Reviewerly/exHarmony huggingface dataset:
Education and training

Apart from the true leadership that one brings to building, leading and managing a digital evidence capability, in addition to quality assurance, there is no more important facet than education and training that you can provide to your team. Although this might seem to be obvious for any professional endeavour, it is especially true in digital evidence. It is not possible to prescribe every scenario in every case that your team will encounter, but if the individuals have sound fundamental and applied knowledge, plus a willingness to continue to learn, they will be able to meet the needs of each case they encounter through the application of good decision-making.
Opportunities and Prospects for the Implementation of Artificial Intelligence Systems in Solar Energy

In connection with the large-scale development of high-rise building projects recently in Russia and abroad and their significant energy consumption, one of the main principles in designing is the use of effective energy-saving technologies. Also important aspects are reducing energy consumption and neutralizing the environmental impact of tall buildings. The most promising areas in the field of integration of solar modules (planar and concentrating) in the construction of buildings are development of BIPV technologies (roofing, film, facade materials), the integration of solar energy concentrators that do not require biaxial tracking (medium and low concentrations) on the facades and roofs of buildings (parabolic concentrators, lenses, and Fresnel mirrors), integration of highly concentrated modules on the roofs of buildings.
Introduction: Stories About Users

You are now reading the second edition of Personas—User Focused Design. Since the first edition, I have done a lot more research, been manager of Center for Persona Research and Application, closed the centre, coached a lot of companies, been in connection with Automated Persona Generation, and experienced how difficult it is to do international personas.
Verifiable Conjunctive Keyword Search with Certificateless Searchable

Certificateless encryption solves the problems of key escrow in identity-based cryptography (IBC) and certificate management in traditional public key cryptography (PKC). This paper proposes a new verifiable certificateless public-key encryption scheme based on the conjunctive keywords, which can transfer files without using secure channels. The new authentication process developed in the scheme ensures the secure transmission of encrypted files in the open channel. At the same time, combining the conjunctive keyword into the scheme has the advantage of narrowing the scope of the query file and locating the file we need quickly and accurately. Furthermore, the strong security of our scheme is further analyzed and proved: in the random oracle model, the double indistinguishable characteristics of ciphertext and trapdoor are satisfied. We compared with other schemes in terms of performance analysis. Experiments show that the execution time and key size of our scheme can be reduced by a certain percentage in the Encryption, Trapdoor, and Test phases, which indicates that the structure of this scheme is more concise and has better security.
Multi-View Metrics Enhanced Heterogeneous Graph Neural Network for Extractive Summarization

Currently, many works regarded extractive summarization as a binary classification problem. However, the metric of the summary classification is always too singular and cannot fully utilize sentence features. To address this issue, we propose a multi-view metrics enhanced heterogeneous graph neural network for extractive summarization (MHGS). Firstly, the text is modeled as a heterogeneous graph, which contains two types of nodes, namely, words and sentences. Secondly, in the graph update layer, cross-sentence relationships and long-distance associations are obtained through the indirect connection of words and the direct connection between sentences. Then, we design multi-view metrics including relevance, redundancy, new information, and Rouge to help the model improve the quality of the summary. Finally, to evaluate the effectiveness of the method, some experiments are conducted on the CNN & DailyMail dataset. The experimental results show a generally higher performance of MHGS compared with related methods.
Deep Learning of Binary Hash Codes for Fast Handwritten character Retrieval

With the fast growing number of Offline Handwritten characters picture uploaded every day, it is very difficult to employ any existing traditional techniques to retrieve them.Hash based approximate nearest neighbor retrieval is an effective method in the field of characters image retrieval,meanwhile it has been shown that the activations invoked by an image within the top layers of a large convolutional neural network provide a high-level descriptor of the visual content of the image The propose network that can efficiently extract highlevel semantic features of character images and generate binary hash codes The learning mode of the network is from one end to the other one piont At the same time, it is also suitable for large-scale data sets Take into account the retrieval time and precision, two-level retrieval to ensure the retrieval speed and high precision The performance of the proposed method is better than that of the existing hash algorithms and traditional feature algorithms on MNIST and CHARS74K datasets The scalability and effectiveness of the proposed method are further verified on the HWDB dataset of large-scale Chinese characte
YOLOC: A Vehicle Counting Method for Surveillance Video Based on YOLO

In this paper, aiming at the problem that the signal lamp mechanical scheme seriously obstructs the intersection traffic, the target detection model and target tracking model are established with the help of YOLO algorithm and IoU algorithm. According to the results of the model, the time period of traffic congestion is obtained, and a reasonable scheme is proposed to improve the congestion problem when vehicles pass the intersection, so that the intersection signal light is more intelligent.
If Humans Fail, Machines Take Action: Assessment of Accounting Error Detection Using Machine Learning

Various accounting scandals have proven that current enforcement systems may fail to detect erroneous reporting or identify fraudulent actions. Therefore, enforcement systems should continuously reassess and update the applied methods. Existing studies have shown that textual information and machine learning techniques can substantially support accounting error detection. We use proprietary data from enforcement investigations conducted by the German enforcement system to gain insights into the feasibility of such applications. Our classification models consider a wide range of classification and feature selection methods based on three indicator categories: financial, linguistic, and content. In contrast to many previous studies, we evaluate the classification performance using a realistic imbalanced holdout sample. We observe that content features are particularly important error indicators, and the combination of indicator categories has a significant impact on error detection. Additionally, feature selection plays an essential role in preventing indicator overload. Our findings have important implications for the development of predictive error-detection systems.
Improving ESP Students' Reading Comprehension Proficiency by Using Scanning Techniques

The present study aimed to reveal if the use of scanning techniques can improve ESP students' reading comprehension proficiency. A sample of 20 ESP students was chosen randomly and divided into two groups, their ages ranged between 20 and 21 years old. The control group was taught through traditional methods of reading comprehension and the experimental group was taught through the scanning technique. Both groups were administered a reading comprehension test and questionnaire to find out if they were aware of using scanning techniques in the learning of enumerating texts. Data was analyzed by using a t-test on the scanning technique. The researcher uses pre-test and post-test. The collected data were analyzed by using the T-test formula. The result of data analysis showed that the mean score in the post-test for the experimental group is higher than the mean score in the controlled group. The pre-test and post-test comparison showed that the participants achieved a higher level of reading comprehension proficiency following the instructions. The findings of the study indicated that the reading scanning technique was more effective in improving the reading comprehension ability of ESP students than traditional methods of reading comprehension.
Automatic Extraction of Geometric Contour Features from Building Remote Sensing Images

with the continuous development of China's modernization, the planning and arrangement of residential buildings and commercial buildings are becoming more and more important. Buildings are closely related to human life. Automatic extraction algorithm plays a key role in geometric contour feature extraction and statistics of buildings. Automatic extraction of built-up area is realized by establishing neural network. Full convolution neural network is widely used in the field of image segmentation, but the size of remote sensing image is usually very large. After the image to be processed is divided into image blocks, the geometric features are used to extract the depth features, and then the depth features are arranged into multi-channel feature maps according to the position of the image blocks, which are input into the full convolution neural network for processing. The experimental results show that the automatic geometric contour feature extraction algorithm for building remote sensing image improves the remote sensing feature extraction efficiency by 16.8%. The traditional feature detection combined with machine learning is the most effective remote sensing image building detection algorithm at present. However, the building size, shape and orientation in remote sensing image are different, Building detection has become a big problem. How to use effective features and how to combine machine learning algorithms can make building detection get twice the result with half the effort. For the limitations of the research on building feature extraction, the automatic extraction algorithm is updated and iterated in an innovative way, and the layout and location information of various buildings are analyzed, discussed and summarized, so as to provide results for the research of remote sensing image extraction algorithm in China.
Consideration of Virtual Reality as Training

In the near future, people and artificial intelligences (AIs) will often be working together. This will require special training for both the people and the AIs. Virtual reality (VR) used for training may come into its own, especially in situations where the people and the AIs must move quickly and safely together. This chapter looks at just such training. In this story, “The Triceratops,” the AIs are in charge of the training; they also control the future employment opportunities of people. This brings out a number of ethical considerations and practical considerations when people's lives are controlled by AIs in important ways, even in training.
Development of a Smart Technique for Mobile Web Services Discovery

Web service (WS) presents a good solution to the interoperability of different types of systems that aims to reduce the overhead of high processing in a resource-limited environment.With the increasing demand for mobile WS (MWS), the WS discovery process has become a significant challenging point in the WS lifecycle that aims to identify the relevant MWSs that best match the service requests.This discovery process is a resource-consuming task that cannot be performed efficiently in a mobile computing environment due to the limitations of mobile devices.Meanwhile, a cloud computing can provide rich computing resources for mobile environments given its unlimited and easily scalable resources.This paper proposes a semantic WS discovery and invocation framework in mobile environments based on cloud and a relationship-aware matchmaking algorithm.The discovery algorithm enriches MWS and user requests semantically with the functional and non-functional properties of Ontology Web Language for Services, such as Quality of Web Service, device context, and user preferences.The WS repository is filtered based on logical reasoning and a parameter-based matching algorithm to minimize the matching space and improve runtime performance.The cosine similarity between the user request and services repository is then assessed to generate the most relevant WS.The relationships among concepts in the ontology are considered to improve the recall and precision ratio.After the WS discovery process, users can invoke and test these services in a mobile environment through a dynamic user interface.The interface of the invocation process is changed according to the WS description document.An application prototype is also developed to evaluate the framework based on a Cordova cross-mobile development framework.
Constructing a Collocation Learning System From the Wikipedia Corpus

The importance of collocations for success in language learning is widely recognized. Concordancers, originally designed for linguists, are among the most popular tools for students to obtain, organize, and study collocations derived from corpora. This paper describes the design and development of a collocation learning system that is built from Wikipedia text and provides language learners with an easy-to-use interface for looking up collocations of any word that occurs in Wikipedia. The use of this corpus exposes learners to contemporary, content-related text, and enables them to search for semantically related words for a given topic. The system organizes collocations by syntactic pattern, sorts them by frequency, and links them to their original context. The paper includes a practical user guide to illustrate how to use the system as a language aid to facilitate academic writing.
Network Flow Classification and Volume Prediction using Novel Ensemble Deep Learning Architectures in the Era of the Internet of Things (IoT)

Network flow and volume optimization have become a challenging task with the rapid growth of IoT devices. Fortunately, network flow classification and network volume prediction techniques are powerful tools to allocate resources efficiently in a given network. Current machine learning models have proven useful since they partition and direct traffic to extremely diverse sets of devices and services. Current machine/deep learning techniques are geared towards specific network configurations and suffer from suboptimal accuracy or lengthy training methodologies. In this paper, we propose a family of novel ensemble techniques for short-term network volume predictions and flow classifications. We explore each architecture and the characteristics of each model to achieve high efficiency. Experimental results on three real network traffic datasets show that the proposed family of deep ensemble models using Long Short-Term Memory (LSTM) and Convolutional LSTM neural network (CNN_LSTM) are best-in-class models for short-term flow classification and volume predictions.
A Route Recommender System Based on Current and Historical Crowdsourcing

Due to the rise of the social networks it's possible to use techniques based on crowdsourcing to easily gather real-time information directly from citizens in order to create recommendation systems capable to employ knowledge that is shared from the crowd. Particularly, in Twitter, the users publish a big amount of short messages; however, to automatically extract useful information from Twitter is a complex task. In order to provide an informed recommendation of the current best route between two city points, this chapter introduces a workflow that integrates natural language techniques to build an vector of features for training two linear classifiers which obtain current information from Twitter, and integrates that information with historical information about possible routes using exponential smoothing; current and historical data to feed a route selection algorithm based on Dijkstra. The effectiveness of the proposed workflow is shown with routes between two interest points in Caracas (Venezuela).
An efficient image algorithm for MRI image compression

A timesaving crop procedure will be planned in that paper. The present algorithm can be based on Haar Wavelets and Spatial oriented tree Wavelets. The two-stage coding scheme is effectively encodes the dynamic range of magnitude coefficients. It has been found that SOTW provides better performance than Embedded Zero tree Wavelet (EZW), Set Partitioning in Hierarchical Tree (SPIHT), Set Partitioning in Hierarchical Tree 3D (SPIHT 3D), in terms of high compression ratio, low MSE and high PSNR values. Thus, the proposed algorithm is preferred in a variety of applications such as medical, satellite image compression. The proposed SOTW based compression method able to achieve a better compression ratio (CR) of 71.10% and PSNR of 56.8 dB.
Formation of Practical Activity of Primary School Students

The most important feature of the elementary mathematics course in the article is its practical orientation, if some issues of the mathematics program in the upper grades are of a theoretical nature, illustrated using examples to be included.
Phonemic variations in similar words of Turkish and Urdu language

Urdu language is a member of Indo-European family tree and within the zone of Indo-Iranian branch, whereas Turkish language is a member of Altaic family tree. Both of these languages belong to different family trees, but these languages have many words in common. Urdu language has 41 consonant sounds and 11 vowel sounds, whereas Turkish language has 21 consonant sounds and 8 vowel sounds. Both of these languages don’t have same number of phonemes. It is interesting to contemplate, how the speakers of both languages produce and perceive these common words in their languages. Therefore, a study is designed to explore the phonemic variations in similar words of Urdu and Turkish languages. In order to find out variations, a list of 75 words, which are common in Urdu and Turkish language was prepared in form of text. The data of this study was collected from Pak-Turk school of Lahore. The prepared words list was given to 10 Turkish speakers. These speakers were selected by using purposive sampling technique. The audios of the speakers were recorded and transcribed into standard Turkish IPA symbols. Levenshtein algorithm framework is used to draw a comparative analysis of Turkish phonemic transcriptions with standard Urdu phonemic transcriptions of the same words. With the help of Levenshtein algorithm, phonemic variations in similar words of both languages was measured. The intriguing result of the study will help in establishing the understanding about the production directions of the similar words used by the speakers of both languages.
Digital Documents

Digital documents tend to replace printed documents in many ways. For publishers it is cheaper to produce them and readers can get them instantly. However, the spectrum of “printed” material is quite big and so there is no way to have one kind of digital document that can satisfy all those involved in the publishing business (e.g., authors, readers, publishers, etc.). Digital document appeared in the 1960s and ever since they evolve. Sometimes this evolution process is slow and sometimes it's rapid, but the fact is that today there a plethora of digital document forms that can be used for many and different tasks and most of them are briefly documented here.
Introduction

Quantum cryptography exploits distinctive quantum properties of nature in order to perform a given cryptographic task. Most quantum cryptographic protocols are —at least in principle— information-theoretically secure, which is a very strong notion of security as it is deduced purely from information theory.
Transfer learning

Transfer learning has been widely applied for training neural networks because it is usually much faster, easier, and more effective than training a network from scratch. This chapter first introduces and analyzes transfer learning strategies. Then, to help readers understand more and improve the practice skills in developing neural networks, two scenarios of building and training network models are implemented. In the first scenario, we build the Inception-v3 model through Keras Applications and train this model from scratch. In the second scenario, the Inception-v3 network is built through TensorFlow Hub, and the transfer learning is applied for training this network model.
Uncovering Limitations of E01 Self-Verifying Files

In computer forensics, it is important to understand the purpose of evidence file formats to maintain continuity of acquired data from storage devices. Evidence file formats such as E01 contain embedded data such as cyclic redundancy check (CRC) and hash values to allow a program to verify the integrity of the data contained within it. Students in computer forensics courses need to understand the concepts of CRC and hash values as well as their use and limitations in evidence files when verifying acquired data. That is the CRC and hash values in evidence file only verify the acquired data and not the evidence file per se. This important difference in E01 files was highlighted by showing students an anomaly in E01 files where certain bytes can be changed in E01 files without detection by computer forensic software using the embedded CRC and hash values. The benefit to students is that they can see the advantages of self-verification and limitations of what is verified giving the opportunity for a deeper understanding of evidence files and good practice.
Fuzzy Neurocomputing

We are concerned with a fundamental idea of fuzzy neurocomputing manifesting as a realization of the synergy of fuzzy sets and neural networks. These two frameworks are complementary to a significant extent. The architectures of neurofuzzy systems benefit from mechanisms of explicit knowledge representation supported by fuzzy sets and a spectrum of learning methods being a genuine forte of neurocomputing. Several categories of fuzzy (logic) neurons are introduced, the topologies of the networks built with the aid of these neurons are discussed and learning schemes are presented. The linkages with Boolean networks are highlighted and mechanisms enhancing the interpretability of the network. The gradient-based learning is discussed as a generic mechanism of supervised learning. Selected architectures of neurofuzzy systems involving autoencoders and relational factorization are put forward.