Reviewerly/exHarmony huggingface dataset:
Twenty-nine years of the BIR Annual Survey part 2: Changing information work

This article is the second part of a review of the Business Information Review (BIR) Annual Survey, which has been published annually since 1991. The article explores changes to the professional context for information work revealed by the surveys over the course of its publication. It uses a combination of content and thematic analysis to develop key themes, which are discussed by reference to the original surveys: the decline of public library business services, changing budgets, globalization of business information, offshoring and outsourcing of information work, rising up the value chain, changing skills in information work, reliability and accuracy of information and rising up the value chain. The article aims to summarize and consolidate longitudinal trends revealed by the survey, acts as a guide to the rich data contained within the surveys themselves and provides a testament to the wealth of professional experience captured in the BIR Annual Surveys. Its findings relate to the changes in the nature of professional practice and the pressures on professional practice over the past 30 years.
A Euclidean metric based voice feature extraction method using IDCT cepstrum coefficient

In this paper, we propose a new method for voice feature extraction by using a hierarchical clustering approach of inverse discrete cosine transform (IDCT) cepstrum coefficient. Since the IDCT cepstrum coefficient is transformed into the feature vector based on Euclidean metric, we call the proposed voice feature as “E-vector”. Comparing with other voice features, e.g. Mel frequency cepstrum coefficient (MFCC) and histogram of DCT cepstrum coefficients (HDCC), the E-vector can represent the dynamic characteristics and interactional relationships of the voice. In the numerical experiments, we use the speech files based on the voice of 630 people in TIMIT corpus, and employ Gaussian mixture model to compare the recognition accuracy of E-vector with that of MFCC and HDCC. The results show that E-vector outperformed other voice features on personal identification, and presented higher extensiveness on voice feature extraction.
Pedestrian Classification Using Self-Training Algorithm

Object recognition is one of the key components in autonomous driving systems. However, many existing systems rely on large amounts of labeled training data which are expensive and difficult to obtain. To enhance the practicability of such system, it is essential to develop an object recognition algorithm that can learn from small number of labeled data in order to reduce the cost of data annotation. For this purpose, this paper presents an investigation on the feasibility of using a semi-supervised learning algorithm called self-training to reduce the number of labeled training data. To this aim, first, a self-training algorithm that uses artificial neural networks as a base classifier is prototyped to evaluate the performance of self-training algorithm alone. Next, self-training is implemented on top of a pedestrian recognition algorithm that adopts real Adaboost learning method with decision trees as weak classifiers. The feasibility of the proposed algorithm is confirmed by computational tests on the MNIST dataset of handwritten digits and the NICTA pedestrian dataset. The classification performances of traditional supervised learning and self-training algorithms trained under equivalent conditions are compared. The results show that self-training algorithm is feasible to reduce the number of labeled data by 78% while keeping the same recognition accuracy level of 95% on the condition that sufficient number of unlabeled data are given. In addition, the results indicate the possibility of replacing color images with grayscale images in object recognition to realize faster computation speed. Overall, it is confirmed that self-training can be applied to any existing classifiers as well as implemented on top of existing recognition algorithms.
Eye-Tracking Based Adaptive Parallel Coordinates

Parallel coordinates is a well-known technique for visual analysis of high-dimensional data. Although it is effective for interactive discovery of patterns in subsets of dimensions and data records, it also has scalability issues for large datasets. In particular, the amount of visual information potentially being shown in a parallel coordinates plot grows combinatorially with the number of dimensions. Choosing the right ordering of axes is crucial, and poor design can lead to visual noise and a cluttered plot. In this case, the user may overlook a significant pattern, or leave some dimensions unexplored. In this work, we demonstrate how eye-tracking can help an analyst efficiently and effectively reorder the axes in a parallel coordinates plot. Implicit input from an inexpensive eye-tracker assists the system in finding unexplored dimensions. Using this information, the system guides the user either visually or automatically to find further appropriate orderings of the axes.
Book review: KJ Varnum (ed.), <i>Beyond Reality: Augmented, Virtual, and Mixed Reality in the Library</i>

This is a book review of: Beyond reality: augmented, virtual, and mixed reality in the library, edited by K.J. Varnum and published in 2019 by the American Library Association. This slim volume is an interesting read. Arranged in nine chapters, it's a sampler of actual and potential applications of the three types of experience listed in the title. Augmented reality might be described as 'reality plus' – an overlay of electronically-generated content on the real world, such as is used in the 'heads-up displays' employed by fighter pilots and in some cars. Mixed reality involves some kind of electronically-delivered content, which is tied in with real-world locations, an example being the recent phenomenon of Pokemon-collecting, in which cartoon characters could be collected (often in proximity to real-world libraries) by use of a smart phone, which overlaid their images on the phone's video display of the user's surroundings. The effort required to communicate that concept in a single sentence is some indication of what a complex process is involved; the fact that it would have made little sense to an average reader of even ten years ago shows how far these concepts have penetrated popular culture. Virtual reality is an 'immersive' experience, often achieved through the use of head-mounted display (HMD) equipment, such as the Oculus Rift, which may permit the user to move around and interact with 'objects' in an environment that is completely generated by software.
Comparative Study of Atmospheric Correction Methods of ASTER Data to Enhance the Delineation of Uranium Mineralized Zones

Multispectral imaging sensors are utilized to discover and identify various surface materials, topographical and geological features. Also it is a used to differentiate lithological units and to provide mineral maps. Although successful processing methodologies have been developed for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery for mineral exploration and geological mapping. Questions still arise regarding the optimal atmospheric correction for these applications. This article provides an outline and an evaluation of existing atmospheric compensation algorithms applied on the visible and shortwave infrared spectral region. This may assist in developing a uranium exploration program using ASTER imagery by delineation of the altered minerals associated with the uranium mineralization
Realtime Recoloring Objects using Artificial Neural Networks through a Cellphone

Recoloring it is a technique for changing the color an image resulting in a different new one.Recoloration is a common photo edition operation since digital images are around every media resource and several algorithms are used for editing these pictures, nevertheless, recent digital cameras have increased enormously the quantity of pixels for producing them.This increase in the size of digital images makes difficult the recoloring operation.In order to solve the recoloring problem, there had been applied several algorithms, some algorithms directly detect the color by performing transformations on color representations to different spaces where color is easily separated but this transformation require several no linear operations.On the other hand, numerical parameters on CNNs make than this approach cannot be trained or implemented on a mobile device, more over the time required for computing an input image will made that the processed pictures be delayed continually.Considering this limitation is proposed a specific short architecture for detecting a specific color in general objects using a feedforward neural network trained with gradient descent backpropagation with variable learning rate.
Deep Multivariate Mixture of Gaussians for Object Detection under Occlusion

In this paper, we consider the problem of detecting object under occlusion. Most object detectors formulate bounding box regression as a unimodal task (i.e., regressing a single set of bounding box coordinates independently). However, we observe that the bounding box borders of an occluded object can have multiple plausible configurations. Also, the occluded bounding box borders have correlations with visible ones. Motivated by these two observations, we propose a deep multivariate mixture of Gaussians model for bounding box regression under occlusion. The mixture components potentially learn different configurations of an occluded part, and the covariances between variates help to learn the relationship between the occluded parts and the visible ones. Quantitatively, our model improves the AP of the baselines by 3.9% and 1.2% on CrowdHuman and MS-COCO respectively with almost no computational or memory overhead. Qualitatively, our model enjoys explainability since we can interpret the resulting bounding boxes via the covariance matrices and the mixture components.
Decentralized and secure delivery network of IoT update files based on ethereum smart contracts and blockchain technology

The pervasiveness of IoT devices makes the delivery mechanism of security updates a challenge. Current IoT systems rely on centralized or brokered paradigms or clouds with huge computational and storage capacities. The existing centralized IoT setups are therefore expensive as the result of factors such as the high costs associated with cloud server and network infrastructures and maintenance. Thus, the need for a fully decentralized peer to peer and secure technology to overcome these problems rises into the realm of existence. Blockchain provides a solution that fulfills the requirements of such a platform. Ideally, the update infrastructure should implement the CIA triad properties (Confidentiality, Integrity, and Availability). In this article, we study how a blockchain application can meet these requirements and propose a novel system to decentrally distribute digital content in a peer-to-peer network using the blockchain technology and smart contracts to overcome the concerns mentioned above. Additionally, in order to prevent the issues stemming from the free-riding challenge in P2P networks (peers refrain to generously share their resources to distribute updates), we exploit a Nash equilibrium micropayment mechanism to grant adequate incentive for peers to participate in distributing IoT update files.
THE PLURALISM OF SCIENTIFIC TRUTHS CRITERIA: LEVEL MODEL

The article presents a new, pluralistic concept of the truth of scientific knowledge. It is based on two epistemological principles: 1) recognition of the social nature of scientific knowledge, 2) fixation of the level nature of the structure of scientific knowledge (in the structure of any science there are four qualitatively different levels of knowledge: sensory, empirical, theoretical and metatheoretical). Due to ontological, epistemological and methodological different levels of scientific knowledge on each of them there are special criteria of its truth. All of them have only two common features: a) each of them is a multicomponent, b) each criterion of truth has in its structure a consensual component.
Security Risks from Vulnerabilities and Backdoors

It is well-known that Alan Mathison Turing, a mathematician, put forward an abstract computing model, Turing Machine, and provided a solution to the computability problem. Another mathematician John Von Neumann came up with the storage program architecture, solving the computational issue of how to control program directions. Later, computer scientists and engineers, one generation after another, have devoted themselves to the promotion of computational capability, the reduction of application threshold and improvement of man-machine efficiency research and practice. And progress in microelectronics and engineering has initiated a digitized, computerized, networking and intelligent era for the human society, creating a computer-controlled cyberspace which exists everywhere. Unfortunately, the security in the computer architecture has long been neglected. Meltdown and Spectre (refer to Sect. 1.1.3) hardware vulnerabilities which have been discovered recently have made people awkward and embarrassed. It seems impossible to find a remedy through pure software. Although people have paid early attention to software vulnerabilities and worked hard to solve the problem, so far no ideal solution has been found. People have to doubt whether software technology alone can overcome the obstacle. Developments in globalized industry work division and the opening of open-source code technology, supply chains or technology chains, and even industrial chains cannot guarantee the trust, which makes threats from potential backdoors even more complicated. The author believes that attack theories and approaches based on software and hardware code defects are the main cause (rather than one of the causes) for the general security threats in the current cyberspace.
Identifying the Within-Statement Changes to Facilitate Change Understanding

As current tree-differencing approaches ignore changes that occur within a statement or do not present them in an abstract way, it is difficult to automatically understand revisions involving statement updates. We propose a tree-differencing approach to identifying the within-statement changes. It calculates edit operations based on an element-sensitive strategy and the longest common sequence algorithm. Then, it generates the metadata for each edit operation. Meta-data include the type of operation, the type of entity and the name of the element part, the content, the content pattern and all references involved. We have implemented the approach as a free accessible tool. It is built upon ChangeDistiller and refines its statement-update type. Finally, to demonstrate how to use the proposed approach for change understanding, we studied the condition-expression changes in four open projects. We analyzed the non-essential condition changes, the effective changes that definitely affect the condition, and other changes. The results show that for revisions with condition-expression changes, nearly 20% contain non-essential changes, while more than 60% have effective changes. Furthermore, we found many common patterns. For example, we found that half of the revisions with effective changes were caused by adding or removing expressions in logical expressions. And, in these revisions, 47% enhanced the condition, while 49% weakened it.
Deep Model Compression via Deep Reinforcement Learning.

Besides accuracy, the storage of convolutional neural networks (CNN) models is another important factor considering limited hardware resources in practical applications. For example, autonomous driving requires the design of accurate yet fast CNN for low latency in object detection and classification. To fulfill the need, we aim at obtaining CNN models with both high testing accuracy and small size/storage to address resource constraints in many embedded systems. In particular, this paper focuses on proposing a generic reinforcement learning based model compression approach in a two-stage compression pipeline: pruning and quantization. The first stage of compression, i.e., pruning, is achieved via exploiting deep reinforcement learning (DRL) to co-learn the accuracy of CNN models updated after layer-wise channel pruning on a testing dataset and the FLOPs, number of floating point operations in each layer, updated after kernel-wise variational pruning using information dropout. Layer-wise channel pruning is to remove unimportant kernels from the input channel dimension while kernel-wise variational pruning is to remove unimportant kernels from the 2D-kernel dimensions, namely, height and width. The second stage, i.e., quantization, is achieved via a similar DRL approach but focuses on obtaining the optimal weight bits for individual layers. We further conduct experimental results on CIFAR-10 and ImageNet datasets. For the CIFAR-10 dataset, the proposed method can reduce the size of VGGNet by 9x from 20.04MB to 2.2MB with 0.2% accuracy increase. For the ImageNet dataset, the proposed method can reduce the size of VGG-16 by 33x from 138MB to 4.14MB with no accuracy loss.
PEDAGOGICAL MANAGEMENT IN THE FORMATION OF THE CREATIVE POTENTIAL OF FUTURE SOCIAL EDUCATION IN THE MASTER'S CONDITIONS

Introduction.Nowadays, innovation plays an important role in the development of any educational institution.In modern conditions, innovation has become a driving force for economic, technological, political and social development.Therefore, innovation must be managed.The management of innovation is a key element, on the effectiveness of which depends the growth of welfare of the educational institution in general and the activation of pedagogical and scientific activity of teachers in particular.Thus, the issue of managing the innovation activity of the educational institution is relevant and timely.Purpose.Study the problem of managing the innovation activity in general secondary educational institution.Methods.Theoretical analysis, synthesis, generalization.Results.The innovation policy of educational institutions requires new methods and forms of organization of the educational process and, as a consequence, other scientific and methodological, financial, legislative provision, it requires new changes and innovations in the management structure.The managerial arsenal of the principal of educational institution includes new functions, in particular, the organization of analytical, diagnostic and research activities, scientific analysis and forecasting.The change of the scope and content of management requires the development of a new system for managing the innovation activity in the educational institution.An organization that aim to participate in the innovation process must take into account its own innovation poten-tial, which is defined as a set of different types of resources, including material and production, financial, intellectual, scientific and technical, etc. which are necessary for the implementation of innovation activity.The bearer of pedagogical innovations are creative, energetic people who are professionally capable, morally and financially interested in carrying out the innovative changes, mastering and implementing the new things.Originality.Modern psychological and pedagogical innovations in the national system of general secondary education include: in the content of education and upbringing of a personality; in the forms and methods of education and upbringing; in education and upbringing technologies; in the content, forms and methods of management of a comprehensive educational institution; in the structure of organization of activity of a comprehensive educational institution.Conclusion.Thus, the management of innovation processes in a general secondary educational institution has a profound socio-pedagogical meaning, because the success of changes in the education system depends on it.In order to realize it, in the educational sphere should prevail creativity and constant search, which is the basic environment for the new ideas, searches and achievements.
A Recommendation Approach Based on Community Detection and Event Correlation Within Social Learning Network

The social learning network is regarded as one of the most widespread types of online learning due to its collaborative and interactive properties. Recommendation systems contribute significantly in delivering relevant contents on social networks. However, learners are not consistently engaged. It is inescapable to develop a strategy for providing recommendations to meet system requirements for non-considerably active periods and to interrelate all events. To mitigate this problem, we propose an approach to fill the information gap and make recommendations more reliable. Our approach therefore introduces community detection and the correlation between data related to all events carried out by learners: consultations, information sharing, discussions, researches, etc. It is based on (a) detecting communities of learners who interact more intensely with each other and share common interests, and (b) calculating recommendations based on communities detected and computed correlations between data associated to learners’ events. Our perspective allows us to jointly embed two crucial guidelines, which are the event correlation and community detection.
Students’ performance via satisfiability reverse analysis method with Hopfield Neural Network

This finding presents a logic mining technique to model student’s performance in terms of specialized attributes. In this study, k Satisfiability based reverse analysis method (kSATRA) will be proposed to extract the logical relationship among key student’s attribute and will be used to classify or project the outcome of the current and future performance trend. The performance of the studied trends was narrowed down to three critical components: difficulties towards assigned subject, the ability to achieve the threshold marks and the decision to continue their study to higher level education. kSATRA is a method that utilized the beneficial feature of Hopfield Neural Network and k Satisfiability representation. The dataset used in this study includes data from several schools which contain insightful features. The robustness of kSATRA in extracting logical rule in student’s performance will be evaluated based on the root mean square error (RMSE), mean absolute error (MAE) and mean percentage error (MAPE). The result obtained from the computer simulation demonstrates the effectiveness of kSATRA in representing the performance of the students.
Action Semantics Network: Considering the Effects of Actions in Multiagent Systems

In multiagent systems (MASs), each agent makes individual decisions but all of them contribute globally to the system evolution. Learning in MASs is difficult since each agent's selection of actions must take place in the presence of other co-learning agents. Moreover, the environmental stochasticity and uncertainties increase exponentially with the increase in the number of agents. Previous works borrow various multiagent coordination mechanisms into deep learning architecture to facilitate multiagent coordination. However, none of them explicitly consider action semantics between agents that different actions have different influence on other agents. In this paper, we propose a novel network architecture, named Action Semantics Network (ASN), that explicitly represents such action semantics between agents. ASN characterizes different actions' influence on other agents using neural networks based on the action semantics between them. ASN can be easily combined with existing deep reinforcement learning (DRL) algorithms to boost their performance. Experimental results on StarCraft II and Neural MMO show ASN significantly improves the performance of state-of-the-art DRL approaches compared with several network architectures.