Reviewerly/exHarmony huggingface dataset:
Short-term prediction for distributed photovoltaic power based on improved similar time period

A short-term prediction method for distributed PV power based on an improved selection of similar time periods (ISTP) is proposed, to address the problem of low output power prediction accuracy due to a large number of influencing factors and the large difference in the degree of influence of various factors. First, the simple correlation coefficient (SCC) based on path analysis is used to screen the main influencing factors with stronger correlation with PV output power, and these factors are classified into three categories. Second, correlations of the three dimensions are calculated, respectively: (i) TOPSIS (with weights optimized by the SCC) determines meteorological correlation, (ii) linear weighting (based on the fuzzy ranking) obtains time correlation, and (iii) load correlation is quantified with existing current parameters. Third, the combined impact correlation (CIC) is obtained by weighting the three correlations above to establish criteria for the selection of similar periods, and a short-term PV power prediction model is established. Finally, experimental results based on real data of Australian Yulara Solar System PV plant demonstrate that errors of proposed ISTP method are respectively improved by 47.06% and 46.09% compared with the traditional ELMAN and similar day method.
A proposal for the inductive categorisation of parenthetical discourse markers in Spanish using parallel corpora

Abstract We propose a method for the automatic induction of categories of Spanish discourse markers using parallel corpora, based on a quantitative and empirical approach that minimises explicit linguistic knowledge. We conducted the analysis the using a large Spanish-English parallel corpus. First, we used this corpus to obtain a list of parenthetical discourse markers in each language. Then, we used it as a “semantic mirror”, inspecting the English equivalences and assessing which Spanish discourse markers fulfil a similar function in discourse and vice versa. The result of this procedure is an emerging categorisation of discourse markers. The main contribution is to offer empirical evidence for the adequacy of existing manually-compiled taxonomies and the potential for discovery of new, unaccounted categories. In this article we focus on units pertaining to the Spanish language but, since the method is purely quantitative, it is possible to apply it to different languages as well.
Variable selection for high-dimensional incomplete data using horseshoe estimation with data augmentation

Bayesian shrinkage methods have been widely employed to perform variable selection with high-dimensional data. However, the presence of missing data hinders the implementation of these methods. Since complete case analyses can lead to biased estimates, applicable and efficient methods of variable selection with imputation are needed to obtain valid results. In order to address this issue, we propose an algorithm that employs the horseshoe shrinkage prior for shrinkage and multiple imputation for missing data in high-dimensional settings with a practical suggestion on model selection decision strategy. Simulation studies and real data analyses are presented and compared with those of other possible approaches. The simulation results suggest that the proposed algorithm can be considered as a general strategy for model selection of incomplete continuous data.
Using Quantum Nodes Connected via the Quantum Cloud to Perform IoT Quantum Network

Computer networks consist of millions of nodes that need constant protection because of their continued vulnerability to attacks. Classical security methods for protecting such networks will not be effective enough if quantum computers become widespread. On the other hand, we can exploit the capabilities of quantum computing and communications to build a new quantum communication network. In this paper, we focused on enhancing the performance of the classical client–server Internet application. For this sake, we introduced a novel Internet of Things (IoT) quantum network that provides high security and Quality of Service (QoS) compared with the traditional IoT network. This can be achieved by adding quantum components to the traditional IoT network. Quantum counterpart nodes, channels, and servers are used. In order to establish a secure communication between the quantum nodes and the quantum server, we defined a new Communication Procedure (CP) for the suggested IoT quantum network. The currently available quantum computer has a small qubit size (from 50 to 433 qubits). The proposed IoT quantum network allows us to overcome this problem by concatenating the computation efforts of multiple quantum nodes (quantum processors).
Op-Trans: An Optimization Framework for Negative Sampling and Triplet-Mapping Properties in Knowledge Graph Embedding

Knowledge graphs are a popular research field in artificial intelligence, and store large amounts of real-world data. Since data are enriched over time, the knowledge graph is often incomplete. Therefore, knowledge graph completion is particularly important as it predicts missing links based on existing facts. Currently, the family of translation models delivers a better performance in knowledge graph completion. However, most of these models randomly generate negative triplets during the training process, resulting in the low quality of negative triplets. In addition, such models ignore the important characteristics of triplet-mapping properties during model learning. Therefore, we propose an optimization framework based on the translation models (Op-Trans). It enhances the knowledge-graph completion effect from both negative sampling and triplet-mapping properties. First, we propose a clustering cache to generate negative triplets, which generate negative triplets based on entity similarity. This sampling method can directly use the cache to track the negative triplets with large scores. In addition, we focus on the different contributions of the triplets to the optimization goal. We calculate the distinct weight for each triplet according to its mapping properties. In this way, the scoring function deals with each triplet depending on its own weight. The experimental results show that Op-Trans can help the state-of-the-art baselines to obtain a better performance in a link prediction task.
Lightweight and Privacy-Preserving Multi-Keyword Search over Outsourced Data

In cloud computing, documents can be outsourced to the cloud server to achieve flexible access control and efficient sharing among multiple users. The outsourced documents can be intelligently searched according to some keywords with the help of cloud server. During the search process, some private information of outsourced documents may be leaked since the keywords may contain sensitive information of users. However, existing privacy-preserving keyword search schemes have high computation complexity, which are not suitable for resource-constrained end devices—that is, the data processing and search trapdoor generation procedures require users to take resource-intensive computations, e.g., high-dimensional matrix operations, exponentiations and bilinear pairings, which are unaffordable by resource-constrained devices. To address the issues of efficiency and privacy for realizing sorted multi-keyword search over outsourced data in clouds, this paper proposes a lightweight privacy-preserving ranked multi-keyword search (PRMS) scheme, which is further extended to allow each outsourced document to be associated with multiple types of keywords. The searched documents can be sorted according to their similarity scores between the search query and the keyword index of documents, so that only when the similarity score exceeds a given threshold, the corresponding searched document will be returned. The security analysis demonstrates that the proposed PRMS schemes can guarantee the privacy of outsourced documents and keywords, and provides unlinkability for search trapdoors. Performance analysis and comparison show the practicality of the proposed PRMS schemes.
Reconstructing Quantum States from Sparse Measurements

Quantum state tomography (QST) is a central technique to fully characterize an unknown quantum state. However, standard QST requires an exponentially growing number of quantum measurements against the system size, which limits its application to smaller systems. Here, we explore the sparsity of underlying quantum state and propose a QST scheme that combines the matrix product states’ representation of the quantum state with a supervised machine learning algorithm. Our method could reconstruct the unknown sparse quantum states with very high precision using only a portion of the measurement data in a randomly selected basis set. In particular, we demonstrate that the Wolfgang states could be faithfully reconstructed using around 25% of the whole basis, and that the randomly generated quantum states, which could be efficiently represented as matrix product states, could be faithfully reconstructed using a number of bases that scales sub-exponentially against the system size.
Improved BDS-2/3 Satellite Ultra-Fast Clock Bias Prediction Based with the SSA-ELM Model

Ultra-fast satellite clock bias (SCB) products play an important role in real-time precise point positioning. Considering the low accuracy of ultra-fast SCB, which is unable to meet the requirements of precise point position, in this paper, we propose a sparrow search algorithm to optimize the extreme learning machine (SSA-ELM) algorithm in order to improve the performance of SCB prediction in the Beidou satellite navigation system (BDS). By using the sparrow search algorithm's strong global search and fast convergence ability, we further improve the prediction accuracy of SCB of the extreme learning machine. This study uses ultra-fast SCB data from the international GNSS monitoring assessment system (iGMAS) to perform experiments. First, the second difference method is used to evaluate the accuracy and stability of the used data, demonstrating that the accuracy between observed data (ISUO) and predicted data (ISUP) of the ultra-fast clock (ISU) products is optimal. Moreover, the accuracy and stability of the new rubidium (Rb-II) clock and hydrogen (PHM) clock onboard BDS-3 are superior to those of BDS-2, and the choice of different reference clocks affects the accuracy of SCB. Then, SSA-ELM, quadratic polynomial (QP), and a grey model (GM) are used for SCB prediction, and the results are compared with ISUP data. The results show that when predicting 3 and 6 h based on 12 h of SCB data, the SSA-ELM model improves the prediction model by ~60.42%, 5.46%, and 57.59% and 72.27%, 44.65%, and 62.96% as compared with the ISUP, QP, and GM models, respectively. When predicting 6 h based on 12 h of SCB data, the SSA-ELM model improves the prediction model by ~53.16% and 52.09% and by 40.66% and 46.38% compared to the QP and GM models, respectively. Finally, multiday data are used for 6 h SCB prediction. The results show that the SSA-ELM model improves the prediction model by more than 25% compared to the ISUP, QP, and GM models. In addition, the prediction accuracy of the BDS-3 satellite is better than that of the BDS-2 satellite.
Detecting Parallel Covert Data Transmission Channels in Video Conferencing Using Machine Learning

Covert communication channels are a concept in which a policy-breaking method is used in order to covertly transmit data from inside an organization to an external or accessible point. VoIP and Video systems are exposed to such attacks on different layers, such as the underlying real-time transport protocol (RTP) which uses Transmission Control Protocol (TCP) or User Datagram Protocol (UDP) packet streams to punch a hole through Network address translation (NAT). This paper presents different innovative attack methods utilizing covert communication and RTP channels to spread malware or to create a data leak channel between different organizations. The demonstrated attacks are based on a UDP punch hole created using Skype peer-to-peer video conferencing communication. The different attack methods were successfully able to transmit a small text file in an undetectable manner by observing the communication channel, and without causing interruption to the audio/video channels or creating a noticeable disturbance to the quality. While these attacks are hard to detect by the eye, we show that applying classical Machine Learning algorithms to detect these covert channels on statistical features sampled from the communication channel is effective for one type of attack.
Unsupervised Cross-Scene Aerial Image Segmentation via Spectral Space Transferring and Pseudo-Label Revising

Unsupervised domain adaptation (UDA) is essential since manually labeling pixel-level annotations is consuming and expensive. Since the domain discrepancies have not been well solved, existing UDA approaches yield poor performance compared with supervised learning approaches. In this paper, we propose a novel sequential learning network (SLNet) for unsupervised cross-scene aerial image segmentation. The whole system is decoupled into two sequential parts—the image translation model and segmentation adaptation model. Specifically, we introduce the spectral space transferring (SST) approach to narrow the visual discrepancy. The high-frequency components between the source images and the translated images can be transferred in the Fourier spectral space for better preserving the important identity and fine-grained details. To further alleviate the distribution discrepancy, an efficient pseudo-label revising (PLR) approach was developed to guide pseudo-label learning via entropy minimization. Without additional parameters, the entropy map works as the adaptive threshold, constantly revising the pseudo labels for the target domain. Furthermore, numerous experiments for single-category and multi-category UDA segmentation demonstrate that our SLNet is the state-of-the-art.
Estimation of Environment of Sedimentary Deposition in Parh Formation of Tertiary Sediments of Jaisalmer Basin, Rajasthan, India

The test well DND-1 is located in the Dandewala structure of Jaisalmer petroliferous basin. Sedimentary samples collected from Parh formation of Tertiary sedimentary sequence were investigated using Mössbauer spectroscopy to understand the relative distribution of iron bearing minerals with depth. Samples showed predominant presence of iron in pyrite, while the relative amount of siderite was quite small. Iron was also found present in clay forming minerals in Fe2+ and Fe3+, [Ca, Mg, Fe] carbonates, but in small amount. A significant variation in presence/absence of pyrite and siderite, presence of [Ca, Mg, Fe] carbonates is discussed in context to the environment of sedimentary deposition.
Analysis of global and net radiation fluxes in relation to surface albedo at DACCIWA site in Ile-Ife, southwest Nigeria

Measurements of global solar radiation (RG) and net radiation (RN) fluxes were made above the grass-covered surface at DACCIWA site, Ile-Ife, southwest Nigeria, from 2017 to 2019. The datasets were obtained from a four-component net radiometer (model NR01, Hukseflux Thermal Sensors B.V., the Netherlands). Observations were made and separated for cases of clear sky and cloudy conditions during the measurement period. The results showed considerable fluctuations for both radiation fluxes. For clear sky conditions, the magnitudes of RG and RN were higher than those observed for cloudy conditions due to attenuation. For the period of observation, LWup fluxes were higher in the dry season than in the wet season due to dry and hotter surface conditions while LWd dominated the wet season at the site. The highest radiation values occurred in 2018, while the lowest were observed in 2017. Daily surface albedo (α) ranged from 0.16 to 0.22. Empirical relationships obtained for RG and RN are RN = 0.754 RG – 17.4 Wm−2 and RN = 0.657 RG – 32.7 Wm−2 for wet and dry seasons, respectively. Based on the empirical relationships, daily RN and RG can be obtained when measurements like these are not available. Linear relationships between RN and RG show that for all days (cloudy and clear sky conditions), daily average RN is about 0.75 RG, and about 0.60 RG for clear sky conditions at the location. However, the results reflect the characteristics of the site and can be applied for gap-filling or careful extension of the measurement period.
Unintentional synchronization with self-avatar for upper- and lower-body movements

The subjective experience of embodying an avatar when immersed in virtual reality (VR) is known to support the sense of presence and to help with the interaction in a virtual environment. Virtual embodiment is often thought of as the consequence of replacement of the physical body by a virtual one, with a sense of agency for the avatar obtained by making the avatar’s body follow the user’s movements. This unidirectional motor link was, however, challenged by studies observing the opposite effect under different circumstances, for example, in a slow-motion context or when an arm movement was snapped on a predefined axis. These reports are, however, still rare or anecdotal. With the idea of a generalized bidirectional relationship between the user and the avatar in mind, we established a methodology to systematically provoke and study the circumstances under which participants follow the movements of their avatar during long repetitive movements without having been instructed to do so. A preliminary study confirmed that our virtual experimental setup, using full-body motion capture, avatar animation, and virtual mirrors, supports a strong sense of agency and body ownership for the avatar while enabling the experimental manipulation of the avatar’s movement. In the main experimental study, where participants performed repetitive upper- and lower-body movements while their avatar animations were either congruent or out-of-phase, we observed that almost all participants synchronized with their avatar at least once, for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mo>∼</mml:mo><mml:mn>47</mml:mn><mml:mi>%</mml:mi></mml:math> of trials for lower limb movements and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m2"><mml:mo>∼</mml:mo><mml:mn>38</mml:mn><mml:mi>%</mml:mi></mml:math> for upper limb movements. Participants still reported low agency and ownership for the avatar under the incongruent condition, but, most interestingly, some of them also reported that their movements were not influenced by the avatar despite the behavioral effect. Our methodological approach and results contribute to the characterization of the conditions of occurrence of the self-avatar follower effect and, thereby, to identifying an enriched interaction design for VR, involving complex avatar–user mutual interdependencies.
Quantum-Inspired Applications for Classification Problems

In the context of quantum-inspired machine learning, quantum state discrimination is a useful tool for classification problems. We implement a local approach combining the k-nearest neighbors algorithm with some quantum-inspired classifiers. We compare the performance with respect to well-known classifiers applied to benchmark datasets.
Pendampingan Perancangan Chatbot Sebagai Media Interaktif Dalam Menghadapi Tantangan Era Digitalisasi

The implementation of this Community Service (PKM) activity is assisting in the design or manufacture of chatbots which is a series of training activities for making chatbots. This PKM is a national activity whose participants come from various districts/cities in Indonesia. The urgency of this PKM for participants is: (a) For teachers and lecturers because the development of educational technology is a necessity in the current digitalization era; (b) For the general public, because the chatbot working mechanism is the primary factor to be applied in various professional fields, chatbots are not only applied to educational institutions but also in business activities. In particular, for participants through this PKM, the aim is for participants to be able to design a chatbot application and can use it according to the instructions given to the system through the Smojo.Ai platform. This PKM method is carried out online using video conference zoom cloud meetings, Google meet, and WhatsAps Group (WAG) which consists of 3 (three) stages starting from the preparation stage to the evaluation stage from September to October 2022. The achievements of this PKM of 90% which is the average value of the tests carried out, namely: (a) Based on the results of the participant chatbot design, the predetermined target is 80%; (b) Based on the results of testing the participant's chatbot accuracy shows a result of 100%. The implication of the results of this PKM is to contribute to literacy and skills for participants in keeping up with the times, especially information technology which currently influences most activities in the education and business sectors. The next stage of development after the PKM has been successfully carried out is that participants are expected to be able to develop their skills to create and design chatbots independently according to their needs, profession, and field of work which can not only be applied to the education sector but can also be applied to various sectors. Chatbots can be developed in the tourism, retail, service, and other sectors that require customer service to answer repeated questions or for other needs.
An omnidirectional approach to touch-based continuous authentication

This paper focuses on how touch interactions on smartphones can provide a continuous user authentication service through behaviour captured by a touchscreen. While efforts are made to advance touch-based behavioural authentication, researchers often focus on gathering data, tuning classifiers, and enhancing performance by evaluating touch interactions in a sequence rather than independently. However, such systems only work by providing data representing distinct behavioural traits. The typical approach separates behaviour into touch directions and creates multiple user profiles. This work presents an omnidirectional approach which outperforms the traditional method independent of the touch direction - depending on optimal behavioural features and a balanced training set. Thus, we evaluate five behavioural feature sets using the conventional approach against our direction-agnostic method while testing several classifiers, including an Extra-Tree and Gradient Boosting Classifier, which is often overlooked. Results show that in comparison with the traditional, an Extra-Trees classifier and the proposed approach are superior when combining strokes. However, the performance depends on the applied feature set. We find that the TouchAlytics feature set outperforms others when using our approach when combining three or more strokes. Finally, we highlight the importance of reporting the mean area under the curve and equal error rate for single-stroke performance and varying the sequence of strokes separately.