Reviewerly/exHarmony huggingface dataset:
“Striking out on your own”—A study of research information management problems on university campuses

Abstract Here, we report on a qualitative study that examined research information management (RIM) ecosystems on research university campuses from the perspectives of research information (RI) managers and librarians. In the study, we identified 21 RIM services offered to researchers, ranging from discovering, storing, and sharing authored content to identifying expertise, recruiting faculty, and ensuring the diversity of committee assignments. In addition, we identified 15 types of RIM service provision and adoption problems, analyzed their activity structures, and connected them to strategies for their resolution. Finally, we report on skills that the study participants reported as being needed in their work. These findings can inform the development of best practice guides for RIM on university campuses. The study also advances the state of the art of RIM research by applying the typology of contradictions from activity theory to categorize the problems of RIM service provision and connect their resolution to theories and findings of prior studies in the literature. In this way, the research expands the theoretical base used to study RIM in general and RIM at research universities in particular.
Knit Stretch Sensor Placement for Body Movement Sensing

Motion capture technology is widely used in movement-related Human-Computer Interaction, especially in digital arts such as digital dance performance. This paper presents a knit stretch sensor-based dance leotard design to evaluate the locations where the sensors best capture the movement on the body. Two studies are undertaken: (1) interviews to determine user requirements of a dance movement sensing system; (2) evaluation of sensor placement on the body. Ten interviewees including dancers, choreographers, and technologists describe their requirements and expectations for a body movement sensing system. The centre of the body (the torso) is determined to be the area of primary interest for dancers and choreographers to sense movement, and technologists find the robustness of textile sensors the most challenging for textile sensing system design. A dance leotard toile is then designed with sensor groupings on the torso along the direction of major muscles, based on the interviewees’ preferred movements to be captured. Each group of the sensors are evaluated by comparing their signal output and a Vicon motion capture system. The evaluation shows sensors which are constantly under tension perform better. For example, sensors on the upper back have a higher success rate than the sensors on the lower back. The dance leotard design was found to capture the movements of standing lean back and standing waist twists the best.
Photovoltaic power missing data filling based on multiple matching and long‐ and short‐term memory network

To address the problem of data missing caused by equipment faults, abnormal transmission, and improper storage in data acquisition systems of photovoltaic (PV) power plants, we hereby propose a method for PV power missing data filling based on multiple matching and a long short-term memory network. First, test samples were divided into three different weather sequences according to the weather type. According to the missing data types, the test samples were divided into two major categories, namely partial missing and complete missing. Each resulting set continued to be divided into four subsets according to the missing proportion. Then, a 450-day data source with 15-minutes intervals was established. The maximal information coefficient was used to reduce the data source and the redundant data space. Finally, coupling meteorological factors were eliminated according to the mutual information theory. A relationship between meteorological factors and short-wave radiation was developed to improve multiple matching accuracy. The correctness of the proposed method and its validity were verified by filling missing power data from a PV power station in Jilin, China.
On the Role of Stablecoins in Cryptoasset Pricing Dynamics

We examine the interactions between stablecoins, Bitcoin, and a basket of altcoins to uncover whether stablecoins represent the investors' demand for trading and investing into cryptoassets or rather play a role as boosting mechanisms during cryptomarkets price rallies. Using a set of instruments covering the standard cointegration framework as well as quantile-specific and non-linear causality tests, we argue that stablecoins mostly reflect an increasing demand for investing in cryptoassets rather than serve as a boosting mechanism for periods of extreme appreciation. We further discuss some specificities of 2017, even though the dynamic patterns remain very similar to the general behavior. Overall, we do not find support for various scandalous claims about stablecoins.
On the vibro-acoustic characterization of two similar violas da gamba

The paper summarizes the main aspects of an experimental vibro-acoustic investigation developed by the authors on two violas da gamba (named in the following as viols). The considered instruments show similar aesthetic and geometric characteristics, as one of them was made later adopting the other as a reference. However, they are characterized by non-negligible different construction solutions, such that is not possible to consider one a faithful replica of the other. The instruments considered are a 16th century viol (hereinafter referred to as ancient viol) and a 21st century viol, inspired by the ancient instrument (hereinafter referred to as the modern viol). The ancient instrument has no bass bar and no sound post and has a rounded back. The modern one was deliberately made with a flat back, to follow the Italian school of construction of this type of instrument and, during this activity, it has been available in different configurations with and without bass bar and sound post. The availability of these two viols made it possible to evaluate how different construction solutions have a significant impact on both structural and acoustic response. Experimental modal analysis and operational vibro-acoustic response techniques have been extensively adopted for this aim. Considering its high intrinsic value, the measurements on the ancient viol have been reduced to the minimum necessary to obtain the suitable identification thereof. On the contrary, broad experimental activity on the modern viol has made its development possible, in analyzing its structural modification, related to the insertion of the bass bar and sound post, in order to verify the acoustic variations resulting from these variants. The different behavior of the ancient viol, with respect to the modern one, has also been analyzed to underline how the sound itself changes from an antique to a contemporary conception of stringed musical instruments.
Vast Topological Learning and Sentient AGI

A novel form of neurocomputing allows machines to generate new concepts along with their anticipated consequences, all encoded as chained associative memories. Knowledge is accumulated by the system through direct experience as network chaining topologies form in response to various environmental input patterns. Thereafter, random disturbances to the connections joining these nets promote the formation of alternative chaining topologies representing novel concepts. The resulting ideational chains are then reinforced or weakened as they incorporate nets containing memories of impactful events or things. Such encodings of entities, actions, and relationships as geometric forms composed of artificial neural nets may well suggest how the human brain summarizes and appraises the states of nearly a hundred billion cortical neurons. It may also be the paradigm that allows the scaling of synthetic neural systems to brain-like proportions to achieve sentient artificial general intelligence (SAGI).
Nonlinear model predictive control of hopping model using approximate step-to-step models for navigation on complex terrain

We consider the motion planning problem of a hopper navigating a terrain comprising stepping stones while optimizing an energy metric. The most widely used approach of discrete searches (e.g., A-star) cannot handle boundary conditions (e.g., end path constraints on position, velocity). However, continuous optimizations can easily deal with the boundary value problem but are not widely used in motion planning because they are computationally intensive and possibly non-convex when one considers the terrain. Here we use a continuous optimization approach within a model predictive control framework. First, we generate a library comprising initial states at an instant in the locomotion cycle (e.g., apex), the controls (e.g., foot placement, amplitude of force), and the states at the same instant at the next step. Next, we fit these step-to-step models with low order polynomials (typically 2nd or 3rd order). Finally, the planner uses these low order step-to-step models to preview a fixed distance ahead and plans the optimal steps and controls. Thereafter, we implement the plan for the first step, followed by replanning. This process continues until the hopper reaches the end of the terrain. The main contributions are low-order polynomial models for fast computation and incorporation of the complex terrain as a cost function.
Video Satellite Imagery Super Resolution for ‘Jilin-1’ via a Single-and-Multi Frame Ensembled Framework

Compared with traditional remote sensing images, satellite remote sensing video contains more useful information and can capture continuous dynamic video. Recently, many deep-learning based methods have been proposed for video super resolution. However, these methods tend to ignore the structural information and characteristics for video satellite imagery such as small ground targets, a wide range of scales and weak textures. To this end, this paper proposes a single-and-multi-frame ensembled framework called SMFE for remote sensing videos super-resolution. The SMFE framework combines a non-local based single image super resolution (SISR) network and a state-of-the-arts multi-frame super resolution (MFSR) network EDVR. Experiments have been performed to demonstrate the effectiveness of the proposed method on Jilin-1.
Regularized Building Segmentation by Frame Field Learning

We add a frame field output to an image segmentation neural network to improve segmentation quality and provide structural information for a subsequent polygonization step. A frame field encodes two directions up to sign at every point of an image. To improve segmentation, we train a network to align an output frame field to the tangents of ground truth contours. In addition to increasing performance by leveraging the multi-task learning effect, our method produces more regular segmentations with sharp building corners. GitHub: github.com/Lydorn/Polygonization-by-Frame-Field-Learning.
A Study on XSS Attacks: Intelligent Detection Methods

Abstract Cross-site scripting is one of the standard web application attacks vulnerable to the application layer. The attacker handles malicious scripting for trusted websites and inject the script. There are numerous types of XSS scripting vulnerable to attack websites incredibly open web applications. The attacker can load or redirect to the malicious webpage. The XSS is susceptible to attack significant websites like medical, e-commerce, banking, etc. The detection and prevention of XSS attacks are still complicated. Plenty of research has been carried out to control the XSS based attack. This paper analyses the XSS attack detection methods by various performance metrics. Numerous works issued in the widespread journals between 2019 and 2020 are reviewed in this paper to accomplish these requirements. The reviewed articles are compared concerning algorithms’ simplicity, the type they belong, and the performance metrics. The work assumed that the movement in the application of elementary methods to detect XSS attacks is better than the recommendations that custom some artificial-intelligence techniques.
Paying attention to rhythm in HCI: Some thoughts on methods

To focus on rhythms within the lived relations between humans and technology is to focus on unfolding processes, dynamic temporality, and patterns of change and continuity. Rhythms infuse the political, the social, the personal and the technological. Through anticipation and expectation rhythms also point towards the future and its tendencies. Drawing inspiration from Rhythmanalysis, Somaesthetic Design and Entanglement HCI this paper explores methods for analysing rhythm within HCI and discusses the challenges of recording the ephemeral, articulating experience and understanding the non-human. Like rhythm, the methods outlined involve process, temporality and difference. There are the embodied practices of developing sensitivity, paying attention and thinking by doing. Then there are the representational practices of revealing through sound, working with moving images, shifting temporal scales and expressing dynamics graphically. These methods are presented as suggestions and tentative principles for ways of thinking with and through rhythm and aim to inspire further exploration.
Cloud Data Security Model Using Modified Decoy Technique In Fog Computing For E-Healthcare

Abstract In recent years, the cloud provides the facility of storing the possible files to a remote database which can be retrieved on demand. Healthcare cloud infrastructure is used to store the medical records which in turn help in managing and tracking the records of the user. The primary care providers use Electronic Medical Record (EMR) for diagnosing and evaluating medical data. Big data analytics is an important technology in enormous business areas which includes banking, social media, machine sensor data and medical welfare. Healthcare cloud provides various storage facilities such as Google drive, dropbox business and one drive in which security issues like data theft attacks to be considered as the serious security breaches of the cloud. In this paper, a modified decoy technique is proposed in order to ensure more security for the user’s Medical Big Data (MBD). The tri-party authentication key uses key agreement protocol which is modelled by bilinear pairing cryptography. The proposed system detects the attacker and sends the information such as IP address, access time and date to the user. It also provides the facility to block the account from further access. Using the triple-des algorithm in bilinear pairing the parameters like throughput is increased and computational complexity is decreased.
An Efficient Vulnerability Extrapolation Using Similarity of Graph Kernel of PDGs

Discovering the potential vulnerabilities in software plays a crucial role in ensuring the security of computer system. This paper proposes a method that can assist security auditors with the analysis of source code. When security auditors identify new vulnerabilities, our method can be adopted to make a list of recommendations that may have the same vulnerabilities for the security auditors. Our method relies on graph representation to automatically extract the mode of PDG(program dependence graph, a structure composed of control dependence and data dependence). Besides, it can be applied to the vulnerability extrapolation scenario, thus reducing the amount of audit code. We worked on an open-source vulnerability test set called Juliet. According to the evaluation results, the clustering effect produced is satisfactory, so that the feature vectors extracted by the Graph2Vec model are applied to labeling and supervised learning indicators are adopted to assess the model for its ability to extract features. On a total of 12,000 small data sets, the training score of the model can reach up to 99.2%, and the test score can reach a maximum of 85.2%. Finally, the recommendation effect of our work is verified as satisfactory.
Instance Segmentation with Oriented Proposals for Aerial Images

Instance segmentation is a challenging issue in remote sensing. The existing state-of-the-art methods use horizontal bounding box (HBB) to infer the instance mask of object. However, the objects in the aerial images have the characteristics of being distributed in arbitrary orientation, densely packed, and so on. In this case, the HBB usually contains a lot of background information and several neighboring objects, leading to coarse and inaccurate mask prediction. To solve the aforementioned problems, we propose a new instance segmentation method, ISOP, by inferring the mask on oriented bounding box (OBB) instead of HBB. We show that the proposed method leading to more accurate mask predictions, especially for densely packed objects. We evaluate our method in the iSAID dataset, and compared to the baseline, the ISOP has achieved around 17% improvement in terms of mAP and 11% for densely packed objects.
FTPB: A Three-Stage DNS Tunnel Detection Method Based on Character Feature Extraction

The domain name system(DNS) protocol is one of the most versatile protocols in the world. If a hacker can control the DNS protocol to pass messages and control the host of victim, then no firewall can effectively intercept the DNS protocol. In recent years, the DNS tunnel is one of the most dangerous threats in the field of steganography, which supports a wide range of criminal activities. In order to detect and distinguish different types of DNS tunnels, we present a three-stage DNS tunnel detection method based on character feature extraction. This novel method named FTPB which uses feature extraction to filter out the domain names of the DNS tunnels by feature extraction classifier and then converts them into a high-dimensional vector by term frequency-inverse document frequency(TF-IDF), and reduces the dimension to 2 by principal components analysis(PCA). Ultimately, the data is classified by a binary vector classifier. Our method only needs to extract the character information of the domain names, and can effectively discover different types of DNS tunnels. Compared with traditional detection methods that can only detect DNS tunnels based on content, our research method can also have the ability to detect DNS tunnels based on codebooks, and the outcome of the evaluation substantially prove the efficacy of our method with accuracy and precision are more than 99%.
Automated visual classification of DOM‐based presentation failure reports for responsive web pages

Summary Since it is common for the users of a web page to access it through a wide variety of devices—including desktops, laptops, tablets and phones—web developers rely on responsive web design (RWD) principles and frameworks to create sites that are useful on all devices. A correctly implemented responsive web page adjusts its layout according to the viewport width of the device in use, thereby ensuring that its design suitably features the content. Since the use of complex RWD frameworks often leads to web pages with hard‐to‐detect responsive layout failures (RLFs), developers employ testing tools that generate reports of potential RLFs. Since testing tools for responsive web pages, like Re DeCheck , analyse a web page representation called the Document Object Model (DOM), they may inadvertently flag concerns that are not human visible, thereby requiring developers to manually confirm and classify each potential RLF as a true positive (TP), false positive (FP), or non‐observable issue (NOI)—a process that is time consuming and error prone. The conference version of this paper presented V iser , a tool that automatically classified three types of RLFs reported by Re DeCheck . Since V iser was not designed to automatically confirm and classify two types of RLFs that Re DeCheck 's DOM‐based analysis could surface, this paper introduces V erve , a tool that automatically classifies all RLF types reported by Re DeCheck . Along with manipulating the opacity of HTML elements in a web page, as does V iser , the V erve tool also uses histogram‐based image comparison to classify RLFs in web pages. Incorporating both the 25 web pages used in prior experiments and 20 new pages not previously considered, this paper's empirical study reveals that V erve 's classification of all five types of RLFs frequently agrees with classifications produced manually by humans. The experiments also reveal that V erve took on average about 4 s to classify any of the RLFs among the 469 reported by Re DeCheck . Since this paper demonstrates that classifying an RLF as a TP, FP, or NOI with V erve , a publicly available tool, is less subjective and error prone than the same manual process done by a human web developer, we argue that it is well‐suited for supporting the testing of complex responsive web pages.
Improved Camera Calibration Method and Accuracy Analysis for Binocular Vision

The quality of the stereo camera calibration method governs the accuracy of the binocular vision and thus determines the precision of the 3D reconstruction. Spurred by that, in this paper, we propose an improved calibration method that increases the calibration accuracy by optimizing the captured corners. Our method is multi-staged, where first, we employ the Harris corner detector to identify the uncertainties of all image corners. Then, for each detected corner, we apply a linear optimization, and finally, we calibrate the intrinsic and extrinsic parameters of the binocular vision setup by employing a nonlinear optimization scheme. We confirm the validity of our technique by analyzing the factors influencing the camera calibration accuracy and confirm the experimental conditions. Ultimately, we evaluate the proposed method and demonstrate that the mean error of our binocular vision architecture is more appealing compared to current literature.
One Covert Channel to Rule Them All: A Practical Approach to Data Exfiltration in the Cloud

The sharing of hardware platforms in multi-tenant environments is a growing security concern. Microarchitectural timing-based covert channels allow tunneling information out of a compromised cloud instance, thus bypassing information flow policies. Significant research efforts have been carried out in order to address the super-set of timing channels. Nevertheless, new attacks keep on being published while disregarding the latest academic efforts, arguing that the relevant defences have not yet been deployed. In order to bridge the gap between vulnerabilities and countermeasures, we challenge state-of-the-art mitigation techniques by constructing the first cross-VM covert channel that is resilient against all known defences, whether they are already deployed or still theoretical. Defence strategies that are relevant with covert channels are surveyed, and a list of requirements is constructed for the new attack. Then, we re-visit the exploitation of the x86 memory bus lock, and launch the proposed covert communication channel across two AWS EC2 instances. While simple in design, the proposed implementation shows that x86 microarchitectures still present salient vulnerabilities, and that state-of-the-art defence strategies-even theoretical ones-remain unsuccessful at hindering data leakage in multi-tenant environments. Finally, a strategy to mitigate the remaining vulnerability is suggested, along with a comparison against the ARMv8 processor architecture.
Considering Cross-Referencing Method for Scalable Public Blockchain

We propose a cross-referencing method for enabling multiple P2P network domains to manage their own public blockchains and periodically exchange among one another the state of the latest fixed block in the blockchain with hysteresis signatures. We evaluated the effectiveness of the proposed method from a theoretical viewpoint by introducing the tamper-resistance-improvement ratio. We confirmed that the tamper-resistance-improvement ratio was high, meaning that it is possible to achieve a more scalable public blockchain balanced with decentralization and tamper resistance with the proposed method.