Reviewerly/exHarmony huggingface dataset:
Employee Behavioural Factors and Information Security Standard Compliance in Nigeria Banks

Information security analysts acknowledge that cyber-attacks, information theft, and internet fraud are prevalent within the banking industry.One of the issues precipitating this trend is non-compliance with standards and policies by employees.In Nigeria, employee behavioral factors that determine compliance with international information security standards and policies have not been empirically assessed.An understanding of these factors is critical in combatting cyber-related crimes, as this provides organizations with accurate information, which enables the strengthening of existing security mechanisms.An investigation into the effect of employee's behavioral factors on information security standards and policies (ISSsPs) was undertaken at selected Nigerian banks.Partial least squares structural equation modelling (PLS-SEM) was adopted for the analysis of data obtained from 370 employees of selected banks in South-West, Nigeria.The findings indicated that behavioral factors such as normative belief, security awareness, perception biases and certainty of detection positively influence employees' ISSsPs compliance.However, the severity of the penalty for non-compliance and perceived effectiveness of ISSP did not influence employees' actions.
TweetScore

The spammers have been grossly detrimental since the inception of Twitter social networks and keep polluting social environments by hiding themselves among a large amount of normal users. In this paper, we aim to address two challenges existing in the spammer detection problem: 1) monitoring tweets that have a higher probability of including spam messages; 2) providing an accurate solution for spam classification. To address these two challenges, we first propose a pseudo-honeypot framework for efficient tweets monitoring and collection. By taking advantage of users' diversity and selecting normal users as the parasitic body, the pseudo-honeypot can harness normal users with features having much more potentials of attracting spammers. This lets the pseudo-honeypot collect tweets that are far more likely to include spam messages. Furthermore, we design a novel spam classification solution called TweetScore by exploring both the intrinsic attributes' and users' relationships in social networks. TweetScore quantifies such relationships into a vector of numerical values to represent each tweet's score, reflecting the associated user's behaviors. The neural network is then employed to take these vectors as input to classify spams and spammers. Through extensive experiments, we demonstrate the efficiency of the pseudo-honeypot system on spam monitoring and the accuracy of TweetScore on spam classification. Specifically, the spam and spammer ratios collected by our pseudo-honeypot system are four times as much as those of a non pseudo-honeypot counterpart while the TweetScore can achieve, on an average, 93.5% accuracy, 93.71% precision, and 1.52% false positive in online spam classification.
A Petz-féle sebészeti gyomorvarrógép; elő és utóélet, és ami közte van. Egy elhallgatott hungarikum

The surgical stapler is a "sine qua non" of any branch of present day thoracoabdominal surgery.However, when the origin of the surgical stapler is mentioned, it is usually referred to as the "Soviet (or Russian) machine" , ignoring the names of the pioneers Petz and Hültl, or being just incidentally mentioned at the best.The hard rock fact is, that the gastric stapler constructed by Aladar Petz and presented to the world in 1921 is the archetype of all subsequent surgical staplers up to now.The concept and the machine became of both a scientific and business success.It took the first half of the 20th century till the stapling line of the hollow organs have got established and became a standard operational procedure.The original machine was excusively for gastric resection, a concept developed further by the Russians in the mid 1950s and applied for different other anatomical structures as well.The Budapest-Moscow knowledge transfer of the concept (and machine) was orchestrated by the world famous Soviet surgeon Boris Petrowsky, who recognised the importance of the idea.The result, the Russian UKL family lung staplers became a Holy Grail of the tuberculosis and later lung cancer surgery.The first lines of the USA chapter of bronchial staplers were written by the eminent Boston surgeon Mark Ravitch (himself descendant of Russian emigrees).Technical moodifications follwed resulting in the TA machines (thoracoabdominal).The metal machines were replaced by the disposable plastic ones in the 1980s, irrespectively to their ecological footprint and economic burden.No branch of modern visceral surgery can afford itself the lack of the surgical staplers: they are required everywhere.The horizon of the application is very wide, indeed: extending from the damage control surgery of torso trauma to the high precision robotic surgical procedures.The spirit of Aladar Petz is all aroundkeep his heritage alive.
Implementation of k-Medoids Clustering Algorithm to Cluster Crime Patterns in Yogyakarta

The increase in crime from day to day needs to be a concern for the police, as the party responsible for security in the community. Crime prevention effort must be done seriously with all knowledge that they have. To increase police performance of crime prevention effort, it is necessary to analyze crime data so that relevant information can be obtained.This study tried to analyze crime data to obtain relevant information using clustering in data mining.Clustering is a data mining method that can be used to extract valuable information by grouping data into groups that have similar characters.The data used in this study were crime patterns which were then grouped using K-medoids clustering algorithm.The obtained results in this study were three crime groups, namely high crime levelwith 4 members, medium crimelevel with 6 members and low crime level with 8 members.It is expected that this information can be used as material for consideration in crime prevention effort
The quantitative prediction of tectonic fractures of Longmaxi formation in Dingshan area, southeastern Sichuan Basin

Tectonic fractures are a significant factor for production of the Longmaxi formation in Dingshan area. The analyse of the distribution and development degree of tectonic fractures is the main research method in present-day. Based on the data of field outcrops and borehole core, the new method-binary method is put forward by rock fracture and energy value to decide tectonic fracture development. According to the method the fitting formula is offered between the relation of the value of rock fracture, energy and the fracture density. Compared the fracture density predictive value and the observed value of 15 wells, all wells relative error less than 25%. The prediction results are accurate. According to the fitting formula of fracture development and distribution in the study area are obtained. The results show that the fracture density is high as a whole of the Longmaxi formation in Dingshan area. Whole shows by south east to gradually reduce the trend of the north west. Supported the future of the oil and gas exploration to provide geological basis and guidance in Dingshan area.
Virtual reality of three‐dimensional surgical field for surgical planning and intraoperative management

None declared.
CSRS: A Chinese Seal Recognition System With Multi-Task Learning and Automatic Background Generation

As an important part of the Chinese painting and calligraphy, the seals not only have a high value of art but also contain a lot of information about the artwork itself. At this digital age, we would like not only be able to represent the seals in the digital format, but also like to use image processing techniques to help us better understand them. With the development of deep learning, convolutional neural networks have been widely used in the fields of feature learning, object localization, and classification. Based on deep learning technology, this paper proposes a highly accurate Chinese seal recognition system (CSRS). With our CSRS, users could simply input a single seal image into the system, then CSRS would automatically recognize the seal and report the relevant information in real-time. The CSRS mainly contains three units. 1) A new Siamese network with multi-task learning (Siamese-MTL), which can effectively solve the similarity measurement problem and improve the generalization of the model. 2) A new online data generation algorithm called automatic background generation (ABG) which could generate numerous seal images with different backgrounds for effective training. 3) A new training method for Siamese network which based on a central constraint. In order to validate the effectiveness of the proposed method, we have established two large scale seals image databases, including 15,000 Chinese seal images and 1,700 background images, respectively. We evaluate our method and compare with the variant methods on these datasets, achieving the highest performance. The extensive experimental results indicate that our proposed method is effective and has a great potential for the practical application in Chinese seals recognition.
Ambient audio authentication

In the IoT environment, many terminal devices are deployed in unattended areas. If these devices are moved elsewhere by an attacker, the wrong environmental sensing values are obtained, which causes a major disaster. In this paper, we propose an ambient authentication mechanism based on audio to be used in multi-factor authentication by using the ambient sensors equipped with a smart phone. An ultrasonic signal that is not detectable by the human ear was transmitted by the authenticator, and the attenuated signal received by the user being authenticated was transmitted back to the authentication server. The transmitted and received decoded symbol sequences of the audio signal are used to calculated the bit error rate, which is used to measure the relative distance. Our proposed method can narrow the authentication distance to less than 0.5 meters, which can greatly improve the security of the authentication system.
Overview of the Intelligent Personal Assistants

The article provides an overview of intelligent personal assistants (IPA) (also known as conversational agents, smart speakers, digital / intelligent personal assistants, or voicecontrolled agents). The main technical features of the IPAs, including Google Assistant, Amazon Alexa, Microsoft Cortana, Apple Siri, are outlined along with their reported adoption in private and public environments. IPA research trends and ideas for IPA research in Ukrainian context are also discussed.
Music Generation Using an Interactive Evolutionary Algorithm

Music generation with the aid of computers has been recently grabbed the attention of many scientists in the area of artificial intelligence. Deep learning techniques have evolved sequence production methods for this purpose. Yet, a challenging problem is how to evaluate a music generated by a machine. In this paper, a methodology has been developed based upon an interactive evolutionary optimization method, with which the scoring of the generated musics are primarily performed by human expertise, during the training. This music quality scoring is modeled using a BiLSTM recurrent neural network. Moreover, the innovative generated music through a Genetic algorithm, will then be evaluated using this BiLSTM network. The results of this mechanism clearly show that the proposed method is able to create pleasurable melodies with desired styles and pieces. This method is also quite fast, compared to the state-of-the-art data-oriented evolutionary systems.
“Appuhamy” - The Fully Automatic Rubber Tapping Machine

"ENGINEER", established in 1973 by The Institution of Engineers, is a Journal for dissemination of Engineering knowledge, published quarterly.If you are interested in contributing an Original Technical Paper based on research by the author(s) (intended for Section I) or an Original Article of Professional or Technical interest related to Engineering. (Section II) to this journal please go through the publication checklist which can be downloaded from here.Cover DescriptionCover images show views of the Giant tank and the Tekkama anicut.
Fast Blind Image Deblurring Using Smoothing-Enhancing Regularizer

Blind deconvolution is a highly ill-posed problem for the restoration of degraded images and requires prior knowledge or regularization. Recently, various priors have been proposed and the models based on these priors have achieved state-of-the-art performances. In this paper, we present a blind image deblurring method based on a computationally efficient and effective image regularizer. The proposed regularizer is motivated by the fact that the success of recent priors mainly stems from their properties, which implicitly generate an unnatural latent image suppressing insignificant structures and preserving only salient edges. These salient edges guide the models to estimate an accurate kernel. In this paper, the proposed regularizer termed smoothing-enhancing regularizer, not only assures that only salient structures in the image are preserved but also enhances these salient structures to help the model estimate the more accurate kernel. To efficiently solve the proposed model, we develop an efficient numerical approach based on the half-quadratic splitting algorithm and the lagged-fixed-point iteration scheme. The optimization scheme only requires a few additional shrinkage operations compared with the original half-quadratic splitting algorithm, making our method much faster than recent leading methods. The qualitative and quantitative experimental results show that our algorithm achieves the state-of-the-art results and can be extended to other challenging deblurring tasks, such as those involving text, face, and low-illuminated images. Furthermore, the proposed method is much more computationally efficient than the recent state-of-the-art algorithms with up to more than 10× faster execution time.
Semi-Supervised Teacher-Student Architecture for Relation Extraction

Generating a large amount of training data for information extraction (IE) is either costly (if annotations are created manually), or runs the risk of introducing noisy instances (if distant supervision is used). On the other hand, semi-supervised learning (SSL) is a cost-efficient solution to combat lack of training data. In this paper, we adapt Mean Teacher (Tarvainen and Valpola, 2017), a denoising SSL framework to extract semantic relations between pairs of entities. We explore the sweet spot of amount of supervision required for good performance on this binary relation extraction task. Additionally, different syntax representations are incorporated into our models to enhance the learned representation of sentences. We evaluate our approach on the Google-IISc Distant Supervision (GDS) dataset, which removes test data noise present in all previous distance supervision datasets, which makes it a reliable evaluation benchmark (Jat et al., 2017). Our results show that the SSL Mean Teacher approach nears the performance of fully-supervised approaches even with only 10% of the labeled corpus. Further, the syntax-aware model outperforms other syntax-free approaches across all levels of supervision.
Revisiting comparison between entanglement measures for two-qubit pure states

Given a non-maximally entangled state, an operationally significant question is to quantitatively assess as to what extent the state is away from the maximally entangled state, which is of importance in evaluating the efficacy of the state for its various uses as a resource. It is this question which is examined in this paper for two-qubit pure entangled states in terms of different entanglement measures such as negativity (N), logarithmic negativity (LN), and entanglement of formation (EOF). Although these entanglement measures are defined differently, to what extent they differ in quantitatively addressing the earlier mentioned question has remained uninvestigated. The theoretical estimate in this paper shows that an appropriately defined parameter characterizing the fractional deviation of any given entangled state from the maximally entangled state in terms of N is quite different from that computed in terms of EOF, with their values differing up to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>∼<!-- ∼ --></mml:mo> </mml:mrow> <mml:mn>15</mml:mn> <mml:mi mathvariant="normal">%<!-- % --></mml:mi> </mml:math> for states further away from the maximally entangled state. Similarly, the values of such fractional deviation parameters estimated using the entanglement measures LN and EOF also strikingly differ among themselves, with the maximum value of this difference being around 23%. This analysis is complemented by illustration of these differences in terms of empirical results obtained from a suitably planned experimental study. Thus, such an appreciable amount of quantitative non-equivalence between the entanglement measures in addressing the experimentally relevant question considered in the present paper highlights the requirement of an appropriate quantifier for such intent. We indicate directions of study that can be explored towards finding such a quantifier.
Human action recognition based on HOIRM feature fusion and AP clustering BOW

In this paper, we propose a human action recognition method using HOIRM (histogram of oriented interest region motion) feature fusion and a BOW (bag of words) model based on AP (affinity propagation) clustering. First, a HOIRM feature extraction method based on spatiotemporal interest points ROI is proposed. HOIRM can be regarded as a middle-level feature between local and global features. Then, HOIRM is fused with 3D HOG and 3D HOF local features using a cumulative histogram. The method further improves the robustness of local features to camera view angle and distance variations in complex scenes, which in turn improves the correct rate of action recognition. Finally, a BOW model based on AP clustering is proposed and applied to action classification. It obtains the appropriate visual dictionary capacity and achieves better clustering effect for the joint description of a variety of features. The experimental results demonstrate that by using the fused features with the proposed BOW model, the average recognition rate is 95.75% in the KTH database, and 88.25% in the UCF database, which are both higher than those by using only 3D HOG+3D HOF or HOIRM features. Moreover, the average recognition rate achieved by the proposed method in the two databases is higher than that obtained by other methods.
Bayesian hidden Markov tree models for clustering genes with shared evolutionary history

Determination of functions for poorly characterized genes is crucial for understanding biological processes and studying human diseases.Functionally associated genes are often gained and lost together through evolution.Therefore identifying co-evolution of genes can predict functional gene-gene associations.We describe here the full statistical model and computational strategies underlying the original algorithm CLustering by Inferred Models of Evolution (CLIME 1.0) recently reported by us (Cell 158 (2014) 213-225).CLIME 1.0 employs a mixture of tree-structured hidden Markov models for gene evolution process, and a Bayesian model-based clustering algorithm to detect gene modules with shared evolutionary histories (termed evolutionary conserved modules, or ECMs).A Dirichlet process prior was adopted for estimating the number of gene clusters and a Gibbs sampler was developed for posterior sampling.We further developed an extended version, CLIME 1.1, to incorporate the uncertainty on the evolutionary tree structure.By simulation studies and benchmarks on real data sets, we show that CLIME 1.0 and CLIME 1.1 outperform traditional methods that use simple metrics (e.g., the Hamming distance or Pearson correlation) to measure co-evolution between pairs of genes.
An Image Is Worth More than a Thousand Favorites: Surfacing the Hidden Beauty of Flickr Pictures

The dynamics of attention in social media tend to obey power laws. Attention concentrates on a relatively small number of popular items and neglecting the vast majority of content produced by the crowd. Although popularity can be an indication of the perceived value of an item within its community, previous research has hinted to the fact that popularity is distinct from intrinsic quality. As a result, content with low visibility but high quality lurks in the tail of the popularity distribution. This phenomenon can be particularly evident in the case of photo-sharing communities, where valuable photographers who are not highly engaged in online social interactions contribute with high-quality pictures that remain unseen. We propose to use a computer vision method to surface beautiful pictures from the immense pool of near-zero-popularity items, and we test it on a large dataset of creative-commons photos on Flickr. By gathering a large crowdsourced ground truth of aesthetics scores for Flickr images, we show that our method retrieves photos whose median perceived beauty score is equal to the most popular ones, and whose average is lower by only 1.5%.
A Grounded Unsupervised Universal Part-of-Speech Tagger for Low-Resource Languages

Ronald Cardenas, Ying Lin, Heng Ji, Jonathan May. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
A framework of procedural considerations for implementing virtual reality in design review

The implementation of immersive virtual reality in the construction sector is experiencing a period of renewed interest since the research efforts of the early 2000s.VR-related applications are increasing in number and scopes, with a focus on communication and analysis of design intents.On the other hand, procedural challenges still obstacle their effective adoption for such purposes.The paper proposes a preliminary set of procedural considerations to take into account when immersive VR is used for feedback collection in design reviews.It is the result of an inductive approach associated with qualitative research methods based on seven semi-structured interviews with representative design stakeholders (i.e., twenty-four), including clients, designers, endusers and an accessibility expert.They were asked to evaluate VR adoption for the usability-focused analysis of a new school building.The framework represents a first step to address in a systematic way the practical implications related to VR-aided design reviews, especially when clients and end-users are involved.