Reviewerly/exHarmony huggingface dataset:
Can Reverse Nearest Neighbors Perceive Unknowns?

A novel open set classifier is presented in this work, where the neighborhood of a test instance is determined using the principles of Reverse k-nearest neighbors (RkNN). The RkNN count of an instance can have any non-negative value less or equal to the size of the training set. While dealing with an open dataset, consisting of known and unknown classes, the zero count can provide a possible solution for detecting the unknown class. Positive RkNN count along with the nearest RkNN distance information are used to determine the known class classifications. Experiments are carried out on ten real world datasets, with various openness values on five state-of-the-art open set learners and the proposed scheme. Their performance is measured on three evaluating metrics namely accuracy, average F1 over known and unknown classes, and Known class F1. Empirical results indicate comparable to superior performance delivered by the proposed method over the state-of-the-art approaches on all but one dataset.
Trust: Anthropomorphic Algorithmic

Computer Science often emulates humanlike behaviours including intelligence that has taken to storms in every other domain where human deals with. A computing system with a defined role and goal is called an agent with humanlike capability for decision making in dynamic and complex real world it is situated in. Largely this aspect of a computing unit needs ability to learn, act and forecast. Broadly the study in Artificial Intelligence (AI) also deals with such aspects. Researchers of both the schools of computing viz. Intelligent Agents and AI systems, propose several algorithms to emulate human like behaviours. These algorithms here, are labelled as anthropomorphic algorithms. In particular, here our discussion is focussed on trust. The idea of trust as conceptualised, computed and applied in different domains, is discussed. Further, it points out the dimensions that need to be looked at, in order to endow computing systems with trust as a humanitics.
The Integration of File Server Function and Task Management Function to Replace Web Application on Cloud Platform for Cost Reduction

The aim of this paper is to propose a new technique to operate a web application on a cloud platform based on the integration of multiple services which could benefit in the cost reduction of renting a cloud. Two of the most important functions are file server function and task management function. The purpose of study is to experiment this concept and technique. It is tested on Microsoft Azure platform since it is a cloud platform that covers all services required. These services are helpful in the operation of storing and retrieving data file in the created file server. Data is then displayed on web browser. Task management function is helpful in making notification to all 3 involved applications which are Trello, Microsoft To-Do, and Microsoft Planner. The operation starts with user's data input being processed in the function to create task list on the application. Test result is satisfactory i.e. the designed technique is able to function completely as normal website function and web application. Its main feature is the reduction of cloud rental fee because when service is open for user to access, the airtime fee is quite costly. Hence, with this new technique, the expense is calculated only when there is a data access from user which makes it much cheaper.
Multimedia Knowledge Design and Processing for Personal Robots

The development of personal robots or service robots has aroused much interest in recent times, witnessing an amazing growth of robotics in different domains. Although sophisticated humanoid robots have been developed, much more effort is needed for improving their cognitive capabilities. Interactions with humans and / or with other agents are still limited and not considered satisfactory. So, the way we store and represent knowledge in a cognitive architecture(CA) is fundamental in order to overcome these limitations and improve human-machine and machine-machine interactions. In this paper we propose a multimedia graph knowledge base using pure formalisms, which can be used by personal robots to represent and share the acquired knowledge, linking it to abstract concepts gifted with semantic relations. Moreover we propose a prototype architecture for sharing the knowledge between two agents following the Knowledge-as-a-service (KaaS) paradigm.
Object Manipulation: Interaction for Virtual Reality on Multi-touch Screen

We propose a virtual reality interaction method based on multitouch screen, which provides a human-computer interaction method for gesture touch control for manipulating 3D virtual objects in the virtual world. The method combines the real touch screen and the touch gestures, and the user can directly interact with the real touch screen like a daily tablet to control objects in the virtual environment. The interaction mode enables the user to obtain the real dynamic feedback information while interacting, and meets the daily operation habits of the user, so the operation of the object with multiple degrees of freedom is more accurate and convenient. Since the virtual object can be operated by translating, rotating, scaling, etc., this interaction can be applied to various display systems, and the user can obtain a more realistic and intuitive feeling through multi-angle observation. In the interactive system we designed, the hands and touch screens in the real world will be mapped in the virtual scene to achieve virtual and real registration, providing visual tracking of the contact between the hand and the touch screen, improving user immersion and realism.
Deep Autoencoders with Value-at-Risk Thresholding for Unsupervised Anomaly Detection

Many real-world monitoring and surveillance applications require non-trivial anomaly detection to be run in the streaming model. We consider an incremental-learning approach, wherein a deep-autoencoding (DAE) model of what is normal is trained and used to detect anomalies at the same time. In the detection of anomalies, we utilise a novel thresholding mechanism, based on value at risk (VaR). We compare the resulting convolutional neural network (CNN) against a number of subspace methods and present results on changedetection.net.
Dynamic Proxemic Distances in Different Soical Environments

Traditional robots are widely used in industrial which is isolated with humans to keep safety. However, with the advance in mobile robotics, they are going to live or work with human beings and increasingly appear in social environments. When navigating in a shared environment, a mobile robot should not only pay attention to obstacle avoidance and keep safety, it should also consider whether it interferes with other people's personal space and keep the human beings comfortable. The social aware robot has been of more interest to the robotics community. In this paper, we present a method to adjust the proxemic distance dynamically to meet the harmonious coexistence between humans and robots with the change of the social scenes. We combine the semantic information with human movements to generating an appropriate proxemic distance for pedestrians. In contrast to most existing methods, not only security regions but also human psychological characteristics are considering in this method.
Generalization Approach for CNN-based Object Detection in Unconstrained Outdoor Environments

Recent advances in object detection using convolutional neural networks provide new opportunities to address various problems faced in agriculture. However, training a reliable object detection model which can be generalized to different environments requires large amounts of data which is difficult to obtain. This is particularly true when considering detection of wilding conifers, which are scattered across a diverse, unconstrained environment. Here we argue that a reliable dataset can be developed for wilding detection by synthetically enlarging the training dataset. A reliable detection model was trained using only 100 real images of wilding conifers.
Pseudonymisation with Break-the-Glass Compatibility for Health Records in Federated Services

Pseudonymisation is a major requirement in recent data protection regulations, and of especial importance when sharing healthcare data outside of the boundaries of the affinity domain. However, healthcare systems require important break-the-glass procedures, such as accessing records of patients in unconscious states. Our work presents a pseudonymisation protocol that is compliant with break-the-glass procedures, established on a (t, n)-threshold secret sharing scheme and public key cryptography. The pseudonym is safely derived from a fragment of public information without any private secret requirement. The protocol is proven secure and scalable under reasonable assumptions.
Extraction and Recognition of Fingerprint Characteristic of Mobile Terminal’s Transient Signal

Abstract The fingerprint characteristic of mobile terminal’s signal is unique and it can be used to identify the source of the signal. In all the characteristics, the characteristic of transient signals has been more favoured because of the greater diversity. However, the duration of transient signal is extremely short and difficult to accurately detect. Therefore, in order to successfully obtain the fingerprint characteristic, most of the research results are based on the laboratory environment. In this paper, through the methods of differential constellation trajectory and neural network, we realize the extraction and recognition of fingerprint characteristic of mobile phone’s transient signal in the real environment. Meanwhile, by controlling the distance between the terminal and the base station, we also studied the recognition of fingerprint characteristic under different noise conditions.
Comparison Of Gesture In Thai Boxing Framework Using Angular Dynamic Time Warping

We developed an algorithm that compared the movements of a Thai boxing trainer and those of a trainee and produced a video clip that a trainer can use for training novice boxers in a training camp or that a trainee could use for self-training. In the developed system, a Microsoft Kinect sensor was used to capture 3D joint positions of the body of a trainer or trainee. A cosine similarity and an angular dynamic time warping were used to determine a similarity score between the postures or joint positions of the trainer and the trainee for static and dynamic poses respectively. Based on these similarity scores, suggestions to trainees on how to improve their boxing postures were overlaid on the recorded video clip, helping the trainee to improve his/her posture and movement. The proposed algorithm and developed framework could be easily used for other training such as yoga, dance, cardio workouts because the system allows to track and compare the whole body of users.
Development of Indonesian Speech Recognition with Deep Neural Network for Robotic Command

Research on speech recognition for several languages has been shown significant improvement for seamless interaction between human and robot. In this study, a system to command assistant robot with Indonesian speech recognition using deep neural network (DNN) has been proposed. The DNN architecture created by convolutional neural networks (CNNs), max pooling, and fully connected layers. The experiments performed on a self-constructed dataset with training, validation, and testing data in 0.8:0.1:0.1 ratio. This network built using Keras (TensorFlow Backend) and the result shows 99.43% accuracy on testing data and 89.57% on actual condition.
Probabilistic obstacle avoidance and object following: An overlap of Gaussians approach

Autonomous navigation and obstacle avoidance are core capabilities that enable robots to execute tasks in the real world. We propose a new approach to collision avoidance that accounts for uncertainty in the states of the agent and the obstacles. We first demonstrate that measures of entropy- used in current approaches for uncertainty-aware obstacle avoidance-are an inappropriate design choice. We then propose an algorithm that solves an optimal control sequence with a guaranteed risk bound, using a measure of overlap between the two distributions that represent the state of the robot and the obstacle, respectively. Furthermore, we provide closed form expressions that can characterize the overlap as a function of the control input. The proposed approach enables model-predictive control framework to generate bounded-confidence control commands. An extensive set of simulations have been conducted in various constrained environments in order to demonstrate the efficacy of the proposed approach over the prior art. We demonstrate the usefulness of the proposed scheme under tight spaces where computing risk-sensitive control maneuvers is vital. We also show how this framework generalizes to other problems, such as object-following.
Application of Ant Colony System to Optimal Thermal Unit Commitment

This study conducted optimal thermal unit commitment (UC) of power systems by using an ant colony system (ACS). Optimal thermal UC involves scheduling the on/off status and real power outputs of generating units at each hour and minimizing the system”s daily total production cost while satisfying all constraints. First, the system”s loss coefficients were obtained by considering transmission line loss and the optimal amounts of each unit were identified through economic dispatch with losses; furthermore., the ACS was applied to acquire the optimal generation schedule. An IEEE 30-bus power system was simulated using the ACS, which yielded excellent results.
Modelling of the Fake Posting Recognition in On-Line Media Using Machine Learning

Discuss content in the online web space has a significant impact on social life in recent years, especially in the political world. The impact of social networks has its advantages and disadvantages. An important disadvantage is a rising of the antisocial content in online communities. The antisocial content represents a serious and actual problem that is reinforced by a simplifying the process of creating and disseminating of antisocial posts. A typical example is a spreading of fake reviews. Detection of fake reviews is becoming one of the most important areas of research in last years. It is easier to track the impact of fake reviews than to detect them. The aim of this paper is to create suitable models for the fake reviews recognition using machine learning algorithms particularly decision tree, random forests, support vector machine and naïve Bayes classifier. Using a confusion matrix, several indicators of binary classification efficiency were quantified in the process of these models testing.
A Novel Algorithm for Ship Detection in SAR Images

The difference between targets and clutter is analysed. Considering the emotion target's gray intensity distribution difference compared to clutter, a novel algorithm is presented for ship detection in SAR images. First, a CFAR method is used to detect ships and wakes. In the detection, the sea clutter is modeled as a Gamma distribution, and the effects of ships and wakes are greatly reduced in the estimation of the Gamma distribution. Second, ships and wakes are distinguished according to their nonhomogeneity, because ships have larger non-uniformities than wakes. The detection performance is much better by using this algorithm. The result of real data show the effectiveness of this algorithm.
Technology around the park

This short paper explores the role of technology in improving tensioned relationships between older adults and park managers in residential retirement parks and villages. In Australia, residential retirement parks and villages are gated communities complete with demountable homes and community recreational facilities. Typically, a park manager oversees the maintenance and general liveability of the village, however issues about residents' agency and wellbeing often arises. Consequently, residents blame park managers for poor practices resulting in a simplistic 'us versus them' dichotomy. An opportunity arose to work with these older adults to co-design solutions to mitigate these tensions. Our co-design project revealed that residents do not feel they have control or agency over their lifestyle choices in residential villages, thus leading to undesirable experiences and tensioned relationships. Further, while residents prefer to communicate openly and efficiently with their park managers to resolve maintenance and dispute resolution issues promptly, this direct line of communication is rarely accommodated. In response, the introduction of technology is explored to augment the means of improving the relationships between residents and managers in residential parks and villages.
Evaluating Imitation of Human Eye Contact and Blinking Behavior Using an Android for Human-like Communication

The appearance of android robots is very similar to that of human beings. From their appearance, we expect that androids might provide us with high-level communication. The imitation of human behavior gives us the feeling of natural behavior even if we do not know what drives high-level communication. In this paper, we evaluate the imitation of human eye behavior by an android. We consider that the android imitates human eye behavior while explaining some research topic and a person acts as a listener. Then, we construct a method to imitate the eye behavior obtained from eye trackers. For the evaluation, we asked seventeen male subjects for their subjective evaluation and compared the imitation with an android that controlled eye-contact duration and eyeblinks by editing the imitation or programming rule-based behavior. From the results, we found out that 1) the rule-based behaviors kept human-likeness, 2) 3-second eye contact obtained better scores regardless of the imitation-based or rule-based eye behavior, and 3) the subjects might regard the longer eyeblinks as voluntary eyeblinks, with the intention to break eye contacts.
A Novel Cultural Evolution-Based Nomadic Pastoralist Optimization Algorithm (NPOA): The Mathematical Models

In this paper, the mathematical models for a proposed novel modified Pastoralist Optimization Algorithm (POA) called the Nomadic Pastoralist Optimization Algorithm (NPOA) inspired by the nomadic pastoralists herding strategies and cultural evolution strategy is presented. The nomadic pastoralist herding strategies which are scouting, camping, herding, splitting and merging were mathematically modeled. The mathematical models will be used to develop the proposed algorithm. The algorithm when developed will be tested on several benchmark functions to ascertain the algorithms exploration and exploitative ability. The performance will also be validated by comparing with POA and other popular and similar metaheuristic algorithms such as GOA, PSO, ABC, BBO and ICA.
Analysis on the Reform Path of College English Teaching in Local Engineering Colleges Under the Background of Big Data

The information revolution in the era of big data has had a huge impact on education, which has led to changes in teaching methods. This paper analyzes the problems in college English teaching in local engineering colleges from the aspects of college English tutorials, teaching resources, methods and teaching objects. The transformation of big data technology based on mobile Internet will have a full impact on college English teaching in China, which will promote the new environment of English teaching. On the basis of analyzing the new teaching environment generated by big data, explore how to further utilize big data information technology and resource reform local colleges and universities college English teaching, it mainly includes: constructing a three-dimensional curriculum system, creating a mixed teaching mode of "Mooc- class, micro-class + flip classroom", and integrating college English teaching resources.
Classification of aspirated and unaspirated sounds in speech using excitation and signal level information

In this work, consonant aspiration and unaspiration phenomena are studied. It is known that, pronunciation of aspiration and unaspiration is characterized by the ’puff of air’ released at the place of constriction in the vocal tract also known as burst. Here, properties of the vowel immediately after the burst are studied for characterization of the burst. Excitation source signal estimated from speech as low pass filtered linear prediction residual signal is used for the task. The signal characteristics of parameters such as glottal pulse, duration of open, closed & return phases; slope of open, & return phases; duration of burst; ratio of highest and lowest frame wise energies of signal and voice onset point are explored as features to characterize aspiration and unaspiration. Three datasets namely TIMIT, IIIT Hyderabad Marathi and IIIT Hyderabad Hindi (IIIT-H Indic Speech Databases) are used to verify the proposed approach. Random forest, support vector machine and deep feed forward neural networks (DFFNNs) are used as classifiers to test the effectiveness of the features used for the task. Optimal features are selected for the classification using correlation based feature selection (CFS). From the results, it is observed that the proposed features are efficient in classifying the aspirated and unaspirated consonants. Performance of the proposed features in recognition of aspirated and unaspirated phoneme is also evaluated. IIIT Hyderabad Marathi is considered for the analysis. It is observed that the performance of recognition of aspirated and unaspirated sounds using proposed features is improved in comparison with the MFCCs based phoneme recognition system.
A Novel Method to Achieve Ordered Dithering in Images

In this paper we propose a simple yet efficient way to achieve ordered dithering in images. The complex wavelet transform serves as the base of the proposed method, which makes it a multiscale approach. A traditional ordered dithering technique has also been incorporated, which not only provides uniformity to the final image but also makes the proposed method time efficient. The proposed method produces pleasant dithered images with a very strong emphasis on image details and enhances contrast. The proposed method outperforms the state of the art techniques from both objective and subjective point of views.
A Stylistic Features Based Approach for Author Profiling

Author profiling is the area of data-driven computational linguistics which is used to predict the demographic profiles such as age, gender, location, nativity language of authors by processing the textual content of their written text. Author profiling is so popular in recent days because of its potential applications such as marketing analysis studies, forensic linguistics, security and literary research. The majority of approaches for Author Profiling tend to focus on the content of the texts. Some researchers argue that focusing on structure of a text is more effective than content of a text. A popular research area called Stylometry is used by these researchers. Stylometry is used for finding the stylistic differences among the texts. In Author Profiling, various researchers proposed several types of stylistic features for differentiating the style of writing of the authors. The main concentration of this work is on feature engineering, the development, evaluation, and application of the feature set in the context of machine learning techniques to author profiling. In this work, we focused on gender prediction of the authors of reviews dataset. It was observed that stylistic features achieved best accuracies for predicting gender when compared with state-of-the-art approaches in many different scenarios, especially when combined with other features.