Reviewerly/exHarmony huggingface dataset:
Build IoT through Virtual Reality

Virtual Reality (VR) is a new form as integration of technologies that overlays computer-generated information on real world. Virtual reality can be used to enhance student learning and engagement. VR education can transform the way educational content is delivered; it works on the premise of creating a virtual world, real or imagined and allows users not only see it but also interact with it. Hence, educator has begun to seek technologies that have potential to be integrated in education in order to help students learn effectively and enhance their understanding. The focus of this research is on development of mobile application on Raspberry Pi topic by using Virtual Reality technology from Computer Architecture and Organization subject in University Pendidikan Sultan Idris (UPSI). Student have difficulty to understand and even not know what is Raspberry Pi besides less usage of mobile application on Virtual Reality, the application could help them understand the functionality of Raspberry Pi better and effectively. By using PROTOTYPING model, evaluation phase consisted of post tested have been carried out on target user as to see the learning effectiveness from student achieved. The result of finding shows that the students think that VR-Pi application is easy to use and the note provided is appropriate and easy to understand. Thus, this mobile application of VR technology could enhance the traditional form of teaching and learning in order to improve student’s knowledge and interest about Raspberry Pi which improves their understanding and experience using technology while learning.
Knowledge and attitudes toward transgender community among speech-language pathologists in India: A questionnaire-based exploration

Background: During the transition process, transgender individuals may require voice and communication services. Speech-language pathologists are increasingly involved in rendering clinical services and assisting transgender clients in voice and communication therapy. Previous studies in different countries have highlighted the lack of competence expressed by the speech-language pathologists toward serving the members of the lesbian, gay, bisexual, transgender, and queer communities. Presently, no such findings are available in the Indian context. Thus, a need was felt for identifying the concerns toward treatment for transgender individuals in speech-language pathology settings in India. Aim: The aim of the present study was to assess the knowledge, comfort levels and attitudes of speech-language pathologists practicing in India regarding the transgender community. Method: An online survey method was used to assess the knowledge and attitudes among speech-language pathologists working in India toward the transgender community. Results: The findings of the study revealed higher comfort levels as compared to self-rated knowledge levels in addressing issues related to transgender healthcare. Evidence-based practices toward transgender healthcare emerged as the topic needing more information. The study also helped to identify several moral beliefs and practices for voice therapy for the transgender population. Conclusion: There is a strong need to educate the speech language pathologists toward transgender healthcare in order to promote better cultural competence. The findings of the present study help to identify the lacunae in knowledge as well as to highlight the need to have continuing education programs in this area.
Shading on Photovoltaic Collectors on Rooftops

Rooftop photovoltaic (PV) systems in urban environments play an important role in solar electric energy generation. Shading on PV collectors, by self-shading, walls and fences on rooftops, affect negatively the output energy of the PV systems. Increasing the distance between the collector rows, and between the walls and fences near to the collectors, may minimize the shading losses. Practically, this option is usually limited, especially on rooftops. Rooftops may be of different types: horizontal, inclined, and saw-tooth, and may have obscuring structures like walls and fences. The distance between the shading objects and the PV collector rows determine the loss of energy due to shading. The study provides the PV system designer with mathematical expressions for distances from obscuring objects for the deployment of PV systems on rooftops. The optimal inclination and azimuths angles of a PV system on a triangular sloped rooftop are also illustrated.
OF-VO: Reliable Navigation among Pedestrians Using Commodity Sensors.

We present a novel algorithm for safe navigation of a mobile robot among pedestrians. Our approach uses commodity visual sensors, including RGB-D cameras and a 2D lidar, for explicitly predicting the velocities and positions of surrounding obstacles through optical flow estimation and object detection. Given these partial observations of the environment, we present a modified velocity-obstacle (VO) algorithm to compute collision-free trajectories for the robot. A key aspect of our work is the coupling between the perception (OF: optical flow) and planning (VO) components for reliable navigation. Overall, our OF-VO algorithm is a hybrid combination of learning-based and model-based methods and offers better performance over prior algorithms in terms of navigation time and success rate of collision avoidance. We highlight the realtime performance of OF-VO in simulated and real-world dynamic scenes on a Turtlebot robot navigating among pedestrians with commodity sensors. A demo video is available at \url{this https URL}
BCGAN-Based Over-Sampling Scheme for Imbalanced Data

Classification is a process of identifying the class to which input data belong. One of the most popular methods to do this is to construct a classification model by training a machine learning algorithm using a given set of data. For better classification performance, the dataset should have a balanced data distribution by class. If the dataset is imbalanced, that is, one class (minority class) has very fewer data than the other class (majority class); a model has little chance to learn about the minority class, and training is biased to the majority class. As a result, the model tends to classify any input to the majority class and does not handle data of the minority class properly. To overcome this data imbalance problem, we propose a novel over-sampling scheme based on Borderline-Conditional Generative Adversarial Networks (BCGAN). Our BCGAN generates data for the minority class, particularly along the borderline between majority class and minority class. Through various experiments on actual imbalanced datasets, we show the performance of our scheme.
Analysing Arbitrary Curves from the Line Hough Transform

The Hough transform is commonly used for detecting linear features within an image. A line is mapped to a peak within parameter space corresponding to the parameters of the line. By analysing the shape of the peak, or peak locus, within parameter space, it is possible to also use the line Hough transform to detect or analyse arbitrary (non-parametric) curves. It is shown that there is a one-to-one relationship between the curve in image space, and the peak locus in parameter space, enabling the complete curve to be reconstructed from its peak locus. In this paper, we determine the patterns of the peak locus for closed curves (including circles and ellipses), linear segments, inflection points, and corners. It is demonstrated that the curve shape can be simplified by ignoring parts of the peak locus. One such simplification is to derive the convex hull of shapes directly from the representation within the Hough transform. It is also demonstrated that the parameters of elliptical blobs can be measured directly from the Hough transform.
Towards Understanding the Instability of Network Embedding

Network embedding algorithms learn a mapping from the discrete representation of nodes to continuous vector spaces that preserve the proximities of nodes. The techniques have a wide range of applications in various downstream tasks such as node classification, link prediction, and network alignment. Despite recent efforts to the design of novel models, little attention has been paid to understanding the instability of network embedding. In this paper, we fill this gap by investigating several state-of-the-art network embedding methods. Node embeddings form a geometric shape in the latent space. Characterizing the geometry is critical to figure out the variance of network embedding. Hence, we define two metrics to characterize the geometric properties and find that node embeddings tremble in different instantiations of an embedding space. Then, we formally define the stability of node embeddings as the invariance of the nearest neighbors of nodes. Experimental results show that existing embedding approaches have significant amounts of instability. We explore the influence factors that affect the stability of different methods and find that both network structure and algorithm models affect the stability of node embeddings significantly. Finally, we examine the implications of embedding instability for downstream tasks and find remarkable impacts on the performance.
Support Vector Machine Algorithm for SMS Spam Classification in The Telecommunication Industry

In recent years, we have withnessed a dramatic increment volume in the number of mobile users grows in telecommunication industry. However, this leads to drastic increase to the number of spam SMS messages. Short Message Service (SMS) is considered one of the widely used communication in telecommunication service. In reality, most of the users ignore the spam because of the lower rate of SMS and limited amount of spam classification tools. In this paper, we propose a Support Vector Machine (SVM) algorithm for SMS Spam Classification. Support Vector Machine is considered as the one of the most effective for data mining techniques. The propose algorithm have been evaluated using public dataset from UCI machine learning repository. The performance achieved is compared with other three data mining techniques such as Naí¯ve Bayes, Multinominal Naí¯ve Bayes and K-Nearest Neighbor with the different number of K= 1,3 and 5. Based on the measuring factors like higher accuracy, less processing time, highest kappa statistics, low error and the lowest false positive instance, it’s been identified that Support Vector Machines (SVM) outperforms better than other classifiers and it is the most accurate classifier to detect and label the spam messages with an average an accuracy is 98.9%. Comparing both the error parameter overall, the highest error has been found on the algorithm KNN with K=3 and K=5. Whereas the model with less error is SVM followed by Multinominal Naí¯ve Bayes. Therefore, this propose method can be used as a best baseline for further comparison based on SMS spam classification.
AIBench: An Industry Standard AI Benchmark Suite from Internet Services.

The booming successes of machine learning in different domains boost industry-scale deployments of innovative AI algorithms, systems, and architectures, and thus the importance of benchmarking grows. However, the confidential nature of the workloads, the paramount importance of the representativeness and diversity of benchmarks, and the prohibitive cost of training a state-of-the-art model mutually aggravate the AI benchmarking challenges. In this paper, we present a balanced AI benchmarking methodology for meeting the subtly different requirements of different stages in developing a new system/architecture and ranking/purchasing commercial off-the-shelf ones. Performing an exhaustive survey on the most important AI domain-Internet services with seventeen industry partners, we identify and include seventeen representative AI tasks to guarantee the representativeness and diversity of the benchmarks. Meanwhile, for reducing the benchmarking cost, we select a benchmark subset to a minimum-three tasks-according to the criteria: diversity of model complexity, computational cost, and convergence rate, repeatability, and having widely-accepted metrics or not. We contribute by far the most comprehensive AI benchmark suite-AIBench. The evaluations show AIBench outperforms MLPerf in terms of the diversity and representativeness of model complexity, computational cost, convergent rate, computation and memory access patterns, and hotspot functions. With respect to the AIBench full benchmarks, its subset shortens the benchmarking cost by 41%, while maintaining the primary workload characteristics. The specifications, source code, and performance numbers are publicly available from the web site this http URL.
SMINT

Sharing a pre-trained machine learning model, particularly a deep neural network via prediction APIs, is becoming a common practice on machine learning as a service (MLaaS) platforms nowadays. Although deep neural networks (DNN) have shown remarkable successes in many tasks, they are also criticized for the lack of interpretability and transparency. Interpreting a shared DNN model faces two additional challenges compared with interpreting a general model. (1) Limited training data can be disclosed to users. (2) The internal structure of the models may not be available. These two challenges impede the application of most existing interpretability approaches, such as saliency maps or influence functions, for DNN models. Case-based reasoning methods have been used for interpreting decisions; however, how to select and organize the data points under the constraints of shared DNN models is not discussed. Moreover, simply providing cases as explanations may not be sufficient for supporting instance level interpretability. Meanwhile, existing interpretation methods for DNN models generally lack the means to evaluate the reliability of the interpretation. In this article, we propose a framework named Shared Model INTerpreter (SMINT) to address the above limitations. We propose a new data structure called a boundary graph to organize training points to mimic the predictions of DNN models. We integrate local features, such as saliency maps and interpretable input masks, into the data structure to help users to infer the model decision boundaries. We show that the boundary graph is able to address the reliability issues in many local interpretation methods. We further design an algorithm named hidden-layer aware p-test to measure the reliability of the interpretations. Our experiments show that SMINT is able to achieve above 99% fidelity to corresponding DNN models on both MNIST and ImageNet by sharing only a tiny fraction of training data to make these models interpretable. The human pilot study demonstrates that SMINT provides better interpretability compared with existing methods. Moreover, we demonstrate that SMINT is able to assist model tuning for better performance on different user data.
Real-time pose tracking of 3D targets by photo-model-based stereo-vision

Nowadays, robots have been utilized to perform a wide variety of tasks instead of humans, in which those equipped with hand-eye cameras have been proved to be useful in factories for handling metal parts. However, there have been difficulties for robots with vision sensors like cameras to detect a target objects’ 3D pose accurately, especially in the case that the target object is unique and the shape is arbitrary, and further, the object is moving. Visual servoing to moving target may enable the robot to pursue an animal to catch it. Aiming at achieving identification ability of a unique object, this paper utilized our previous study concerning photo-model-based object recognition. Based on the study, this paper applies the photo-model-based pose estimation method to video-rate stereo-vision position and orientation (pose) tracking, and it has been applied to an arbitrary-shaped moving target. To confirm the abilities of the proposed stereo-vision-based method to pursue an arbitrary target, real-time 3D pose estimation experiment to track the relative pose between a moving target and hand-eye robot have been conducted. The real-time experimental results show the proposed system can recognize the designated arbitrary-shaped target and track its pose in real-time.
Earlier stage for straggler detection and handling using combined CPU test and LATE methodology

Using MapReduce in Hadoop helps in lowering the execution time and power consumption for large scale data. However, there can be a delay in job processing in circumstances where tasks are assigned to bad or congested machines called "straggler tasks"; which increases the time, power consumptions and therefore increasing the costs and leading to a poor performance of computing systems. This research proposes a hybrid MapReduce framework referred to as the combinatory late-machine (CLM) framework. Implementation of this framework will facilitate early and timely detection and identification of stragglers thereby facilitating prompt appropriate and effective actions.
Enforcing dissipative entanglement by feedback

We study the possibility of enhancing the stationary entanglement achievable with two-qubit dissipating into a common environment by means of feedback. We contrast the effect of Markovian with Bayesian feedback and show that, depending on the initial state, the performance of the latter is from 16% to 33% superior.
Property Suffix Array with Applications in Indexing Weighted Sequences

The suffix array is one of the most prevalent data structures for string indexing; it stores the lexicographically sorted list of suffixes of a given string. Its practical advantage compared to the suffix tree is space efficiency. In Property Indexing , we are given a string x of length n and a property Π, i.e., a set of Π- valid intervals over x so that a pattern p occurs in x if and only if x has an occurrence of p that lies entirely within an interval of Π. A suffix-tree-like index over the valid prefixes of suffixes of x can be built in time and space O ( n ). We show here how to directly build a suffix-array-like index, the Property Suffix Array (PSA), in time and space O ( n ). We mainly draw our motivation from weighted (probabilistic) sequences: sequences of probability distributions over a given alphabet. Given a probability threshold 1/ z , we say that a string p of length m matches a weighted sequence x of length n at starting position i if the product of probabilities of the letters of p at positions i , …, i + m − 1 in x is at least 1/ z . Our algorithm for building the PSA can be directly applied to build an O ( nz )-sized suffix-array-like index over x in time and space O ( nz ). Finally, we present extensive experimental results showing that our new indexing data structure is well suited for real-world applications.
Decrypting the Central Mystery of Quantum Mathematics:

&#x0D; &#x0D; &#x0D; The Theory of Elementary Waves (TEW) is based on three new Axioms that lead to a different understanding of quantum mathematics. There is a massive amount of research data that supports TEW. This article will take six well established experiments from mainstream scientific journals and re-interpret their axioms from a TEW point of view. Although it is usually asserted that QM explains all existing quantum experiments, that is only true if you can convince yourself that the quantum world is weird. If you adopt TEW axioms, suddenly the quantum world transforms itself into looking ordinary, like everyday Nature. If, for example, time only goes forwards, never backwards; if there is no such thing as a quantum eraser; if nothing is transmitted faster than the speed of light, then TEW axioms allow you to make sense of a quantum world which QM can only explain if you allow for weirdness throughout Nature. TEW consists of axioms that allow us to understand the quantum world in a way that makes sense from the viewpoint of our everyday experience.&#x0D; &#x0D; &#x0D;
On game-based control systems and beyond

This perspective first expounds on the reasons behind the introduction of the game-based control systems (GBCS), delineates their features, provides some examples, and put forward some research problems. The paper then points out that more complex regulation problems on the now rapidly developing man-machine integration systems may also be investigated by expanding the scope of the GBCS.
Centralized Admission System for Advanced Level Private Schools: Case of Kilimanjaro Region, Tanzania

Globally, it is desirable to have fair and transparent student admissions into both public and private universities, colleges and schools. A case in point, in Tanzania 35% of students enrolled each year in higher level learning institutions and technical education are from Advanced Level (A-level) private schools. Of concern is that, this paper confirms and quantify that 93.5% of admissions into A-level schools were performed on paper based. Such admissions were characterized by multiple admissions, being costly, inconsistency, inaccuracy, and difficulties in following admission procedures. On the other hand, existing manual admission systems were considered unfair and not transparent. To mitigate these challenges a centralized web-based solution named Tanzania Central Processing Admission System (TCPAS) has been conceptualized to resolve the identified admission challenges. This paper presents on an ongoing research work aimed to address the challenges facing the current admission procedures of A-Level private schools in Tanzania. The proposed TCPAS is designed to be a web-mobile solution. The TCPAS tool is intended to reduce admission costs by reducing turnaround time for entire admission processes; encourage the use of paperless admission; control forgery over entry qualifications (certificates) during the admission process; has a centralized data handling capability; saves admission vacancies; and reach many geographically scattered applicants. Moreover, questionnaires were used to gather requirements from 150 respondents from the case study (Kilimanjaro region).
Sentiment classification with adversarial learning and attention mechanism

Abstract Sentiment classification is a key task in sentiment analysis, reviews mining, and other text mining applications. Various models have been proposed to build sentiment classifiers, but the classification performances of some existing methods are not good enough. Meanwhile, as a subproblem of sentiment classification, positive and unlabeled learning (PU learning) problem widely exists in real‐world cases, but it has not been given enough attention. In this article, we aim to solve the two problems in one framework. We first build a model for traditional sentiment classification based on adversarial learning, attention mechanism, and long short‐term memory (LSTM) network. We further propose an enhanced adversarial learning method to tackle PU learning problem. We conducted extensive experiments in three real‐world datasets. The experimental results demonstrate that our models outperform the compared methods in both traditional sentiment classification problem and PU learning problem. Furthermore, we study the effect of our models on word embedding. Finally, we report and discuss the sensitivity of our models to parameters.