Reviewerly/exHarmony huggingface dataset:
Adaptive Pooling for Convolutional Neural Networks with Arbitrary Input Sizes

Convolutional neural networks have been widely used in deep learning recently. This paper presents an adaptive scheme to modify the input layers of the conventional convolutional neural networks such that images of arbitrary sizes can be directly input. Specifically, motivated by the advantage of content-aware image resizing, which takes the regions of interest into account for effective displaying on various screens with different dimensions and aspect ratios, it is beneficial to incorporate content-aware image resizing into convolutional neural networks. Experimental results show that image classification can be improved in terms of mean average precision.
pytest

In Chapter 4 , you explored nose, which is an advanced and better framework for Python testing. Unfortunately, nose has not been under active development for the past several years. That makes it an unsuitable candidate for a test framework when you want to choose something for a long-term project. Moreover, there are many projects that use unittest or nose or a combination of both. You definitely need a framework that has more features than unittest, and unlike nose, it should be under active development. nose2 is more of a test-runner for unittest and an almost defunct tool. You need a unit test framework that’s capable of discovering and running tests written in unittest and nose. It should be advanced and must be actively developed, maintained, and supported. The answer is pytest.
Variational Contrastive Log Ratio Upper Bound of Mutual Information for Training Generative Models

Theoretically, a Generative adversarial network minimizes the Jensen-Shannon divergence between real data distribution and generated data distribution. This divergence is another form of mutual information between a mixture distribution and a binary distribution. It implies that we can build a similar generative model by optimizing the mutual information. This research proposes variational contrastive log-ratio upper bound vCLUB mutual information estimation on mixture distribution and the optimization algorithm to train two generative models. We call the models CLUB-sampling generative network (vCLUB-sampling GN) and vCLUB-non sampling generative network (vCLUB-non sampling GN). The results show that vCLUB-sampling outperforms GAN and vCLUB-non sampling GN on the MNIST dataset and has competitive results with GAN on the CIFAR-10 dataset. However, GAN outperforms vCLUB-non sampling GN on both datasets.
Reference Framework for Coordinating Robots for Movement of Objects

One of the common use cases in many factories is that loads of materials is to be moved from one place to another. It is desirable to have payload carriers for automating the tasks to improve productivity and reduce the burden on humans. The automation becomes even more important for hazardous environments. This framework is developed for a concept of multiple robots that are deployed by enabling a form of coordination from a centralised system. The work is an attempt to create a framework for such applications in which multiple robots work to accomplish payload movements. The master is a computer that uses a vision system and identifies the number of robots, the payload objects to be transferred, their initial positions, the destination points that they all need to be moved to and the obstacles in the environment that the robot needs to manoeuvre around. The master commands the robot motion, picking up and transfer the payloads to destination. The realisation also includes algorithms for optimisation of the paths with the objective of minimizing the distances to be traversed. In the prototype developed, the proposed system is evaluated based on one master running on a PC system and three robots working on payload transfer. The proof of concept is based on a prototype with robots and arena of area 1.5m x 1.5m with representative payloads, obstacles, and targets. The search algorithm is developed in Python for path optimisation with real time video feedback of the workspace from a camera. The framework can be extended to applications comprising of multiple robots as each of the robots will communicate wirelessly with the master. Scenarios were created based on different positions for payload and obstacles to determine the time taken for payloads to be moved to target locations. The optimal time taken was found to be 130.09s and 136.39s as the robots traverse the shortest path and at constant speed. The framework is found to work for real time environment changes in terms of moving robots and obstacles.
SegmentPerturb: Effective Black-Box Hidden Voice Attack on Commercial ASR Systems via Selective Deletion

Voice control systems continue becoming more pervasive as they are deployed in mobile phones, smart home devices, automobiles, etc. Commonly, voice control systems have high privileges on the device, such as making a call or placing an order. However, they are vulnerable to voice attacks, which may lead to serious consequences. In this paper, we propose SegmentPerturb which crafts hidden voice commands via inquiring the target models. The general idea of SegmentPerturb is that we separate the original command audio into multiple equal-length segments and apply maximum perturbation on each segment by probing the target speech recognition system. We show that our method is as efficient, and in some aspects outperforms other methods from previous works. We choose four popular speech recognition APIs and one mainstream smart home device to conduct the experiments. Results suggest that this algorithm can generate voice commands which can be recognized by the machine but are hard to understand by a human.
A new playful language based on pedagogical tools for explaining genetic algorithm basic concepts : Creating educational pedagogical tools to disseminate basic concepts of computational algorithms

This document shows the conversion of the general concept of genetic algorithm widely used in A.I for making predictions which has multiple applications in the field of robotics and engineering design, but in this case its application will be in an educational and playful context in order to make its using more known in an everyday context that can also be adapted to new technologies in the future.
Spectral Aesthetics and ecological listening in the music of Helena Tulve

Abstract This chapter analyzes Estonian composer Helena Tulve's unique timbral process in four works: Sula (1999); L'Équinoxe de l'âme for soprano, triple harp or kannel, and string quartet (2008); Arboles lloran por luvia for voices and nyckelharpa (2006); and I am a River for chamber choir (2009). L'Équinoxe de l'âme (The Equinox of the Soul) is based on a text by the Persian mystic and philosopher Shahab al-Din Suhrawardi on the mythical Persian firebird Simurgh. Rather than portraying dissolving as in the orchestral work ice—Sula means to thaw or melt—the timbral transformations of Équinoxe evoke sparks bursting into flame, as well as a philosophical rumination on existence. The remaining vocal works similarly treat their texts with a programmatic flair that blurs the line between the materiality of the sound and its contemplative mood. Tulve's compositional approach can be considered post-spectral in its influences and attention to timbral detail. However, she is also motivated by ecological concerns, and counts herself among a small group of contemporary Estonian composers devoted to expressing this philosophy through music. Her influences meld in a music that prioritizes “slowness”: a sustained focus on spectral detail that reflects the composer's deep-rooted ecological concerns. The chapter thus draws on ideas from ecological theory, timbral analysis, and speculative aesthetics to show how Tulve's compositions express form through timbre and its expression in space, and reflect an ecological aesthetic of listening.
Blockchain-based Self-scoring Method

Quiz sites are typically made up of a front end, a back end, and a database of questions. Our objective is to further commonize this structure. Problem verification and solution keeping are aspects that have already been thoroughly studied and modified. However, the fairness of answer validation is often unclear to the end user. We propose a system that reduces the cost of creating a quiz site and a quiz scoring system for e-learning by putting part of the structure on a blockchain. Our system verifies answers using zero-knowledge proofs, stores the answers with Ethereum, an open-source platform for decentralized applications, and tracks them. Blockchain can be used to prevent the falsification of answer records. Ethereum is a new technique that has attracted considerable attention in recent years, and we use its program executions, i.e., smart contracts, to validate answers. After receiving an answer, the system stores it as an Ethereum transaction event.
Thermal coherence of Heisenberg model with dipole interaction and magnetic external field

The dynamics of coherence in a two-qubit Heisenberg XXX model with magnetic field in the presence of the intrinsic decoherence is investigated. We discuss the influence of dipole parameter D , spin distance r , magnetic field B z and initial state parameter α on coherence of the long time limit situation. Then, we discuss how decoherence changes with time with the initial state parameter α and the decoherence parameter γ . • We consider a two-qubit Heisenberg XXX model with magnetic field in the presence of the intrinsic decoherence. • The dynamics of coherence with magnetic field, spin distance and dipole parameter are investigated. • The ways to maintain the coherence are found. • Our results will be benefit for the future research on decoherence.
Requirements Behind Reviews: How do Software Practitioners See App User Reviews to Think of Requirements?

The popularity of mobile computing has led to the development of several mobile apps to solve end-users everyday problems. Nowadays, app stores allow users to write reviews reporting their experiences using the apps. Negative experiences can reveal the reasons why users are moving from one app to another and drive app adoption by future users. Thus, software companies should listen to users’ voices to meet their needs and improve the quality of their apps. In this context, Data-Driven Requirements Engineering (DDRE) emerges as an alternative approach to develop and manage requirements from large volumes of data such as user reviews. Most research on DDRE has focused on assessing different machine learning, information retrieval, and natural language processing techniques for automatic requirements mining and classification. However, we need to move forward to understand how to support the DDRE adoption by software practitioners who will apply the paradigm in practice. In this paper, we present a tool-supported approach for data-driven requirements elicitation. It applies text mining techniques to obtain user reviews from app stores and presents the most frequent terms with their associated reviews through a visual representation. To advance the understanding of DDRE adoption, we conducted a feasibility study with industry practitioners to investigate how our approach supports the requirements elicitation process. Our main findings indicated a positive acceptance by practitioners. They perceived it as easy to use and useful to think of requirements while demonstrating an intention to adopt it in the requirements elicitation process.
Shopping and Basket Analysis by Using an Improved Apriori Algorithm in WEKA

In the past years, e-commerce and online shopping grew fast. It became more helpful by letting people buy the desired product online. Also, to help their users to find the product of their desire easily and make the process simpler, the online shopping websites use some kinds of an algorithm to provide recommendation systems. Often, these systems use techniques like basket analyzing and association rules which is finding the relation between the products together or between users too, so apriori algorithm is one of the famous ones among the recommendation systems. Although it has some limitations while implementing which makes the algorithm less confident or even useless, Let us assume we have 100K records in the sold item list in a system in which about 10K refers to the customers buying only one or two items in their purchase. Therefore, this ten per cent will not affect finding the relation between the items, at the same time these records will make the system less efficient and take more time to analyze, in this paper, we try to show how we can improve the apriori algorithm efficiency and accuracy by some preprocessing on the dataset before applying apriori algorithm by eliminating the unnecessary records, this process helps to make the algorithm better because of reducing the number of transactions, hence finding strong relationships between items easier for the rest of the records.
The Mediating Effect of Competitive Advantage on the Relationship Between Human IT Resources and Performance

Human IT capability gained mainly from the organization based on the strategic perspective of human resources. Human capital is a valuable resource for organizations as intellectual assets. Hence, human IT resources can be perceived as a strategic capability that directly impacts performance in the Jordanian banking sector. This research aims to examine the mediating effect of competitive advantage between IT human resources on performance in the Jordanian banking sector from 2014 to 2019. To achieve the research objectives, it is expected from this study to generate practical knowledge that can be used by the IT human resources of the respective banks to create a competitive advantage and improve their performance. Additionally, this study is expected to add to knowledge in various ways, including demonstration of how IT human resources as a dynamic resource could impact the competitive advantage and firm performance in light of the Resource-Based View (RBV) theory. Thus, a quantitative method is used to collect and analyze data from 16 Jordanian banks; the targeted population is about 853 management staff of the Jordanian banks. The empirical results showed the mediating effect of competitive advantage on the relationship between IT human resources and performance; this conclusion related to the sufficient capturing of the respective setting that in turn becomes a positive impact. Furthermore, human IT resources in the given context could be revealed with other resources and capabilities, or through different business routines.
Quantifiers with Split Scope

Quantifiers in intensional contexts cause difficulty in explaining their scope phenomena in the standard linguistics.A strong quantifier has narrower scope than an intensional operator, but the nominal predicate has the de re interpretation, and an indefinite can have wide scope over an intensional operator over a syntactic island.In the paper, I propose a new way of interpreting a strong quantifier, assuming that a strong quantifier triggers the presupposition that there is a non-empty set determined by the nominal predicate.A presupposition tends to be projected over an intensional operator.This gives the effect that the nominal predicate gets the de re interpretation.On the other hand, the nuclear scope of the quantifier, together with the nominal predicate, determines another set in the local context, and the quantificational force is determined by the relation of the two sets.A weak quantifier is ambiguous, and it can be interpreted as triggering a presupposition, as a strong quantifier does.A quantifier in this use can be analyzed in the same way, but it leads to the effect that the quantifier, not just the nominal predicate, has scope over an intensional operator because of a semantic property of the quantifier.This new way of interpreting a quantifier is independently motivated by the observation that the two sets are referred to at a later discourse.
Development Process and Hot Spot Analysis of Object Detection Based on Mapping Knowledge Domains

The object detection technology undergoes rapid development, has been widely used in the fields of transportation, medical treatment, Internet security etc., and has received extensive attention from scholars and industry participants at home and abroad. To intuitively and deeply understand the development process and application progress in the field of object detection, we use bibliometrics software citespace to visualize and analyze 5,500 papers about object detection from 2001 to 2021 in Web of Science database, draw the mapping knowledge domains such as countries (regions) and institutional cooperation networks, co-citation networks, keyword sequence diagrams, etc. in the field of object detection research, which allows people to systematically review the development process and key research achievements in this field in the past over 20 years, and carry out the visual analysis, content analysis and statistical analysis with respect to the research hotspots and current situation in different periods. According to the research results, all countries and institutions continue to invest more in research of object detection, and put focus on the accuracy and speed of the model. The development opportunity and challenge will still coexist in terms of directions and applications of small object detection, weakly supervised detection and multi-modal detection.
Digital footprint in predicting school graduates’ educational strategy

This research article aims at evaluating the results of the school graduates’ educational strategy transformation with the help of digital footprint data. The analysis of official and unofficial Internet communities of universities in the social network «VKontakte» shows that their active users are school graduates, who thus receive the necessary information about the university. The method presented can become a promising tool to forecast the demand for higher education. The study covers the period from 2019 to 2021, a total of 502 thousand user profiles having been identified, 246 thousand accounts included in the final sample. The results show that during the analyzed period the number of user subscriptions to university communities has decreased in all Russian regions. The orientation towards universities located in other regions has also changed: these were more popular before the pandemic, being subscribed to by the majority of students, whereas after the spread of COVID-19, most of the graduates began to choose universities located in their native region. A significant limitation of this method is that a number of profiles are closed, which logically does not allow to use them in further analysis. The results of the study show, however, high validity and wide opportunities for the use of the digital footprint method when predicting young people’s educational trajectory and planning the enrollment campaign, both on the scale of the university and the whole country.
A Comprehensive Travel Recommendation System

Planning plays the major role in organizing a trip or an outing. Though there are various resources in the web, and various travel agencies that provide planned trips, the automation provided by them is low. This project is an attempt to build a comprehensive travel recommendation system, which builds the itinerary to the user based on his priorities and inputs, along with providing personalized recommendations. The project makes use of machine learning algorithms like collaborative filtering and content-based filtering, along with Google APIs to build the itinerary and provide recommendations.
Fast Center Location Method of Edge Detection Based on Canny Operator

The positioning of the circular target is a very important task in image processing, and it is the basis for image analysis to proceed. The current classic Hough circle detection method has the advantages of high accuracy and strong anti-interference ability. However, because the parameter space of this method is three-dimensional, the time and space required to accumulate evidence in the three-dimensional space are huge, which is not applicable in practical applications. In order to overcome these shortcomings, this paper proposes a fast center location method. This method can detect the circle even if it is defective. It has the characteristics of fast, stable and accurate, and can meet the real-time requirements.
The Saline Terrain Prediction Model Based on Random Forest

With the development of science and technology, the trend of combining computers with terrain surveying is increasing. The Lop Nur Salt Field in Xinjiang has extremely rich mineral resources. In order to rationally develop and utilize salt lake resources, the establishment of a three-dimensional model for the distribution of salt field minerals is of great significance to the economic benefits and sustainable development of the enterprise. This paper adopts a data analysis method based on deep learning, and the proposed saline terrain prediction model first removes noise from the collected point cloud data, then combines multiple machine learning model training and testing sample data, and finally evaluates the experimental results through various performance indicators. The experimental results show that the accuracy of combining random forests with other network models is higher, and the mean square error value is 0.0021, which has the certain value for actual industrial development and production.
FAIR: Fully-Adaptive Framework for Improving Resource Provisioning in Collaborative CPU-FPGA Cloud Environments

Cloud Warehouses have been exploiting CPU-FPGA collaborative environments to accelerate multi-tenant applications to achieve scalability and maximize resource utilization. However, resource provisioning is challenging in these environments since kernels may be dispatched to CPU and FPGA concurrently in a scenario with highly variant workloads and demands. The provisioning complexity is further aggravated due to diverse CPU and FPGA architectures being used at Cloud Warehouses (e.g., different FPGA/CPU devices between nodes). That means that the resource manager needs to consider the workload to be allocated and the characteristics of the Cloud infrastructure, which can be non-uniform. This paper shows that efficient resource provisioning in CPU-FPGA cloud environments requires different strategies depending on the demand, architecture, and workload. To provide the best use of resources in this complex environment, we propose FAIR, a Fully-Adaptive approach for Improving Resource provisioning in Collaborative CPU-FPGA Cloud. FAIR is end user-transparent and, in contrast to existing approaches, exploits the benefits of multiple provisioning strategies by dynamically selecting the most appropriate depending on the warehouse needs, workload properties, and target architecture. Over a varied set of scenarios, FAIR significantly improves the performance and energy efficiency of the environment compared to the use of fixed single strategies. On average, FAIR provides 32&#x0025; performance improvements over the use of the best fixed single strategy. Compared to an Oracle that always selects the best energy strategies, FAIR achieves only 3&#x0025; energy degradation.
Regularities of Adaptation of Communication Didactic Processes to Modern Digital Information

The modern educational process with included communication and information technologies has a number of pressing problems. Difficulties and threats in the work of the digital educational space determined the purpose and methods of research. The main tasks are the need to study the level of adaptation of communication educational processes to the content field of information security of the educational environment; research of risks and threats to the information infrastructure of an educational organization. As a result of the study, it is shown that most teachers lack basic professional knowledge and skills in the field of communication educational processes in the modern rapidly changing conditions of the digital educational process.