Reviewerly/exHarmony huggingface dataset:
Geostatistical Determination of Ore Shoot Plunge and Structural Control of the Sizhuang World-Class Epizonal Orogenic Gold Deposit, Jiaodong Peninsula, China

The Jiaodong Peninsula in eastern China is the third largest gold-mining area and one of the most important orogenic gold provinces in the world. Ore shoots plunging in specific orientations are a ubiquitous feature of the Jiaodong lode deposits. The Sizhuang gold deposit, located in northwestern Jiaodong, is characterized by orebodies of different occurrences. The orientation of ore shoots has remained unresolved for a long time. In this paper, geostatistical tools were used to determine the plunge and structural control of ore shoots in the Sizhuang deposit. The ellipses determined by variogram modeling reveal the anisotropy of mineralization, plus the shape, size, and orientation of individual ore shoots. The long axes of the anisotropy ellipses trend NE or SEE and plunge 48° NE down the dip. However, individual ore shoots plunge almost perpendicular to the plunge of the ore deposit as a whole. This geometry is interpreted to have resulted from two periods of fluid flow parallel to two sets of striations that we identified on ore-controlling faults. Thrust-related lineations with a sinistral strike-slip component were associated with early-stage mineralization. This was overprinted by dextral and normal movement of the ore-controlling fault that controlled the late-stage mineralization. This kinematic switch caused a change in the upflow direction of ore-forming fluid, which in turn controlled the orientation of the large-scale orebodies and the subvertical plunge of individual ore shoots. Thus, a regional transition from NW-to-SE-trending compression to NW-to-SE-trending extension is interpreted as the geodynamic background of the ore-forming process. This research exemplifies an effective exploration strategy for studying the structural control of the geometry, orientation, and grade distribution of orebodies via the integration of geostatistical tools and structural analysis.
Collaborative Futures

Designing new technologies to support the lived experience of dementia is of increasing interest within HCI. While there is guidance on qualitative research methods to use in areas such as dementia, there is a need for more appropriate ways to research in the younger demographic. In Younger Onset Dementia (YOD), the circumstances and experiences are markedly different from dementia in the later stage of life requiring a different approach. This paper presents insights into the methods and approaches used in fieldwork with five people living with YOD; where they engaged as co-researchers in a co-directed inquiry into their lived experiences. Through this, we make a number of methodological contributions to HCI and Participatory Action Research (PAR) for research in the YOD setting. This includes productive approaches that are sensitive, respectful and empowering to the participants. It also extends current approaches to using probes in HCI and dementia research.
AI@ntiPhish — Machine Learning Mechanisms for Cyber-Phishing Attack

This study proposes a novel machine learning architecture and various learning algorithms to build-in anti-phishing services for avoiding cyber-phishing attack. For the rapid develop of information technology, hackers engage in cyber-phishing attack to steal important personal information, which draws information security concerns. The prevention of phishing website involves in various aspect, for example, user training, public awareness, fraudulent phishing, etc. However, recent phishing research has mainly focused on preventing fraudulent phishing and relied on manual identification that is inefficient for real-time detection systems. In this study, we used methods such as ANOVA, X2, and information gain to evaluate features. Then, we filtered out the unrelated features and obtained the top 28 most related features as the features to use for the training and evaluation of traditional machine learning algorithms, such as Support Vector Machine (SVM) with linear or rbf kernels, Logistic Regression (LR), Decision tree, and K-Nearest Neighbor (KNN). This research also evaluated the above algorithms with the ensemble learning concept by combining multiple classifiers, such as Adaboost, bagging, and voting. Finally, the eXtreme Gradient Boosting (XGBoost) model exhibited the best performance of 99.2%, among the algorithms considered in this study.
Comparative Research of Swarm Intelligence Clustering Algorithms for Analyzing Medical Data

As the Internet of medical Things emerge in the field of medicine, the volume of medical data is expanding rapidly and along with its variety.As such, clustering is an important procedure to mine the vast data.Many swarm intelligence clustering algorithms, such as the particle swarm optimization (PSO), firefly, cuckoo, and bat, have been designed, which can be parallelized to the benefit of mass data computation.However, few studies focus on the systematic analysis of the time complexities, the effect of instances (data size), attributes (dimensionality), number of clusters, and agents of these algorithms.In this paper, we performed a comparative research for the PSO, firefly, cuckoo, and bat algorithms based on both synthetic and real medical data sets.Finally, we conclude which algorithms are effective for the medical data mining.In addition, we recommend the more suitable algorithms that have been developed recently for the different medical data to achieve the optimal clustering.
Mouse-Tracking

Mouse- and hand-tracking has become a popular method for studying the cognitive processes involved in a variety of domains, including language processing, memory functions, social cognition, and preferential and moral decision making, to name just a few. The popularity of mouse- and hand-tracking derives from its promise to provide a window into the evolution of cognitive processes with an unrivaled temporal resolution. This window is opened by linking characteristics of the movement, such as the shape of its trajectory, to characteristics of the underlying cognitive process. In current research, this means, more often than not, inferring the response competition created by different conditions or stimuli based on the curvature of aggregate movement trajectories. However, we will argue that this aggregate-level approach risks obscuring important trial-level variability in movement trajectories that can paint a different picture of the underlying cognitive process than the aggregate-level results do. Fortunately, mouse-tracking can do much more. In this chapter, we present a new approach to analyzing mouse-trajectories based on trajectory clustering that overcomes the limitations of aggregation-based analyses of movement trajectories.
Parameter Transfer Unit for Deep Neural Networks

Parameters in deep neural networks which are trained on large-scale databases can generalize across multiple domains, which is referred as “transferability”. Unfortunately, the transferability is usually defined as discrete states and it differs with domains and network architectures. Existing works usually heuristically apply parameter-sharing or fine-tuning, and there is no principled approach to learn a parameter transfer strategy. To address the gap, a Parameter Transfer Unit (PTU) is proposed in this paper. PTU learns a fine-grained nonlinear combination of activations from both the source domain network and the target domain network, and subsumes hand-crafted discrete transfer states. In the PTU, the transferability is controlled by two gates which are artificial neurons and can be learned from data. The PTU is a general and flexible module which can be used in both CNNs and RNNs. It can be also integrated with other transfer learning methods in a plug-and-play manner. Experiments are conducted with various network architectures and multiple transfer domain pairs. Results demonstrate the effectiveness of the PTU as it outperforms heuristic parameter-sharing and fine-tuning in most settings.
Construction of Green's functions on a quantum computer: Quasiparticle spectra of molecules

We propose a scheme for the construction of one-particle Green's function (GF) of an interacting electronic system via statistical sampling on a quantum computer. Although the non-unitarity of creation and annihilation operators for the electronic spin orbitals prevents us from preparing specific states selectively, probabilistic state preparation is demonstrated to be possible for the qubits. We provide quantum circuits equipped with at most two ancillary qubits for obtaining all the components of GF. We perform simulations of such construction of GFs for LiH and H$_2$O molecules based on the unitary coupled-cluster (UCC) method to demonstrate the validity of our scheme by comparing the spectra exact within UCC and those from full configuration interaction calculations. We also examine the accuracy of sampling method by exploiting the Galitskii--Migdal formula, which gives the total energy only from the GF.
Can internal factors improve innovation performance via innovation culture in SMEs?

Purpose Innovation performance is an issue that has a profound effect not only on Malaysian large companies but also among small and medium enterprises (SMEs) especially Bumiputera SMEs. The purpose of this paper is to explore the theoretical review of innovation culture pertaining to innovation performance by conducting a literature review on SME studies. The previous reference on innovation performance in Malaysian SMEs is still scarce, even though it is the key benchmark to measure firm performance. It has been demonstrated by the literature that innovation culture is significantly associated with innovation performance. Nonetheless, its effect on Bumiputera SMEs is still underexplored. Thus, this study examines the importance of internal factors (specific human capital and social capital) to innovation culture in driving innovation performance. Design/methodology/approach A questionnaire-based survey was conducted to gather data from Bumiputera SMEs. A total of 140 responses were obtained and analyses were carried out using Smart-PLS software to produce interesting findings. Findings The findings indicate that social capital (relational capital and social network) has a significant impact on innovation culture and indirectly impacts innovation performance. The findings also reveal that specific human capital does not have significant impact on innovation culture and innovation performance. This paper shows the importance of social capital and how it directly influences Bumiputera SMEs and innovation performance. This result will be encouraging to firms in other developing countries. Practical implications Although SMEs play an important role in economic development, their contribution to innovation is small and marginal. This study makes an important contribution by providing information to the Malaysian SMEs, especially those that are of Bumiputera status on the factors that could enhance innovation performance and nurture innovation culture in their organisations. Thus, it is hoped that this study will generate interest among researchers to attain more conclusive evidence about the practice of innovation culture among Malaysian SMEs. Originality/value This paper is one of the first to address the mediating effect of innovation culture on specific human capital, social capital and innovation performance.
ChiMed: A Chinese Medical Corpus for Question Answering

Question answering (QA) is a challenging task in natural language processing (NLP), especially when it is applied to specific domains.While models trained in the general domain can be adapted to a new target domain, their performance often degrades significantly due to domain mismatch.Alternatively, one can require a large amount of domain-specific QA data, but such data are rare, especially for the medical domain.In this study, we first collect a large-scale Chinese medical QA corpus called ChiMed; second we annotate a small fraction of the corpus to check the quality of the answers; third, we extract two datasets from the corpus and use them for the relevancy prediction task and the adoption prediction task.Several benchmark models are applied to the datasets, producing good results for both tasks.
LGCN: Learnable Gabor Convolution Network for Human Gender Recognition in the Wild

Human gender recognition in the wild is a challenging task due to complex face variations, such as poses, lighting, occlusions, etc. In this letter, learnable Gabor convolutional network (LGCN), a new neural network computing framework for gender recognition was proposed. In LGCN, a learnable Gabor filter (LGF) is introduced and combined with the convolutional neural network (CNN). Specifically, the proposed framework is constructed by replacing some first layer convolutional kernels of a standard CNN with LGFs. Here, LGFs learn intrinsic parameters by using standard back propagation method, so that the values of those parameters are no longer fixed by experience as traditional methods, but can be modified by self-learning automatically. In addition, the performance of LGCN in gender recognition is further improved by applying a proposed feature combination strategy. The experimental results demonstrate that, compared to the standard CNNs with identical network architecture, our approach achieves better performance on three challenging public datasets without introducing any sacrifice in parameter size.
Named-Entity Recognition on Indonesian Tweets using Bidirectional LSTM-CRF

The massive amount of Twitter data allow it to be analyzed using Named-Entity Recognition. Named-Entity Recognition (NER) is a sub-task of Information Extraction that can recognize entities in a text. Most NERs are trained to handle formal text such as news articles, but when applied to informal texts such as tweets, it provides poor performance. The limited number of words, informal and messy grammar on tweets makes it difficult to classify the entities needed. In this study, it was built the model using a combination of deep learning and machine learning approaches, Bidirectional Long Short-Term Memory (BLSTM) and Conditional Random Field (CRF) as the solutions. Entities identified in the form of Person, Location and Organization. The corpus tested included 600 Indonesian tweets comprising 250 formal tweets and 350 informal tweets. The model got the best F1 score results by adding the word embedding type FastText, which are 86,13% for formal tweets, 81,17% for informal tweets, and 84,11% for combined tweets.
Building robust models for Human Activity Recognition from raw accelerometers data using Gated Recurrent Units and Long Short Term Memory Neural Networks

Human Activity Recognition (HAR) is a growing field of research in biomedical engineering and it has many potential applications in the treatment and prevention of several diseases. Due to the recent advancement in technology, devices that collect position and orientation measurements (e.g. accelerometers and gyroscopes) are becoming ubiquitous. These measurements can then be used to train machine learning models for HAR. In this research, we propose one recurrent neural network architecture and a data augmentation approach for building robust and accurate models for HAR. We compared models with Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) layers. The proposed data augmentation approach was used to make the models robust to the cases where one or more sensors are missing. In this empirical study, we could also understand some relations between the ideal locations of the sensors in the participants and the types of activities performed. The proposed approaches were tested in the GOTOv dataset from a study which involved 35 participants performing 16 sedentary, ambulatory and lifestyle activities in a semi-structured environment. The results presented, clearly show that the models are able to detect these activities in a robust way.
Securely computing the n-variable equality function with 2n cards

Research in the area of secure multi-party computation using a deck of playing cards, often called card-based cryptography, started from the introduction of the five-card trick protocol to compute the logical AND function by den Boer in 1989. Since then, many card-based protocols to compute various functions have been developed. In this paper, we propose two new protocols that securely compute the $n$-variable equality function (determining whether all inputs are equal) $E: \{0,1\}^n \rightarrow \{0,1\}$ using $2n$ cards. The first protocol can be generalized to compute any doubly symmetric function $f: \{0,1\}^n \rightarrow \mathbb{Z}$ using $2n$ cards, and any symmetric function $f: \{0,1\}^n \rightarrow \mathbb{Z}$ using $2n+2$ cards. The second protocol can be generalized to compute the $k$-candidate $n$-variable equality function $E: (\mathbb{Z}/k\mathbb{Z})^n \rightarrow \{0,1\}$ using $2 \lceil \lg k \rceil n$ cards.
Through the Eyes of Faculty: Using Personas as a Tool for Learner-Centered Professional Development

College science instructors need continuous professional development (PD) to meet the call to evidence-based practice. New PD efforts need to focus on the nuanced blend of factors that influence instructors’ teaching practices. We used persona methodology to describe the diversity among instructors who were participating in a long-term PD initiative. Persona methodology originates from ethnography. It takes data from product users and compiles those data in the form of fictional characters. Personas facilitate user-centered design. We identified four personas among our participants: Emma the Expert views herself as the subject-matter expert in the classroom and values her hard-earned excellence in lecturing. Ray the Relater relates to students and focuses on their points of view about innovative pedagogies. Carmen the Coach coaches her students by setting goals for them and helping them develop skill in scientific practices. Beth the Burdened owns the responsibility for her students’ learning and feels overwhelmed that students still struggle despite her use of evidence-based practice. Each persona needs unique PD. We suggest ways that PD facilitators can use our personas as a reflection tool to determine how to approach the learners in their PD. We also suggest further avenues of research on learner-centered PD.
Quantum butterfly effect in polarized Floquet systems

We explore quantum dynamics in Floquet many-body systems with local conservation laws in one spatial dimension, focusing on sectors of the Hilbert space which are highly polarized. We numerically compare the predicted charge diffusion constants and quantum butterfly velocity of operator growth between models of chaotic Floquet dynamics (with discrete spacetime translation invariance) and random unitary circuits which vary both in space and time. We find that for small but nonzero density of charge (in the thermodynamic limit), the random unitary circuit correctly predicts the scaling of the butterfly velocity but incorrectly predicts the scaling of the diffusion constant. We argue that this is a consequence of quantum coherence on short time scales. Our work clarifies the settings in which random unitary circuits provide correct physical predictions for nonrandom chaotic systems and sheds light into the origin of the slow down of the butterfly effect in highly polarized systems or at low temperature.
Spectral collapse in the two-photon quantum Rabi model

Spectral collapse, the transition from a discrete to a continuous spectrum, is a characteristic in quantum Rabi models. We explore this phenomenon in the two-photon quantum Rabi model using optical phase space, and we find that, in the so-called degenerate qubit regime, the collapse is similar to the transition from a harmonic to an inverted oscillator with the free-particle potential as a critical transition point. In the degenerate qubit regime, we construct Dirac-normalizable eigenfunctions with well-defined parity for the model. In the general model, we use parity to diagonalize the system in the qubit basis and numerically find that the qubit frequency does not change the critical point where spectral collapse occurs. We numerically confirm the existence of an exceptional state at the critical coupling, and we argue its analytic provenance from both a Born-Oppenheimer and a variational approximation.
Potential of Airborne Hyperspectral Data for Geo-Exploration over Parts of Different Geological/Metallogenic Provinces in India based on AVIRIS-NG Observations

In this article, we discuss the potential of airborne hyperspectral data in mapping host rocks of mineral deposits and surface signatures of mineralization using AVIRIS-NG data of a few important geological provinces in India.We present the initial results from the study sites covering parts of northwest India, as well as the Sittampundi Layered Complex (SLC) of Tamil Nadu and the Wajrakarur Kimberlite Field (WKF) of Andhra Pradesh from southern India.Modified spectral summary parameters, originally designed for MRO-CRISM data analysis, have been implemented on AVIRIS-NG mosaic of Jahazpur, Rajasthan for the automatic detection of phyllosilicates, carbonates and Fe-Mg-silicates.Spectral analysis over Ambaji and the surrounding areas indicates the presence of calcite across much of the study area with kaolinite occurring as well in the north and east of the study area.The deepest absorption features at around 2.20 and 2.32 μm and integrated band depth were used to identify and map the spatial distribution of phyllosilicates and carbonates.Suitable thresholds of band depths were applied to map prospective zones for marble exploration.The data over SLC showed potential of AVIRIS-NG hyperspectral data in detecting mafic cumulates and chromitites.We also have demonstrated the potential of AVIRIS-NG data in detecting kimberlite pipe exposures in parts of WKF.
Understanding Medical Conversations with Scattered Keyword Attention and Weak Supervision from Responses

In this work, we consider the medical slot filling problem, i.e., the problem of converting medical queries into structured representations which is a challenging task. We analyze the effectiveness of two points: scattered keywords in user utterances and weak supervision with responses. We approach the medical slot filling as a multi-label classification problem with label-embedding attentive model to pay more attention to scattered medical keywords and learn the classification models by weak-supervision from responses. To evaluate the approaches, we annotate a medical slot filling data and collect a large scale unlabeled data. The experiments demonstrate that these two points are promising to improve the task.