Reviewerly/exHarmony huggingface dataset:
CSTA-2P1D UNet: Consecutive Spatio-Temporal Attention for Multi-Scale 3D Pancreas Segmentation

Accurate pancreas segmentation is a crucial task for the diagnosis of diseases as well as for developing modern surgical tools. In this paper, a novel architecture, named Consecutive Spatio-Temporal Attention (CSTA) 2 Plus 1 Dimensional (2P1D) UNet, is proposed for the volumetric segmentation of the pancreas in CT images. CSTA mechanism is inspired by recent advancements in Squeeze & Excitation (SE) module. It recalibrates feature maps, by multiplying learnable spatial and channel-wise attention maps, sequentially. Further, incorporation of Multi-Sale Output (MSO) and Group Normalization (GN) improves the localization performance and training stability of the model, respectively. Moreover, 2P1D convolution is introduced to enhance the segmentation results of our proposed architecture. Smoothed Dice Loss (SDL) is used as a loss function for training of the proposed model. Collectively, a state-of-the-art performance of 83.70%, 87.70%, and 85.60% on dice score, sensitivity, and precision is achieved, verifying the model's effectiveness for the pancreas segmentation.
Sec-IoT: A Framework for Secured Decentralised IoT Using Blockchain-Based Technology

Blockchain technology has been used recently as a secure method for authenticating digital information in many applications. Inspired by the success of the technology, we envision the potential of the blockchain for secured communication in a decentralised Internet of things (IoT). In this paper, we envisage a framework for a secured IoT and describe the infrastructure and mechanism of the entire system. Also, we provide solutions to overcome some of the limitations of blockchain technology including miner selection and reaching consensus, for a decentralised IoT by incorporating a learning-to-rank method for node selection. We also contemplate using hybrid consensus algorithm in the blockchain to detect faulty node and to improve the node convergence.
Using a 360° camera as a mobile data collection method towards understanding information types and use in running

Abstract This paper presents a mobile data collection method using a 360° camera developed in an ongoing qualitative doctoral research project. The method captures audio, participant action and the running environment visuals of a situation in an immersive way compared to traditional audio‐video capture. The method contributes towards an understanding of information types, use and value amongst those who run. This paper describes the mobile method, its research context and provides an insight to how the method has contributed to developing findings about information types and use.
Euclidean Distance based Loss Function for Eye-Gaze Estimation

The Loss function is an integral component in a Neural network. It affects the performance of CNN network in its classification. In this paper, we propose a Euclidean distance based Loss function for the CNN model, in an eye-gaze memory card game. We compared the Euclidean distance loss function with the well-known cross-entropy loss function. The performance parameters used in our comparison are prediction accuracy and average Euclidean distance prediction error. The results show that cross-entropy has better prediction accuracy. However, the Euclidean distance loss function provides a better average Euclidean distance prediction error resulting in better user experience. This is because the wrongly predicted eye gaze cards are near to the user intended card. In the case of cross-entropy, the predicted card error is quite evenly spread across the screen.
On the Use of FPGAs to Implement CNNs: A Brief Review

Convolutional Neural Network (CNN) is a subclass of deep neural network that has gained popularity in recent years. CNN has revolutionized the execution of tasks such as natural language processing, image classification, and voice recognition. However, the performance of CNNs is often limited by the hardware available for training large sets of data. Graphical Processing Units (GPUs) have been shown to achieve good performance with CNN-based applications, however, GPU is expensive and is not suitable for all applications. In recent years, and for various reasons researchers have shifted their focus to Field Programmable Gate Arrays (FPGAs) and even other edge devices like microcontrollers to execute CNN models. This paper provides a survey of a number of applications where FPGAs are used in the implementation of various CNN-based models. The survey provides the reader with a compact and informative insight into recent efforts in this domain.
Evaluating Length of a Shortest Adaptive Homing Sequence for Weakly Initialized FSMs

There are many research papers devoted to the state identification problem of finite state machines (FSMs) which are widely used for analysis of discrete event systems. A deterministic complete reduced FSM always has a homing sequence (HS) but a number of non-deterministic FSMs not necessarily have a preset HS; nevertheless, those FSMs can still have an adaptive HS. Moreover, adaptive HS can be shorter than the preset. If each state can be an initial state of a complete FSM, i.e., an FSM is non-initialized, then a shortest adaptive HS (if it exists) has polynomial length in the number of FSM states. We show that it is not the case for weakly-initialized FSMs: such FSMs can have a shortest adaptive HS of exponential length in the number of FSM states / transitions. However, the experimental evaluation shows that the non-polynomial upper bound for a shortest adaptive HS was never reached for randomly generated weakly-initialized two-input FSMs.
M-Cypher

Graph databases witness the rise of Graph Query Language (GQL) in recent years, which enables non-programmers to express a graph query. However, the current solution does not support motif-related queries on knowledge graphs, which are proven important in many real-world scenarios. In this paper, we propose a GQL framework for mining knowledge graphs, named M-Cypher. It supports motif-related graph queries in an effective, efficient and user-friendly manner. We demonstrate the usage of the system by the emerging Covid-19 knowledge graph analytic tasks.
Effect of Coding Styles in Detection of Web Application Vulnerabilities

Web application security has become paramount for the organisation's operation, and therefore, static analysis tools (SAT) for vulnerability detection have been widely researched in the last years. Nevertheless, SATs often generate errors (false positives & negatives), whose cause is recurrently associated with very diverse coding styles, i.e., similar functionality is implemented in distinct manners, and programming practices that create ambiguity, such as the reuse and share of variables. The paper presents an analysis of SAT's behaviour and results when they process various relevant web applications coded with different coding styles. Furthermore, it discusses if the SQL injection vulnerabilities detected by SATs as true positives are really exploitable. Our results demonstrate that SATs are built having in mind how to detect specific vulnerabilities, without considering such forms of programming. They call to action for a new generation of SATs that are highly malleable to be capable of processing the codes observed in the wild.
Learning to Re-Rank with Contextualized Stopwords

The use of stopwords has been thoroughly studied in traditional Information Retrieval systems, but remains unexplored in the context of neural models. Neural re-ranking models take the full text of both the query and document into account. Naturally, removing tokens that do not carry relevance information provides us with an opportunity to improve the effectiveness by reducing noise and lower document representation caching-storage requirements. In this work we propose a novel contextualized stopword detection mechanism for neural re-ranking models. This mechanism consists of training a sparse vector in order to filter out document tokens from the ranking decision. This vector is learned end-to-end based on the contextualized document representations, allowing the model to filter terms on a per occurrence basis. This leads to a more explainable model, as it reduces noise. We integrate our component into the state-of-the-art interaction-based TK neural re-ranking model. Our experiments on the MS MARCO passage collection and queries from the TREC 2019 Deep Learning Track show that filtering out traditional stopwords prior to the neural model reduces its effectiveness, while learning to filter out contextualized representations improves it.
Bengali Question Answering System Using Seq2Seq Learning Based on General Knowledge Dataset

Context-based QA system is a leading research area in NLP. An automatic QA system that can respond to the answer, and which is related to the given context. A deep learning-based model provides a more factual result for today's QA system. Here we introduce a deep learning-based Seq2Seq model for Bengali context-based QA system using general knowledge dataset. Where context and question are part of the encoder and related answer is part of the decoder. All automatic system can discern any language by machine translation. Sequence wise learning is a good solution for those types of automatic system learning. Input tokens are encoded by the encoder and output tokens are decoded by the decoder. Each sequence is stored in LSTM cell that maintains the sequence of input and output. Most of the AI system is developed in different languages. Compared with other languages the Bengali language needs to expand research this field. The major perspective of this research to develop an AI based QA system for the Bengali language. For experiment total, two thousand Bengali general knowledge data is used that also provides a dataset for Bengali QA system. In the dataset, the context contains the main feature for the question. After training our model it gives 99 % accuracy for this dataset and 89% accuracy for validation. The trained model gives a good response in answer prediction.
A Spanish Corpus for Talking to the Elderly

In this work, a Spanish corpus that was developed, within the EMPATHIC project ( http://www.empathic-project.eu/ ) framework, is presented. It was designed for building a dialogue system capable of talking to elderly people and promoting healthy habits, through a coaching model. The corpus, that comprises audio, video an text channels, was acquired by using a Wizard of Oz strategy. It was annotated in terms of different labels according to the different models that are needed in a dialogue system, including an emotion based annotation that will be used to generate empathetic system reactions. The annotation at different levels along with the employed procedure are described and analysed.
GIS Mechanical Fault Diagnosis and Test Analysis Based on S Transform-SVM-AlexNet Model

To effectively test Gas Insulated Switchgear (GIS) mechanical faults, test simulation systems of three kinds of that were installed. Vibration signals were measured by the opening and closing operation of the GIS circuit breaker as an excitation source and a GIS mechanical fault diagnosis method based on S Transform-SVM-AlexNet Model was proposed. Vibration signals were processed by S Transform and the time-frequency diagram containing equipment characteristics was obtained. The S Transform-SVM-AlexNet Model was established and the pre-trained AlexNet Neural Network Model was used to extract S Transform image features as predictive variables. Support Vector Machine (SVM) was fitted for image pre-classification. According to the classification result of the fuzzy matrix, effective measuring points were screened out. Time-frequency diagrams of effective measuring points were sent into AlexNet Model for transfer learning, the fine-tuned neural network model obtained, which can realize the effective diagnosis of GIS mechanical faults.
Shapley Values and Meta-Explanations for Probabilistic Graphical Model Inference

Probabilistic graphical models, such as Markov random fields (MRF), exploit dependencies among random variables to model a rich family of joint probability distributions. Inference algorithms, such as belief propagation (BP), can effectively compute the marginal posteriors for decision making. Nonetheless, inferences involve sophisticated probability calculations and are difficult for humans to interpret. Among all existing explanation methods for MRFs, no method is designed for fair attributions of an inference outcome to elements on the MRF where the inference takes place. Shapley values provide rigorous attributions but so far have not been studied on MRFs. We thus define Shapley values for MRFs to capture both probabilistic and topological contributions of the variables on MRFs. We theoretically characterize the new definition regarding independence, equal contribution, additivity, and submodularity. As brute-force computation of the Shapley values is challenging, we propose GraphShapley, an approximation algorithm that exploits the decomposability of Shapley values, the structure of MRFs, and the iterative nature of BP inference to speed up the computation. In practice, we propose meta-explanations to explain the Shapley values and make them more accessible and trustworthy to human users. On four synthetic and nine real-world MRFs, we demonstrate that GraphShapley generates sensible and practical explanations.
Named Entity Recognition in Judicial Field Based on BERT-BiLSTM-CRF Model

The recognition of named entity in judicial documents is the key to realize automatic trial, and how to effectively distinguish the entities from text is the focus of this paper. However, in special fields, such as the judicial field, many experiments show that the artificial features selection based on domain knowledge have a great influence on the results of the neural network models. Therefore, how to obtain a better named entity recognition performance in judicial field without relying on artificial features is a problem to be solved. In this paper, we propose a neural network model based on BERT-BiLSTM-CRF. Firstly, we use the BERT pre-trained language model to generate the word vectors according to the context of the words, enhance the semantic representation of words, then the word vector sequence is input into BiLSTM-CRF for training. Experiments show that our method is effective, at the same time, it solves the problem that the traditional word vector representation maps the word into a single vector and cannot characterize the ambiguity of words.
Assessing the Usability of Truck Hiring Mobile Applications in Bangladesh Using Heuristic and Semiotic Evaluation

The digital truck hiring systems facilitate the transfer of goods effectively and efficiently, and greatly impact the performance of the logistics industry of any country including Bangladesh. Thus, a digital truck hiring system needs to be usable and accessible to users irrespective of their level of familiarity with digital systems. Earlier researches did not focus to design, develop, and evaluate such applications for making them usable, intuitive, and accessible to end-users. Therefore, the objectives of this research are to assess the usability of truck hiring applications and to provide possible design recommendations for enhancing their usability. To attain these objectives, the usability of three truck hiring applications (DigiTruck, Truck Lagbe, and Trux24) currently operating in Bangladesh was evaluated through heuristic evaluation and semiotic evaluation techniques. As outcomes of heuristic evaluation, a noticeable number of usability problems were found in each application primarily related to maintaining consistency, flexibility, conventions, and error management, while the severity levels of these problems were ranged from cosmetic (level 1) to major (level 3). The semiotic evaluation depicted the intuitiveness of the interface elements of the selected applications. Finally, a set of design recommendations and implications for practitioners are presented to design and develop such applications with enhanced usability and user experience.
Retrieval of Particle Size of Natural Granite From Multiangular Bidirectional Reflectance Spectra Using the Hapke Model (June 2020)

Quantitative determination of the physical properties of natural granite has been attempted from remotely sensed information, for which the Hapke model is a popular method. However, using the model to retrieve the photometric properties of terrestrial rocks (slab or particulate samples), especially for those with complex surface conditions such as natural granite, remains a challenge. In this study, we have approached the dilemma by coupling both radiative transfer (Hapke’s isotropic multiple scattering approximation (IMSA) model) and an empirical relationship between particle sizes with its critical parameter, the single-scattering albedo (SSA, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\omega$ </tex-math></inline-formula> ), determined from bidirectional reflectance (BDR) measurements. The results clearly indicated that the particle size of natural granite systematically controlled the BDR, which can be well fit by the Hapke model with varying parameters. The retrieved photometric parameters of the coefficients in the phase function ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$b$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$c$ </tex-math></inline-formula> ) can effectively indicate the scattering behavior of natural granite, but the variations in their values did not strongly correlate with the change in particle sizes. Instead, a good linear relationship between the SSA values and particle sizes has been established. By coupling the relationship into the Hapke model, we found a practical approach to estimate the particle size for measured samples from inversely retrieved SSA. Through this method, we are able to retrieve the physical properties of granite under natural surface conditions, and we foresee that the approach will be widely used in the future.
"Keep it Simple, Lazy" -- MetaLazy: A New MetaStrategy for Lazy Text Classification

Recent advances in text-related tasks on the Web, such as text (topic) classification and sentiment analysis, have been made possible by exploiting mostly the "rule of more": more data (massive amounts) more computing power, more complex solutions. We propose a shift in the paradigm to do "more with less" by focusing, at maximum extent, just on the task at hand (e.g., classify a single test instance). Accordingly, we propose MetaLazy, a new supervised lazy text classification meta-strategy that greatly extends the scope of lazy solutions. Lazy classifiers postpone the creation of a classification model until a given test instance for decision making is given. MetaLazy exploits new ideas and solutions, which have in common their lazy nature, producing altogether a solution for text classification, which is simpler, more efficient, and less data demanding than new alternatives. It extends and evolves the lazy creation of the model for the test instance by allowing: (i) to dynamically choose the best classifier for the task; (ii) the exploration of distances in the neighborhood of the test document when learning a classification model, thus diminishing the importance of irrelevant training instances; and (iii) a better representational space for training and test documents by augmenting them, in a lazy fashion, with new co-occurrence based features considering just those observed in the specific test instance. In a sizeable experimental evaluation, considering topics and sentiment analysis datasets and nine baselines, we show that our MetaLazy instantiations are among the top performers in most situations, even when compared to state-of-the-art deep learning classifiers such as Deep Network Transformer Architectures.
Blockchain framework for real-time streaming data generated in image sensor networks for smart monitoring

The smart city concept is attracting increasing attention from society. Smart monitoring, which enables the detection and prevention of road traffic accidents, is one promising application of the smart city concept. The deployment of three-dimensional (3D) image sensor networks formed by light detection and ranging (LIDAR) devices interconnected via a network is a key enabler for smart monitoring. Data collected by image sensor networks for smart monitoring are sensitive because the usage of the data is often related to public safety or law enforcement. Managing sensor data using the blockchain technology is one way to address these sensitivity concerns, as a blockchain network helps prevent data from being tampered with even by the administrators of the system. However, prior works have not considered how to handle streaming data such as image sensor data generated by LIDAR devices in real-time, which means there is a risk of overflow in the network if the data are handled frame by frame. In response to this issue, we propose a blockchain framework for real-time monitoring in a smart city using image sensor networks. Our key concept with this framework is to aggregate hash values converted from multiple frames of image sensor data into one hash value. The proposed framework reduces the number of data ‘writes’ on a blockchain network, thus preventing any overflow. Our framework also enables the estimation of the optimal number of aggregated hash values that minimizes delay while avoiding overflow. Measurements taken in actual environments using a real-world LIDAR dataset demonstrated the effectiveness of the proposed framework.
Incremental Learning Algorithm of Data Complexity Based on KNN Classifier

In practice, new data are constantly generated, and the existing data complexity algorithms are based on the idea of batch learning. In the face of the dynamic increase of data scale, how to measure the characteristic information of data has become an urgent problem to be solved in the field of data mining. This paper focuses on this problem and further studies its incremental learning function on the basis of in-depth discussion of data complexity proposed by TK Ho et al. Among them, N3 and N4 are the complexity indexes based on KNN classifier (K=1). In this paper, the incremental learning algorithm I1NN was proposed on the basis of 1-NN classifier, and its feasibility and validity were verified on both the artificial data set and the UCI public data set.
Spoken Language Identification with Deep Temporal Neural Network and Multi-levels Discriminative Cues

The language cue is an important component in the task of spoken language identification (LID). But it will take a lot of time to align language cue to speech segment by the manual annotation of professional linguists. Instead of annotating the linguistic phonemes, we use the cooccurrence in speech utterances to find the underlying phoneme-like speech units by unsupervised means. Then, we model phonotactic constraint on the set of phoneme-like units for finding the larger speech segments called the suprasegmental phonemes, and extract the multi-levels language cues from them, including phonetic, phonotactic and prosodic. Further, a novel LID system is proposed based on the architecture of TDNN followed by LSTM-RNN. The proposed LID system is built and compared with the acoustic feature based methods and the phonetic feature based methods on the task of NIST LRE07 and Arabic dialect identification. The experimental results show that our LID system helps to capture robust discriminative information for short duration language identification and high accuracy for dialect identification.