Reviewerly/exHarmony huggingface dataset:
Preservice teachers’ TPACK learning trajectories in an online educational technology course

This mixed-methods study analyzed Technological Pedagogical Content Knowledge (TPACK) learning trajectories of preservice teachers enrolled in an online seven-week long educational technology course. Timeline analysis was used to identify and interpret emerged patterns of learning trajectories. The study suggests that continuous growth of the group mean TPACK could be attributed to the inclusion of immersion - theory - analysis - digital content development cycles, focus on high-impact teaching strategies, and a choice of interactive online platform. Level of participation, support from the instructors, and confidence in using technology for teaching and learning moderated the course influence leading to several distinct patterns in individual TPACK learning trajectories.
Cooperative Concurrency Control for Write-Intensive Key-Value Workloads

Key-Value Stores (KVS) are foundational infrastructure components for online services. Due to their latency-critical nature, today’s best-performing KVS contain a plethora of full-stack optimizations commonly targeting read-mostly, popularity-skewed workloads. Motivated by production studies showing the increased prevalence of write-intensive workloads, we break down the KVS workload space into four distinct classes, and argue that current designs are only sufficient for two of them. The reason is that KVS concurrency control protocols expose a fundamental tradeoff: avoiding synchronization by partitioning writes across threads is mandatory for high throughput, but necessarily creates load imbalance that grows with core count and write fraction. We break this tradeoff with C-4, a co-design between NIC hardware and KVS software that judiciously separates write requests into two classes: independent ones that can be balanced across threads, and dependent ones which must be queued. C-4 dynamically partitions independent writes with the NIC to increase the load balancing flexibility of current KVS designs, and adds a software layer to the KVS to compact dependent writes into batches. Our evaluation shows that for write-intensive workloads, C-4 reduces 99th% tail latency by 1.3−5× and improves throughput by up to 1.7×.
Challenges of blockchain application in the financial sector: a qualitative study

Purpose The novel blockchain technology can be leveraged, owing to the growth in computing power and its widespread applications. This study aims to understand the challenges of adopting blockchain technology in the financial sector, organise them into a model and classify them for systematic address. Design/methodology/approach Interpretive Structural Modeling (ISM) has been carried out along with MICMAC (Matrice d’impacts croisés multiplication appliquée á un classment) analysis to hierarchically structure blockchain adoption problems and categorise the challenges into four classes-autonomous, dependent, linkage and independent for better addressing. The study also uses content analysis using NVivo software. Findings The digraph depicts the hierarchical challenge model. Vulnerability to financial crimes and glitches, privacy issues and geopolitical tensions due to cross-border transactions are the dependent variables. Complex architecture to comprehend, code and fix, the need for new financial intermediaries, complexity in auditing and the lack of unified governance and coordination among institutions and regulators are the independent variables. The digraph, which is also justified by the qualitative content analysis, is beneficial for stakeholders to systematically address the interdependent challenges associated with blockchain implementations in finance to foster its favourable adoption. Practical implications The challenges in the adoption of blockchain should be resolved to allow the implementation of this technology in various finance domains. This study enables organisations to carry out resource planning and systematically address these challenges to leverage the advantages of blockchain. Social implications The results of the present study can help in promoting the proliferation of blockchain for faster, cost-effective, transparent and secure financial transactions and foster innovative and new business models for economic growth. Originality/value The development of technology has brought about significant changes in the financial sector. Blockchain is a technological advancement that aims to bring security and transparency to transactions. There has been no research leveraging ISM-MICMAC to hierarchically organise and classify the blockchain challenges in the financial sector, a critical one. The research also uses content analysis which is seldom found along with ISM-MICMAC.
Group‐guided artificial bee colony algorithm with elastic adjustment strategy

Summary Artificial bee colony algorithm (ABC) is a new intelligent optimization method based on swarm intelligence. It is an algorithm that simulates the social behavior of bees in nature and has a performance of good search in all kinds of optimization problems. Over the past few years, ABC algorithm has become popular because of its stable performance and good structure. ABC algorithm performance, however, is often influenced by both exploitation and exploration abilities, and balancing exploitation and exploration is particularly important. This article proposes a novel variant of ABC, which is called elastic adjusted group guided artificial bee colony algorithm (EGABC). In EGABC, we propose a group guidance strategy with flexible ability to adjust. The main purpose is to effectively use the known information, speed up convergence, and play a balance between search exploitation and exploration ability. To verify the validity of EGABC, we conducted experiments on 13 classic benchmark problems and CEC2013 test suite. Based on experimental results, the EGABC strategy is highly competitive and can improve the ABC algorithm's performance significantly.
Adaptive encoding-based evolutionary approach for Chinese document clustering

Abstract Document clustering has long been an important research direction in intelligent system. When being applied to process Chinese documents, new challenges were posted since it is infeasible to directly split the Chinese documents using the whitespace character. Moreover, many Chinese document clustering algorithms require prior knowledge of the cluster number, which is impractical to know in real-world applications. Considering these problems, we propose a general Chinese document clustering framework, where the main clustering task is fulfilled with an adaptive encoding-based evolutionary approach. Specifically, the adaptive encoding scheme is proposed to automatically learn the cluster number, and novel crossover and mutation operators are designed to fit this scheme. In addition, a single step of K -means is incorporated to conduct a joint global and local search, enhancing the overall exploitation ability. The experiments on benchmark datasets demonstrate the superiority of the proposed method in both the efficiency and the clustering precision.
Memory-Side Acceleration and Sparse Compression for Quantized Packed Convolutions

Neural Network compression techniques, such as parameter quantization and weight pruning have made deep neural network (DNN) inference more efficient for low-power devices such as MCUs and edge devices by reducing the memory and computation overhead required with minimal impact on model accuracy. To avoid storing and computing zeros, these techniques necessitate the use of sparse data representations, which introduces execution overhead to locate values required by a computation. Sparse matrix formats like Compressed Sparse Row (CSR) and other more recent designs are computationally inefficient when applied to the convolution algorithm as well as inefficient for storing quantized values. In this paper, we outline an intuitive extension of CSR called Partitioned Sparse Representation (PSR) in conjunction with a convolution algorithm that hides the cost of indexing overhead via a simple memory-side RISC-like core. PSR divides the entire weight array for a convolution layer into partitions that allow for smaller (e.g., 8-bit) indexes to reduce storage overhead. We also rely on a memory-side accelerator called HHT, a programmable, near-memory RISC-like co-processor that enables efficient processing of sparse data (including PSR). We show that HHT together with PSR allows the CPU to maximize the advantage of RISC-V packed instructions on sparse quantized data. We show as much as 10x speedup for sparse CONV with HHT over a baseline of the CPU performing all computations on dense data. HHT performs 2.7x faster on end-to-end image classification inference over the baseline and achieves 70% energy savings over sparse CONV with CPU performing all computations.
Lattice‐based cryptosystems in standardisation processes: A survey

Abstract The current widely used public‐key cryptosystems are vulnerable to quantum attacks. To prepare for cybersecurity in the quantum era, some projects have been launched to call for post‐quantum alternatives. Due to solid security and desirable performance, lattice‐based cryptosystems are viewed as promising candidates in the upcoming standardisation of post‐quantum cryptography. This study surveys the lattice‐based cryptosystems in the post‐quantum standardisation processes including the NIST Post‐Quantum Cryptography Standardisation and the Chinese Cryptographic Algorithm Design Competition, from both design and security aspects. We present generic design paradigms of lattice‐based schemes and describe several representative proposals and recent progress. We also recap some main cryptanalytic results and methods for estimating the concrete security of lattice‐based schemes.
Modelling the Dynamics of Language Change: Logistic Regression, Piotrowski’s Law, and a Handful of Examples in Polish

The study discusses modelling diachronic processes by logistic regression. The phenomenon of nonlinear changes in language was first observed by Raimund Piotrowski (hence labelled as Piotrowski’s law), even if actual linguistic evidence often speaks against using the notion of a ‘law’ in this context. In our study, we apply logistic regression models to changes which occurred between 15th and 18th century in the Polish language. The attested course of the majority of these changes closely follow the expected values, which proves that the language change might indeed resemble a nonlinear phase change scenario. We also extend the original Piotrowski’s approach by proposing polynomial logistic regression for these cases which can hardly be described by its standard version. Also, we propose to consider individual language change cases jointly, in order to inspect their possible collinearity or, more likely, their different dynamics in the function of time. Last but not least, we evaluate our results by testing the influence of the subcorpus size on the model’s goodness-of-fit.
Development of High-level Synthesis Oriented Background Scroll Function Considering Hierarchy of Game Display Frame

We are developing background scrolling function in High-Level Synthesizable game programing library to realize high-performance and power-saving mobile terminals executing game applications by using hardware execution in Field Programmable Gate Array, FPGA. High-Level Synthesis converts software into hardware automatically and it can reduce heavy burden of hardware development. In game application, display resolution has not to be always same as game screen size. The hierarchy layering game screens with varying sizes on the display frame may provide more engaging effect to the game application. In such case like the size of game screen differs from the display resolution, the appropriate integration of the updated game screen into the display frame is required. Performing updating and embedding the game screen individually may lead to degrade the performance of hardware because useless memory copies become bottleneck. To solve this problem for the background scrolling on the game screen, we extend our function to fit the background scrolled to physical display resolution simultaneously. That is, this paper proposes a high-level synthesizable software description method for background scrolling considering hierarchy of display frame and evaluates this effect.
Integrated Visual-Based ASL Captioning in Videoconferencing Using CNN

An assistive videoconferencing tool that combines hand tracking, region extraction, and a convolutional neural network (CNN) for gesture recognition has been developed to improve accessibility for sign language users.
A Low Error Face Recognition System Based on A New Arrangement of Convolutional Neural Network and Data Augmentation

This paper represents a low error face recognition system constructed on convolutional neural network structure. The proposed method introduces a new layer arrangement for the CNN with added normalization layers. In addition, a data augmentation step consisting of vertical flip, scaling, rotation, and shift is implemented into the framework of our system to improve the accuracy. This augmentation helps the system to overcome the issue of having low number of samples per individuals in our dataset. The support vector machine (SVM) and Softmax are considered as classifiers of the proposed system, and results are evaluated for both classification methods. The system is tested on the ORL face image dataset. In our experiment SVM showed a higher accuracy compared to Softmax. The results compared to other existing face recognition methods show better performance and higher accuracy of the proposed system. The proposed method is evaluated with %50 of the dataset as training samples and the rest as test samples by random. Our technique achieves %98.64 with Softmax and %99.7 with SVM recognition rate.
A Comparison between Using Fuzzy Cognitive Mapping and Machine Learning to Predict Students’ Performance in Higher Education

Higher education quality (HEQ) stakeholders increasingly see the student as the core of the teaching operation. To improve HEQ a good prediction of students’ performance is a considerable step. In this paper we discuss the use of two approaches used in the literature in students’ performance (SP) prediction: Machine Learning (ML) techniques and Fuzzy Cognitive Mapping (FCM). Our main contribution is a comparative analysis of these approaches and recommendations in context of the Moroccan Higher Education institutions.
Back-Translation-Style Data Augmentation for Mandarin Chinese Polyphone Disambiguation

Conversion of Chinese Grapheme-to-Phoneme (G2P) plays an important role in Mandarin Chinese Text-To-Speech (TTS) systems, where one of the biggest challenges is the task of polyphone disambiguation. Most of the previous polyphone disambiguation models are trained on manually annotated datasets, and publicly available datasets for polyphone disambiguation are scarce. In this paper we propose a simple back-translation-style data augmentation method for mandarin Chinese polyphone disambiguation, utilizing a large amount of unlabeled text data. Inspired by the back-translation technique proposed in the field of machine translation, we build a Grapheme-to-Phoneme (G2P) model to predict the pronunciation of polyphonic character, and a Phoneme-to-Grapheme (P2G) model to predict pronunciation into text. Meanwhile, a window-based matching strategy and a multi-model scoring strategy are proposed to judge the correctness of the pseudo-label. We design a data balance strategy to improve the accuracy of some typical polyphonic characters in the training set with imbalanced distribution or data scarcity. The experimental result shows the effectiveness of the proposed back-translation-style data augmentation method.
On the Applications and Parallelization of Multichannel Source Coding

Each codeword in Multichannel Source Coding (MSC) naturally uses multiple channels simultaneously.Previous works only focused on the theoretical aspects of MSC.In this paper, we discuss the applications and parallelization of MSC.
Increasing pedestrian detection performance through weighting of detection impairing factors

Object detection is a matured technique, converging to the detection performance of human vision. This paper presents a method to further close the remaining gap of detection capability by investigating visual factors impairing the detectability of objects. As some of these factors are hard or impossible to measure in real sensor data, a detector is trained on synthetic data making perfect measurements and ground truth data available at a large scale. The resulting detector is then used to calibrate an empirical weighting loss, which weights samples of real training data and their corresponding detection impairing factors. The method is applied to the task of pedestrian detection in traffic scenes. The effectiveness of the empirical detection impairment weighting loss (DIW loss) is demonstrated on a detector trained on the CityPersons dataset and reaches a new state-of-the-art detection performance on this benchmark, improving the previous by .
Automatic Classification of White Blood Cells Using Pre-Trained Deep Models

White blood cells (WBCs), which are part of the immune system, help our body fight infections and other diseases. Certain diseases can cause our body to produce fewer WBCs than it needs. For this reason, WBCs are of great importance in the field of medical imaging. Artificial intelligence-based computer systems can assist experts in the analysis of WBCs. In this study, an approach is proposed for the automatic classification of WBCs over five different classes using a pre-trained model. ResNet-50, VGG-19, and MobileNet-V3-Small pre-trained models were trained with ImageNet weights. In the training, validation, and testing processes of the models, a public dataset containing 16,633 images and not having an even class distribution was used. While the ResNet-50 model reached 98.79% accuracy, the VGG-19 model reached 98.19% accuracy, the MobileNet-V3-Small model reached the highest accuracy rate with 98.86%. When the predictions of the MobileNet-V3-Small model are examined, it is seen that it is not affected by class dominance and can classify even the least sampled class images in the dataset correctly. WBCs were classified with high accuracy using the proposed pre-trained deep learning models. Experts can effectively use the proposed approach in the process of analyzing WBCs.
A Novel Image Encryption Algorithm Based on 2D Self-Coupling Sine Map

Since the current general image encryption algorithms based on chaotic systems and classical permutation diffusion structures typically suffer from stochastic degradation, simple algorithm structures, and reduced execution efficiency when dealing with images with large data, this paper proposes a new multiprocess image encryption algorithm based on a new hyperchaotic coupled sine map. To begin with, by performing dynamic analyses of the map, including equilibrium points, bifurcation diagrams, Lyapunov exponent diagrams, and phase diagrams, associated performance analyses show high spectral entropy (SE) values and permutation entropy (PE) values. Meanwhile, sequences generated by the proposed map are able to pass the NIST randomness test, which shows its suitability for the design of encryption algorithms. Then, a chaos-based image encryption algorithm is proposed in this paper: In the preprocessing stage, the images are sparsely sampled using a compressed-sensing method; in the permutation stage, the image is decomposed into eight bit-plane images containing only 0s and 1s and a clockwise cyclic shift operation based on a chaotic sequence is applied in each bit-plane image to achieve a bit-level permutation effect; in the diffusion stage, the block DNA random computing is applied to the permuted image to modify the pixel values and the final cipher image is obtained. In addition, the initial value of the map is determined by the preset initial value, user-defined strings, and the Hash value of the plain image, where the Hash value of the plain image is calculated by the SHA-256 algorithm. Finally, the analysis of the algorithm demonstrates that the algorithm exhibits extremely strong plaintext correlation externally and excellent encryption performance in terms of attack resistance and execution efficiency.
Digital Leadership: Managing Schools’ Virtual Spaces in Times of Crisis

The COVID-19 pandemic changed schools’ reality and posed a wide range of challenges for school leaders, such as a re-examination of principals’ and teachers’ authority and leadership in the schools’ virtual spaces. Teaching methods and social-emotional aspects of learning were challenged as well. The school faculty had to redesign their pedagogical environments to provide an extensive and complete response to these new challenges. The main difficulty lay in the need to create an immediate distance-learning environment that would provide solutions for the pedagogical, emotional, and social challenges in the new and virtual space that had been created. We investigated teachers’ perceptions of their and their principals’ roles as digital educational leaders (e-leaders) in the schools’ virtual spaces. The research was conducted using a qualitative method with a population of 16 female teachers in elementary schools in Israel. Data analysis yielded four major themes: new virtual space in the school's organization, principals’ and teachers’ roles in managing virtual spaces, opportunities in virtual space management, and challenges and difficulties. These themes were closely interrelated in the context of schoolwork. Findings highlight the importance of more fully understanding the role of extensive digital leadership, i.e., the management of virtual school spaces. It is proposed that management of schools’ virtual spaces can enhance overall school effectiveness, which in turn can help achieve better cooperation between school faculty and the surrounding community.
Accessible decision support for sustainable energy systems in developing countries

Abstract With rising electricity demand through digitization and innovation, the urgency of climate change mitigation, and the recent geopolitical crisis, stakeholders in developing countries face the complex task to build reliable, affordable, and low-emission energy systems. Information inaccessibility, data unavailability, and scarce local expertise are major challenges for planning and transitioning to decentralized solutions. Motivated by the calls for more solution-oriented research regarding sustainability, we design, develop, and evaluate the web-based decision support system NESSI4D web + that is tailored to the needs and capabilities of various stakeholders in developing countries. NESSI4D web + is open access and considers location-specific circumstances to facilitate multi-energy planning. Its applicability is demonstrated with a case study of a representative rural village in southern Madagascar and evaluated through seven interviews with experts and stakeholders. We show that NESSI4D web + can support the achievement of the United Nations Sustainable Development Goals and enable the very prerequisite of digitization: reliable electrification.