Reviewerly/exHarmony huggingface dataset:
Pengembangan Komik Manga Biologi Berbasis Android Untuk Peserta Didik Kelas XI Ditingkat SMA/MA

&lt;p&gt;In learning biology facilities and infrastructure supporting learning and technological developments such as android owned by learners have not been used as a medium of learning. The alternative is to develop android-based digital manga comic learning media to facilitate the understanding of biological materials. This study aims to: 1.) Knowing how to develop android-based digital manga comic learning media on hormonal system material for class XI in MAN 2 Bandar Lampung that facilitate biology learning materials by learners. 2.) To determine the feasibility of developing comic-based digital manga media based on the hormonal system material for students of class XI based on the assessment of material experts, linguists, media experts, biology teachers, and learners. This research represents (R &amp;amp; D) Research and Development which is defined by the Borg and Gall procedure. Stages used are only up to the seventh stage of the ten stages. This research was conducted at MAN 2 BandarLampung on students of class XI MIA 1. Assessment of material experts is very decent percentage 83.75%, the assessment of linguists is very decent berpententase 93.75%, assessment of media experts is very decent percentage 85.8%. While the repson teachers berpersentase 86.25%, and the response class XI students berpersentase 82.4% in MAN 2 Bandarlampung. Based on the assessment of the quality of learning media of digital manga comic on hormonal system material expressed in criteria is very suitable to be used as a medium of learning.&lt;/p&gt;
Soil information management and knowledge sharing in Victoria, Australia—user perspectives

Abstract For twenty‐one years, soil information and knowledge in Victoria, Australia has been shared via the Victorian Resources Online ( VRO ) website. VRO has been the primary online repository for soil and landscape information generated from land resource assessment studies, field days, training workshops, and the visualization and animation of soil and landscape processes. User surveys and qualitative feedback have highlighted the value of online soil information to users, with many considering it a useful “tool of trade,” particularly those involved in agricultural research, extension, planning and education. An extensive series of “Healthy Soils” training workshops (2007–2010) focused on topics such as “soil types and structure,” “soil chemical testing,” “soil biology,” “soil erosion” and “organic matter.” Evaluation has demonstrated their usefulness and relevance, with learning outcomes achieved by &gt;80% of overall participants, many of whom planned to make better management decisions and change management practices on‐farm. The information and knowledge generated through the series of workshops remains a valuable online resource. The development of more sophisticated knowledge visualization tools, such as animations, has broadened the potential audience for soil‐related information. Analytical profiling of VRO users in recent years has highlighted it as a source of information and knowledge for a local, national and international audience. This supports the notion that increasing access to public‐sector data and information provides significant benefits. Lessons learnt from enduring programs such as VRO can inform future information and knowledge management initiatives.
Context Awareness Computing in Smart Spaces Using Stochastic Analysis of Sensor Data

In building a smart space, it becomes more critical to develop a recognition system which enables to be aware of contexts, since the appropriate services can be provided under the accurate recognition. As services satisfying for desires of individual human residents are more demanding, the necessity for more sophisticated recognition algorithms is increasing. This paper proposes an approach to discover the current context by stochastically analyzing data obtained from sensors deployed in the smart space. The approach proceeds in two phases, which is to build context models and to find one context model matching the current state space, however we mainly focus on the phase building context models. Experimental validation supports the approach and approved validity.
Interactive Text Analysis and Information Extraction

A lot of work that has been done in the text mining field concerns the extraction of useful information from the full-text of publications. Such information may be links to projects, acknowledgements to communities, citations to software entities or datasets and more. Each category of entities, according to its special characteristics, requires different approaches. Thus it is not possible to build a generic mining platform that could text mine various publications to extract such info. Most of the time, a field expert is needed to supervise the mining procedure, decide the mining rules with the developer, and finally validate the results. This is an iterative procedure that requires a lot of communication among the experts and the developers, and thus is very time-consuming. In this paper, we present an interactive mining platform. Its purpose is to allow the experts to define the mining procedure, set/update the rules, validate the results, while the actual text mining code is produced automatically. This significantly reduces the communication among the developers and the experts and moreover allows the experts to experiment themselves using a user-friendly graphical interface.
The Cross-Modality Disparity Problem in Multispectral Pedestrian Detection.

Multispectral pedestrian detection has shown great advantages under poor illumination conditions, since the thermal modality provides complementary information for the color image. However, real multispectral data suffers from the position shift problem, i.e. the color-thermal image pairs are not strictly aligned, making one object has different positions in different modalities. In deep learning based methods, this problem makes it difficult to fuse the feature maps from both modalities and puzzles the CNN training. In this paper, we propose a novel Aligned Region CNN (AR-CNN) to handle the weakly aligned multispectral data in an end-to-end way. Firstly, we design a Region Feature Alignment (RFA) module to capture the position shift and adaptively align the region features of the two modalities. Secondly, we present a new multimodal fusion method, which performs feature re-weighting to select more reliable features and suppress the useless ones. Besides, we propose a novel RoI jitter strategy to improve the robustness to unexpected shift patterns of different devices and system settings. Finally, since our method depends on a new kind of labelling: bounding boxes that match each modality, we manually relabel the KAIST dataset by locating bounding boxes in both modalities and building their relationships, providing a new KAIST-Paired Annotation. Extensive experimental validations on existing datasets are performed, demonstrating the effectiveness and robustness of the proposed method. Code and data are available at this https URL.
On the Value of Diploma from the Perspective of Undergraduate Students

The purpose of the study was to identify specific emotionally colored ideas about the future that determine the value of a diploma of higher education for modern undergraduate students in full-time education. The students of the first and fourth years of full-time education of five universities and five branches of universities in different regions of Russia, majoring in the different areas of training (specialty) took part in the study. The method of unfinished sentences was used. It has been shown that undergraduate students in their majority do not consider the diploma as a document that reflects the qualification and level of education of the owner, which determines the prospects for employment and career growth. The presence of a diploma, from the point of view of students, should guarantee its owner the loyalty of the society.
Special issue editorial: Virtual Agents for Social Skills Training

The topic of this special issue of the Journal on Multimodal User Interfaces is Virtual Agents for Social Skills Training. In this issue four papers investigate how interactive, multimodal technology such as virtual agents can be used in social skills training.
A Dynamic Recommender System with Fused Time and Location Factors

A recommender system is an important tool to help users obtain content and overcome information overload. It can predict users’ interests and offer recommendations by analyzing their history behaviors. However, traditional recommender systems focus primarily on static user behavior analysis. Recently, with the promotion of the Netflix recommendation prize and the open dataset with location and time information, many researchers have focused on the dynamic characteristics of the recommender system (including the changes in the dynamic model of user interest), and begun to offer recommendations based on these dynamic features. Intuitively, these dynamic user features provide us with an effective method to learn user interests deeply. Based on the observations above, we present a dynamic fusion model by integrating geographical location, user preferences, and the time factor based on the Gibbs sampling process to provide better recommendations. To evaluate the performance of our proposed method, we conducted experiments on real-world datasets. The experimental results indicate that our proposed dynamic recommender system with fused time and location factors not only performs well in traditional scenarios, but also in sparsity situations where users appear at the first time.
Effect-Driven Flow Analysis

Traditional machine-based static analyses use a worklist algorithm to explore the analysis state space, and compare each state in the worklist against a set of seen states as part of their fixed-point computation. This may require many state comparisons, which gives rise to a computational overhead. Even an analysis with a global store has to clear its set of seen states each time the store updates because of allocation or side-effects, which results in more states being reanalyzed and compared. In this work we present a static analysis technique, Modf, that does not rely on a set of seen states, and apply it to a machine-based analysis with global-store widening. Modf analyzes one function execution at a time to completion while tracking read, write, and call effects. These effects trigger the analysis of other function executions, and the analysis terminates when no new effects can be discovered. We compared Modf to a traditional machine-based analysis implementation on a set of 20 benchmark programs and found that Modf is faster for 17 programs with speedups ranging between 1.4x and 12.3x. Furthermore, Modf exhibits similar precision as the traditional analysis on most programs and yields state graphs that are comparable in size.
Automatic Image Annotation for Small and Ad hoc Intelligent Applications using Raspberry Pi

The cutting-edge technology Machine Learning (ML) is successfully applied for Business Intelligence. Among the various pre-processing steps of ML, Automatic Image Annotation (also known as automatic image tagging or linguistic indexing) is the process in which a computer system automatically assigns metadata in the form of captioning or keywords to a digital image. Automatic Image Annotation (AIA) methods (which have appeared during the last several years) make a large use of many ML approaches. Clustering and classification methods are most frequently applied to annotate images. In addition, these proposed solutions require a high computational infrastructure. However, certain real-time applications (small and ad-hoc intelligent applications) for example, autonomous small robots, gadgets, drone etc. have limited computational processing capacity. These small and ad-hoc applications demand a more dynamic and portable way to automatically annotate data and then perform ML tasks (Classification, clustering etc.) in real time using limited computational power and hardware resources. Through a comprehensive literature study we found that most image pre-processing algorithms and ML tasks are computationally intensive, and it can be challenging to run them on an embedded platform with acceptable frame rates. However, Raspberry Pi is sufficient for AIA and ML tasks that are relevant to small and ad-hoc intelligent applications. In addition, few critical intelligent applications (which require high computational resources, for example, Deep Learning using huge dataset) are only feasible to run on more powerful hardware resources. In this study, we present the framework of “Automatic Image Annotation for Small and Ad-hoc Intelligent Application using Raspberry Pi” and propose the low-cost infrastructures (single node and multi node using Raspberry Pi) and software module (for Raspberry Pi) to perform AIA and ML tasks in real time for small and ad-hoc intelligent applications. The integration of both AIA and ML tasks in a single software module (with in Raspberry Pi) is challenging. This study will helpful towards the improvement in various practical applications areas relevant to small intelligent autonomous systems.
Geochemical Soil Survey for Base and Precious Metals in Dagbala-Atte District, Southwestern Nigeria

Geochemical survey of residual soils was undertaken in Dagbala-Atte District of Igarra Schist Belt, southwestern Nigeria to explore for base and precious metals (Cu, Pb, Zn, Au and Ag).It involved sampling residual soil from 49 sites in a grid pattern; analyzing the soil samples for the base and precious metals and other commonly associated elements including As, Cd, Fe, Hg and Sb; subjecting the geochemical data generated to statistical analysis; and preparing the geochemical distribution map of the district for each element.On the basis of the multivariate statistical analysis (correlation matrix and factor analysis) of the soil geochemical data, three mineralization types were inferred to be present in the district.The suspected types of mineralization consist of: 1) Cu-Pb-Zn-Ag mineralization with associated As, Hg, Sb and Fe; 2) Au-Cu mineralization with associated As; and 3) minor Hg-bearing Au-Pb mineralization.With the aid of the elemental geochemical distribution maps, the suspected mineralization was linked to rock types from which the sampled residual soils were apparently derived.Hence, the suspected Cu-Pb-Zn-Ag mineralization is linked to the quartz-biotite schist at the southwestern part of the district while the suspected Au-Cu mineralization is located on the silicified sheared rock at the northern and southern parts and the minor Hg-bearing Au-Pb mineralization within the granitic gneiss at the northeastern and eastern areas of the district.Based on the foregoing findings, it is recommended that lithogeochemical survey for the base and precious metals should be done in the area.
The Art of Phishing

Nowadays there are many threats that a company needs to protect itself. Everyone knows someone who has fallen for a coup by using an email, message or phone. People who pass by someone they trust, to extract data and money from the victim. These three ways are used to try convince someone to deliver accounts, credit card and document data in companies and at a particular level. According to Symantec, more than 6 hundreds of companies per day are targeted for Phishing, specifically Business E-Mail Compromise (BEC). In it, criminals pass through a central figure in the company, usually the CEO, and try to extract information or get employees to transfer money. This type of attack has generated in the last years losses of billions of dollars for the businesses affected. It is urgent that all company employees and individuals know as soon as possible what Phishing is and what steps to take.
Remote sensing signature of geological structures inferred on landsat imagery of Afikpo area Southeastern Nigeria

Satellite remote sensing has contributed immensely to the interpretation of linear features and other geological structures.In the study area, lineaments mapped were linear and planar features on satellite imageries that are expression of fractures, or faults within the subsurface.Image lines of different contrast may extend from a few centimetres to several of metres in length.The Landsat imagery used has a spatial resolution of 30 x 30 m, and eleven spectral bands covering the visible and thermal region of electromagnetic spectrum.Four spectral bands were selected and used for the purpose of structural mapping namely; thermal infra-red band (Band 11), 11.5 to 12.51 µm; Short-wave infrared (Band 6), 1.57 -1.65 µm; near Infrared (Band 5) 0.85 to 0.88 µm and Red (Band 4) 0.64 -0.67 µm.Lineaments extracted from the digital satellite scene are concentrated mostly on the sandstone belt in the North-East close to Amasiri area.A total of 116 lineaments were generated.Seven thematic maps were produced; digital elevation model (DEM) was used to create surface features maps, shaded relief and slope while the regional lineament map was digitized from the enhanced colour composites of Landsat ETM (bands 7, 5 and 2) image using ARCGIS 10.0 software for visual extraction and delineation of geological structures which were also ground-truth.The area indicates major NE -SW to minor E -W orientation of lineaments.The lineament density identified indicates regional lineaments are of great importance as indexes for groundwater exploration, mineralization targets, rocks quarrying and structural interpretation.
Skeleton-Based Human Action Recognition through Third-Order Tensor Representation and Spatio-Temporal Analysis

Given the broad range of applications from video surveillance to human–computer interaction, human action learning and recognition analysis based on 3D skeleton data are currently a popular area of research. In this paper, we propose a method for action recognition using depth sensors and representing the skeleton time series sequences as higher-order sparse structure tensors to exploit the dependencies among skeleton joints and to overcome the limitations of methods that use joint coordinates as input signals. To this end, we estimate their decompositions based on randomized subspace iteration that enables the computation of singular values and vectors of large sparse matrices with high accuracy. Specifically, we attempt to extract different feature representations containing spatio-temporal complementary information and extracting the mode-n singular values with regards to the correlations of skeleton joints. Then, the extracted features are combined using discriminant correlation analysis, and a neural network is used to recognize the action patterns. The experimental results presented use three widely used action datasets and confirm the great potential of the proposed action learning and recognition method.
‘I have a different kind of brain’—a script-centric approach to interactive narratives in games

In a computer game narrative, a user influences the ordering of events. To model this behaviour, game designers and writers need to use some kind of programming primitives. A computer game script will hence differ from, for instance, a movie screenplay in that traditional dialogue text is complemented with some textual or visual logic formalism. Not all groups involved in production of a game have a programming background and may therefore be unable to easily comprehend such formalisms. This paper presents a novel approach to game dialogue writing where traces from play-throughs are used as the core of the script. Alternative branches are identified and presented in relation to the main trace. The approach has been implemented in a tool that has been used successfully by three professional writers in mobile game production. The results indicate that this is a promising approach to enable non-programmers to work with interactive narratives.
Automated Negotiations Under User Preference Uncertainty: A Linear Programming Approach

Autonomous agents negotiating on our behalf find applications in everyday life in many domains such as high frequency trading, cloud computing and the smart grid among others. The agents negotiate with one another to reach the best agreement for the users they represent. An obstacle in the future of automated negotiators is that the agent may not always have a priori information about the preferences of the user it represents. The purpose of this work is to develop an agent that will be able to negotiate given partial information about the user’s preferences. First, we present a new partial information model that is supplied to the agent, which is based on categorical data in the form of pairwise comparisons of outcomes instead of precise utility information. Using this partial information, we develop an estimation model that uses linear optimization and translates the information into utility estimates. We test our methods in a negotiation scenario based on a smart grid cooperative where agents participate in energy trade-offs. The results show that already with very limited information the model becomes accurate quickly and performs well in an actual negotiation setting. Our work provides valuable insight into how uncertainty affects an agent’s negotiation performance, how much information is needed to be able to formulate an accurate user model, and shows a capability of negotiating effectively with minimal user feedback.
Partial and Conditional Expectations in Markov Decision Processes with Integer Weights

Abstract The paper addresses two variants of the stochastic shortest path problem (“optimize the accumulated weight until reaching a goal state”) in Markov decision processes (MDPs) with integer weights. The first variant optimizes partial expected accumulated weights, where paths not leading to a goal state are assigned weight 0, while the second variant considers conditional expected accumulated weights, where the probability mass is redistributed to paths reaching the goal. Both variants constitute useful approaches to the analysis of systems without guarantees on the occurrence of an event of interest (reaching a goal state), but have only been studied in structures with non-negative weights. Our main results are as follows. There are polynomial-time algorithms to check the finiteness of the supremum of the partial or conditional expectations in MDPs with arbitrary integer weights. If finite, then optimal weight-based deterministic schedulers exist. In contrast to the setting of non-negative weights, optimal schedulers can need infinite memory and their value can be irrational. However, the optimal value can be approximated up to an absolute error of $$\epsilon $$ in time exponential in the size of the MDP and polynomial in $$\log (1/\epsilon )$$ .
Digital Motion Acquisition to Assess Spinal Cord Injured (SCI) Patients

The process of motorcycle seat styling is a grey system with partially known and partially unknown information and is influenced by various factors.In this study, Grey Modelling (GM)(1,1) is used to predict the style of a motorcycle seat, and the shape features of the seat are extracted via morphological analysis and are parameterized.The process of shape evolution is established, and the modelling characteristics are predicted by GM(1,1).The kansei study is performed using five adjectives describing the seat styles to establish the equation of kansei regression analysis.The regression analysis is employed to modify predictive modelling.A certain brand of motorcycle seats is modelled to analyse and verify the feasibility and scientific applicability of adopting GM(1,1) in predicting motorcycle seat styling, which provided a feasible and effective reference for the motorcycle seat design.