Reviewerly/exHarmony huggingface dataset:
Mobile Phishing Websites Detection and Prevention Using Data Mining Techniques

&lt;strong&gt;Abstract&lt;/strong&gt;— The widespread use of smart phones nowadays makes them vulnerable to phishing. Phishing is the process of trying to steal user information over the Internet by claiming they are a trusted entity and thus access and steal the victim's data(user name, password and credit card details). Consequently, the need for mobile phishing detection system has become an urgent need. And this is what we are attempting to introduce in this paper, where we introduce a system to detect phishing websites on Android phones. That predicts and prevents phishing websites from deceiving users, utilizing data mining techniques to predict whether a website is phishing or not, relying on a set of factors (URL based features, HTML based features and Domain based features). The results shows system effectiveness in predicting phishing websites with 97% as prediction accuracy.
The Creation of Maze in Order to Hide Data, and the Proposal of Method Based on AES Data Encryption Algorithm

The field of science, which enables the transmission of private data in multimedia environments such as display, audio and video, is called steganography.The widespread use of the internet in our daily lives has brought about the problem of transmitting confidential information.Hence, the need for steganography science has also increased.In this study, a labyrinth is produced in the size of the environment in which the data can hide with the help of a private key.Color images with 24-bit depth were used as data hiding environment in different measures and different formats.For the production of maze, the Depth First Search algorithm, which ensures both the creation of the maze and the finding of solution path together with this, was utilized.It is made the information hide inside the pixels on the image, corresponding to the coordinates of the solution path obtained from the maze.The Lowest Bit Insertion method was used to hide information.In order to make the proposed method more robust, the data, which will be hidden before the data hiding process was encrypted with the AES encryption algorithm.This makes it difficult for third parties to obtain confidential data.The proposed method was tested with detailed security analysis and it was observed that data hiding / data extraction processes were successful.
Study on the Influence of Diversity and Quality in Entropy Based Collaborative Clustering

The aim of collaborative clustering is to enhance the performances of clustering algorithms by enabling them to work together and exchange their information to tackle difficult data sets. The fundamental concept of collaboration is that clustering algorithms operate locally but collaborate by exchanging information about the local structures found by each algorithm. This kind of collaborative learning can be beneficial to a wide number of tasks including multi-view clustering, clustering of distributed data with privacy constraints, multi-expert clustering and multi-scale analysis. Within this context, the main difficulty of collaborative clustering is to determine how to weight the influence of the different clustering methods with the goal of maximizing the final results and minimizing the risk of negative collaborations—where the results are worse after collaboration than before. In this paper, we study how the quality and diversity of the different collaborators, but also the stability of the partitions can influence the final results. We propose both a theoretical analysis based on mathematical optimization, and a second study based on empirical results. Our findings show that on the one hand, in the absence of a clear criterion to optimize, a low diversity pool of solution with a high stability are the best option to ensure good performances. And on the other hand, if there is a known criterion to maximize, it is best to rely on a higher diversity pool of solution with a high quality on the said criterion. While our approach focuses on entropy based collaborative clustering, we believe that most of our results could be extended to other collaborative algorithms.
Assessing incitement to hatred as a crime against humanity of persecution

The main issue this paper aims to tackle is whether incitement to hatred, independently of direct incitement to violence should amount to the actus reus of persecution as a crime against humanity. Before determining whether hate speech can be treated as an international crime, this article assesses theories and current international legal standards in the field of freedom of expression in order to identify the space for such a drastic intrusion into this freedom. The most significant justification for an international criminalisation of hate speech in certain contexts is that it enables large-scale discrimination and threatens basic human rights, including the right to life, of the targeted out-group. Current international criminalisation, as interpreted by the jurisprudence of the Yugoslavia and Rwanda tribunals, still shows an overly narrow approach, excluding much of the hate propaganda that plays a vital role in the enabling of mass crimes. This article concludes that systematic incitement to hatred should be treated as a form of persecution, regardless of a call to violence, when the inciter has knowledge of the fact that his words might contribute to the commission of grave crimes or large-scale discrimination against the targeted out-group and the denial of human rights of its members.
Designing and Implementing an Organoleptic Test Application for Food Products Using Android Based Decision Tree Algorithm

&lt;p&gt;The development of food and beverage products increases over the years. The high level of food consumption led to the presence of new food and beverage brands produced in Salatiga. In accordance with this fact, Organoleptic Tests or in other words evaluation of food products could be conducted well if the obtained dataset had a complete, rapid, and accurate information. The required information could be clarified into the dataset that was used to calculate the testing. Manually calculating the process was a problem that usually appeared. As a result, losing or having a broken report on the organoleptic test often occurred. Mileage of the research setting became another problem that appeared when the research conducted out of the town, Salatiga. A combination of R&amp;amp;D method and application of algorithm was implemented in this research by using the performed stages. These stages started from the identification of problems to the testing of the systems. During the process of organoleptic testing, &lt;em&gt;Weka 3.8.2&lt;/em&gt; was used as a tool to test the C4.5 algorithm and an implementation of the applied algorithm on the android. The results of this study revealed that the average classification accuracy of the J48 algorithm in Weka could achieve above 90% accuracy and average score of hedonic scaling system was in “agree” category. It also showed that the algorithm and the testing of the systems which implemented on android has a credible performance in classifying data.&lt;/p&gt;
Triple-adjacent-frame generative network for blind video motion deblurring

Photos and videos captured by handheld imaging devices are often accompanied by unwanted blur because of hand jitters and fast movement of objects during the exposure time. Most previous studies discussed single image deblurring and video deblurring but neglected detailed analyses of the spatiotemporal continuity between adjacent frames, which limits the deblurring effect. We propose a novel end-to-end blind video motion deblurring network that takes triple adjacent frames as input to deblur a blurry video frame. In our approach, a bidirectional temporal feature transfer between triple adjacent frames is implemented to pass the latent features of the central frame on to a group encoder of its neighbors. Then, a hybrid decoder decodes group features and estimates a sharper video frame relative to the central frame. Experimental results show that our model outperforms previous excellent methods in terms of traditional metrics (PSNR and SSIM) and visual quality within an acceptable time cost. The code is available at https://github.com/BITLIULONGEE/Triple-Adjacent-Frame-Generative-Network.
Initial Experiments with Duet Benchmarking: Performance Testing Interference in the Cloud

Accurate performance testing may require many measurements and therefore many machines to execute on. When many machines are needed, the cloud offers a tempting solution, however, measurements conducted in the cloud are generally considered unstable. In the context of comparing performance of two workloads, we propose a measurement procedure that improves accuracy by executing the workloads concurrently and using the measurements to filter outside interference. Depending on the platform used, experiments show average accuracy improvement ranging from 114% to 683% over sequential measurements on workloads running the ScalaBench suite with the Graal compiler.
Social Network Sensitive Area Perturbance Method Based on Firefly Algorithm

Social network analysis has many essential applications, but it depends on sharing and publishing the underlying graph. The real dataset published by social networks has great appeal to researchers and research institutions. However, publishing datasets can create many security and privacy problems. A standard technique for achieving link privacy is to randomize a link over the space for node pairs based on probabilistically. Aiming at the problem of social network link privacy, a Sensitive Area Perturbance Based on Firefly (SAPBF) algorithm is proposed. Improve the data availability of the published graph by limiting the range of random perturbations. The specific method is to measure the influence of nodes, such as k-core, degree, and PageRank algorithms to obtain nodes with different influences. The firefly algorithm is applied to the social network to find high-influence nodes. The low-influence nodes are gathered around the high-influence nodes to form sensitive areas. Finally, according to different edge retention probability, the random perturbation algorithm protects privacy in the sensitive area. The content of the social network is continuously enriched and the scale is increasing. The performance of the anonymous algorithm of the single-machine social network graph is limited. The SAPBF algorithm is parallelized based on the Pregel model. The results of the algorithm in the real experimental dataset show that the proposed algorithm guarantees the property of the graph while ensuring the anonymity. Compared with the traditional random perturbation algorithm, the distributed processing graph improves the efficiency while ensuring the data availability of the graph.
The Effect of Motivation on Employee Creativity: Evidence from NGO Sector in Southern Punjab, Pakistan

IntroductionEvery Organization has required human and material (tangible) resources to function efficiently which help it to achieve its goals.Role of HR is more important than tangible resources because a human has to utilize tangible resources effectively and efficiently for a better outcome.Similarly, the role of creativity considers as an important factor in the performance of every firm in this era of the highly competitive environment.The importance of creativity could not be ignored in a single firm.Creative and innovative firms have a better ability in response to unpredictable challenges and threatening (Reiter-Palmon & Illies, 2004).Moreover, it is identified as an essential part of the organizational survival and remains in the competition.Therefore the firms motivate their employees which are capable of creativity.Motivation is something which makes an employee capable of doing some creative work because of its leads an employee towards doing some action.Motivation is a process which inspires, encourages, and enhances confidence to do some creative work.Creativity is defined as "The generation of ideas has to be organized and initiated by the senior management of an organization" (Wu, McMullen, Neubert, & Yi, 2008).Managers from different organizations use different tactics to motivate employees such as, an employee of the month, bonuses and promotions.These are very useful tactics which surely contribute to employee's performance.At the point when supervisors focus on building up representatives' capabilities and testing, fascinating work, workers' psychological or persuasive procedures are upgraded (Oldham & Cummings, 1996).Employee creativity improves with the help of colleagues, cooperation, open correspondence and informative feedback (Zhou & George, 2001).Bringing creativity needs motivation from organizations, the employees must be encouraged in suggesting new ideas, which could be used.Employees of an organization will have assurance of their survival and support of their seniors their efficiency and creative performance will be increased (Eder & Sawyer, 2007).Employees of an organization are considered as asset of the organization.Some of them are performing very well, but some are not.Every employee has different mind from other.
Analisis Perbandingan Algoritma Static Round-Robin dengan Least-Connection Terhadap Efisiensi Load Balancing pada Load Balancer Haproxy

In IT networking, load balancing used to share the traffic between backend servers. The idea is to make effective and efficient load sharing. Load balancing uses scheduling algorithms in the process includes Static round-robin and Least-connection algorithm. Haproxy is a load balancer that can be used to perform the load balancing technique and run by Linux operating systems. In this research, Haproxy uses 4 Nginx web server as backend servers. Haproxy act as a reverse proxy which accessed by the client while the backend servers handle HTTP requests. The experiment involves 20 Client PCs that are used to perform HTTP requests simultaneously, using the Static round-robin algorithm and Least-connection on the haproxy load balancer alternately. When using Static round-robin algorithm, the results obtained average percentages of CPU usage successively for 1 minute; 5 minutes; and 15 minutes are; 0.1%; 0.25%; and 1.15% with average throughput produced is 14.74 kbps. Average total delay produced 64.3 kbps. The average total delay and jitter is 181.3 ms and 11.1 ms, respectively. As for the Least-connection algorithm average percentage obtained successively for 1 minute; 5 minutes; and 15 minutes are 0.1%; 0.3%; and 1.25% with the average throughput produced is 14.66 kbps. The average total delay and jitter is 350.3 ms and 24.5 ms, respectively. It means Static round-robin algorithm is more efficient than the algorithms Least-connection because it can produce a greater throughput with less CPU load and less total delay.
Knowledge Topic-Structure Exploration for Online Innovative Knowledge Acquisition

Researchers and practitioners have long realized the importance of acquiring innovative knowledge from online innovation communities. However, it has still been a challenge to identify valuable information of interest from unstructured and noisy user-generated text. The extant research trying to solve this problem is limited by the generalizability issue and the lack of topic-level investigation. In this article, we proposed an automatic hybrid model to simultaneously extract knowledge topics and knowledge structures from free text in online innovation communities. By employing a topic model, one important contribution of this article is the elimination of manual intervention and addressing of the generalizability issue faced by existing supervised approaches. Another major contribution of this article is the design of a novel solution for knowledge structure construction by combining a topic model with association analysis; this solution addresses the weakness of the traditional topic model. We evaluate the validity of the proposed hybrid model in experiments with a real-world dataset. The results show that by providing a new angle for innovative knowledge acquisition and management, this proposed method is effective in extracting meaningful innovative knowledge and in uncovering knowledge structures.
Deep CNN-based Features for Hand-Drawn Sketch Recognition via Transfer Learning Approach

Image-based object recognition is a well-studied topic in the field of computer vision.Features extraction for hand-drawn sketch recognition and retrieval become increasingly popular among the computer vision researchers.Increasing use of touchscreens and portable devices raised the challenge for computer vision community to access the sketches more efficiently and effectively.In this article, a novel deep convolutional neural network-based (DCNN) framework for hand-drawn sketch recognition, which is composed of three wellknown pre-trained DCNN architectures in the context of transfer learning with global average pooling (GAP) strategy is proposed.First, an augmented-variants of natural images was generated and sum-up with TU-Berlin sketch images to all its corresponding 250 sketch object categories.Second, the features maps were extracted by three asymmetry DCNN architectures namely, Visual Geometric Group Network (VGGNet), Residual Networks (ResNet) and Inception-v3 from input images.Finally, the distinct features maps were concatenated and the features reductions were carried out under GAP layer.The resulting feature vector was fed into the softmax classifier for sketch classification results.The performance of proposed framework is comprehensively evaluated on augmented-variants TU-Berlin sketch dataset for sketch classification and retrieval task.Experimental outcomes reveal that the proposed framework brings substantial improvements over the state-of-the-art methods for sketch classification and retrieval.
Hydrogeochemical and hydrodynamic features affecting redox processes in groundwater

This research deals with some hydrogeochemical and hydrodynamic features that affect redox processes in groundwater, presenting three different case studies and methodological approaches. This information is vital when characterizing contaminated sites, planning monitoring, selecting remedial techniques, and assessing health risks. In the San Pedro Sula site case study (Honduras), new insights and an advanced method for the aquifer redox zonation were provided. The Multi-Collocated Factorial Kriging, a multivariate geostatistical technique borrowed from agriculture and soil science, identified simultaneous redox processes, acting at different scales and mainly due to eutrophicated surface-water/groundwater interaction: at short-range, Mn oxihydroxide reductive dissolution and Fe oxi-hydroxide colloidal phase precipitation; at long-range, Fe mobilization. The obtained results are supported by the Principal Component Analysis and hydrogeochemical numerical modeling. The interaction among different contamination events in the VR site allowed to get a deeper insight into the competition for organic substrate among different redox processes. To this purpose, a 1-D Advective-Reactive- Dispersive transport numerical model was implemented, and its results highlighted that the reductive dechlorination is inhibited by the inorganic Terminal Electron Accepting Processes, enriching groundwater in highly toxic by-products of the reductive dechlorination (i.e. Vinyl Chloride). As a matter of fact, the bacteria using inorganic Terminal Electron Acceptors to oxidize the organic substrate are more efficient than the halo-respiring bacteria, that favor reductive dechlorination. The contaminated site of Bussi Officine case study allowed to clarify the hydrodynamic effect on the redox processes and the anoxic condition distribution. The comparison between dissolved chlorinated solvents and the inorganic Terminal Electron Acceptors distribution in the aquifer pointed out that dispersion inhibits all the redox processes because it dilutes the organic substrate concentration in groundwater. On the other hand, the lacustrine deposits rich organic matter (i.e. peat) can be considered as “chemical reactors”, that releases by back-diffusion toxic by-product of the reductive dichlorination in groundwater.
LambdaGAN: Generative Adversarial Nets for Recommendation Task with Lambda Strategy

The Top-N recommendation task aims to recommend users the items they like most. Generative Adversarial Net (GAN) has achieved good results on recommendation, which learns user-item matrix by a generative adversarial training process. There are two main scenarios, pointwise scenarios and pairwise scenarios. However, for recommendation, GANs in pairwise scenarios perform not as well as these in pointwise scenarios. As pairwise rank is a position-independent algorithm, it does not consider Top-N ranking sufficiently. Recommendation task is position-dependent. Especially the Top-N item ranking accuracy is much more important than the ranking accuracy of the tail item. In this paper, we propose LambdaGAN for Top-N recommendation. LambdaGAN introduces lambda rank into generative adversarial training process in order to consider the ranking information of the item. The proposed model enables generative adversarial training in pairwise scenarios available for recommendation by optimizing the rank based metrics directly. Moreover, we adjust lambda function according to the characteristics of recommendation. Two new designed lambda functions are proposed. Experimental results show that LambdaGAN outperforms state-of-the-art algorithms including BPR, PRFM, LambdaFM and IRGAN in terms of four standard evaluation metrics on two widely used datasets, Movielens-100K and Netflix.
Automatic Seal Imprint Verification Systems Using Edge Difference

In Asian countries, seals are widely used for authenticating the identity of a person or an organization. Therefore, the ability to efficiently verify whether a seal is either genuine or forged is important. Both rectangular and circular seal imprints are considered in this work. Geometric transformation is performed to align the perspective of the detected imprint image with the reference imprint. After the edge-differences between the reference image and the detected image are computed, a distance transformation is applied to extract several kinds of weighting matrices. The edge-difference images are weighted element-wise with the edge-difference images to calculate some predefined similarity metrics which serve as the input to the SVM (support vector machine). The imprint is then determined to be either genuine or forged. The experimental results show the effectiveness of the proposed verification approach.
Future of Platform Economy: Digital Platform as New Economic Actor and Instance of Social Control

The digital technologies have irreversible impact on social structures, institutions and mundane practices. This forces to raise again the classical social-philosophical question about social control, its forms, modes of realization, points of application and stakes, and about emerging economic regimes, claiming to be basic and defining. This article presents the attempt to describe and analyze digital platforms as new economic actor and instance of social control.