Reviewerly/exHarmony huggingface dataset:
TSVM-M<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e3165" altimg="si1.svg"><mml:msup><mml:mrow /><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:math>: Twin support vector machine based on multi-order moment matching for large-scale multi-class classification

For multi-class classification, many existing methods, such as multiple weighted linear loss twin support vector machine (MWLTSVM), construct multiple decision hyperplanes by minimizing the positive points loss’s first-order moment (mean), which may lead to sensitivity to outliers. Also, when faced with a large-scale classification problem, how to speed up the process of solving the optimization model is also a challenge. An alternative is to use rectangular kernel technology (RKT) to reduce computational complexity. However, RKT is based on the uniform point selection method, which can be proven to be ineffective in improving classifier performance. To address these problems, a novel classifier under the structure of “one-versus-rest” for multi-class classification is proposed in this paper, named twin support vector machine based on multi-order moment matching (TSVM-M3). When constructing the decision hyperplanes, TSVM-M3 takes the first-order and second-order moments (mean and variance) of positive points loss into consideration and implements this by introducing an adjusting factor into the objective function. A theoretical analysis of the robustness of the proposed TSVM-M3 is also provided. Meanwhile, a novel RKT based on the density-dependent data selection method is proposed for large-scale classification. We demonstrate that the proposed RKT can benefit from reducing modeling error. Experimental results show the effectiveness of the proposed TSVM-M3.
Teaching English to First-year Students in Russia: Addressing the Challenges of Distance Learning

(1) The aim of this study is to investigate the psychological climate in first-year students’ academic groups in Russia after they experienced the period of distance learning at the initial stage of university studies (due to the COVID-19 pandemic) and to find out how different it is compared with the academic groups of first-year students whose university studies were not influenced by the COVID-19 pandemic. (2) Methods: Mixed quantitative and qualitative methods were applied to collect data using A. N. Lutoshkin’s questionnaire with closed-ended questions. The questionnaire determines the psychological climate in students’ academic groups. (3) Results: The results of the study reveal considerable differences in the psychological climate of the investigated groups. The findings suggest that the students whose university studies were not influenced by the COVID-19 pandemic considered their academic groups to have more mutual understanding, a greater desire to cooperate with other teams, a better mood in the team, a greater desire to participate in the joint affairs, etc., in comparison with the students who experienced a period of distance learning at the initial stage of their studies. (4) Conclusions: The transition to distance learning allows students to continue their studies under epidemiological restrictions. However, the factors affecting the speed of the adaptation of first-year students in distance learning and the determination of the degree of their influence on the socio-psychological state of the students remain insufficiently studied. This study contributes to the field by defining the peculiarities of the psychological climate in first-year students’ academic groups after they experienced a period of distance learning at the initial stage of university studies. It also specifies how the psychological climate is different from the academic groups of first-year students who were taught in a traditional format. As psychological climate plays a crucial role for EFL instruction, measures must be taken to ease the first-year students’ adaptation process while studying online.
Comments on “A blockchain-based attribute-based signcryption scheme to secure data sharing in the cloud”

Secure data sharing deserves special attention in cloud computing since the operating environment is semi-trusted. Recently, Eltayieb et al. proposed a blockchain-based attribute-based signcryption scheme to secure data sharing in the cloud (Journal of Systems Architecture, doi: https://doi.org/10.1016/j.sysarc.2019.101653). However, after carefully revisiting this scheme, we found out that Eltayieb et al.’s scheme is not secure against confidentiality attack. Thus, the data confidentiality of this scheme is broken, and any data user can have unauthorized access control on outsourced data.
Defending against FakeBob Adversarial Attacks in Speaker Verification Systems with Noise-Adding

Speaker verification systems use human voices as an important biometric to identify legitimate users, thus adding a security layer to voice-controlled Internet-of-things smart homes against illegal access. Recent studies have demonstrated that speaker verification systems are vulnerable to adversarial attacks such as FakeBob. The goal of this work is to design and implement a simple and light-weight defense system that is effective against FakeBob. We specifically study two opposite pre-processing operations on input audios in speak verification systems: denoising that attempts to remove or reduce perturbations and noise-adding that adds small noise to an input audio. Through experiments, we demonstrate that both methods are able to weaken the ability of FakeBob attacks significantly, with noise-adding achieving even better performance than denoising. Specifically, with denoising, the targeted attack success rate of FakeBob attacks can be reduced from 100% to 56.05% in GMM speaker verification systems, and from 95% to only 38.63% in i-vector speaker verification systems, respectively. With noise adding, those numbers can be further lowered down to 5.20% and 0.50%, respectively. As a proactive measure, we study several possible adaptive FakeBob attacks against the noise-adding method. Experiment results demonstrate that noise-adding can still provide a considerable level of protection against these countermeasures.
ParamMacros: Creating UI Automation Leveraging End-User Natural Language Parameterization

Prior work in programming-by-demonstration (PBD) has explored ways to enable end-users to create custom automation without needing to write code. We propose a new end-user specification model &#x2013; asking the end-user to explicitly identify parts of their natural language query that can be generalized. We built a PBD system, ParamMacros, where users first generalize a concrete natural language question &#x2013; identifying parameters and their possible values &#x2013; and then create a demonstration of how to answer the question on the website of interest. ParamMacros then infers a generalized program by using the user-provided parameter values to identify relevant patterns in the website&#x2019;s structure. In a lab study we found that participants were able to meaningfully parameterize natural language queries and felt such a parameterization and demonstration process would be useful for creating custom automation.
The Gamma: Programmatic Data Exploration for Non-programmers

Data exploration tools based on code can access any data source, result in reproducible scripts and encourage users to verify, reuse and modify existing code. Unfortunately, they are hard to use and require expert coding skills. Can we make data exploration tools based on code accessible to non-experts?We present The Gamma, a novel text-based data exploration environment that answers the question in the affirmative. The Gamma takes the idea of code completion to the limit. Users create transparent and reproducible scripts without writing code, by repeatedly choosing from offered code completions.The Gamma is motivated by the needs of data journalists and shows that we may not need to shy away from code for building accessible, reproducible and transparent tools that allow a broad public to benefit from the rise of open data.
Generating Rational Commonsense Knowledge-Aware Dialogue Responses With Channel-Aware Knowledge Fusing Network

Dialogues systems endow machines with the ability to converse with humans using natural language. Nonetheless, previous Seq2Seq-based generative dialogue systems often generate safe but meaningless responses, such as &#x2018;I don&#x0027;t know&#x0027; or &#x2018;I think so&#x0027;. To this end, researchers proposed to infuse external knowledge into dialogue generation, and such knowledge-enhanced methods have achieved remarkable improvements in the open-domain dialogue systems. External knowledge is an exogenous input, where the estrangement inevitably exists between knowledge and dialogue context. Although previous knowledge-enhanced works can already use commonsense knowledge to generate informative responses, they always use knowledge in a single-channel paradigm, which is hard to accurately handle different data-flows and then tends to generate irrational dialogue responses. Thus, they tend to be confused and generate strange responses when infusing the knowledge into dialogue generation, such as &#x2018;I just ate a basketball&#x2019;, dramatically degrading the user experience. To address this problem, this paper proposes a novel <i>Channel-Aware Knowledge Fusing</i> Network (CAKF). Rather than following the traditional single-channel paradigm, CAKF employs three unique channels to handle different data-flows more clearly and rationally: a <i>base</i> channel serves like a vanilla Seq2Seq decoder; a <i>context</i> channel to utilize the contextual information, and a <i>knowledge</i> channel to infuse commonsense knowledge into the dialogue generation. Above such three channels, a <i>Sequential Manager</i> is built to maintain the global sequential decision state, aggregate the local data-flows, and make the final prediction. Experiments on two open-released datasets (a Chinese Weibo and an English Reddit) demonstrated the superior performance of this work against various state-of-the-art approaches.
GradDT: Gradient-Guided Despeckling Transformer for Industrial Imaging Sensors

The speckle noise is a granular disturbance that often brings negative side effects on the detection and recognition of targets of interest in industrial imaging sensors. From the statistical point of view, this type of noise can be modeled as a multiplicative formula. The nonlinear multiplicative property makes despeckling more intractable with respect to noise reduction and details preservation. To blindly remove the undesirable speckle noise, we combine the gradient model and machine learning technology for despeckling. In particular, we first introduce the logarithmic transformation to transform the multiplicative speckle noise into an additive version. A gradient-guided despeckling transformer (termed GradDT) is then proposed to blindly reduce the additive noise in the transformed noisy images. To be specific, the proposed method mainly includes two modules, i.e., the spatial feature extraction module (SFEM) and the efficient transformer module (ETM). The SFEM can extract the spatial feature of speckle noise and the gradient maps corresponding to the noise-free image. The ETM module can calculate the spatial domain's cross-channel cross-covariance and produce global attention maps to reconstruct the sharp image. The proposed GradDT thus can effectively distinguish the speckle noise and vital image features (e.g., edge and texture) to balance the degree of noise suppression and details preservation. Extensive experiments have been implemented on both synthetic and realistic degraded images. Compared with several state-of-the-art speckle noise reduction methods, our GradDT could generate superior imaging performance in terms of both quantitative evaluation and visual quality.
A trust‐based mechanism for drones in smart cities

Smart cities equipped with intelligent devices can enhance the lifestyle and quality of humans by automatically and collaboratively acting as a sustainable resource to the ecosystem. In addition, the technological advancement can be further empowered by interconnecting various types of technologies, such as IoT, Artificial Intelligence, drones and robotics which will clearly improve the Quality of Services, energy efficiency and connectivity to the overall system. The integration of drones hovering over smart cities with the other devices in the smart city network brings a lot of benefits. However, it can also lead to various security and privacy concerns in the network. The aim of this article is to put forward a secure and safe smart city communication environment by proposing a trust establishment scheme for the ad hoc Unmanned Aerial Vehicles network. In which, malicious devices can be traced and blocked by analysing and evaluating their historical interactions within the system and calculating their trust values. A behaviour-based and local trust value scheme is used to analyse the trust of each communicating device that is further associated with a blockchain distributed ledger. The proposed mechanism is measured over various networking and security metrics, including throughput, latency, accuracy and block updating compared to the existing state-of-the-art solutions.
PK-APF: Path-Keeping Algorithm for USVs Based on Artificial Potential Field

Path-keeping requires unmanned surface vehicles (USVs) to follow a planned path in autonomous navigation. It is essential for USVs to carry out autonomous tasks such as collecting various data of water quality and surrounding terrain for exploration and protection of water environments. However, due to obstacle avoidance and other factors such as wind, water waves, and dynamics of USVs, USVs usually deviate from the original planned path during autonomous navigation. This paper proposes a novel path-keeping algorithm based on the artificial potential field method (PK-APF) for USVs. To minimize the deviation between the actual path and the original planned path, the vertical distance and the virtual foot points of the current position of USVs to the original path (a line connecting the previous navigation point and the next navigation point) are calculated. When the vertical distance is larger than a threshold, we regard the vertical foot point as a virtual goal point to guide the USVs to navigate the original path in real-time to achieve high-precision path-keeping. Obstacle avoidance is simulated in the MATLAB and Virtual RobotX (VRX) simulators, and the influence of wind and water waves is considered in VRX. Experiments are conducted in typical scenarios and results show that PK-APF outperforms the traditional APF by at least 22%. The work provides an important basis for real-life environments. Substantial further work is planned for applying the method on a physical USV.
Smart Surveillance with Facial Recognition Using Inception Resnet-V1

Security is a major concern of all public places with large and busy movement of people throughout the day. The various aspects of the current security system employed includes security personnel, cameras and live footages that are continuously monitored. These systems provide a reactive means of responding and handling a security threat. The response time for handling such a situation is very important and it is directly dependent on the time taken to detect a threat. These systems are largely dependent on in-person monitoring at all times and hence are prone to human errors and efficiency.
Exploiting Rank-Based Filter Pruning for Real-Time Uav Tracking

UAV tracking is an emerging task and has wide potential applications in such as agriculture, navigation, entertainment and public security. However, the limitations of computing resources, battery capacity, and maximum load of UAV hinder the deployment of DL-based tracking algorithms on UAV. In contrast to deep learning trackers, discriminative correlation filters (DCF)-based trackers stand out in the UAV tracking community because of their high efficiency. However, their precision is usually much lower than trackers based on deep learning. Model compression is a promising way to narrow the gap (i.e., effciency, precision) between DCF- and deep learning- based trackers, which has not caught much attention in UAV tracking community. In this paper, We propose the P-SiamFC++ tracker, which is the first to use rank-based filter pruning to compress the SiamFC++ model, achieving a remarkable balance between efficiency and precision. Our method is general and may encourage further studies on UAV tracking with model compression in the future. Extensive experiments on four UAV benchmarks, including UAV123@10fps, DTB70, UAVDT and Vistrone2018, show that P-SiamFC++ tracker significantly outperforms state-of-the-art UAV tracking methods.
Creating a Model for Developing and Evaluating Technical Instructions that use Extended Reality

Purpose: Extended reality (XR) is an umbrella term for the many ways that we can now design 3D, interactive, and real-time environments as in combining virtual and real elements, and experience immersion in a completely virtual reality (VR). The use of XR is increasing in popularity across a range of industries. Although researchers are becoming increasingly interested in the benefits and challenges of using XR to convey technical instructions, more comprehensive research is required. I aim to address this need in the present article by introducing an affordance model of Technical Instructions in Extended Reality, the TIER model. Two earlier categorizations, the affordances of technical instructions, and the phases of data handling in XR, formed the point of departure for this article. The analysis utilized a four-category model of affordances by Rantakokko and Nuopponen (2019) that comprised: accessing, finding, understanding, and relying on. Data handling in XR drew on a phase-based model by Rantakokko (2022) featuring: collection, processing, storage, transfer, combining, and presentation. Methods: The two sets of categorizations were combined into a holistic model with an iterative process in order to offer a tool for analyzing and describing the possibilities and challenges that XR brings to designing technical instructions. The iterative process was conducted by adding examples from existing research into matrix tables to understand how the affordances of technical instructions and the phases of data handling in XR are connected. Results: The TIER model is introduced with examples to illustrate how it can be used to view every phase of XR data handling in terms of the affordances of technical instructions based on the laws, regulations, principles of good guidance, and the design process. Conclusion: The TIER model can be used as a tool for an organized, step-by-step design process as well as testing XR-based technical instructions to ensure that the features of XR support the intended affordances of technical instructions. technical instructions, affordances, extended reality, mixed reality,
Emerging Tools for the Interpretation of Glacial and Periglacial Landscapes with Geomorphological Interest—A Case Study Using Augmented Reality in the Mountain Pass of San Isidro (Cantabrian Range, Northwestern Spain)

The application of Augmented Reality (AR), a technology that complements and enriches the live view of a place with virtual elements, is a relatively new approach that has been demonstrated to be useful as an educational tool for the interpretation of geological and geographic features. In addition, it is a technology consistent with challenges related to enhancing the geotourism experience in geologically diverse terrains, and consistent with the sustainable use of resources in rural and natural areas. The focus of AR in this contribution is primarily for academic use. Here, we developed a pioneering AR project, at the San Isidro Pass, located in the north-west of the Iberian Peninsula, more specifically, in the central-eastern sector of the Cantabrian Mountain range (the Asturian Massif) and the northern slope of the Sierra de Sentiles. This is a high mountain area where the low incidence of Pliocene and Quaternary fluvial erosion has favored the preservation of a great geodiversity. In this case study, the approaches of geology and physical geography as subjects in official university studies have been used as a reference framework. Fieldwork was carried out to design an itinerary, or geotrail, of Points of Interest (POIs), that highlight the geological values of this montane region by visualising and interpreting nine glacial and periglacial landscapes using dynamic graphic resources (animated videos, panoramic and spherical tours and rotating and expandable 3D models combining conventional terrestrial photography and 360° photography, aerial photography, thematic cartography, etc.) composed by graphic processing software. The result is the design of AR tools which could be used by public and private entities as a product suitable for university teaching but also a work that could guide other geoeducational actions or institutional action for official heritage recognition. In isolation, these POIs function as ‘geosites’; when combined in sequence (POI 1 to POI 9), these POIs function as a ‘geotrail’ to highlight the diversity of glacial and periglacial landscapes and their sedimentary deposits; when viewed as an ensemble, POIs combine to be similar to a ‘geopark’ that emphasises glacial and periglacial landscapes and sedimentary deposits.
Touch-Move-Release: Studies of Surface and Motion Gestures for Mobile Augmented Reality

Despite a broad range of mobile AR applications available to date, the majority still primarily use surface gestures, i.e., gesturing on the touch screen surface of the device and do not utilise the affordance of three-dimensional user interaction that AR interfaces support. In this research, we compared two methods of gesture interaction for mobile AR applications: Surface Gestures and Motion Gestures, which take advantage of the spatial information of the mobile device. We have conducted two user studies: an elicitation study ( n = 21) and a validation study ( n = 10). The first study elicited two sets of 504 gestures, surface and motion gestures, for 12 everyday mobile AR tasks. The two sets of gestures were classified and compared in terms of goodness, ease of use, and engagement. As expected, the participants’ elicited surface gestures are familiar and easy to use, while motion gestures were found more engaging. Using design patterns derived from the elicited motion gestures, we proposed a novel interaction technique called “TMR” (Touch-Move-Release). Through validation study we found that the TMR motion gesture enhanced engagement and provided a better game experience. In contrast, the surface gesture provided higher precision resulting in higher accuracy and was easier to use. Finally, we discuss the implications of our findings and give our design recommendations for using the elicited gestures.