Reviewerly/exHarmony huggingface dataset:
An improved SIFT algorithm for registration between SAR and optical images

Aiming at improving the performance of scale invariant feature transform (SIFT) algorithm during the registration of optical and synthetic aperture radar (SAR) images, a new SIFT algorithm is proposed. Firstly, the nonlinear diffusion scale space of optical and SAR images is constructed by using nonlinear diffusion filtering, the uniform gradient information is calculated by using multi-scale Sobel operator and multi-scale exponential weighted mean ratio operator respectively. Then, after removing the first layer of the scale space with the image blocking strategy, the scale space is partitioned, and Harris feature points are extracted on the basis of consistent gradient information to obtain stable and uniform point features. Descriptors are constructed based on gradient position and direction histogram templates and normalized to overcome nonlinear radiation differences between images. Finally, the correct matching point pairs are obtained by using bilateral fast approximate nearest neighbor (FLANN) search matching method and random sampling consistent (RANSAC) method, and the affine transformation model parameters are obtained. Compared with the other two algorithms, the CMR of this algorithm is improved by 80.53%, 75.61% and 81.74% respectively in the three groups of images, and the RMSE is reduced by 0.6491, 1.0287 and 0.6306 respectively.
Fast exact synthesis of two-qubit unitaries using a near-minimum number of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>T</mml:mi></mml:math> gates

This paper focuses on exact synthesis of two-qubit unitaries using Clifford and $T$ gates. We propose an ancilla-free synthesis algorithm (i) which uses $T$ gates no more than ten times the minimum possible number of $T$ gates, also known as the $T$ count, and (ii) whose time complexity is linear with the $T$ count and thus instance optimal. Our synthesis algorithm relies on a characterization of the $T$ count of two-qubit unitaries based on Lie group homomorphism, which may be interest of its own. Precisely, we show that for any two-qubit unitary generated by Clifford and $T$ gates, its $T$ count is equivalent to the least denominator exponent of its SO(6) representation up to a factor of at most 10.
An arbitrable outsourcing data audit scheme supporting credit reward and punishment and multi-user sharing

In the era of the Internet of Things, cloud storage allows users to outsource their data without retaining a local copy. However, a serious problem caused by this pattern is that the data owner loses control of the data. In this case, to ensure outsourced storage data integrity, researchers proposed Provable Data Possession. However, most systems cannot deal with disagreements between entities throughout the process. Due to this, we propose an auditing system for outsourced data. By using undeniable blockchain information, it solves disputes between entities and avoids accounting delays by third-party auditors. A credit chain based on the public blockchain is designed. A novel dynamic credit calculation algorithm is adopted in the credit chain to realize an effective reward and punishment mechanism. Security proofs are provided in detail for the proposed scheme. Evaluation experiments show that compared with existing similar schemes, the scheme achieves arbitrable audit and has a lower computational cost.
Dynamic hierarchical multi-scale fusion network with axial MLP for medical image segmentation

Medical image segmentation provides various effective methods for accuracy and robustness of organ segmentation, lesion detection, and classification. Medical images have fixed structures, simple semantics, and diverse details, and thus fusing rich multi-scale features can augment segmentation accuracy. Given that the density of diseased tissue may be comparable to that of surrounding normal tissue, both global and local information are critical for segmentation results. Therefore, considering the importance of multi-scale, global, and local information, in this paper, we propose the dynamic hierarchical multi-scale fusion network with axial mlp (multilayer perceptron) (DHMF-MLP), which integrates the proposed hierarchical multi-scale fusion (HMSF) module. Specifically, HMSF not only reduces the loss of detail information by integrating the features of each stage of the encoder, but also has different receptive fields, thereby improving the segmentation results for small lesions and multi-lesion regions. In HMSF, we not only propose the adaptive attention mechanism (ASAM) to adaptively adjust the semantic conflicts arising during the fusion process but also introduce Axial-mlp to improve the global modeling capability of the network. Extensive experiments on public datasets confirm the excellent performance of our proposed DHMF-MLP. In particular, on the BUSI, ISIC 2018, and GlaS datasets, IoU reaches 70.65%, 83.46%, and 87.04%, respectively.
STA: An efficient data augmentation method for low-resource neural machine translation

Transformer-based neural machine translation (NMT) has achieved state-of-the-art performance in the NMT paradigm. However, it relies on the availability of copious parallel corpora. For low-resource language pairs, the amount of parallel data is insufficient, resulting in poor translation quality. To alleviate this issue, this paper proposes an efficient data augmentation (DA) method named STA. Firstly, the pseudo-parallel sentence pairs are generated by translating sentence trunks with the target-to-source NMT model. Furthermore, two strategies are introduced to merge the original data and pseudo-parallel corpus to augment the training set. Experimental results on simulated and real low-resource translation tasks show that the proposed method improves the translation quality over the strong baseline, and also outperforms other data augmentation methods. Moreover, the STA method can further improve the translation quality when combined with the back-translation method with the extra monolingual data.
Analysing domain-specific problem-solving processes within authentic computer-based learning and training environments by using eye-tracking: a scoping review

Abstract Recently, many studies have been published on the use of eye-tracking to analyse complex problem-solving processes within authentic computer-based learning and training environments. This scoping review aims to provide a systematic report of the current state-of-the-art for related papers. Specifically, this work offers a scoping review of studies that analyse problem-solving processes by using eye-tracking (alongside additional process data such as log files, think aloud, facial expression recognition algorithms, or psychophysiological measures) within authentic technology-based learning and training environments for professional and vocational education and training (VET). A total of 12 studies were identified. The most commonly calculated measures in eye-tracking research are position measures, and these are almost exclusively position duration measures such as the proportion of fixation times or total dwell times. Count measures are also mostly related to the number or proportion of fixations and dwells. Movement measures are rarely computed and usually refer to saccade directions or a scan path. Also, latency and distance measures are almost never calculated. Eye-tracking data is most often analysed for group comparisons between experts vs. novices or high vs. low-performing groups by using common statistical methods such as t-test, (M)ANOVA, or non-parametric Mann–Whitney-U. Visual attention patterns in problem-solving are examined with heat map analyses, lag sequential analyses, and clustering. Recently, linear mixed-effects models have been applied to account for between and within-subjects differences. Also, post-hoc performance predictions are being developed for future integration into multimodal learning analytics. In most cases, self-reporting is used as an additional measurement for data triangulation. In addition to eye-tracking, log files and facial expression recognition algorithms are also used. Few studies use shimmer devices to detect electrodermal activity or practice concurrent thinking aloud. Overall, Haider and Frensch’s (1996, 1999) “information reduction hypothesis” is supported by many studies in the sample. High performers showed a higher visual accuracy, and visual attention was more focused on relevant areas, as seen by fewer fixation counts and higher fixation duration. Low performers showed significantly fewer fixation durations or substantially longer fixation durations and less selective visual attention. Performance is related to prior knowledge and differences in cognitive load. Eye-tracking, (in combination with other data sources) may be a valid method for further research on problem-solving processes in computer-based simulations, may help identify different patterns of problem-solving processes between performance groups, and may hold additional potential for individual learning support.
Piano Soundboard Analysis at Radiated Sound

The piano is a complex musical instrument consisting of several components influencing vibration and sound production. By understanding the sound production mechanism virtual instruments can be created (physics-based sound synthesis) and the design and manufacturing of soundboards can be supported (virtual prototyping). Based on previous results published in the literature, a piano model was built and extended by a near field sound radiation model capable for physics-based sound synthesis. In this paper a simplified piano model is presented, including hammer strike and hysteretic felt models, coupled lossy string model and a 2D FEM based stiffened plate model for soundboard. This paper contains a parametric study where the soundboard parameters, such as its material characteristics and boundary conditions, are modified and their effect on the soundboard's modal behavior and the radiated sound is analyzed. Instead of using only musical (qualitative) descriptors, e.g. brightness or coloring, the piano sounds are characterized based on standard quantitative descriptors (e.g. harmonic ratio, spectral centroid). It is shown that these descriptors are determined by soundboard admittance, string characteristics and position on the soundboard; radiated sound from wooden soundboards can be characterized as harmonic for wide range of initial material descriptors; the string position is essential, and the perceived sound can differ significantly for different listening positions, even for the same harmonic decay pattern.
The Future of Design + Journalism: A Manifesto for Bridging Digital Journalism and Design

In this introduction to the special issue Design + Journalism, we present a manifesto about the future of design and journalism. This manifesto discusses the key areas that need attention in order to develop a more robust bridge between design and journalism. The manifesto is structured by the following three notions: (1) Recognizing design contributions in the field; (2) Integrating design into digital journalism studies through cultural shift; and (3) Widening the scope of the journalism design industry-academia interface. First, we argue that design artifacts, requirements, methods, and implications ought to be recognized as scholarly contributions in digital journalism studies. Second, we need a profound cultural shift in digital journalism studies, which entails conceptualizing design as a cultural practice, understanding design as a continuous process of shaping, and updating pedagogical approaches. Third, we argue that transversal integration between academia and industry should be more heterogeneous in terms of geopolitics, cultural norms, and values. A varied set of industries should be engaged beyond North America and Western Europe. We argue that integrating these three aspects in digital journalism studies will strengthen the understanding of design as an integral part of journalism and digital journalism studies.
Virtual Bank Assistance: An AI Based Voice BOT for Better Banking

Chatbot, a computer program that simulates human conversation, or chat, through artificial intelligence an intelligence chat bot will be used to give information or answers to any question asked by user related to bank. It is more like a virtual assistant, people feel like they are talking with real person. They speak the same language we do, can answer questions. In banks, at user care centres and enquiry desks, human is insufficient and usually takes long time to process the single request which results in wastage of time and also reduce quality of user service. The primary goal of this chat bot is user can interact with mentioning their queries in plain English and the chat bot can resolve their queries with appropriate response in return The proposed system would help duplicate the user utility experience with one difference that employee and yet get the queries attended and resolved. It can extend daily life, by providing solutions to help desks, telephone answering systems, user care centers. This paper defines the dataset that we have prepared from FAQs of bank websites, architecture and methodology used for developing such chatbot. Also this paper discusses the comparison of seven ML classification algorithm used for getting the class of input to chat bot.
Swarm Exploration and Communications: A First Step towards Mutually-Aware Integration by Probabilistic Learning

Swarm exploration by multi-agent systems relies on stable inter-agent communication. However, so far both exploration and communication have been mainly considered separately despite their strong inter-dependency in such systems. In this paper, we present the first steps towards a framework that unifies both of these realms by a “tight” integration. We propose to make exploration “communication-aware” and communication “exploration-aware” by using tools of probabilistic learning and semantic communication, thus enabling the coordination of knowledge and action in multi-agent systems. We anticipate that by a “tight” integration of the communication chain, the exploration strategy will balance the inference objective of the swarm with exploration-tailored, i.e., semantic, inter-agent communication. Thus, by such a semantic communication design, communication efficiency in terms of latency, required data rate, energy, and complexity may be improved. With this in mind, the research proposed in this work addresses challenges in the development of future distributed sensing and data processing platforms—sensor networks or mobile robotic swarms consisting of multiple agents—that can collect, communicate, and process spatially distributed sensor data.
A Full-Coverage Path-Planning Algorithm for a Glass-Curtain-Wall-Cleaning Robot Driven by Ropes

Rope-driven robots are increasingly being applied for the efficiently cleaning of glass curtain walls. However, increasingly complex wall surfaces and the various shapes of obstacles may block the robot and reduce coverage. In this study, three-DOF rope-driven cleaning robots and a full-coverage path-planning algorithm were developed to achieve global operation. The robot adopts a five-rope parallel configuration, and four winches are mounted on the wall and one on the ground to produce 3D motion performance. We used a grid method to build the wall model to mark obstacles, and then we decomposed it according to the wall curvature to better access cleaning subareas. To further increase the cleaning coverage rate, a full-coverage path-planning algorithm based on an improved priority heuristic was designed, which does not ignore the inset area of U-shaped obstacles. By introducing two sets of priority criteria to judge the forward direction, the robot can switch directions to cover a whole area when encountering U-shaped obstacles. Furthermore, by planning a return route requiring the least amount of time when entering a dead zone, an escape strategy was developed to prevent the robot from being unable to choose a direction. The experimental results show that the robot, after applying the proposed path-planning algorithm, could complete the global cleaning of complex glass walls with various obstacles.
Exploiting the Rolling Shutter Read-Out Time for ENF-Based Camera Identification

The electric network frequency (ENF) is a signal that varies over time and represents the frequency of the energy supplied by a mains power system. It continually varies around a nominal value of 50/60 Hz as a result of fluctuations over time in the supply and demand of power and has been employed for various forensic applications. Based on these ENF fluctuations, the intensity of illumination of a light source powered by the electrical grid similarly fluctuates. Videos recorded under such light sources may capture the ENF and hence can be analyzed to extract the ENF. Cameras using the rolling shutter sampling mechanism acquire each row of a video frame sequentially at a time, referred to as the read-out time (Tro) which is a camera-specific parameter. This parameter can be exploited for camera forensic applications. In this paper, we present an approach that exploits the ENF and the Tro to identify the source camera of an ENF-containing video of unknown source. The suggested approach considers a practical scenario where a video obtained from the public, including social media, is investigated by law enforcement to ascertain if it originated from a suspect’s camera. Our experimental results demonstrate the effectiveness of our approach.
Managing the security of information systems with partially observable vulnerability

We consider the security maintenance of information systems where the extent of vulnerability is partially observable. However, the exact extent of the vulnerability can be observed by paying an inspection fee. In each period, the decision‐maker needs to take one of three decisions: (i) do nothing, (ii) inspect and implement (fix the vulnerability) if needed, and (iii) directly implement. We prove that the optimal policy follows a threshold structure. For each value of k (the known vulnerability), there are two thresholds for the partial information: the lower of the two thresholds dictates whether for this value of k, inspection is optimal before a possible implementation or whether direct implementation (i.e., without inspection) is optimal. If inspection is done, another threshold determines whether an implementation is done or not. If neither threshold applies, it is optimal to do nothing. We develop a numerical procedure to find the decision variables in the maintenance policy. We extend the main model to include variable implementation and inspection costs. The optimality of the threshold policy is shown to hold under more general settings. We apply the model to a real‐world problem and demonstrate its applicability and value in managing security systems. Here, we study the security maintenance policies for three different real‐world telecommunications operators and find that these operators can significantly reduce the cost of managing their security by adopting our proposed policy. Another finding is that inspection is more beneficial for medium‐sized to large‐sized operators.
Digital touch in sponsorship: Getting closer to the brand through virtual reality

Abstract Supporters are consuming more and more sporting events through digital platforms. Brands should adapt to these new consumption patterns in order to strengthen their connection with their customers. Today, researchers and practitioners can create more interactive and enjoyable experiences through virtual reality (VR) applications. Through two studies conducted on VR during sporting events, we show that mentally representing the touch experiences of players on the field reduces the psychological distance between the supporter and the player, which strengthens the supporter's attachment to the team and, through the same attachment, to the sponsor (Study 1). We also highlight that these positive effects of VR compared to real touch (using a player's jersey) is linked to the improvement of immersion, which stimulates touch imagery (Study 2). We discuss the implications of this research for theory and practice in the sports business and other sectors.
Tourmaline occurrence and gold mineralization at a granitoid-metasediment contact in the Upper Lom Basin, east Cameroon

Abstract Gold mineralization of the Upper Lom Basin in the eastern Cameroon goldfield is spatially associated with tourmaline. The goldfield belongs to the Adamawa-Yadé Domain of the Central African Fold Belt and is characterized by eluvial, alluvial and lode gold deposits of Pan-African age. This paper examines the chemistry and morphology of saprock gold and its link with tourmaline in the host rock. Tourmaline chemistry was obtained using the electron microprobe analysis (EMPA) technique while gold grains were examined for morphology and microchemistry using the EMPA equipped with scanning electron microscopy and energy dispersive spectrum instruments. The tourmaline compositional data plot in the dravite field, and belongs to the alkali group showing major element variations that are typical of tourmaline associated with granite-related orogenic gold deposits common along shear zones. Gold alloy composition (Ag, Cu) suggests multiple gold precipitation events due to episodic fluid influx, with a range of 572–1000 gold fineness. We infer that tourmaline development and gold deposition were coeval within the aureole around the pluton and involved reduced hydrothermal fluids with low salinity. The high δ 11 B in the tourmaline also points to fluid derivation from the Pan African granitic basement widely recognized to be fertile in gold in eastern Cameroon. Tourmaline textural-chemical features such as low Na content and gold microchemistry point to granite-related hydrothermal style of primary gold mineralization, and has implications for exploration as tourmaline-rich rocks in the vicinity of the felsic plutons in the Lom Basin would be optimal target areas for future exploration.
Exploring Bi-Directional Context for Improved Chatbot Response Generation Using Deep Reinforcement Learning

The development of conversational agents that can generate relevant and meaningful replies is a challenging task in the field of natural language processing. Context and predictive capabilities are crucial factors that humans rely on for effective communication. Prior studies have had a significant limitation in that they do not adequately consider the relationship between utterances in conversations when generating responses. This study aims to address this limitation by proposing a novel method that comprehensively models the contextual information of the current utterance for response generation. A commonly used approach is to rely on the information of the current utterance to generate the corresponding response, and as such it does not take advantage of the context of a multi-turn conversation. In our proposal, different from other studies, we will use a bi-directional context in which the historical direction helps the model remember information from the past in the conversation, while the future direction enables the model to anticipate its impact afterward. We combine a Transformer-based sequence-to-sequence model and the reinforcement learning algorithm to achieve our goal. Experimental results demonstrate the effectiveness of the proposed model through qualitative evaluation of some generated samples, in which the proposed model increases 24% average BLEU score and 29% average ROUGE score compared to the baseline model. This result also shows that our proposed model improves from 5% to 151% for the average BLEU score compared with previous related studies.