Reviewerly/exHarmony huggingface dataset:
Detection of Malaria Infection Using Convolutional Neural Networks

A parasite that causes the potentially fatal disease malaria is spread to humans by female Anopheles mosquitoes carrying this parasite. Despite global efforts to eradicate the disease, malaria is still a major public health problem, particularly in underdeveloped nations. The timely and accurate detection of malaria infection is necessary for effective treatment and for reducing the morbidity and mortality associated with the disease. However, identifying malaria-infected cells can be challenging, especially in areas with limited access to trained healthcare professionals. Recent advances in deep learning and computer vision have shown promising results in identifying several medical images. This paper proposes a new CNN model to identify whether cells are parasitized or uninfected with malaria to help health personnel save the lives of infected people. In various ways, the proposed CNN architecture is compared to other pre-trained models, VGG-19, ResNet50, DenseNet121, and Inception V3. The malaria cell dataset of 27,558 images is used for model evaluations. The findings show that the proposed model performs the best, with an accuracy score of 97%.
HENCE-X: Toward Heterogeneity-Agnostic Multi-Level Explainability for Deep Graph Networks

Deep graph networks (DGNs) have demonstrated their outstanding effectiveness on both heterogeneous and homogeneous graphs. However their black-box nature does not allow human users to understand their working mechanisms. Recently, extensive efforts have been devoted to explaining DGNs' prediction, yet heterogeneity-agnostic multi-level explainability is still less explored. Since the two types of graphs are both irreplaceable in real-life applications, having a more general and end-to-end explainer becomes a natural and inevitable choice. In the meantime, feature-level explanation is often ignored by existing techniques, while topological-level explanation alone can be incomplete and deceptive. Thus, we propose a heterogeneity-agnostic multi-level explainer in this paper, named HENCE-X, which is a causality-guided method that can capture the non-linear dependencies of model behavior on the input using conditional probabilities. We theoretically prove that HENCE-X is guaranteed to find the Markov blanket of the explained prediction, meaning that all information that the prediction is dependent on is identified. Experiments on three real-world datasets show that HENCE-X outperforms state-of-the-art (SOTA) methods in generating faithful factual and counterfactual explanations of DGNs.
CORE-Sketch: On Exact Computation of Median Absolute Deviation with Limited Space

Median absolute deviation (MAD), the median of the absolute deviations from the median, has been found useful in various applications such as outlier detection. Together with median, MAD is more robust to abnormal data than mean and standard deviation (SD). Unfortunately, existing methods return only approximate MAD that may be far from the exact one, and thus mislead the downstream applications. Computing exact MAD is costly, however, especially in space, by storing the entire dataset in memory. In this paper, we propose COnstruction-REfinement Sketch (CORE-Sketch) for computing exact MAD. The idea is to construct some sketch within limited space, and gradually refine the sketch to find the MAD element, i.e., the element with distance to the median exactly equal to MAD. Mergeability and convergence of the method is analyzed, ensuring the correctness of the proposal and enabling parallel computation. Extensive experiments demonstrate that CORE-Sketch achieves significantly less space occupation compared to the aforesaid baseline of No-Sketch, and has time and space costs relatively comparable to the DD-Sketch method for approximate MAD.
Sentence-Level Readability Assessment Based on Dependency Grammar

The research on the sentence-level readability assessment is of great significance in the field of linguistics research and language teaching. Under the guidance of the language system view, this research constructed a linguistic feature system based on dependency grammar, and its validity is verified. In the sentence-level readability task from international Chinese language education textbooks, the machine learning models of new dependency features and traditional features were constructed respectively, and the validity of the models was compared and verified. The results show that the new dependency feature system performs well and was incremental to the traditional scoring research results.
Research on Constructing a Precise Financial Aid System in Colleges in the Era of Big Data

Facilitating financial aid work on college students from economically disadvantaged families is an essential requirement for carrying out the basic task of fostering virtue through education and running universities that are deeply rooted in China. Crucially, financial aid offerings, an important part of college education management and services, concern the vital interests of students in general. As our country's social economy and higher education keep on booming, colleges are working harder to provide better financial support for students in need. In the meantime, there are risks and challenges yet to be tackled, and relevant work needs to be optimized. This research delves into ways of constructing a precise financial aid system in colleges in the era of big data so that the financial aid policies of the Party and the State can benefit every student, therefore maximizing equity in education and mitigating the burden of needy students.
Cognition, criminal conduct, and virtual reality: Understanding and reducing offending using simulated environments

Over the past years, research has shown that virtual reality (VR) technology can be used to observe, interpret, and change human behavior and cognition in a variety of domains. This chapter explores the potential of VR as a tool to observe, interpret, and change human behavior and cognition as they relate to antisocial behavior. We review the criminological research literature as well as research literature from related disciplines on VR applications that has focused on observing and reducing antisocial behavior. The main findings of our review suggest that the key merits of VR in the domain of crime and antisocial behavior are its ability to provide safe learning environments that would otherwise involve risk, the possibility of generating ethical and ecologically valid virtual alternatives for real-life situations, and the development of stimuli that are impossible to create in real life. These unique characteristics make VR a promising tool to observe criminal behavior as it takes place and develop intervention programs to reduce antisocial behavior.
Regional Language Translator and Event Detection Using Natural Language Processing

Many languages and cultures are followed by the peoples in India. Most of the peoples are known only their mother tongue. But in everyday life, the peoples are in the position to get some essence from the bills and drafts passed by the government. These bills and drafts are commonly scripted by the language Hindi and English. These languages are not known to all the peoples of India. In this case, when a draft is passed within the specific languages, individuals of different states will not get the essence of this passed draft. They require an interpreter physically to communicate the pros and cons present in the drafts, bills or amendments. In this case, to make the individuals effortlessly get the essence of the drafts or bills passed by the government, a programmed territorial dialect interpreter is required. Here, it needs some language translation like Natural language Processing. This paper concentrates on various techniques used in language translation. In this proposed method using the NLP to convert the sentence into string and for the language translation. This paper uses the knuth morris pratt for searching the string and boyer moore horspool algorithm is used to find the substrings in the string. Through this idea all the peoples can able to know the essence in the bills or drafts passed by the individual as well as by the government.
Machine Learning Based Recommender Systems for Crop Selection: A Systematic Literature Review

This work draws a systematic literature review about the use of Machine Learning based recommender systems for crop selection in agriculture, following the PRISMA protocol for systematic reviews. Agriculture is one of the vital sectors of every country’s economy and crop selection is a paramount problematic to ensure optimal yield depending on economical, technological and environmental factors. Yet, it is up to today underrepresented research thematic, although our study shows a significant increasing trend in the number of studies in the last years. We present the outline of the study, the selection process, as well as the method of content analysis and a literature review table. A detailed analysis of 40 articles published about the CR problem as well as the main achievements, and current challenges are discussed. One of the main outcomes of this study is that, in future works, much attention should be paid to crop selection by using smart and automated solutions due to the huge positive impact it has on agricultural yields, as well as incorporating more diverse recommender-systems algorithms to address this problematic. Finally, we shed light on some future perspectives worthwhile pursuing, concerning the whole ML pipeline and including model evaluation.
A Quantum Machine Learning Model for Medical Data Classification

The use of healthcare systems has a big impact on people's well-being. To create efficient healthcare systems, new models are always being developed. The quick growth of the use of such models in the medical disciplines has created great possibilities for the development of new applications. However, the development of quick, precise, effective models suitable for medical applications still faces significant hurdles. In this chapter, the feasibility of applying the quantum support vector classifier algorithm (QSVC) is evaluated and tested on medical datasets. Ten datasets obtained from the UCI machine learning repository were adopted for this study. The experimental results revealed that the proposed intelligent model based on the QSVC obtained very promising results. The results showed the high classification outcomes of QSVC compared with state of art models. These high classification results can offer technical assistance for the enhancement of medical data classification.
Accelerating Neural Networks Using Open Standard Software on RISC-V

Deep neural networks have the ability to learn patterns from huge amounts of data and hence have been adopted in many high performance computing and scientific applications. To achieve cost effective performance on such applications, vendors and chip designers are increasingly looking at domain-specific accelerators. To facilitate adapting the design to the needs of the workload, we need a generic open standard solution all through the stack - software to hardware. This paper explores one such approach. On the hardware side, RISC-V ISA has a minimal base integer set and provides custom extensions which works as a good starting point for designing these special accelerators. This design can further benefit from the RISC-V vector extensions which help achieving high compute density leading to performance improvement for user applications. On the software side, SYCL provides a C++-based portable parallel programming model to target various devices. Thus, enabling SYCL applications to run on RISC-V accelerators provides an open standard way of accelerating neural networks. This paper elaborates the usage of open standards and open source technology to run complex SYCL applications on RISC-V vector processors.
Experimental Shot Noise Measurement Using the Imperfect Detection—A Special Case for Pulsed Laser

Measuring the quantum fluctuations of a laser source is the first task in performing continuous variable quantum key distribution protocols. The quantum fluctuations of the source are measured using balanced homodyne detection. In this paper, we have measured the shot noise of a pulsed laser using imperfect homodyne detection. The imperfections accounted for in the detection process are a delay between the homodyne output arms and also due to the selection of the pulse integration window larger as well as smaller than the photo-current pulse width during the analysis. We have analyzed the imperfect detection results for two different experimental layouts, and a comparative study has been performed. From our analysis, it is evident that these imperfections play a significant role in balanced homodyne detection and must be optimized properly. Our results indicate that balanced homodyne detection can be performed using limited resources, which paves the way for easy experimental realization of optical homodyne tomography and continuous variable quantum key distribution in a laboratory setting.
Bayesian Network Structure Learning Algorithm Combining Improved Dragonfly Optimization

Bayesian network structure learning is one of the current research hotspots in fields such as statistics and machine learning. Although it has great potential and application prospects, when there are too many variables, this type of algorithm will not be able to accurately and efficiently provide the optimal solution. In response to this issue, this study improved the dragonfly swarm optimization algorithm and solved the problem of variable type conflicts through binary discretization, applying it to the Bayesian network structure learning algorithm. According to the algorithm testing results, when the sample size is 1000 and the missing rate is 30%, the Bayesian Information Criterion (BIC) of the proposed algorithm is -7896. Under the same missing rate, when the sample size is 2000, the proposed algorithm BIC is -15114. Their BIC scores are superior to the greedy search algorithm and the sine cosine algorithm used for comparison. The proposed algorithm provides a promising development direction for the field of Bayesian network structure learning.
Improving Recommender Systems by a Further Factorization of the Factor Matrices

Due to the availability of massive numbers of items for any product on the Web, the burden of selecting an item is borne by the user.A Recommender System (RS) is a useful tool that has been employed to save the user's time by recommending preferred items for him/her efficiently.Collaborative-based RS's use the latent factor or/and the neighbourhood techniques including matrix factorization (MF), which is an efficient approach utilizing latent factors.The idea of the approach is based on calculating similarities through between users and items simultaneously, to predict appropriate recommendations.MF can be seen as mathematical model capable to split an entity into multiple smaller entries through an ordered rectangular array of functions, to discover the features or information underlying the interactions between users and items.The technique factorizes the user-item rating matrix (R) (first level factorization) into users (P) and items (Q) matrices.A recent RS system, named NLM, combines the neighbourhood and the latent factor techniques to produce attractive results.This paper proposes to improve the NLM method by proposing a more effective technique, named NLM+, by further factorizing P and Q using MF using a second level factorization.To the best of our knowledge, current studies in this field have not considered the use of the aforementioned two levels of factorizing.To validate our method, CiaoDVD, MovieLens, and FilmTrust datasets have been used and incorporated to demonstarte that NLM+ improves NLM by 48% and 42% in the recall and the precision values, respectively.
KeyScrub: A Reliable Key Backup and Recovery Method for Blockchain

On a blockchain platform, if a user has exposed or lost his/her private key, it may result in the loss of all the cryptocurrencies that he/she owns. It is critical to back up a user’s private key and recover it at any time, without compromising the decentralized feature of the blockchain. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t-out-of-n</i> secret sharing technique is typically used to support key backup and recovery in a blockchain, where a user’s private key is divided into <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> partial keys, and the original private key can be restored only when at least <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> partial keys are available. This paper proposes a key scrubbing method, KeyScrub, to guarantee that at least <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> partial keys are available at any time in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t-out-of-n</i> secret sharing. The key scrubbing operation periodically (i.e., the scrubbing interval) checks how many partial keys are correctly maintained, and issues the recovery of partial keys without full recovery of the key. The reliability analysis of the proposed method shows a tradeoff relationship between the scrubbing interval and the values of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> to meet the given reliability requirement for a key backup and recovery service.
User Experience in Large-Scale Robot Development: A Case Study of Mechanical and Software Teams

User experience integration within software companies has been studied extensively, but studies on organizations that develop robots are scant. The physical and socially situated presence of robots poses unique design and development challenges, which companies should be able to address. This case study examines how mechanical and software teams involved in a large-scale robot development project embed UX in robot design. The case offers new perspectives on HCI research, which traditionally explores UX integration in companies from the point of views of UX specialists and software developers, with little consideration of how mechanical and software design interact. During our 12+ months collaboration with the company, we conducted non-participant observations of 30 project SCRUM meetings. Based on this data, we identify four themes concerning the role of UX in robot development, workarounds in design evaluation, requirements handling, and coordination mechanisms.
Exploring Natural Language Processing Methods for Interactive Behaviour Modelling

Analysing and modelling interactive behaviour is an important topic in human-computer interaction (HCI) and a key requirement for the development of intelligent interactive systems. Interactive behaviour has a sequential (actions happen one after another) and hierarchical (a sequence of actions forms an activity driven by interaction goals) structure, which may be similar to the structure of natural language. Designed based on such a structure, natural language processing (NLP) methods have achieved groundbreaking success in various downstream tasks. However, few works linked interactive behaviour with natural language. In this paper, we explore the similarity between interactive behaviour and natural language by applying an NLP method, byte pair encoding (BPE), to encode mouse and keyboard behaviour. We then analyse the vocabulary, i.e., the set of action sequences, learnt by BPE, as well as use the vocabulary to encode the input behaviour for interactive task recognition. An existing dataset collected in constrained lab settings and our novel out-of-the-lab dataset were used for evaluation. Results show that this natural language-inspired approach not only learns action sequences that reflect specific interaction goals, but also achieves higher F1 scores on task recognition than other methods. Our work reveals the similarity between interactive behaviour and natural language, and presents the potential of applying the new pack of methods that leverage insights from NLP to model interactive behaviour in HCI.
Modeling and Assessing User Interaction in Big Data Visualization Systems

When users interact with large data through a visualization system, its response time is crucial in keeping engagement and efficacy as high as possible, and latencies as low as 500 ms can be detrimental to the correct execution of the analysis. This can be due to several causes: (i) for large data or high query rates, database management systems (DBMS) may fail to meet the performance needs; (ii) modeling all the interactions with a visualization system is challenging due to their exploratory nature, where not all of them are equally demanding in terms of computation time; (iii) there is a lack of models for integrating optimizations in a holistic way, hampering consistent evaluation across systems. In response to these problems, we propose a conceptual interaction-driven framework that enhances the visualization pipeline by adding a new Translation layer between the Data-, Visualization- and Interaction- layers, leveraging the modeling of interactions with augmented statecharts. This new layer aims to collect information about queries and rendering computations, linking such values to interactions in the statechart. To make the Translation layer actionable, we contribute a software component to automatically model the user interactions for a generic web-based visualization system through augmented statecharts, in which each interaction is labeled with its latency threshold. We first demonstrate its generality on ten state-of-the-art visualization systems. Then we perform a user study (n = 50), collecting traces by asking users to perform already established exploratory tasks on the well-known Crossfilter interface. Finally, we replay those traces over its generated statechart, assessing the capability to model the user interactions correctly and describing violations in the latency thresholds.
Empirical Grounding for the Interpretations of Natural User Interface: A Case Study on Smartpen

The emergence of Natural User Interface (NUI) approximately two decades ago promised to support intuitive and multimodal interactions by leveraging human sensorimotor skills such as touching, speaking, and gazing. Despite the development and introduction of commercial NUI hardware, traditional user interfaces (e.g., GUIs) continue to dominate in many sectors, prompting inquiry into the claims of ‘naturalness’. To examine this phenomenon, we investigated empirically two interpretations of naturalness: innateness and intuitiveness. The study involved asking 56 participants to complete learning tasks with a smartpen system and a laptop system representing innateness and intuitiveness, respectively. A mixed-method design was implemented to collect participants’ perception and performance while using both interfaces. Results indicated that, despite the smartpen system was highly learnable, the perception of naturalness was significantly linked to participants’ prior experiences rather than to innate abilities. The implications of these findings are discussed.