Reviewerly/exHarmony huggingface dataset:
Improving performance of sorting small arrays on MIPS CPUs using bitonic sort and SIMD instructions

Even though sorting has been widely examined problem in computer science, its parallelization is still subject to many challenges, and draws much interest, even in recent years. This paper presents and examines a possible implementation of parallelized sorting for MIPS CPUs that relies on bitonic sorting networks and SIMD instructions. The main goal of the implementation was to create a set of fast algorithms for sorting arrays of small size (4, 8 and 16) that could be, in turn, applied within a larger, more general, sorting system. Developed algorithms are compared to corresponding cases of stl::sort algorithm in terms of execution time. Measured performance gains were significant.
KeySED:An Efficient Keyword based Search over Encrypted Data in Cloud Environment

To maintain confidentiality of certain specific data, it is required to encrypt the data by using Ciphertext-Policy Attribute-Based Encryption (CPABE) mechanism, before its upload onto a untrusted cloud server. Moreover either to search an item in a given encrypted data or to carry out required computation is not feasible without compromising the confidentiality. To ease the process of search operation over outsourced encrypted data, it is delegated to the cloud server. The CSP performs the outsourced search only when the user is authorized as per the data owner. Further, the searched result returned by the cloud server should be verified by the user. To address the above issue we propose a fine grained attribute based keyword search model. The authenticity of the user is verified by the cloud server using CPABE technique. Further only authorized users can generate valid trapdoor messages which are used later to perform search operation by the cloud server. The proposed model takes comparatively less computation time in comparison to the considered related existing models. The empirical results and mathematical proof of the proposed model shows that it would be suitable for integration into resource constraint devices.
A Blockchain Based Secure Data Transmission Mechanism for Electric Company

Data security is an important request for electric company that takes responsibility of energy supply for our daily lives. In this paper, a blockchain based framework is proposed to grantee the integrity and authenticity of data transmission in electric company. We employ the blockchain to implement a decentralized system that records every data transformation operations in the network and a double-account strategy is designed to fulfill the anonymity data transmission. We implement the proposed method in a test network with five nodes, and the experimental results indicate that the proposed method is effective to improve the data security in electric company.
Fast and Safe Policy Adaptation via Alignment-based Transfer

Applying deep reinforcement learning to physical systems, as opposed to learning in simulation, presents additional challenges in terms of sample efficiency and safety. Collecting large amounts of hardware demonstration data is time-consuming and the exploratory behavior of reinforcement learning algorithms may lead the system into dangerous states, especially during the early stages of training. To address these challenges, we apply transfer learning to reuse a previously learned policy instead of learning from scratch. In this paper, we propose a method where given a source policy, policy adaptation is performed via transfer learning to produce a target policy suitable for real-world deployment. For policy adaptation, alignment-based transfer learning is applied to trajectories generated by the source policy and their corresponding safe target trajectories. We apply this method to manipulators and show that the proposed method is applicable to both inter-task and inter-robot transfer whilst considering safety. We also show that the resulting target policy is robust and can be further improved with reinforcement learning.
A Hybrid Editor for Fast Robot Mission Prototyping

We present an editor that creates an interactive live preview for simple robot missions. The preview provides instant feedback and the statements in the script can be adapted through direct manipulation of the visualization. While the program is executed to draw the preview, it decorates each value with location information and the expression that produced it. If a value is changed in the visualization, the change can be traced back to the source to produce the desired result. An application prototype demonstrates that our approach produces sensible and generalizing results even in the presence of control flow structures in our DSL.
Can AI Close the Design-Code Abstraction Gap?

Aligning the design of a system with its implementation improves product quality and simplifies product evolution. While developers are empowered with AI/ML augmented tools and techniques that increasingly assist them in implementation tasks, the abstraction gap between code and design limits automation for design tasks. In this position paper, we argue that the software engineering community can take advantage of the experiences built with AI/ML techniques to advance automation in design analysis. We summarize research challenges and describe two efforts that apply machine learning to codebases to extract design constructs and detect deviation from intended designs and to use search-based refactoring to make design improvements for extracting functionality.
Review of Research Progress on Small Sample Learning

The neural network model has achieved very good results in the field of computer vision, but the requirements for the amount of data are directly proportional to performance. Data hungry is not only the cause of the high performance of the neural network model, but also a hindrance to its further development. There is not a lot of available data in real-world application scenarios, so how to obtain better performance and more robust models in a small sample environment becomes the goal of less sample learning research. This paper reviews the past small sample learning methods, sorts out its performance and theoretical principles, and points out the existing deficiencies and proposes possible future research directions.
Proposed Methods for Real-Time Visualization of Panoramic Stadium Tribune Images in High Resolution

This paper describes and compares two slightly different methods for “real-time” visualization of panoramic stadium tribune images in high resolution using a virtual camera. The methods are tested as a part of the novel automatic panoramic surveillance camera system for the protection of people and property at sports stadiums. In the following sections, we introduce and compare our two methods, which are tested with four industrial 4k cameras. The first method processes input video streams composed of already transformed images and most of the operations are done on the CPU. The second method processes original video streams from cameras and specific operations are done on the GPU. Using our two methods and specific PC configuration, it is possible to display a full resolution panoramic video sequence with a maximal frame rate of 11.68 ± 2.11 fps and 6.82 ± 1.86 fps. Focusing on a real-time application, more experimental results with the computational efficiency of particular stages in our methods are presented and discussed.
Specification-Based Maneuvering of Quadcopters Through Hoops

In this paper, we study the problem of navigating quadcopters through a sequence of hoops. The specification may be given directly or indirectly via a linear temporal logic (LTL) formula. We approach this problem in three phases. First, we introduce a planner that generates a path through a given sequence of hoops. Second, we augment our planner to leverage a given specification in linear temporal logic (LTL) and generate a sequence that satisfies this specification. Third, we implement cross-entropy optimization on this planner to enhance trajectory performance where quadcopter trajectories are modified within the solution space to optimize over a cost function. We implement this planner as a novel interaction modality between users and quadcopters on the Robotarium. Simulation and experimental results are provided.
Understanding social interactions for the design of tangible user interfaces

Several research works talk about benefits of Tangible User Interfaces in Collaborative work. However, it is still convenient to understand how people interact with tangibles medias in order to design Tangible Interfaces to support Collaboration. In the CSCW domain, we study collaborative activities in order to identify Social Interactions that enable people achieve common goals. Our interest is the development of Tangible User Interfaces to support Collaboration. So, as first step we analysed a group of users in a collaborative task, that allowed us to identify on working space the social interactions between users who used pieces of paper as tangible medias. Then we describe the process of identifying social interactions using the MARS model and introduce some challenges for the design and implementation of Tangible User Interfaces that supports collaborative work.
Stability Assessment Machine Learning Models Interpretation Based on Surrogate Models and Clustering

Machine Learning has been wildly employed to power system stability assessment researches. However, most of machine learning models are lack of interpretability, which hampers their application in engineering. To overcome this deficiency, a model agnostic interpretation method is proposed for stability assessment machine learning. First, in the neighborhood of single data, a local surrogate model construction method is introduced to reveal the inner logic between the system state variables and stability level. Then, in order to get more general interpretations, a state data clustering method based on the Gaussian mixture model is proposed. The cluster interpretation is obtained by surrogate models of representative data. The interpretation results provide a reference for trusting the model and stability control. Finally, the interpretation method is applied to the voltage stability margin assessment machine learning model. The effectiveness of the proposed methods is verified.
Fine-Grained Isolation to Protect Data against In-Process Attacks on AArch64

In-process attacks are a new class of attacks that circumvent protection schemes centered around inter-process isolation. Against these attacks, researchers have proposed fine-grained data isolation schemes that can protect sensitive data from malicious accesses even during the same process. Their proposals based on salient hardware features, such as ARM® processor architecture’s domain protection, are quite successful, but it cannot be applied to a specific architecture, namely AArch64, as this does not provide the same hardware features. In this paper, therefore, we present Sealer, a fine-grained data isolation scheme applicable in AArch64. Sealer achieves its objective by brilliantly harmonizing two hardware features of AArch64: The eXecute-no-Read and the cryptographic extension. Sealer provides application developers with a set of application programming interface (API) so that the developers can enjoy the fine-grained data isolation in their own way.
Question Classification for Health Care Domain Using Rule Based Approach

Question Classification (QC) System for Health Care domain is a specific component of Health Care Question Answering System (QAS). A Question Classification System implementation is usually a computer program that may find its answer type of the question by input the question. The QC system for Health Care domain is very important role in medical field. The Experiment on 427 health questions collected from many different web sites. We have used 9 type of health questions like what, when, how, how many, who, which, why, how much, where, when i.e. that we have get after question preprocessing. After preprocessing we have create question classifier and the experimental result overall accuracy of our system is achieved 80.79% accuracy. Future work has also been discussed as proposed system can be designed a healthcare based question answering system and as the targeted system mismatched in giving answers type and some fruitful efforts will be made so that this drawback of the proposed system can be resolve.
Human—Hand Posture Classification For Robotic Teleoperation Using Wearable Sensor

The aim of this work is to control a teleoperated robotic gripper to grasp objects of different shapes using an efficient posture classification algorithm. Two Inertial Measurement Unit sensors are attached to a wearable glove and using it the human operator performs 5 different postures related to grasping. The feature selected for experimentation is Unit Quaternion. Classification is done with 2 different classifier algorithms, Neural Networks and Random Forests. This is followed by a basic mapping to a 10 degrees of freedom robotic hand. A comparative study of the classifier algorithms is carried out to select the most efficient algorithm for sensor based posture recognition.
Robust Graph Topology Learning and Application in Stock Market Inference

In many applications, there are multiple interacting entities, generating time series of data over the space. To describe the relation within the set of data, the underlying topology may be used. In many real applications, not only the signal/data of interest is measured in noise, but it is also contaminated with outliers. The proposed method, called RGTL, infers the graph topology from noisy measurements and removes these outliers simultaneously. Here, it is assumed that we have no information about the space graph topology, while we know that graph signal are sampled consecutively in time and thus the graph in time domain is given. The simulation results show that the proposed algorithm has a better performance for different graph orders, compared with the conventional graph topology inference methods. Due to the nature of stock market data and the presence of noise and high power outliers, the proposed method is also applied to find some relations among selected ticker symbol prices in the USA market.
Facial Expression Recognition Using Shearlet Transform and Kirsch Masking

In this paper, a new feature extractor is proposed that is combined of Discrete Shearlet Transform (DST) system and Kirsch Compass Kernel (KCK) with Local Binary Pattern (LBP) for extracting features from images. Discrete Shearlet Transform gives multiresolute images of an image by highlighting the major edges along with detail features. On the other hand, Kirsch Compass Kernel (KCK) provides 8 different directional images with prominent edges which sharpen the edges in each direction. These characteristics of DST and KCK help LBP to extract the significant features of an image. The extracted features by LBP help to classify the images by using Support Vector Machine (SVM).
Normal Forms of Conditional Knowledge Bases Respecting Entailments and Renamings

Normal forms of conditional knowledge bases are useful to create, process and compare the knowledge represented by them. In this paper, we propose the reduced antecedent normal form (RANF) for conditional knowledge bases. Compared to the antecedent normal form, it represents conditional knowledge with significantly fewer conditionals. A set of transformation rules maps every knowledge base to a model equivalent knowledge base in RANF. The new notion of renaming normal form ($$\rho $$NF) of a conditional knowledge base takes signature renamings into account. We develop an algorithm for systematically generating conditional knowledge bases over a given signature that are both in RANF and in $$\rho $$NF. The generated knowledge bases are consistent, pairwise not antecedentwise equivalent and pairwise not equivalent under signature renaming. Furthermore, the algorithm is complete in the sense that, taking signature renamings and model equivalence into account, every consistent knowledge base is generated.
Deep Neural Network-Inspired Approach for Human Gesture-Triggered Control Actions Applied to Unmanned Aircraft Systems

Deep Neural Networks have positively impacted the development of autonomous navigation strategies, allowing the integration of object detection capabilities to the vast array of already existing control methods. In this project, a Deep Neural Network is implemented to distinguish particular hand gestures, as well as user’s arm movement, in order to trigger a specific activity of an Unmanned Aircraft System. We present real-time experimental results of how this technique is applied, making use of a Natural User Interface to control a the autonomous aircraft.
Complex Event Processing as an Approach for real-time Analytics in industrial Environments

Real-time analysis of Internet of Things sensor data is crucial for players in the industrial sector for staying competitive. That is, why highly capable and easy to integrate solutions are needed for this, which can be deployed close to the data sources. In this paper, we argue that Complex Event Processing (CEP), which is a model-driven data analytics approach, is such a technique. CEP is able to achieve high throughput of data without the need of the computing power available in modern cloud infrastructures, while producing semantically higher value data in real time.Our here presented solution using CEP is easily integrated, scalable and capable of processing big amounts of data while giving semantic assurances through meta data modeling. Users of our solution do not need to learn any languages to model patterns, but can do that with an intuitive, graphical approach running on mobile devices, which makes it a good fit for domain experts working in industrial environments today.Solutions like the one presented in this paper can be a key-enabler for new business models in the industrial sector and smart factories.
The use of YouTube for teaching English in high schools

Finding an aspect of life in which we do not rely on technology is almost impossible in an era in which its benefits are used to the utmost. Rapid technological changes and quickly growing knowledge base demand from us to acquire informational skills efficiently, which indirectly lead to improvements and updates in the teaching process. The aim of this paper is to investigate the scope of modernization of the teaching process in our country. A research was conducted in order to determine the extent of use of YouTube (as an important digital medium) and the Internet in general at a high school level. An overview of the prevailing attitudes on this topic is given, as well as the advantages and disadvantages of YouTube in the context of language learning. Results of the research that was conducted among the high school students in Leposavić are given in the main part of the paper. Finally, suggestions for selection of suitable teaching/learning video-contents were given, as well as recommendations for potential additional research in the future.
Determination of features of cyber-attack goals based on analysis of data in open security data sources

Abstract The paper analyzes security databases including attack patterns database, vulnerability database and weaknesses database. Special attention is given to the schema that underlies the attack patterns database and specifies its objects and relations between them. This scheme is used for selection of features that characterize different classes of cyber-attack goals. The paper outlines metrics of security related objects, such as attacks, weaknesses and vulnerabilities provided by different schemas, the classes of cyber-attack goals, and analyzes relations between different goals and features of cyber-attacks. The experiments demonstrated dependency between the values of selected features and their applicability for determination of different classes of cyber-attack goals.
Trend-GRU Model based Time Series Data Prediction in Melt Transport Process

Temperature and pressure are two important performance indicators during the melt transport of polyester fiber production, which can affect the overall properties of the melt. Therefore, the accurate prediction of these two indicators is crucial for the control of melt properties. This paper proposes a data prediction model, Trend-GRU (Gated Recurrent Unit), which can extract the feature of unstable change of the melt data. On the premise that the model is not over-complicated, a new structure is designed to extract the feature of unstable change to improve the prediction accuracy. Two data sets on temperature and pressure collected from the actual production process of a spinning factory are used for comparative experiments. The results show that the accuracy of the data prediction of the proposed model is better than the original one.
Are You With Me? Determining the Association of Individuals and the Collective Social Space

The increasing use of autonomous mobile robots in different parts of society, and not restricted only to industrial environments, makes it important to propose techniques that will allow them to behave in the most socially acceptable way as possible. In most real-world scenarios, individuals in the environment are interacting with each other and are arranged into groups. Therefore, it is paramount the proposition of techniques to efficiently and correctly identify and represent such groups. This information can be useful in different tasks such as approaching and initiating an interaction, escorting, and the navigation itself. In this work, we propose a novel graph-based approach to evaluate the possible association of individuals in the environment based on their position and body orientation. Next, based on this association, we propose a representation of the combined social space of individuals in the same group. The methodology was evaluated using synthetic and real-world datasets, showing that it achieves results comparable to or better than the state-of-the-art.
Machine Comprehension-Incorporated Relevance Matching

In current web search engines, the relevance between a query and web pages (i.e. documents) is measured by Text Matching (TM) models. The documents retrieved mainly focus on matching text queries themselves but fail to find target information towards the user intent (e.g. the direct answer to question-style queries). Thus the document containing target information that users want may not be ranked at the top 1 in the search result or even not be recalled. Besides, as voice search and voice-powered assistants are entering our life, queries tend to be long tail ones, which needs a search engine evolved into a higher level of semantic relevance matching. Therefore, it is necessary to build an intent-target relevance matching model in modern search scenarios. This paper proposes a unified model of Machine Comprehension-incorporated Relevance Matching (MCRM). Totally, MCRM models how web users choose the relevant documents to read by observing the titles or summaries, and further look for target information from them. To accomplish that, we first formulate two tasks as Text Matching and Target Extracting. For learning each task, a Context-augmented Matching network (ContMatch) and a Matching-fused machine Comprehension network (MatComprehend) are proposed. Then, they are integrated into an end-to-end framework that can not only measure the semantic relevance but also extract the intent-related target, by deeply comprehending the semantics hidden in queries and exploiting intent-target relations between queries and documents. In MCRM, the two tasks are jointly learned by a multi-task learning approach, where the semantic relevance measured by ContMatch, and the intent-target relevance captured by MatComprehend, are combined and enhanced mutually for boosting the final performance. We conduct extensive experiments on real-world data. The experimental results demonstrate the superiority of MCRM against the state-of-the-art relevance models.