followed so the implemented systems offer an interoperable service to their researchers.Previous research revealed that Albania is ready to take its first steps toward Open Science, and future ideas for how to contribute to the field were proposed. There were two significant findings: first, there is no CRIS implementation in Albanian institutions, and second, there is no strategy or policy to support Open Science. The research also proposed an Albanian Open Science Digital Repository.In this paper, we show how the implementation of the system is done and how it relates to the newly proposed Open Science Policy. The goal is to demonstrate how Albanian institutions are implementing advances towards Open Science.In conclusion, this paper demonstrates Albanian researchers' support for Open Science and the European Open Science Cloud.
Explaining the Decisions of Deep Policy Networks for Robotic Manipulations

Deep policy networks enable robots to learn behaviors to solve various real-world complex tasks in an end-to-end fashion. However, they lack transparency to provide the reasons of actions. Thus, such a black-box model often results in low reliability and disruptive actions during the deployment of the robot in practice. To enhance its transparency, it is important to explain robot behaviors by considering the extent to which each input feature contributes to determining a given action. In this paper, we present an explicit analysis of deep policy models through input attribution methods to explain how and to what extent each input feature affects the decisions of the robot policy models. To this end, we present two methods for applying input attribution methods to robot policy networks: (1) we measure the importance factor of each joint torque to re ect the influence of the motor torque on the end-effector movement, and (2) we modify a relevance propagation method to handle negative inputs and outputs in deep policy networks properly. To the best of our knowledge, this is the first report to identify the dynamic changes of input attributions of multi-modal sensor inputs in deep policy networks online for robotic manipulation.
A planning framework of environment detection for unmanned ground vehicle in unknown off-road environment

This paper describes a planning framework of environment detection for unmanned ground vehicle (UGV) in the completely unknown off-road environment, which is able to quickly guide the UGV with nonholonomic constraints to detect the environmental information as much as possible. The contributions of this paper contain four fold. First, due to the sensor characteristics of camera and lidar, we present a two-layer combined detection map which can accurately represent the detected and undetected area. Second a frontier extraction algorithm based on RRT considering information acquisition and nonholonomic constraints of UGV is used to extract the target pose. Third, we use a search path planning method based on motion primitive which is able to handle obstacle constraints of environment, nonholonomic constraints of UGV. Fourth the heuristic fusion is proposed to guide the extension of motion primitives to generate a kinodynamically feasible and collision-free trajectory in real-time. And it works well in both simulation and real scene.
ADS-B Anomaly Data Detection Using SVDD-based LSTM Encoder-Decoder Algorithm

Due to the high accuracy, favorable data share and large coverage, Automatic Dependent Surveillance - Broadcast (ADS-B) surveillance is regarded as the core technology in the next generation air traffic management. However, the ADS-B data is broadcasted with absence of adequate data integrity and authentication support, which leads to various security challenges on information leakage and tampering in the ADS-B system. Hence, anomaly ADS-B data detection is vital to minimize security threats in ADS-B system application, especially for ADS-B data attack with high concealment. In this paper, a novel anomaly data detection method was designed based on the long short-term memory (LSTM) encoder-decoder architecture and Support Vector Domain Description (SVDD) hyper-sphere classifier. The proposed algorithm provided a solution for the ADS-B data reconstruction and adaptive anomalies detection threshold determination. Experiments conducted on the real ADS-B data to compare the proposed method with previous approaches, and the comparison results from which illustrated that the effectiveness and accuracy of the proposed method.
Multivariate log‐contrast regression with sub‐compositional predictors: Testing the association between preterm infants' gut microbiome and neurobehavioral outcomes

To link a clinical outcome with compositional predictors in microbiome analysis, the linear log-contrast model is a popular choice, and the inference procedure for assessing the significance of each covariate is also available. However, with the existence of multiple potentially interrelated outcomes and the information of the taxonomic hierarchy of bacteria, a multivariate analysis method that considers the group structure of compositional covariates and an accompanying group inference method are still lacking. Motivated by a study for identifying the microbes in the gut microbiome of preterm infants that impact their later neurobehavioral outcomes, we formulate a constrained integrative multi-view regression. The neurobehavioral scores form multivariate responses, the log-transformed sub-compositional microbiome data form multi-view feature matrices, and a set of linear constraints on their corresponding sub-coefficient matrices ensures the sub-compositional nature. We assume all the sub-coefficient matrices are possible of low-rank to enable joint selection and inference of sub-compositions/views. We propose a scaled composite nuclear norm penalization approach for model estimation and develop a hypothesis testing procedure through de-biasing to assess the significance of different views. Simulation studies confirm the effectiveness of the proposed procedure. We apply the method to the preterm infant study, and the identified microbes are mostly consistent with existing studies and biological understandings.
Cross-aligned and Gumbel-refactored Autoencoders for Multi-view Anomaly Detection

Multi-view anomaly detection (AD) is a challenging task due to the complicated data distributions across different views. Specifically, there exist two types of anomalies in multi-view distributions: attribute anomaly that exhibits consistent anomalous pattern in each view and class anomaly that exhibits inconsistent traits (e.g., semantic label) across multiple views. Existing methods detect these anomalies in an unsupervised manner with the clustering assumption: normal data share consistent clustering structure across views while anomalous data exhibits inconsistent clusters across views. However, these methods would fail for complex multi-view data distributions where there is no obvious clusters. Moreover, existing models suffer from robustness since they are undermined by anomalies during training time. To get rid of the clustering assumption, we propose a Cross-aligned and Gumbel-refactored AutoEncoders (CGAEs) model to effectively detect two types of multi-view anomalies. In CGAEs, we devise a cross-reconstruction module to detect class anomaly by recovering one view from another view. Class anomalies would lead to high cross-reconstruction loss since they do not have the correct information in one view to generate another. We further design a view-alignment module to detect attribute anomaly by the alignment distance among multiple views in the latent space. Attribute anomalies possess large distances since they are less aligned due to fewer anomalous training instances. To handle the robustness issue, we propose a Gumbel-refactored reconstruction loss to replace the mean square error (MSE) in original autoencoders. The cross entropy loss is calculated between the discreterized input and Gumbel-sampled output, thus disregarding the irrelevant details to achieve model robustness. Experimental results validate the superiority of the proposed CGAEs model on both the benchmark datasets and real world datasets.
DENSITY OPERATOR REPRESENTATION IN MULTI-MATRIX VECTOR COHERENT STATES: LANDAU PROBLEM IN A HARMONIC POTENTIAL BACKGROUND

The density operator representation in the context of multi-matrix vector coherent states basis is performed and applied to Landau levels of an electron in an electromagnetic field coupled to an isotropic harmonic potential. Main relevant statistical properties such as the Mandel Q -parameter and the signal-to-quantum-noise ratio are derived and discussed. The Q -Husimi distribution is also investigated from the viewpoint of the Glauber-Sudarshan- P representation of the density operator.
Mobile Internet based TELEDOCTOR CONSULTATION APP

Nowadays visiting doctor for even a normal consultation has been a thought of worry. Taking patients to doctor for normal consultation is still in practice. Sometimes family doctor advices to consult a specialist who is not from your town. So, it always a question whether to consult that doctor because it is a cumbersome process to move the patient. The aim of this project is to make the Doctor’s consultation possible through video call. This will reduce the chances of getting affected by other patients when physically visiting the clinic. Also, can consult to any other specialist from different city which will reduce the pain of patient to physically visit the clinic for normal consultation. Mobile phones and computers are the Teledoc devices, and they can be accessed by no of peoples. Persons living in rural and urban areas who cannot directly connect with provider the facility should be improved. Caregivers can easily monitor the conditions of patient by these devices. Doctors can collect the information of patient and can give treatment to them without meeting physically to patients.
Improving the Performance of Local Binary Convolutional Neural Networks for Bilingual Digit Recognition

Bilingual handwritten digit recognition is a challenging problem that is not investigated deeply. In many applications, most public documents in Arabic countries are written in bilingual forms, Arabic and English. Arabic digit recognition and English digit recognition were studies using several machine learning techniques, including a convolutional neural network that is used in many applications and modified to produce other models such as Local Binary Convolutional Neural Networks (LBCNN). However, the parameters used for building the LBCNN model were not tuned in proportion to the LBCNN technique, including the number of depths or convolutional layers, anchor weights, sparsity level, and the learning rate approach. This paper enhances the performance, in terms of accuracy, of the LBCNN technique for both Arabic and English digit recognition models by introducing optimum parameters values. The proposed parameters enhance the accuracies of LBCNN models by 0.04% and 0.08% for MNIST and MADBase datasets, respectively.