Reviewerly/exHarmony huggingface dataset:
When Digital Transformation Meets Brains the Role of Instructors as Innovators in Higher Education

This paper presents evidence that successful digital transformation is associated with the level and scope of an innovator’s experience. We select higher education as an example of a knowledge-based sector, and investigate digital innovation in delivering university courses as a response to the COVID-19 shutdown. A difference-in-differences model is employed to isolate the effect of a shift from a face-to-face delivery mode to an asynchronous digital delivery mode on student grades. While many instructors and students associate digital classrooms with a screen of black tiles, the digital transformation of higher education reaches far beyond this. Our focus is on genuinely transformed digital delivery modes, such as test bank offerings and tutorial videos. We find a significant improvement in student performance among only instructors who teach quantitative courses, have high levels of experience or expend high levels of effort. The results suggest that these individual characteristics of the digital innovators are of primary importance. In contrast, university spending does not contribute to this benefit of asynchronous digital delivery modes. Digital transformation in a knowledge-based sector such as higher education is not equally effective across the board but crucially depends on the brains behind it.
A comparative study and implementation of neuro-fuzzy and decision tree for malignant tumour detection system

Breast cancer is one of the most chronic diseases found in women. There are two types of tumours found in the breasts: malignant and benign. A patient who has more percentage of malignant tumours is suffering from breast cancer. A model based on neuro-fuzzy is proposed to classify the tumour as malignant or benign. The designed system works on various attributes of tumour like tumour thickness, shape, size, etc. The classification process completes in three phases; phase 1 classifies the attributes as cat1 or cat2 on the basis of information gain. Then in phase 2, cat1 attributes are used to select the class of tumour by using the radial bias function neural network while the cat2 attributes use the fuzzy to select the class of tumour. The results of both techniques are collaborated by using the fuzzy inference system in the phase 3. The effectiveness of the technique is easily identified by the results. The results are compared for the accuracy of cancer detection of cat1 and cat2 with neuro-fuzzy system and decision tree.
Scene graph representation robust against graph errors for scene graph classification

In this research, we have developed a new scene graph representation for scene graph classifiers. Scene graphs are powerful scene representations that can represent scene structure (semantic and spatial structure). Recent progress in Graph Convolutional Network (GCN) has enabled training of scene graph classifier. However, the effective scene graph descriptors for GCN have not been established yet. In this paper, we developed a new method of scene graph representation using local features (PatchNetVLAD) that are robust against graph errors. We verified the effectiveness of the proposed method by experiments using the NCLT dataset.
A Short History of TLS

This chapter describes the differences between the older version of TLS to our reference TLS 1.2, even if they are marked as obsolete. Attacks like DROWN have shown that knowledge of obsolete standards is essential to understanding the security of complex systems. Soon, TLS 1.3 will probably become the most important version of TLS and is described in greater detail here.
The Impact of Applying Human Resource Management Strategies On Employee’s Performance (Applied Study on Jordanian Islamic Banks)

This study aimed to identify the impact of human resources strategies with its all dimensions (recruitment, Staffing, training, and compensation), on performance by its dimensions (quality of work performed, quantity of work performed, speed of work performed, relationship with superiors). A special questionnaire developed consisting of (41) items, which were distributed to (346) employees working in Jordanian Islamic banks (Jordan Islamic Bank, Arab Islamic Bank, Safwa Islamic Bank) in a stratified proportional sampling method. It reached (77.2%), and the researcher excluded (8) questionnaires due to their unsuitability for statistical analysis processes. Statistical analyzes were carried out on (259) questionnaires. Using the appropriate statistical analysis methods, the study found a statistically significant impact of the strategies of human resource management with its combined and sporadic dimensions (recruitment, staffing, training, and compensation), on performance by its dimensions (quality of work performed, quantity of work performed, speed of work performed, relationship with superiors). The study recommended the necessity of continuing to activate modern methods of attracting from different sources through electronic applications, given the emergency conditions that have affected the business environment in recent times. In addition to the need to reconsider the bases of appointment and work to provide equal opportunities for all regardless of gender, and to involve women in Islamic banking more effectively. In addition, activating modern training methods, such as electronic training, given the importance of this type of training in providing workers with the skills they need and providing for training expenses.
Impact of Type of Convolution Operation on Performance of Convolutional Neural Networks for Online Signature Verification

An Online signature is a multivariate time series, a commonly used biometric source for user verification. Deep learning (DL) is increasingly becoming ubiquitous as a paradigm for solving problems that come with a wealth of data. Convolution has been its main workhorse. Recently, DL had marked its entry in online signature verification (OSV), a standard bio-metric method that has been mostly dealt with in traditional settings. However, embracing a DL solution to a problem requires certain issues to be tackled, viz. (i) type of convolution, (ii) order of convolution, and (iii) input representation. In this work, we experimentally analyse each of the issues mentioned above regarding OSV, and subsequently present a superior model that reports state-of-the-art (SOTA) performance on three widely used data-sets namely MCYT-100, SVC, and Mobisig. Specifically, the proposed model reports an equal error rate (EER) of 9.72% and 3.1% in Skilled_01 categories of MCYT-100 and SVC data-sets, with gains of around 4% and 3% over the next best performing methods, respectively. The experimental outcome confirms that the interrelationship between the type and order of convolution operation and the input signature representation plays a significant role in the performance of OSV frameworks.
Chapter 2: Structure of a C Program

Those already familiar with a programming language will no doubt find many parallels with C. Languages such as C++ and Java have very strong similarities, even including much of the syntax, but all languages have the same underlying structure. No matter what language is used, it is absolutely essential to comment the code. To be an effective programmer, code should be made reusable, and it is reusable only if it is clear what each function needs for input and what it produces for output. It is not reusable if its purpose and usage are easily forgotten. When working in a team, it is even more essential that comments are clear and consistent so that others can reliably use your work. Careful and frequent comments can help the reader understand the logic of the program so that if changes are necessary later, it can be done easily and confidently. This chapter introduces the core components of a program: where it starts, how input values can be included on the command line, and how to finish. It also covers the different styles of comments and what is good practice for commenting your code.
Virtuality and its relationship with the Teaching Performance of General Studies of the Universidad Autonoma del Perú, in the Context of the COVID-19 Pandemic

At the beginning of 2020, the pandemic began due to COVID-19, in which the need arose to carry out a total restructuring in the teaching-learning modality, having virtuality as the main means, with the use of technology and digital tools. Currently, due to the continuing social isolation dictated by the government, it has been possible to show in our house of studies that the performance of teachers was affected by this new modality. For this reason, this paper analyzes the relationship between virtuality and the performance of General Studies teachers at the Autonomous University of Peru in the context of the COVID-19 pandemic. For this, a sample of 50 teachers who carried out academic activities in the semester 2021 -II was used, using a questionnaire built and validated by expert judgment and the reliability test, using Cronbach's Alpha in the SPSS program. . The results were recorded with respect to the academictechnical-digital, socio-emotional and physical-mental health dimensions, which show that there is a significant positive average relationship between virtuality and the performance of the General Studies teachers of the Autonomous University of Peru in the context of the COVID-19 pandemic.
Online assessment of learning outcomes: the experience of Poland

The epidemiological situation and periodic restrictions imposed in Poland in 2020-2021 forced universities to quickly transform off-line learning into distance one. For this reason, the offered standard e-learning platforms and/or the created own implemented internet access applications were used. Full-fledged distance learning at a university has also become possible thanks to recording the students’ grades in their electronic indexes. The article analyses the process of organizing the assessment of students' knowledge in Polish universities. The situation of online assessment was considered on the example of the Faculty of Economics and Pedagogy, the Academy of Management and Administration in Opole. The place of the lecturer's thematic blog in the preparation of students' final works was also analysed. The authors argue that the experience of a Polish university can be used in organizing the assessment of students' learning outcomes in Ukrainian universities and secondary schools.
Voice Reminder Assistant based on Speech Recognition and Speaker Identification using Kaldi

A user often wants to do some tasks which don’t need a specific time to complete but are important, due to busy routines and possible memory loss problems user forgets to accomplish such tasks or delay them to manage later. The task may be about conveying some information to a person when the user meets him, sort things in his wardrobe, or locker, etc. A user can meet the person anywhere or anytime so he cannot set a reminder for a specific time. Such tasks can be accomplished if the user has a reminder system, which reminds him about the tasks at runtime. In this paper, we present a reminder system based on speech recognition &amp; speaker identification; personal voice- controlled aid for the user to remind about the tasks when some specific keyword/phrase is spoken by the user or user reaches a specific location so that the user can accomplish the important time independent tasks without being time-bound. The specified keyword can be anything, person name, place name, and any phrase. The user can speak to the assistant to remind about some task without un- locking his mobile or opening the application for a specific keyword/phrase &amp; location. When the specific keyword/phrase is spoken or user reaches the specific location the assistant also speaks and reminds the user about the task. Voice reminder assistant supports Text-Independent Speaker Identification unlike other voice assistants already available.
Virtual field trip as a resource in environmental education during the pandemic period - translation to real space

The pandemic and the consequent social isolation have changed the perception of space and the relationship between the real and the virtual. This paper analyzes an experience of remote teaching carried out during the pandemic period of COVID-19, to reflect on how the appropriation, translation, and re-signification of the virtual space into the real one happened, that is, what was the construction made by students regarding the understanding of contents and places explored virtually. The results obtained allow us to infer the aggregative role of non-formal teaching spaces and the importance of using different didactic tools in the search for overcoming the most varied limitations, including moments of social isolation.
Research on a Fully Actuated UAV with Two 2-DOF Tiltable Rotors

In recent years, a variety of unmanned aerial vehicles (UAVs) have been developed to replace human tasks, such as carrying cargo and inspecting infrastructures, and have attracted attention in many fields. However, conventional quad-rotor UAVs are forced to change in attitude when moving in the air. Therefore, aircraft that multiple rotors are assigned with an inclination or are tiltable has been developed, but most of them are redundant actuated systems. Therefore, in this study, it aims to develop an aircraft with a 2-DOF tiltable rotors to realize a fully-actuated system, which is capable of independent flight in position and attitude, using a small number of rotors. In this aircraft, rotors being different rotational directions are arranged up and down, one by one, so that it can reduce the power consumption and the risk of failures, because the number of rotors is only two.
The Abduction of Sherlock Holmes: A Dataset for Visual Abductive Reasoning

Humans have remarkable capacity to reason abductively and hypothesize about what lies beyond the literal content of an image. By identifying concrete visual clues scattered throughout a scene, we almost can’t help but draw probable inferences beyond the literal scene based on our everyday experience and knowledge about the world. For example, if we see a “20 mph” sign alongside a road, we might assume the street sits in a residential area (rather than on a highway), even if no houses are pictured. Can machines perform similar visual reasoning? We present Sherlock, an annotated corpus of 103K images for testing machine capacity for abductive reasoning beyond literal image contents. We adopt a free-viewing paradigm: participants first observe and identify salient clues within images (e.g., objects, actions) and then provide a plausible inference about the scene, given the clue. In total, we collect 363K (clue, inference) pairs, which form a first-of-its-kind abductive visual reasoning dataset. Using our corpus, we test three complementary axes of abductive reasoning. We evaluate the capacity of models to: i) retrieve relevant inferences from a large candidate corpus; ii) localize evidence for inferences via bounding boxes, and iii) compare plausible inferences to match human judgments on a newly-collected diagnostic corpus of 19K Likert-scale judgments. While we find that fine-tuning CLIP-RN50 $$\,\times \,$$ 64 with a multitask objective outperforms strong baselines, significant headroom exists between model performance and human agreement. Data, models, and leaderboard available at http://visualabduction.com/ .
Expandable-Convolutional-Block Neural Inference with Spatial-Fused Multi-resolution Features and Terse Convolution for IoT-Based Intelligent Automated Systems

We proposed two model compression methods by adjusting the structure of the deep network model, aiming to provide a small-sized model and realize the application requirement of edge computing. Edge Device has weak computing power and small storage capacity. The key idea of our proposed scheme is to minimize the computational cost of Expandableconvolutional-block neural network (ECB-Net) [7] to compress model size and optimize the model performance of ECB-Net. In order to achieve this, it is necessary to combine two model compression methods that adjust the ECB-Net network structure, including replacing Transpose Convolution in SIC-Block with SPP-Layer and replacing FC-Layer with TC-Layer. The experimental results reveal that the proposed scheme does have effective model compression capabilities and indeed prediction accuracy.
Specifying the Semantics of a Programming Language

As we noted in Chap. 3, syntax concerns the textual or graphical form of programs, but not what these programs mean.
Knowledge-Enhanced Classification: A Scheme for Identification of High-Quality Articles

How to find high-quality articles from many articles is the topic of this competition and also the problem that many enterprises want to solve. In this classification problem, from TF-IDF to word2vec, then to RNN and LSTM, and now to transformer-based models, such as Bert, have achieved great improvement in NLU tasks. However, for many specific problems, such as recognition of high-quality article, directly inputting the text content into the transformer model cannot get the optimal solution, and many other optimizations are needed. In this paper, we try to add statistical features of articles and knowledge graphs, and add entities name of knowledge graph into Bert-based model, specific methods are in Sect. 2 and Sect. 3. Finally, our model achieved 83.6 F1-score in the official test set and ranked first among all teams in task 2 of CCKS-2022. This paper is divided into four parts: 1) The introduction of our task; 2) Main ideas of our model; 3) Other innovation strategies; 4) Experiments and result.
CLID: A Chunk-Level Intent Detection Framework for Multiple Intent Spoken Language Understanding

Multi-intent spoken language understanding (SLU) that can handle an utterance containing multiple intents is more practical and attracts increasing attention. However, existing state-of-the-art models are either too coarse-grained (Utterance-level) or too fine-grained (Token-level) in intent detection, and thus may fail to recognize the intent transition point and the correct intents in an utterance. In this paper, we propose a Chunk-Level Intent Detection (CLID) framework, where we introduce a sliding window-based self-attention (SWSA) scheme for regional chunk intent detection. Based on the SWSA, an auxiliary task is introduced to identify the intent transition point in an utterance and obtain sub-utterances with a single intent. The intent of each sub-utterance is then predicted by assembling the intent predictions of the chunks (in a sliding window manner) within it. We conduct experiments on two public datasets, MixATIS and MixSNIPS, and the results show that our model achieves state-of-the-art performance.
Introduction

To be effective, data-intensive systems require extensive ongoing customisation to reflect changing user requirements, organisational policies, and the structure and interpretation of the data they hold. Manual customisation is expensive, time-consuming, and error-prone. In large complex systems, the value of the data can be such that exhaustive testing is necessary before any new feature can be added to the existing design. In most cases, precise details of requirements, policies and data will change during the lifetime of the system, forcing a choice between expensive modification and continued operation with an inefficient design.
Competence-based approach in teaching the discipline "Speech Culture and Business communication" at a technical university

The purpose of the study is to reveal the features of the implementation of the competence approach in the process of teaching the discipline "Speech culture and business communication" for students of the training direction 35.03.06 Agroengineering. The scientific novelty of the research consists in a comparative analysis of general cultural and universal competencies formed during the study of the humanities disciplines "Russian language and speech culture " (FSES 3+) and "Speech culture and business communication" (FSES 3++); in the presentation of the experience of teaching the latter discipline in a technical university, describing the forms of work with students, learning outcomes. As a result of the research, the theoretical and practical significance of the discipline is revealed, which contributes to the development of communicative competence, which further determines the self-realization of the graduate.
Pedagogical Importance of Ecological Education of Preschool Children

The pedagogical significance of ecological education of preschool children, the love of mother nature, its care issues such as cultivating a child's love for nature and living beings through ecological education and the formation of devotion to the motherland are widely covered.
KH-FC: krill herd-based fractional calculus algorithm for text document clustering using MapReduce structure

In this paper, krill herd-based fractional calculus (KH-FC) using MapReduce framework is proposed for effective text document clustering. Here, the stop word removal and stemming model is applied in the pre-processing step, helps to remove redundant information and hence the size of the information is reduced, which further enhances the clustering accuracy. Furthermore, term frequency (TF) and inverse document frequency (IDF) are employed for extracting significant features. Finally, the developed KH-FC model is utilised for clustering the text documents. The developed KH-FC algorithm is developed by combining the FC concept into the KH technique. In this method, pre-processing and feature extraction is performed in the mapper phase, whereas the clustering process is executed in the reducer phase. The performance of the developed approach is evaluated based on performance metrics, like accuracy, Jaccard coefficient, and F-measure. The developed KH-FC approach obtained better performance in terms of accuracy, Jaccard coefficient, and F-measure is 0.983, 0.936 and 0.967, respectively.
Process evaluation of the Dutch COVID-19 QR code app: an example of citizen science

<b>Background:</b> The Dutch Ministry of Health developed and implemented the CoronaCheck app. With the app, citizens can show proof of "non-infectivity" when traveling within the EU and to gain access to e.g. restaurants or events. The study was carried out among citizens, companies and civil society organisations. The results of were used by the Ministry for optimization. <b>Method:</b> 42 interviews and 353 questionnaires were conducted in the general population, visitors to events, managers of test lanes and employees of society organisations. Field observations were performed at 3 events. The interviews were transcribed verbatim and thematically coded. Descriptive analyses were used for the questionnaires. <b>Findings:</b> 49% of the citizens see the added value and necessity of CoronaCheck yet 47% fear that CoronaCheck will lead to a divide in society. As the vaccination rate increases, doubts increase among respondents about the necessity of the app. The travelling distance to a testing location and the digital accessibility were bottlenecks. Companies had difficulties to find the right information. It turned out to be difficult to avoid fraud with QR codes.&nbsp;The interviews with civil society organisations showed that there is too little attention for low digital literate people. <b>Conclusion:</b> Citizens, companies and civil society organisations all find the concept CoronaCheck relevant. However, there are some points of interest: testing should be easily accessible, information about the control procedure and a telephone helpline should be available for organisers and the procedure should also be easily available in analogue form.