Reviewerly/exHarmony huggingface dataset:
A few-shot learning framework for air vehicle detection by similarity embedding

Air vehicles such as aircrafts and drones have played an important role in surveillance and transportation for both civil and military applications. In this paper, we proposed a few-shot learning framework for air vehicle detection by similarity embedding, with a single moving camera mounted on another flying object. Firstly, we presented the example embedding with similarity conditioned LSTM-model for air vehicle detection. Secondly, we described the support set embedding with bidirectional LSTM-model of air vehicle training samples. Thirdly, we introduced the label prediction for air vehicle image blocks by attention kernel. Finally, we applied the fully convolutional network to segment air vehicle in the accurate bounding box. Experiment results of air vehicle detection show the effectiveness of our approach.
The classification of gene expression profiles based on improved rotation forest algorithm

In order to improve the classification effect of the gene expression profile with high dimension and small sample, this paper proposes an improved rotation forest algorithm. Firstly, the information index to classification algorithm (IIC) can’t deal with multi-class problems, and then an improved IIC algorithm is proposed to achieve the attribute reduction so that the noise genes are eliminated and the dimensions of feature space are reduced. Secondly, the rotation forest algorithm was improved from the two aspects of improving diversity and accuracy of the base classifiers to classify gene expression profiles. The simulation experimental results on the benchmark gene expression profile datasets show that our proposed algorithm is better than rotation forest algorithm in classification accuracy, stability and running time.
Hand Gesture Controller for Robotic-Wheelchair Using Microelectromechanical Sensor ADXL 345

Robotic wheelchairs help in improving the mobility and safety of people with severe motor handicaps. In this paper, we propose a microelectromechanical sensor integrated in a data glove to record the hand tilt angle. An Arduino microcontroller processes the formulated angle and transmits a corresponding hand command wirelessly to a prototype robotic wheelchair via a Zigbee module employing IEEE 802.15.4 protocol. A Proportional, Integrative, and Derivative controller is added by interfacing the MATLAB® SIMULINK and Arduino® programming environments to optimise the DC motor speed control response of the robotic wheelchair. The resulting automatic tuning system causes the wheelchair speed to remain constant in both ramping up and down conditions. Experimental outcomes show that the proposed mechanism responds accurately to hand gestures of users.
An observation on NORX, BLAKE2, and ChaCha

This note extends the first-order truncated differentials found by Das, Maitra, and Meier [1] on 2 rounds of the NORX32 permutation. These lead to stronger 2.5-round differential biases for NORX and BLAKE2's permutation, and for 5 rounds of the ChaCha permutation. These biases lead to efficient “inside-out” distinguishers on 4 rounds of the NORX and BLAKE2 permutations, and 8 rounds of the ChaCha permutation. These distinguishers do not directly affect the security claims of NORX, BLAKE2, or ChaCha in their respective modes of operation.
International Comparison of Radiologists’ Assessment of the Perceptual Quality of Medical Ultrasound Video

Telemedicine can provide timely and high-quality clinical health care from a distance, improving access to and delivery of medical services in resource-poor settings. It can also save lives in situations of emergency. In many circumstances, the success of telemedicine practice heavily relies on the transmission of medical videos over large distances. However, video communication systems are prone to distortion in visual signals, affecting the task performance and thus putting patients at risk. It is critical to understand how practitioners perceive the quality of visual media and use such knowledge to improve clinical practice in telemedicine. In this paper, we investigate the hypothesis that visual quality perception varies between clinicians who work in different practice settings. To evaluate this hypothesis, we performed a subjective experiment where French and Chinese radiologists were asked to rate the quality of ultrasound videos compressed using different compression configurations. The results show that the way the perceived quality changes with the compression configuration is consistent among the both settings studied, however, French radiologists were more bothered by the compression artifacts. The findings can help inform future studies to develop tailored telemedicine systems for specific settings or individuals.
Exploring and Conceptualising Attestation

When formalising the rules of trust in the remote attestation of TPM-based computer systems it is paramount that the rules are precisely understood, supporting unambiguous communication of information about system requirements between engineers. We present a diagrammatic approach to modelling rules of trust using an extended version of concept diagrams. Within the context of our proof-of-concept Network Function Virtualisation and Attestation environment, these rules allow different level of trust to be explored and, importantly, allow us to identify when a computer system should not be trusted. To ensure that the modelling approach can be applied to general systems, we include generic patterns for extending our domain model and rules of trust. Consequently, through the use of a formal, yet accessible, diagrammatic notation, domain experts can define rules of trust for their systems.
Selection of talented archery athletes using weighted product method

Many talented archers come from Indonesia. They all want to participate in ASIAN-GAMES, SEA-GAMES, or the Olympics. Sometimes, the coach needs to choose the most talented athlete who will take part in the competition. Each athlete has several different advantages and its uniqueness. Based on the reason for the coach's difficulties, we built a decision support system that can help coaches choose talented archer athletes. It is based on information technology. The method that has the simplest complexity and the fastest computing time is the Weighted Product Method. The parameters/criteria used in the selection of talented archery athletes include mentality, flexibility, endurance, equipment, and technique. The Decision Support Systems are made with the Delphi Programming Language while storing databases using MySQL. We tested the Decision Support System that was built using the Black Box method. Based on the results of the BlackBox test, the Decision Support System for talented Archery Athletes Selection using Weighted Product (WP) Method has a success percentage of 100%.
Modeling and Evaluating a Human Resource Management Ontology

Over the last few years, there is a growing attention toward Human Resources (HRs) as one of the most valuable asset of any organizations; and managing successfully this asset is crucial in project management. However, despite its relevance, the literature review was shown a lack of a shared, interoperable framework on Human Resource Management (HRM) that allow HRs as a team to interchange their knowledge, skills and facilitates their proper use of Tools and Techniques (T&T); further, HRM processes are not enough to perform all HR duties due to a lack of a common terminology and a complete understanding about project requirements. This paper deals with two main contributions. First, we model a semantic point of view of the main concepts related to HRM domain in the context of PMBOK 5th Guide; the outcome of this model will be an ontology promoting interoperability among HRs as well as their efficient use of T&T. After that, we propose an evaluation of the proposed ontology using both criteria-based evaluation approach to focus on the ontology content and its structure and automated consistency checking approach in order to consider ontology consistency.
Zener diode vs rectifier diode: The comparation of Gaussian probability distribution charts from full-wave rectifier circuits

The aim of this research is to get an explanation why the use of zener diode in full-wave rectifier circuit is not suitable. The diode used in this research is IN4728 zener diode and IN4002 rectifier diode, which is connected to 1,200 ohm resistor. The circuit is supplied with 5-volt AC power supply with frequency of 50 Hz. The output voltage data of the diode is clipped by using LoggerPro voltage sensor. The data is processed by fitting the data according to the Gaussian probability distribution. The results showed that the Gaussian probability distribution chart of the circuit using IN4728 zener diode has an asymmetric shape, unlike the Gaussian probability distribution chart of the circuit using an IN4002 rectifier diode that has a symmetrical shape. The IN4728 zener diode has breakdown voltage of 3.3 V. When reversed bias is occur and the source voltage exceeds the breakdown voltage of the zener diode, the voltage still pass through the zener diode at 3.3 V. This causes the charts of its Gaussian probability distribution has an asymmetric shape. So it can be concluded that the use of IN4728 zener diode for rectifier circuit is not suitable.
Image Enhancement with Applications in Biomedical Processing

The images obtained by X-Ray or computed tomography (CT) may be contaminated with different kinds of noise or show lack of sharpness, too low or high intensity and poor contrast. Such image deficiencies can be induced by adverse physical conditions and by the transmission properties of imaging devices. A number of enhancement techniques in image processing may improve the quality of the image. These include: point arithmetic operations, smoothing and sharpening filters and histogram modifications. The choice of the technique, however, depends on the type of image deficiency. In this paper, the primary aim is to propose an efficient image enhancement method based on nonparametric estimation so as to enable medical images to have better contrast. To evaluate the method performance, X-Ray and CT images have been studied. Experimental results verify that applying this approach can engender good image enhancement performance when compared with classical techniques.
An explorative survey of formal and agile software development methods

This research work explores the trends in formal and agile software development methods. Software development has evolved, from the era of “code and fix” to methods categorized as “heavyweight” and “lightweight”. The heavyweight methods are championed by the waterfall method, while agile methods are considered the lead in lightweight methods of software development. Both methods have proven records of successes and failures. Bridging the divide between them and harmonizing their symbiotic properties has the synergy of creating beneficial and more robust methodology with complementary advantage which is termed ambidexterity.Ambidexterity allows for a high level approach of selecting a methodology on the basis of the problem requirements, and coordinates their independent processes complementarily without conflict.Keywords: Global Software Development, Agile Software Development, off Shoring, Near-shoring, Distribution
Local stereo matching algorithm for low-texture areas

Aiming at the problem that the matching accuracy for the low-texture areas in the stereo matching is so low that the matching failure is likely to occur. This paper proposes a stereo matching algorithm for low-texture areas. Firstly, using the horizontal and vertical gradient of the pixel and the specificity of the pixel itself to perform the calculation of the matching cost. At the same time, introducing the HSV color space to obtain the adaptive support arm length of each pixel in the image, thereby obtaining the aggregate area of each pixel. Then, in the acquired aggregation area, by using the bilateral filtering method to calculate the aggregation cost. Finally, in the disparity computation and refinement stage, Left-Right Consistency (LRC) check is combined with the iterative guided filtering method to reduce the error matching. At the end of the article, by using the standard images on the Middlebury platform to experiment. The experimental results are compared with the traditional experimental results, which proves the effectiveness of the proposed algorithm in the low-texture areas.
A Comparison of Scene Flow Estimation Paradigms

This paper presents a comparison between two core paradigms for computing scene flow from multi-view videos of dynamic scenes. In both approaches, shape and motion estimation are decoupled, in accordance to a large segment of the relevant literature. The first approach is faster and considers only one optical flow field and the depth difference between pixels in consecutive frames to generate a dense scene flow estimate. The second approach is more robust to outliers by considering multiple optical flow fields to generate scene flow. Our goal is to compare the isolated fundamental scene flow estimation methods, without using any post-processing, or optimization. We assess the accuracy of the two methods performing two tests: an optical flow prediction, and a future image prediction, both on a novel view. This is the first quantitative evaluation of scene flow estimation on real imagery of dynamic scenes, in absence of ground truth data.
Convolutional neural network with uncertainty estimates for no-reference image quality assessment

The use of convolutional neural networks (CNNs) for general no-reference image quality assessment (NR-IQA) has seen tremendous growth in the research community. Most these methods used the patches cropped from the original images for training. For these patch-based methods, the ‘ground truth’ quality of patches is essential. In practice, these methods often took the quality score of an original image directly as the labels of its patches’ quality. However, the perceptual quality of image patches generally differs from the corresponding image quality. Thus, the noise in patches’ labels may hinder effective training of the CNN. In this paper, we propose a CNN with two branches for general noreference image quality assessment. One branch of this model predicts the patch quality, and the other predicts the uncertainty, which denotes the degree of deviation of the patch quality from the image quality. Our model can be trained in an end-to-end manner by minimizing a joint loss. We tested our model on widely used image quality databases and showed that it performed better or comparable with those of state-of-the-art NR-IQA algorithms.
Making big data small

Big data analytics is often prohibitively costly and is typically conducted by parallel processing with a cluster of machines. Is big data analytics beyond the reach of small companies that can only afford limited resources? This paper tackles this question by presenting Boundedly EvAlable SQL (BEAS), a system for querying big relations with constrained resources. The idea is to make big data small. To answer a query posed on a dataset, it often suffices to access a small fraction of the data no matter how big the dataset is. In the light of this, BEAS answers queries on big data by identifying and fetching a small set of the data needed. Under available resources, it computes exact answers whenever possible and otherwise approximate answers with accuracy guarantees. Underlying BEAS are principled approaches of bounded evaluation and data-driven approximation, the focus of this paper.
The cross-covariance for heterogeneous track-to-track fusion

Track-to-track fusion (T2TF) has been studied widely for both homogeneous and heterogeneous cases, these cases denoting common and disparate state models. However, as opposed to homogeneous fusion, the cross-covariance for heterogeneous local tracks in different state spaces that accounts for the relationship between the process noises of the heterogeneous models seems not to be available in the literature. The present work provides the derivation of the cross-covariance for heterogeneous local tracks of different dimensions where the local states are related by a nonlinear transformation (with no inverse transformation). First, the relationship between the process noise covariances of the motion models in different state spaces is obtained. The cross-covariance of the local estimation errors is then derived in a recursive form by taking into account the relationship between the local state model process noises. In our simulations, linear minimum mean square (LMMSE) fusion is carried out for a scenario of two tracks of a target from two local trackers, one from an active sensor and one from a passive sensor.
Social Engineering to The Development Plan of Experimental Power Reactor (RDE)

Social Engineering Study has been conducted to the development plan of Experimental Power Plant (RDE). BATAN plans to build and operate the RDE in area Puspiptek Serpong, South Tangerang City. The RDE development will directly or indirectly provide social impact for South Tangerang City. The social impact of RDE development needs to be anticipated since the beginning to ensure that communities receive the benefits of RDE. Therefore, assessment of the social engineering of the RDE development plan needs to be done in an integrated manner with relevant stakeholders. Social Engineering is designed to involve the active participation of key stakeholders in connecting information in a persuasive and open communication pattern to answer the public information needs of the RDE project. The aim of the study was to determine the social engineering strategy for the RDE development plan. The methods used include secondary data collection and primary, coordination and discussion through the activities of consignation and focus group discussions (FGD), followed by analysis and evaluation of data. The results of the study provide the benefits to compile strategies and design of specific dissemination activities in each stakeholder group to encourage stakeholder participation in the success of the RDE program and increase public acceptance.
Additive depth maps, a compact approach for shape completion of single view depth maps

In this paper we introduce a 2D convolutional neural network (CNN) which exploits the additive depth map, a minimal representation of volume, for reconstructing occluded portions of objects captured using commodity depth sensors. The additive depth map represents the amount of depth needed to transform the input into the “back” depth map taken with a sensor exactly opposite of the input camera. A union of the input and back depth map is then the completed 3D shape. To accomplish this task we employ a residual encoder-decoder with skip connections as the overall architecture. We train, and benchmark, our network using existing synthetic datasets as well as real world data captured from a commodity depth sensor. Our experiments show that the additive depth map, despite its minimal 2D representation of volume, can produce comparable results to existing state-of-the-art 3D CNN approaches for shape completion from single view depth maps.
The Weak Completion Semantics Can Model Inferences of Individual Human Reasoners

The weak completion semantics (WCS) based on three-valued Łukasiewicz logic has been demonstrated to be an adequate model for general human reasoning in a variety of different domains. Among the many experimental paradigms in cognitive psychology, the Wason Selection Task (WST) is a core problem with more than 200 publications demonstrating key factors of the systematic deviation of human reasoning from classical logic. Previous attempts were able to model general response patterns, but not the individual responses of participants. This paper provides a novel generalization of the weak completion semantics by using two additional principles, abduction and contraposition: This extension can model the four canonical cases of the WST for the Abstract, Everyday, and Deontic problem domain. Finally, a quantitative comparison between the WCS predictions of the extended model and the individual participants’ responses in the three problem domains is performed. It demonstrates the power of the WCS to adequately model human reasoning on an individual human reasoner level.
Efficient Dynamic Resource Allocation in Hadoop Multiclusters for Load- Balancing Problem

Background: ‘Map-Reduce’ is the framework and its processing of data by rationalizing the distributed servers. also its running the various tasks in parallel way. The most important problem in map reduce environment is Resource Allocation in distributed environments and data locality to its corresponding slave nodes. If the applications are not scheduled properly then it leads to load unbalancing problems in the cloud environments. Objective: This Research suggests a new dynamic way of allocating the resources in hadoop multi cluster environment in order to effectively monitor the nodes for faster computation, load balancing and data locality issues. The dynamic slot allocation is explained theoretically in order to address the problems of pre configuration, speculative execution, delay in scheduling and pre slot allocation in hadoop environments. Experiment is done with Hadoop cluster which increases the efficiency of the nodes and solves the load balancing problem. Methods: The Current design of Map Reduce Hadoop systems are affected by underutilization of slots. The reason is the number of maps and reducer is allotted is smaller than the available number of maps and reducers. In Hadoop most of the times its noticed that dynamic slot allocation policy, the mapper or reducers are idle. So we can use those unused map tasks to overloaded reducer tasks in-order to increase the efficiency of MR jobs and vice versa. Results: The real time experiment was implemented with Multinode Hadoop cluster map reduce jobs of file size 1giga bytes to 5 giga bytes and various performance test has been taken. Conclusion: This paper focused on Hadoop map reduce resource allocation management techniques for multi cluster environments. It proposes a novel dynamic slot allocation policy to improve the performance of yarn scheduler and eliminates the load balancing problem. This work proves that dynamic slot allocation is outperforms more than yarn framework. In future it considered to concentrate on CPU bandwidth and processing time.