Reviewerly/exHarmony huggingface dataset:
Interactive Natural Language Technology for Explainable Artificial Intelligence

We have defined an interdisciplinary program for training a new generation of researchers who will be ready to leverage the use of Artificial Intelligence (AI)-based models and techniques even by non-expert users. The final goal is to make AI self-explaining and thus contribute to translating knowledge into products and services for economic and social benefit, with the support of Explainable AI systems. Moreover, our focus is on the automatic generation of interactive explanations in natural language, the preferred modality among humans, with visualization as a complementary modality.
Multiscale energy profile of maximally nonlocal quantum CHSH scenarios

The outcomes measured by Alice and Bob in quantum CHSH scenarios are collected as digital signals and decomposed into a set of trend and fluctuation subsignals. We show that the shortest scales subsignals contain the highest energy concentrations. Later, the subsignals' energy is characterized in terms of the entanglement degree. For maximally entangled qubits, the dominant energy in the signals measured locally by the observers is the fluctuation energy (ratios from 0.5 to 1), whereas in the signals considering the outputs of both parties is the trend energy (ratios from 0.5 to 0.75). The energy profile attained is a key step toward the compression of quantum signals via wavelet techniques.
High Performance Kernel Architecture for Convolutional Neural Network Acceleration

Convolutional neural networks (CNNs) have emerged as a prominent choice in artificial intelligence tasks. Recent advancements in CNN designs have greatly improved the performance and energy-efficiency of several computation-intensive applications. However, in real-time applications, greater accuracy of CNN is attained at the expense of very high computational cost and complexity. Further, the implementation of real-time CNN on embedded platforms is highly challenging due to resource and power constraints. This paper addresses the aforesaid computational complexity and presents an accelerator architecture accompanied by a novel kernel design to improve overall CNN performance. The proposed kernel design introduces a computing mechanism that reduces the data movement cost in terms of computational cycle count (latency) by parallelizing the convolution processing elements. This architecture takes advantage of the overlap of spatially adjacent data. The performance of the proposed architecture is also analyzed for multiple hyper-parameter configurations. The proposed accelerator achieves an average of [Formula: see text] improvement in reduction of execution time than the conventional computing architecture. To analyze the proposed architecture’s performance, we validate the architecture with AlexNet and VGG-16 CNN models. The proposed accelerator architecture achieves an average of [Formula: see text] throughput improvement over state-of-the-art accelerators.
An implementation of sign language alphabet hand posture recognition using geometrical features through artificial neural network (part 2)

In the sign language alphabet, several hand signs are in use. Automatic recognition of performed hand signs can facilitate the communication between hearing and none hearing people. This framework proposes hand posture recognition of the American Sign Language alphabet based on a neural network (NN) which works on geometrical feature extraction of the hand. The user's hand is captured by a 3D depth-based sensor camera. Consequently, the hand is segmented according to the depth features. The proposed system is called 'Depth-based Geometrical Sign Language Recognition' (DGSLR). The DGSLR adopted an easier hand segmentation approach, which is further used in other segmentation applications. The proposed geometrical feature extraction framework improves the accuracy of recognition due to unchangeable features against hand orientation or rotation compared to Discrete Cosine Transform (DCT) and Moment Invariant. As a support vector machine (SVM) is a type of artificial neural network (ANN), it is used to drive desired outcomes. Since there are 26 different signs in the Sign Language alphabet, a multi-class SVM versus a single SVM classifier with 26 classes by an RBF kernel was used to validate each class. The proposed framework is proficient to hand posture recognition and provides an accuracy of up to 96.78%. The findings of the iterations demonstrated that the combination of the extracted features resulted in a better accuracy rate in the recognition process in the classification step.
Prediction and Rule Generation for Leukemia using Decision Tree and Association Rule Mining

Leukemia is familiar as the cancer of the bone marrow. Bone marrow is known as the spongy tissue which creates blood cells. That's why for blood cell productivity problems, Leukemia can boost heavily. It generally impacts the leukocytes or white blood cells. And also it grows gradually in human bodies. Both children and adults can be affected by leukemia. In this research, our objective is to detect leukemia applying Machine Learning algorithms, which can help medical science to detect leukemia. Therefore, we need a system to decide to detect Leukemia. That's why we propose some well-known algorithms to compare accuracy to detect Leukemia in our study. We have used some different types of machine learning algorithms like Decision Tree, Logistic Regression and Random Forest algorithm to get the result that a patient is a cancer patient or not. We collected our data from Z H Shikder Medical College and Hospital, which includes 401 data in our dataset. Among the algorithms, the Decision tree algorithm gives the best result with 84.15% accuracy of prediction. In this paper, we show the comparative results analyzing different algorithms precision, recall and F1 score for our all data samples. We also try to extract rules from features with the help of Apriori algorithm and association rule.
Enhancing The User Experience of Portal Website using User-Centered Design Method

Many of the activities are done online nowadays. The portal website is one of the websites that the users need to visit. This makes the portal website need to meet user satisfaction. The user satisfaction can be reached by increasing the User Experience (UX) value. User-Centered Design (UCD) is a method used to develop a product by involving users in its development. Products developed using the UCD method will have a better user experience so that the users will be more comfortable using the product. The purpose of this research is to improve UX on an portal website using the UCD method. The initial value and final product value will be assessed using the User Experience Questionnaire (UEQ). The results of the study show there is an increase in the UEQ scale, attractiveness from 1.16 to 1.60, perspicuity from 1.10 to 1.54, efficiency from 1.11 to 1.42, dependability from 1.02 to 1.32, stimulation from 0.87 to 1.61 and novelty from 0.59 to 1.14 and overall are in above average or even good in one criteria. The results represent the feedback from the respondents about the portal website improvement is going better.
Skeletal Feature Compensation for Imitation Learning with Embodiment Mismatch

Learning from demonstrations in the wild (e.g. YouTube videos) is a tantalizing goal in imitation learning. However, for this goal to be achieved, imitation learning algorithms must deal with the fact that the demonstrators and learners may have bodies that differ from one another. This condition — “embodiment mismatch” — is ignored by many recent imitation learning algorithms. Our proposed imitation learning technique, SILEM (Skeletal feature compensation for Imitation Learning with Embodiment Mismatch), addresses a particular type of embodiment mismatch by introducing a learned affine transform to compensate for differences in the skeletal features obtained from the learner and expert. We create toy domains based on PyBullet's HalfCheetah and Ant to assess SILEM's benefits for this type of embodiment mismatch. We also provide qualitative and quantitative results on more realistic problems — teaching simulated humanoid agents, including Atlas from Boston Dynamics, to walk by observing human demonstrations.
Artificial Intelligence for Smart Governance; towards Jambi Smart City

Abstract The declaration of Jambi City towards smart city in 2017 has changed the trend of information technology development in Jambi. One of the famous smart city icons is SIKOJA that was launched in 2019. SIKOJA is an application that houses various e-government platforms in Jambi City. This research aims to analyse the potential use of artificial intelligence (AI) in the SIKOJA system toward Smart Governance indicators. This research used qualitative method of which the primary data were collected through observation, interview and documentations. The result of this study indicated that database in the SIKOJA integrated system is not yet maximized to develop further decision. Meanwhile, such database will give a powerful impact if it can be transformed into something, a decision, for example. SIKOJA can provide data about a sudden shortage of supply in the market without providing further decision options. SIKOJA is still focused on the information system rather than maximizing the data utilization for the next step using the AI. Finally, Jambi City Government should develop AI which can maximize the utility of the data gathered by SIKOJA integrated system.
Deep Convolutional Neural Network Based on Wavelet Transform for Super Image Resolution

Recently, the biggest change in machine learning has demonstrated its ability to solve super resolution (SR) problems. Deep learning (DL) has been successfully applied to solve such problems. One of the most commonly used categories of SR is single image super resolution (SISR) which aims to reconstruct a high-resolution (HR) image from its low resolution (LR) image. This paper presents a deep neural network DNN based on convolutional Wavelet Transform for SISR. The DWT is applied to the LR image to divide it into various frequency components. Then, our deep convolutional neural network (DCNN) is fed with only the approximated image while dismissing their details in order to remove noise. To validate the effectiveness of the proposed method, extensive experiments are performed using Set5, Set14, and urban 100 datasets. The obtained results demonstrated the efficiency of the proposed approach.
Long-Tail Hashing

Hashing, which represents data items as compact binary codes, has been becoming a more and more popular technique, e.g., for large-scale image retrieval, owing to its super fast search speed as well as its extremely economical memory consumption. However, existing hashing methods all try to learn binary codes from artificially balanced datasets which are not commonly available in real-world scenarios. In this paper, we propose Long-Tail Hashing Network (LTHNet), a novel two-stage deep hashing approach that addresses the problem of learning to hash for more realistic datasets where the data labels roughly exhibit a long-tail distribution. Specifically, the first stage is to learn relaxed embeddings of the given dataset with its long-tail characteristic taken into account via an end-to-end deep neural network; the second stage is to binarize those obtained embeddings. A critical part of LTHNet is its dynamic meta-embedding module extended with a determinantal point process which can adaptively realize visual knowledge transfer between head and tail classes, and thus enrich image representations for hashing. Our experiments have shown that LTHNet achieves dramatic performance improvements over all state-of-the-art competitors on long-tail datasets, with no or little sacrifice on balanced datasets. Further analyses reveal that while to our surprise directly manipulating class weights in the loss function has little effect, the extended dynamic meta-embedding module, the usage of cross-entropy loss instead of square loss, and the relatively small batch-size for training all contribute to LTHNet's success.
A Step Towards Sentiment Analysis of Assamese News Articles Using Lexical Features

With the growth of the internet, more and more digital content gives rise to each day, resulting in an ‘age of data’. It brings a trend for reading online news from various digitally available newspapers. Positive news spread positivity, and negative news spread negativity to our minds and our society. Since the last few decades, sentiment analysis has become a fascinating and salient area for researchers in natural language processing to understand the sentiment of the news. Therefore, it becomes necessary to classify into positive and negative polarity to measure the daily and overall news sentiment. In this paper, we aim to carry a sentiment polarity classification model by applying machine learning classifiers on low resource Assamese language using lexical features on the news domain. The baseline system works only with a bag of words without any feature-based polarity. But, our proposed model uses lexical features like adjectives, adverbs, and verbs. The proposed model has shown improvement over our baseline model in terms of F1-score on the standard data set.
Development of an underwater hand gesture recognition system

In the maritime environment, proper diver-robot communication is a bottleneck to enable collaborative work underwater. Many applications like companion or safety robots require a robust system to communicate underwater. In this paper, we propose a four-step vision-based underwater hand gesture recognition system to address this need. First, the input images are enhanced to account for the frequency-dependent loss underwater and made scale and rotation invariant. This allows the segmentation and extraction of the arm region. In a second step, a wrist line normalization is performed to extract the visible hand region. Then a hand posture detection is performed. Therefore, a new underwater posture dataset is created. Two detection algorithms are compared. A convex hull defect-based approach achieves 92.22% accuracy and a finger segmentation-based algorithm achieves an accuracy of 94.81%. In the last step, a dynamic hand gesture recognition is implemented. The presented system is based on the creation of generalizable posture parameters and can be adapted to any given posture. This makes the system easy to customize and therefore enables a wide range of new applications for human-robot interaction underwater.
A phonetic model of non-native spoken word processing

Non-native speakers show difficulties with spoken word processing.Many studies attribute these difficulties to imprecise phonological encoding of words in the lexical memory.We test an alternative hypothesis: that some of these difficulties can arise from the non-native speakers' phonetic perception.We train a computational model of phonetic learning, which has no access to phonology, on either one or two languages.We first show that the model exhibits predictable behaviors on phone-level and word-level discrimination tasks.We then test the model on a spoken word processing task, showing that phonology may not be necessary to explain some of the word processing effects observed in non-native speakers.We run an additional analysis of the model's lexical representation space, showing that the two training languages are not fully separated in that space, similarly to the languages of a bilingual human speaker.
Understanding machine learning classifier decisions in automated radiotherapy quality assurance

The complexity of generating radiotherapy treatments demands a rigorous quality assurance (QA) process to ensure patient safety and to avoid clinically significant errors. Machine learning classifiers have been explored to augment the scope and efficiency of the traditional radiotherapy treatment planning QA process. However, one important gap in relying on classifiers for QA of radiotherapy treatment plans is the lack of understanding behind a specific classifier prediction. We develop explanation methods to understand the decisions of two automated QA classifiers: (1) a region of interest (ROI) segmentation/labeling classifier, and (2) a treatment plan acceptance classifier. For each classifier, a local interpretable model-agnostic explanation (LIME) framework and a novel adaption of team-based Shapley values framework are constructed. We test these methods in datasets for two radiotherapy treatment sites (prostate and breast), and demonstrate the importance of evaluating QA classifiers using interpretable machine learning approaches. We additionally develop a notion of explanation consistency to assess classifier performance. Our explanation method allows for easy visualization and human expert assessment of classifier decisions in radiotherapy QA. Notably, we find that our team-based Shapley approach is more consistent than LIME. The ability to explain and validate automated decision-making is critical in medical treatments. This analysis allows us to conclude that both QA classifiers are moderately trustworthy and can be used to confirm expert decisions, though the current QA classifiers should not be viewed as a replacement for the human QA process.
Enhanced Representation Learning for Examination Papers with Hierarchical Document Structure

Representation learning of examination papers is the cornerstone of the Examination Paper Analysis (EPA) in education area including Paper Difficulty Prediction (PDR) and Finding Similar Papers (FSP). Previous works mainly focus on the representation learning of each test item, but few works notice the hierarchical document structure in examination papers. To this end, in this paper, we propose a novel Examination Organization Encoder (EOE) to learn a robust representation of the examination paper with the hierarchical document structure. Specifically, we first propose a syntax parser to recover the hierarchical document structure and convert an examination paper to an Examination Organization Tree (EOT), where the test items are the leaf nodes and the internal nodes are summarization of their child nodes. Then, we applied a two-layer GRU-based module to obtain the representation of each leaf node. After that, we design a subtree encoder module to aggregate the representation of each leaf node, which is used to calculate an embedding for each layer in the EOT. Finally, we feed all the layer embedding into an output module, the process is over and we get the examination paper representation that can be used for downstream tasks. Extensive experiments on real-world data demonstrate the effectiveness and interpretability of our method.
Survey on Differences of Requirements Engineering for Traditional and Agile Development Processes

Traditional and Agile are two different software development approaches, where recently the Agile have attracted the attention of the software development industry due to its capability to overcome the limitations of the traditional software development approaches and to cope with increasing complexity in system development. This paper presents a literature review that is focusing on requirements engineering in both traditional and Agile development processes with a discussion of their existing challenges. In addition, it illustrates how Agile approaches can help to overcome the challenges of managing requirements in traditional methods. The paper also shows the importance of activity planning of requirements engineering in improving the product quality, cost, and scheduling. Finally, the paper points out some drawbacks of Agile practices and available solutions.
Intrinsic Plagiarism Detection System Using Stylometric Features and DBSCAN

Plagiarism is the act of using someone else’s words or ideas without giving them due credit and representing it as one’s own work. In today's world, it is very easy to plagiarize others' work due to advancement in technology, especially by the use of the Internet or other offline sources such as books or magazines. Plagiarism can be classified into two broad categories on the basis of detection namely extrinsic and intrinsic plagiarism. Extrinsic plagiarism detection refers to detecting plagiarism in a document by comparing it against a given reference dataset, whereas, Intrinsic plagiarism detection refers to detecting plagiarism with the help of variation in writing styles without using any reference corpus. Although there are many approaches which can be adopted to detect extrinsic plagiarism, few are available for intrinsic plagiarism detection. In this paper, a simplified approach is proposed for developing an intrinsic plagiarism detector which is helpful in detecting plagiarism even when no reference corpus is available. The approach deals with development of an intrinsic plagiarism detection system by identifying the writing style of authors in the document using stylometric features and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering. The proposed system has an easy to use interactive interface where user has to upload a text document to be checked for plagiarism and the result is displayed on the web page itself. In addition, the user can also see the analysis of the document in the form of graphs.
Weld Seam Tracking Based on Laser Imaging Binary Image Preprocessing

In the current welding seam tracking research, the laser tracking system of welding seam in the v-shaped welding process, the camera's anti-interference ability is low in the process of image acquisition, image noise is too much, and so on. A welding seam tracking method of laser imaging binary image preprocessing is proposed. The denoising point method of open operation and OSTU algorithm were used to obtain image binarization threshold and generate binarization image. MATLAB programming was used to obtain the binary image of the laser structural light obtained by the camera, and the image was further de-noised by corrosion and expansion operations to obtain the accurate data of the weld. The laser imaging features in the V-shaped weld image were enhanced to further improve the identification accuracy of using the laser vision triangulation method, as well as the tracking accuracy of the weld during the process. The error could be controlled within 0.5mm.
A People Counting System for Use in CCTV Cameras in Retail

This paper focuses on the feasibility of implementing a vision-based people counting system using footage from an existing surveillance camera in a restaurant establishment. The main challenge is to do so given the unique fixed viewpoint of the camera, which is optimized for security instead of data analytics. A three-step approach, namely people detection, tracking, and then people counting, is employed in creating the system. Neural networks such as YOLOv3 and Deep SORT are used. The proponents then partnered with a retail establishment in a high-traffic business district, to test the system. The results show that it is possible to achieve an accuracy of 82.76% for days when the restaurant waiting area is not crowded. The system also achieved an overall accuracy of 66.17% over five days of extensive testing, which includes extreme conditions wherein people in the video are densely packed and occluded. However, the system performance and accuracy can still be improved through downsizing the frames, retraining the models, and exploring other models.