Reviewerly/exHarmony huggingface dataset:
Emojis: A Grapholinguistic Approach

The present article stands at the interface of CMC research andgrapholinguistics. After outlining which features are typical of the writing of pri­vate text messages, the focus of the first part of the paper (Sections 2 and 3) lieson the use of emojis. Notably, emoji use is not—as is commonly done—analyzedunder a pragmatic perspective, but grapholinguistically, at the graphetic andgraphematic levels: emojis are conceptualized as visual shapes that may assumegraphematic functions within a given writing system. In the second part (Sec­tion 4), it is underlined that all variants of written digital communication (suchas the use of emojis, but also all other characters) are made possible only due tothe Unicode Consortium’s decisions; this, finally, is argued to have far­reaching consequences for the future of writing.
Early Detection of Osteoarthritis based on Cartilage Thickness in Knee X-ray Images

Arthritis is a joint disorder featuring inflammation.There are numerous forms of Arthritis.Arthritis essentially causes joint dis-functioning which may further tend to cause deformity and disability.Osteoarthritis (OA) is one form of arthritis which is mostly seen in old age group.A patient suffering from OA needs to visit medical experts where clinical and radiographic examination is carried out.Analysis of bone structures in initial stage is bit complex.So any vague conclusion drawn from the radiographic images may make the treatment faulty and troublesome.Thus to overcome this we have developed an algorithm that computes the cartilage area/thickness using various shape descriptors.The computed descriptors obtained the accuracy of 99.81% for K-nearest neighbour classifier and 95.09% for decision tree classifier.The estimated cartilage thickness is validated by radiographic experts as per KL grading framework which will be helpful to the doctors for quick and appropriate analysis of ailment in the early stage.The results are competitive and promising as reported in the literature.
Deep Learning Black Box Problem

Application of neural networks in deep learning is rapidly growing due to their ability to outperform other machine learning algorithms in different kinds of problems. But one big disadvantage of d ...
Realizing a Mobile Multimodal Platform for Serious Games Analytics

This paper presents the design and development of "StoryPlay Multimodal", a mobile multimodal analytics platform for the evaluation of Serious Games. It is intended to serve researchers, teachers and educational game developers as a means to assess their Serious Game Design. This is done by capturing, pre-processing, synchronizing and visualizing multimodal serious games analytics and mobile sensor data from playtesting sessions. By linking log data with multimodal data, it is possible to uncover relations between design elements, gameplay interactions, context parameters and affective and cognitive states. This is crucial for gaining full insight into the session, even if not present with the player at the same location. After discussing design requirements, the architecture of the software, the different modules, additional features, implementation challenges and solutions are presented. The testing settings, participants and results are also discussed to demonstrate how the evaluation procedure can be applied to deliver valuable outcomes for Serious Games Research.
Artificial Techniques for Language Disorders

&lt;p class="0abstract"&gt;This review focuses on artificial techniques which include the artificial intelligent techniques and applications, the robot technology and the serious games supporting the procedure of learning and teaching of language disorders and deficits as well the evolution of speech. Especially, the written language, the oral language and the phoneme pronunciations, the communication and consequently the social interaction are benefited and are assisted by these achievements of technology.&lt;/p&gt;
Explainable Subgraphs with Surprising Densities: A Subgroup Discovery Approach

The connectivity structure of graphs is typically related to the attributes of the nodes. In social networks for example, the probability of a friendship between two people depends on their attributes, such as their age, address, and hobbies. The connectivity of a graph can thus possibly be understood in terms of patterns of the form ‘the subgroup of individuals with properties X are often (or rarely) friends with individuals in another subgroup with properties Y'. Such rules present potentially actionable and generalizable insights into the graph. We present a method that finds pairs of node subgroups between which the edge density is interestingly high or low, using an information-theoretic definition of interestingness. This interestingness is quantified subjectively, to contrast with prior information an analyst may have about the graph. This view immediately enables iterative mining of such patterns. Our work generalizes prior work on dense subgraph mining (i.e. subgraphs induced by a single subgroup). Moreover, not only is the proposed method more general, we also demonstrate considerable practical advantages for the single subgroup special case.MSC codesGraph miningSubgroup DiscoverySubjective interestingnessCommunity detection
A comparative analysis of classification techniques on predicting flood risk

Flood is a temporary overflow of a dry area due to overflow of excess water, runoff surface waters or undermining of shoreline. In Malaysia itself in 2014, the country grieved with the catastrophic flood event in Kuala Krai, Kelantan, which caused of human lives, public assets and money lost. Due to uncertainties in flooding event, it is vital for Malaysia to have pre-warning system that assist related agencies in to categorize land areas that face high risk of flood so preventive actions can be planned in place. This paper conducts a comparative analysis of three classifications in classifying the risk of flood, whether high or low. The classification experiment conducts using three variants of Bayesian approaches, which are Bayesian Networks (BN), Naive Bayes (NB), and Tree Augmented Naive Bayes (TAN). The outcome of this research shows that Tree Augmented Naive Bayes (TAN) has the best algorithms as compared to others algorithms in classifying the risk of flood.
An Implicit Form of Krasulina's k-PCA Update without the Orthonormality Constraint

We shed new insights on the two commonly used updates for the online k-PCA problem, namely, Krasulina's and Oja's updates. We show that Krasulina's update corresponds to a projected gradient descent step on the Stiefel manifold of orthonormal k-frames, while Oja's update amounts to a gradient descent step using the unprojected gradient. Following these observations, we derive a more implicit form of Krasulina's k-PCA update, i.e. a version that uses the information of the future gradient as much as possible. Most interestingly, our implicit Krasulina update avoids the costly QR-decomposition step by bypassing the orthonormality constraint. A related update, called the Sanger's rule, can be seen as an explicit approximation of our implicit update. We show that the new update in fact corresponds to an online EM step applied to a probabilistic k-PCA model. The probabilistic view of the update allows us to combine multiple models in a distributed setting. We show experimentally that the implicit Krasulina update yields superior convergence while being significantly faster. We also give strong evidence that the new update can benefit from parallelism and is more stable w.r.t. tuning of the learning rate.
Automated Synthesis of Social Laws in STRIPS

Agents operating in a multi-agent environment must consider not just their actions, but also those of the other agents in the system. Artificial social systems are a well-known means for coordinating a set of agents, without requiring centralized planning or online negotiation between agents. Artificial social systems enact a social law which restricts the agents from performing some actions under some circumstances. A robust social law prevents the agents from interfering with each other, but does not prevent them from achieving their goals. Previous work has addressed how to check if a given social law, formulated in a variant of ma-strips, is robust, via compilation to planning. However, the social law was manually specified. In this paper, we address the problem of automatically synthesizing a robust social law for a given multi-agent environment. We treat the problem of social law synthesis as a search through the space of possible social laws, relying on the robustness verification procedure as a goal test. We also show how to exploit additional information produced by the robustness verification procedure to guide the search.
Enhancement of the Multi-Particle Collision Algorithm by mechanisms derived from the Opposition-Based Optimization

New versions of the metaheuristic Multi-Particle Collision Algorithm (MPCA) are presented.In order to provide more effective candidate solutions for an optimization problem, the concept of opposition and reflection is introduced to improve the capacity to find a solution in the search space.Four different strategies to compute the reflected and opposite points are implemented.The performance of all implementations is evaluated over thirty objective functions with different complexities, using serial and parallel versions of the algorithms.
A Unifying View on Individual Bounds and Heuristic Inaccuracies in Bidirectional Search

In the past few years, new very successful bidirectional heuristic search algorithms have been proposed. Their key novelty is a lower bound on the cost of a solution that includes information from the g values in both directions. Kaindl and Kainz (1997) proposed measuring how inaccurate a heuristic is while expanding nodes in the opposite direction, and using this information to raise the f value of the evaluated nodes. However, this comes with a set of disadvantages and remains yet to be exploited to its full potential. Additionally, Sadhukhan (2013) presented BAE∗, a bidirectional best-first search algorithm based on the accumulated heuristic inaccuracy along a path. However, no complete comparison in regards to other bidirectional algorithms has yet been done, neither theoretical nor empirical. In this paper we define individual bounds within the lower-bound framework and show how both Kaindl and Kainz's and Sadhukhan's methods can be generalized thus creating new bounds. This overcomes previous shortcomings and allows newer algorithms to benefit from these techniques as well. Experimental results show a substantial improvement, up to an order of magnitude in the number of necessarily-expanded nodes compared to state-of-the-art near-optimal algorithms in common benchmarks.
Improved Steganography Scheme based on Fractal Set

Steganography is the art of hiding secret data inside digital multimedia such as image, audio, text and video. It plays a significant role in current trends for providing secure communication and guarantees accessibility of secret information by authorised parties only. The Least-Significant Bit (LSB) approach is one of the important schemes in steganography. The majority of LSB-based schemes suffer from several problems due to distortion in a limited payload capacity for stego-image. In this paper, we have presented an alternative steganographic scheme that does not rely on cover images as in existing schemes. Instead, the image which includes the secure hidden data is generated as an image of a curve. This curve is resulted from a series of computation that is carried out over the mathematical chaotic fractal sets. The new scheme aims at improving the data concealing capacity, since it achieves limitless concealing capacity and disposes of the likelihood of the attackers to realise any secret information from the resulted stego-image. From the security side, the proposed scheme enhances the level of security as the scheme depends on the exact matching between secret information and the generated fractal (Mandelbrot-Julia) values. Accordingly, a key stream is created based on these matches. The proposed scheme is evaluated and tested successfully from different perspectives
Tandem Inference: An Out-of-Core Streaming Algorithm for Very Large-Scale Relational Inference

Statistical relational learning (SRL) frameworks allow users to create large, complex graphical models using a compact, rule-based representation. However, these models can quickly become prohibitively large and not fit into machine memory. In this work we address this issue by introducing a novel technique called tandem inference (ti). The primary idea of ti is to combine grounding and inference such that both processes happen in tandem. ti uses an out-of-core streaming approach to overcome memory limitations. Even when memory is not an issue, we show that our proposed approach is able to do inference faster while using less memory than existing approaches. To show the effectiveness of ti, we use a popular SRL framework called Probabilistic Soft Logic (PSL). We implement ti for PSL by proposing a gradient-based inference engine and a streaming approach to grounding. We show that we are able to run an SRL model with over 1B cliques in under nine hours and using only 10 GB of RAM; previous approaches required more than 800 GB for this model and are infeasible on common hardware. To the best of our knowledge, this is the largest SRL model ever run.
Sentiment Analysis on Grab User Reviews Using Support Vector Machine and Maximum Entropy Methods

One business that has developed along with the increase in information and communication technology is the transportation service business. The last decade has emerged as a technology-based transportation business innovation, for example Grab. It is important for a company or organization to find out about people's responses to their services or products. Public opinion on the product is not small in number, even though it is undeniable that public opinion has an impact on the company's image. Therefore, a technique for analyzing the opinion is needed so that the company can monitor and organize their services. Public opinion is classified into positive or negative sentiment classes using SVM and Maximum Entropy methods. The labeling results are then analyzed by text association to find the relationship of each information obtained. Classification with SVM method produces 89.01% accuracy. Whereas Maximum Entropy obtained higher accuracy that is 90.46%. Text associations obtained from the positive sentiment class and negative sentiment class. The results of negative reviews are analyzed for causes and consequences using fishbone diagrams for problem solving.
Diversified Bayesian Nonnegative Matrix Factorization

Nonnegative matrix factorization (NMF) has been widely employed in a variety of scenarios due to its capability of inducing semantic part-based representation. However, because of the non-convexity of its objective, the factorization is generally not unique and may inaccurately discover intrinsic “parts” from the data. In this paper, we approach this issue using a Bayesian framework. We propose to assign a diversity prior to the parts of the factorization to induce correctness based on the assumption that useful parts should be distinct and thus well-spread. A Bayesian framework including this diversity prior is then established. This framework aims at inducing factorizations embracing both good data fitness from maximizing likelihood and large separability from the diversity prior. Specifically, the diversity prior is formulated with determinantal point processes (DPP) and is seamlessly embedded into a Bayesian NMF framework. To carry out the inference, a Monte Carlo Markov Chain (MCMC) based procedure is derived. Experiments conducted on a synthetic dataset and a real-world MULAN dataset for multi-label learning (MLL) task demonstrate the superiority of the proposed method.
Design and Application of an Intelligent Patrol Algorithm for Forest Management and Protection Based on Global Positioning System

Forests are critical to the ecological balance of the earth.However, natural disasters and manmade damages are posing a severe threat to forest resources, calling for effective means of forest management and protection (M&P).Therefore, this paper designs and applies an intelligent patrol algorithm for forest M&P based on cutting-edge techniques like the global positioning system (GPS).Firstly, the information of forest road and the forest road network were obtained with the aid of the GPS.Next, the Dijkstra's algorithm was adopted to identify the shortest patrol path for the M&P personnel and realize the intelligent patrol algorithm, in the light of the key points in forest M&P and the responsible areas of the M&P personnel.Together, the forest road network, M&P route planning and intelligent patrol form an effective framework for high-quality forest M&P.The research results shed new light on the protection of forest resources.
Causal Transfer for Imitation Learning and Decision Making under Sensor-Shift

Learning from demonstrations (LfD) is an efficient paradigm to train AI agents. But major issues arise when there are differences between (a) the demonstrator's own sensory input, (b) our sensors that observe the demonstrator and (c) the sensory input of the agent we train.In this paper, we propose a causal model-based framework for transfer learning under such “sensor-shifts”, for two common LfD tasks: (1) inferring the effect of the demonstrator's actions and (2) imitation learning. First we rigorously analyze, on the population-level, to what extent the relevant underlying mechanisms (the action effects and the demonstrator policy) can be identified and transferred from the available observations together with prior knowledge of sensor characteristics. And we device an algorithm to infer these mechanisms. Then we introduce several proxy methods which are easier to calculate, estimate from finite data and interpret than the exact solutions, alongside theoretical bounds on their closeness to the exact ones. We validate our two main methods on simulated and semi-real world data.
Hot-film/hot-wire anemometer calibration for low velocities using image processing

Calibration of hot-wire and hot-film probes at low velocities is a difficult task because the dynamic pressure at these velocities is very low and may not be measured easily. To overcome this problem, substituent techniques have been presented in the literature that rely on other phenomena and utilize different hardware. This paper describes a simple and low-cost method which proposes to move the anemometer probe in quiescent air (here by means of a swinging arm) and track this motion with a camera. After processing of the images, velocity time history of the probe is found by numerical calculations. Calibration curve is then obtained without any predetermined relationship. Using a medium-speed video camera that is often found in laboratories would avoid the need to a position sensor and a complicated arm on which this sensor is mounted. This technique can be used not only for pendulums but also for other means of moving probes in quiescent medium.
Securing Web Applications through a Framework of Source Code Analysis

Source code analysis is becoming extremely important for the universal acceptance of web applications because the automated source code analysis tools play a key role in identifying and fixing security-related vulnerabilities.This paper proposes a framework for securing web applications through source code analysis.The framework has three prescriptive phases including executing and monitoring, classifying and controlling and refining and managing.The framework helps to examine the web application source code related to security issues.The executing and monitoring phase employs five different open source tools for statically analyzing the source code.According to the literature, there are nine broad categories of vulnerabilities in web applications.After filtration of these vulnerabilities, classifying and controlling phase categorize the vulnerabilities according to their severity level with the help of fuzzy analytical analysis process and suggestive measures.The refining and managing phase takes these measures and suggests changes to the source code to make it more secure.This framework was validated through a web-based hospital management system.The results of the validation showed that the framework implementation made the source code more robust towards the upcoming vulnerabilities and bugs.
An analysis of Politeness Strategies in Indonesian English Textbooks

The research aimed explores politeness approaches in EFL textbooks, especially Indonesian English textbooks. There were two textbooks which analysed as the sample for this study. The textbooks entitled “Talk Active 1” and “Talk Active 2: Language and Culture Programme”. The researcher limited the analysis on the dialogs which provided as materials in the textbooks. Brown &amp; Levinson's (1987) politeness strategies were applied to categorize the data. The researcher applied content analysis method proposed by Denscombe, (2010) as the research method. The results of the study revealed bald on-record, positive politeness, negative politeness, and off-record were reflected in the Indonesian English textbooks. Both types of bald on-record strategies were found in the Indonesian English textbooks. However, there were only three types of off-records strategies were reflected in the textbooks. The finding revealed 11 types of positive politeness as strategy follows: S1: Notice, attend to hearer; S2: Exaggerate, S3: Intensify interest to hearer; S4: Use in-group identity markers; S6: Avoid disagreement; S7: Presuppose/assert/raise common ground; S8: Joke; S10: Offer, promise; S12: Include both speaker and hearer in the activity; S13: Give (or ask for) reasons; S14: assume or assert reciprocity. The result also presented 8 types of negative politeness as follows: S1: Be conventionally indirect; S2: Question, hedge; S3: Be pessimistic; S4: Minimize the imposition; S5: Give deference; S6: Apologize; S7: Impersonalize speaker and hearer and S10: Go on record as incurring a debt, or as not indebting hearer. The analysis showed positive and negative politeness strategies were central strategies that reflected in the textbooks. It proved that Indonesian English textbooks have pragmatics input for the students.
Generalized Planning with Positive and Negative Examples

Generalized planning aims at computing an algorithm-like structure (generalized plan) that solves a set of multiple planning instances. In this paper we define negative examples for generalized planning as planning instances that must not be solved by a generalized plan. With this regard the paper extends the notion of validation of a generalized plan as the problem of verifying that a given generalized plan solves the set of input positives instances while it fails to solve a given input set of negative examples. This notion of plan validation allows us to define quantitative metrics to asses the generalization capacity of generalized plans. The paper also shows how to incorporate this new notion of plan validation into a compilation for plan synthesis that takes both positive and negative instances as input. Experiments show that incorporating negative examples can accelerate plan synthesis in several domains and leverage quantitative metrics to evaluate the generalization capacity of the synthesized plans.