Reviewerly/exHarmony huggingface dataset:
Nonlinear wavelet-based estimation to spectral density for stationary non-Gaussian linear processes

Nonlinear wavelet-based estimators for spectral densities of non-Gaussian linear processes are considered. The convergence rates of mean integrated squared error (MISE) for those estimators over a large range of Besov function classes are derived, and it is shown that those rates are identical to minimax lower bounds in standard nonparametric regression model within a logarithmic term. Thus, those rates could be considered as nearly optimal. Therefore, the resulting wavelet-based estimators outperform traditional linear methods if the degree of smoothness of spectral densities varies considerably over the interval of interest, such as sharp spike, cusp, bump, etc., since linear estimators are not able to attain these rates. Unlike in classical nonparametric regression with Gaussian noise errors where thresholds are determined by normal distribution, we determine the thresholds based on a Bartlett type approximation of a quadratic form with dependent variables by its corresponding quadratic form with independent identically distributed (i.i.d.) random variables and Hanson-Wright inequality for quadratic forms in sub-gaussian random variables. The theory is illustrated with some numerical examples, and our simulation studies show that our proposed estimators are comparable to the current ones.
Deep-fake video detection approaches using convolutional – recurrent neural networks

Deep-Fake is an emerging technology used in synthetic media which manipulates individuals in existing images and videos with someone else's likeness. This paper presents the comparative study of different deep neural networks employed for Deep-Fake video detection. In the model, the features from the training data are extracted with the intended Convolution Neural Network model to form feature vectors which are further analysed using a dense layer, a Long Short-Term Memory and Gated Recurrent by adopting transfer learning with fine tuning for training the models. The model is evaluated to detect Artificial Intelligence based Deep fakes images and videos using benchmark datasets. Comparative analysis shows that the detections are majorly biased towards domain of the dataset but there is a noteworthy improvement in the model performance parameters by using Transfer Learning whereas Convolutional- Recurrent Neural Network has benefits in sequence detection.
Facial makeup transfer with GAN for different aging faces

Facial aging is widely used in criminal tracking and the search for lost children. If the aging face is made up, it will greatly affect the discrimination of the tracking system. Therefore, the research on the makeup of different aging faces is extremely important. Existing studies have achieved a good transition from the non-makeup domain to the makeup domain in facial makeup transfer. But few studies involve the transfer of facial makeup at different ages. In addition, existing datasets rarely contain both age and makeup attributes, which make the transfer of facial makeup for different ages full of challenges. To solve the above problems, we propose a learning framework, called AM-Net, which can realize facial makeup transfer for different ages while protecting identity information. AM-Net is composed of two sub-network modules: Aging-Net and Makeup-Net. AM-Net first learns the aging mechanism of faces through Aging-Net, and then, it feeds the learned aging mode to Makeup-Net. After that, AM-Net trains Makeup-Net to realize the mapping relationship between the non-makeup domain to the makeup domain and transfer the makeup style to the face of the non-makeup. Throughout the network, multiple losses are applied to ensure AM-Net preserve information about the identity, background, etc. Extensive experiments are conducted on different datasets with different state-of-the-art methods, which prove the effectiveness of AM-Net.
FxP-QNet: A Post-Training Quantizer for the Design of Mixed Low-Precision DNNs With Dynamic Fixed-Point Representation

Deep neural networks (DNNs) have demonstrated their effectiveness in a wide range of computer vision tasks, with the state-of-the-art results obtained through complex and deep structures that require intensive computation and memory. Now-a-days, efficient model inference is crucial for consumer applications on resource-constrained platforms. As a result, there is much interest in the research and development of dedicated deep learning (DL) hardware to improve the throughput and energy efficiency of DNNs. Low-precision representation of DNN data-structures through quantization would bring great benefits to specialized DL hardware. However, the rigorous quantization leads to a severe accuracy drop. As such, quantization opens a large hyper-parameter space at bit-precision levels, the exploration of which is a major challenge. In this paper, we propose a novel framework referred to as the Fixed-Point Quantizer of deep neural Networks (FxP-QNet) that flexibly designs a mixed low-precision DNN for integer-arithmetic-only deployment. Specifically, the FxP-QNet gradually adapts the quantization level for each data-structure of each layer based on the trade-off between the network accuracy and the low-precision requirements. Additionally, it employs post-training self-distillation and network prediction error statistics to optimize the quantization of floating-point values into fixed-point numbers. Examining FxP-QNet on state-of-the-art architectures and the benchmark ImageNet dataset, we empirically demonstrate the effectiveness of FxP-QNet in achieving the accuracy-compression trade-off without the need for training. The results show that FxP-QNet-quantized AlexNet, VGG-16, and ResNet-18 reduce the overall memory requirements of their full-precision counterparts by 7.16x, 10.36x, and 6.44x with less than 0.95%, 0.95%, and 1.99% accuracy drop, respectively.
Privacy risk assessment and privacy-preserving data monitoring

Privacy regulations press organisations to handle personal data with reinforced caution. Moreover, organisations are dealing with increasing amounts of Personally Identifiable Information in their systems. Thus, there is a high demand not only for privacy-preserving data processing mechanisms but also privacy-enhancing services. As such, we propose the Personal Data Analyser, a tool that increases privacy assurances and minimises privacy risks through automated privacy-preserving data monitoring and privacy risk assessment mechanisms. Automated data monitoring is achieved with a hybrid mechanism that employs Regular Expressions, Natural Language Processing tools, and machine learning models such as Multilayer Perceptron and Random Forests. Our privacy risk assessment mechanism is based on custom-built crisp and fuzzy models, that consider information such as data processor reputation, data sensitiveness and other inputs in order to assess privacy risk associated with data transactions. Our work is integrated and validated under real use cases of the PoSeID-on platform and warns users whenever potential privacy risks are detected. Validation under PoSeID-on's pilots and its users proved beneficial not only to assess our solution but also to raise users' awareness of their data. The results of this work show that our solution is an effective Privacy Enhancing Technology that increases privacy assurances between organisations and their users.
Research on Simulation Analysis of Physical Training Based on Deep Learning Algorithm

Aging is the trend of the global population in the 21st century. Physical degradation of the elderly and related care is a major challenge in the face of an aging society. Exercise can delay physiological aging and promote the metabolism of body functions. Although aging is an irreversible natural law, proper physical training can help prevent aging. Therefore, relevant personnel attach great importance to the training of physical fitness. To this end, a 12-week elderly functional fitness training experiment was conducted with elderly residents in a village in Nanjing. In the detection process, the gait analysis system is mainly used for the subject’s motion detection and recording and records the data into the gait analysis software system based on the improved deep learning algorithm for sports training simulation analysis. After completing the physical training simulation experiment, the RTM model is used for simulation analysis. The results were evaluated. The evaluation data show that the homogeneity test results of the designed physical training simulation experiment are very reasonable. Since the result is much larger than 0.10, it can be inferred that the results of the physical training simulation analysis have been expected and also meet the national GB/T 31054–2014 standard requirements.
Educational futures after COVID-19: Big tech and pandemic profiteering versus education for democracy

To address the dramatic economic contraction brought on by the global pandemic, governments at all levels have taken on tremendous debt in order to provide economic stability and prevent a more dramatic collapse. It is likely that, as the initial phase of the pandemic passes, familiar neoliberal austerity claims about the necessity to trim education budgets will gain greater force and acceptance. However, I suggest that these neoliberal policies demand sacrifices of the wrong constituency: Given that Big Tech has amassed huge sums of money over the course of the pandemic, how is it morally justifiable that tech companies benefit from the pandemic while educational institutions shoulder the financial fallout of pandemic government spending? In this paper, I first outline how Big Tech profits from the education sector during the pandemic even as it undermines the democratic function of education in doing so. I then situate these more specific critiques within a broader consideration of the role technology plays in undermining a democratic society. In conclusion, I argue that a pandemic profiteering tax for Big Tech represents the best short-term solution to get ahead of the “austerity curve” and ensure that the COVID-19 crisis serves as an opportunity to deepen our commitments to promoting the democratic function education. Without such commitments, the pandemic will become the turning point at which Big Tech effectively coopts public education for its own ends, to the detriment of democracy. My underlying claim is that technology is in conflict with both democracy and education. This runs against the widespread notion that technology will help promote learning, and that technology helps inform and connect people and therefore helps promote democracy. In what follows I dispel such notions.
Virtual performatives as face-work practices on Twitter: Relying on self-reference and humour

Self-referential third-person predications functioning as stand-alone virtual performatives, such as ∗shrugs∗, abound across modes of computer-mediated communication and interactive multimodal platforms. Their continued use, commonly involving manual addition of typographic elements, testifies to non-trivial communicative needs not satisfied by easy access to graphical icons, like the shrug emoji. This study investigates their contributions to the construction of face in mass messaging. Focusing on face-work practices on Twitter, the aim is to account for ways in which users employ virtual performatives in that noisy, opaque, and socially complex environment. Virtual performatives are examined in a sample of publicly visible tweets which are not retweets or replies to other users and do not contain names of people. Manifesting minimal addressivity, the constructions are shown to rely on self-reference for face-work, inviting imagined audiences of the like-minded to treat the action or emotion thus enacted virtually by an externalized self as something they can relate to. Virtual performatives are inherently playful, and the humour conveyed in the tweets including them is predominantly benevolent and tolerant. A central finding is the conspicuous presence of benign self-deprecating humour: as users seek social acceptance at their own expense, they seem not to take themselves too seriously.
Iterative brain tumor retrieval for MR images based on user’s intention model

Generally, medical content-based image retrieval (CBIR) systems select low-level visual features as image descriptors. However, these descriptors fail to provide clues for understanding the content of medical images in a similar way as a human expert, which makes the retrieval results inconsistent with the user’s intention. To solve this problem, we propose a closed-loop brain tumor retrieval system for MR images with an eye-tracking based relevance feedback mechanism. In our method, we first model the intention of the user by training a convolutional neural network based on the temporal and spatial features extracted from his/her eye-tracking data collected when inspecting the relevance between different images. Upon using visual features as a bridge, the relevancy degree to the query image of any of the database images is computed with our user’s intention model by transferring to it the eye movement data from the most visually similar image amongst images iteratively accumulated in the canvas. Our proposed retrieval system is implemented in an iterative manner. In each round of iteration, user’s eye movement data when inspecting the system returns are collected and the canvas collection of images is also updated by appending to it the user inspected system returns. With the updated canvas collections, the relevancy degree of database images can be recomputed and the system can begin a new round search of the most relevant images. Extensive experiments have been performed on a publicly available T1-weighted contrast-enhanced magnetic resonance image (CE-MRI) dataset that consists of three types of brain tumors (glioma, meningioma, and pituitary tumor) collected from 233 patients with a total of 3064 images across the axial, coronal, and sagittal views. Experimental results of 22 volunteers (11 males and 11 females, with an average age of 24.4 years) from our medical school show that upon implicit involvement of users in the brain tumor retrieving process, our proposed system significantly outperforms state-of-the-art methods and achieves Prec@10 to 99.94%, mAP to 97.95% after the third round of iteration.
Global trends in digital transformation and media positioning of universities in social networks

For modern universities, social media is a competitive environment and a platform to increase their brand awareness in global and national rankings and promote educational, scientiﬁc and innovative services to a social media audience using marketing tools. The positioning of a modern university in social media is an activity focused on presenting the university and its services in the most advantageous way, popularising science. The aim of the study is to examine global trends in the positioning and digital transformation of university media activity in social media based on open statistical data. The authors analysed university presence indices in eight social networks (VK, Instagram, Facebook, YouTube, Telegram, Twitter, OK, Tik-Tok) of universities from the top 10 media activity rankings (2021), taking into account the speciﬁcs of each social network. The universities with the maximum media presence in each social network are highlighted. It is noted that a prominent presence on social media is based on working on the university’s positive media image and implementing global media positioning trends into strategic development. Two different strategies for positioning in the media space have been detected, and social networks with great potential for positioning higher education institutions in terms of age and the market segment capacity they cover are noted. The study can be useful to sociologists, economists, marketing experts and university professors.
Evaluation of the trends in jobs and skill-sets using data analytics: a case study

Abstract Introduction Fast-emerging technologies are making the job market dynamic, causing desirable skills to evolve continuously. It is therefore important to understand the transitions in the job market to proactively identify skill sets required. Case description A novel data-driven approach is developed to identify trending jobs through a case study in the oil and gas industry. The proposed approach leverages a range of data analytics tools, including Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA), Factor Analysis and Non-Negative Matrix Factorization (NMF), to study changes in the market. Further, our approach is capable of identifying disparities between skills that are covered by the educational system, and the skills that are required in the job market. Discussion and evaluation The results of the case study show that, while the jobs most likely to be replaced are generally low-skilled, some high-skilled jobs may also be at risk. In addition, mismatches are identified between skills that are imparted by the education system and the skills required in the job market. Conclusions This study presents how job market and skills required evolved over time, which can help decision-makers to prepare the workforce for highly demanding jobs and skills. Our findings are in line with the concerns that automation is decreasing the demand for certain skills. On the other hand, we also identify the new skills that are required to strengthen the need for collaboration between minds and machines.
Robots and Sustainability: Robots as Persuaders to Promote Recycling

Abstract Humans tend to interact socially with humanoid devices, such as robots. Therefore, a possible application of robotics technology is the promotion of pro-social behavior, namely recycling. To test the effectiveness of this application, an experimental setting in which participants were required to dispose of waste was created. Two types of electronic instructors, a robot and a tablet computer, were located close to the disposal area to provide instructions on appropriate waste disposal. The comparison of the effectiveness of the two types of electronic instructors found that participants exposed to the robot sorted the waste more accurately than participants exposed to the tablet computer. Scores for perceived anthropomorphism and induced empathy were higher for the robot than the tablet computer. We conclude that robots, because of their anthropomorphic features, are more likely to evoke empathy than tablet computers, and thus robots can be more effective in promoting pro-social behavior.
The research data life cycle, legacy data, and dilemmas in research data management

Abstract This paper presents findings from an interview study of research data managers in academic data archives. Our study examined policies and professional autonomy with a focus on dilemmas encountered in everyday work by data managers. We found that dilemmas arose at every stage of the research data lifecycle, and legacy data presents particularly vexing challenges. The iFields' emphasis on knowledge organization and representation provides insight into how data, used by scientists, are used to create knowledge. The iFields' disciplinary emphasis also encompasses the sociotechnical complexity of dilemmas that we found arise in research data management. Therefore, we posit that iSchools are positioned to contribute to data science education by teaching about ethics and infrastructure used to collect, organize, and disseminate data through problem‐based learning.
Deep Clustering and Visualization for End-to-End High-Dimensional Data Analysis

High-dimensional data analysis for exploration and discovery includes two fundamental tasks: deep clustering and data visualization. When these two associated tasks are done separately, as is often the case thus far, disagreements can occur among the tasks in terms of geometry preservation. Namely, the clustering process is often accompanied by the corruption of the geometric structure, whereas visualization aims to preserve the data geometry for better interpretation. Therefore, how to achieve deep clustering and data visualization in an end-to-end unified framework is an important but challenging problem. In this article, we propose a novel neural network-based method, called deep clustering and visualization (DCV), to accomplish the two associated tasks end-to-end to resolve their disagreements. The DCV framework consists of two nonlinear dimensionality reduction (NLDR) transformations: 1) one from the input data space to latent feature space for clustering and 2) the other from the latent feature space to the final 2-D space for visualization. Importantly, the first NLDR transformation is mainly optimized by one Clustering Loss, allowing arbitrary corruption of the geometric structure for better clustering, while the second NLDR transformation is optimized by one Geometry-Preserving Loss to recover the corrupted geometry for better visualization. Extensive comparative results show that the DCV framework outperforms other leading clustering-visualization algorithms in terms of both quantitative evaluation metrics and qualitative visualization.
Adaptive Perspective Distillation for Semantic Segmentation

Strong semantic segmentation models require large backbones to achieve promising performance, making it hard to adapt to real applications where effective real-time algorithms are needed. Knowledge distillation tackles this issue by letting the smaller model (student) produce similar pixel-wise predictions to that of a larger model (teacher). However, the classifier, which can be deemed as the perspective by which models perceive the encoded features for yielding observations (i.e., predictions), is shared by all training samples, fitting a universal feature distribution. Since good generalization to the entire distribution may bring the inferior specification to individual samples with a certain capacity, the shared universal perspective often overlooks details existing in each sample, causing degradation of knowledge distillation. In this paper, we propose Adaptive Perspective Distillation (APD) that creates an adaptive local perspective for each individual training sample. It extracts detailed contextual information from each training sample specifically, mining more details from the teacher and thus achieving better knowledge distillation results on the student. APD has no structural constraints to both teacher and student models, thus generalizing well to different semantic segmentation models. Extensive experiments on Cityscapes, ADE20K, and PASCAL-Context manifest the effectiveness of our proposed APD. Besides, APD can yield favorable performance gain to the models in both object detection and instance segmentation without bells and whistles.