Reviewerly/exHarmony huggingface dataset:
Exploiting Local Shape Information for Cross-Modal Person Re-identification

In computer vision, person re-identification (Re-id) is an important problem, aiming to match people across multiple camera views. Most of the existing Re-id systems widely use RGB-based appearance cues, which is not suitable when lighting conditions are very poor. However, for many security reasons, sometimes continued surveillance via camera in low lighting conditions is inevitable. To overcome this problem, we take advantage of the Kinect sensor based depth camera (e.g., Microsoft Kinect), which can be installed in dark places to capture video, while RGB based cameras can be installed in good lighting conditions. Such types of heterogeneous camera networks can be advantageous due to the different sensing modalities available but face challenges to recognize people across depth and RGB cameras. In this paper, we propose a body partitioning method and novel HOG based feature extraction technique on both modalities, which extract local shape information from regions within an image. We find that combining the estimated features on both modalities can sometimes help to better reduce visual ambiguities of appearance features caused by lighting conditions and clothes. We also propose an effective metric learning approach to obtain a better re-identification accuracy across RGB and depth. Experimental results on two publicly available RGBD-ID datasets show the effectiveness of our proposed method.
A generic gust definition and detection method based on wavelet-analysis

Abstract. Wind gusts can have destructive effects on many structures and objects deemed valuable to humans. The aviation industry, for example, views gusts as a major hazard. Their destructive effect is proportional to the momentum that a specific gust imposes onto an object. The actual definition of a gust has a strong influence on how its impact can be quantified. Existing gust definitions, however, are largely based on fixed parameters describing shape requirements and thresholds and are often developed only for specific use cases. These gust definitions do not provide a direct link to the physical impact a particular gust has on a structure or object. The overall goal of this study is to provide such a direct link. The application of a wavelet-analysis to a turbulence-resolving wind velocity signal allows for the localization of signal amplitudes in the period as well as in the time domain. In this paper, we use wavelet-analysis in order to develop a straight-forward method of deriving information about gusts from a wind velocity signal. In order to define what a particular gust might be, we suggest the specification of a characteristic period and amplitude in the time-domain. We define a generic gust as a section of a wind velocity signal, where the wavelet-analysis detects that characteristic amplitude to be matched or exceeded within that characteristic period. The characteristic amplitudes and periods are generic and span a two-dimensional space of generic gust definitions. The method can be applied to turbulence resolving simulation data as well as high-resolution wind velocity measurement data. It can detect gusts of any shape, it is unbiased regarding any specific use case and invariant to changes of the mean wind. We provide a detailed description of the method, its capabilities and demonstrate its application to high resolution wind velocity measurement data.
A self-adaptive quantum steganography algorithm based on QLSb modification in watermarked quantum image

As one of important research branches of quantum information hiding, quantum steganography embeds secret information into quantum images for covert communication by integrating quantum secure communication technology and classical steganography. In this paper, based on the novel enhanced quantum representation (NEQR), a novel quantum steganography algorithm is proposed to transfer secret information by virtue of quantum watermarked image. In order to achieve this goal, the least significant qubit (LSQb) of quantum carrier image is replaced with the secret information by implementing quantum circuit. Compared with the previous quantum steganography algorithms, the communicating parties can recover the secret information tampered, meanwhile the tampers can be located effectively. In the experiment result, the peak signal-to-noise ratios (PSNRs) are calculated for different quantum watermarked images and quantum watermarks, which demonstrate the imperceptibility of the algorithm is good and the secret information embedded can be recovered by virtue of its self-adaptive mechanism.
Improving Robustness of DASH Against Network Uncertainty

Most video players use adaptive bitrate (ABR) algorithms to ensure good quality-of-experience (QoE) across diverse network conditions. To balance conflicting QoE factors, state-of-the-art ABR algorithms select bitrate by optimizing a defined QoE function. However, this scheme relies on throughput prediction that is sensitive to network conditions, so the achieved QoE can be poor in unstable networks. In this paper, we propose a robust ABR algorithm called RBC to avoid the impact of prediction error on QoE. RBC controls the buffer occupancy within a safe range while maximizing the QoE, and employs an adaptive bitrate controller to ensure a good control performance in various network condition. Trace-driven experiments show that RBC achieves much less playback freezing and an improvement of 13.5% on average QoE over the best approach RobustMPC, which confirms the effectiveness of buffer control and adaptive controller in improving system robustness against network uncertainty.
Eye-Gaze to Screen Location Mapping for UI Evaluation of Webpages

This paper presents a way to track eye-gaze by using webcam and mapping the eye-gaze data compensating head pose and orientation on the display screen. First, we have shown a blank screen with red dots to 10 individuals and recorded their eye-gaze pattern and head orientation associated with that screen location by automated annotation. Then, we trained a neural network to learn the relationship between eye-gaze and head pose with screen location. The proposed method can map eye-gazes to screen with 68.3% accuracy. Next, by using the trained model to estimate eye gaze on screen, we have evaluated content of a website. This gives us an automated way to evaluate the UI of a website. The evaluation metric might be used with several other metrics to define a standard for web design and layout. This also gives insight to the likes and dislikes, important areas of a website. Also, eye tracking by only a webcam simplifies the matter to use this technology in various fields which might open the future prospect of enormous applications.
Two proofs of the algebraic completeness theorem for multilattice logic

Shramko [(2016). Truth, falsehood, information and beyond: The American plan generalized. In K. Bimbo (Ed.), J. Michael Dunn on information based logics, outstanding contributions to logic (pp. 191–212). Dordrecht: Springer] formulated multilattice logic and the algebraic completeness theorem for it. However, the proof has not been presented. In this paper, we consider Kamide and Shramko's multilattice logic MLn [Kamide & Shramko (2017a). Embedding from multilattice logic into classical logic and vice versa. Journal of Logic and Computation, 27(5), 1549–1575] which is an extension of Shramko's original multilattice logic by several implications and coimplications. Using the technique of algebraic embedding, we show that Kamide and Shramko's sequent calculus for multilattice logic MLn is sound and complete with respect to multilattices. Moreover, we introduce yet another algebraic semantics for this logic based on the notion of a De Morgan multilattice. Using Lindenbaum-Tarski algebras, we show that MLn is sound and complete with respect to De Morgan multilattices. Besides, we modify Kamide and Shramko's notion of modal multilattice [Kamide & Shramko (2017b). Modal multilattice logic. Logica Universalis, 11(3), 317–343], i.e. we present the concept of De Morgan modal multilattice. We prove that Kamide and Shramko's modal multilattice logic (Kamide & Shramko, 2017b) is adequate with respect to De Morgan modal multilattices.
automaTA

When online learners have questions that are related to a specific task, they often use Q&A boards instead of web search because they are looking for context-specific answers. While lecturers, teaching assistants, and other learners can provide context-specific answers on the Q&A boards, there is often a high response latency which can impede their learning. We present automaTA, a prototype that suggests context-specific answers to online learners' questions by capturing the context of the questions. Our solution is to automate the response generation with a human-machine mixed approach, where humans generate high-quality answers, and the human-generated responses are used to train an automated algorithm to provide context-specific answers. automaTA adopts this approach as a prototype in which it generates automated answers for function-related questions in an online programming course. We conduct two user studies with undergraduate and graduate students with little or no experience with Python and found the potential that automaTA can automatically provide answers to context-specific questions without a human instructor, at scale.
A Comparative Study on Data Stream Clustering Algorithms

Data stream mining is trending today due to enormous data generation by many applications. Most of the non-stationary systems generate data which are massive in volume, non-static and fast changing. Data available is huge, so to augment the calibre of data, it is essential to cluster them. This in turn will boost the data processing speed and that holds a great level of importance in data stream mining. Data Streams being enormous, complicated, fast changing and infinite puts some additional challenges in clustering techniques i.e. time limitation, memory constraint. For mining data streams, many clustering algorithm has been emerged. Also, it is also required to identify cluster of arbitrary shape. And Density based clustering algorithm play important role there. These algorithms fall under notable class having potential to find clusters of arbitrary shapes and to detect noise. Over and above, these algorithms do not require information of the sum total of clusters to be formed, as a part of prior knowledge. The primary focus of these algorithms is to use density-based methods while forming clusters and simultaneously curbing the constraints, which are inherent in the nature of data streams. The objective of this paper is to throw discuss algorithms that are density based and their pros and cons.
Comparative and Evaluative Economic Analysis of Ground Mounted Photovoltaic Plants

Energy produced from renewable sources has assumed an increasingly central role, thanks to the investments in renewable energy and the agreements reached during the last Conferences of the Parties. This paper deals with the economic-estimative evaluation of two photovoltaic plants with similar peak power and geographic position located precisely in the areas of Mandatoriccio and Campana (Calabria, South Italy). The evaluation method used is the Discounted Cash Flow (DCF), a financial procedure that allows to simulate the entire life cycle of the photovoltaic system, from the acquisition date. This method has been implemented by applying an experimental combination of the Build up Approach and the analytical method based on the ascending and descending influences that act on the specific risks related to photovoltaic investment. This paper also highlights the importance of the energy potentially produced by the photovoltaic plants to obtain a very objective estimation value. For this purpose, the annual energy production was estimated using the simplified Siegel method. Thus it was possible to compare Siegel data with real ones supplied by their managers, to carry out a comparative estimative analysis of the two photovoltaic plants covered by our article.
Rank-Based Tensor Factorization for Student Performance Prediction.

One of the essential problems, in educational data mining, is to predict students' performance on future learning materials, such as problems, assignments, and quizzes. Pioneer algorithms for predicting student performance mostly rely on two sources of information: students' past performance, and learning materials' domain knowledge model. The domain knowledge model, traditionally curated by domain experts maps learning materials to concepts, topics, or knowledge components that are presented in them. However, creating a domain model by manually labeling the learning material can be a difficult and time-consuming task. In this paper, we propose a tensor factorization model for student performance prediction that does not rely on a predefined domain model. Our proposed algorithm models student knowledge as a soft membership of latent concepts. It also represents the knowledge acquisition process with an added rank-based constraint in the tensor factorization objective function. Our experiments show that the proposed model outperforms state-of-the-art algorithms in predicting student performance in two real-world datasets, and is robust to hyper-parameters.
Impact of social media use on brand awareness: an applied study on Jordanian banks that uses Facebook

The study aimed to examine the impact of the social media use on brand awareness, the importance of this research stems from the importance of branding and brand awareness in today ever-changing world. The research focuses on the effectiveness of using social media to build brand awareness in Jordanian banks. A descriptive analytical methodology approach was adopted; a questionnaire was used and distributed to consumers using Facebook bank page. After data were analysed and hypothesis tested it has been found that the use of social media has a positive impact on brand awareness, brand recognition, brand recall, brand top-of-mind and brand dominance. Based on the findings of the research recommendations include: marketing department in Jordanian banks sector should build social media marketing strategy that allows more interaction with customer, enhance the knowledge about social media, assign employees to update content and stay in touch with the clients on bank page.
Air Combat Situation Assessment Based on Improved Cloud Model Theory

In order to improve the processing ability of fuzzy information in air combat situation assessment, this paper improves the cloud model theory and establishes an improved cloud fighter air combat situation assessment model. Firstly, the air combat factors are reduced and the weights are assigned. Then, the cloud model theory is used to generate the cloud diagram of air combat situation. Compared with the evaluation cloud diagram, the air combat situation is evaluated. Finally, the reliability of the model is verified through example calculation.
Deep Robust Single Image Depth Estimation Neural Network Using Scene Understanding

Single image depth estimation (SIDE) plays a crucial role in 3D computer vision. In this paper, we propose a two-stage robust SIDE framework that can perform blind SIDE for both indoor and outdoor scenes. At the first stage, the scene understanding module will categorize the RGB image into different depth-ranges. We introduce two different scene understanding modules based on scene classification and coarse depth estimation respectively. At the second stage, SIDE networks trained by the images of specific depth-range are applied to obtain an accurate depth map. In order to improve the accuracy, we further design a multi-task encoding-decoding SIDE network DS-SIDENet based on depthwise separable convolutions. DS-SIDENet is optimized to minimize both depth classification and depth regression losses. This improves the accuracy compared to a single-task SIDE network. Experimental results demonstrate that training DS-SIDENet on an individual dataset such as NYU achieves competitive performance to the state-of-art methods with much better efficiency. Ours proposed robust SIDE framework also shows good performance for the ScanNet indoor images and KITTI outdoor images simultaneously. It achieves the top performance compared to the Robust Vision Challenge (ROB) 2018 submissions.
Accurate querying of frequent subgraphs in power grid graph data

With the development of information technology, the amount of power grid topology data has gradually increased. Therefore, accurate querying of this data has become particularly important. Several researchers have chosen different indexing methods in the filtering stage to obtain more optimized query results because currently there is no uniform and efficient indexing mechanism that achieves good query results. In the traditional algorithm, the hash table for index storage is prone to “collision” problems, which decrease the index construction efficiency. Aiming at the problem of quick index entry, based on the construction of frequent subgraph indexes, a method of serialized storage optimization based on multiple hash tables is proposed. This method mainly uses the exploration sequence to make the keywords evenly distributed; it avoids conflicts of the stored procedure and performs a quick search of the index. The proposed algorithm mainly adopts the “filter-verify” mechanism; in the filtering stage, the index is first established offline, and then the frequent subgraphs are found using the “contains logic” rule to obtain the candidate set. Experimental results show that this method can reduce the time and scale of candidate set generation and improve query efficiency.
Cluster Mechanism of Development of Educational Technology in the Region

Goal: This paper is aimed at developing an advanced cluster mechanism for the development of high-tech education in the region and substantiation of the need for its real world application in modern Russia through the example of the regions of the Southern Federal District. Methodology: In order to provide scientific substantiation of the need for the development of the higher education market in the regions of the Southern Federal District of the Russian Federation, the method of regression analysis was used to determine the dependence of the socioeconomic situation of these regions on the number of graduated bachelors, specialists and masters in 2018. Results: It has been proved that higher education institutions of the regions of the Southern Federal District of the Russian Federation perform an important function involving training of personnel for the regional economics. The higher education services market plays a critical part in the formation of the GDP of the regions of the Southern Federal District of the Russian Federation. The causes of the slight share of high-tech education in the structure of the higher education services market of Russia have been established: lack of higher-education teaching personnel, lack of financial resources, and high degree of depreciation of fixed assets. The expediency of the use of European and American models of involvement of higher education institutions in the cluster processes for the development of high-tech education (EdTech) in the regions of modern Russia has been demonstrated, as it assumes development of educational clusters, since it makes it possible to create a favorable environment in clusters and provide incentives for the reorientation of Russian universities to the provision of high-tech education services. Recommendations: A cluster mechanism for the development of high-tech education (EdTech) is available and indispensible in the regions of Russia. This mechanism should be based on the successful global practice of creating educational clusters. For that end, it is recommended to use the elaborated advanced cluster mechanism for the development of high-tech education (EdTech) in the region reflecting the logic of this process.
A new efficient TKHC-based image sharing scheme over unsecured channel

The major problems of Visual Secret Sharing (VSS) are the pixel expansion and lossy recovery. The former creates large-sized shared images and makes their handling, storage, and speed transmission via networks challenging, whereas the latter leads to poor contrast of the recovered images. In addition, sharing a huge volume of images and transmitting the shared images through one less channel is a critical problem of VSS where any unauthenticated user can attack, discover the generated shares, and recover the secret image. In this paper, an efficient TKHC algorithm is proposed to augment the privacy and safety of the shared images. Moreover, the new TKHC-based VSS scheme is utilized to sharing a huge RGB and grayscale images which are subjected to be encrypted and decrypted by means of TKHC and providing strong security to transmit all the generated shares via one public channel. In comparison to the existing schemes, the proposed scheme shows significant improvement in encryption quality with lightweight computation cost. Furthermore, it withstands the known-plaintext and brute-force attacks and overall creates a balance between security, cost, and performance.
Continual Learning in Practice

This paper describes a reference architecture for self-maintaining systems that can learn continually, as data arrives. In environments where data evolves, we need architectures that manage Machine Learning (ML) models in production, adapt to shifting data distributions, cope with outliers, retrain when necessary, and adapt to new tasks. This represents continual AutoML or Automatically Adaptive Machine Learning. We describe the challenges and proposes a reference architecture.
Discrete Cuckoo Search Algorithm for MIMO Detection

This paper proposes a discrete cuckoo search algorithm, which is a meta-heuristic optimization algorithm used to solve real-world problems, and it is applied to the multiple-input and multiple-output symbol detection problem. However, simply applying the cuckoo search algorithm is problematic since it does not consider the convex and discrete nature of the symbol detection problem. Thus, a discrete cuckoo search algorithm is applied to the decoded process to enhance the error performance of symbol detection.
VR-Replay: Capturing and Replaying Avatars in VR for Asynchronous 3D Collaborative Design

Distributed teams rely on asynchronous CMC tools to complete collaborative tasks due to the difficulties and costs surrounding scheduling synchronous communications. In this paper, we present VR-Replay, a new communication tool that records and replays avatars with both nonverbal behavior and verbal communication in VR asynchronous collaboration. We describe a study comparing VR-Replay with a desktop-based CVE with audio annotation and a VR immersive CVE with audio annotation. Our results suggest that viewing the replay avatar in VR-Replay improves teamwork, causing people to view their partners as more likable, warm, and friendly. 75% of the users chose VR-Replay as the preferred communication tool in our study.