Reviewerly/exHarmony huggingface dataset:
Was that me?

Traditional Western musical instruments have evolved to be robust and predictable, responding consistently to the same player actions with the same musical response. Consequently, errors occurring in a performance scenario are typically attributed to the performer and thus a hallmark of musical accomplishment is a flawless musical rendition. Digital musical instruments often increase the potential for a second type of error as a result of technological failure within one or more components of the instrument. Gestural instruments using machine learning can be particularly susceptible to these types of error as recognition accuracy often falls short of 100%, making errors a familiar feature of gestural music performances. In this paper we refer to these technology-related errors as system errors, which can be difficult for players and audiences to disambiguate from performer errors. We conduct a pilot study in which participants repeat a note selection task in the presence of simulated system errors. The results suggest that, for the gestural music system under study, controlled increases in system error correspond to an increase in the occurrence and severity of performer error. Furthermore, we find the system errors reduce a performer's sense of control and result in the instrument being perceived as less accurate and less responsive.
Combining Biometric Data with Focused Document Types Classifies a Success of Program Comprehension

Program comprehension is one of the important cognitive processes in software maintenance. The process typically involves diverse mental activities such as understanding of source code, library usages, and requirements. Systematic supports would be improved if the supports can be aware of such fine-grained mental activities during program comprehension. Here we aim to investigate whether biometric data can be varied according to such mental activity classes and conduct an experiment with program comprehension tasks involving multiple documents. As a result, we successfully classified the success/failure of the tasks at 85.2% from electroencephalogram (EEG) combined with focused document types. This result suggests that our metrics based on EEG and focused document types might be beneficial to detect developers' diverse mental activities triggered by different documents.
A Framework to Reverse Engineer Database Memory by Abstracting Memory Areas

The contents of RAM in an operating system (OS) are a critical source of evidence for malware detection or system performance profiling. Digital forensics focused on reconstructing OS RAM structures to detect malware patterns at runtime. In an ongoing arms race, these RAM reconstruction approaches must be designed for the attack they are trying to detect. Even though database management systems (DBMS) are collectively responsible for storing and processing most data in organizations, the equivalent problem of memory reconstruction has not been considered for DBMS-managed RAM.
Crypto access: Is it possible to use cryptocurrencies in scholarly periodicals?

Key points The integration of crypto assets into the traditional journal model offers potential for both user incentives and financial security, especially for new or small journals. The case study reveals low uptake by users, which may be attributed to a resistance to new technology and underestimation of the incentives required. Implementation of cryptocurrencies is likely to be more successful if it is implemented by many journals and in an open environment for publishers.
Energy-Efficient Workload Allocation in Distributed HPC System

A vital problem of energy management in a High Performance Computing (HPC) system comprised of remote clusters and data centers is investigated. The paper presents the results of research on energy-efficient management of workload in distributed data centers and computing clusters. The optimisation scheme for allocation of clusters to batches of computing tasks is presented and discussed. The aim is to minimize the energy used by the HPC system, assuming that the required level of quality of services is guaranteed. The results of the performance evaluation of the developed decision system through simulation are presented and discussed.
Pedestrian Counting Without Tracking for the Security Application of IoT Framework

Currently, the security of the Internet of Things (IoT) has aroused great concern. Pedestrian counting under video surveillance has become a key problem affecting social security. In this paper we describe a novel and real-time pedestrian counting framework without using any tracking algorithms. Current research under wide overhead cameras are mainly focus on the tracking-based algorithms, however, effective tracking is difficult in most cases. Therefore, we design a line sampling process, based on this strategy, we can achieve a temporal slice image that contains useful head feature information, which can be used for pedestrian counting without the necessity for visual tracking. As is expected, our algorithm is more stable and accurate than existing approaches. In addition, we also design a two-stage detection algorithm, which is used to locate head position. Experimental results indicate that our constructed algorithm can obtain better performance.
Underwater Sonar Visual Tracking with Obstacle Solving Method

In the field of visual target tracking, the efficient convolution operators (ECO) algorithm has performed very well in accuracy and efficiency. Unlike optical images, underwater sonar images exist the problems of low resolution, high similarity between foreground and background, no obvious texture features. And the underwater moving organisms will deform frequently in a short time. We refer to these problems collectively as obstacles. Because of these obstacles, if an excellent target tracker is directly applied to underwater sonar image sequences, it will not achieve ideal results. Therefore, this paper proposes an underwater sonar visual tracker (USVT) capable of stable tracking. Based on the ECO tracker, first of all, for the problem of high similarity between the foreground and background, we change the original ECO model update method, and make it update under the condition of high-confidence tracking results to repress model drift. Second, we add a deformation suppression model that can repress the change of the response map, which lead to a better effect on targets that are frequently deformed at low resolution. Experiments were performed on the underwater sonar image datasets (USDT). Compared with the algorithm before, it proved that the new method proposed in this paper has better tracking performance for underwater sonar image sequences.
Reducing Storage Requirement in Blockchain Networks Using Overlapping Data Distribution

Blockchain technology first gained attention via public blockchain platforms like Bitcoin and Etherium. Over the years, researchers have continuously explored the potential of blockchains in a more restricted environment, which in turn has paved the way for the creation of many private blockchain platforms. In a private blockchain system, the identity of every entity is known and thus, the issue of trust is less prominent. In addition to the added customizability of permissions regarding who gets to do what, private blockchains also reduce the resource requirement as no proof-of-work is needed here. However, each node in a private blockchain network still needs to store the whole blockchain consisting of all the transactions from the beginning. If the number of transactions in a private blockchain network rises to a very large number, the storage requirement can rise proportionately. In addition to that, public blockchain platforms like Bitcoin and Etherium are already in need of storage optimizations because of their size. We propose a method to divide the whole blockchain of transactions into some non-overlapping shards and make multiple copies of them. Then, we distribute these shards uniformly across the nodes in the network. We show theoretically that this approach not only improves the storage requirement but also ensures the integrity of the data in blockchain in case of node failures.
Attribute Rule performance in Data Mining for Software Deformity Prophecy Datasets Models

In recently, all the developers, programmer and software engineers, they are working specially on software component and software testing to compete the software technology in the world. For this competition, they are using different kind of sources to analysis the software reliability and importance. Nowadays Data mining is one of source, which is used in software for overcome the problem of software fault which occur during the software test and its analysis. This kind of problem leads software deformity prophecy in software. In this research paper, we are also trying to overcome the software deformity prophecy problem with the help of our proposed solution called ONER rule attribute. We have used REPOSITORY datasets models, these datasets models are defected and non-defected datasets models. Our analysis class of interest is defected models. In our research, we have analyzed the efficiency of our proposed solution methods. The experiments results showed that using of ONER with discretize, have improved the efficiency of correctly classified instances in all. Using percentage split and training datasets with ONER discretize rule attribute have improved correctly classified in all datasets models. The analysis of positive accuracy f-measure is also increased in percentage split during the use of ONER with discretize but in some datasets models, the training data and cross validation is better with use of ONER rule attribute. The area under curve (ROC) in both scenarios using ONER rule attribute and discretize with ONER rule attribute is almost same or equal with each other.
Research overview to the quality evaluation of strong-motion observation data

Abstract The quality of strong-motion observation data can directly influence the reliability of earthquake disaster prevention, seismic intensity rapid reporting, earthquake early warning and other results, so it’s of great significance to analyse and evaluate the quality of observation data. In this paper, a detailed survey is made considering the status of domestic and overseas studies on the quality of strong-motion observation data, followed by elaborations about the method and deficiencies in the quality evaluation of strong-motion data; Meanwhile, the main factors influencing the quality of strong-motion observation data are also illustrated, and indexes of quality evaluation are summarized by combining with characteristics and application objectives of strong earthquake observation data; Furthermore, this paper also provides discussions about the development trend of data quality evaluation based on the actual strong-motion observation conditions in China, and puts forward some suggestions.
Quantifying Blocks World State Space

Blocks World is a prototype artificial intelligence problem used to exemplify problem solving using searching and planning algorithms. In this paper we present and experimentally evaluate combinatorial algorithms for measuring the size of the search graph associated to this problem. We focus on evaluating exactly a few metrics on the Blocks World state space graph, including the number of states, the average number of stacks per state, the number of transitions, and the average branching factor.
SimplyHover

Code comprehension is a vital mental process in any maintenance activity. It becomes decisive in large code blocks, in particular those that are governed by if-else statements. These large blocks increase the spatial distance between the if statement and its else counterpart or other dependent else statements. This increased spatial complexity makes it hard for the developer to recall the if logical expression that he has just read and understood, so he needs to go back and read it again, then understand it, and eventually negate it to get the else implicit logical expression. In extreme cases the if-else pair might occur in different pages so the developer has to scroll back and forth to grasp them both. Furthermore, to understand the else implicit negated expression he might want to go back to the farthest if statement and understand the aggregated logical expression all the way to the else which makes the understanding of the else even harder.
Improved wavelet domain centroid-based adaptive audio steganography

The discrete wavelet transform has strong stability, and small modifications to the audio have a little impact on the coefficients in the DWT domain. Centroid refers to the center of energy distribution and also has strong stability. Therefore, this paper proposes an improved adaptive steganography based on wavelet domain centroid, which can be used to hide secret information in digital audio. The secret information in the scheme is mainly represented by XOR operation and adaptive hiding. The original audio is decomposed by a 2-level integer wavelet transform, which is then divided into a number of segments to calculate the centroid of each segment. The secret information is then embedded into each of the low frequency wavelet coefficients by a three-bit XOR result. If the XOR result is not equal to the secret information, the next bit of the centroid bit is flipped. The scheme has a considerable capacity. Compared with the existing methods, it has better robustness, and the imperceptibility is also acceptable.
Preventing Overlaps in Agglomerative Hierarchical Conceptual Clustering

Hierarchical Clustering is an unsupervised learning task, whi-ch seeks to build a set of clusters ordered by the inclusion relation. It is usually assumed that the result is a tree-like structure with no overlapping clusters, i.e., where clusters are either disjoint or nested. In Hierarchical Conceptual Clustering (HCC), each cluster is provided with a conceptual description which belongs to a predefined set called the pattern language. Depending on the application domain, the elements in the pattern language can be of different nature: logical formulas, graphs, tests on the attributes, etc. In this paper, we tackle the issue of overlapping concepts in the agglomerative approach of HCC. We provide a formal characterization of pattern languages that ensures a result without overlaps. Unfortunately, this characterization excludes many pattern languages which may be relevant for agglomerative HCC. Then, we propose two variants of the basic agglomerative HCC approach. Both of them guarantee a result without overlaps; the second one refines the given pattern language so that any two disjoint clusters have mutually exclusive descriptions.
Spacecraft Fault Diagnosis Based on Improved A* Algorithm

The development of fault diagnosis systems has received more and more attention from countries and regions, especially the fault diagnosis of spacecraft. In this paper, the fault diagnosis search problem is also compared to a relationship of finding a path. An inference search tree is established. By finding the fault point in the inference search tree, a constraint condition-cost function is added to expand the branch in the search tree. At the time, the cost function is used to judge and filter, so that it can reduce many unnecessary searches and search for the optimal solution with the least amount of time. In this paper, the traditional A* algorithm is improved. By adding constraints, that is, the cost function, based on the traditional A* algorithm and adding additional structure, especially considering the state in the variable assignment, and the constraint set is Decomposed into a set of constraints. By using mutual priority independence (MPI) to solve the optimal constraint satisfiability problem (OCSP), the optimal constraint satisfiability problem is constructed as a state space search problem, and each search state corresponds to the partial allocation of decision variables. Combine the cost of each allocation to calculate the invalidity, and achieve the purpose of fast fault search.
Advancement of Textual Answer Triggering: Cognitive Boosting

Answer Triggering is still perceived as a challenging task in Question Answering (QA) despite the recent successes chalked up by deep learning models. Its demand for near-human sentence comprehension and answer selection has made previous works on the task seemingly incapable of solving the task. This article introduces an Answer Triggering dataset, CogQA, that contains cognitive features of sentences to enhance the performance of answer triggering systems. It also presents the first deep hierarchical end-to-end neural model that leverages the cognitive elements of CogQA to establish neural correlations to its corresponding answer(s). Our results demonstrate the utility of the dataset and its capability of enabling better triggering of answers in QA systems. Furthermore, our hierarchical model's performance transcends previous works on the WIKIQA benchmark by an appreciable extent.
A Comparative Examination of AR and Video in Delivering Assembly Instructions

Recent developments in technology have made augmented reality (AR) accessible through smartphones, thus allowing millions of users to experience AR. With such unlimited market availability, every industry is attempting to leverage the potential of AR. In this paper, we examine the effectiveness of AR in providing assembly instructions to lay (or novice) users. We compare video and AR instructions in assisting lay users to complete an assembly process; we manipulate the complexity of the assembly task to help validate results. Using task accuracy, task efficiency and user engagement proxies to measure effectiveness, we find that as the task complexity increases, AR instructions begin to yield higher objective accuracy and efficiency than video instructions do. We observe a learning curve associated with using the headset, the device through which the AR instructions were delivered; consequently, less complex tasks were better supported by video instructions than AR ones. We believe this study provides important insights for companies considering the use of AR to provide assembly instructions to their user base.
Intercultural usability of large public displays

One component of smart or major cities are large public displays that are used for many purposes - advertising, warning, informing. These devices appeal to a large heterogeneous user group as especially in major cities people from many countries and cultures live together. Even though, researchers have identified intercultural usability recommendations for general and private devices - mobile phones, websites, etc. -, there is still a gap of information about how to design large non-personal, public displays for intercultural user groups. This dissertation explores the challenges and opportunities within the intercultural design space of large public displays. The expected results are design recommendations and guidelines regarding relevant aspects for intercultural usability of large public displays. Further, these results can be used by user interface designers and researchers that want to design, improve, evaluate or further explore large public displays for intercultural settings.
Accessibility of Block-Based Introductory Programming Languages and a Tangible Programming Tool Prototype

Visual programming languages (VPLs) were designed to assist children in introductory programming courses. Unfortunately, despite the positive results in teaching, VPLs are believed to be inaccessible for children with visual impairments and low vision due to the dependency of visual graphics as both input and output methods. To identify the barriers that users with visual impairments and low vision face while using Block-based programming environments, as well as to acquire feedback regarding the design of a new tangible programming tool prototype, a usability study was conducted which involved nine adult participants with visual impairments and low vision. This paper presents the findings of this usability study and provides a list of features that are needed in order to make Block-based environments accessible. Furthermore, based on observations, interviews, and post-surveys this study demonstrates that our prototype can be used by users with visual impairments and low vision and provides a guideline for the design of tangible interfaces to teach programming concepts.
Relational Facts Extraction with Splitting Mechanism

Relational fact extraction is aimed to extract triples from sentences. Recent years, Sequence-to-sequence learning has been utilized for this task because of its advantage of modeling three different entity overlapping types. In their model, they utilized the same RNN cell to decode entities and relation in a triplet. Actually, the information required to predict entities and relation are different. So we shouldn't mix the process of extracting entities from the original sentence and predicting the relation between entities. Based on the above observation, we propose a novel mechanism to split the process of decoding entities and relation. We conduct extensive experiments on NYT and WebNLG datasets. The experimental results show that our Splitting-Mechainsm (SM) helps to promote performance.
Implementation of Person Tracking System in Panorama Based on Personalized Distribution

Panoramic video is a video collected by multi-channel fisheye lens and obtained by splicing and fusion. Compared with traditional video, panoramic video has better integrity, immersion and presence. Therefore, panoramic video has been widely used in the video field. In order to meet the client's personalized tracking needs in the panoramic broadcast system and panoramic monitoring system, this system designs and implements a people-tracking system based on personalized distribution in the panorama, and provides personalized services for the client to select target objects. Compared with the traditional manual sliding screen for viewing, this automatic live broadcast mode with the target person as the video center greatly improves the user experience. This system uses the method of deep learning to process the panoramic video. After the video is processed by the detection unit based on YOLOv3 and the recognition unit based on ResNet50, the coordinates of the target person in the panoramic video will be obtained. According to the different objects selected by each client, the local video of the corresponding target object is distributed to each client through a streaming media server to meet the personalized tracking needs of different clients.
Study of Accurate and Fast Estimation Method of Vehicle Length Based on YOLOs

With the development of AIoT, Smart City Traffic Management System based on artificial intelligence and big data has gradually become an effective urban management system. Instead of raw data from sensors and cameras, some specific information, such as the length of vehicles are more expected to be gained in Smart City. In this paper, a vehicle length estimation method is proposed based on the Convolutional Neural Networks (CNN) and image processing. The vehicles will be detected by YOLOs, a CNN model for object detection. Then the approximate length of vehicles will be estimated. Experiment results verify that the vehicle length estimation based on YOLOs approach a high accuracy at low time consumption.