Reviewerly/exHarmony huggingface dataset:
Internet of Things Technological Evolution. Threats Mission in Stealing or Corrupting User's Data

The Internet of Things as a part of the digitalization process will completely replace our daily habits in the upcoming years, by establishing a more complex interconnected network which collects data in real time, helps the users to exchange it and also provides different ways to access information that some time ago was difficult to obtain. In this way, even if the industrial evolution will bring a significant number of advantages, will also have its own treats. Considering the software and hardware technological advancements, the last decade can be described as being the period in which the continuous war between users of interconnected devices and different types of threats as computer hackers, known and unknown forms of software malwares and privacy data or Intellectual Property theft have arisen. This paper aims to obtain the different ways of threats evolution, to provide an overview regarding them behaviour and how an IoT adhered company will be affected, highlighting aspects as the impact, customer relationship and the company's reputation on the market. The methods used to perform the research will consist in a short literature review regarding how the threats evolved and them behaviour, the analysis of different case studies regarding the impact of cyber-attacks on the aforementioned aspects and the different crisis strategies used by companies in case of cyber-attacks. In this way, this paper also aims to raise awareness through different IoT adhered companies for whom the cyber- security is not an important aspect, yet.
Visual feature recognition of human motion in sequence images based on 3D skeleton

The recognition accuracy of human motion visual feature recognition method is low, and the recognition information integrity is low. Based on 3D skeleton, a new visual feature recognition method of human motion in sequence images is proposed. The 3D skeleton information is used to represent human actions, and then human actions are treated as manifolds. Based on 3D skeleton information, main pose features and main action trend features are proposed to represent human actions and measure the distance between actions; Based on the local linearity measurement of human motion manifold, the decomposition of human action is realized, which is conducive to the representation and recognition of classification tasks. The experimental results show that the method based on 3D skeleton can effectively improve the recognition accuracy and integrity, which proves the effectiveness of the proposed method.
Method for Identifying Cracks In Mass Concrete of Bridge Engineering based on Image Processing

Due to the influence of the initial video image quality of concrete cracks, there are obvious identification errors in traditional bridge engineering mass concrete crack identification. Therefore, the image processing technology is used to optimize the design of mass concrete crack identification method. The image acquisition equipment is installed and calibrated to obtain the initial crack image of mass concrete of bridge engineering. The image processing technology is used to preprocess the initial image through the steps of graying, denoising, smoothing and smoothing. On this basis, the crack image features are extracted and fused to obtain the crack recognition results of mass concrete of bridge engineering including crack category, crack width and other information. Compared with the traditional recognition method, the average correct recognition rate of the design method is increased by 9.3%.
HUBUNGAN SISTEM INFORMASI AKUNTANSI PERSEDIAAN TERHADAP PENGENDALIAN STOCK DI PT. KARYA SEMANGAT MANDIRI

Abstract This research was conducted with the aim of examining the relationship between the 2 variables used, namely the inventory accounting information system varibale (X) and the stock control variable (Y). This research was conducted at PT. Karya Semangat Mandiri with 25 respondents in which the respondent is an employee of the company. Statistical testing was carried out with the SPSS version 23 statistical program.Based on the results of the research conducted, it uses a non-parametric test with the Spearman rank method, where the results of the test show a coefficient value of 0.822, the results are in a very strong category with a significant value of 0.00 where the results are small from 0.05. The researcher can conclude that the inventory accounting information system variable with the stock control variable has a very strong and significant relationship. Keywords: Inventory Accounting Information System, Stock Control
Graph-Based and Personalized Content Recommendations for Software Developers

Informal learning through online resources like Stack Exchange is quite popular among the software developers to ask or seek a software-related content. Personalized content recommendation for software developers is still under-explored. With years of accumulation, Stack Exchange has collected thousand of questions and answers in many fields, particularly in software engineering. In this paper, we propose a recommendation system that extracts topics from Stack Exchange and considers the developer's topical navigation from browser search logs to recommend personalized content (i.e. Stack Exchange posts) to the developer. The results shows that our trained model predicts the topics with 92% accuracy, and it is found highly reliable and transparent in terms of recommendation.
Crime Data Analysis, Visualization and Prediction

Crimes are common social problems that can even affect the quality of life, even the economic growth of a country.Big Data Analytics (BDA) is used for analyzing and identifying different crime patterns, their relations, and the trends within a large amount of crime data.Here, BDA is applied to criminal data in which, data analysis is conducted for the purpose of visualization.Big data analytics and visualization techniques were utilized to analyze crime big data within the different parts of India.Here, we have taken all the states of Indian for analysis, visualization and prediction. The series of operations performed are data collection, data pre-processing, visualization and trends prediction, in which LSTM model is used. The data includes different cases of crimes with in different years and the crimes such as crime against women and children in which, kidnap, murder, rape. The predictive results show that the LSTM perform better than neural network models. Hence, the generated outcomes will benefit for police and law enforcement organizations to clearly understand crime issues and that will help them to track activities, predict the similar incidents, and optimize the decision making process.
Dynamic Range Mode Enumeration

The range mode problem is a fundamental problem and there is a lot of work about it. There is also some work for the dynamic version of it and the enumerating version of it, but there is no previous research about the dynamic and enumerating version of it. We found an efficient algorithm for it.
Layer-Wise Interpretation of Deep Neural Networks Using Identity Initialization

The interpretability of neural networks (NNs) is a challenging but essential topic for transparency in the decision-making process using machine learning. One of the reasons for the lack of interpretability is random weight initialization, where the input is randomly embedded into a different feature space in each layer. In this paper, we propose an interpretation method for a deep multilayer perceptron, which is the most general architecture of NNs, based on identity initialization (namely, initialization using identity matrices). The proposed method allows us to analyze the contribution of each neuron to classification and class likelihood in each hidden layer. As a property of the identity-initialized perceptron, the weight matrices remain near the identity matrices even after learning. This property enables us to treat the change of features from the input to each hidden layer as the contribution to classification. Furthermore, we can separate the output of each hidden layer into a contribution map that depicts the contribution to classification and class likelihood, by adding extra dimensions to each layer according to the number of classes, thereby allowing the calculation of the recognition accuracy in each layer and thus revealing the roles of independent layers, such as feature extraction and classification.
Pengelolaan Arsip Dinamis Inaktif di Lembaga Pendidikan Sekolah Menengah Kejuruan

This study aims to find out what are the obstacles faced in managing archives in the administration section and the things that are done to optimize the management of inactive dynamic records. The method in this research is qualitative and data collection is obtained by observation, interview, and documentation methods. The results of this study indicate that the management of archives in the Administration of Vocational High Schools has not been carried out optimally because there are still obstacles such as manual information systems in managing archives, uncluttered workspace layout, limited archive storage space, limitation of supporting infrastructure for archiving. and limited human resources in managing records. Based on the results of these studies, the archive management system is still not optimal and needs to be paid attention by the leadership so that archive management can be well organized and effective.
Federated Learning without Revealing the Decision Boundaries

We consider the recent privacy preserving methods that train the models not on original images, but on mixed images that look like noise and hard to trace back to the original images. We explain that those mixed images will be samples on the decision boundaries of the trained model, and although such methods successfully hide the contents of images from the entity in charge of federated learning, they provide crucial information to that entity about the decision boundaries of the trained model. Once the entity has exact samples on the decision boundaries of the model, they may use it for effective adversarial attacks on the model during training and/or afterwards. If we have to hide our images from that entity, how can we trust them with the decision boundaries of our model? As a remedy, we propose a method to encrypt the images, and have a decryption module hidden inside the model. The entity in charge of federated learning will only have access to a set of complex-valued coefficients, but the model will first decrypt the images and then put them through the convolutional layers. This way, the entity will not see the training images and they will not know the location of the decision boundaries of the model.
WEB-BASED ONLINE GROCERY ORDERING SYSTEM FOR APU STUDENTS

Nowadays, online shopping is popular and e-commerce is expected to be the next major retail area to be disrupted. The goal of this project to facilitate APU students to get their needs from a grocery store over the internet with affordable service, features, enjoyable experience, secure and good user interface. In this application, users have the ability to order their grocery needs online. To this end, the developer has developed a mobile web online grocery ordering system for APU students called Steps. This application offers affordable services to students. The application would help APU students to save their time and conserve money rather than pay the cost of petrol and parking with affordable service, enjoyable experience and amazing features. Moreover, security features are implemented to increase the security of the application. In recent years it has been observed that APU students have been facing difficulties in doing their grocery shopping. Since most of the students do not have transportation so in order to get their grocery they need to spend more money on transportation and waste some time to get their needs. It has been put forward to develop a web mobile application to meet their needs so that it saves them time, money and it is more convenient for them as students. However, these services best suit their needs. Lastly, the application offers a much faster, more effective and intuitive design.
Investigating the Mediating role of Goal Clarity in the Relationship between Spiritual Leadership and project Success: Evidence from Software Industry

The basic aim of the current study is to examine whether there is any relationship between Spiritual leadership (SL) and project Success (PS) through mediating role of goal clarity (GC). For this purpose this study used quantitative research approach and test the all statistical results through SmartPLS for 326 responded gathered from employs that were associated with software projects in the education and health care industries of Islamabad and Rawalpindi region, Pakistan. All the proposed hypothesis were accepted and result validated that SL is positively and significantly backing to GC and PS. In addition to this, findings also show that GC is shown positive and significant impact on PS and GC is positively and significantly mediate the linkages between SL and PS. There is a key evidence that research on SL and PS is too limited. Therefore, this study using the causal SL theory that identified that how SL is crucial for PS and provide a valuable extension to the SL in the project management literature. This research also disscuss the theortical, practical of this reearch and provide the future recomeneation for further exploration.
Specific information literacy instruction, in several different languages, for pupils studying for university entrance examinations

The purpose of this paper is to describe the instruction in specific information literacy skills, in several different languages, for final examination pupils, which has taken place during 2005. This instruction is part of a structure or matrix for information literacy instruction and training, for pupils aged between 11 and 18 years of age, which has been developed in the School Library and Information Centre at the Kalsbeek College, Woerden, the Netherlands.
Demystifying Distributed Systems with a Storytelling Method

On a daily basis, the general public uses online services without any knowledge of the concepts these services rely on. Even though this is fine most of the time, there are situations where such obliviousness puts the users in trouble. We aim to popularize key distributed systems concepts and make them accessible to K-16 students, so as to give future generations a better idea of what to expect of online services. This should help make better decisions regarding when to use them, and when to trust them. We adopt the method of storytelling and embed distributed systems concepts into an interactive story available on the Internet. Our preliminary assessment on CS1 students shows that our interactive game may have a positive effect on their overall grasp of the embedded notions. The effect is marginal, but our study provides interesting insights on how to make the technique effective.
unzipFPGA: Enhancing FPGA-based CNN Engines with On-the-Fly Weights Generation

Single computation engines have become a popular design choice for FPGA-based convolutional neural networks (CNNs) enabling the deployment of diverse models without fabric reconfiguration. This flexibility, however, often comes with significantly reduced performance on memory-bound layers and resource underutilisation due to suboptimal mapping of certain layers on the engine's fixed configuration. In this work, we investigate the implications in terms of CNN engine design for a class of models that introduce a pre-convolution stage to decompress the weights at run time. We refer to these approaches as on-the-fly. To minimise the negative impact of limited bandwidth on memory-bound layers, we present a novel hardware component that enables the on-chip on-the-fly generation of weights. We further introduce an input selective processing element (PE) design that balances the load between PEs on suboptimally mapped layers. Finally, we present unzipFPGA, a framework to train on-the-fly models and traverse the design space to select the highest performing CNN engine configuration. Quantitative evaluation shows that unzipFPGA yields an average speedup of 2.14x and 71% over optimised status-quo and pruned CNN engines under constrained bandwidth and up to 3.69x higher performance density over the state-of-the-art FPGA-based CNN accelerators.
Base-Package Recommendation Framework Based on Consumer Behaviours in IPTV Platform

Internet Protocol TeleVision (IPTV) provides many services such as live television streaming, time-shifted media, and Video On Demand (VOD). However, many customers do not engage properly with their subscribed packages due to a lack of knowledge and poor guidance. Many customers fail to identify the proper IPTV service package based on their needs and to utilise their current package to the maximum. In this paper, we propose a base-package recommendation model with a novel customer scoring-meter based on customers behaviour. Initially, our paper describes an algorithm to measure customers engagement score, which illustrates a novel approach to track customer engagement with the IPTV service provider. Next, the content-based recommendation system, which uses vector representation of subscribers and base packages details is described. We show the significance of our approach using local IPTV service provider data set qualitatively. The proposed approach can significantly improve user retention, long term revenue and customer satisfaction.
Multi-aperture digital telescopic system with super-resolution

The development results of a telescopic system that allows high-quality digital images of distant objects to be obtained in horizontal routes with a high spatial resolution, which in an extreme case tends toward the diffraction limit, are reported in this study. A three-channel multi-aperture digital telescopic system with super-resolution realizes two methods of digital image processing that can compensate the distortions introduced by a non-uniform distribution of the refractive index along the optical observation path. A comparative analysis of these methods was conducted, and their advantages and limitations are described in this paper. The results of theoretical investigations are confirmed experimentally by observing stationary and dynamic objects under the conditions of heavy atmospheric turbulence. The advantages of a three-channel multiple-aperture digital telescopic system design with a super-resolution over optical systems built based on traditional schematics are demonstrated.
Research on Civil Engineering Condition Monitoring Based on Wireless Sensor

With the continuous development and progress of the times, a variety of large-scale engineering construction is constantly advancing, but due to the high technical requirements of large-scale civil engineering, if the operation does not meet the standards, it may lead to very serious consequences, so the monitoring of large-scale civil engineering is very necessary. Comprehensive and efficient detection and evaluation of large-scale civil engineering can greatly improve the safety of the project. Therefore, civil engineering detection has become a new research hotspot. Through continuous in-depth research, it is found that wireless sensor technology has obvious advantages, which can help relevant personnel to monitor large-scale civil engineering. This paper mainly studies the application of wireless sensor technology in civil engineering condition monitoring, introduces the new concept of wireless sensor, compares and analyzes the existing monitoring technology, and discusses the important role of wireless sensor technology in civil engineering monitoring.
Passive Copy-Move Image Forgery Detection Techniques: A Study

Due to the tremendous technological development in the digital world, there is a proliferation in the popularity of digital images in all spheres of human life. However, the introduction of state of the art Digital Image-Editing Software packages such as Pic Monkey, Adobe Lightroom, Corel PaintShop, Skylum Luminar, etc. have made image forgery non-observable and much easier than earlier times. Thus, there is a need for image authentication and forgery detection. This paper presents the active and passive image forgery detection techniques in use and then draws a comparative study of several existing passive frequency-based copy-move forgery detection approaches depending on evaluation metrics such as precision, recall and F-Measure. This paper also discusses the various forgery detection datasets with their features, advantages, and disadvantages along with the different practical evaluation metrics used by researchers for assessing the performance of the image forgery detection algorithms.
Spectrum Congruency of Multiscale Local Patches for Edge Detection

This paper proposes a novel feature called spectrum congruency for describing edges in images. The spectrum congruency is a generalization of the phase congruency, which depicts how much each Fourier components of the image are congruent in phase. Instead of using fixed bases in phase congruency, the spectrum congruency measures the congruency of the energy distribution of multiscale patches in a data-driven transform domain, which is more adaptable to the input images. Multiscale image patches are used to acquire different frequency components for modeling the local energy and amplitude. The spectrum congruency coincides nicely with human visions of perceiving features and provides a more reliable way of detecting edges. Unlike most existing differential-based multiscale edge detectors which simply combine the multiscale information, our method focuses on exploiting the correlation of the multiscale patches based on their local energy. We test our proposed method on synthetic and real images, and the results demonstrate that our approach is practical and highly robust to noise.
Introduction to a Hypergraph Logic Unifying Different Variants of the Lambek Calculus

In this paper hypergraph Lambek calculus ($\mathrm{HL}$) is presented. This formalism aims to generalize the Lambek calculus ($\mathrm{L}$) to hypergraphs as hyperedge replacement grammars extend context-free grammars. In contrast to the Lambek calculus, $\mathrm{HL}$ deals with hypergraph types and sequents; its axioms and rules naturally generalize those of $\mathrm{L}$. Consequently, certain properties (e.g. the cut elimination) can be lifted from $\mathrm{L}$ to $\mathrm{HL}$. It is shown that $\mathrm{L}$ can be naturally embedded in $\mathrm{HL}$; moreover, a number of its variants ($\mathrm{LP}$, $\mathrm{NL}$, $\mathrm{NLP}$, $\mathrm{L}$ with modalities, $\mathrm{L}^\ast(\mathbf{1})$, $\mathrm{L}^{\mathrm{R}}$) can also be embedded in $\mathrm{HL}$ via different graph constructions. We also establish a connection between $\mathrm{HL}$ and Datalog with embedded implications. It is proved that the parsing problem for $\mathrm{HL}$ is NP-complete.
Finding Hyperplanes Using Support Vectors

The preceding chapter presented a linear classification model called linear discriminant analysis (LDA), which distributes groups equally when covariance matrices are equivalent. Although the classifier is one of the optimum linear classification models, it has its limits. Foremost, we cannot estimate the dependent variable using a categorical variable. Second, we train and test the model under strict assumptions of normality. This chapter brings together an alternative linear classification model called support vector machine (SVM). It is part of the ensemble family; it estimates either a continuous variable or a categorical variable. It applies a kernel function to transform data in such a way that a hyperplane best fits the data. Unlike LDA, SVM makes no assumptions about the underlying structure of the data.