Reviewerly/exHarmony huggingface dataset:
Key Recovery Under Plaintext Checking Attack on LAC

The National Institute of Standards and Technology (NIST) is working on the standardization of post-quantum algorithms. In February 2019, NIST announced 26 candidate post-quantum cryptosystems had entered the Round 2. Prior work has shown how to mount key recovery attacks on several candidates like FrodoKEM, NewHope, and Kyber, but their methods do not work for LAC, which uses a different encoding scheme and rounding method. To address this gap, we describe a powerful new attack on LAC. In particular, we propose a simple and effective method to recover the reused secret key of LAC.CPA. Following the method we show that, using the recommended parameters, thousands of queries are sufficient to recover the full secret key with a 100% probability, which is verified by experiments. Since LAC.KE is based on LAC.CPA, our method can be used to assess the key-reuse resilience of LAC.KE. In particular, if Alice reuses a secret key, Bob can recover it by communicating with Alice thousands of times. Since LAC is a Round 2 candidate in the NIST PQ process, the presented result may well have a high impact on the understanding of this important cryptosystem.
The The Correlation of Hemodynamic Status and Oxygen Saturation with The Level of Consciousness in Head Injury Patients

Introduction: Head injury is a traumatic disorder caused by the mechanical strength of a blunt object or penetration that can cause temporary or permanent changes in tissue function and structure. The latest developing concept on poor prognosis among head injury patients was occur related to the secondary injuries, where there was an increase in intracranial, cerebral ischemia, and decreased consciousness. The important thing in the management of head injuries is to maintain hemodynamic stability and to prevent hypoxia by maintaining oxygen saturation &gt;95%. The purpose of this study was to analyze the correlation of hemodynamic status and oxygen saturation with the level of consciousness in head injury patients at RSUD Mardi Waluyo, Blitar.&#x0D; Methods: The research used correlational analytical with a cross-sectional design. The sampling technique used consecutive sampling within a total sample of 30 respondents. Spearman's test was used for data analysis.&#x0D; Results: The results showed a correlation between mean arterial pressure (p-value = .004), respiratory rate (p-value = .000), body temperature (p-value = .017), and oxygen saturation (p-value = .000) with level of consciousness (GCS) respectively.&#x0D; Conclusion: It can be concluded that mean arterial pressure, respiratory rate, body temperature, and oxygen saturation can affect the level of consciousness (GCS). There was a need to improve hemodynamic and oxygen saturation monitoring to prevent the bad prognosis in head injury patients at RSUD Mardi Waluyo, Blitar.
Design of Hash Function Using Two Dimensional Cellular Automata

Haldar, Tanushree Chowdhury, Dipanwita RoyThis paper proposes a new hash function using two-dimensional cellular automata. The cellular structure of 5-neighborhood 2D CA is exploited to use it as the basic building block of the hash function. It has been shown that the excellent randomness along with the inherent diffusion makes 2D CA an ideal candidate of Hash function. This work also focuses on the security analysis of the Hash function. We demonstrate Birthday attack and the analysis shows that the design is secure against this attack.
The SWEBOK Guide — More Than 20 Years down the Road

The proof of concept document of the Guide to the Software Engineering Body of Knowledge (SWEBOK) was made available in 1998 - now more than 20 years ago. The keynote speaker has played a key role in all published versions of the SWEBOK Guide. In this talk, the speaker will reflect on the impact of the SWEBOK Guide, make some observations about its current content after recently spending a year in industry, and provide some remarks on moving forward.
Aggregating data center measurements for availability analysis

Summary A data center infrastructure is composed of heterogeneous resources divided into three main subsystems: IT (processor, memory, disk, network, etc.), power (generators, power transformers, uninterruptible power supplies, distribution units, among others), and cooling (water chillers, pipes, and cooling tower). This heterogeneity brings challenges for collecting and gathering data from several devices in the infrastructure. In addition, extracting relevant information is another challenge for data center managers. While seeking to improve the cloud availability, monitoring the entire infrastructure using a variety of (open source and/or commercial) advanced monitoring tools, such as Zabbix, Nagios, Prometheus, CloudWatch, AzureWatch, and others is required. It is often common to use many monitoring systems to collect real‐time data for data center components from different subsystems. Such an environment brings an inherent challenge stemming from the need to aggregate and organize the whole collected infrastructure data and measurements. This first step is necessary prior to obtaining any valuable insights for decision‐making. In this paper, we present the Data Center Availability (DCA) System, a software system that is able to aggregate and analyze data center measurements aimed toward the study of DCA. We also discuss the DCA implementation and illustrate its operation, monitoring a small University research laboratory data center. The DCA System is able to monitor different types of devices using the Zabbix tool, such as servers, switches, and power devices. The DCA System is able to automatically identify the failure time seasonality and trend present in the collected data from different devices of the data center.
COVID-19: A Semantic-Based Pipeline for Recommending Biomedical Entities

With the increasing number of publications about COVID-19, it is a challenge to extract personalized knowledge suitable for each researcher.This work aims to build a new semantic-based pipeline for recommending biomedical entities to scientific researchers.To this end, we developed a pipeline that creates an implicit feedback matrix based on Named Entity Recognition (NER) on a corpus of documents, using multidisciplinary ontologies for recognizing and linking the entities.Our hypothesis is that by using ontologies from different fields in the NER phase, we can improve the results for state-of-the-art collaborative-filtering recommender systems applied to the dataset created.The tests performed using the COVID-19 Open Research Dataset (CORD-19) dataset show that when using four ontologies, the results for precision@k, for example, reach the 80%, whereas when using only one ontology, the results for precision@k drops to 20%, for the same users.Furthermore, the use of multi-fields entities may help in the discovery of new items, even if the researchers do not have items from that field in their set of preferences.
QuLBIT: Quantum-Like Bayesian Inference Technologies for Cognition and Decision

This paper provides the foundations of a unified cognitive decision-making framework (QulBIT) which is derived from quantum theory. The main advantage of this framework is that it can cater for paradoxical and irrational human decision making. Although quantum approaches for cognition have demonstrated advantages over classical probabilistic approaches and bounded rationality models, they still lack explanatory power. To address this, we introduce a novel explanatory analysis of the decision-maker's belief space. This is achieved by exploiting quantum interference effects as a way of both quantifying and explaining the decision-maker's uncertainty. We detail the main modules of the unified framework, the explanatory analysis method, and illustrate their application in situations violating the Sure Thing Principle.
Aspect Level Sentiment Analysis Using Bi-Directional LSTM Encoder with the Attention Mechanism

Aspect level sentiment analysis (ALSA) is a fine-grained task in sentiment analysis. It classifies the different polarities (positive, negative) for the specific aspects of each review. In general, document and sentence level sentiment analysis has achieved remarkable results, but they can’t make the right prediction for the specific aspects in the given review. In the past few years, many researchers have worked sentiment analysis using machine learning approaches, but they have a problem in polarity detection for each aspect in the sentence. Therefore, this paper proposes to classify the sentiment polarity for each aspect by using Bi-directional LSTM (Bi-LSTM) encoder combining with the attention mechanism. The attention mechanism pays attention to the aspect of a specific target in the given review. It is a type of deep learning model and also an extension of the LSTM network, which can learn the long sequences of text. Deep learning models take a long time to train and when the network is deep, it encounters the vanishing gradient problem, a long-standing issue in the neural network model. This paper also considers the hyperparameter tuning approach to solve the vanishing gradient problem for ALSA task. Hyperparameter may be the weight, bias, the number of epochs, batch size, and so on. Experiments are conducted on IMDB and SemEval 2014 Task 4 datasets and the results show that the accuracy of our sentiment model reaches 88.5%, which is higher than other LSTM-based methods like standard AE-LSTM, AT-LSTM, standard LSTM, TC-LSTM, and TD-LSTM.
Machine Learning and Security: The Good, The Bad, and The Ugly

I would like to share my thoughts on the interactions between machine learning and security.
Evaluation of the Cleft-Overstep Algorithm for Linear Regression Analysis

Optimization algorithms have been applied to improve the learning speed of machine learning. In the backpropagation phase, the weights of the neural network will be updated in each epoch with the purpose that the difference between the actual output and predicted one will be reached to a value smaller than a predefined number epsilon. In essence, the backpropagation process only uses the idea of optimization algorithm to formulate a weighted update formula, rather than a complete optimization problem. The enhancement of machine learning speed is meaningful when the application of artificial intelligence is rising. The cleft-overstep optimal algorithm has been introduced since the 1990s and implemented in optimal control, which has not been popularized and evaluated in other fields. This paper aims to use the idea of cleft-overstep method, also apply and program probing in linear regression analysis - a simple machine learning algorithm. Results were compared with classic gradient descent method based on the known problem and showed a significant learning speed improvement.
Psamathe: a DSL for safe blockchain assets

Blockchains host smart contracts for voting, tokens, and other purposes. Vulnerabilities in contracts are common, often leading to the loss of money. Psamathe is a new language we are designing around a new flow abstraction, reducing asset bugs and making contracts more concise than in existing languages. We present an overview of Psamathe, and discuss two example contracts in Psamathe and Solidity.
SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detection

As mobile hardware technology advances, on-device computation is becoming more and more affordable.
On Modeling Labor Markets for Fine-Grained Insights

The labor market consists of job seekers looking for jobs, and job openings waiting for applications. Classical labor market models assume that salary is the primary factor explaining why job-seekers select certain jobs. In practice, job seeker behavior is much more complex and there are other factors that should be considered. In this paper, we therefore propose the Probabilistic Labor Model (PLM) which considers salary satisfaction, topic preference matching, and accessibility as important criteria for job seekers to decide when they apply for jobs. We also determine the user and job latent variables for each criterion and define a graphical model to link the variables to observed applications. The latent variables learned can be subsequently used in downstream applications including job recommendation, labor market analysis, and others. We evaluate the PLM model against other baseline models using two real-world datasets. Our experiments show that PLM outperforms other baseline models in an application prediction task. We also demonstrate how PLM can be effectively used to analyse gender and age differences in major labor market segments.
Blockchain and Bitcoin Security

Launched in 2008, blockchain technology with a typical product of Bitcoin is being applied and has great development potential in many areas such as finance, trade, manufacturing, and telecommunications. Bitcoin as a popular cryptocurrency with the highest total market capitalization of the virtual money market has always been an attractive target for hackers. In this chapter, we focus on attack and security on blockchain technology. In the first section, some targeted attacks on Bitcoins will be repeated. In the next section, we present potential attack techniques on blockchain technology in general and Bitcoin in particular, such as double-spending attack technique on mining pool or Bitcoin network. The general security solutions for block-chain will be presented in the next section. Besides, we devote a section to presenting the issue of how to secure wallets and Bitcoin transactions for those who use this virtual currency. The summary and discussion of future security orientation for blockchain technology will be presented at the end of the chapter.
CBPM: An Approach for Contextual Business Process Modeling

With the evolution of ubiquitous computing, the consideration of the context in applications became indispensable and this is the case for business process modeling. In recent years, several approaches have been proposed to introduce context into BP modeling. In this article, we will present an approach which allows to model business process while integrating context notion, we will propose a meta-model of context that allows to model the context in a generic way, and also a framework to gather this context meta-model with the BPMN model in order to elaborate a contextual business process.
Research on Image Fuzzy Enhancement Algorithm with Tanh Operator

Abstract Improvements are proposed in this paper to overcome the drawbacks of traditional fuzzy enhancement algorithms. Tanh operator is used as membership function, and fuzzy enhancement function are made up of simple power functions, and the image is divided into two regions by adaptive segmentation method, one is high grey region, the other is low grey region, pixels in one region are enhanced, and pixels in the other region are reduced. Simulation results show that this algorithm has good ability to increase image’s contrast, and it is a simple and efficient way to enhance blur edges.
Embedded Object Detection System Based on Deep Neural Network

Object detection is widely used in many fields, such as intelligent security monitoring, smart city, power inspection, and so on. The object detection algorithm based on deep learning is a kind of storage intensive and computing intensive algorithm which is difficult to achieve on the embedded platform with limited storage and computing resources. In this paper, we choose mobinetv2, a lightweight neural network with few model parameters and strong feature extraction ability, to replace darknet53 as the backbone network of YOLOv3 algorithm. In addition, we use a model compression method based on channel pruning to compress the network model. This method compresses model to detecting objects on embedded ARM platform. Neon instruction and OpenMP technology are further used to optimize and accelerate the intensive computing of convolutional network, and finally achieve a real-time embedded object detection system.
A Novel Face Detector Based on YOLOv3

Face detection has broad applications. Recently, there has been lots of advancement in face detection based on deep learning methods. However, small face detection in a real-world environment is still a challenging task due to its low resolution, variability in size, different poses and occlusions. YOLOv3 is one of the main approaches for object detection, which has achieved comparatively better performance for small target detection in real-time. However, it still struggles to detect a group of small size faces with inaccurate localization as well as an increasing number of false positives. In this paper, we propose an efficient multiscale deep learning network based on YOLOv3 to detect a group of small faces. First, we select the optimum number of anchors, and this will help us understand the small face targets better; secondly, we change the bounding box regression loss in the YOLOv3 to a new CIoU loss to improve the false positives; thirdly, we extend the detection scale from 3 to 4 in YOLOv3 especially for detecting small faces; fourthly, we simplify the four convolutional layers to two residual blocks from six convolutional layers in each detection scale to avoid the derivative vanishing. The proposed model can achieve the state-of-the-art performance on the WIDER FACE face detection benchmark, especially in the hard subset that has a high number of small faces with the variability of scale, poses and occlusions. Our model has achieved 86.5%AP in the WIDER FACE hard validation subset compared to 72.9%AP by the YOLOv3. The run-time is also satisfactory for real application for VGA resolution image with 64.3 FPS using the Nvidia Titan RTX.
Mmnet: A Multi-Method Network for Multi-Label Classification

In the mainstream convolutional neural network structure framework, a single-label classification network uses a backbone network as a feature extraction network to map the image to a global feature vector, and then the global feature vector is input to the classifier for classification. In the context of multi-label image classification, the classification results of multiple types of labels will be fed back to the global feature vector through a back-propagation algorithm. In this process, the backpropagation information of multi-labels will generate a certain degree of competition. Due to the competition between different object categories, some categories with complex features but small sample numbers are difficult to get correctly classified. This paper proposes an end-to-end convolutional neural network based on a multi-path structure, which not only retains the characteristics of end-to-end and parallelizable operations of the convolutional neural network, but also reduces the feature competition between different types of objects and improves The recognition performance of the network. In addition, the structure has the characteristics of easy expansion. For newly added object categories, the network can well continue the previous learning results, quickly complete the classification of new objects, and can also design specific tributary network structures for specific types of objects.
Moral Circuits [Special Section]

In this essay, I offer an ethnographic-oriented cartoon as a reflection on a personal experience in moral circuits. First prepared for the 4S meeting in Boston (2017)—under the title “Pedal Transcriba, an Ethnographic Device of (and for) Qualitative Research”—it condenses one dimension of the work of multi-sited research across community spaces for alternative computing in the Pacific. My motivation was to derive the human circuitry from a simple technical object and research moral economies of “open” digital fabrication through the very practice of design and fabrication.
Siamese Convolutional Neural Network for ASL Alphabet Recognition

American sign language is an important communication way to convey information among the deaf community in North America and is primarily used by people who have hearing or speech impairments. The deaf community faces a struggle in schools and other institutions because they usually consist primarily of hearing people. Besides, deaf people often feel misunderstood by people who do not knowsign language, for example, family members. In the last two decades, researchers have been proposing automatic sign language recognition systems to facilitate the learning of sign language, and nowadays, computer scientists have focused on using artificial intelligence in order to develop a system capable of reducing the communication gap between hearing and deaf people. In this paper, it is proposed a siamese convolutional neural network for American sign language alphabet recognition. This siamese architecture allows the computer to reduce the high interclass similarity and high intraclass variations. The results show that the proposed method outperforms the state of the art systems.
Quantum Algorithms for String Processing

In the paper, we investigate two problems on strings. The first one is the String matching problem, and the second one is the String comparing problem. We provide a quantum algorithm for the String matching problem that uses exponentially less quantum memory than existing ones. The algorithm uses the hashing technique for string matching, quantum parallelism, and ideas of Grover’s search algorithm. Using the same ideas, we provide two algorithms for the String comparing problem. These algorithms also use exponentially less quantum memory than existing ones. Additionally, the second algorithm works exponentially faster than the existing one.
Method of information obtaining from ontology on the basis of a natural language phrase analysis

A method for phrases analyzing in natural languages of inflective type (Ukrainian and Russian) has been developed. The method allows one to outline main expressed ideas and groups of words in the text by which they are stated. The semantic trees of propositions formed in this way, each of which expresses one specific idea, are a convenient source material for constructing queries to the ontology in the SPARQL language. The analysis algorithm is based on the following sequence of basic steps: word tokenize, determining of marker words and phrases, identifying available type of proposition, identifying nouns groups, building a syntactic graph of a sentence, building semantic trees of propositions based on existing types of propositions, substituting parameters from semantic trees of propositions in the corresponding SPARQL query templates. The choice of an appropriate template depends on the type of proposition expressed by a given semantic tree of a proposition. The sets of concepts received as an answer are tied as corresponding answers to the previously defined semantic tree of proposition. In case of non-receipt of information from the ontology, the reduction of noun groups is carried out to express more general concepts and the building queries using them. This allows us to get some answer, although not as accurate as when we use the full noun group. The use of SPARQL query templates requires an a priori known ontology structure, which is also proposed in this paper. Such a system is applicable for dialogue using chat-bots or for automatically receiving answers to questions from the text.
Uma nova realidade: aulas remotas de atividade física para idosos na pandemia de Covid-19

The aim of this study was to assess adherence, perception of social distance and the role of physical activity in the elderly from a university extension program with remote activities during the COVID-19 pandemic. A cross-sectional study was carried out with 60 elderly people. A questionnaire with questions about adherence to classes, perception of social distance, the role of physical activity and sociodemographic variables was used. The results showed participants' adherence to classes, ambivalent perceptions about distance and the importance of physical activity in terms of social and physical aspects. Remote classes have become a new reality for the elderly in the face of the pandemic, as a way of maintaining a routine of activities, benefiting from social approach and physical and mental well-being.