Search results for: emotion mining

Authors: Franklin Nantui Mabe, Robert Osei

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Activities and operations of artisanal small scale mining (ASM) have recently appealed to the attention of policymakers, researchers, and the general public in Ghana. This stems from the negative impacts of ASM operations on the environment and livelihoods of local inhabitants, as well as the disregard for available ASM mining policies. This study, therefore, investigates whether or not artisanal small-scale miners have enough knowledge of the mining policies and their implementations. The study adopted the Knowledge, Attitudes, and Practices (KAP) framework approach to design the research, collect and analyze primary data. The most aware ASM policy provision is the one that mandates the government to reserve demarcated ASM areas for Ghanaians, whilst the least aware provision is the one that admonishes the government to promote co-operative saving among ASM. The awareness index is lower than the attitude index towards the policy provisions. In terms of practices, miners continued to use bad practices with the associated negative impacts on the environment and rural livelihoods. It is therefore important for the government through mineral commission, district, municipal and metropolitan assemblies to intensify the education on the ASM policies. These could be done with the help of ASM associations. The current systems where a cluster of districts have a single Mineral Commission Office should be restructured to make sure that each mining district has an office.

Keywords: mining policies, KAP, awareness, artisanal small-scale mining

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Authors: Colette Faucher

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In modern asymmetric conflicts, the Armed Forces generally have to intervene in countries where the internal peace is in danger. They must make the local population an ally in order to be able to deploy the necessary military actions with its support. For this purpose, psychological operations (PSYOPs) are used to shape people’s behaviors and emotions by the modification of their attitudes in acting on their perceptions. PSYOPs aim at elaborating and spreading a message that must be read, listened to and/or looked at, then understood by the info-targets in order to get from them the desired behavior. A message can generate in the info-targets, reasoned thoughts, spontaneous emotions or reflex behaviors, this effect partly depending on the means of conveyance used to spread this message. In this paper, we focus on psychological operations that generate emotions. We present a method based on the Intergroup Emotion Theory, that determines, from the characteristics of the conveyed message and of the people from the population directly reached by the means of conveyance (direct info-targets), the emotion likely to be triggered in them and we simulate the propagation of the effects of such a message on indirect info-targets that are connected to them through the social networks that structure the population.

Keywords: military psychological operations, social identity, social network, emotion propagation

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Authors: Simon Mensah Ofosu

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Illicit surface mining activities pollutes agricultural lands and water bodies and results in accumulation of heavy metals in vegetables cultivated in such areas. Heavy metal (HM) accumulation in vegetables is a serious food safety issues due to the adverse effects of metal toxicities, hence the need to investigate the levels of these metals in cultivated vegetables in the eastern region. Cocoyam leaves, cabbage and cucumber were sampled from selected farms in mining areas (Atiwa District) and non -mining areas (Yilo Krobo and East Akim District) of the region for the study. Levels of Cadmium, Lead, Mercury and Arsenic were investigated in the vegetables with Atomic Absorption Spectrometer, and the results statistically analyzed with Microsoft Office Excel (2013) Spread Sheet and ANOVA. Cadmium (Cd) and arsenic (As) were the highest and least concentrated HM in the vegetables sampled, respectively. The mean concentrations of Cd and Pb in cabbage (0.564 mg/kg, 0.470 mg/kg), cucumber (0.389 mg/kg, 0.190 mg/kg), cocoyam leaves (0.410 mg/kg, 0.256 mg/kg) respectively from the mining areas exceeded the permissible limits set by Joint FAO/WHO. The mean concentrations of the metals in vegetables from the mining and non-mining areas varied significantly (P<0.05). The Target Hazard Quotient (THQ) was used to assess the health risk posed to the human population via vegetable consumption. The THQ values of cadmium, mercury, and lead in adults and children through vegetable consumption in the mining areas were greater than 1 (THQ >1). This indicates the potential health risk that the children and adults may be facing. The THQ values of adults and children in the non-mining areas were less than the safe limit of 1 (THQ<1), hence no significant health risk posed to the population from such areas.

Keywords: food safety, risk assessment, illicit mining, public health, contaminated vegetables

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Authors: Rashmi Gupta

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It is unclear whether arousal or valence modulates the response inhibition process. It has been suggested that irrelevant positive emotional information (e.g., happy faces) and negative emotional information (e.g., angry faces) interact with attention differently. In the present study, we used arousal-matched irrelevant happy and angry faces. These faces were used as stop-signals in the stop-signal paradigm. There were two kinds of trials: go-trials and stop-trials. Participants were required to discriminate between the letter X or O by pressing the corresponding keys on go-trials. However, a stop signal was occasionally presented on stop trials, where participants were required to withhold their motor response. A significant main effect of emotion on response inhibition was observed. It indicated that the valence of a stop signal modulates inhibitory control. We found that stop-signal reaction time was faster in response to irrelevant angry faces than happy faces, indicating that irrelevant angry faces facilitate the response inhibition process compared to happy faces. These results shed light on the interaction of emotion with cognitive control functions.

Keywords: attention, emotion, response inhibition, inhibitory control

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Authors: M. V. Manoj Kumar, Likewin Thomas, Annappa

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Process mining provides methods and techniques for analyzing event logs recorded in modern information systems that support real-world operations. While analyzing an event-log, state-of-the-art techniques available in process mining believe that the operational process as a static entity (stationary). This is not often the case due to the possibility of occurrence of a phenomenon called concept drift. During the period of execution, the process can experience concept drift and can evolve with respect to any of its associated perspectives exhibiting various patterns-of-change with a different pace. Work presented in this paper discusses the main aspects to consider while addressing concept drift phenomenon and proposes a method for detecting and localizing the sudden concept drifts in control-flow perspective of the process by using features extracted by processing the traces in the process log. Our experimental results are promising in the direction of efficiently detecting and localizing concept drift in the context of process mining research discipline.

Keywords: abrupt drift, concept drift, sudden drift, control-flow perspective, detection and localization, process mining

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Authors: Zeba Mahmood

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The modern business organizations are adopting technological advancement to achieve competitive edge and satisfy their consumer. The development in the field of Information technology systems has changed the way of conducting business today. Business operations today rely more on the data they obtained and this data is continuously increasing in volume. The data stored in different locations is difficult to find and use without the effective implementation of Data mining and Knowledge management techniques. Organizations who smartly identify, obtain and then convert data in useful formats for their decision making and operational improvements create additional value for their customers and enhance their operational capabilities. Marketers and Customer relationship departments of firm use Data mining techniques to make relevant decisions, this paper emphasizes on the identification of different data mining and Knowledge management techniques that are applied to different business industries. The challenges and issues of execution of these techniques are also discussed and critically analyzed in this paper.

Keywords: knowledge, knowledge management, knowledge discovery in databases, business, operational, information, data mining

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Authors: B. Chandra

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In Association Rule Mining, much attention has been paid for developing algorithms for large (frequent/closed/maximal) itemsets but very little attention has been paid to improve the performance of rule generation algorithms. Rule generation is an important part of Association Rule Mining. In this paper, a novel approach named NARG (Association Rule using Antecedent Support) has been proposed for rule generation that uses memory resident data structure named FCET (Frequent Closed Enumeration Tree) to find frequent/closed itemsets. In addition, the computational speed of NARG is enhanced by giving importance to the rules that have lower antecedent support. Comparative performance evaluation of NARG with fast association rule mining algorithm for rule generation has been done on synthetic datasets and real life datasets (taken from UCI Machine Learning Repository). Performance analysis shows that NARG is computationally faster in comparison to the existing algorithms for rule generation.

Keywords: knowledge discovery, association rule mining, antecedent support, rule generation

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Authors: G. Surendra Babu

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We all know the importance of mineral wealth, which has been buried inside the layers of the earth for decades. These are the natural energy sources that are used in our day to day life like fuel, electricity, construction, etc. but the process of extraction causes damage to the nature that can’t be returned back and which are left over after completion of mining we can see these are barren from decades these remain unused degraded land. Most of them are covered with vegetation before the start during mining which damages the native vegetation of the region and disturbs the watershed boundary of the regions and it also disturbs the biodiversity of the reign. The major motto of the study is to understand the various issues that are found and to understand various methods of reclamations process that are suitable for revegetating and also variously practiced which are carried out in the different case studies and government guidelines procedure of lease licenses which includes the environmental clearances and also to study the vegetation pattern according to the major issues identified. And finally suggesting the new guidelines with respect to the old guidelines which helps in the revegetation of the mine-sites which helps in establishing of its own sustainable ecosystem in future.

Keywords: reclamation, open-pit mining, revegetation, reclamation methods

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Authors: Ali Asheibi

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The rapid increase in the size of databases required to store power quality monitoring data has demanded new techniques for analysing and understanding the data. One suggested technique to assist in analysis is data mining. Preparing raw data to be ready for data mining exploration take up most of the effort and time spent in the whole data mining process. Clustering is an important technique in data mining and machine learning in which underlying and meaningful groups of data are discovered. Large amounts of harmonic data have been collected from an actual harmonic monitoring system in a distribution system in Australia for three years. This amount of acquired data makes it difficult to identify operational events that significantly impact the harmonics generated on the system. In this paper, harmonic data preparation processes to better understanding of the data have been presented. Underlying classes in this data has then been identified using clustering technique based on the Minimum Message Length (MML) method. The underlying operational information contained within the clusters can be rapidly visualised by the engineers. The C5.0 algorithm was used for classification and interpretation of the generated clusters.

Keywords: data mining, harmonic data, clustering, classification

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Authors: Karima Qayumi, Alex Norta

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The rapid generation of high volume and a broad variety of data from the application of new technologies pose challenges for the generation of business-intelligence. Most organizations and business owners need to extract data from multiple sources and apply analytical methods for the purposes of developing their business. Therefore, the recently decentralized data management environment is relying on a distributed computing paradigm. While data are stored in highly distributed systems, the implementation of distributed data-mining techniques is a challenge. The aim of this technique is to gather knowledge from every domain and all the datasets stemming from distributed resources. As agent technologies offer significant contributions for managing the complexity of distributed systems, we consider this for next-generation data-mining processes. To demonstrate agent-based business intelligence operations, we use agent-oriented modeling techniques to develop a new artifact for mining massive datasets.

Keywords: agent-oriented modeling (AOM), business intelligence model (BIM), distributed data mining (DDM), multi-agent system (MAS)

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Authors: Marcin Michalak

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Due to the increasing efforts on saving our natural environment a change in the structure of energy resources can be observed - an increasing fraction of a renewable energy sources. In many countries traditional underground coal mining loses its significance but there are still countries, like Poland or Germany, in which the coal based technologies have the greatest fraction in a total energy production. This necessitates to make an effort to limit the costs and negative effects of underground coal mining. The longwall complex is as essential part of the underground coal mining. The safety and the effectiveness of the work is strongly dependent of the diagnostic state of powered roof supports. The building of a useful and reliable diagnostic system requires a lot of data. As the acquisition of a data of any possible operating conditions it is important to have a possibility to generate a demanded artificial working characteristics. In this paper a new approach of modelling a leg pressure in the single unit of powered roof support. The model is a result of the analysis of a typical working cycles.

Keywords: machine modelling, underground mining, coal mining, structure

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Authors: Faisal Aburub, Wael Hadi

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Data mining is the process of extracting useful or hidden information from a large database. Extracted information can be used to discover relationships among features, where data objects are grouped according to logical relationships; or to predict unseen objects to one of the predefined groups. In this paper, we aim to investigate four well-known data mining algorithms in order to predict groundwater areas in Jordan. These algorithms are Support Vector Machines (SVMs), Naïve Bayes (NB), K-Nearest Neighbor (kNN) and Classification Based on Association Rule (CBA). The experimental results indicate that the SVMs algorithm outperformed other algorithms in terms of classification accuracy, precision and F1 evaluation measures using the datasets of groundwater areas that were collected from Jordanian Ministry of Water and Irrigation.

Keywords: classification, data mining, evaluation measures, groundwater

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Authors: Vivian Khamis

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Exposure to war and forced migration have been widely linked to child subsequent adaptation. What remains sparse is research spanning multiple risk and protective factors and examining their unique and relative implications to difficulties in mental health among refugee girls. This study investigated the mechanisms through which posttraumatic stress disorder (PTSD), emotion dysregulation , neuroticism, and behavioral and emotional disorders in Syrian refugee girls is impacted by exposure to war traumas, age, and other risk and protective factors such as coping styles, family relationships, and school environment. The sample consisted of 539 Syrian refugee girls who ranged in age from 7 to 18 years attending public schools in various governorates in Lebanon and Jordan. Two school counselors carried out the interviews with children at school. Results indicated that war trauma, older age, and a combination of negative copying style associated with conflict in the family could lead to an overall state of emotion dysregulation, neuroticism, behavioral and emotional disorders, and PTSD in refugee girls. On the other hand, lapse of time since resettlement in host country, positive copying style, cohesion, and expressiveness in the family would lead to more positive mental health status, including lower levels of emotion dysregulation, neuroticism, behavioral and emotional disorders, and PTSD . Enhanced understanding of the mechanistic role of risk and protective factors in contributing to difficulties in mental health in refugee girls may contribute to the development of effective interventions to target the psychological effects of the refugee experience.

Keywords: refugee girls, PTSD, emotion dysregulation, neuroticism, behavioral and emotional disorders

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Authors: Seulbi Choi, Hyunchul Ahn

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Collaborative filtering (CF) algorithm has been popularly used for recommender systems in both academic and practical applications. It basically generates recommendation results using users’ numeric ratings. However, the additional use of the information other than user ratings may lead to better accuracy of CF. Considering that a lot of people are likely to share their honest opinion on the items they purchased recently due to the advent of the Web 2.0, user's review can be regarded as the new informative source for identifying user's preference with accuracy. Under this background, this study presents a hybrid recommender system that fuses CF and user's review mining. Our system adopts conventional memory-based CF, but it is designed to use both user’s numeric ratings and his/her text reviews on the items when calculating similarities between users.

Keywords: Recommender system, Collaborative filtering, Text mining, Review mining

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Authors: Carla Nardelli, Jérome Holtzmann, Céline Baeyens, Catherine Bortolon

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Background. Emotion regulation (ER) is a process associated with difficulties in mental health. Given its transdiagnostic features, its improvement could facilitate the recovery of various psychological issues. A limit of current studies is the lack of knowledge regarding whether available interventionsimprove ER flexibility (i.e., the ability to implement ER strategies in line with contextual demands), even though this capacity has been associated with better mental health and well-being. Therefore, the aim of the study is to test the efficacy of a 9-weeks ER group program (the Affect Regulation Training-ART), using the most appropriate measures (i.e., experience sampling method) in a student population. Plus, the goal of the study is to explore the potential mediative role of ER flexibility on mental health improvement. Method. This Randomized Controlled Trial will comparethe ER program group to an active control group (a relaxation program) in 100 participants. To test the mediative role of ER flexibility on mental health, daily measures will be used before, during, and after the interventions to evaluate the extent to which participants are flexible in their ER. Expected outcomes. Using multilevel analyses, we expect an improvement in anxious-depressive symptomatology for both groups. However, we expect the ART group to improve specifically on ER flexibility ability and the last to be a mediative variable on mental health. Conclusion. This study will enhance knowledge on interventions for students and the impact of interventions on ER flexibility. Also, this research will improve knowledge on ecological measures for assessing the effect of interventions. Overall, this project represents new opportunities to improve ER skills to improve mental health in undergraduate students.

Keywords: emotion regulation flexibility, experience sampling method, psychological intervention, emotion regulation skills

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Authors: D. I. George Amalarethinam, A. Emima

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Educational Data Mining (EDM) is one of the newest topics to emerge in recent years, and it is concerned with developing methods for analyzing various types of data gathered from the educational circle. EDM methods and techniques with machine learning algorithms are used to extract meaningful and usable information from huge databases. For scientists and researchers, realistic applications of Machine Learning in the EDM sectors offer new frontiers and present new problems. One of the most important research areas in EDM is predicting student success. The prediction algorithms and techniques must be developed to forecast students' performance, which aids the tutor, institution to boost the level of student’s performance. This paper examines various classification techniques in prediction methods and data mining tools used in EDM.

Keywords: classification technique, data mining, EDM methods, prediction methods

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Authors: Ylva Fältholm, Cathrine Norberg

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One of the major threats identified to successful future mining is that women do not find the industry attractive. Many attempts have been made, for example in Sweden and Australia, to create organizational structures and mining communities attractive to both genders. Despite such initiatives, many mining areas are developing into gender-segregated fly-in/fly out communities dominated by men with both social and economic consequences. One of the challenges facing many mining companies is thus to break traditional gender patterns and structures. To do this increased knowledge about gender in the context of mining is needed. Since language both constitutes and reproduces knowledge, increased knowledge can be gained through an exploration and description of the mining discourse from a gender perspective. The aim of this study is to explore what conceptual ideas are activated in connection to the physical/geographical mining area and to work within the mining industry. We use a combination of critical discourse analysis implying close reading of selected texts, such as policy documents, interview materials, applications and research and innovation agendas, and analyses of linguistic patterns found in large language corpora covering millions of words of contemporary language production. The quantitative corpus data serves as a point of departure for the qualitative analysis of the texts, that is, suggests what patterns to explore further. The study shows that despite technological and organizational development, one of the most persistent discourses about mining is the conception of dangerous and unfriendly areas infused with traditional notions of masculinity ideals and manual hard work. Although some of the texts analyzed highlight gender issues, and describe gender-equalizing initiatives, such as wage-mapping systems, female networks and recruitment efforts for women executives, and thereby render the discourse less straightforward, it is shown that these texts are not unambiguous examples of a counter-discourse. They rather illustrate that discourses are not stable but include opposing discourses, in dialogue with each other. For example, many texts highlight why and how women are important to mining, at the same time as they suggest that gender and diversity are all about women: why mining is a problem for them, how they should be, and what they should do to fit in. Drawing on a constitutive view of discourse, knowledge about such conflicting perceptions of women is a prerequisite for succeeding in attracting women to the mining industry and thereby contributing to the development of future mining.

Keywords: discourse, corpus linguistics, gender, mining

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Authors: Siqing Guan, Fumiyo Oshima, Eiji Shimizu, Nozomi Tomita, Toru Takahashi, Hiroaki Kumano

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Risk factors for social anxiety in autism spectrum conditions involve executive attention, emotion regulation, and thought regulation as processes of cognitive dysregulation. Social camouflaging behaviors as strategies used to mask and/or compensate for autism characteristics during social interactions in autism spectrum conditions have also been emphasized. However, the role of cognitive dysregulation and social camouflaging related to social anxiety in autism spectrum conditions has not been clarified. Whether these factors are specific to social anxiety in autism spectrum conditions or common to social anxiety independent of autism spectrum conditions needs to be clarified. Here, we explored risk factors specific to social anxiety in autism spectrum conditions and general risk factors for social anxiety independent of autism spectrum conditions. From the Japanese participants in early adulthood (age=18~39) of the online survey in Japan, those who exceeded the Japanese version Autism-Spectrum Quotient cutoff (33 points or more )were divided into the autism spectrum conditions group (ASC; N=255, mean age=32.08, SD age=5.16)and those who did not exceed the cutoff were divided into the non-autism spectrum conditions group (Non-ASC; N=255, mean age=31.70, SD age=5.09). Using the Japanese versions of the Social Phobia Scale, the Social Interaction Anxiety Scale, and the Short Fear of Negative Evaluation Scale, a composite score for social anxiety was calculated using a method of principal. We also measured emotional control difficulties using the Difficulties in Emotion Regulation Scale, executive attention using the Effortful Control Scale for Adults, rumination using the Rumination-Reflection Questionnaire, and worry using the Penn State Worry Questionnaire. This study was passed through the review of the Ethics Committee. No conflicts of interest. Multiple regression analysis with forced entry method was used to predict social anxiety in the ASC and non-ASC groups separately, based on executive attention, emotion dysregulation, worry, rumination, and social camouflage. In the ASC group, emotion dysregulation (β=.277, p<.001), worry (β=.162, p<.05), assimilation (β=.308, p<.001) and masking (β=.275, p<.001) were significant predictors of social anxiety (F (7,247) = 45.791, p <.001, R2=.565). In the non-ASC groups,emotion dysregulation (β=.171, p<.05), worry (β=.344，p <.001), assimilation (β=.366,p <.001) and executive attention (β=-.132,p <.05) were significant predictors of social anxiety (F (7,207) =47.333, p <.001, R2=.615).The findings suggest that masking was shown to be a risk factor for social anxiety specific to autism spectrum conditions, while emotion dysregulation, worry, and assimilation were shown to be common risk factors for social anxiety, regardless of autism spectrum conditions. In addition, executive attention is a risk factor for social anxiety without autism spectrum conditions.

Keywords: autism spectrum, cognitive control, social anxiety, social camouflaging

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Authors: Bharat P. Modi, Jayesh M. Patel

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Mobile Internet is acting as a major source of data. As the number of web pages continues to grow the Mobile web provides the data miners with just the right ingredients for extracting information. In order to cater to this growing need, a special term called Mobile Web mining was coined. Mobile Web mining makes use of data mining techniques and deciphers potentially useful information from web data. Web Usage mining deals with understanding the behavior of users by making use of Mobile Web Access Logs that are generated on the server while the user is accessing the website. A Web access log comprises of various entries like the name of the user, his IP address, a number of bytes transferred time-stamp etc. A variety of Log Analyzer tools exists which help in analyzing various things like users navigational pattern, the part of the website the users are mostly interested in etc. The present paper makes use of such log analyzer tool called Mobile Web Log Expert for ascertaining the behavior of users who access an astrology website. It also provides a comparative study between a few log analyzer tools available.

Keywords: mobile web access logs, web usage mining, web server, log analyzer

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Authors: Ashutosh Mishra, Nikhil Goyal

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A 3D facial emotion recognition model based on deep learning is proposed in this paper. Two convolution layers and a pooling layer are employed in the deep learning architecture. After the convolution process, the pooling is finished. The probabilities for various classes of human faces are calculated using the sigmoid activation function. To verify the efficiency of deep learning-based systems, a set of faces. The Kaggle dataset is used to verify the accuracy of a deep learning-based face recognition model. The model's accuracy is about 65 percent, which is lower than that of other facial expression recognition techniques. Despite significant gains in representation precision due to the nonlinearity of profound image representations.

Keywords: facial recognition, computational intelligence, convolutional neural network, depth map

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Authors: Telma Fontao de Castro, Kylee Miller, Maria Xavier Araújo, Isabel Brandao, Sandra Torres

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Objective: There is a dearth of research investigating the long-term emotional functioning of individuals recovered from anorexia nervosa (AN). This 15-year longitudinal study aimed to examine whether difficulties in cognitive processing of emotions persisted after long-term AN recovery and its link to anxiety and depression. Method: Twenty-four females, who were tested longitudinally during their acute and recovered AN phases, and 24 healthy control (HC) women, were screened for anxiety, depression, alexithymia, and emotion regulation difficulties (ER; only assessed in recovery phase). Results: Anxiety, depression, and alexithymia levels decreased significantly with AN recovery. However, scores on anxiety and difficulty in identifying feelings (alexithymia factor) remained high when compared to the HC group. Scores on emotion regulation difficulties were also lower in HC group. The abovementioned differences between AN recovered group and HC group in difficulties in identifying and accepting feelings and lack of emotional clarity were no longer present when the effect of anxiety and depression was controlled. Conclusions: Findings suggest that emotional dysfunction tends to decrease in AN recovered phase. However, using an HC group as a reference, we conclude that several emotional difficulties are still increased after long-term AN recovery, in particular, limited access to emotion regulation strategies, and difficulty controlling impulses and engaging in goal-directed behavior, thus suggesting to be a trait vulnerability. In turn, competencies related to emotional clarity and acceptance of emotional responses seem to be state-dependent phenomena linked to anxiety and depression. In sum, managing emotions remains a challenge for individuals recovered from AN. Under this circumstance, maladaptive eating behavior can serve as an affect regulatory function, increasing the risk of relapse. Emotional education and stabilization of depressive and anxious symptomatology after recovery emerge as an important avenue to protect from long-term AN relapse.

Keywords: alexithymia, anorexia nervosa, emotion recognition, emotion regulation

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Authors: Setareh Shayanfar

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Given the increasing importance of peer relationships during adolescence, the present study aimed to examine peer interactions that facilitate or hinder adolescents’ regulation of negative emotions. Using the Enhanced Critical Incident Technique, 1-hour semi-structured interviews were conducted with 16 junior high school adolescents. Participants were asked to recall situations when they experienced strong negative emotions during the past school year, indicate the peer interactions that helped or hindered their emotion regulation, and identify prospective interactions with the potential to help regulate their emotions. Data analysis extracted 182 critical incidents, including 109 helping incidents, 45 hindering incidents, and 28 wish list items, which generated 10 categories nested within four overarching themes: Positive Personal Support included (a) supportive presence, (b) expressing concern, (c) empathizing, and (d) encouraging and cheering up; while Strategy Transmission included (e) sharing perspective, and (f) giving advice; Activated Support included (g) taking action, and (h) distracting; while Negative Personal Interactions included (i) withdrawing and (j) punishing. Implications for mental health and service providers, as well as recommendations for future research, are presented.

Keywords: adolescence, emotion regulation, enhanced critical incident technique, peers

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Authors: Julio Vega

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Expression recognition is a field of Artificial Intelligence whose main objectives are to recognize basic forms of affective expression that appear on people’s faces and contributing to behavioral studies. In this work, a ROS node has been developed that, based on Deep Learning techniques, is capable of detecting the facial expressions of the people that appear in the image. These algorithms were optimized so that they can be executed in real time on an embedded platform. The experiments were carried out in a PC with a USB camera and in a Raspberry Pi 4 with a PiCamera. The final results shows a plausible system, which is capable to work in real time even in an embedded platform.

Keywords: python, low-cost, raspberry pi, emotion detection, human-robot interaction, ROS node

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Authors: Mahsa Tavakoli, Seyed Mohammad Hojjati, Yahya Kooch

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Considering that coal mining is one of the important industrial activities, it may cause damages to environment. According to the author’s best knowledge, the effect of traditional coal mining activities on plant biodiversity has not been investigated in the Hyrcanian forests. Therefore, in this study, the effect of coal mining activities on vegetation and tree diversity was investigated in Hyrcanian forest, North Iran. After filed visiting and determining the mine, 16 plots (20×20 m²) were established by systematic-randomly (60×60 m²) in an area of 4 ha (200×200 m²-mine entrance placed at center). An area adjacent to the mine was not affected by the mining activity, and it is considered as the control area. In each plot, the data about trees such as number and type of species were recorded. The biodiversity of vegetation cover was considered 5 square sub-plots (1 m²) in each plot. PAST software and Ecological Methodology were used to calculate Biodiversity indices. The value of Shannon Wiener and Simpson diversity indices for tree cover in control area (1.04±0.34 and 0.62±0.20) was significantly higher than mining area (0.78±0.27 and 0.45±0.14). The value of evenness indices for tree cover in the mining area was significantly lower than that of the control area. The value of Shannon Wiener and Simpson diversity indices for vegetation cover in the control area (1.37±0.06 and 0.69±0.02) was significantly higher than the mining area (1.02±0.13 and 0.50±0.07). The value of evenness index in the control area was significantly higher than the mining area. Plant communities are a good indicator of the changes in the site. Study about changes in vegetation biodiversity and plant dynamics in the degraded land can provide necessary information for forest management and reforestation of these areas.

Keywords: vegetation biodiversity, species composition, traditional coal mining, Caspian forest

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Authors: Azzaya Enkhjargal

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Context: The mining industry has a long history of human rights abuses, including forced labor, environmental pollution, and displacement of communities. In recent years, there has been growing international pressure to hold mining companies accountable for these abuses. Research Aim: This study explores the legal liabilities of mining companies for human rights abuses. The study specifically examines the case of Erdenet Mining Corporation (EMC), a large mining company in Mongolia that has been accused of human rights abuses. Methodology: The study used a mixed-methods approach, which included a review of legal literature, interviews with community members and NGOs, and a case study of EMC. Findings: The study found that mining companies can be held liable for human rights abuses under a variety of regulatory frameworks, including soft law and self-regulatory instruments in the mining industry, international law, national law, and corporate law. The study also found that there are a number of challenges to holding mining companies accountable for human rights abuses, including the lack of effective enforcement mechanisms and the difficulty of proving causation. Theoretical Importance: The study contributes to the growing body of literature on the legal liabilities of mining companies for human rights abuses. The study also provides insights into the challenges of holding mining companies accountable for human rights abuses. Data Collection: The data for the study was collected through a variety of methods, including a review of legal literature, interviews with community members and NGOs, and a case study of EMC. Analysis Procedures: The data was analyzed using a variety of methods, including content analysis, thematic analysis, and case study analysis. Conclusion: The study concludes that mining companies can be held liable for human rights abuses under a variety of legal and regulatory frameworks. There are positive developments in ensuring greater accountability and protection of affected communities and the environment in countries with a strong economy. Regrettably, access to avenues of redress is reasonably low in less developed countries, where the governments have not implemented a robust mechanism to enforce liability requirements in the mining industry. The study recommends that governments and mining companies take more ambitious steps to enhance corporate accountability.

Keywords: human rights, human rights abuses, ESG, litigation, Erdenet Mining Corporation, corporate social responsibility, soft law, self-regulation, mining industry, parent company liability, sustainability, environment, UN

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Authors: Devina Aprilia Gunawan, Tasya Aspiranti, Inugrah Ratia Pratiwi

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This research aims to classify the mining sector companies based on Altman’s Z-score model, and providing an analysis based on the Altman’s Z-score model’s financial ratios to provide a picture about the financial condition in mining sector companies in Indonesia and their viability in the future, and to find out the partial and simultaneous impact of each of the financial ratio variables in the Altman’s Z-score model, namely (WC/TA), (RE/TA), (EBIT/TA), (MVE/TL), and (S/TA), toward the financial condition represented by the Z-score itself. Among 38 mining sector companies listed in Indonesia Stock Exchange (IDX), 28 companies are selected as research sample according to the purposive sampling criteria.The results of this research showed that during 3 years research period at 2010-2012, the amount of the companies that was predicted to be healthy in each year was less than half of the total sample companies and not even reach up to 50%. The multiple regression analysis result showed that all of the research hypotheses are accepted, which means that (WC/TA), (RE/TA), (EBIT/TA), (MVE/TL), and (S/TA), both partially and simultaneously had an impact towards company’s financial condition.

Keywords: Altman’s Z-score model, financial condition, mining companies, Indonesia

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Authors: H. S. Saini, K. Vijayalakshmi, Rishi Sayal

Abstract:

Network-based education has been growing rapidly in size and quality. Knowledge clustering becomes more important in personalized information retrieval for web-learning. A personalized-Learning service after the learners’ knowledge has been classified with clustering. Through automatic analysis of learners’ behaviors, their partition with similar data level and interests may be discovered so as to produce learners with contents that best match educational needs for collaborative learning. We present a specific mining tool and a recommender engine that we have integrated in the online learning in order to help the teacher to carry out the whole e-learning process. We propose to use sequential pattern mining algorithms to discover the most used path by the students and from this information can recommend links to the new students automatically meanwhile they browse in the course. We have Developed a specific author tool in order to help the teacher to apply all the data mining process. We tend to report on many experiments with real knowledge so as to indicate the quality of using both clustering and sequential pattern mining algorithms together for discovering personalized e-learning systems.

Keywords: e-learning, cluster, personalization, sequence, pattern

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Authors: Rika Yuliana, Budi Rahardjo

Abstract:

The Role of ICT in the organization will continue to experience growth in line with business growth. However, in reality, there is a gap between ICT initiatives with the development (needs) of company business that is caused by yet inadequate of ICT strategic alignment. Therefore, this study was conducted with the aim to create an enterprise architectural model rule, particularly in mining companies, using the TOGAF framework. The results from the design development phase of the mining enterprise architecture meta model represents the domain of business, applications, data, and technology. The results of the design as a whole were analyzed from four perspectives, namely the perspective of contextual, conceptual, logical and physical. In the end, the quality assessment of the mining enterprise architecture is conducted to assess the suitability of the design standards and architectural principles.

Keywords: design and development the information technology architecture, enterprise architecture, enterprise architecture design result, TOGAF architecture development method (ADM)

Procedia PDF Downloads 406

Authors: Melissa R. Quispe-Zuniga, Daniel Callo-Concha, Christian Borgemeister, Klaus Greve

Abstract:

The Peruvian Andes have a high potential for mining, but many of the mining areas overlay with campesino community lands, being these key actors for agriculture and livestock production. Lead by economic incentives, some communities are renting their lands to mining companies for exploration or exploitation. However, a growing number of campesino communities, usually social and economically marginalized, have developed resistance, alluding consequences, such as water pollution, land-use change, insufficient economic compensation, etc. what eventually end up in Socio-Environmental Conflicts (SEC). It is hypothesized that disclosing the information on environmental pollution and enhance the involvement of communities in the decision-making process may contribute to prevent SEC. To assess whether such complains are grounded on the environmental impact of mining activities, we measured the heavy metals concentration in 24 indicative samples from rivers that run across mining exploitations and farming community lands. Samples were taken during the 2016 dry season and analyzed by inductively-coupled-plasma-atomic-emission-spectroscopy. The results were contrasted against the standards of monitoring government institutions (i.e., OEFA). Furthermore, we investigated the water/environmental complains related to mining in the neighboring 14 communities. We explored the relationship between communities and mining companies, via open-ended interviews with community authorities and non-participatory observations of community assemblies. We found that the concentrations of cadmium (0.023 mg/L), arsenic (0.562 mg/L) and copper (0.07 mg/L), surpass the national water quality standards for Andean rivers (0.00025 mg/L of cadmium, 0.15 mg/L of arsenic and 0.01 mg/L of copper). 57% of communities have posed environmental complains, but 21% of the total number of communities were receiving an annual economic benefit from mining projects. However, 87.5% of the communities who had posed complains have high concentration of heavy metals in their water streams. The evidence shows that mining activities tend to relate to the affectation and vulnerability of campesino community water streams, what justify the environmental complains and eventually the occurrence of a SEC.

Keywords: mining companies, campesino community, water, socio-environmental conflict

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Authors: Erin Lynne Plettenberg, Jeremy Vickery

Abstract:

In an access-control situation, the role of a user determines whether a data request is appropriate. This paper combines vetted web mining and logic modeling to build a lightweight system for determining the role of a health care provider based only on their prior authorized requests. The model identifies provider roles with 100% recall from very little data. This shows the value of vetted web mining in AI systems, and suggests the impact of the ICD classification on medical practice.

Keywords: electronic medical records, information extraction, logic modeling, ontology, vetted web mining

Procedia PDF Downloads 147