Reviewerly/exHarmony huggingface dataset:
Efficient Image Super-Resolution with Collapsible Linear Blocks

In this paper, we propose a simple but effective architecture for fast and accurate single image super-resolution. Unlike other compact image super-resolution methods based on hand-crafted designs, we first apply coarse-grained pruning for network acceleration, and then introduce collapsible linear blocks to recover the representative ability of the pruned network. Specifically, each collapsible linear block has a multi-branch topology during training, and can be equivalently replaced with a single convolution in the inference stage. Such decoupling of the training-time and inference-time architecture is implemented via a structural re-parameterization technique, leading to improved representation without introducing extra computation costs. Additionally, we adopt a two-stage training mechanism with progressively larger patch sizes to facilitate the optimization procedure. We evaluate the proposed method on the NTIRE 2022 Efficient Image Super-Resolution Challenge and achieve a good trade-off between latency and accuracy. Particularly, under the condition of limited inference time (≤ 49.42ms) and parameter amount (≤ 0.894M), our solution obtains the best fidelity results in terms of PSNR, i.e., 29.05dB and 28.75dB on the DIV2K validation and test sets, respectively.
Overview of PAN 2022: Authorship Verification, Profiling Irony and Stereotype Spreaders, and Style Change Detection

The paper gives a brief overview of three shared tasks which have been organized at the PAN 2022 lab on digital text forensics and stylometry hosted at the CLEF 2022 conference. The tasks include authorship verification across discourse types, multi-author writing style analysis and author profiling. Some of the tasks continue and advance past editions (authorship verification and multi-author analysis) and some are new (profiling irony and stereotypes spreaders). The general goal of the PAN shared tasks is to advance the state of the art in text forensics and stylometry while ensuring objective evaluation on newly developed benchmark datasets.
Activity Classification and Analysis During a Sports Training Session Using a Fuzzy Model

Training has great significance and should be an integral part of the daily routines of all elite athletes. Training allows the body to gradually develop strength and endurance, increase skill levels, and build trust, motivation, and ambition. The risk factors in sports training sessions include fragile or low self-confidence, Breakdowns in self-assurance, and high expectations are considered an important factors. In this paper, Dynamic Activity Adaptive Physical Fuzzy Model (DAAPFM) is proposed to become better athletes and meet their mental challenges. Mel does time-frequency analysis, and they tend to experience anxiety about performance. Fuzzy wrapping optimization analysis is integrated with DAAPFM to build confidence in the belief in one’s ability to execute a task or win an event. The experimental results show that the data fusion approach can analyze the activities of athletes effectively. The simulation results give the average accuracy of the classification of sports activity 99.40% framework’s efficiency to gradually develop strength and endurance, increase skill levels, and build trust, motivation, and ambition.
Autism, Autonomy, and Touch Avoidance

Touch is an important component of many social experiences for many people. Autistic children commonly avoid social touch more than non-autistic peers. It is generally thought that this is due to autistic individuals experiencing hyper- or hyposensitivity of touch. While this is undoubtedly the case at least some of the time, studies of touch and autism have often involved decontextualised experimental settings or post-hoc reports on touch by autistic people or their common social interactants (i.e., parents). As such, there is very little research that looks at social touch in interactions involving autistic people and studies how it naturally occurs and how it is managed in the moment. Using multimodal Conversation Analysis, I analyse a collection of cases of social touch in the form of parents' cuddles or embraces with their autistic children. I demonstrate here what these cuddles can look like, how they can unfold over time with both autistic children and their parents mutually participate in building intimate sensorial moments. I also show more problematic moments where the child resists, abandons, or misunderstands a cuddle from their parent (or attempt to secure one) demonstrating that, in these cases, the trouble for the autistic children was not touch sensitivity but the prioritisation of courses of action that social touch would impede. As such the children display that the social touch is avoided or negatively evaluated due to its social nature, not its physical/sensational one. In demonstrating this I argue that not everything that might look like a sign of sensory difference is one.
Sound Encryption Algorithm with Perfect Reconstruction using Tent Map and Multidimensional Arnold Chaotic Systems

This paper proposes an encryption algorithm for speech signals based on two chaotic systems: Arnold 3D Map and Tent Map. The algorithm obtains the same results for audio signals. Encryption and decryption processes were considered to be error-free. The architecture of the algorithm has been built so that encryption leads to good performance metrics results and also is suitable for long sound pauses (specific to speech communications). By splitting the signal, the independent processing of each segment of length N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> is ensured, leading to a real-time encryption and decryption. The influence of the segment length on the algorithm speed was also studied.
Quantum treatment of cavity-assisted entanglement of three-level atoms and two fields in an electromagnetically-induced-transparency configuration

We examine the field-field and field-atom entanglements generated by \ensuremath{\Lambda}-type three-level atoms placed in an optical cavity driven above threshold by two coherent input optical fields. It is shown that under realistic experimental conditions entanglement between the two output cavity fields as well as between the fields and the atoms can be achieved when the two cavity fields are both one- and two-photon (Raman) resonant with the atomic transitions, as in the configuration for electromagnetically induced transparency (EIT). The entanglement has similar features to pump-signal-idler three-color entanglement in an above-threshold optical parametric oscillator [Villar et al., Phys. Rev. Lett. 97, 140504 (2006)]. This cavity-enhanced atom-field cooperative coupling could enable convenient and efficient generation of bright, nondegenerate, narrow-band entangled fields, which may find potential applications in realistic quantum communications and networking protocols.
Precise Re-Os dating of molybdenite using Carius tube, NTIMS and ICPMS

The technique described in this paper represents isotope dilution, the digestion in reverse aqua regia in Carius tube at 230 °C, purifying Os using normal distillation and microdistillation, purifying Re using acetone extraction and anion exchange chromatography, isotopes ratio determination using NTIMS. We have used this technique to analyze for repeatedly a molybdenite HLP powder as a reference standard. Seventeen samples from twelve small bottles were analyzed over a period of six months. The average Re-Os age was 221.3 ± 0.3 Ma with 95% confidence limit. Median age and mean absolute deviation were 221.34 ± 0.12 Ma. Using improved ICPMS and modified procedure, we also got good Re-Os age 222.0 ± 1.4 Ma with 95% confidence limit for same HLP above. The 187Re-1870s isochrone age of molybdenite is 322.7 ± 2.5 Ma with 95 % confident Limit, MSWD 0.51 and correlation constant 0.99999 for the molybdenite of porphyry copper deposit in East Tianshan, Xinjiang Province.
Exploration and Evaluation of Self-Clocked Rate Adaptation for Multimedia (SCReAM) Congestion Control Algorithm in 5G Networks

The new emerging mobile technologies have led to increased complexity in the networking paradigm. These technologies are incorporated into each device to support to-day's trends. Although this process offers many advantages, it brought several issues when new use-cases and services are introduced such as requiring regular updating of infrastructure, high network throughput, low latency, and minimum packet loss to provide proper performance. One of the main interesting applications that can benefit from such features is the conver-sational video for WebRTC which is becoming more popular since the developing community is growing and the industry is expressing its interest by striving towards designing new schemes for it. The main concept that controls the conversational video for WebRTC is the congestion control algorithm. Many approaches have been designed to address certain issues to result in optimum performance. In this study, we aim to explore the possibility of achieving optimal streaming in a 5G environment. We are especially interested in choosing the appropriate congestion control algorithm to achieve this purpose. As a candidate, we are focusing on the Self-Clocked Rate Adaptation for Multimedia (SCReAM) congestion control algorithm. Also, we aim to provide guidelines for achieving the video streaming performance of SCReAM in 5G networks.
Implementation of Blockchain Domain Control Verification (B-DCV)

Security in the communication systems rely mainly on a trusted Public Key Infrastructure (PKI) and Certificate Authorities (CAs). Besides the lack of automation, the complexity and the cost of assigning a signed certificate to a device, several allegations against CAs have been discovered, which has created trust issues in adopting this standard model for secure systems. The automation of the servers certificate assignment was achieved by the Automated Certificate Management Environment (ACME) method, but without confirming the trust of assigned certificate. This paper presents a complete tested and implemented solution to solve the trust of the Certificates provided to the servers by using the blockchain platform for certificate validation. The Blockchain network provides an immutable data store, holding the public keys of all domain names, while resolving the trust concerns by applying an automated Blockchain-based Domain Control Validation (B-DCV) for the server and client server verification. The evaluation was performed on the Ethereum Rinkeby testnet adopting the Proof of Authority (PoA) consensus algorithm which is an improved version of Proof of Stake (Po <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$S$</tex> ) applied on Ethereum 2.0 providing superior performance compared to Ethereum 1.0.
Grad-CAM++ is Equivalent to Grad-CAM With Positive Gradients

The Grad-CAM algorithm provides a way to identify what parts of an image contribute most to the output of a classifier deep network. The algorithm is simple and widely used for localization of objects in an image, although some researchers have point out its limitations, and proposed various alternatives. One of them is Grad-CAM++, that according to its authors can provide better visual explanations for network predictions, and does a better job at locating objects even for occurrences of multiple object instances in a single image. Here we show that Grad-CAM++ is practically equivalent to a very simple variation of Grad-CAM in which gradients are replaced with positive gradients.
Image Semantic Segmentation of Underwater Garbage with Modified U-Net Architecture Model

Autonomous underwater garbage grasping and collection pose a great challenge to underwater robots. To assist underwater robots in locating and recognizing underwater garbage objects efficiently, a modified U-Net-based architecture consisting of a deeper contracting path and an expansive path is proposed to accomplish end-to-end image semantic segmentation. In addition, a dataset for underwater garbage semantic segmentation is established. The proposed architecture is further verified in the underwater garbage dataset and the effects of different hyperparameters, loss functions, and optimizers on the performance of refining the predicted segmented mask are examined. It is confirmed that the focal loss function will lead to a boost in solving the target–background unbalance problem. Eventually, the obtained results offer a solid foundation for fast and precise underwater target recognition and operations.
Development of Games Online Features Educandy to Children Aged 5-6 Years

Recognizing letters is important to be developed in early childhood for the next child's language skills. By introducing letters to children, it is hoped that children can understand letters by knowing the sounds and shapes of letters. The learning media that will be developed in this research is an online game on the educandy. The purpose of this study was to determine the development of online games on the educandy to introduce characters to children aged 5-6 years at KB Darul Falah Terbanggi Besar. Types of research and development (research and development). The development procedure according to the Borg and Gall theory consists of seven stages, namely potential and problems, data collection, product design, expert testing, product revision, product testing, and product revision. Based on the results of the study, it can be concluded that, games feature educandy to introduce script to children aged 5-6 years at KB Darul Falah Terbanggi Besar, the development of online game feature educandy children are able to name symbols of letters that are known, children are able to recognize the sound of the initial letters of the names of objects in the vicinity. The Effectiveness of Using Online Games Features Educandy to Improve Literacy Skills for Children aged 5-6 years, namely after research was conducted at the Darul Falah Family Planning Terbanggi Besar, it was proven that the literacy abilities of early childhood can be categorized as starting to develop.
A Smart Video Surveillance System for Helping Law Enforcement Agencies in Detecting Knife Related Crimes

With recent technological developments, criminal investigation has witnessed a revolutionary change in identifying crimes. This has empowered Law Enforcement Agencies (LEAs) to take benefit of such revolution and build a smart criminal investigation ecosystem. Generally, LEAs collect data through surveillance systems (e.g., cameras); which are implemented on public places in order to recognize people behaviors and visually identify those who may form any danger or risk. In this paper, we focus on knives-related crimes or attacks that have been increased in recent years. In order to ensure public safety, it is crucial to detect such type of attacks in an accurate and efficient way in order to help LEAs in reducing potential consequences. We propose a smart video surveillance system (SVSS), which is based on a modified Single Shot Detector (SSD) and is combined with InceptionV2 and MobileNetV2 models. The proposed system is believed to enable LEAs to analyze big data collected from sensor cameras in a real-time and to accurately detect knives-based attacks. Experimental result show that SVSS can achieve better results in real-life scenario in terms of obtaining rapid and accurate attack warnings.
Using Word Embedding Methods for Product Recommendation

Recommendation systems have become increasingly popular in recent years. In today&#x2019;s world where the competition is extremely high, many service providers, from e-commerce to music industry, use recommendation systems. Considering the number of products and customers, it is crucial to deliver the right products to the right customers. Numerous methods are applied in recommendation systems. In our study, it is aimed to examine the purchasing association between products and to offer alternative products to the customers based on what they have already bought. For this purpose, Apriori, and Word2Vec and FastText, a natural language processing method, which extract the relationship space between words according to their frequency of occurrence, are used. Then, these models are compared. The results from word embedding methods are promising and will be used in future studies.
The Effect of Ambient Temperature On Device Classification Based On Radio Frequency Fingerprint Recognition

Physical layer authentication is an important technique for cybersecurity, especially in military scenarios. Device classification using radio frequency fingerprinting, which is based on recognizing device-unique characteristics of the transient waveform observed at the beginning of a transmission from a radio device, is a promising method in this context. In this study, the effect of the ambient temperature on the performance of radio device classification based on RF fingerprinting is investigated. The waveforms of the transient regions of the transmissions are recorded as images, and ResNet50 and InceptionV3 networks for image classification are used to determine the radio devices. The radio devices used in the study belong to the same brand, model, and production date, making the problem more difficult than classifying radio devices of different brands or models. Our results show that high levels of accuracy can be attained using convolutional neural network models such as ResNet50 and InceptionV3 when the test data and the training data are collected at the same temperature, whereas performance suffers when the test data and the training data belong to different temperature values. We provide the performance figures of a blended training model that uses training data taken at various temperature values. A comparison of the two networks is also provided.
Cooperative Fall Detection with Multiple Cameras

We propose a fall detection mechanism based on multi-camera cooperation in home space. Cameras capture image-based falling events, and self-organize a group using deep reinforcement learning. Neighbor cameras exchange sensing data and statuses in local network proximity. With information sharing in a group, cameras can improve the accuracy of decision making on falling events and cope with the limited fields of view against physical deployment of cameras in residential areas.
Research on Application of Virtual Reality Technology to Create Metaverse Podcasts

This study proposes a metaverse podcast by avatar scene that combines VRM 3D Model, facial expressions and body language, by means of real-time import of face capture and motion capture into the mirror-like software, and outputs the image as a virtual camera. By faithfully presenting the process of verbal expression and behavioral response, the image of the virtual avatar is more vivid and closer to the other self. The importance of the facial expression of the virtual avatar in the expression process is studied, which also increases the possibility of development of virtual reality technology.
Prediction of Prospecting Target Based on Selective Transfer Network

In recent years, with the integration and development of artificial intelligence technology and geology, traditional geological prospecting has begun to change to intelligent prospecting. Intelligent prospecting mainly uses machine learning technology to predict the prospecting target area by mining the correlation between geological variables and metallogenic characteristics, which usually requires a large amount of data for training. However, there are some problems in the actual research, such as fewer geological sample data and irregular mining features, which affect the accuracy and reliability of intelligent prospecting prediction. Taking the Pangxidong study area in Guangdong Province as an example, this paper proposes a deep learning framework (SKT) for prospecting target prediction based on selective knowledge transfer and carries out intelligent prospecting target prediction research based on geochemical data in Pangxidong. The irregular features of different scales in the mining area are captured by dilation convolution, and the weight parameters of the source network are selectively transferred to different target networks for training, so as to increase the generalization performance of the model. A large number of experimental results show that this method has obvious advantages over other state-of-the-art methods in the prediction of prospecting target areas, and the prediction effect in the samples with mines is greatly improved, which can effectively alleviate the problems of a small number of geological samples and irregular features of mining areas in prospecting prediction.
Towards Personalized Adaptive Learning in e-Learning Recommender Systems

An adaptive e-learning scenario not only allows people to remain motivated and engaged in the learning process, but it also helps them expand their awareness of the courses they are interested in.e-Learning systems in recent years had to adjust with the advancement of the educational situation.Therefore many recommender systems have been presented to design and provide educational resources.However, some of the major aspects of the learning process have not been explored quite enough; for example, the adaptation to each learner.In learning, and in a precise way in the context of the lifelong learning process, adaptability is necessary to provide adequate learning resources and learning paths that suit the learners' characteristics, skills, etc. e-Learning systems should allow the learner to benefit the most from the presented learning resources content taking into account her/his learning experience.The most relevant resources should be recommended matching her/his profile and knowledge background not forgetting the learning goals she/he would like to achieve and the spare time she/he has in order to adjust the learning session with her/his goals whether it is to acquire or reinforce a certain skill.This paper proposes a personalized elearning system that recommends learning paths adapted to the users profile.
HGCNN: Deep Graph Convolutional Network for Sensor-Based Human Activity Recognition

Sensor-based human activity recognition(HAR) tasks often face issues of low-quality data, such as insufficient or incorrect data labels. But natural human activities are often con-textual, for example, an athlete needs to stretch before exercising. Therefore, we hypothesize unlabeled activity can be identified by contextual activity associations. Early statistical machine learning algorithms that rely on manual feature extraction frequently have knowledge constraints and can only capture superficial features, which prevents them from producing acceptable results in unsupervised or semisupervised circumstances. Deep learning on graph has made enormous strides in several disciplines in recent years because of its quick development. To address the issues highlighted above, we design a deep learning model called HGCNN based on graph neural network (GNN). Besides, we also make the sensor's time series data model as a fully connected subgraph with a sliding window size. Afterwards, a spectral graph convolution method is used to extract activity contextual relationship on the subgraph. The classification accuracy of this method on the Extra-Sensory dataset reaches 99.54% and HGCNN shows superior performance relative to previously CNN baseline, improving on macro F1-score reached up to 0.87.