Reviewerly/exHarmony huggingface dataset:
Corporate foresight organizational learning and performance: The moderating role of digital transformation and mediating role of innovativeness in SMEs

Corporate foresight is a capability that includes any structural or cultural element that enables the company to detect discontinuous change early. The purpose of the present study is to examine the direct impact of corporate foresight, and organizational learning on the performance dynamic of SMEs working in the United Arab Emirates (UAE). In addition, the study tries to analyze the moderating as well as mediating effect of digital transformation and innovativeness on the relationship between exogenous and endogenous constructs. A sample of 576 questionnaires were distributed among the owners/managers of different SMEs working in the region of UAE. However, a final sample of 354 respondents was empirically tested. The data was analyzed through a two-step approach where structural equation modelling (SEM) under SmartPLS was found to be very helpful to examine the direct and indirect relationship between the study variables. The study findings show that there is an insignificant but positive impact of corporate foresight on organizational performance whereas significant impact of organizational learning on organizational performance. Furthermore, the study found evidence for the moderating effect of digital transformation between organizational learning and innovation. Additionally, it is observed that innovativeness mediates the relationship between corporate foresight and performance dynamics. The study findings suggest that for exploring the relationship between corporate foresight, digital transformation, and organization the role of innovation and digital transformation is quite significant. The study findings suggest that both owners and managers at SMEs of UAE should attach more importance to innovative capabilities and digital transformation for achieving higher levels of organizational performance. Policy makers should reasonably consider the direct and indirect effect of study variables while considering high performance at the workplace.
Path-optimized nonadiabatic geometric quantum computation on superconducting qubits

Quantum computation based on nonadiabatic geometric phases has attracted a broad range of interests, due to its fast manipulation and inherent noise resistance. However, it is limited to some special evolution paths, and the gate-times are typically longer than conventional dynamical gates, resulting in weakening of robustness and more infidelities of the implemented geometric gates. Here, we propose a path-optimized scheme for geometric quantum computation on superconducting transmon qubits, where high-fidelity and robust universal nonadiabatic geometric gates can be implemented, based on conventional experimental setups. Specifically, we find that, by selecting appropriate evolution paths, the constructed geometric gates can be superior to their corresponding dynamical ones under different local errors. Numerical simulations show that the fidelities for single-qubit geometric Phase, $\pi/8$ and Hadamard gates can be obtained as $99.93\%$, $99.95\%$ and $99.95\%$, respectively. Remarkably, the fidelity for two-qubit control-phase gate can be as high as $99.87\%$. Therefore, our scheme provides a new perspective for geometric quantum computation, making it more promising in the application of large-scale fault-tolerant quantum computation.
Assessing the Extent and Types of Hate Speech in Fringe Communities: A Case Study of Alt-Right Communities on 8chan, 4chan, and Reddit

Recent right-wing extremist terrorists were active in online fringe communities connected to the alt-right movement. Although these are commonly considered as distinctly hateful, racist, and misogy...
Explaining transformer-based image captioning models: An empirical analysis

Image Captioning is the task of translating an input image into a textual description. As such, it connects Vision and Language in a generative fashion, with applications that range from multi-modal search engines to help visually impaired people. Although recent years have witnessed an increase in accuracy in such models, this has also brought increasing complexity and challenges in interpretability and visualization. In this work, we focus on Transformer-based image captioning models and provide qualitative and quantitative tools to increase interpretability and assess the grounding and temporal alignment capabilities of such models. Firstly, we employ attribution methods to visualize what the model concentrates on in the input image, at each step of the generation. Further, we propose metrics to evaluate the temporal alignment between model predictions and attribution scores, which allows measuring the grounding capabilities of the model and spot hallucination flaws. Experiments are conducted on three different Transformer-based architectures, employing both traditional and Vision Transformer-based visual features.
Understanding Affective, Normative &amp; Continuance Commitment through the Lens of Training &amp; Development

The purpose of the study was to analyze nurses' perceptions of the benefits of training and development, as well as their commitment to the organization. Training and development and three facets of organizational commitment are studied using a quantitative survey research design. Structured equation modelling using Smart PLS 3 was used to analyze 290 of 313 responses from public sector hospitals. Training and development, according to the findings, have a strong positive relationship with affective, normative, and continuance commitment. Some of these findings have ramifications for management and administrative staff of hospitals. They must first commit their nursing staff to the hospital in order to ensure the best quality services for their patients. Nurses respond with greater affective, normative, and continuance commitment when they believe a training programmed is designed to meet their professional developmental needs.
The Interpretation of Reaction Time in Information-Processing Research 1

This chapter examines some of the assumptions and principles upon which the interpretation of reaction time measurement is based. It is hoped that the juxtaposition of these sets of issues will lead to the more careful use and interpretation of reaction time measures in the understanding of human information processing. Reaction time most typically is defined as the interval between the presentation of a stimulus to a subject and the subject’s response. Aside from questions about the conditions under which reaction time measures are obtained, the further interpretation of reaction time is dependent upon the experimental logic involved in specific situations. The Additive Factor Method, developed by Sternberg, has as its principal concern the first of the problems discussed above in conjunction with the limitations on the use of the Subtraction Method. There are a number of technical statistical considerations that dictate great caution in the application of this covariance technique to the error problem in reaction time studies.
Firefly-Based Maintainability Prediction for Enhancing Quality of Software

In a broad spectrum, software metrics play a vital role in attribute assessment, which successively moves software projects. The metrics measure gives many crucial facets of the system, enhancing the system quality of software developed. Moreover, maintenance is the correction process that works out in the software system once the software is initially made. The noteworthy characteristic of any software is ‘change,’ and as a result, additional concern ought to be taken in developing software. So, the software is expected to be modified effortlessly (maintainable). Predicting software maintainability is still challenging, and accurate prediction models with low error rates are required. Since there are so many modern programming languages on the horizon. To accurately measure software maintainability, new techniques have to been introduced. This paper proposes a maintainability index (MI) by considering various software metrics by which the error gets minimized. It also intends to adopt a renowned optimization algorithm, namely Firefly (FF), for the optimum result. The proposed Base Model-FF is compared to other traditional models like BM-Differential Evolution (BM-DE), BM-Artificial Bee Colony (BM-ABC), BM-Particle Swarm Optimization (BM-PSO), and BM-Genetic Algorithm (BM- GA) in terms of performance metrics like Differential ratio, correlation coefficient, and Random Mean Square Error (RMSE).
Opinion mining towards blockchain technology adoption for accessing digital library resources

Purpose Blockchain technology is a distributed and decentralized public digital ledger, which is employed to save dynamic transaction data and static records across several computers so that each record could not be modified retroactively without the collusion of the network and alteration of all subsequent blocks. Recently, it has become immensely popular in digital resource sharing in different research areas such as healthcare, smart cities, cryptocurrency and libraries. Since the current eLibrary systems are vulnerable to issues such as unauthorized access, plagiarism, etc., there is a lack of access control system that can efficiently address these issues. Design/methodology/approach The authors designed a conceptual model for evaluating the users' intention in the use of blockchain-based digital libraries, which can facilitate the resource organization and provide additional security to interactive processes between users. To conduct our survey, the authors devised and shared two versions, English and Chinese, among 298 participants. Moreover, 7 PhD students participated in the pre-testing of the questioner design. The authors analyzed the demographic data using the Jamovi software and SmartPLS in order to generate the path modeling. Findings This study revealed that blockchain technology adaption in eLibraries is essential for enhancing the quality of services, infrastructure and resources for libraries. The study’s results show that optimism, informativeness, perceived usefulness, perceived ease of use, attitude and intention to use blockchain technology for accessing digital resources in libraries. Originality/value This study contributes to the adoption of blockchain technology in the digital library. To the best of the authors’ knowledge, this work is the first empirical attempt to provide a new perspective of developing digital libraries based on security policies. This model shows the underpinning knowledge to manage digital resources, which can facilitate the design phases and enhance the management costs in eLibraries.
Shadow couplings

A classical result of Strassen asserts that given probabilities $\mu , \nu$ on the real line which are in convex order, there exists a
3D Sensor Based Pedestrian Detection by Integrating Improved HHA Encoding and Two-Branch Feature Fusion

Pedestrian detection is vitally important in many computer vision tasks but still suffers from some problems, such as illumination and occlusion if only the RGB image is exploited, especially in outdoor and long-range scenes. Combining RGB with depth information acquired by 3D sensors may effectively alleviate these problems. Therefore, how to utilize depth information and how to fuse RGB and depth features are the focus of the task of RGB-D pedestrian detection. This paper first improves the most commonly used HHA method for depth encoding by optimizing the gravity direction extraction and depth values mapping, which can generate a pseudo-color image from the depth information. Then, a two-branch feature fusion extraction module (TFFEM) is proposed to obtain the local and global features of both modalities. Based on TFFEM, an RGB-D pedestrian detection network is designed to locate the people. In experiments, the improved HHA encoding method is twice as fast and achieves more accurate gravity-direction extraction on four publicly-available datasets. The pedestrian detection performance of the proposed network is validated on KITTI and EPFL datasets and achieves state-of-the-art performance. Moreover, the proposed method achieved third ranking among all published works on the KITTI leaderboard. In general, the proposed method effectively fuses RGB and depth features and overcomes the effects of illumination and occlusion problems in pedestrian detection.
Adversarial Robustness of Deep Code Comment Generation

Deep neural networks (DNNs) have shown remarkable performance in a variety of domains such as computer vision, speech recognition, and natural language processing. Recently they also have been applied to various software engineering tasks, typically involving processing source code. DNNs are well-known to be vulnerable to adversarial examples, i.e., fabricated inputs that could lead to various misbehaviors of the DNN model while being perceived as benign by humans. In this paper, we focus on the code comment generation task in software engineering and study the robustness issue of the DNNs when they are applied to this task. We propose ACCENT (Adversarial Code Comment gENeraTor) , an identifier substitution approach to craft adversarial code snippets, which are syntactically correct and semantically close to the original code snippet, but may mislead the DNNs to produce completely irrelevant code comments. In order to improve the robustness, ACCENT also incorporates a novel training method, which can be applied to existing code comment generation models. We conduct comprehensive experiments to evaluate our approach by attacking the mainstream encoder-decoder architectures on two large-scale publicly available datasets. The results show that ACCENT efficiently produces stable attacks with functionality-preserving adversarial examples, and the generated examples have better transferability compared with the baselines. We also confirm, via experiments, the effectiveness in improving model robustness with our training method.
Enterprise risk management and information technology security in the financial sector

Purpose This study aims to empirically investigate the relationship between enterprise risk management (ERM) and information technology (IT) security within the financial sector. Design/methodology/approach Risk officers of financial institutions licensed by the Central Bank of Ghana constituted the sample frame. A structured questionnaire was used to elicit data from the respondents. The structural equation modeling method was employed to analyze the hypothesized model. Findings The results revealed that ERM has a strong positive substantial effect on IT security within financial institutions. However, organizational culture failed to moderate the relationship between ERM and IT security. Practical implications A well-managed risk helps to eliminate ineffective, archaic and redundant technology as the originator of rising perils and organizational concerns in today's corporate financial institutions since ERM established a substantially strong positive correlation among the variables. Originality/value ERM studies in the African context are rare. This paper adds to contemporary literature by providing a new perspective toward the understanding of the relationship between ERM and IT security, especially in the financial industry.
Extracting Language Content from Speech Sounds: The Information Theoretic Approach

Speech comprehension involves recovering a speaker’s intended meaning from the speech sounds that they produce. While the sensory-driven components of this process have been widely investigated, the impact of speech content (i.e., linguistic information) on sensory processing is much less understood. Here we summarize the growing body of research demonstrating that neural processing of speech sounds is influenced by morpheme- and word-level statistical properties of the information conveyed. We introduce and review evidence that information theoretic measures such as entropy and surprisal are apparent in neural responses. These findings help uncover fundamental organizational principles of the language system: what units are stored and how they are accessed. Modeling sensitivity to the information content of the speech signal helps explain the interface between (i) auditory processes operating on speech sounds and (ii) the words and meanings that those sounds convey.
Arduino based Electronic Voting System with Biometric and GSM Features

India is a democratic country, where voting system has created massive impact on elections. The soul of any social equality lies in the authenticity that citizens decide their own representatives. Electronic voting machines (EVM) are introduced for achieving fair election system and also shrink the man power requirement during the election. In modern era, these electronic voting machines are prone to tampering and electoral frauds which introduces prime challenges for fair election process. However, the prospect of tampering memory chip and double voting are the two main vulnerabilities in current voting system. Hence, it is essential to modernize the conventional voting systems with a transparent authentication module, security and reliability in order to achieve fairness in the elections. In this work a fingerprint authentication for the voter based on stored data has been proposed which enables identification of individuals with high degree of accuracy and GSM feature enhances the transparency of election. Whenever, a fake vote has casted, and then an alert message for the candidate has been sent to registered mobile number of a voter. The proposed voting system based on Arduino is trustworthy in terms of security, reliability, fairness and transparency of election.
Automatic Pronunciation Assessment using Self-Supervised Speech Representation Learning

Self-supervised learning (SSL) approaches such as wav2vec 2.0 and HuBERT models have shown promising results in various downstream tasks in the speech community. In particular, speech representations learned by SSL models have been shown to be effective for encoding various speech-related characteristics. In this context, we propose a novel automatic pronunciation assessment method based on SSL models. First, the proposed method fine-tunes the pre-trained SSL models with connectionist temporal classification to adapt the English pronunciation of English-as-a-second-language (ESL) learners in a data environment. Then, the layer-wise contextual representations are extracted from all across the transformer layers of the SSL models. Finally, the automatic pronunciation score is estimated using bidirectional long short-term memory with the layer-wise contextual representations and the corresponding text. We show that the proposed SSL model-based methods outperform the baselines, in terms of the Pearson correlation coefficient, on datasets of Korean ESL learner children and Speechocean762. Furthermore, we analyze how different representations of transformer layers in the SSL model affect the performance of the pronunciation assessment task.
Improving the Performance of Radiologists Using Artificial Intelligence-Based Detection Support Software for Mammography: A Multi-Reader Study

To evaluate whether artificial intelligence (AI) for detecting breast cancer on mammography can improve the performance and time efficiency of radiologists reading mammograms.A commercial deep learning-based software for mammography was validated using external data collected from 200 patients, 100 each with and without breast cancer (40 with benign lesions and 60 without lesions) from one hospital. Ten readers, including five breast specialist radiologists (BSRs) and five general radiologists (GRs), assessed all mammography images using a seven-point scale to rate the likelihood of malignancy in two sessions, with and without the aid of the AI-based software, and the reading time was automatically recorded using a web-based reporting system. Two reading sessions were conducted with a two-month washout period in between. Differences in the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and reading time between reading with and without AI were analyzed, accounting for data clustering by readers when indicated.The AUROC of the AI alone, BSR (average across five readers), and GR (average across five readers) groups was 0.915 (95% confidence interval, 0.876-0.954), 0.813 (0.756-0.870), and 0.684 (0.616-0.752), respectively. With AI assistance, the AUROC significantly increased to 0.884 (0.840-0.928) and 0.833 (0.779-0.887) in the BSR and GR groups, respectively (p = 0.007 and p < 0.001, respectively). Sensitivity was improved by AI assistance in both groups (74.6% vs. 88.6% in BSR, p < 0.001; 52.1% vs. 79.4% in GR, p < 0.001), but the specificity did not differ significantly (66.6% vs. 66.4% in BSR, p = 0.238; 70.8% vs. 70.0% in GR, p = 0.689). The average reading time pooled across readers was significantly decreased by AI assistance for BSRs (82.73 vs. 73.04 seconds, p < 0.001) but increased in GRs (35.44 vs. 42.52 seconds, p < 0.001).AI-based software improved the performance of radiologists regardless of their experience and affected the reading time.
Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations

Topic models have been the prominent tools for automatic topic discovery from text corpora. Despite their effectiveness, topic models suffer from several limitations including the inability of modeling word ordering information in documents, the difficulty of incorporating external linguistic knowledge, and the lack of both accurate and efficient inference methods for approximating the intractable posterior. Recently, pretrained language models (PLMs) have brought astonishing performance improvements to a wide variety of tasks due to their superior representations of text. Interestingly, there have not been standard approaches to deploy PLMs for topic discovery as better alternatives to topic models. In this paper, we begin by analyzing the challenges of using PLM representations for topic discovery, and then propose a joint latent space learning and clustering framework built upon PLM embeddings. In the latent space, topic-word and document-topic distributions are jointly modeled so that the discovered topics can be interpreted by coherent and distinctive terms and meanwhile serve as meaningful summaries of the documents. Our model effectively leverages the strong representation power and superb linguistic features brought by PLMs for topic discovery, and is conceptually simpler than topic models. On two benchmark datasets in different domains, our model generates significantly more coherent and diverse topics than strong topic models, and offers better topic-wise document representations, based on both automatic and human evaluations.1
Robust Depth Completion with Uncertainty-Driven Loss Functions

Recovering a dense depth image from sparse LiDAR scans is a challenging task. Despite the popularity of color-guided methods for sparse-to-dense depth completion, they treated pixels equally during optimization, ignoring the uneven distribution characteristics in the sparse depth map and the accumulated outliers in the synthesized ground truth. In this work, we introduce uncertainty-driven loss functions to improve the robustness of depth completion and handle the uncertainty in depth completion. Specifically, we propose an explicit uncertainty formulation for robust depth completion with Jeffrey's prior. A parametric uncertain-driven loss is introduced and translated to new loss functions that are robust to noisy or missing data. Meanwhile, we propose a multiscale joint prediction model that can simultaneously predict depth and uncertainty maps. The estimated uncertainty map is also used to perform adaptive prediction on the pixels with high uncertainty, leading to a residual map for refining the completion results. Our method has been tested on KITTI Depth Completion Benchmark and achieved the state-of-the-art robustness performance in terms of MAE, IMAE, and IRMSE metrics.