Reviewerly/exHarmony huggingface dataset:
Effect of latency on simulator sickness in smartphone virtual reality

Abstract Virtual reality (VR) technologies have experienced rapid development in recent years, leading to low‐cost consumer smartphone VR systems (with 3DoF tracking). One issue with the smartphone VR systems, which rely on existing generic displays, is simulator sickness, which arises due to factors that induce sensory conflicts, especially related to visual–vestibular conflict. Display update latency with respect to head position is one of the most widely implicated factors for simulator sickness. However, studies of the effects of latency on simulator sickness have given inconsistent results. Moreover, these studies were done on systems with latencies much higher than current smartphone VR systems. Therefore, it is hard to generalize those results to today's VR systems. To address this, we present in this paper a study on the effect of latency on simulator sickness for latency levels around 20 ms. We present a systematic evaluation of simulator sickness in smartphone VR, testing for a difference between conditions with latencies of 18 and 28 ms, on a task of natural game playing in smartphone VR. We noted differences between individuals in their reported simulator sickness for the two latency conditions but no sample‐wide significant difference.
Lossless Image Compression Algorithm Based on Haar Transform

Resolution of images processed by modern systems on chip (SoC) tends to increase. The volume of data to be transmitted increases accordingly. Therefore, the main factor slowing down the performance of the entire system is the data bus bottleneck limiting the maximum I/O bandwidth. So, one way to solve this problem is to reduce the amount of transferred data. For this purpose, a lossless image compression algorithm based on the Haar transform is proposed. The coding algorithm is described in detail. The results of the algorithm modeling on various datasets of images are presented. Average compression ratio obtained is approximately 2.2. Hardware implementation of the algorithm is also discussed.
Optimum Tilt and Orientation Angle Determination with Application of Solar data

Predicting and analyzing solar resource has been a prerequisite requirement to promote solar as an effective sustainable energy source. Solar radiation knowledge incident on inclined surface is the fundamental prerequisite for solar technology deployment and monitoring. Suggestions for efficient methods to capture and utilize the sun's energy up to the maximum extent is important to perform Active Energy Management. Tilt and orientation angle play an important part in collecting the maximum amount of solar radiation. An effective solution to reduce the cost of generating power by adjusting the tilt angle and orientation angle based on various factors such as time, season and location. It is important to estimate irradiance of random custom orientations and tilt angles, as the orientations of the installation and tilt angles may differ at different locations. Maximizing the potential power generation by the solar panels has been the key engineering by proper positioning and placement of the Solar panels. Hence, an effort has been made to determine optimum tilt and orientation angles on basis of solar irradiance data for single and dual axis tracking system.
The Application of Artificial Intelligent in Analysing Ruling of Court for Corruption Case

Abstract A ruling of courts contains a full transcript of the defendant’s examination in chief and its cross-examination, as well as the transcript of the witnesses’ testimony under oath, and the concomitant cross-examination of the witnesses, a list of documentary evidence, and applied sentences. By applying Artificial Intelligent, the researcher is able to open all possible connections and interrelation of all actors and evidence which are almost impossible to be done manually. From six of the ruling of the Supreme Courts regarding corruption, we identified and mapped the process, forms, sociograms, crosstab of corruptions, and interconnections of all the persons and institutions involved. The huge and various documents can be easily categorized and be more homogeny. This study, therefore, demonstrates that Artificial Intelligent is increasingly playing a role in qualitative as well as quantitative corruption research.
Deep Matrix Factorization for Cross-Domain Recommendation

Recommender system can make personalized recommendation services for all users. Collaborative filtering is the main method can select suitable items to specific users among the recommendation algorithm. Common CF-based approaches use the users' history behaviors which include explicit information or implicit feedback to make recommendation decisions. Matrix Factorization is the most popular idea in the field of collaborative filtering. Currently, many MF-based methods have been proposed and achieve great improvement. But matrix factorization can only fit linear features which limit the their performance in real-world dataset which contains complex and nonlinear feature, moreover, the sparsity of user-item interaction information is another bottleneck of matrix factorization methods. Recently, Deep learning is widely applied to many fields, there are many research has applied deep learning to recommender system. In this paper, we use multi-layer perceptron structures to learn the representation of users and items in ML-based method. On the other hand, in order to address the data sparsity problem exists in collaborative filtering, Cross-Domain Recommendation is a promising way. Combining with collaborative approach to extract the latent feature, we propose Deep Matrix Factorization for Cross Domain Recommendation (DMF-CDR). We test the proposed method on real-world dataset and show that it outperforms several recent popular models.
Cloud Computing Load Balancing Using Amazon Web Service Technology

Amazon.com is a high and most important trafficked web services around the globe. It is an infrastructure-based web service which provides a wide range of products and services via Internet. AWS is a kind of platform which allows development of different applications by providing solutions for messaging, data storage with high scalability. Though AWS provides a wide range of services, there is a chance that servers are getting heavily loaded due to increasing in numbers of task being requested for the execution by the end users. Therefore, load balancing is required to balance the loads on different servers located on different regions available around the globe.
Attention-LSTM Fused U-Net Architecture for Organ Segmentation in CT Images

During the treatment planning stage of the radiotherapy, the medical physicists or doctors have to delineate the contour of the tumor and the organs at risk in order to accurately deliver enough radiation energy to the tumor and reduce such exposure to the surrounding normal tissues. Organ contouring is a time-consuming and laborious task. An automatic contouring tool is definitely required to fulfill the needs of the increasing cancer population. In this work, we proposed a fusion model that combines the network characteristics of a sequential model (sensor3D) and Attention U-Net. In which the convolutional LSTM layer is applied to study the spatial correlation between different layers in a CT image data set. The attention mechanism suppresses the irrelevant features from the complex image content and focuses on the useful messages of target organs. The clinical data contained CT image series of 108 patients and was acquired from a local hospital with IRB approval. The proposed model segmented five types of organs, lung, liver, stomach, esophagus, and heart. The segmentation accuracy rates of Dice Similarity Coefficient (DSC) were 99.27%, 95.48%, 88.19%, 80.81%, and 93.8%, respectively. We further developed a user interface that converts the AI-generated results into DICOM-RT format. Therefore the radiologists can fine-tune the results under the software used to do the routine manual-delineation tasks.
Face Alignment Algorithm Based on an Improved Cascaded Convolutional Neural Network

Aiming at the problem of a large number of parameters and high time complexity caused by the current deep convolutional neural network models, an improved face alignment algorithm of a cascaded convolutional neural network (CCNN) is proposed from the network structure, random perturbation factor (shake), and data scale. The algorithm steps are as follows: 3 groups of lightweight CNNs are designed; the first group takes facial images with face frame as input, trains 3 CNNs in parallel, and weighted outputs the facial images with 5 facial key points (anchor points). Then, the anchor points and 2 different windows with a shake mechanism are used to crop out 10 partial images of human faces. The networks in the second group train 10 CNNs in parallel and every 2 networks’ weighted average and colocated a key point. Based on the second group of networks, the third group designed a smaller shake mechanism and windows, to achieve more fine-tuning. When training the network, the idea of parallel within groups and serial between groups is adopted. Experiments show that, on the LFPW face dataset, the improved CCNN in this paper is superior to any other algorithm of the same type in positioning speed, algorithm parameter amount, and test error.
Injurious Comment Detection and Removal utilizing Neural Network

There are a lot of ways to communicate in this cyber world. With this increasingly growing era there is also much obstruction in a safe and secure environment. There has been an exponential growth in cyber bullying and abusing. Deep learning methods have recently begun to be used to detect abusive comments made in online forums. Detecting, and classifying, online abusive language is a non-trivial NLP challenge because online comments are made in a wide variety of contexts, and contain words from many different formal and informal lexicons. For this to overcome we design a model that detects the level of toxicity in a message and replaces it with another phrase. It uses a Deep Neural network model that takes a message/comment as an input and checks for various parameters such as Toxic, Severe Toxic, Identity hate, threat, etc. And the application finally then replaces the portion with another word/phrase. Examining things, you care about can be troublesome. The danger of misuse and provocation online implies that numerous individuals quit communicating and offer up on looking for changed thoughts. Stages battle to adequately encourage discussions, driving numerous networks to restrict or totally shut down client remarks.
Secure Decentralized Application Development of Blockchain-based Games

The purpose of this paper is to present Ethereum decentralized application development methodology with focus on security issues and its verification. We introduce key concepts that are related to developing decentralized applications and Crypto Collectibles games. Moreover, the requirements for blockchain projects were presented along with a selection of use case examples. The paper concerns the application design process issues, starting from the methodology used, going through the description of requirements and specification, ending up with the implementation. Finally, an overview of the issues associated with the security of Ethereum decentralized applications is presented. We compared guidelines from Ethereum Smart Contract Best Practices by ConsenSys, Smart Contract Security Verification Standard created by SecuRing, Decentralized Application Security Project introduced by NCC Group, Security Considerations from Solidity documentation, Ethereum Smart Contracts Security Recommendations from Guylando Knowledge Lists, and Smart Contract Weakness Classification and Test Cases. It was discussed which guideline should be followed and when should the verification take place, considering the life cycle of the application. The paper covers different security risks related to blockchain games along with examples of how vulnerabilities can arise, how they can be detected during security verification phase, and countermeasures to address them.
Recovery of the Solar Irradiance Data using Artificial Neural Network

Abstract Global solar irradiance (GHI) data plays a major role in the design, performance assessment, and monitoring of a solar energy conversion system. However, data loss happens sometimes due to various reasons. Hence, it is important to recover the lost data. Unfortunately, due to the high cost of measurement devices, other meteorological parameters are often not available to aid the data recovery. Nevertheless, a photovoltaic (PV) system could be installed nearby the site which the output power is recorded. This paper explored the capability of using output power from a PV panel to predict the lost or unavailable GHI data in the past via the Artificial Neural Network (ANN) technique. 1 day, 10 days, and 30 days of GHI data, time, and output power of a solar panel in July 2020 were used to train three ANN models. The ANN models were then used to predict one day and thirty days GHI data in July 2019 based on the PV output power of that period. The result shows that the ANN model with a higher number of training data can predict the GHI data with lower statistical errors. The results prove that PV output power can be used to recover the unavailable GHI data in the past with acceptable accuracy. This technique is useful for the industry to recover the historical time series of GHI for designing a new renewable energy project or assessing the past performance of a renewable energy project.
Augmenting Visual Information in Knowledge Graphs for Recommendations

Knowledge graphs (KGs) have been popularly used in recommender systems to leverage high-order connections between users and items. Typically, KGs are constructed based on semantic information derived from metadata. However, item images are also highly useful, especially for those domains where visual factors are influential such as fashion items. In this paper, we propose an approach to augment visual information extracted by popularly used image feature extraction methods into KGs. Specifically, we introduce visually-augmented KGs where the extracted information is integrated by using visual factor entities and visual relations. Moreover, to leverage the augmented KGs, a user representation learning approach is proposed to learn hybrid user profiles that combine both semantic and visual preferences. The proposed approaches have been applied in top-N recommendation tasks on two real-world datasets. The results show that the augmented KGs and the representation learning approach can improve the recommendation performance. They also show that the augmented KGs are applicable in the state-of-the-art KG-based recommender system as well.
Chinese Automatic Text Simplification Based on Unsupervised Learning

In this paper, a Chinese automatic text simplification(ATS) method based on unsupervised learning was introduced. Automatic text simplification is a research field of natural language processing. In terms of Chinese texts, the reliance on the hand-made simplified corpus or dictionary is not applicable due to a large number of texts. Chinese is a diverse language, and numerous factors need to be taken into consideration. An automatic simplification method based on Chinese text and a readability formula based on linear regression was proposed in this paper. Based on our method, just input a set of Chinese sentences and the more comprehensible sentences can be obtained through syntactic simplification and lexical simplification. Through the automatic evaluation of the hand-made simplified corpus, the readability score of our system increased by 3.68 compared with that of the original text, and the SARI score reached 36.02.
GeoWINE: Geolocation based Wiki, Image, News and Event Retrieval

In the context of social media, geolocation inference on news or events has become a very important task. In this paper, we present the GeoWINE (Geolocation-based Wiki-Image-News-Event retrieval) demonstrator, an effective modular system for multimodal retrieval which expects only a single image as input. The GeoWINE system consists of five modules in order to retrieve related information from various sources. The first module is a state-of-the-art model for geolocation estimation of images. The second module performs a geospatial-based query for entity retrieval using the Wikidata knowledge graph. The third module exploits four different image embedding representations, which are used to retrieve most similar entities compared to the input image. The last two modules perform news and event retrieval from EventRegistry and the Open Event Knowledge Graph (OEKG). GeoWINE provides an intuitive interface for end-users and is insightful for experts for reconfiguration to individual setups. The GeoWINE achieves promising results in entity label prediction for images on Google Landmarks dataset. The demonstrator is publicly available at http://cleopatra.ijs.si/geowine/.
Facial Landmarks and Expression Label Guided Photorealistic Facial Expression Synthesis

Facial expression manipulation plays an increasingly important role in the field of computer graphics and has been widely used in generating facial animations.However, it is still a very challenging task as it needs full understanding of the input face and very depending on the facial appearance.In this paper, we present an end-to-end generative adversarial network for facial expression synthesis.Given the facial landmarks and the expression label of a target image, our method automatically generates a corresponding expression facial image with the identity information and facial details well preserved.Both qualitative and quantitative experiments are conducted on the CK+ and Oulu-CASIA datasets.Experimental results show that our method has the compelling perceptual results even there exist large differences in facial shapes for unseen subjects.
Mining contour sequences for significant closed patterns

Sequential pattern mining in music is a central part of automated music analysis and music generation. This paper evaluates sequential pattern mining on a corpus of Mozarabic chant neume sequences that have been annotated by a musicologist with intra-opus patterns. Significant patterns are discovered in three settings: all closed patterns, maximal closed patterns, and minimal closed patterns. Each setting is evaluated against the annotated patterns using the measures of recall and precision. The results indicate that it is possible to retrieve all known patterns with an acceptable precision using significant closed pattern discovery.
The Efficient Resource Scheduling Strategy in Cloud: A Metaheuristic Approach

Abstract The cloud computing is evolving as a high-performance computing platform due to broad-scale, flexible computational architecture and heterogeneous collection of autonomous systems. Cloud technology uses concept of virtualization for managing resources, which develops resource scheduling as a key issue. The scheduling of cloud tasks is an NP-complete problem and therefore irreconcilable with particular solution. Also, with the huge collection of a database system, the management of resources and tasks becomes complex with specific to the completion time requirements and cost constraints. To resolve this problem, a number of meta-heuristic algorithms have been developed. Due to redundant wastage of resources and time, the under and over-provisioning is one kind of issues leads to either degradation in performance or wastage of cloud resources. To overcome these kinds of problems, we introduce a task scheduling approach by incorporating reinforcement learning along with the nature-inspired meta-heuristic optimization to maximizing cloud throughput, minimizing completion time &amp; production cost in IaaS cloud. By reinforcement learning, the agent will choose appropriate action among a set of available actions and the scheduler succeeds towards task allocation resulted to decrease makespan and increasing system utilization rate.
Mitigating excessive vCPU spinning in VM-agnostic KVM

In virtualized environments, oversubscribing virtual CPUs (vCPUs) on physical CPUs (pCPUs) is common to utilize CPU resources efficiently. Unfortunately, excessive vCPU spinning, which occurs when a vCPU is waiting in a spin loop for an event from a descheduled vCPU, causes serious performance degradation. Usually, the VM-agnostic hypervisor tries to prevent excessive vCPU spinning by rescheduling vCPUs when an excessive spin is detected by hardware support for virtualization.
Studying and diagnosing the heavy and light sand Minerals for some of the soils in southern and northern Iraq

Abstract A study and diagnosis of heavy and light sand minerals were conducted for some soils in southern and northern Iraq, as the soils in southern Iraq from Maysan Governorate were represented in four regions and included soils (Ali Al-Gharbi, Al-Batira, Al-Maymouna, Al-Tayyib). And four areas of the soils of northern Iraq from the governorate of Erbil and included soils (Shaqlawa, Salah El-Din area, Kori, and Harir), as the surface depths (0-30) cm were chosen for all the soils studied. The results showed the predominance of quartz minerals in all soils of the study within the light part of light sand minerals and attributed the dominance to their resistance to weathering in addition to being inherited from the original material while the dominance of the opaque minerals and all the studied soils appeared within the heavy fraction of sand minerals, the sovereignty was attributed to the nature of the sediments, their source, and the severity of the weathering process that affected them. The results also showed the similarity in the mineral composition with the difference in the proportions of their distribution. This difference in the relative distribution is due to the effect of sedimentation processes. Which is a reflection of the variation in the physiographic sites and its effect on the mineral content of the sediments, and because the soil samples are located within the flood plains that were formed by floods occurring in the river basins, which reflected the difference in the relative distribution of sand minerals for the soils of Erbil Governorate, as they are outside the range of riverine operations which It is attributed to the nature of the topography and geography of the land in northern Iraq that differs from its south.
An Efficient and Secured Data Sharing Approach through Image Transmission

The privacy of transmitting data in the era of super-fast internet and high-volume data generation has become a tough job. To solve this problem, the combination of steganography and visual cryptography together is considered. Steganography is the process of concealing some data inside any form of digital data. The proposed method takes the original image and the secret message is encoded in its LSB (least significant bits), where genetic algorithm modifies the pixel values of encrypted image, therefore making the secret of the message tough to crack and then secretly hides the data in the original image and it is detected after undergoing visual cryptography which helps to encrypt visual information so that on decryption, data or the information appears as an image. The aim of this model is to design an efficient and secured algorithm for better reliability, security and efficiency for a secret message.