Reviewerly/exHarmony huggingface dataset:
ARMyPat: Mobile Application in Learning Malay Historical Patriots Using Augmented Reality

In the era of evolving technology, the generation who was born in 1995 onwards or known as Gen Z in Malaysia is becoming less interested in knowing and learning about Malay Historical Figures. Currently, the medium or tool applied in learning about Malay Historical Figures is only by using textbook as reference in the classroom. Looking at this scenario, this project attempts to attract their interest in learning historical information, especially about Malay Figure by converting the text or image information into an interactive learning method. The approach for this project is by implementing an augmented reality technology where the interactive fighting game has been embedded. The objectives of this study are to identify the requirements, design and develop Augmented Reality mobile learning application about Malay Historical Figures with the commercial name of ARMyPat (Augmented Reality based in Learning about Malay Patriots). This application prototype will be compatible for the Android platform mobile devices and the target users for this application are students aged between 10 and 15 years old who learn the historical subject at school. The project methodology is ADDIE model that covered analyses, design, develop, implement and evaluation phase since this methodology is suitable for the interactive learning development process. The students can obtain the information such as the summary of character, biodata, heritage and contribution interactively. For 3D character model view, they can learn and know the attraction of the martial art which is Silat. Therefore, they can learn a few movements of the Silat martial art. The last part is an interactive fighting game whereby it can bring the students to the fighting action in real environment and encourage them to know about the heroism characters of that Malay Historical Figure. In the end, this application is to encourage young generation patriotism spirits towards Historical Figure in Malaysia by utilizing augmented reality technology.
CarVideos: A Novel Dataset for Fine-Grained Car Classification in Videos

We investigate the fine-grained object classification problem of determining make, model and year of car from a video. To this end we introduce a new dataset called CarVideos. It is a novel dataset for fine-grained object classification in videos. The CarVideos dataset contains over a million video frames annotated with bounding boxes around the visible cars as well as the specific year, make and model of each car. We implemented several state-of- the-art methods for object classification in videos and compared them using the dataset to establish a baseline performance level for future research. We also introduce a novel approach to fine-grained object classification in videos that combines a Single Shot Multibox Detector (SSD) with a single stream multi-region convolutional neural network (CNN). Our experiments show that our novel method significantly outperforms previous methods in terms of accuracy on the dataset. Our approach outperforms Temporal Segment Networks (TSN) and 3D Convolutional Networks, which are state-of-the-art on human action recognition in videos.
Thrust Vectoring Control for Infrastructure Inspection Multirotor Vehicle

The aging infrastructure is the global problem, thus inspection become very important. With introduction of the multi-rotor many research and inspection organization has started to adapt such vehicle to assist the inspection works. However, the state of the art technology is mostly visual inspection only, thus the image stabilization and control method is important. The research group in Chung Yuan Christian University use the inspection results and instead of design camera gimbal to image stabilization, the team developed a completely new control method “Thrust Vectoring Control” to allow the vehicle to have translation without any attitude change, which leads to significant improvement of captured image qualities, thus more useful for visual inspection.
A Framework for SLO-driven Cloud Specification and Brokerage

The diversity of cloud offerings motivated the proposition of cloud modelling languages (CMLs) to abstract complexities related to selection of cloud services. However, current CMLs lack the support for modelling service level objectives (SLOs) that are required for the customer applications. Consequently, we propose an application- and provider-independent SLO modelling language (SLO-ML) to enable customers to specify the required SLOs. We also sketch the architecture to realise SLO-ML.
Domain specific architectures, hardware acceleration for machine/deep learning

This article aims to provide the reader with a clear understanding of a subdiscipline in artificial intelligence, Deep Neural Networks. In addition to this, we cover a set of proposed Domain Specific Architectures, Accelerators, that are optimized for these types of computations. In optimizing these computations, we are able to reduce data transfers by keeping data at the processing unit in their individual register files thus increasing energy efficiency per computation.
An Ontology-Based Interactive System for Understanding User Queries

The use of ontologies in applications like dialogue systems, question-answering or decision-support is gradually gaining attention. In such applications, keyword-based user queries are mapped to ontology entities and then the respective application logic is activated. This task is not trivial as user queries may often be vague and imprecise or simply don't match the entities recognised by the application. This is for example the case in symptom-checking dialogue systems where users can enter text like "I am not feeling well", "I sleep terribly", and more, which cannot be directly matched to entities found in formal medical ontologies. In the current paper we present a framework for automatically building a small dialogue for the purposes of bridging the gap between user queries and a set of pre-defined (target) ontology concepts. We show how we can use the ontology and statistical techniques to select an initial small set of candidate concepts from the target ones and how these can then be grouped into categories using their properties in the ontology. Using these groups we can ask the user questions in order to try and reduce the set of candidates to a single concept that captures the initial user intention. The effectiveness of this approach is hindered by well-known underspecification of ontologies which we address by a concept enrichment pre-processing step based on information extraction techniques. We have instantiated our framework and performed a preliminary evaluation largely motivated by a real-world symptom-checking application obtaining encouraging results.
Short-time expansion of Heisenberg operators in open collective quantum spin systems

We present a new method to compute short-time expectation values in large collective spin systems with generic Markovian decoherence. Our method is based on a Taylor expansion of a formal solution to the equations of motion for Heisenberg operators. This expansion can be truncated at finite order to obtain virtually exact results at short times that are relevant for metrological applications such as spin squeezing. In order to evaluate the expansion for Heisenberg operators, we compute the relevant structure constants of a collective spin operator algebra. We demonstrate the utility of our method by computing spin squeezing, two-time correlation functions, and out-of-time-ordered correlators for $10^4$ spins in strong-decoherence regimes that are otherwise inaccessible via existing numerical methods. Our method can be straightforwardly generalized to the case of a collective spin coupled to bosonic modes, relevant for trapped ion and cavity QED experiments, and may be used to investigate short-time signatures of quantum chaos and information scrambling.
GC-Wise: A Self-adaptive approach for memory-performance efficiency in Java VMs

High-level language runtimes are ubiquitous in every cloud deployment. From the geo-distributed heavy resources cloud provider to the new Fog and Edge deployment paradigms, all rely on these runtimes for portability, isolation and resource management. Across these clouds, an efficient resource management of several managed runtimes involves limiting the heap size of some VMs so that extra memory can be assigned to higher priority workloads. The challenges in this approach rely on the potential scale of systems and the need to make decisions in an application-driven way, because performance degradation can be severe, and therefore it should be minimized. Also, each tenant tends to repeat the execution of applications with similar memory-usage patterns, giving opportunity to reuse parameters known to work well for a given workload. This paper presents GC-Wise, a system to determine, at run-time, the best values for critical heap management parameters of the OpenJDK JVM, aiming to maximize memory-performance efficiency. GC-Wise comprises two main phases: 1) a training phase where it collects, with different heap resizing policies, representative execution metrics during the lifespan of a workload; and 2) an execution phase where an oracle matches the execution parameters of new workloads against those of already seen workloads, and enforces the best heap resizing policy. Distinctly from other works, the oracle can also decide upon unknown workloads. Using representative applications and different hardware setting (a resourceful server and a fog-like device), we show that our approach can lead to significant memory savings with low-impact on the throughput of applications. Furthermore, we show that we can predict with high accuracy the best heap resizing configuration in a relatively short period of time.
Efficient Algorithm for Mining High Utility Pattern Considering Length Constraints

High utility itemset (HUI) mining is one of the popular and important data mining tasks. Several studies have been carried out on this topic, which often discovers a very large number of itemsets and rules, which reduces not only the efficiency but also the effectiveness of HUI mining. In order to increase the efficiency and discover more interesting HUIs, constraint-based mining plays an important role. To address this issue, the authors propose an algorithm to discover HUIs with length constraints named EHIL (Efficient High utility Itemsets with Length constraints) to decrease the number of HUIs by removing tiny itemsets. EHIL adopts two new upper bound named sub-tree and local utility for pruning and modify them by incorporating length constraints. To reduce the dataset scans, the proposed algorithm uses transaction merging and dataset projection techniques. The execution time improvements ranged from a modest five percent to two orders of magnitude across benchmark datasets. The memory usage is up to twenty-eight times less than state-of-the-art algorithm FHM+.
Spiking networks for improved cognitive abilities of edge computing devices

This concept paper highlights a recently opened opportunity for large scale analytical algorithms to be trained directly on edge devices. Such approach is a response to the arising need of processing data generated by natural person (a human being), also known as personal data. Spiking Neural networks are the core method behind it: suitable for a low latency energy-constrained hardware, enabling local training or re-training, while not taking advantage of scalability available in the Cloud.
A methodology for gestural interaction relying on user-defined gestures sets following a one-shot learning approach

Human communication has been studied from different approaches and resulting in contributions to several disciplines. From the computer sciences point of view, the findings made in the area have inspired the development of Natural User Interfaces (NUI), interaction mechanisms aimed at replicating t he way in which people communicate, so the information exchange with computational systems happens in similar fashion. Gestural interfaces are a specific type of NUI focused on analyzing the relationship between body motion and semantic meanings. Although, from a technical perspective, proposals found in the literature had proven high efficiency and accuracy on gestural recognition, several authors had reported lack of naturalness in the interaction with gesture-based applications, leading to the conclusion that NUIs are not usually as natural as they claim to be. Moreover, gestures are culture and language specific, which makes them ambiguous, incompletely specified, and difficult to match with semantic meaning when the context is unknown. In this paper, we propose a methodology for enabling the development of gesture-based applications, considering that accuracy and efficiency in recognition tasks must not be affected, and prioritizing the flexibility for allowing the use of gestures that are suitable for different user contexts through the exploration of user-defined gesture sets and Machine Learning techniques, and using a one-shot learning approach.
Bayesian Inference Using Expected A Posterior Estimation for Prediction of Thermal Index and Power Consumption of Air Conditioner Based on Mean-field Theory

Based on statistical mechanics of information due to the mean-field theory (MFT), we predict environmental variables and electric power consumed by air conditioner in small-scale systems by making use of Bayesian inference using the expected a posterior (EAP) estimation. Here, we clarify that the present method succeeds in providing an optimal thermal index due to the temperature-humidity index (THI) at each sampling point and power used due to air conditioner, if we set parameters under optimal conditions and that this fact is confirmed by exact proof via equality. Also, we find that the provided THI is further improved, if we introduce correlations of environmental quantities between sampling points into the conventional model.
Fuzzy Cognitive Maps as a Tool to Forecast Emotions in Refugee and Migrant Communities for Site Management

Refugees and migrants arrivals in the Mediterranean since 2014 have resulted in humanitarian relief operations at national and international levels. Decision making by relevant actors in all aspects of humanitarian response impacts on the well-being of People of Concern (PoC). Site management decisions take into account a wide range of criteria in diverse sectors, including protection of PoC. Criteria address basic relief response, such as water, sanitation and nutrition, social characteristics, as well as protection of PoC. In particular, site management decisions may affect relationships between PoC, leading to tensions or peaceful coexistence between PoC. In this paper a decision making process is proposed for the forecasting of emotions in PoC sites in order to assist site management decisions, particularly in the context of avoidance of tensions. The method uses Fuzzy Cognitive Maps (FCM) to forecast emotions based on input data obtained from site management reports. Historical data from site management reports in Greece have been used for deriving the indicators used in the input layer of the designed FCM. The proposed method will be applied to forecast potential tensions in PoC sites in Greece.
Is the light on or off?

Everyday activities and routines should be easy to perform for everyone, including those who are totally blind. This paper introduces a simple audio-based tool aimed at supporting visually-impaired people in checking whether the light in a room is on or off. In this context, we discuss the main issues and considerations for totally blind users with regard to a specific seemingly simple task, which is light detection. The proposed prototype is based on a simple circuit and a form of auditory feedback which informs the user whether they are switching on or off the light. The evaluation conducted by involving blind and sighted people confirmed the usefulness of the proposed tool.
IoT devices discovery and identification using network traffic data

The Internet of Things (IoT) has been erupting world widely over the decade. However, the security and privacy leakage issues from IoT devices are surfaced to a major flaw for IoT device operators. An attacker may use network traffic data to identify IoT devices and launch attacks on their target devices. To explore the severity and extent of this privacy threat, we design a hybrid ML-based IoT device identification framework. Our key insight is that typically an IoT device has a unique traffic signature and it is already embedded in its network traffic. Unlike other existing work using complex modeling, we show that the majority of IoT devices can be easily identified using our empirical models, and the other devices can also be correctly classified using our ML-based models. We instrument a smart IoT experiment environment to verify and evaluate our approaches. Our framework paves the way for operators of smart homes to monitor the functionality, security and privacy threat without requiring any additional devices.
A Secure and Sustainable Framework to Mitigate Hazardous Activities in Online Social Networks

Recent years have seen continuous exposure of online social network (OSN) users to the security vulnerability due to socio-technical cyber hazards committed by OSN associates. Unfortunately, the current OSN platforms has lack of functionalities for automatic users protection in initiating online friendship and online users interaction within their circle. Moreover, the users cannot analyze their associates' time-varying and changing behavior, which is a strong indicator of malicious activities. To address this issue, this paper proposes a design of automatic two-phase pre- and post-filtering approach with the objective of mitigating the cyber threats in OSN platforms. In the pre-filtering phase, we propose a method where each user can access a reliable technique to select a friend after getting his/her details authenticity. In the post-filtering phase, a control mechanism is established to recognize malicious posts/actions of the associates in order to limit the spread of the corresponding cyber threats. Our empirical analysis shows some significant comparison data towards hybrid (combined pre- and post-filtering) method, where the users' consents for our proposed method exceed significantly over the other competing techniques. In fact, the proportional mean value of the hybrid method is increased almost twice (2.03) of the existing methods.
Research on urban anti-terrorism intelligence perception system from the perspective of Internet of things application

Objective/significance Based on the urgent needs of the status quo of urban anti-terrorism, combining the present situation of intelligent city construction, we carry out the research on the key problems such as the collection and analysis of counter-terrorism information for urban environment. This paper puts forward a kind of city anti-terrorism information perception system based on the Internet of things, and realizes to improve the level of surveillance and early warning of terrorist activities facing the urban environment. Method/Process The system collects and merges the heterogeneous information of terrorism, which is based on the information perception layer, the intelligence identification layer and the intelligence inference layer, which is based on the city's focal area. To filter suspicious information by constructing a semantic database of terrorist action signals Combined with complex event processing and machine learning technology, real-time monitoring and mining prediction of mass event flow, using the comparison between historical data of terrorist attacks and real-time data, increased the area of counter-terrorism intelligence. Results/Conclusions We hope to provide a better model and an exploratory study on the construction of the counter-terrorism early warning for the urban environment.
A general methodology for identifying the writer of codices. Application to the celebrated “twins”

In the present work, a method of general applicability is introduced for classifying a given set of ancient documents to their writer. A principal motive and, at the same time, a main result of this work was to test if two sets of documents preserving the Homeric Iliad have been written by the same hand or not. To achieve this, the authors have developed and/or improved two dedicated methods, which may be described very briefly as follows: the first method compares all realizations of the same alphabet symbol appearing in two documents and it offers related quantitative criteria. The second method aims at determining the ideal form(s) of each alphabet symbol the writer had in his/her mind when writing a document. In both methods, decision is made by means of novel statistical criteria. Application of this methodology furnished the following results in a fully quantitative manner: 1) all twenty-six (26) Byzantine codices kept at Real Monasterio de San Lorenzo de El Escorial or at the Biblioteca Marciana in Venice that have been studied, have been written by four (4) distinct writers only. 2) The codices written by each one of the four hands have been spotted with maximum likelihood and 3) the celebrated “twins” have indeed been written by the same hand. We once more emphasize that the approach introduced here may be immediately applied to classifying any set of documents to the distinct hands that have written them.
Superposition of sound waves: beats

In the current study, the subject of beats is addressed as a context to teach the subject of superposition of sound waves. In addition to this, by revealing the mathematical aspect of the subject, the context of beats was first investigated mathematically and then interpreted from a physical point of view. WolframAlpha was used for graphical drawing and Tone Generator was used for perceiving by hearing.
Population Diversity Analysis in Adaptive Differential Evolution Variants with Unconventional Randomization Schemes

This research represents a detailed insight into the modern and popular hybridization of unconventional quasiperiodic/chaotic sequences and evolutionary computation. It is aimed at the influence of different randomization schemes on the population diversity, thus on the performance, of two selected adaptive Differential Evolution (DE) variants. Experiments are focused on the extensive investigation of totally ten different randomization schemes for the selection of individuals in DE algorithm driven by the default pseudo-random generator of Java environment and nine different two-dimensional discrete chaotic systems, as the unconventional chaotic pseudo-random number generators. The population diversity is recorded for 15 test functions from the CEC 2015 benchmark set in 10D.
Research on Differentially Private Bayesian Classification Algorithm for Data Streams

Considering the privacy leakage risk of data streams mining, a differential private Naive Bayes classification algorithm for data streams (DP-NB) is proposed in the paper. The Gaussian Bayesian model is firstly adopted and the methods of feature selection, cross-validation and weighting mechanism are used to improve the model and solve the problem of concept drift. Then noise perturbation is added to the model parameters and incremental update is presented to optimize the Gaussian classifier model. Theoretical analysis of the DP-NB algorithm proves that it achieves ε-differential privacy. Finally, experimental results show that the DP-NB algorithm has better classification accuracy while satisfying privacy protection.