Reviewerly/exHarmony huggingface dataset:
Style-Label-Free: Cross-Speaker Style Transfer by Quantized VAE and Speaker-wise Normalization in Speech Synthesis

Cross-speaker style transfer in speech synthesis aims at transferring a style from source speaker to synthesised speech of a target speaker’s timbre. Most previous approaches rely on data with style labels, but manually-annotated labels are expensive and not always reliable. In response to this problem, we propose Style-Label-Free, a cross-speaker style transfer method, which can realize the style transfer from source speaker to target speaker without style labels. Firstly, a reference encoder structure based on quantized variational autoencoder (Q-VAE) and style bottleneck is designed to extract discrete style representations. Secondly, a speaker-wise batch normalization layer is proposed to reduce the source speaker leakage. In order to improve the style extraction ability of the reference encoder, a style invariant and contrastive data augmentation method is proposed. Experimental results show that the method outperforms the baseline. We provide a website with audio samples <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
The Impact of Innovative Leadership on Crisis Management Strategies in Public Institutions in the State of Qatar

The study aimed to examine the impact of innovative leadership practices on crisis management strategies in public institutions in the State of Qatar. The study population consisted of public institutions in the State of Qatar. In order to achieve the objectives of the study, a questionnaire was developed to collect data from the analytical unit, the Social Statistical Sciences Package (SPSS V.25) was adopted. The AMOS V.25 program were adopted for path analysis. The study showed an impact of Innovative leadership on crisis management strategies. The study recommended the need for Qatari public institutions to focus on creating strong relationships with strategic partners in the internal Qatari environment and the external environment.
Wheel tracks, rutting a new Oregon Trail: A survey of autonomous vehicle cybersecurity and survivability analysis research

The rapid development of autonomous vehicles during the past decade has caused increasingly grave cybersecurity challenges to be associated with their use. Among these challenges are vulnerabilities involving existing vehicular technology, which have been subject to well-publicized exploits that bring into question the survivability of these vehicles under failure or attack. This chapter is a survey of the research landscape of autonomous vehicles, focusing on security and survivability; related attributes such as performability are also considered. Research areas are visualized in a taxonomy and gaps are discussed throughout the paper. We conclude with recommendations and a discussion of future research opportunities.
Local Hashing and Fake Data for Privacy-Aware Frequency Estimation

Data collected from services and application users contain identifying attributes. The categorical attributes of user data capture information contained in a fixed set of domain values <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{D}_{\boldsymbol{m}}$</tex> . The statistical analysis of the collected data drives modeling, which in the case of categorical attributes is frequency estimation. It gives the approximate number of individuals who reported a specific value from set <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{D}_{\boldsymbol{m}}$</tex> . Under the conditions where the user data is collected repeatedly, frequency estimation may exhibit disclosure potential risks. Therefore it is important to privatize the user data such that the statistics are relevant yet minimize privacy risks. This is achieved by a set of algorithms called Frequency Oracles. Local Differential Privacy is a widely-used technique for the concerning circumstances. Additionally, several methods are used to amplify its privacy guarantees including sampling and randomization. In this paper, I propose the first sample-based frequency oracle which used Optimized Local Hashing (OLH) and was further enhanced by the replacement of some attribute values with fake data. The adaptive solution utilized the benefits offered by OLH for large-dimensioned dataset and a variance independent of dimensionality. The privacy-utility trade-off given by the proposed solution was found to be better than existing solutions for certain general and strict privacy regimes for multi-dimensional datasets.
Detecting multi-class kidney abnormalities using Deep learning

Deep learning now days is powerful tool to perform task like image classification and in the medical domain there is lack of data it's an issue to get balanced amount of quality dataset, for deep learning model it requires good amount of data for algorithms to train and to enhance their performance. Issues related to Kidney like stone, cyst and tumor are common. In this research we are focused on deep learning models that will classify kidney CT-scan images. Using state of the art classification models like CNN and Data Augmentation, normalization and Transfer learning are techniques that are used to enhance the models performance and improve the accuracy of model. The model of CNN that is being used is VGG-16 and after that custom model is developed to train it further. To balance the distribution of dataset techniques like data augmentation is being used, finally we came up with the accuracy of 98 and F1 score around 99.
The Role of Distance Learning Technology in Mitigating Unknown-Unknown Risks: Case of Covid-19

With the changing circumstances across the world due to the pandemic COVID-19, the concept of distance learning and the use of technology has increased at a very fast pace. This has resulted different online classes and different associated risks related to security with these online classes for the organizations. However, with the passage of time, it has been realized that COVID-19 has now become a part of our lives, and what we considered a matter of days or weeks might last for a very long period, which is still unknown to us. Most importantly, this new phase of life has become a new normal for the people today. It has now become important for us to change the course of our lives and to hold different activities according to this new normal. Most of the business activities are also now being adapted through this social distancing and many other activities involving the education system to be on top of everything else. The methodology adopted for the study was qualitative study and the method adopted for the study was interviewed and generating the study through literature reviews.
The Impact of Strategic Orientation on Organizational Ambidexterity at the Hotels Sector in Jordan

The aim of the study was to examine the impact of strategic orientation on organizational ambidexterity. Therefore, it focused on five-star hotels in Jordan. Data were primarily gathered through self-reported questionnaires created by Google Forms which were distributed to a random-stratified sample of (215) managers. In total, (193) responses were received including (8) invalid to statistical analysis due to uncompleted or inaccurate. Hence, the final sample contained (185) responses suitable to analysis requirements. Structural equation modeling (SEM) was conducted to test hypotheses. The results showed that strategic orientation had a positive impact on organizational ambidexterity. Moreover, the results indicated that the highest impact was for technology orientation. Based on the study results, the researchers recommend hotel managers to develop strategic directions by updating their general strategies to contribute to increasing the market share compared to other competing hotels.
Mix-Guided VC: Any-to-many Voice Conversion by Combining ASR and TTS Bottleneck Features

Due to the difficulty of obtaining parallel data, there are many works focus on non-parallel voice conversion(VC) recently. Bottleneck features(BNFs) from automatic speech recognition(ASR) and text-to-speech(TTS) models play an important role in feature disentangling for VC. In this work, we propose Mix-Guided VC, a non-parallel any-to-many voice conversion model by combining ASR-BNFs and TTS-BNFs. We demonstrate that ASR-BNFs and TTS-BNFs are complementary. ASR-BNFs are more robust especially in any-to-many tasks, but suffer from leaking source speaker’s timbre information; TTS-BNFs are closely correlated with text, but lack robustness. Experiments show that the proposed model achieves the best balance in speech quality, timbre similarity and robustness compares with baseline models. Furthermore, the whole modules in the proposed model can be trained jointly and no more pre-training data is needed.
A Micro-Doppler Based Human Pose Estimation Framework for Single-channel Ultra-Wideband Radar

Human pose estimation based on optical sensors is difficult to solve the situation under harsh environment and shielding. In this paper, a Micro-Doppler (MD) based human pose estimation for the single-channel ultra-wideband (UWB) radar, called MD-Pose, is proposed. It is found that the MD signature contains the time-varying motion of the human torso, arms, legs, hands, and feet, which can be used in the pose estimation task. The MD spectrum of human echo is obtained by the STFT transform, which is the input of proposed MD-Pose. An asymmetric U-Net network is trained with UWB radar MD spectrum, which can estimate the human keypoints. The experimental results of a single-channel UWB radar validate the effectiveness of the proposed method.
Factors and Challenges Influencing Women Leadership in Management: A Systematic Review

Leadership and management have always been common among men, yet women are stepping and succeeding into these roles too. However, with any new change that is projected to a system, challenges arise to reach adaptation or even acceptance of the change. This paper aims in pointing out the challenges and difficulties faced by women in their leadership and management roles. Six factors were studied which are competence, perception, stereotyping, harassment, organizational culture, and gender inequality on the performance and participation of a person, and thus how they impact gender discrimination. The results have presented a set of impressive challenges faced by women in their leadership roles such as gender and sex-based discrimination, stereotyping, harassment, gender inequality and perceived incompetence. This paper articulates the importance of paying attention to the factors affecting women in leadership and management in order to empower them and decrease gender discrimination in the workspace.
A Novel System for Regional Twitter Hate Speech Analysis and Detection using Deep Learning Models and Web Scraping

Instances of hate speech on popular social media platforms such as Twitter are becoming increasingly common and intense. However, there still exists a lack of comprehensive deeplearning models to combat Twitter hate speech. In this project, a comprehensive detection and reporting platform, entitled “TweetWatch,” was created to solve this issue. A binary classification CNN (Convolutional Neural Network) and a multi-class CNN were created to detect hate speech from real-time Twitter data and classify tweets with hate speech into five categories. The binary classification model has an AUC score of 98.95% and an F1 score of 97.88%. The multi-class classification model has an AUC score of 89.46%. All metrics reached over a targeted 5% increase from previous models in multiple papers, validating the proposed solution. Additionally, the only real-time choropleth map for hate speech in the United States was successfully created.
Estimating the Quality of Information Technology Services Using the Generalized Additive Regression

When covid-19 attacked the world, all activities in the economic, social, cultural, and educational fields which were initially carried out onsite, were all done online, especially education at Telkom University which mostly uses website-based applications such as LMS, Igracias, SiRAMA as learning media center. So, the possibility of problems with the website is very large. To overcome this, students can issue a ticketing to ask for assistance from IT support at Telkom University, namely the Information Technology Services. Therefore, the role of the Information Technology Services in online education is very important, because the service is directly dealing with problems that occur in every student of Telkom University. In this research, the author proposes a prediction system for the quality of service of the Information Technology Services to predict whether the services of the Information Technology Services have been maximized and have greatly helped Telkom University students. The author uses the Generalized Additive Model for regression algorithm to estimate the quality of the Information Technology Servicesector’s service which is selected due to its flexibility.
Self-Supervised Augmentation of Quality Data Based on Classification-Reinforced GAN

In deep learning, the quality of ground truth training data is crucial for the resulting performance. However, depending on applications, collecting a sufficient amount of quality data from a realistic setting is problematic. In this case, data augmentation can play an important role as long as augmentation ensures data quality and diversity for training, preferably in an unsupervised way. Recently, a number of GAN variants have been emerged for improved quality in data augmentation. Although successful, further improvement is necessary for enhancing diversity in addition to quality in data augmentation. In this paper, we propose a GAN-based approach to self-supervised augmentation of quality data based on Classification-Reinforced GAN referred to here as CLS-R GAN, to extending diversity as well as quality in data augmentation. In CLS-R GAN, a discriminator-independent classifier additionally self-trains the generator by classifying the fake data, as well as augmenting the real data in an unsupervised way. Extensive experiments were conducted, including an application to augmenting liver ultrasonic image data, to verify the effectiveness of CLS-R GAN based on standard evaluation metrics. The results indicate the effectiveness of CLS-R GAN for improved quality and diversity in augmented data.
Dynamic Group Signatures with Message Dependent Opening and Non-Interactive Signing

The group signature with message dependent opening (GS-MDO) is a valiant of the group signature in a sense that the opening authority is split into two parties called the opener and the admitter. Most known constructions of GS-MDO employ the static model. The only scheme using the dynamic model by Sun and Liu has a problem on the anonymity against the admitter in the real-world usage because the signing process requires the interaction between the signer and the admitter. In this paper, we restart the research line of GS-MDO in the dynamic setting. We introduce the definition of the dynamic group signature with message dependent opening (DGS-MDO), the security requirements and propose a generic construction. By instantiating our construction with appropriate primitives, we can obtain a DGS-MDO scheme with the standard model security, constant signature size and non-interactive signing process.
A Method to Eliminate Fruitless Cycles for Pollard's Rho Method by Splitting a Seed-point Table for a Random Walk

The most efficient method for solving ECDLP is the Pollard-Rho method, which is known to be more efficient in solving ECDLP by using skew Frobenius mapping and splitting a seed-point table to eliminate the fruitless cycles, but some fruitless cycles increase non-negligibly. In this paper, we propose a method to further reduce the number of fruitless cycles in the Pollard's rho method using skew Frobenius mapping. We also apply the proposed method to a 21-bit BN curve and confirm its effectiveness.
Impact of Knowledge Management on Administrative Innovation of Software Companies in Jordan

The major aim of the study was to examine the impact of knowledge management on administrative innovation. A qualitative method based on a questionnaire was used in this study for data collection and sample selection. Therefore, it focused on of Software Companies in Jordan. Data were primarily gathered through self-reported questionnaires creating by Google Forms which were distributed to a purposive sample of managers via email. Statistical program AMOSv24 was used to test the study hypotheses. The study results show that the highest impact on administrative innovation was for knowledge generation, followed by knowledge applying, then knowledge storing, and finally the lowest impact was for knowledge dissemination. Researchers recommend decision-makers to pay attention to the tacit knowledge and experiences of employees, and turn them into tangible ideas and methods that contribute to the development of work performance, and to refine the personalities of their employees by supporting their ideas and suggestions and activating their role in solving problems.
BiLSTM Based Phase Modulation Detection of Radar Emitters

In modern Electronic Warfare (EW) systems, recognition of the intrapulse modulation schemes of intercepted Low Probability of Intercept (LPI) radar signals in real-time is a crucial survival task. Lately, Convolutional Neural Network (CNN) had proven effective for emitter recognition but the problem lies with the recognition of phase-coded waveforms at low Signal to Noise Ratio (SNR). We propose a Bidirectional Long Short Term Memory (BiLSTM) network-based recognition technique for the analysis and recognition of phase-coded waveforms. In order to avoid heavy processing, the time-domain radar signals are directly fed as an input to the BiLSTM network for recognition without any preprocessing and feature extraction. The BiLSTM layer can extract the contextual information of signals well and is followed by a fully connected layer. Finally, a softmax classifier is employed to accomplish the recognition task. Six distinct types of phase-coded waveforms are simulated corrupted with Additive White Gaussian Noise (AWGN) with the SNR ranging from −10 to 10 dB. It has been demonstrated that the proposed methodology considerably enhances recognition accuracies over existing techniques.
QoE-Aware Content Oriented Path Optimization Framework with Egress Peer Engineering

We propose COFFEE, a Content Oriented Flexible Framework with Egress Engineering, improving client QoE in content delivery. BGP lacks visibility into performance, so they do not know which path maximizes the QoE of their content. COFFEE engineers egress paths based on the strategies formulated by content operators. Thus, it realizes performance-aware routing that reflects the characteristics of each content. The framework performs passive measurement, calculates a QoE score, and performs EPE-based routing control, for each content server. We deployed COFFEE on a real production network to evaluate its feasibility. It was verified that the paths could be routed to the QoE-optimal paths according to the application contents prepared for the experiment. In the experiment environment, the change from the content user's access to the optimal path that matched the content strategy was applied within 2 seconds. The degradation of forwarding performance due to the introduction of the framework was also confirmed to be negligible.
Examining Employee Performance During Covid-19 Pandemic: A Study of Aerospace Manufacturing Industry in Malaysia

During Covid-19 pandemic situation, a lot of company facing problem in sustaining their businesses. Most organization has response to the current situation in maintaining the business sustainability. Unfortunately, the changes have been made by management has given significant impact on employee performance. This quantitative study is to examine the relationship between motivation, work environment and work from home towards employee performance. The target respondents of this study are among employee in an aerospace manufacturing company in Malaysia during Covid-19 pandemic situation. Statistical Product and Service Solutions (SPSS) and Analysis of a Moment Structures AMOS will be used in analyzing the data that has been collected to get the accuracy of result. The finding shows that motivation, work environment and WFH have a significant relationship with employee performance. The finding of this study is to provide insights to manufacturing company in managing strategic change management during Covid-19 pandemic as well as for future studies in revealing the factors that influence employee performance.
The Impact of Work-Life Balance on Organizational Commitment

The study aimed to explore the effect of work-life balance on the dimensions of organizational commitment (affective, continuance, and normative commitment) by collecting data by means of a questionnaire distributed to a sample of workers in one of the big food companies. The results showed a positive significant effect of work-life balance on the affective and normative commitment, and a negative effect of work-life balance on continuance commitment. These results agreed with some previous studies and differed with others. The study recommended the need to pay more attention to the balance of work because it improves employee emotional attachment and obligation to the organization and reduces their assessment of their relationship with the organization through the costs they incur in the case of leaving the organization. Researchers are recommended to study work-life balance by focusing on its dimensions and not as a whole variable.
Aircraft Turnaround Manager (ATM): A Solution to Airport Operations

The set of activities performed during the preparation of an inbound aircraft for the succeeding outbound flight, which are planned for the same aircraft is called turnaround operation. The turnaround operations consist of activities the exchange of passengers for both inbound and outbound, crew, baggage and cargo handling, catering services. The evaluation and control of activities at the present time, is being carried out manually, which lead to flight delays and unreliable data. The proposed solution is to show the aircraft turnaround operations in real time and is based on a rule based systems called Aircraft Turnaround Manager (ATM) that is used to manage and monitor the flight groundwork throughout the turnaround process. The Critical Path Method (CPM) and Project Evaluation and Review Technique (PERT) are exhibited in the system. The study has shown, real time monitoring of turnaround operation can decrease delays occurring from operations belongs to airline and can optimize the aircraft ground time.