Reviewerly/exHarmony huggingface dataset:
Path Finding Algorithms

In this survey we have compared five different path finding algorithms, namely, Dijkastra’s algorithm, A* Search, Greedy Best First Search, Breadth First Search and Depth First Search on the basis of the following criteria: Complexity: It refers to the rate at which the execution time grows with respect to the input size. Reliability: It refers to whether the algorithm is able to guarantee the shortest path. Weighted/unweighted: Some algorithms are not suited for weighted graphs and thus have a major limitation. Their respective advantages and disadvantages have also been discussed.
Equipment data-based activity recognition of construction machinery

Heavy equipment plays an essential role in construction projects. However, to optimize the allocation of that resource (e.g., via discrete-event simulation), the correct monitoring of the current usage of the equipment is imperative. Machine learning methods are usually based on the mounting of additional sensors such as accelerometers or gyroscopes on the equipment. However, many kinds of equipment already transmit production data to a proprietary platform. This paper explores the possibilities of performing activity recognition based on already available equipment data transmitted by the equipment, without the need for additional sensors. The proposed approach is based on hybrid models consisting of convolutional neural networks (CNN) and recurrent neural networks (RNN). Additionally, the influence of the use of bidirectional RNN is investigated. The hybrid models are compared with other baseline models. Both hybrid models demonstrate good results compared to the baseline models. Furthermore, the influence of the sample size is examined.
Real-Time Stripe Noise Removal Method for Endoscope Image

Endoscopes use small-size image sensors to achieve imaging, so the image is usually transmitted in high-speed serial signal or analog signal, which are susceptible to interference during transmission, resulting in stripe noise in the image. The endoscope designed in this paper is applied to the urinary system, and the ultra-compact image sensor OV6946 is used to realize in-vivo imaging. In medical endoscope system, stripe noise will seriously affect image quality, which affect the doctor's diagnosis of the disease and even cause misdiagnosis to patients. Therefore, removing the stripe noise in endoscope image has great application value. Most of the stripe phenomenon in image is the introduction of specific frequency noise components. In order to make the stripe noise removal method universal, a real-time method to remove stripe noise is proposed on the basis of analyzing the characteristics of stripe noise. Fourier transform on each line of noise image is implemented in FPGA. The stripe noise spectrum is identified and suppressed in frequency domain to achieve the effect of real-time removal of stripe noise. The experimental results show that the proposed method can effectively suppress the stripe noise of OV6946 output image, preserve the edge and detail information of the image. At the same time, the processing time is only 4.143 μs under the system clock of 300MHz, which can well meet the real-time requirements of endoscopic imaging.
A Bayesian networks approach to infer social changes from burials in northeastern Taiwan during the European colonization period

Burials provide valuable information to study social structures based on the assumption that burials and associated grave goods can represent social roles and relations in a society. To study social relationships, network analysis has been increasingly applied to archaeological data to infer interactions and relationships between entities. Statistical approaches to network analysis, such as exponential random graph models (ERGMs), provide a way to test hypotheses about dynamic processes of network formation. However, computational difficulties and sensitivity to uncertainties limit the application of ERGMs. In this paper, we introduce a Bayesian framework on ERGMs that enables an efficient computational process, effective quantification of uncertainty, and robust model evaluation of network properties. We tested a hypothesis of social change relative to the arrival of Europeans by studying burial data from Kiwulan, an Iron Age site in northeastern Taiwan. The results indicate a transition among the burials from network ties based on ritual objects to wealth objects, and a more centralized structure with increased social differentiation after the European presence was established in the 17th century. Our case study demonstrates the effectiveness of Bayesian network analysis for archaeological data, and expands the use of burials in understanding the impacts of colonial presence on Indigenous groups in a pericolonial context.
It Was “All” for “Nothing”: Sharp Phase Transitions for Noiseless Discrete Channels

We establish a phase transition known as the “all-or-nothing” phenomenon for noiseless discrete channels. This class of models includes the Bernoulli group testing model and the planted Gaussian perceptron model. Previously, the existence of the all-or-nothing phenomenon for such models was only known in a limited range of parameters. Our work extends the results to all signals with arbitrary sublinear sparsity. Over the past several years, the all-or-nothing phenomenon has been established in various models as an outcome of two seemingly disjoint results: one positive result establishing the “all” half of all-or-nothing, and one impossibility result establishing the “nothing” half. Our main technique in the present work is to show that for noiseless discrete channels, the “all” half implies the “nothing” half, that is, a proof of “all” can be turned into a proof of “nothing.” Since the “all” half can often be proven by straightforward means—for instance, by the first-moment method—our equivalence gives a powerful and general approach towards establishing the existence of this phenomenon in other contexts.
Deep Ensembles for Semantic Segmentation on Road Detection

Semantic segmentation is a significant technique that can provide valuable insights into the context of driving scenes. This work discusses several mechanisms: data augmentation, transfer learning, transposed convolutions and focal loss function for improving the performance of neural networks for image segmentation. Experiments on two traditional model architectures – U-net and MobileUNetV2 – are conducted and the results are evaluated in terms of – Intersection-over-Union (IoU) and F-score. The KITTI Road dataset is utilised for training and testing the algorithms on road segmentation. More specifically, data augmentation and the task-specific focal loss provide the highest improvement of 6.68% and 5.23%, respectively. To further enhance segmentation performance, an ensemble scheme is adopted where several models are executed simultaneously and their outputs are fused together to derive the final prediction. Such a design can reduce incorrect predictions of individual models and produce more precise segmentation masks.
On the Vulnerability of Hardware Masking in Practical Implementations

Masking is a widely used technique to improve security against side channel analysis (SCA) by dividing sensitive information into multiple shares. One of the primary assumptions in masking is that the leakage of each share is independent of the other shares. A successful attack by utilizing the leakage from a single share cannot be performed unless the leakage information from all of the shares is available. When implementing hardware masking, this assumption of independent shares, however, is no longer valid when different shares are connected to the same power delivery network. In this paper, the relationship between different masking shares are quantitatively analyzed. The impact of the power/ground noise in the shared power delivery network on security of masking is investigated. By analyzing the behavior of dependence based on different physical factors, certain insights are presented to improve the security of hardware masking in practical implementations.
Multimodal Video Indexing (MVI): A New Method Based on Machine Learning and Semi-Automatic Annotation on Large Video Collections

Indexing video by the concept is one of the most appropriate solutions for such problems. It is based on an association between a concept and its corresponding visual sound, or textual features. This kind of association is not a trivial task. It requires knowledge about the concept and its context. In this paper, we investigate a new concept detection approach to improve the performance of content-based multimedia documents retrieval systems. To achieve this goal, we are going to tackle the problem from different plans and make four contributions at various stages of the indexing process. We propose a new method for multimodal indexation based on (i) a new weakly supervised semi-automatic method based on the genetic algorithm (ii) the detection of concepts from the text in the videos (iii) the enrichment of the basic concepts thanks to the usage of our method DCM. Subsequently, the semantic and enriched concepts allow a better multimodal indexation and the construction of an ontology. Finally, the different contributions are tested and evaluated on a large dataset (TRECVID 2015).
Palette-Based Recoloring of Natural Images Under Different Illumination

Palette-based image recoloring is a popular method in the field of color processing by Intuitively converting the main color of the image into a palette that the user can directly manipulate. However, the traditional palette-based image recoloring method for natural images under different illuminations is affected by the light, colors on the extracted palette may be too close, which will cause color overflow and serious distortion after recoloring. This paper proposes a palette-based recoloring algorithm based on intrinsic decomposition. By using an intrinsic decomposition system based on different illumination images for unsupervised deep learning, natural image is decomposed into reflectance image and shading image. Only the reflectance image is recolored, and then combined with the shading image to obtain a natural recolored image. At the same time, in order to balance accuracy and ease of operation, we use the gap statistic algorithm to get the palette size. The recoloring results of natural images show the superiority of this method in terms of color transfer quality and user experience.
CCMC: Code Completion with a Memory Mechanism and a Copy Mechanism

Code completion tools are increasingly important when developing modern software. Recently, statistical language modeling techniques have achieved great success in the code completion task. However, two major issues with these techniques severely affect the performance of neural language models (NLMs) of code completion. a) Long-range dependences are common in program source code. b) New and rare vocabulary in code is much higher than natural language. To address the challenges above, in this paper, we propose code completion with a memory mechanism and a copy mechanism (CCMC). To capture the long-range dependencies in the program source code, we employ Transformer-XL as our base model. To utilize the locally repeated terms in program source code, we apply the pointer network into our base model and design CopyMask to improve the training efficiency, which is inspired by masked multihead attention in the transformer decoder. To combine the long-range dependency modeling ability from Transformer-XL and the ability to copy the input token to output from the pointer network, we design a memory mechanism and a copy mechanism. Through our memory mechanism, our model can uniformly manage the context used by Transformer-XL and pointer network. Through our copy mechanism, our model can either generate a within-vocabulary token or copy an out-of-vocabulary (OOV) token from inputs. Experiments on a real-world dataset demonstrate the effectiveness of our CCMC on the code completion task.
Deep learning on Sleptsov nets

Sleptsov nets are applied as a uniform language to specify models of unconventional computations and artificial intelligence systems. A technique for specification of neural networks, including mul...
Aesthetic of Friction for Exercising Motivation: a Prototyping Journey

Despite an intention to exercise, it remains a challenge for many people to establish a workout routine over a period of time. Amongst identified barriers and enablers to exercise, getting dressed for a workout is considered as one of the tipping points of actually going. Implementing the Aesthetic of Friction in this specific context, could imply the right course of action for the user, while it also allows freedom and encourages meaning-making. In this Research-through-Design project, we designed an interactive shrinking hanger, that implements these key principles, to encourage exercise motivation. We followed an iterative process focusing on the aesthetics of the interaction to find out how a careful consideration of the look and feel of an interactive artefact influences the acceptance of the implemented friction. We document the design process of this aesthetics of friction exemplar, and reflect on how to implement friction in design.
Modelling of the process of a program compilation according to the training sample data using the collective intelligence of the simplest computers

Abstract The description of the system intended for genetic programming and consisting of a large number of elementary computers is given. Commands and data are placed in a cell consisting of four fields – the first address field, the second address field, the arithmetic field and the result field. This architecture made it possible to place a software binary tree formed by commands and addresses into sequentially located cells of an elementary computer. This technique made it possible to simplify the crossover and mutation operators, as well as the coding of the problem without involving complex programming systems and supercomputers. In the future, it is planned to use table processors tested on complex problems of genetic programming to significantly reduce power consumption in on-board computers of aviation and space technology.
PBL-based VR course for preservice teachers’ designing skills in applied university under coronavirus

Smart education and ubiquitous learning have come over globally, especially under coronavirus regime. Visual media and interactive operation can draw children's attention and interests, so the VR educational software becomes a prominent trend for training children. However, in K12 education, the VR technology is just in its initial period. In the face of global coronavirus context, and children's visual learning characteristics, training preservice teachers with abilities to develop the VR educational software is crucial. In the project, it designs the VR course for preservice teachers in applied university that cultivates preservice teachers, trains them to develop the VR educational software, and enables them to develop it. It designs the VR course to accomplish the above two objectives in two aspects: creating PBL-based VR teaching cases to give them a sense of achievement and leading them to develop the VR educational software as the summarizing test. The results are presented in three views. Firstly, their developed VR educational software shows that the PBL cases are effective in training students' ability of developing the VR educational software. Secondly, it investigates the effectiveness of PBL-based VR course by t-test. Finally, it surveys their satisfaction for the course, ideas about what other technologies they want to learn, what subjects they want to develop for, and what types of software they want to design to guide course research in the future.
Fluid Speculations: Drawing Artefacts in Watercolour as Experimentation in Research Through Design

This pictorial paper presents design experimentations using drawing, watercolour and storytelling as forms of speculation about artefacts and technologies. The use of watercolour, a medium that bleeds and has leaky boundaries, allowed ambiguity, defamiliarization and critical reflection to enter the research space, encouraging new understandings of artefacts and their latent possibilities. Watercolour was also valuable for speculating about designs that deal with bodily fluids, and for Drawing Conversations that facilitated reinterpretations of artefacts through drawings created by two different people. Intersecting themes about cognition, creativity and technology, this pictorial presents examples that illustrate how watercolour drawing can impact on the representation of speculative concepts, including the creation of visual conversations. The drawings shown here are presented as representations embodying families of ideas and themes related to the objects they symbolize, together with the design spaces they suggest and the stories they invite us to imagine.
An Anomaly Detection Study on Automotive Sensor Data Time Series for Vehicle Applications

Anomaly detection in automotive systems has been a strong challenge: first, during the development phase, then after the manufacturing approval in ramp-up production and finally during the vehicles life cycle management. The numerous sensors positioned inside a vehicle generate more than a gigabyte of data at each second timeframe. These sensors are connected through the vehicle network, which comprises Electronic Control Units (ECUs) and Controller Area Network (CAN) buses. Each ECU gets input from its sensors, executes specific instructions and aims to monitor the vehicle's normal state detecting any irregular action corresponding to its observed behavior. The aggregator of all sensor data and control actions detects the anomalies in vehicle systems, which poses a multi-source big data problem. Detecting anomalies during manufacturing has turned out to be another research challenge after the introduction of Industry 4.0. This paper presents a performance comparison of different anomaly detection algorithms on time series originating from automotive sensor data. Interquartile range, isolation forest, particle swarm optimization and k-means clustering algorithms are used to detect outlier data in the study.
Ultrasound for Gaze Estimation—A Modeling and Empirical Study

Most eye tracking methods are light-based. As such, they can suffer from ambient light changes when used outdoors, especially for use cases where eye trackers are embedded in Augmented Reality glasses. It has been recently suggested that ultrasound could provide a low power, fast, light-insensitive alternative to camera-based sensors for eye tracking. Here, we report on our work on modeling ultrasound sensor integration into a glasses form factor AR device to evaluate the feasibility of estimating eye-gaze in various configurations. Next, we designed a benchtop experimental setup to collect empirical data on time of flight and amplitude signals for reflected ultrasound waves for a range of gaze angles of a model eye. We used this data as input for a low-complexity gradient-boosted tree machine learning regression model and demonstrate that we can effectively estimate gaze (gaze RMSE error of 0.965 ± 0.178 degrees with an adjusted R2 score of 90.2 ± 4.6).
CIAO: An Optimization Framework for Client-Assisted Data Loading

Data loading has been one of the most common performance bottlenecks for many big data applications, especially when they are running on inefficient human-readable formats, such as JSON or CSV. Parsing, validating, integrity checking and data structure maintenance are all computationally expensive steps in loading these formats. Regardless of these costs, many records may be filtered later during query evaluation due to highly selective predicates - resulting in wasted computation. Meanwhile, the computing power of client ends is typically not exploited. Here, we explore investing limited cycles of clients on prefiltering to accelerate data loading and enable data skipping for query execution. In this paper, we present CIAO, a tunable system to enable client cooperation with the server to enable efficient partial loading and data skipping for a given workload. We proposed an efficient algorithm that would select a near-optimal predicate set to push down within a given budget. Our experiments show that CIAO can significantly accelerate the data loading processing and query execution.
BAOD: Budget-Aware Object Detection

We study the problem of object detection from a novel perspective in which annotation budget constraints are taken into consideration, appropriately coined Budget Aware Object Detection (BAOD). When provided with a fixed budget, we propose a strategy for building a diverse and informative dataset that can be used to optimally train a robust detector. We investigate both optimization and learning-based methods to sample which images to annotate and what type of annotation (strongly or weakly supervised) to annotate them with. We adopt a hybrid supervised learning framework to train the object detector from both these types of annotation. We conduct a comprehensive empirical study showing that a handcrafted optimization method outperforms other selection techniques including random sampling, uncertainty sampling and active learning. By combining an optimal image/annotation selection scheme with hybrid supervised learning to solve the BAOD problem, we show that one can achieve the performance of a strongly supervised detector on PASCAL-VOC 2007 while saving 12.8% of its original annotation budget. Furthermore, when 100% of the budget is used, it surpasses this performance by 2.0 mAP percentage points.
Measuring Camera Exposure Time Using Equivalent Sampling

This paper presents a novel method to measure the exposure time of digital cameras. The measurement relies on a sequence of pictures taken by the camera in video mode. The hardware requirements of the proposed method are low: only a signal generator, driving an LED source, is required. The proposed method is illustrated by real measurements. The measurements indicate that the proposed solution is able to provide estimates with uncertainties in the low microsecond range.
Personalized Healthcare Monitoring System

Healthcare is necessary for every person, who needs regular monitoring and it cannot be available at home, especially under developed region, where health facilities are not easily accessible. These days everyone is very keen and particular when it comes to their health condition. People want to grasp their current health condition and also want proper care rapidly. Hence, this research work will first review such existing systems that aim to monitor various vital parameters using android application. Android is an open-source platform that is primarily designed for touch screen mobile devices such as smart phone, tablets etc. that allow maximum number of users to access the application for maximum time and make user's life easier. Then, this research work proposes a model called Android based Personalized Health Care Monitoring and Appointment Application in terms of questionnaire based on vital health parameters such as body temperature, blood pressure, heart rate, saturation percentage of oxygen in blood, etc. that helps in observing the patient health condition and provide people with in-home medical services. This model will be a low-cost android application with a web-based application that will reduce the manual work, save time and cost for both patients as well as doctors. This android application will be developed to easily view the user/patient's health report information by their respective doctors and the user/patient's family members. This application will be advantageous for users/patients to maintain their health condition and provide them in-home medical services without visiting hospitals or clinics.