Reviewerly/exHarmony huggingface dataset:
Exact and Linear-Time Gas-Cost Analysis

Blockchains support execution of smart contracts: programs encoding complex transactions between distrusting parties. Due to their distributed nature, blockchains rely on third-party miners to execute and validate transactions. Miners are compensated by charging users with gas based on the execution cost of the transaction. To compute the exact gas cost, blockchains track gas cost dynamically creating its own overhead. This paper presents a static exact gas-cost analysis technique that can be employed to eliminate dynamic gas tracking. This approach presents further benefits such as providing miners with a trusted gas bound that can be verified in linear time, and eliminating out-of-gas exceptions. To handle recursion and unbounded computation, we propose a novel amortization technique that stores gas inside data structures. We have implemented our analysis technique in a tool called GasBoX that takes a contract as input and infers the gas cost of its functions automatically. We have evaluated GasBoX on $$13$$ standard smart contracts borrowed from real-world blockchain projects. Our soundness theorem proves that the gas bound inferred by GasBoX exactly matches the gas cost at runtime and no dynamic gas tracking is necessary.
A Trust-Based Approach to Extract Social Relationships for Recommendation

Recommender systemsShokeen, Jyoti areRana, Chhavi gainingRani, Poonam momentum with the increasing set of user choices in recent years. Also, growing proliferation of social networking sites is using these systems to accelerate their usage. User preferences are likely to be similar or influenced by their friends on social networks. Due to this, social relations can help the recommender systems in solving cold-start and data sparsity problems. This paper presents a trust-based approach to extract social relations for recommendation in social networks. We use matrix factorization approach to factorize the user-item rating matrix. Both rating matrix and trusted relations play a crucial role in calculating the weighted trust between users in our proposed approach. Based on the weighted trust value, the algorithm recommends top-N items to the user. The motivation behind the extraction of social relationships is to use the recommendations of friends.
Comparative Analysis of Marker and Marker-less Augmented Reality in Education

Augmented Reality (AR) is the rendering of elements such as text, images, videos, 3D objects, etc into our real-world environment. In this paper, we have used this technology to bring a change in the vision of education (online mostly while staying remotely) to help students understand the concepts for example Engineering Mathematics, Electromagnetism, etc. by bringing 3D models of the concepts superimposed into the real world. The paper has been divided into two parts: The first part consists of the marker-based AR approach where an image target is brought in front of the camera to superimpose it with 3D model here. It consists of feature extraction, feature description and feature matching, homography, projection and bringing 3D object to scene. The second part is the development of an Android application using Marker less AR approach explaining an engineering concept. We have used 3D models and necessary UI elements. A comparison of both the parts of the work has been made.
Using Machine Learning to Automate Mammogram Images Analysis

Breast cancer is the second leading cause of cancer-related death after lung cancer in women. Early detection of breast cancer in X-ray mammography is believed to have effectively reduced the mortality rate since 1989. However, a relatively high false positive rate and a low specificity in mammography technology still exist. In this work, a computer-aided automatic mammogram analysis system is proposed to process the mammogram images and automatically discriminate them as either normal or cancerous, consisting of three consecutive image processing, feature selection, and image classification stages. In designing the system, the discrete wavelet transforms (Daubechies 2, Daubechies 4, and Biorthogonal 6.8) and the Fourier cosine transform were first used to parse the mammogram images and extract statistical features. Then, an entropy-based feature selection method was implemented to reduce the number of features. Finally, different pattern recognition methods (including the Back-propagation Network, the Linear Discriminant Analysis, and the Naive Bayes Classifier) and a voting classification scheme were employed. The performance of each classification strategy was evaluated for sensitivity, specificity, and accuracy and for general performance using the Receiver Operating Curve. Our method is validated on the dataset from the Eastern Health in Newfoundland and Labrador of Canada. The experimental results demonstrated that the proposed automatic mammogram analysis system could effectively improve the classification performances.
Faster Convergence to N-Queens Problem Using Reinforcement Learning

Algorithmic complexity has been a constraint to solving problems efficiently. Wide use of an algorithm is dependent on its space and time complexity for large inputs. Exploiting an inherent pattern to solve a problem could be easy compared to an algorithm-based approach. Such patterns are quite necessary at cracking games with a vast number of possibilities as an algorithm-based approach would be computationally expensive and time-consuming. The N-Queens problem is one such problem with many possible configurations and realizing a solution to this is hard as the value of N increases. Reinforcement Learning has proven to be good at building an agent that can learn these hidden patterns over time to converge to a solution faster. This study shows how reinforcement learning can outperform traditional algorithms in solving the N-Queens problem.
A Saudi Sign Language Recognition System based on Convolutional Neural Networks

Sign language is the main communication method for deaf people.It is a collection of signs that deaf people use to deal with each other.Deaf people find it difficult to communicate with normal people, as most of them do not understand the signs of the sign language.Sign language recognition systems translate the signs into natural languages and thus shorten the gap between deaf and normal people.Many studies have been done on different sign languages.There is a considerable number of studies on the standard Arabic sign language.In Saudi Arabia, deaf people use the Saudi language, which is different from standard Arabic.This study proposes a smart recognition system for Saudi sign language based on convolutional neural networks.The system is based on the Saudi Sign language dictionary, which was published recently in 2018.In this study, we constructed a dataset of 40 Saudi signs with about 700 images for each sign.We then developed a deep convolutional neural network and trained it on the constructed dataset.To have better recognition, we took images of the signs with different hand sizes, skin colors, lighting, backgrounds, and with/without accessories.The results showed that the recognition model achieved an accuracy of 97.69% for training data and 99.47% for testing data.The model was implemented in two versions: mobile and desktop.
Patterns of interpredictability and principal parts in Latin verb paradigms: an entropy-based approach

Abstract This paper provides a fully word-based, abstractive analysis of predictability in Latin verb paradigms. After reviewing previous traditional and theoretically grounded accounts of Latin verb inflection, a procedure is outlined where the uncertainty in guessing the content of paradigm cells given knowledge of one or more inflected wordforms is measured by means of the information-theoretic notions of unary and n- ary implicative entropy, respectively, in a quantitative approach that uses the type frequency of alternation patterns between wordforms as an estimate of their probability of application. Entropy computations are performed by using the Qumin toolkit on data taken from the inflected lexicon LatInfLexi. Unary entropy values are used to draw a mapping of the verbal paradigm in zones of full interpredictability, composed of cells that can be inferred from one another with no uncertainty. N- ary entropy values are used to extract categorical and near principal part sets, that allow to fill the rest of the paradigm with little or no uncertainty. Lastly, the issue of the impact of information on the derivational relatedness of lexemes on uncertainty in inflectional predictions is tackled, showing that adding a classification of verbs in derivational families allows for a relevant reduction of entropy, not only for derived verbs, but also for simple ones.
Neural Network Design: Learning from Neural Architecture Search

Neural Architecture Search (NAS) aims to optimize deep neural networks' architecture for better accuracy or smaller computational cost and has recently gained more research interests. Despite various successful approaches proposed to solve the NAS task, the landscape of it, along with its properties, are rarely investigated. In this paper, we argue for the necessity of studying the landscape property thereof and propose to use the so-called Exploratory Landscape Analysis (ELA) techniques for this goal. Taking a broad set of designs of the deep convolutional network, we conduct extensive experimentation to obtain their performance. Based on our analysis of the experimental results, we observed high similarities between well-performing architecture designs, which is then used to significantly narrow the search space to improve the efficiency of any NAS algorithm. Moreover, we extract the ELA features over the NAS landscapes on three common image classification data sets, MNIST, Fashion, and CIFAR-10, which shows that the NAS landscape can be distinguished for those three data sets. Also, when comparing to the ELA features of the well-known Black-Box optimization Benchmarking (BBOB) problem set, we found out that the NAS landscapes surprisingly form a new problem class on its own, which can be separated from all 24 BBOB problems. Given this interesting observation, we, therefore, state the importance of further investigation on selecting an efficient optimizer for the NAS landscape as well as the necessity of augmenting the current benchmark problem set.
RETRACTED: Beach sports image detection based on heterogeneous multi-processor and convolutional neural network

Many of the broadcasting requirements, setting beach motion data from a dedicated camera and extracting meaningful information from the data raise important research agendas. Therefore, computer vision and machine learning are essential for the automatic or semiautomatic processing of heterogeneous multiprocessors moving to the beach. The detection accuracy of moving images is low, which takes more time. The proposed system has emerged to identify and classify beach sports services from the deployment of arm-worn gyroscopes for semi-professional beach sportspeople. It is based on off-the-shelf heterogeneous multiprocessors of convolutional neural networks that can distinguish between common service types. This shows the potential of wearable technology for beach sports that can provide accurate motion analysis. Preprocessing is used to remove noisy and irrelevant data. Segmentation is the process of splitting a moving image into components or objects in the image. Classification analysis, and offer heterogeneous multi-processor analyzes the latest detailed performance evaluation based on Convolutional Neural Network system. Therefore, this study will use Heterogeneous Multi-Processor beach sports as raw material image detection technology analysis. In this study, an equal edge detection image detection technique, gradation processing, target acquisition and target recognition to the sports video's actual needs combined to meet the needs of different dynamic image detection.
Semi-parametric survival analysis via Dirichlet process mixtures of the First Hitting Time model

Time-to-event data often violate the proportional hazards assumption inherent in the popular Cox regression model. Such violations are especially common in the sphere of biological and medical data where latent heterogeneity due to unmeasured covariates or time varying effects are common. A variety of parametric survival models have been proposed in the literature which make more appropriate assumptions on the hazard function, at least for certain applications. One such model is derived from the First Hitting Time (FHT) paradigm which assumes that a subject's event time is determined by a latent stochastic process reaching a threshold value. Several random effects specifications of the FHT model have also been proposed which allow for better modeling of data with unmeasured covariates. While often appropriate, these methods often display limited flexibility due to their inability to model a wide range of heterogeneities. To address this issue, we propose a Bayesian model which loosens assumptions on the mixing distribution inherent in the random effects FHT models currently in use. We demonstrate via simulation study that the proposed model greatly improves both survival and parameter estimation in the presence of latent heterogeneity. We also apply the proposed methodology to data from a toxicology/carcinogenicity study which exhibits nonproportional hazards and contrast the results with both the Cox model and two popular FHT models.
RETRACTED: Research on teaching based on tennis-assisted robot image recognition

Design and development of an autonomous robot will collect the scattered tennis balls on the tennis court. To make the camera active, the robot uses a camera placed above from these recently collected the ball to roll. And, given the machine design, because it falls to prevent the ball, catch the ball. Image processing, the use of color filters, the detected edge detection algorithm, corresponds to the Raspberry Pi 3, can be achieved by the distance to the library implementation of robot OpenCV, it is sorted. After the desired robot angle is generated, it is translated to the appropriate motor speed value is sent to the motor control unit. To realize this behavior, it has four DC motors to be used. For the movement platform, one of which is a scanning operation of the ball, which is used to control the camera angle, the last one will be used for the ball detent mechanism. The realized by the discrete PI controller with two DC motors position and speed control motor controller boards of the rotary encoder. Experimental tests of the real world validate the design of the system.
Peculiarities of forming high-demanded soft skills in the educational space of the escape room

The problem and the aim of the study. Including game elements and digital services into the students' cognitive activity are the current trends in the development of the didactic system. The designing of an interactive escape room as an organizational form of cognition and learning technology is one of the ways of gamification of the digital educational space. The authors investigate the problems of forming the high-demanded competencies through interactive quest games. These competencies are necessary for graduates to solve future professional tasks and for successful socialization. Research methods. The analysis of fundamental scientific works on the problem of gamification in education and the usage of digital resources for forming high-demanded soft skills were used to obtain theoretical generalizations. The escape room was designed using modern interactive technologies. 48 students of specialty 44.03.01 Pedagogical education (31% of boys and 69% of girls) of Vyatka State University, Kirov, were involved in the designing of game areas for educational purposes. At the control stage of the experiment, the G-criterion was used to evaluate the changes in over-professional competencies being formed. Results. The essence of the concept of an escape room has been clarified as a game space fully adapted to a specific story, and each single element of which is designed to respond to one common plot, including thematic tasks. The stages of designing and implementation of the escape room for educational purposes were described: determination of the system of planned results, over-subject competencies; development of the game scenario; reasonable choice of software and hardware support; correlation of the emotional and psychological component with the filling of the interactive educational space. The empirical value of Gemp=3&lt;Gcr=9 (for p=0.01) obtained in the course of the experiment confirms that the shift towards improving the quality of learning after applying the proposed approach is not random. In conclusion, the authors generalized the factors that provide the effective use of interactive resources of escape rooms to form the high-demanded soft skills: taking into account the peculiarities of game mechanics; professional aspirations and interests; students' cognition style; correlation of actually formed soft skills with planned ones, etc.
A Password Meter without Password Exposure

To meet password selection criteria of a server, a user occasionally needs to provide multiple choices of password candidates to an on-line password meter, but such user-chosen candidates tend to be derived from the user’s previous passwords—the meter may have a high chance to acquire information about a user’s passwords employed for various purposes. A third party password metering service may worsen this threat. In this paper, we first explore a new on-line password meter concept that does not necessitate the exposure of user’s passwords for evaluating user-chosen password candidates in the server side. Our basic idea is straightforward; to adapt fully homomorphic encryption (FHE) schemes to build such a system but its performance achievement is greatly challenging. Optimization techniques are necessary for performance achievement in practice. We employ various performance enhancement techniques and implement the NIST (National Institute of Standards and Technology) metering method as seminal work in this field. Our experiment results demonstrate that the running time of the proposed meter is around 60 s in a conventional desktop server, expecting better performance in high-end hardware, with an FHE scheme in HElib library where parameters support at least 80-bit security. We believe the proposed method can be further explored and used for a password metering in case that password secrecy is very important—the user’s password candidates should not be exposed to the meter and also an internal mechanism of password metering should not be disclosed to users and any other third parties.
Cloud Computing: The New World of Technology

In this era of technology, data storage and its management plays an important role. Every organization include loads of data, storing and managing bundle of data is a major issue. A convenient approach is required to handle it so that accessing and updating it become simple and easy. Apart from storage and management, freelancing is an emerging trend nowadays. It requires programming languages and their frameworks to cater to the consumer interests. Yet the availability of all frameworks on a single system is an arduous task which can be eradicated by creating our own database for the purpose of data storage, multiple system usage and software development. But it is costly and time-consuming task. For proper utilization of time, money and space, Cloud Computing comes into play these days. Therefore, in this module, we will discuss about Cloud Computing its working, deployment and service mode of Cloud Computing along with their pros and cons.
TMA: Tera‐MACs/W neural hardware inference accelerator with a multiplier‐less massive parallel processor

Summary Computationally intensive inference tasks of deep neural networks have brought about a revolution in accelerator architecture, aiming to reduce power consumption as well as latency. The key figure‐of‐merit in hardware inference accelerators is the number of multiply‐and‐accumulation operations per watt (MACs/W); the state‐of‐ the‐art MACs/W, so far, has been several hundreds Giga‐MACs/W. We propose a Tera‐ MACS/W neural hardware inference accelerator (TMA) with 8‐bit activations and scalable integer weights less than 1‐byte. The architecture's main feature is a configurable neural processing element for matrix‐vector operations. The proposed neural processing element uses a multiplier‐less massive parallel processor that works without multipliers, which makes it attractive for energy efficient high‐performance neural network applications. We benchmark our system's latency, power, and performance using Alexnet trained on ImageNet. Finally, we compared our accelerator's throughput and power consumption to that of the prior works. The proposed accelerator outperforms the state‐of‐the‐art counterparts, in terms of the energy and area efficiency, achieving 2.3 TMACs/W@1.0 V on a 28‐nm Virtex‐7 FPGA chip.
Virtual trainer system: a tool to increase exercise participation and work productivity

Purpose Due to lack of motivation, individuals often fail to perform regular exercise. In view of this, we developed a virtual trainer system (VTS) to encourage users to perform simple exercise routines regularly. Design/methodology/approach A design science approach is adopted to develop a VTS to motivate users to exercise. Study findings are based on a field experiment with 91 participants recruited from a university in Hong Kong. Findings Outcome-oriented reminders foster stronger perceived risks of health and perceived value of exercises, whereas virtual trainer attractiveness has insignificant effect. Perceived value of exercises is positively related to exercise participation, which has a positive relationship with work productivity. Research limitations/implications The findings answer question of how to motivate people to continue exercising. Practical implications Findings provide insights for fitness companies to design online exercise training for users. Social implications VTS can promote regular exercise and healthy life. Originality/value This research shows that interactive virtual agents can motivate users to exercise regularly. It contributes to the burgeoning research on the use of IT artifacts for improving exercise participation and provides practical insights into VTS designs.
Offensive Security of Keyboard Data Using Machine Learning for Password Authentication in IoT

In this paper, to increase the attack success rate, we analyzed the distribution of all collected keyboard data based on the distance of time and keyboard scancode data, which presents the crucial data from the previous study. To achieve this, we derived time-distance based features that have higher attack success rates than in previous studies. The proposed attack method defines 6 features, and evaluates the performance based on 18 datasets. For performance evaluation, the accuracy, precision, recall, F1-score, and AUC of Datasets (1 to 3) were compared, and two experiments showed improved overall performance by at least 10.6 % and up to 16.1 % compared to previous studies in terms of the performance evaluation for each feature, comparison of variations in maximum performance, comparison of variations in performance of each feature, and comparison of variations in overall performance. Moreover, the best accuracy, which represents the probability of password exposure, was 96.7 %, which suggests that our proposed attack method has a higher accuracy than the previous study (96.2 %). In conclusion, we demonstrated that password authentication is neutralized by stealing the user password more effectively. For future research, we will focus on improving the attack success rate with respect to accuracy and overall performance numbers, using not only machine learning, but also deep learning.