Reviewerly/exHarmony huggingface dataset:
Work-in-Progress: A Preliminary Study on Students’ Acceptance of Chatbots for Studio-Based Learning

Studio-based learning (SBL) is a pedagogy that encompasses collaborative active learning strategies focusing on creativity, peer learning, and problem-solving. The iterative design procedures and critique sessions are the primary learning practices of SBL, which relays to a need to have effective communication between instructors and students. Due to this, we designed a Telegram chatbot using the TextIt to facilitate some of the interactions that are common in SBL. In this preliminary study, students’ acceptance of chatbot was investigated based on the Technology Acceptance Model (TAM) using a mix-method approach. Preliminary results indicated positive acceptance and intention to use chatbots due to ease of use, mobile accessibility, human-like communications, and privacy in communicating and providing feedback.
Ruta, a Loom for Making Sense of Industrial Weaving

Weaving is the technique of making fabric through interlacing yarn, and it can be seen as a way to program a material. However, due to this programming complexity, the novice weaver finds it hard to quickly engage in weaving. We designed Ruta through an iterative process involving weave experts and novice weavers. In effect, Ruta is a tool for sensemaking the complexity of Jacquard weaving which enables novel weavers to understand and feel what weaving actually entails. We present the Ruta loom and demonstrate how it imparts meaning to the process of complex weaving.
MASSD: Multi-scale attention single shot detector for surgical instruments

Surgical instrument detection is a significant task in computer-aided minimal invasive surgery for providing real-time feedback to physicians, evaluating surgical skills, and developing a training plan for surgeons. In this study, a multi-scale attention single detector is designed for surgical instruments. In the field of object detection, accurate detection of small objects is always a challenging task. We propose an innovative feature fusion technique aimed at small surgical instrument detection. First, the attention map is created from high-level features to act on the low-level features and enrich the semantic information of the low-level features. The original and processed features are then fused by skip connection. Finally, multi-scale feature maps are created to predict fusion features. The experiments on the ATLAS Dione dataset yielded results with a detection time of 0.066 s per frame and a mean average precision of 90.08%. Our proposed feature fusion module can obtain more semantic information for low-level features and significantly enhance the performance of small surgical instrument detection.
Hybrid Cryptography Algorithm For Secure And Low Cost Communication

In a client-server architecture or open networks such as the internet, transmission of data may lead to leak of confidential information. The objective of encryption is to secure or protect data from unauthorized access or modifications. In this paper a hybrid cryptography algorithm is proposed in order to achieve confidentiality and increase security in the communications taking place over the internet. The paper also focuses on the time taken for the encryption and decryption process so that the algorithm is not CPU exhaustive.
Age-Oriented Face Synthesis With Conditional Discriminator Pool and Adversarial Triplet Loss

The vanilla Generative Adversarial Networks (GAN) are commonly used to generate realistic images depicting aged and rejuvenated faces. However, the performance of such vanilla GANs in the age-oriented face synthesis task is often compromised by the mode collapse issue, which may result in the generation of faces with minimal variations and a poor synthesis accuracy. In addition, recent age-oriented face synthesis methods use the L1 or L2 constraint to preserve the identity information on synthesized faces, which implicitly limits the identity permanence capabilities when these constraints are associated with a trivial weighting factor. In this paper, we propose a method for the age-oriented face synthesis task that achieves a high synthesis accuracy with strong identity permanence capabilities. Specifically, to achieve a high synthesis accuracy, our method tackles the mode collapse issue with a novel Conditional Discriminator Pool (CDP), which consists of multiple discriminators, each targeting one particular age category. To achieve strong identity permanence capabilities, our method uses a novel Adversarial Triplet loss. This loss, which is based on the Triplet loss, adds a ranking operation to further pull the positive embedding towards the anchor embedding resulting in significantly reduced intra-class variances in the feature space. Through extensive experiments, we show that our proposed method outperforms state-of-the-art methods in terms of synthesis accuracy and identity permanence capabilities, qualitatively and quantitatively.
HR Based Interactive Chat bot (PowerBot)

In the age of machine intelligence, Computer Science has had a lot of advancements in the past decade, and Artificial Intelligence definitely stands distinguished among them. A chat bot is a type of application which is generated by a computer. It's capable of having a simulated interaction with the user in such a way that they don't feel like talking to the machine directly. The human-like response during a conversation is initiated by a computer program which is a verbose operator, commonly recognized as a chat bot. For a chat bot to imitate a human dialogue, the input inured by a user should be precisely analyzed and should forge significant and pertinent feedback. Now a day, people interact with systems more than humans. This project is aimed to implement an HR chat bot using tools that expose artificial intelligence methods such as natural language Understanding. Allowing users to interact with the chat bot using natural language input and to train the chat bot using appropriate methods so it will allow to generate a response. This chat bot will allow users to view all details regarding the company all from within the chat bot. As we have seen, for any of the basic requirements, the employee has to go to the team leader or to the HR. To overcome these challenges faced by the employees, we are trying to present a chat bot which would fulfil the requirements such as applying for leave, reimbursement, applying for a allowance and many such. The Chabot will provide personal and efficient communication between the employee and their HR to manage their job and get assistance when needed, such as; answering any queries and requesting for leave. The Chabot will allow users to feel confident and comfortable when using this service regardless of the employee's computer literacy due to the natural language used in messages. It also provides a very accessible and efficient service as all interactions will take place within the one chat conversation negating the employee for the user to navigate through a system.
Multiautonomous underwater vehicle consistent collaborative hunting method based on generative adversarial network

The time-varying ocean currents and the delay of underwater acoustic communication have caused the uncertainty of single autonomous underwater vehicle (AUV) tracking target and the inconsistency of multi-AUV coordination, which make it difficult for multiple AUVs to form a hunting alliance. To solve the above problems, this article proposes the multi-AUV consistent collaborative hunting method based on generative adversarial network (GAN). Firstly, the three-dimensional (3D) kinematic model of AUV is established for the underwater 3D environment. Secondly, combined with the Laplacian matrix, the topology of the hunting alliance in the ideal environment is established, and the control rate of AUV is calculated. Finally, using the GAN network model, the control relationship after environmental interference is used as the input of the generative model. The control rate in the ideal environment is used as the comparison object of the discriminative model. Using the iterative training of GAN to generate a control rate that adapts to the current interference environment and combining multi-AUV topological hunting model to achieve successful hunting of noncooperative target, the experimental results show that the algorithm reduces the average hunting time to 62.53 s and the success rate of hunting is increased to 84.69%, which is 1.17% higher than the particle swarm optimization-constant modulus algorithm (PSO-CMA) algorithm.
Structural Correlation Based Method for Image Forgery Classification and Localization

In the image forgery problems, previous works has been chiefly designed considering only one of two forgery types: copy-move and splicing. In this paper, we propose a scheme to handle both copy-move and splicing image forgery by concurrently classifying the image forgery types and localizing the forged regions. The structural correlations between images are employed in the forgery clustering algorithm to assemble relevant images into clusters. Then, we search for the matching of image regions inside each cluster to classify and localize tampered images. Comprehensive experiments are conducted on three datasets (MICC-600, GRIP, and CASIA 2) to demonstrate the better performance in forgery classification and localization of the proposed method in comparison with state-of-the-art methods. Further, in copy-move localization, the source and target regions are explicitly specified.
ADR‐Net: Context extraction network based on M‐Net for medical image segmentation

Purpose Medical image segmentation is an essential component of medical image analysis. Accurate segmentation can assist doctors in diagnosis and relieve their fatigue. Although several image segmentation methods based on U‐Net have been proposed, their performances have been observed to be suboptimal in the case of small‐sized objects. To address this shortcoming, a novel network architecture is proposed in this study to enhance segmentation performance on small medical targets. Methods In this paper, we propose a joint multi‐scale context attention network architecture to simultaneously capture higher level semantic information and spatial information. In order to obtain a greater number of feature maps during decoding, the network concatenates the images of side inputs by down‐sampling during the encoding phase. In the bottleneck layer of the network, dense atrous convolution (DAC) and multi‐scale residual pyramid pooling (RMP) modules are exploited to better capture high‐level semantic information and spatial information. To improve the segmentation performance on small targets, the attention gate (AG) block is used to effectively suppress feature activation in uncorrelated regions and highlight the target area. Results The proposed model is first evaluated on the public dataset DRIVE, on which it performs significantly better than the basic framework in terms of sensitivity (SE), intersection‐over‐union (IOU), and area under the receiver operating characteristic curve (AUC). In particular, the SE and IOU are observed to increase by 7.46% and 5.97%, respectively. Further, the evaluation indices exhibit improvements compared to those of state‐of‐the‐art methods as well, with SE and IOU increasing by 3.58% and 3.26%, respectively. Additionally, in order to demonstrate the generalizability of the proposed architecture, we evaluate our model on three other challenging datasets. The respective performances are observed to be better than those of state‐of‐the‐art network architectures on the same datasets. Moreover, we use lung segmentation as a comparative experiment to demonstrate the transferability of the advantageous properties of the proposed approach in the context of small target segmentation to the segmentation of large targets. Finally, an ablation study is conducted to investigate the individual contributions of the AG block, the DAC block, and the RMP block to the performance of the network. Conclusions The proposed method is evaluated on various datasets. Experimental results demonstrate that the proposed model performs better than state‐of‐the‐art methods in medical image segmentation of small targets.
Crafting Everyday Resistance through Lightweight Design

This pictorial examines the relationship between inquiry and activism within academic settings through the design of protest artifacts. Inspired by lineages of feminist print production, we illustrate our own process of creating simple acts of resistance through electronic posters and buttons. Naming these interventions "lightweight design interactions," we hold on to the ways design practice might work as modest, partial, and incremental shifts in the circumstances through which design futures unfold. Lightweight design interactions encourage us as design researchers to look beyond the bold creation of alternatives (new design artifacts) to the subtle nurturing of the circumstances that make alternatives possible.
A Systematic Mapping Study in Cloud for Educational Innovation

For some years now there has been a growing use of cloud computing technologies in education. This paper reports a systematic mapping study (SMS) about the interest in the innovation of methods, techniques, and tools applied to teaching activities based on the use of cloud computing. The SMS presented here was designed using a consolidated, reliable, rigorous methodology and implemented in a replicable and verifiable manner. The process adopted is based on the selection of papers in accredited online digital libraries. This research made it possible to identify and analyze the empirical evidence in relation to the use of cloud computing in education with the aim of identifying both the main topics currently shared by researchers and the less explored areas to be integrated into a research agenda. The results show that only 17% of the 940 works examined report empirical research about the innovation introduced in educational environments of all levels and degrees by the cloud. The results also show that the type of method most widely used was that of feedback. The areas that have attracted the most interest from the research community are those of the new learning environment, collaboration platforms, and virtual laboratories. However, since the same areas are less supported by empirical research than others, we believe that these should be more closely monitored by the scientific community.
Similar case matching with explicit knowledge-enhanced text representation

Similar case matching is one of the important practical applications of text matching, which is a crucial issue in natural language processing. For a well-structured natural language document, the sentence-based representation and explicit knowledge elements should be effectively integrated so as to fit the original structure of the text, when constructing the document representation. In this paper, we propose a multi-task learning framework with “de- and re-construction”, which leverages the extraction of sub-tasks based on sentence-level knowledge elements to enhance the representation at the document level, to improve the performance of the model in the main task of similar case matching. Extensive experiments have proved that our proposed model outperforms methods like latent dirichlet allocation (LDA), LSTM-RNN, BERT, etc, which only focus on constructing document representations.
Modulation Classification Method based on Deep Learning under Non-Gaussian Noise

The arrival of 5G has accelerated the development of the Internet of things and vehicular technology, which often need to transmit large amounts of data through wireless networks. Modulation classification plays an important role in wireless communication. Recent years, deep learning has been applied to solve the modulation classification problem and achieved good classification results. At present, almost all the papers that use deep learning to solve modulation classification are in Gaussian White noise environment. However, the error source mainly comes from non-Gaussian noise in practical wireless communication. In this paper, a modulation classification method in non-Gaussian environment based on Deep Learning is proposed. The proposed algorithm can effectively suppress the sharp pulse in non-Gaussian noise and improve the modulation recognition accuracy. MPSK and MQAM signals which are difficult to distinguish are adopted in the simulation experiment. The simulation results show that validity of the proposed method. At the same time, experiments show that this method is robust to the characteristic exponent of noise.
Quantum thermometry based on a cavity-QED setup

We present a quantum thermometry scheme based on a cavity-QED setup, which attains a sensitivity with Heisenberg scaling. A stream of identical two-level systems passes through a thermal bath to be tested. Each system partially thermalizes, carrying information on the temperature of the thermal bath, and it then interacts with a dissipative single-mode cavity. The Heisenberg scaling is attained from the initial coherence of each system. The systems cannot be fully thermalized by the thermal bath. The optimal interaction time between the system and the thermal bath is derived. Direct photon detection is proved to be an optimal measurement when there is no extra thermal bath in the cavity. In the case where there is an extra thermal bath in the cavity independent of the thermal bath to be tested, homodyne detection is the optimal measurement. Moreover, we show that direct photon detection obtains less information in the case where the extra thermal bath in the cavity is in thermal equilibrium with the thermal bath. However, the optimal measurement can utilize the information from the extra thermal bath to improve the estimation precision of the temperature.
RF-IDH: An intelligent fall detection system for hemodialysis patients via COTS RFID

Unhealthy habits lead to a growing population of hemodialysis patients. The single treatment of hemodialysis is about four hours long. Therefore, patients usually go to the toilet during treatment and need to be checked for safety. However, existing fall detection techniques are often limited by factors such as privacy, signal interference, and the like. In this paper, we propose RF-IDH tackle the above issues, a dedicated system for detecting falls caused by complications in hemodialysis patients using RF signal. In RF-IDH, after collecting the signal, we process the collected data by three functional module clusters, namely signal preprocessing, residual feature extraction, hemodialysis patient’s fall detection, all of which are well-designed to achieve high performance in patient’s fall detection. In particular, we design a residual feature extraction (RFE) algorithm based on the hemodialysis patient safety process model, and the fall detection of hemodialysis patients is treated as a machine learning problem where four classification models are built via learning residual feature space. We implement our system on commercial off-the-shelf RFID devices and compared the evaluation metrics of four different methods in terms of system performance, efficiency, robustness, and latency. The evaluation results show that our proposed RF-IDH that optimizes the 2NN–RFE method achieves superior performance compared to other methods.
A Method for Classification of Missing Values using Data Mining Techniques

This paper is intended for research in the area of Data Mining. The classification accuracy is improved by attributing the missing values of data. The effective attributing method model based Missing value attribution using Correlation is proposed. It represents the missing value using the correlation among the attributes. It works adequately for both categorical and numeric data. It handles the missing values for classification. It increases the accuracy of classification. The proposed imputation method handles the missing values during classification is contributed because it will be suitable for real-time applications. In this article moreover directs a proper review of the missing value approach. A catalog of missing techniques which are normally recognized by the society under which missing data can happen - MCAR (Missing Completely at Random), MAR (Missing at Random), NMAR (Not Missing at Random). This paper discusses several techniques for treating missing data. It discusses numerous strategies for management of missing values there in the dataset.
Text Normalization Using Encoder–Decoder Networks Based on the Causal Feature Extractor

The encoder–decoder architecture is a well-established, effective and widely used approach in many tasks of natural language processing (NLP), among other domains. It consists of two closely-collaborating components: An encoder that transforms the input into an intermediate form, and a decoder producing the output. This paper proposes a new method for the encoder, named Causal Feature Extractor (CFE), based on three main ideas: Causal convolutions, dilatations and bidirectionality. We apply this method to text normalization, which is a ubiquitous problem that appears as the first step of many text-to-speech (TTS) systems. Given a text with symbols, the problem consists in writing the text exactly as it should be read by the TTS system. We make use of an attention-based encoder–decoder architecture using a fine-grained character-level approach rather than the usual word-level one. The proposed CFE is compared to other common encoders, such as convolutional neural networks (CNN) and long-short term memories (LSTM). Experimental results show the feasibility of CFE, achieving better results in terms of accuracy, number of parameters, convergence time, and use of an attention mechanism based on attention matrices. The obtained accuracy ranges from 83.5% to 96.8% correctly normalized sentences, depending on the dataset. Moreover, the proposed method is generic and can be applied to different types of input such as text, audio and images.
Improving neuroevolutionary transfer learning of deep recurrent neural networks through network-aware adaptation

Transfer learning entails taking an artificial neural network (ANN) that is trained on a source dataset and adapting it to a new target dataset. While this has been shown to be quite powerful, its use has generally been restricted by architectural constraints. Previously, in order to reuse and adapt an ANN's internal weights and structure, the underlying topology of the ANN being transferred across tasks must remain mostly the same while a new output layer is attached, discarding the old output layer's weights. This work introduces network-aware adaptive structure transfer learning (N-ASTL), an advancement over prior efforts to remove this restriction. N-ASTL utilizes statistical information related to the source network's topology and weight distribution in order to inform how new input and output neurons are to be integrated into the existing structure. Results show improvements over prior state-of-the-art, including the ability to transfer in challenging real-world datasets not previously possible and improved generalization over RNNs without transfer.