Reviewerly/exHarmony huggingface dataset:
Smart Medication Box Based on Android Mobile Application

Medication consumption is an important thing that must be done by patients. Taking medication needs to be on schedule. Problems that often occur when taking drugs by patients are inappropriate consumption schedules and even missed the schedule. This study creates an IoT-based medication schedule reminder box. This system uses microcontroller of NodeMCU esp8266 to process data and send data to MySQL database via internet connection. Data from the database is accessed using an android-based application. This system has an actuator in the form of a DC motor to drive the conveyor. Conveyor serves as a medium to deliver drugs from the drug storage to the drug tray. The results of the DC motor rotational speed test have an average rotation of 51.72 RPM. This system uses input in the form of two infrared sensors. The infrared sensor A has a function to detect drug availability and determine the status of the drug that has been taken by the user when it reaches the drug tray. The infrared sensor B serves to detect the availability of drugs in the drug tray from the belt conveyor. The average lag time for data synchronization between hardware to database, and from database to android application is 0.82 seconds. This android application consists of a monitoring menu and a parameter input menu from the user.
Dynamic Key-Value Memory Networks based on Concept Structure for Knowledge Tracing

Knowledge tracing (KT) is a popular research topic in adaptive personalized assisted learning. In recent years, a Deep Knowledge Tracing (DKT) model based on recurrent neural networks has emerged based on the development of big data-driven and deep learning. However, it is impossible to specify which specific concepts students are proficient in the DKT model, so a deep knowledge tracing model based on a dynamic key-value memory network (DKVMN) emerges, which uses a static key matrix to store knowledge concepts and a dynamic value matrix to store the mastery of corresponding concepts. Although the DKVMN model explicitly singles out concepts for individual processing, it does not consider the association relationship between concepts. Even though it can mine the potential association, we think it is far from enough, so we propose a DKVMN model based on concept structure (DKVMN-CS), which introduces the concept association relationship a priori knowledge through concept structure graph, acting on both the static matrix of stored concepts and the weight calculation of the value matrix. Experiments show that our proposed DKVMN-CS model has a significant improvement in performance metrics compared to mainstream deep knowledge tracking models such as DKVMN.
Weak-Supervision for Prolonged Hospital Length of Stay Prediction

Predicting whether a patient will have a prolonged length of stay (LoS) once admitted to a hospital can help ensure medical resources are allocated to where they are needed most. However, prior works on classifying prolonged-LoS patients define a prolonged-LoS as being greater than a single, flat number-of-days cutoff. Using a flat cutoff, means that the classification occurs without reference to a baseline LoS, fails to control for any covariates, and is generally only effective for a specific medical subgroup. Instead, in this work, we introduce an approach where the algorithm designer specifies a LoS percentile that should be used as the cutoff for prolonged-LoS. In a method known as weak-supervision, we use the LoS percentile cutoff to train a model to produce the actual labels for classification machine learning training. Contrary to a number-of-days cutoff, the LoS percentile cutoff coupled with weak-supervision, provides what we claim is a more principled and flexible approach to defining what constitutes a prolonged-LoS.Specifically, we train a quantile regression model to predict the designated LoS percentile value for each patient, which importantly allows us to control for covariates that access to medical care should be equalized across (such as primary medical condition, hospital facility, and admission time of day). The regression output is cast as a noisy binary label for prolonged-LoS, which is then used to train a machine learning model for prolonged-LoS classification. We empirically demonstrate that this weak-supervision based approach provides usable classification performance despite using noisy labels.
MRC-Based Medical NER with Multi-task Learning and Multi-strategies

Medical named entity recognition (NER), a fundamental task of medical information extraction, is crucial for medical knowledge graph construction, medical question answering, and automatic medical record analysis, etc. Compared with named entities (NEs) in general domain, medical named entities are usually more complex and prone to be nested. To cope with both flat NEs and nested NEs, we propose a MRC-based approach with multi-task learning and multi-strategies. NER can be treated as a sequence labeling (SL) task or a span boundary detection (SBD) task. We integrate MRC-CRF model for SL and MRC-Biaffine model for SBD into the multi-task learning architecture, and select the more efficient MRC-CRF as the final decoder. To further improve the model, we employ multi-strategies, including adaptive pre-training, adversarial training, and model stacking with cross validation. Experiments on both nested NER corpus CMeEE and flat NER corpus CCKS2019 show the effectiveness of the MRC-based model with multi-task learning and multi-strategies.
Learning Deterministic One-Clock Timed Automata via Mutation Testing

In active learning, an equivalence oracle is supposed to answer whether a hypothesis model is equivalent to the system under learning. Its implementation in real applications is considered a major bottleneck for active automata learning. The problem is especially difficult in the context of learning timed automata due to the infinitely large state space involved. In this paper, following the framework of combining mutation analysis and random testing, we propose an implementation of equivalence oracle in the context of learning deterministic one-clock timed automata (DOTAs). This includes two learning-friendly mutation operators, a heuristic test-case generation method, and a score-based test-case selection method. We implemented a prototype applying our approach by extending an existing tool on active learning of DOTAs and conducted extensive experiments. The results indicate that our method improves upon existing methods on the rate of learning correct models, the number of test cases required, and accumulated delay time in test cases.
INTEGRATION WITH THE SOFTWARE INTERFACE OF THE COM SERVER FOR AUTHORIZED USER

The article is devoted to the development of a software controller for automation of access to tools and object model of the multifunctional graphic editor Adobe Photoshop. The work of the graphic editor is initiated in the form of a COM object, which contains methods available to the software controller through the COM interface, which allows the software to use the functionality of the editor. To restrict unauthorized access, a software authorization control protocol is proposed, which is based on the use of binding to the computer hardware and encryption by a 128-bit MD5 public key hashing algorithm.
Text Summarization using NLP Technique

The text data online is increasing massively; hence, producing a summarized text document is essential. We can create the summarization of multiple text documents either manually or automatically. A manual approach may be tedious and a time-consuming process. The resulting composition may not be accurate when processing lengthy articles; hence the second approach, i.e., the automated summary generation process, is essential. Training machine learning models using these processes makes space and time-efficient summary generation possible. There are two widely used methods to generate summaries, namely, Extractive summarization and abstractive summarization. The extractive technique scans the original document to find the relevant sentences and extracts only that information from it. The abstractive summarization technique interprets the original text before generating the summary. This process is more complicated, and transformer architecture-based pre-trained models are used for comparing the text & developing the outline. This research analysis uses the BBC news dataset to evaluate and compare the results obtained from the machine learning models.
A Novel Image-Based Homomorphic Approach for Preserving the Privacy of Autonomous Vehicles Connected to the Cloud

Autonomous vehicles are taking a leap forward by performing operations without human intervention through continuous monitoring of their surroundings using multiple sensors. Images gathered through vehicle mounted cameras can be large, requiring specialized storage such as cloud. However, cloud data centres can be prone to security and privacy challenges. A partial image-based, homomorphic searchable encryption scheme is proposed, which uses pixel-level encryption to identify objects within encrypted images. The scheme provides Object-Trapdoor and Trapdoor-Image indistinguishability – as the trapdoors are probabilistic. The proposed scheme is deployed on a cloud data centre and tested over a real data set. The proposed scheme reduces storage overhead by approximately 20 times, and is 33 times more efficient compared to the generic Paillier homomorphic searchable encryption scheme. Security analysis demonstrates that the scheme maintains high levels of security and privacy.
Immersion Measurement in Watching Videos Using Eye-tracking Data

Immersion plays a crucial role in video watching, leading viewers to a positive experience, such as increased engagement and decreased fatigue. However, few studies measure immersion while watching videos, and questionnaires are typically used in the measurement of immersion for other applications. These methods may rely on the viewer's memory and cause biased results. Therefore, we propose an objective immersion detection model by leveraging people's gaze behavior while watching videos. In a lab study with 30 participants, an in-depth analysis is carried out on a number of gaze features and machine learning (ML) models to identify the immersion state. Several gaze features are highly indicative of immersion and ML models with these features are able to detect an immersion state of video watchers. Post-hoc interviews demonstrate that our approach is applicable to measure immersion in the middle of watching a video, where some practical issues are discussed as well.
Discovering High Utility Episodes in Sequences

Sequence data are more commonly seen than other types of data (e.g., transaction data) in real-world applications. For the mining task from sequence data, several problems have been formulated, such as sequential pattern mining, episode mining, and sequential rule mining. As one of the fundamental problems, episode mining has often been studied. The common wisdom is that discovering frequent episodes is not useful enough. In this article, we propose an efficient utility mining approach, namely, UMEpi: utility mining of high-utility episodes from complex event sequences. We propose the concept of remaining utility of episodes and achieve a tighter upper bound, namely, episode-weighted utilization (EWU), which will provide better pruning. Thus, the optimized EWU-based pruning strategies can achieve better improvements in mining efficiency. The search space of UMEpi w.r.t. a prefix-based lexicographic sequence tree is spanned and determined recursively for mining high-utility episodes, by prefix-spanning in a depth-first way. Finally, extensive experiments on four real-life datasets demonstrate that UMEpi can discover the complete high-utility episodes from complex event sequences. Furthermore, the improved variants of UMEpi significantly outperform the baseline in terms of execution time, memory consumption, and scalability.
Dear Design Journal: A Visual Journey and a Reflective Account of a Practice-Led Doctoral Research

A pictorial about design journaling as a critical documentation method for doctoral practice-led re- search. It presents various tools and methods that support the researcher’s reflexivity, illustrated in the author’s analysis of his journaling work. It concludes on the importance of journaling to identify new insights and generate theory based on capturing the researcher’s reflection.
Training Multilingual and Adversarial Attack-Robust Models for Hate Detection on Social Media

Social media provide plenty of textual information in various languages. This information can contain or provoke hatred towards different social or religious groups. In this paper, we study methods to process short text messages in English, Hindi, and Russian and identify such intolerance with cross-lingual Transformer models. Moreover, these models can be easily adapted to analyze other languages. We fine-tuned these models with several training techniques to build accurate hate speech detectors that are robust to adversarial attacks. Additional preprocessing was carried out for all datasets to improve the quality of model training. Also, for one of the training datasets, we applied the text attack algorithm that replaces some words with synonyms. For some languages, such an attack can greatly reduce the quality of the model. Experiment results show that mixing adversarial examples to a training dataset and combining deep models to randomized ensembles allows not only to reduce test error on attacked data for languages from the dataset (Hindi, Russian) but also to achieve better accuracy in other languages.
Towards Regression-Free Neural Networks for Diverse Compute Platforms

Our work tackles the emergent problem of reducing predictive inconsistencies arising as negative flips: test samples that are correctly predicted by a less accurate model, but incorrectly by a more accurate one. We introduce REGression constrained Neural Architecture Search (REG-NAS) to design a family of highly accurate models that engender fewer negative flips. REG-NAS consists of two components: (1) A novel architecture constraint that enables a larger model to contain all the weights of the smaller one thus maximizing weight sharing. This idea stems from our observation that larger weight sharing among networks leads to similar sample-wise predictions and results in fewer negative flips; (2) A novel search reward that incorporates both Top-1 accuracy and negative flips in the architecture search metric. We demonstrate that REG-NAS can successfully find desirable architectures with few negative flips in three popular architecture search spaces. Compared to the existing state-of-the-art approach [35], REG-NAS enables 33–48 $$\%$$ relative reduction of negative flips.
Zero-Shot Learning for Reflection Removal of Single 360-Degree Image

The existing methods for reflection removal mainly focus on removing blurry and weak reflection artifacts and thus often fail to work with severe and strong reflection artifacts. However, in many cases, real reflection artifacts are sharp and intensive enough such that even humans cannot completely distinguish between the transmitted and reflected scenes. In this paper, we attempt to remove such challenging reflection artifacts using 360-Degree images. We adopt the zero-shot learning scheme to avoid the burden of collecting paired data for supervised learning and the domain gap between different datasets. We first search for the reference image of the reflected scene in a 360-degree image based on the reflection geometry, which is then used to guide the network to restore the faithful colors of the reflection image. We collect 30 test 360-Degree images exhibiting challenging reflection artifacts and demonstrate that the proposed method outperforms the existing state-of-the-art methods on 360-Degree images.
Time-Interleaved Noise Jamming

Modern jamming systems are faced with the reality that multiple threat radars will be encountered simultaneously, and potentially, all simultaneously-encountered threat radars will need to be countered to prevent detection.Modern jammer systems are capable of extremely rapid reconfiguration, allowing them to counter multiple simultaneous threats in a time-interleaved manner.However, simultaneously-encountered threats cause jamming pulses to coincide in time, leading to the problem of deciding which of the threats to jam when such coincidences occur.An approach to determining the relative priority of jammer pulses is proposed and evaluated to show that time-interleaved jamming is a viable approach to effectively countering multiple simultaneous threats.
An Improved Anchor-Free Object Detection Method

Object detection plays an important role in various industries. However, a fully convolutional one-stage detector (FCOS) has high computational cost and low detection accuracy. Therefore, in this paper, we fused the attention module (CBAM) with feature pyramid (FPN) to extract important feature information, suppress useless information and improve detection accuracy. Finally, we use inverted residual convolution block to replace the detection head of the original method, and the improved detection head reduces the calculation cost and amount of calculation. We use PASCAL VOC to train and evaluate our network. Experimental results show that, compared with the traditional method, the detection accuracy is improved by 1.2%, and the number of parameters is reduced by 4.2m.
optimizing Diffusion Layer with AVX Shuffling: A Study on SKINNY

The development of the Internet of Things (IoT) requires efficient information transmission among resource-limited devices. Designing lightweight block ciphers with high software and hardware efficiency becomes a research hotspot. The design of block ciphers needs to follow the principles of diffusion and confusion. Nonlinear sboxes are broadly used in confusion layers. In contrast, linear bit/byte permutations or diffusion matrices are generally used in the diffusion layer. This paper designs a lightweight diffusion layer for iterative lightweight 8-bit sboxes. The fast shuffling operations of AVX instructions are used in block cipher designs. In addition, the hardware and software performances are related to the number of XORs for the diffusion matrix operation. For different sboxes, the selection of the optimal diffusion layer is diverse. The main idea is to replace the ShiftRows of the SKINNY with a byte permutation and design a diffusion matrix. We use the MILP method to search the differential and linear paths of the modified SKINNY. Our scheme can improve the lower bounds on the number in both differential and linear active sboxes. Moreover, software performance is also improved by using AVX512 instructions.
Characterizing UX Evaluation in Software Modeling Tools: A Literature Review

Model-Based Software Engineering (MBSE) has a high potential to play a critical role in the whole process of software engineering, bringing many benefits to all stakeholders, yet it is not used by most software developers today, due to both lack of tool capabilities and poor user experience (UX) of tools. This study aims to understand the evaluation types and methods applied by researchers when studying UX in modeling tools (modeling experience or MX) and the types of issues uncovered in these studies. We conducted a literature review using a snowballing approach to gather all studies of this topic. A total of 41 research papers were reviewed. Data extraction was performed based on research questions and a categorization of discussed issues was presented. Several gaps and future opportunities were identified and discussed, which include 1) utilizing interview method in research design; 2) distributing testing tasks based on user profiles; 3) involving UX experts in analysis; 4) scalability testing using large models; 5) assessing MX in areas other than just usability and utility; 6) considering collaborative modeling as an important factor contributing to MX; 7) considering both language issues and tool issues in UX evaluation of software modeling tools. 8) improving the taxonomy of MX challenges; 9) triangulating using multiple methods; and 10) developing and validating MX tool design heuristics.
Dramatic Situations for Emergent Narrative System Authorship

When designing and developing an emergent narrative system, one finds themselves in the difficult situation of working with low-level mechanisms while aspiring for high-level, longer-term emergent outcomes. To make things worse, the desired output is not even a concrete artifact but an ambiguous mental construct: something recognized as a story by a human. We think that dramatic situations, as conceptualized by Georges Polti, can act as a useful in-between heuristic: they are objective enough to inform the design of low-level operations, but abstract enough so as not to overdetermine the output. We have embedded 18 dramatic situations in the possibility space of our emergent narrative system and have documented three emergent stories through a constrained process. We find that identifying a dramatic situation prompts the construction of a story as it incites tracing its causes and finding out its outcome. Observing the behavior of the involved parties significantly contributes to their characterization. As dramatic situations are relatively rare in a character's life and typically have high stakes, they are likely to be noteworthy. A potential issue is the discernable recurrence of some dramatic situation emergent patterns which might negatively impact players' interest, a common problem of procedural generation. We hypothesize that this issue might be alleviated by the unique situatedness of players and their characters in the world as they encounter these patterns, creating special conditions of relevance.
Estimation and Interception of a Spiralling Target on Reentry in the Presence of non-Gaussian Measurement Noise

This work addresses the problem of tracking and interception of a ballistic target having spiralling motion on re-entry. Interception is achieved by an interceptor missile which collects the required measurements using an inbuilt seeker, such that accurate estimates for target states are generated. The usual assumption that these measurements are corrupted by Gaussian noise is revisited, as significant outliers are observed in radar measurements. Since the conventional estimators tend to diverge in the presence of measurement outliers, this work propose an accurate and robust estimation algorithm by incorporating the maximum correntropy (MC) criterion. Hence, a Cauchy kernel based MC unscented Kalman filter (CM-UKF) is proposed for accurate state estimation. Also, proportional navigation guidance (PNG) law is implemented such that a possible interception is realized. The estimation accuracy of CM-UKF along with the PNG law is compared with that of the traditional UKF and Gaussian kernel based MC UKF (MC-UKF), by evaluating the average miss-distance and root mean square error (RMSE) in states.