Reviewerly/exHarmony huggingface dataset:
Eye Movements in the “Morris Maze” Spatial Working Memory Task Reveal Deficits in Strategic Planning

Analysis of eye movements can provide insights into processes underlying performance of cognitive tasks. We recorded eye movements in healthy participants and people with idiopathic Parkinson disease during a token foraging task based on the spatial working memory component of the widely used Cambridge Neuropsychological Test Automated Battery. Participants selected boxes (using a mouse click) to reveal hidden tokens. Tokens were never hidden under a box where one had been found before, such that memory had to be used to guide box selections. A key measure of performance in the task is between search errors (BSEs) in which a box where a token has been found is selected again. Eye movements were found to be most commonly directed toward the next box to be clicked on, but fixations also occurred at rates higher than expected by chance on boxes farther ahead or back along the search path. Looking ahead and looking back in this way was found to correlate negatively with BSEs and was significantly reduced in patients with Parkinson disease. Refixating boxes where tokens had already been found correlated with BSEs and the severity of Parkinson disease symptoms. It is concluded that eye movements can provide an index of cognitive planning in the task. Refixations on locations where a token has been found may also provide a sensitive indicator of visuospatial memory integrity. Eye movement measures derived from the spatial working memory task may prove useful in the assessment of executive functions as well as neurological and psychiatric diseases in the future.
Evaluating Recommender System Stability with Influence-Guided Fuzzing

Recommender systems help users to find products or services they may like when lacking personal experience or facing an overwhelming set of choices. Since unstable recommendations can lead to distrust, loss of profits, and a poor user experience, it is important to test recommender system stability. In this work, we present an approach based on inferred models of influence that underlie recommender systems to guide the generation of dataset modifications to assess a recommender’s stability. We implement our approach and evaluate it on several recommender algorithms using the MovieLens dataset. We find that influence-guided fuzzing can effectively find small sets of modifications that cause significantly more instability than random approaches.
P–T–X conditions on the genesis of orogenic Au (As, Bi, Ag) deposit in metasedimentary rocks of the Buracão Area, Araí Group, Brasília Fold Belt, Brazil

The orogenic Au (As, Bi, Ag) mineralization (up to 31 g/t) of the Buracão area is hosted in the metasedimentary Paleo–Mesoproterozoic Traíras Formation, Araí Group, Brasília Fold Belt, Brazil. In order to discuss the P–T–X conditions that generated this mineralization, we present mineralogical, geochemical and fluid inclusion data of veins, gold and wall rocks. Coupled with previous structural data these additional data allow us to link metamorphism and deformation with fluid migration and ore genesis. The integration of fluid inclusion microthermometry with mineral geothermobarometers and structures show that rocks of the study area evolved from: (i) syn–D1a metamorphic peak (400–560 °C, <6 kbar), through to the (ii) development of syn–D1b type I barren veins from low salinity H2O–NaCl fluids during syn– to late–metamorphic peak; and to the (iii) development of syn–D2a type II veins with the pyritic ore precipitation from low to high salinity H2O–CO2–CH4–NaCl ± HCO3 fluids during post–metamorphic peak (345–285 °C, 2.7–1.6 kbar). The high fineness gold nuggets accompanied by As, Bi and Ag within pyrite–arsenopyrite–bismuthinite association in the pyritic ore allows the Buracão area to be classified as “a gold-only orogenic ore deposit”. Fluid inclusion compounds and the enrichment of As, Bi, Pb, Cu, Te, Se and Ag in pyrite from the pyritic ore point out to metamorphism of carbonaceous phyllites and carbonate rocks of the Traíras Formation as a source for ore–forming fluids. We suggest that sulfur and gold in these fluids were derived by metamorphism of sedimentary rocks and conversion of pyrite into pyrrhotite in a metamorphic–hydrothermal system. The Au(HS)2−–bearing fluids percolated along intrabasinal D2a regional shear zones through the Traíras Formation towards the Buracão area, where they precipitated type II ore–bearing veins within local structures. The ore precipitation was induced by contrasting fO2–fS2 related to the interaction between the sulfur-bearing fluid and host magnetite–graphite phyllites. The lithological control is represented by a series of reactions such as 2FeO(in silicates/magnetite) + 4Au(HS)2−(aq) + 4H+(aq) ↔ 2FeS2(s) + 4Au(s) + 4H2S(aq) + 2H2O(l). The deposits of the Buracão area and other orogenic gold deposits hosted in metasedimentary rocks in the Brasília Fold Belt and worldwide confirm the potential of ore genesis in zones that do not show any relation with (meta)igneous rocks, thus, expanding the exploration possibilities in metasedimentary successions.
Unconstrained Monitoring Method for Heartbeat Signals Measurement using Pressure Sensors Array

An unconstrained monitoring method for a driver’s heartbeat is investigated in this paper. Signal measurement was carried out by using pressure sensors array. Due to the inevitable changes of posture during driving, the monitoring place for heartbeat measurement needs to be adjusted accordingly. An experiment was conducted to attach a pressure sensors array to the backrest of a seat. On the basis of the extreme learning machine classification method, driving posture can be recognized by monitoring the distribution of pressure signals. Then, a band-pass filter in heart rate range is adapted to the pressure signals in the frequency domain. Furthermore, a peak point array of the processed pressure frequency spectrum is derived and has the same distribution as the pressure signals. Thus, the heartbeat signals can be extracted from pressure sensors. Then, the correlation coefficient analysis of heartbeat signals and electrocardio-signals is performed. The results show a high level of correlation. Finally, the effects of driving posture on heartbeat signal extraction are discussed to obtain a theoretical foundation for measuring point real-time adjustment.
Sharing Emotions at Scale: The Vent Dataset

The continuous and increasing use of social media has enabled the expression of human thoughts, opinions, and everyday actions publicly at an unprecedented scale. We present the Vent dataset, the largest annotated dataset of text, emotions, and social connections to date. It comprises more than 33 millions of posts by nearly a million users together with their social connections. Each post has an associated emotion. There are 705 different emotions, organized in 63 “emotion categories”, forming a two-level taxonomy of affects. Our initial statistical analysis describes the global patterns of activity in the Vent platform, revealing large heterogeneities and certainly remarkable regularities regarding the use of the different emotions. We focus on the aggregated use of emotions, the temporal activity, and the social network of users, and outline possible methods to infer emotion networks based on the user activity. We also analyze the text and describe the affective landscape of Vent, finding agreements with existing (small scale) annotated corpus in terms of emotion categories and positive/negative valences. Finally, we discuss possible research questions that can be addressed from this unique dataset.
Building LEGO Towers

Global software engineering has changed the way software is developed today. To address the new challenges, many universities have launched specially tailored courses to train young professionals to work in globally distributed projects. However, a mere acknowledgment of the geographic, temporal, and cultural differences does not necessarily lead to a deep understanding of the underlying practical implications. Therefore, many universities developed alternative teaching and learning activities, such as multi-university collaborative projects and small-scale simulations or games. In this article, we present a small-scale exercise that uses LEGO bricks to teach skills necessary for global work. We describe the many different interventions that could be implemented in the execution of the exercise. We had seven runs of the exercises and report our findings from executing seven runs of the exercise with the total of 104 students from five different courses in two different universities. Our results suggest that the exercise can be a valuable tool to help students dealing with troublesome knowledge associated with global software engineering and a useful complement to the courses dedicated to this subject.
Gagasan Model Pembelajaran Mobile–NOS Untuk Peningkatan Literasi Sains Siswa

This article describes the idea of a mobile learning model - NOS for enhancingstudents' scientific literacy and student responses to these ideas. This article iscompiled based on literature studies, simple observations on several variablessupporting mobile learning at IKIP Mataram, and evaluating prospective userresponses to the development idea using a questionnaire. Respondents were 54students of IKIP Mataram Chemistry education. The response data is processedby percentage formula based on category criteria. Mobile-NOS learning islearning that applies the Nature of Science (NOS) learning steps with the supportof learning media in the form of mobile applications that can be applied in smartmobilephone. The six main steps in implementing learning are oriented to Natureof Science (NOS), namely: (1) Reading the context on electronic articles; (2) Indepthquestioning; (3) Observation of cases and targets; (4) DemonstrationProcedure; (5) Library Search; (6) Implementing Procedures; (7)Communicating Science Knowledge; and (8) Authentic Assessment.The results ofthis study showed an average response to the idea of developing NOS-orientedmobile learning at 85.23% with a very good category.
Gaze Augmented Hand-Based Kinesthetic Interaction: What You See is What You Feel

Kinesthetic interaction between the user and the computer mainly utilizes the hand-based input with force-feedback devices. There are two major shortcomings in hand-based kinesthetic interaction: physical fatigue associated with continuous hand movements and the limited workspace of current force-feedback devices for accurately exploring a large environment. To address these shortcomings, we developed two interaction techniques that use eye gaze as an additional input modality: HandGazeTouch and GazeTouch. Hand GazeTouch combines eye gaze and hand motion as the input for kinesthetic interaction, i.e., it uses eye gaze to point and hand motion to touch. GazeTouch replaces all hand motions in touch behavior with eye gaze, i.e., it uses eye gaze to point and gaze dwell time to trigger the touch. In both interaction techniques, the user feels the haptic feedback through the force-feedback device. The gaze-based techniques were evaluated in a softness discrimination experiment by comparing them to the traditional kinesthetic interface, HandTouch, which only uses the hand-based input. The results indicate that the HandGazeTouch technique is not only as accurate, natural, and pleasant as the traditional interface but also more efficient.
Rician Noise Removal via a Learned Dictionary

This paper proposes a new effective model for denoising images with Rician noise. The sparse representations of images have been shown to be efficient approaches for image processing. Inspired by this, we learn a dictionary from the noisy image and then combine the MAP model with it for Rician noise removal. For solving the proposed model, the primal-dual algorithm is applied and its convergence is studied. The computational results show that the proposed method is promising in restoring images with Rician noise.
Performance evaluation of cloud service with hadoop for twitter data

&lt;p&gt;In the era of rapid growth of cloud computing, performance calculation of cloud service is an essential criterion to assure quality of service. Nevertheless, it is a perplexing task to effectively analyze the performance of cloud service due to the complexity of cloud resources and the diversity of Big Data applications. Hence, we propose to examine the performance of Big Data applications with Hadoop and thus to figure out the performance in cloud cluster. Hadoop is built based on MapReduce, one of the widely used programming models in Big Data. In this paper, the performance analysis of Hadoop MapReduce WordCount application for Twitter data is presented. A 4-node in-house Hadoop cluster was setup and experiment was carried out for analyzing the performance. Through this work, it was concluded that Hadoop is efficient for BigData applications with 3 or more nodes with replication factor 3. Also, it was observed that system time was relatively more compared to user time for BigData applications beyond 80GB. This experiment had also thrown certain pattern on actual data blocks used to process the WordCount application. &lt;/p&gt;
Impact of Deterrence and Inertia on Information Security Policy Changes

ABSTRACT This study examines the impact of deterrence and inertia on information security policy changes. Corporations recognize the need to prioritize information security, which sometimes involves designing and implementing new security measures or policies. Using an online survey, we investigate the effect of deterrent sanctions and inertia on respondents' intentions to comply with modifications to company information security policies. We find that certainty and celerity associated with deterrent sanctions increase compliance intentions, while inertia decreases respondents' compliance intentions related to modified information security policies. Therefore, organizations must work to overcome employees' reluctance to change in order to improve compliance with security policy modifications. They may also consider implementing certain and timely sanctions for noncompliance.
Single Underlying Models for Projectional, Multi-View Environments

Multi-view environments provide different views of software systems optimized for different stakeholders. One way of ensuring consistency of overlapping and inter-dependent information contained in such views is to project them “on demand” from a Single Underlying Model (SUM). However, there are various ways of building and evolving such SUMs. This paper presents criteria to distinguish them, describes three archetypical approaches for building SUMs, and analyzes their advantages and disadvantages. From these criteria, guidelines for choosing which approach to use in specific application areas are derived.
Dynamic Granularity Matrix Space Based Adaptive Edge Detection Method for Structured Light Stripes

Structured light has been widely applied to 3D shape measurement with the capabilities of rapidness, high-accuracy, and noncontact. Because of uneven illumination and noise, it is difficult to distinguish the light stripes and background of the image, which reduces the measurement accuracy. In this paper, an adaptive Canny edge detection method with two phases is proposed for structured light stripes. Firstly, the idea of dynamic granularity is introduced and the dynamic granularity matrix space is established, in which image segmentation problems are described as the transformations and jumping of image at different granularity layers. The hierarchical structure and optimal granularity layer of image are obtained as the basis of adaptive edge detection. Secondly, a quantum-inspired group leader hybrid algorithm is adopted to calculate the optimal threshold from the optimal granularity layer, which is taken as the adaptive threshold for Canny operator. Finally, experimental tests and comparisons have been conducted to verify the effectiveness of the adaptive method proposed. The experiments show that the proposed method achieves high segmentation accuracy, improves the segmentation efficiency, and has strong robustness to noise.
Higher-Education Ecosystem Construction and Innovative Talents Cultivating

In the era of a knowledge economy, lots of requirements, such as innovative talents cultivation, life-long education, are put forward for the higher education, with the object of endowing the students the ability of succeeding, integrating, and disseminating knowledge, which depends on the establishment of a sustainable ecological environment and a practical platform provided for the students.The paper finds out the concept and connotation of educational ecology, analyzes the rules or principles of high-education ecosystem, discusses the creation of a balanced ecological environment and innovation platform, and summarizes the working and experience of our educational project.
Utility of WhatsApp as a Tool for Tele-oncopathology for Oral Lesions

Traditional telepathology techniques like whole slide imaging and robotic microscopy are too expensive to be used in developing countries. With the advent of smartphones with high-resolution cameras and faster internet services, it is now possible to send good-quality images to pathologist for diagnosis. To study the utility of WhatsApp image transfer in the histopathological diagnosis of common oral malignant and benign lesions. A total of 100 cases of oral biopsy were included in the study. These comprised of 58 cases of squamous cell carcinoma, 33 cases of oral leukoplakia, 3 cases of oral lichen planus and 6 cases of moderate to severe dysplasia. The conventional microscopy diagnosis made by glass slide viewing was taken as the gold standard and the concordance of the WhatsApp diagnosis with the gold standard was calculated for each category of diagnosis and also for all the 100 cases. The concordance rate for squamous cell carcinoma was 96.6%(56/58). For leukoplakia, there was 100%(33/33) concordance between the WhatsApp diagnosis and conventional microscopy. The concordance rates for dysplasia (4/6) and oral lichen planus (2/3) were 66.7% each. Hence, for all the categories taken together, the overall concordance rate was 95%. A concordance rate of 95% is very encouraging; however, for routine diagnostic use in telepathology, there is still a long way to go. With the availability of better smartphone cameras and faster internet, it will be possible to send better quality images in the future. The pathologists will also learn where to draw the line and ask for glass slides for final diagnosis.
Patients Health Monitoring System Using IOT

The past health survey shows death of few thousand people due to their heavy workload and mental pressure. Death of more patients is unavailability of doctors in critical situation. With the advancement in technology, the proposed system can monitor the patient's body condition uninterruptedly from anywhere in the world. The researcher is planned to make an inter-net of things for health care system which can monitor health of human being. In emergency situation notification will be send to doctors immediately. It takes the body parameters like Temperature, humidity, and pulse sensor and body movements. Analysis of patients’ body data is done against the normal situation to track abnormal physiological parameters.
Dynamic Anomaly Detection Using Vector Autoregressive Model

Identifying vandal users or attackers hidden in dynamic online social network data has been shown a challenging problem. In this work, we develop a dynamic attack/anomaly detection approach using a novel combination of the graph spectral features and the restricted Vector Autoregressive (rVAR) model. Our approach utilizes the time series modeling method on the non-randomness metric derived from the graph spectral features to capture the abnormal activities and interactions of individuals. Furthermore, we demonstrate how to utilize Granger causality test on the fitted rVAR model to identify causal relationships of user activities, which could be further translated to endogenous and/or exogenous influences for each individual’s anomaly measures. We conduct empirical evaluations on the Wikipedia vandal detection dataset to demonstrate efficacy of our proposed approach.
EFFECTS OF GLOBAL DECLINE IN OIL PRICE ON THE FINANCIAL PERFORMANCE OF SELECTED DEPOSIT MONEY BANKS IN NIGERIA

The global decline in oil price have had a significant effect on the Nigerian economy especially the banking sector and this has led to some banks; revenue shortfalls, increase in their non-performing loans, layoff of staff, decrease in the bank deposit base, deterioration of the bank's asset quality, reduction in the banks ticket transactions e.tc. Therefore, the main objective of this research is to examine the effects of the global decline in oil price on the Nigerian banking sector. Descriptive statistics and Simple linear regression was used as a tool for data analysis which was run with a pool ordinary least square (OLS) and the findings revealed that there exists a positive and significant relationship between oil prices and the financial performance (three key performance index were used to measure performance such as; profit after tax, current ratio, and net interest margin) of the banks especially in the period characterized by the decline in oil price. This paper, therefore, recommends that the government should take proactive steps to revamp the Agricultural sector by empowering our local farmers while commercial farming should be encouraged. Banks should endeavor to imbibe diversification of their investments and engage in financial innovation in other to improve their performance so as to enable them to be competitive globally.Keywords: Oil price, Syndicated loans, Oil and gas sector, Profitability, Deposit money banksJEL Classifications: Q40, Q43, G21DOI: https://doi.org/10.32479/ijeep.7514
A new 3D descriptor for human classification: application for human detection in a multi-kinect system

In this paper we present a new 3D descriptor for human classification and a human detection method based on this descriptor. The proposed 3D descriptor allows classification of an object represented by a point cloud, as human or non-human. It is derived from the well-known Histogram of Oriented Gradient by employing surface normals instead of gradients. The process consists in an appropriate subdivision of the object point cloud into blocks. These blocks provide the spatial distribution modeling of the surface normal orientation into the different parts of the object. This distribution modelling is expressed as a histogram. In addition we have set up a multi-kinect acquisition system that provides us with Complete Point Clouds (CPC) (i.e. 360° view). Such CPCs enable a suitable processing, particularly in case of occlusions. Moreover they allow for the determination of the human frontal orientation. Based on the proposed 3D descriptor, we have developed a human detection method that is applied on CPCs. First, we evaluated the 3D descriptor over a set of CPC candidates by using the Support Vector Machine (SVM) classifier. The learning process was conducted with the original CPC database that we have built. The results are very promising. The descriptor can discriminate human from non-human candidates and provides the frontal direction of humans with high precision. In addition we demonstrated that using the CPCs improves significantly the classification results in comparison with Single Point Clouds (i.e. points clouds acquired with only one kinect). Second, we compared our detection method with two others, namely the HOG detector on RGB images and a 3D HOG-based detection method that is applied on RGB-depth data. The obtained results on different situations show that the proposed human detection method provides excellent performances that outperform the other two detection methods.