Reviewerly/exHarmony huggingface dataset:
BALS: Blocked Alternating Least Squares for Parallel Sparse Matrix Factorization on GPUs

Matrix factorization on sparse matrices has been proven to be an effective approach for data mining and machine learning. However, the prior parallel implementations for matrix factorization fail to capture the internal social property embedded in real-world use cases. This article presents an efficient implementation of the alternative least squares (ALS) algorithm called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BALS</i> built on top of a new sparse matrix format for parallel matrix factorization. The BALS storage format organizes the sparse matrix into 2D tiles to avoid repeated data loads and improve data reuses. We further propose a data reordering technique to sort sparse matrices according to nonzeros. The experimental results show that BALS can yield a superior performance than state-of-the-art implementations, i.e., our BALS generally runs faster than Gates’ implementation over different latent feature sizes, with a speedup of up to 2.08× on K20C, 3.72× on TITAN X and 3.13× on TITAN RTX. When compared with alternative matrix factorization algorithms, our BALS consistently outperforms CDMF, cuMF_CCD, and cuMF_SGD over various latent feature sizes and datasets. The reordering technique can provide an extra improvement of up to 23.68 percent on K20C, 19.87 percent on TITAN X and 20.38 percent on TITAN RTX.
Cervical single cell of squamous intraepithelial lesion classification using shape features and extreme learning machine

Abstract Cervical cancer is an abnormal growth of cells found on the cervix. In general, cervical cancer is identified early by doing a pap smear test. However, this examination is still manually performed by doctors and the results are still subjective. Therefore, this study aims to determine the classification of Squamous Intraepithelial Lesion automatically from cervical single cells. The classification of those Squamous Intraepithelial Lesion includes normal cervical cells, Low-Grade Squamous Intraepithelial Lesion (LSIL), and High-Grade Squamous Intraepithelial Lesion (HSIL). We used Extreme Learning Machine (ELM) as a classifier and tried to compare the ELM’s performances with Backpropagation Neural Network method. We used 225 data and 3 classes include normal, LSIL, and HSIL. The classification was carried out by manual cropping and segmentation as the image pre-processing and the feature extraction was based on shape features consisting of Circularity, Semi Major and Minor Axis Length, Equivalent Diameter, Average Radius, and Compactness. This study concluded that Squamous Intraepithelial Lesion classification by using ELM had better performances than Backpropagation Neural Network. The highest accuracy result of 96.67% was obtained in Backpropagation training, while the highest accuracy in ELM’s training was 100% when both methods were tried by using 225 data.
Error Rates in Deterministic Ion Implantation for Qubit Arrays

The theoretical error rates in deterministic ion implantation when using an ion beam governed by a Poisson point process with a detector that counts the impacts are investigated. It is concluded that if the error rates are small, then for spots with nominally one implanted ion the probability of failure to implant the correct number is ≈ for a synchronous (i.e., pulsed) system or for an asynchronous (i.e., continuous beam) system, where is the probability that the detector misses an ion impact, and and are the number of ions (dark counts) per unit time and per pulse, respectively. t s is the system reaction time for an asynchronous system. This approximation allows easy identification of the greatest need for engineering effort. Some experimental efforts to measure these parameters and their uncertainties are examined.
Accelerated execution via eager-release of dependencies in task-based workflows

Task-based programming models offer a flexible way to express the unstructured parallelism patterns of nowadays complex applications. This expressive capability is required to achieve maximum possible performance for applications that are executed in distributed execution platforms. In current task-based workflows, tasks are launched for execution when their data dependencies are satisfied. However, even though the data dependencies of a certain task might have been already produced, the execution of this task will be delayed until its predecessor tasks completely finish their execution. As a consequence of this approach of releasing dependencies, the amount of parallelism inherent in applications is limited and performance improvement opportunities are wasted. To mitigate this limitation, we propose an eager approach for releasing data dependencies. Following this approach, the execution of tasks will not be delayed until their predecessor tasks completely finish their execution, instead, tasks will be launched for execution as soon as their data requirements are available. Hence, more parallelism is exposed and applications can achieve higher levels of performance by overlapping the execution of tasks. Towards achieving this goal, in this paper we propose applying two changes to task-based workflow systems. First, modifying the dependency relationships of tasks to be specified not only in terms of predecessor and successor tasks but also in terms of the data that caused these dependencies. Second, triggering the release of dependencies as soon as a predecessor task generates the output data instead of having to wait until the end of the predecessor execution to release all of its dependencies. We realize this proposal using PyCOMPSs: a task-based programming model for parallelizing Python applications. Our experiments show that using an eager approach for releasing dependencies achieves more than 50% performance improvement in the total execution time as compared to the default approach of releasing dependencies.
Detecting body parts from natural disaster victims using You Only Look Once (YOLO)

Abstract Natural disasters are unpredictable events in both the location and the time of occurrence. Natural disasters can cause property loss and can even be claimed by life. To reduce the amount of losses, the handling of rapid evacuation should be conducted by the SAR team to help victims of natural disasters. But in fact, there are a lot of obstacles in the evacuation process. Starting from the difficulty of searching the victim’s body, the difficulty of the terrain reached until limited equipment needed. In this study designed the body detection system of natural disaster victims using image processing where the shooting of victims was carried out using drones aiming to help find victims in a difficult or prone location when reached directly by humans. Background of the problem, in this research proposed a development method for the detection of victims of natural disaster that aims to help the SAR team as well as natural disaster volunteers in the search for victims who are in a difficult to reach place. The method used by You Only Look Once (YOLO) uses the Python programming language associated with image processing. From the research has been obtained accuracy detection object disaster victims with good accuracy. Based on the experiments that have been done obtained a good accuracy value of 95.49% with epoch of 12000.
PoNW

The original consensus algorithm - Proof of Work (PoW) has been widely utilized in the blockchain systems and is been adopted by many cryptocurrencies, such as Bitcoin and Ethereum, among many others. Nevertheless, the concept has received criticisms over its high energy consumption. This is induced by the necessity for all nodes in the network to communicate synchronously for consensus over the ledger state to be reached. Additionally, the concept has also shown clear limitations regarding performance and throughput. In trying to rectify this issue, the paper proposes the introduction of a new hybrid consensus protocol known as the Proof of Notarized Work (PoNW). The PoNW concept reduces the number of nodes that need to achieve consensus, thereby reducing the overall energy consumption in the current PoW. In addition, we propose using a decentralized random beacon to select nodes to participate in the mining process randomly. Therefore, our algorithm promises to achieve higher scalability and consistency levels without conceding its decentralization. When this is paired with a Byzantine Fault Tolerance (PBFT) verification, the system gains the ability to replace the probabilistic finality in current PoW with absolute finality in a matter of seconds, solving the issue of scalability. Finally, the study will look into the proposed algorithm's security and provides threats model to insure an acceptable failure probability. Results from the security analysis have shown that our consensus algorithm ensures forks cannot occur, and it remains secure and consistent even amid numerous attacks.
Nowhere to Hide: A Novel Private Protocol Identification Algorithm

In recent years, with the rapid development of mobile Internet and 5G technology, great changes have been brought to our lives, and human beings have stepped into the era of big data. These new features and techniques in 5G support many different types of mobile applications for users, which makes network security extremely challenging. Among them, more and more applications involve users’ private data, such as location information, financial information, and biological information. In order to prevent users’ privacy disclosure, most applications choose to use private protocols. However, such private protocols also provide a means for malware and malicious applications to steal users’ privacy and confidential data. From a more secure point of view, we need to provide a way for users to know how many private protocols are running on their mobile phones and distinguish which are authorized applications and which are not. Therefore, the analysis and identification of private protocols have become a hot topic in current research. How to extract the characteristics of network protocol effectively and identify the private protocol accurately becomes the most important part of this research. In this paper, we combine genetic algorithm and association rule algorithm and then propose a set of feature extraction algorithm and protocol recognition algorithm for unknown protocols. The experimental analysis based on the actual data shows that these methods can effectively solve the problems of feature extraction and recognition for unknown protocols and can greatly improve the accuracy of private protocol recognition.
Comparison Study of Load Balancing Algorithm

Cloud computing is in vogue and, playing a compelling role in internet world. The cloud computing is an enumerate version of grid and cluster computing. Cloud computing are provides the utility of computer services such as PaaS,SaaS, and IaaS, and all services provided in a cost effective way. This cloud computing services performance is an important factor, if this utility service is slow and cost is high then it will obsolete in internet world. Hence researcher keep researched on performance of cloud computing services. The cloud server, when receive lots of service request then server need to assign load equally and this process is called load balancing. Load balancer are two type, hardware and software load balancer. The hardware load balancer implementation cost is so high therefore these types of load balancer are not popular. The software load balancer is cost effective;therefore, cloud servicer adopts these types of load balancer. In this paper our discussion is about load balancing and performance factor those are helping to manage the resources such as CPU, memory, secondary storage. It's an important of any cloud service to provide load balancing in an effective manner. In this paper we are going to do comparative study and analysis of existing major load balancing algorithms, and study and analyze for the algorithms various performance factor such as peak hour usage and distribution of services. Our focus on LB challenges in cloud computing, high concern on improvement of performance factor such resources utilization, response time, and throughput. In cloud computing network consist of different servers, storage devices and network unit.
Reactive motion planning using time-layered C-spaces for a collaborative robot PaDY

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Improved Ring Radius Transform-Based Reconstruction for Video Character Recognition

Character shape reconstruction in video is challenging due to low contrast, complex backgrounds and arbitrary orientation of characters. This work proposes an Improved Ring Radius Transform (IRRT) for reconstructing impaired characters through medial axis prediction. At first, the technique proposes a novel idea based on the Tangent Vector (TV) concept that identifies each actual pair of end pixels caused by gaps in impaired character components. Next, the actual direction to predict medial axis pixels using IRRT for each pair of end pixels is proposed with a new normal vector concept. The process of prediction repeats iteratively to find all the medial axis pixels for every gap in question. Further, medial axis pixels with their radii are used to reconstruct the shapes of impaired characters. The proposed technique is tested on benchmark datasets consisting of video, natural scenes, objects and multi-lingual data to demonstrate that it reconstructs shapes well, even for heterogeneous data. Comparative studies with different binarization and character recognition methods show that the proposed technique is effective, useful and outperforms existing methods.
Design and Construction of a Low Cost Portable Cardiopulmonary Resuscitation and Ventilation Device

Recently, after the global spread of the new coronavirus (known as COVID-19), the demand for large numbers of ventilators and CPRs has increased in hospitals and health centers. In fact, providing a large number of these devices is difficult, in addition to their high cost and providing a suitable place for them. Therefore, there is an urgent need to use the concepts of reverse engineering in the design and manufacture of low-cost portable devices for use by patients on site. This paper presents an attempt to apply the concepts of reverse engineering to the design and construction of a low-cost portable cardiopulmonary resuscitation and ventilator device. This device can be used in ambulances, health centers, or hard-to-reach remote places. The main parameters of the implemented device can be adjusted directly by the operator or remotely by a specialist. The device is considered as a node in the wireless sensor network that can be accessed by a specialist to update the parameters according to the patient's condition.
Secure voice cryptography based on Diffie-Hellman algorithm

Abstract This article introduces a new technique for voice signals encryption &amp; decryption to ameliorate the information security during transferring over unsecure network. The presented mechanism is based on a particular type of asymmetric key cryptography called Diffie-Hellman Algorithm. The main reason of using this algorithm is enabling users to encrypt and decrypt their messages via a shared session key that is known only for them. Firstly, the input voice signal in this scheme is enciphered by utilizing the shared secret key at the dispatcher. Secondly, the scrambled voice signal is delivered to the destination through open telecommunication network. Eventually, the cipher text signal is deciphered at the receptor by exploiting the same key to retrieve the plaintext signal. The performance of the presented voice cryptosystem is assessed via different known ciphering/deciphering voice measures for different voice signals. The computed and visual results in addition to comparison outcomes confirm that the presented cryptosystem can fulfill good enciphering and deciphering results and it is capable of tolerating various cryptographic analyses.
Information-theoretic characterization of eye-tracking signals with relation to cognitive tasks

Eye tracking is being increasingly used as a more powerful diagnosis instrument when compared with traditional pen-and-paper tests in psychopedagogy and psychology. This technology may significantly improve neurocognitive assessments in gathering indirect latent information about the subjects’ performance. However, the meaning and implications of these data are far from being fully understood. In this work, we present a comprehensive study of eye tracking time series in terms of statistical complexity measures. We registered the eye tracking movements of several subjects solving the two parts of the commonly applied Trail Making Test (TMT-A and TMT-B) and studied their Shannon entropy, disequilibrium, statistical complexity, and Fisher information with respect to three different probability distributions. The results show that these quantifiers reveal information about different features of the gaze depending on the distribution considered. As a meaningful result, we found that Fisher information in the position distribution reflects the difficulties encountered by the subject when solving the task. Such a characterization may be of interest to understand the underlying cognitive tasks performed by the subjects, and, additionally, it can serve as a source of valuable parameters to quantitatively assess how and why the subjects budget their attention, providing psychologists and psychopedagogues with more refined neuropsychological evaluation features and tools.
Case Study on well-known Topic Modeling Methods for Document Classification

Topic modeling has numerous applications like text categorization, topic clustering, document tagging, feature extraction on wide document collections. In this study, practical exploration method of topic modeling of Latent Dirichlet Allocation, transformers based machine learning method Bidirectional Encoder Representations from Transformers and Term Frequency - Inverse Document Frequency method were applied to the document set separately. It includes sport and education articles collected from internet by graduate students, 801 number totally. The purpose of this study is to observe which method best suits to the topic modeling and if possible in order to increase the accuracy rate via ensemble of these methods. As a result of this study, it was observed that even it has some disadvantages, BERT classified the documents with the correct topic with an average of %92.6 success ratio, overwhelming the others.
Sia-RAE: A Siamese Network based on Recursive AutoEncoder for Effective Clone Detection

Code clone helps improving programming productivity, while at the same time leads to many negative effects on software maintenance. Many approaches have been proposed to detect clones, but most of them fail on detecting low similarity code snippets. In this paper, we propose a Siamese network which links two recursive autoencoders (RAE) with a comparator network for clone detection. The unweighted recursive autoencoder is designed to learn code representation and then the comparator network is employed for similarity evaluation. In this Siamese network, it takes full advantages of lexical, semantic and structure information, and achieves high accuracy in revealing tiny similarity. We conduct comprehensive experiments on BigCloneBench using tagged clones as well as the whole repository respectively. The results suggest that our approach achieves good accuracy, and its recall reaches 93.02 % in WT3/T4, which outperforms state-of-the-art.
Dynamic out-of-vocabulary word registration to language model for speech recognition

Abstract We propose a method of dynamically registering out-of-vocabulary (OOV) words by assigning the pronunciations of these words to pre-inserted OOV tokens, editing the pronunciations of the tokens. To do this, we add OOV tokens to an additional, partial copy of our corpus, either randomly or to part-of-speech (POS) tags in the selected utterances, when training the language model (LM) for speech recognition. This results in an LM containing OOV tokens, to which we can assign pronunciations. We also investigate the impact of acoustic complexity and the “natural” occurrence frequency of OOV words on the recognition of registered OOV words. The proposed OOV word registration method is evaluated using two modern automatic speech recognition (ASR) systems, Julius and Kaldi, using DNN-HMM acoustic models and N-gram language models (plus an additional evaluation using RNN re-scoring with Kaldi). Our experimental results show that when using the proposed OOV registration method, modern ASR systems can recognize OOV words without re-training the language model, that the acoustic complexity of OOV words affects OOV recognition, and that differences between the “natural” and the assigned occurrence frequencies of OOV words have little impact on the final recognition results.
Automatic analysis of insurance reports through deep neural networks to identify severe claims

Abstract In this paper, we develop a methodology to automatically classify claims using the information contained in text reports (redacted at their opening). From this automatic analysis, the aim is to predict if a claim is expected to be particularly severe or not. The difficulty is the rarity of such extreme claims in the database, and hence the difficulty, for classical prediction techniques like logistic regression to accurately predict the outcome. Since data is unbalanced (too few observations are associated with a positive label), we propose different rebalance algorithm to deal with this issue. We discuss the use of different embedding methodologies used to process text data, and the role of the architectures of the networks.
The Effect of Virtual Agent Gender and Embodiment on the Experiences and Performance of Students in Virtual Field Trips

In this paper, we explore how the embodiment and gender of a virtual instructor can affect social presence, spatial presence, perceived learning effectiveness, and performance of students in virtual field trips. A pilot study with 22 students was conducted in the spring of 2020 in a geoscience course at The Pennsylvania State University. Our results show that both gender and embodiment of a virtual instructor can affect the social and learning experiences of students. A female virtual instructor elicited higher levels of social presence, spatial presence, and perceived learning effectiveness compared to a male virtual instructor; and an embodied virtual instructor elicited a higher feeling of spatial presence compared to a disembodied virtual instructor.
Scalable Backdoor Detection in Neural Networks

Recently, it has been shown that deep learning models are vulnerable to Trojan attacks. In the Trojan attacks, an attacker can install a backdoor during training to make the model misidentify samples contaminated with a small trigger patch. Current backdoor detection methods fail to achieve good detection performance and are computationally expensive. In this paper, we propose a novel trigger reverse-engineering based approach whose computational complexity does not scale up with the number of labels and is based on a measure that is both interpretable and universal across different networks and patch types. In experiments, we observe that our method achieves a perfect score in separating Trojan models from pure models, which is an improvement over the current state-of-the-art method.
COVID-19 and International Freedom of Movement: a Stranded Human Right?

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI