Reviewerly/exHarmony huggingface dataset:
Towards a data-driven adaptive anomaly detection system for human activity

Research in the field of ambient intelligence allows for the utilisation of different computational models for human activity recognition and abnormality detection to promote independent living and to improve the quality of life for the increasing ageing population. The existing monitoring systems are not adaptive to the overly changing human behavioural routine leading to a high rate of false predictions. An adaptive system pipeline is proposed in this paper for adapting to changes in human behaviour based on data ageing and data dissimilarity forgetting factors. The forgetting factor feature allows adaptation of the model to the current routines of an individual while forgetting outdated behavioural patterns. The data ageing forgetting factor discard old behavioural routine based on the age of the activity data while in the data dissimilarity approach, this is achieved by measuring the similarity of the activity data. Behaviour modelling is achieved using an ensemble of novelty detection models termed as Consensus Novelty Detection Ensemble consisting of One-Class Support Vector Machine, Local Outlier Factor, Robust Covariance Estimation and Isolation Forest. The proposed approach is data-driven and environment-invariant, making it feasible for deployment in heterogeneous environments. A comparative analysis carried out with other abnormality detection models for human activities across two datasets shows that the proposed approach achieved better results.
Physical Layer Security: Detection of Active Eavesdropping Attacks by Support Vector Machines

This article presents a framework for converting wireless signals into structured datasets, which can be fed into machine learning algorithms for the detection of active eavesdropping attacks at the physical layer. More specifically, a wireless communication system, which consists of an access point (AP), K legitimate users and an active eavesdropper, is considered. To detect the eavesdropper who breaks into the system during the authentication phase, we first build structured datasets based on different features and then apply sophisticated support vector machine (SVM) classifiers to those structured datasets. To be more specific, we first process the signals received by the AP and then define a pair of statistical features based on the post-processing of the signals. By arranging for the AP to simulate the entire process of transmission and the process of constructing features, we form the so-called artificial training data (ATD). By training SVM classifiers on the ATD, we classify the received signals associated with eavesdropping attacks and nonattacks, thereby detecting the presence of the eavesdropper. Two SVM classifiers are considered, including a classic twin-class SVM (TC-SVM) and a single-class SVM (SC-SVM). While the TC-SVM is preferred in the case of having perfect channel state information (CSI) of all channels, the SC-SVM is preferred in the realistic scenario when we have only the CSI of legitimate users. We also evaluate the accuracy of the trained models depending on the choice of kernel functions, the choice of features and on the eavesdropper's power. Our numerical results show that careful parameter-tuning is required for exceeding an eavesdropper detection probability of 95%.
Gulf Cooperation Council Countries’ Climate Change Mitigation Challenges and Exploration of Solar and Wind Energy Resource Potential

Climate change mitigation is one of the most critical challenges of this century. The unprecedented global effects of climate change are wide-ranging, including changing weather patterns that threaten food production, increased risk of catastrophic floods, and rising sea levels. Adapting to these impacts will be more difficult and costly in the future if radical changes are not made now. This review paper evaluates the Gulf Cooperation Council (GCC) countries’ potential for solar and wind energy resources to meet climate change mitigation requirements and assesses the ability of the GCC region to shift towards low-carbon technologies. The review demonstrates that the GCC region is characterized by abundant solar energy resources. The northwestern, southeastern, and western mountains of the region are highlighted as locations for solar energy application. Oman displays the highest onshore wind speed range, 3–6.3 m s⁻1, and has the highest annual solar radiation of up to 2500 kWh/m2. Kuwait has the second highest onshore wind speed range of 4.5–5.5 m s⁻1. The western mountains and northwestern Saudi Arabia have a wind speed range of 3–6 m s⁻1. The United Arab Emirates (UAE) has the second highest annual solar radiation, 2285 kWh/m2, while Saudi Arabia and the state of Kuwait have equal annual solar radiation at 2200 kWh/m2. This review demonstrates that abundant offshore wind energy resources were observed along the coastal areas of the Arabian Gulf, as well as a potential opportunity for wind energy resource development in the Red Sea, which was characterized by high performance. In addition, the GCC countries will not be able to control and address the interrelated issues of climate change in the future if they do not eliminate fossil fuel consumption, adhere to the Paris Agreement, and implement plans to utilize their natural resources to meet these challenges.
Teaching Evaluation System by use of Machine Learning and Artificial Intelligence Methods

To explore the adoption of artificial intelligence (AI) technology in the field of teacher teaching evaluation, the machine learning algorithm is proposed to construct a teaching evaluation model, which is suitable for the current educational model, and can help colleges and universities to improve the existing problems in teaching. Firstly, the existing problems in the current teaching evaluation system are put forward and a novel teaching evaluation model is designed. Then, the relevant theories and techniques required to build the model are introduced. Finally, the experiment methods and process are carried out to find out the appropriate machine learning algorithm and optimize the obtained weighted naive Bayes (WNB) algorithm, which is compared with traditional naive Bayes (NB) algorithm and back propagation (BP) algorithm. The results reveal that compared with NB algorithm, the average classification accuracy of WNB algorithm is 0.817, while that of NB algorithm is 0.751. Compared with BP algorithm, WNB algorithm has a classification accuracy of 0.800, while that of BP algorithm is 0.680. Therefore, it is proved that WNB algorithm has favorable effect in teaching evaluation model.
Fractal Pull-in Stability Theory for Microelectromechanical Systems

Pull-in instability was an important phenomenon in microelectromechanical systems (MEMS). In the past, MEMS were usually assumed to work in an ideal environment. But in the real circumstances, MEMS often work in dust-filled air, which is equivalent to working in porous media, that's mean fractal space. In this paper, we studied MEMS in fractal space and established the corresponding model. At the same time, we can control the occurrence time and stable time of pull-in by adjusting the value of the fractal index, and obtain a stable pull-in phenomenon.
Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention Networks

This paper addresses the task of (complex) conversational question answering over a knowledge graph.For this task, we propose LASAGNE (muLti-task semAntic parSing with trAnsformer and Graph atteNtion nEtworks).It is the first approach, which employs a transformer architecture extended with Graph Attention Networks for multi-task neural semantic parsing.LASAGNE uses a transformer model for generating the base logical forms, while the Graph Attention model is used to exploit correlations between (entity) types and predicates to produce node representations.LASAGNE also includes a novel entity recognition module which detects, links, and ranks all relevant entities in the question context.We evaluate LASAGNE on a standard dataset for complex sequential question answering, on which it outperforms existing baseline averages on all question types.Specifically, we show that LASAGNE improves the F1-score on eight out of ten question types; in some cases, the increase in F1-score is more than 20% compared to the state of the art.
Emoji as Affective Symbols: Affective Judgments of Emoji, Emoticons, and Human Faces Varying in Emotional Content

An important function of emoji as communicative symbols is to convey emotional content from sender to receiver in computer-mediated communication, e. g., WhatsApp. However, compared with real faces, pictures or words, many emoji are ambiguous because they do not symbolize a discrete emotion or feeling state. Thus, their meaning relies on the context of the message in which they are embedded. Previous studies investigated affective judgments of pictures, faces, and words suggesting that these stimuli show a typical distribution along the big two emotion dimensions of valence and arousal. Also, emoji and emoticons have been investigated recently for their affective significance. The present study extends previous research by investigating affective ratings of emoji, emoticons and human faces and by direct comparison between them. In total, 60 stimuli have been rated by 83 participants (eight males, age: 18–49 years), using the non-verbal Self-Assessment Manikin Scales for valence and arousal. The emotionality of the stimuli was measured on a 9-point Likert scale. The results show significant main effects of the factors “stimulus category” and “discrete emotion” including emotionality, valence and arousal. Also, the interaction between these two main factors was significant. Emoji elicited highest arousal, whereas stimuli related to happiness were rated highest in valence across stimulus categories. Angry emoji were rated highest in emotionality. Also, the discrete emotion was best recognized in emoji, followed by human face stimuli and lastly emoticons.
Is Supervised Syntactic Parsing Beneficial for Language Understanding Tasks? An Empirical Investigation

Traditional NLP has long held (supervised) syntactic parsing necessary for successful higher-level semantic language understanding (LU).The recent advent of end-to-end neural models, self-supervised via language modeling (LM), and their success on a wide range of LU tasks, however, questions this belief.In this work, we empirically investigate the usefulness of supervised parsing for semantic LU in the context of LM-pretrained transformer networks.Relying on the established fine-tuning paradigm, we first couple a pretrained transformer with a biaffine parsing head, aiming to infuse explicit syntactic knowledge from Universal Dependencies treebanks into the transformer.We then fine-tune the model for LU tasks and measure the effect of the intermediate parsing training (IPT) on downstream LU task performance.Results from both monolingual English and zero-shot language transfer experiments (with intermediate target-language parsing) show that explicit formalized syntax, injected into transformers through IPT, has very limited and inconsistent effect on downstream LU performance.Our results, coupled with our analysis of transformers' representation spaces before and after intermediate parsing, make a significant step towards providing answers to an essential question: how (un)availing is supervised parsing for high-level semantic natural language understanding in the era of large neural models?
Generative Adversarial Networks–Enabled Human–Artificial Intelligence Collaborative Applications for Creative and Design Industries: A Systematic Review of Current Approaches and Trends

The future of work and workplace is very much in flux. A vast amount has been written about artificial intelligence (AI) and its impact on work, with much of it focused on automation and its impact in terms of potential job losses. This review will address one area where AI is being added to creative and design practitioners’ toolbox to enhance their creativity, productivity, and design horizons. A designer’s primary purpose is to create, or generate, the most optimal artifact or prototype, given a set of constraints. We have seen AI encroaching into this space with the advent of generative networks and generative adversarial networks (GANs) in particular. This area has become one of the most active research fields in machine learning over the past number of years, and a number of these techniques, particularly those around plausible image generation, have garnered considerable media attention. We will look beyond automatic techniques and solutions and see how GANs are being incorporated into user pipelines for design practitioners. A systematic review of publications indexed on ScienceDirect, SpringerLink, Web of Science, Scopus, IEEExplore, and ACM DigitalLibrary was conducted from 2015 to 2020. Results are reported according to PRISMA statement. From 317 search results, 34 studies (including two snowball sampled) are reviewed, highlighting key trends in this area. The studies’ limitations are presented, particularly a lack of user studies and the prevalence of toy-examples or implementations that are unlikely to scale. Areas for future study are also identified.
OSDDY: embedded system-based object surveillance detection system with small drone using deep YOLO

Abstract Computer vision is an interdisciplinary domain for object detection. Object detection relay is a vital part in assisting surveillance, vehicle detection and pose estimation. In this work, we proposed a novel deep you only look once (deep YOLO V3) approach to detect the multi-object. This approach looks at the entire frame during the training and test phase. It followed a regression-based technique that used a probabilistic model to locate objects. In this, we construct 106 convolution layers followed by 2 fully connected layers and 812 × 812 × 3 input size to detect the drones with small size. We pre-train the convolution layers for classification at half the resolution and then double the resolution for detection. The number of filters of each layer will be set to 16. The number of filters of the last scale layer is more than 16 to improve the small object detection. This construction uses up-sampling techniques to improve undesired spectral images into the existing signal and rescaling the features in specific locations. It clearly reveals that the up-sampling detects small objects. It actually improves the sampling rate. This YOLO architecture is preferred because it considers less memory resource and computation cost rather than more number of filters. The proposed system is designed and trained to perform a single type of class called drone and the object detection and tracking is performed with the embedded system-based deep YOLO. The proposed YOLO approach predicts the multiple bounding boxes per grid cell with better accuracy. The proposed model has been trained with a large number of small drones with different conditions like open field, and marine environment with complex background.
Correcting Chinese Spelling Errors with Phonetic Pre-training

Chinese spelling correction (CSC) is an important yet challenging task.Existing state-ofthe-art methods either only use a pre-trained language model or incorporate phonological information as external knowledge.In this paper, we propose a novel end-to-end CSC model that integrates phonetic features into language model by leveraging the powerful pre-training and fine-tuning method.Instead of conventionally masking words with a special token in training language model, we replace words with phonetic features and their sound-alike words.We further propose an adaptive weighted objective to jointly train error detection and correction in a unified framework.Experimental results show that our model achieves significant improvements on SIGHAN datasets and outperforms the previous state-of-the-art methods.
Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and Baseline

Depth maps obtained by commercial depth sensors are always in low-resolution, making it difficult to be used in various computer vision tasks. Thus, depth map super-resolution (SR) is a practical and valuable task, which up-scales the depth map into high-resolution (HR) space. However, limited by the lack of real-world paired low-resolution (LR) and HR depth maps, most existing methods use down-sampling to obtain paired training samples. To this end, we first construct a large-scale dataset named "RGB-D-D", which can greatly promote the study of depth map SR and even more depth-related real-world tasks. The "D-D" in our dataset represents the paired LR and HR depth maps captured from mobile phone and Lucid Helios respectively ranging from indoor scenes to challenging outdoor scenes. Besides, we provide a fast depth map super-resolution (FDSR) baseline, in which the high-frequency component adaptively decomposed from RGB image to guide the depth map SR. Extensive experiments on existing public datasets demonstrate the effectiveness and efficiency of our network compared with the state-of-the-art methods. Moreover, for the real-world LR depth maps, our algorithm can produce more accurate HR depth maps with clearer boundaries and to some extent correct the depth value errors.
Learning Language Specific Sub-network for Multilingual Machine Translation

Multilingual neural machine translation aims at learning a single translation model for multiple languages.These jointly trained models often suffer from performance degradation on rich-resource language pairs.We attribute this degeneration to parameter interference.In this paper, we propose LaSS to jointly train a single unified multilingual MT model.LaSS learns Language Specific Sub-network (LaSS) for each language pair to counter parameter interference.Comprehensive experiments on IWSLT and WMT datasets with various Transformer architectures show that LaSS obtains gains on 36 language pairs by up to 1.2 BLEU.Besides, LaSS shows its strong generalization performance at easy adaptation to new language pairs and zero-shot translation.LaSS boosts zero-shot translation with an average of 8.3 BLEU on 30 language pairs.Codes and trained models are available at https: //github.com/NLP-Playground/LaSS.
Fully Non-autoregressive Neural Machine Translation: Tricks of the Trade

Fully non-autoregressive neural machine translation (NAT) simultaneously predicts tokens with single forward of neural networks, which significantly reduces the inference latency at the expense of quality drop compared to the Transformer baseline.In this work, we target on closing the performance gap while maintaining the latency advantage.We first inspect the fundamental issues of fully NAT models, and adopt dependency reduction in the learning space of output tokens as the primary guidance.Then, we revisit methods in four different aspects that have been proven effective for improving NAT models, and carefully combine these techniques with necessary modifications.Our extensive experiments on three translation benchmarks show that the proposed system achieves the state-of-the-art results for fully NAT models, and obtains comparable performance with the autoregressive and iterative NAT systems.For instance, one of the proposed models achieves 27.49BLEU points on WMT14 En-De with 16.5× speed-up compared to similar sized autoregressive baseline under the same inference condition.The implementation of our model is available here 1 .
Survey of Promising Technologies for Quantum Drones and Networks

Due to recent advancements in quantum drones, the Internet of Quantum Drones (IoQDs), and Drone-to-Satellite connectivity, several advantages have been anticipated for real-time applications.This work examines quantum computing issues, including quantum data processing, techniques, circuits, and algorithms important for quantum drones or their networks.Here, we discussed the current research trends on quantum computing, quantum-safe computing, or post-quantum cryptography important to quantum networks, followed by the numerous advantages, limitations, future advancements, and research issues connected with quantum technologies, drones, and their network.This work has also prepared a taxonomy of quantum-related areas depending upon the logic of their learning, followed by a review of each of these areas.We review the most recent work over quantum algorithms used in various-quantum-related areas and networks, the role of quantum satellites for drone-based networks and communications, how quantum artificial intelligence and quantum machine learning are important for quantum drones, networks and futuristic applications, quantum attacks, quantum genetic algorithms, and the importance of post-quantum and quantum-safe cryptography.The challenges and research directions in these domains are explored as well.Lastly, this work presents an overview of the current state of knowledge in various promising technologies that are recently found to be important for quantum drones and networks.
The Boon and Bane of Blockchain: Getting the Governance Right

Countless enterprise blockchains fail to live up to high expectations, often because the supporting governance structures are insufficiently established or have become stagnant. Based on interviews with 153 blockchain executives and an analysis of publicly documented use cases, this article offers a guide for blockchain scholars and practitioners. Its framework highlights the coordination and control challenges that exist in blockchain governance contexts and presents four generic governance modes to address them: chief, clan, custodian, and consortium. Managers can use these governance modes as a basis for four strategic moves (connecting, isolating, loosening, and tightening) to navigate blockchain governance challenges.
Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning

Recent pretrained language models extend from millions to billions of parameters.Thus the need to fine-tune an extremely large pretrained model with a limited training corpus arises in various downstream tasks.In this paper, we propose a straightforward yet effective fine-tuning technique, CHILD-TUNING, which updates a subset of parameters (called child network) of large pretrained models via strategically masking out the gradients of the non-child network during the backward process.Experiments on various downstream tasks in GLUE benchmark show that CHILD-TUNING consistently outperforms the vanilla fine-tuning by 1.5 ∼ 8.6 average score among four different pretrained models, and surpasses the prior fine-tuning techniques by 0.6 ∼ 1.3 points.Furthermore, empirical results on domain transfer and task transfer show that CHILD-TUNING can obtain better generalization performance by large margins.