Reviewerly/exHarmony huggingface dataset:
Lightweight Real-time Object Detection Model for UAV Platform

Real-time detecting objects on captured images on UAV (Unmanned Aerial Vehicle) platforms, rather than barely transmitting images back to supporting equipment for post-processing, is a core requirement for advanced UAV applications. However, due to limited computing capacity and memory of UAV platforms, it is very challenging to deploy real-time detection models on them. In addition, there are more small objects in aerial images, which makes it more difficult to detect accurately. To solve these problems, this paper brings dense connection to Yolo(You Only Look Once)v3 network, and proposes Yolo-LiteDense model. The backbone of Yolo-LiteDense is densely connected, which improves the performance of feature extraction. Then, we enforce channel pruning to Yolo-LiteDense model by pruning less informative channels with less scaling factors. After pruning, parameters and weight size of the model are compressed significantly, and inference time is also shortened. Evaluation results on VisDrone2018-DET show that parameters and weight size of Yolo-LiteDense are 83% and inference time is 30% less than Yolov3-SPP with comparable average precision. In addition, this paper also proposes the lighter version of Yolo-LiteDense, Yolo-DenseNano. Parameters and weight size of Yolo-LiteDense are 70% less than Yolov3-tiny with 2.68 times greater average precision.
Neural Morphology Dataset and Models for Multiple Languages, from the Large to the Endangered

We train neural models for morphological analysis, generation and lemmatization for morphologically rich languages. We present a method for automatically extracting substantially large amount of training data from FSTs for 22 languages, out of which 17 are endangered. The neural models follow the same tagset as the FSTs in order to make it possible to use them as fallback systems together with the FSTs. The source code, models and datasets have been released on Zenodo.
The Effectiveness of Wi-Fi-Network Technology on Campuses and Residences for an Improved Learning Experience and Engagement

The information and communication technology revolution is broadly recognised for its fundamental role in the effective operation of higher education institutions. There is a need to understand the way in which wireless local area networks at universities are viewed by registered users, the people that develop, implement and maintain these networks, and those planning on adopting these networks. The study’s objective is to unravel the usefulness of these networks in a higher education environment to promote effective learning engagements at campuses of the University of Johannesburg in South Africa. Universities provide Wi-Fi network initiatives on campuses to create an effective learning environment, and wireless local area network connections at universities mean that Wi-Fi-enabled devices can be leveraged for educational purposes. The provision of Wi-Fi-enabled computers, mobile devices and electronic gadgets has revolutionised the realm and methods of communication, which is channelled towards improving and enhancing internet coverage on campuses and at student residences. This study explores the effectiveness of Wi-Fi networks and hotspots on campuses and at student residences to improve students’ learning engagement. The study used mixed-research methods, including a document analysis to gather information from information and communication systems and a survey to gather responses from the respondents. The findings suggest that Wi-Fi availability at universities is perceived as useful and effective since students benefit from a range of improved learning experiences, easy access to educational content, enhanced performance, and quality and education delivery.
End-to-end Network Embedding Unsupervised Key Frame Extraction for Video-based Person Re-identification

At present, regarding the task of video-based person re-identification, the input sequences have subtle differences and large redundancies because there are not enough effective interventions in the extraction of frame sequences. Although some studies have mentioned that key frame should be extracted first, they have not jointed the key frame extraction and the person re-identification. Consequently, it is difficult to evaluate whether the extracted key frames are effective for person re-identification. In this paper, we introduce an End-to-end Network Embedding Unsupervised Key Frame Extraction (EKEN) to address the above problems. First, we design a key frame extraction module and train it using pseudo labels generated by hierarchical clustering to extract key frames. Second, we embed the key frame extraction module into the person re-identification task. The results of the key frame extraction and the pedestrian re-recognition are fed back to each other in time. The instant feedback promotes the synchronization optimization of these two modules. The mAP achieved by our method in the MARS dataset is improved by 0.7%, 2.9%, 2.1% and 2.3% over the methods based on Random, Evenly, Cluster and Frame difference, respectively. Particularly, our method is more fit for the real-world application comparing to existing methods.
Accelerating Secure (2+1)-Party Computation by Insecure but Efficient Building Blocks

Secure multi-party computation (MPC) is a cryptographic tool that enables a set of parties to compute a function jointly while keeping each input secret. Since MPC based on secret sharing (SS) achieves high throughput and works fast, many applications have been developed. However, SS-based MPC requires many communication rounds in general, and this becomes a performance bottleneck in real-world applications under high-latency networks. In this paper, we propose SS-based secure three-party computation with almost no preprocessing based on our new (small-)constant-round fundamental gates, by revisiting a framework in a few previous works where a number of parties are assisted by another party who may partially learn secret information. Instead of ordinary logical gates, our fundamental gate is an efficient Equality, for which the result leaks to the third party, and we develop novel two-round constructions of secure building-block protocols (LessThan Comparison, RightShift, Table LookUp, etc.) from the insecure Equality. To show the practicality of our protocols, we implement a secure exact edit distance protocol for two genome strings. Our experiments show that in some network setting our protocol is about 2 times faster (14 times faster taking preprocessing into consideration) than the state-of-the-art SS-based protocol (Ohata and Nuida, FC 2020).
ROCK-CNN

The paper is dedicated to distributed convolutional neural networks on a resource constrained devices cluster. The authors focus on requirements that meet the users' needs. Based on this, architecture of the system is proposed. Two use cases of CNN computations on a ROCK-CNN cluster are mentioned, and algorithms for organizing distributed convolutional neural networks are described. Experiments to validate proposed architecture and algorithms for distributed deep learning computations are conducted as well.
Ensemble of Multiple Classifiers for Accelerometer Based Human Fall Detection

Fall is a major health threat for the elderly. Automatic detection of human fall using machine learning algorithms is necessary to reduce the consequence of human fall. In the proposed method, nine different features resultant, variance, standard deviation, root mean square, Euclidean norm, skewness, kurtosis, and geometric mean have been calculated from accelerometer data. Stacking based ensemble of Naive Bayes, KNN, J48, and Random forest classifier has been used to discriminate fall activities for ADL. The performance of each classifier has also been calculated for comparing the performance of each classifier with a stacking-based ensemble method. It was found that even some individual classifier gives better result in sensitivity or specificity but stacking based ensemble of these classifier gives better results in a combination of sensitivity and specificity. With stacking-based ensemble classifiers, 89% sensitivity and 95% specificity have been achieved which is higher than the performance of individual classifiers.
Correlations of random classifiers on large data sets

Classification of large data sets by feedforward neural networks is investigated. To deal with unmanageably large sets of classification tasks, a probabilistic model of their relevance is considered. Optimization of networks computing randomly chosen classifiers is studied in terms of correlations of classifiers with network input–output functions. Effects of increasing sizes of sets of data to be classified are analyzed using geometrical properties of high-dimensional spaces. Their consequences on concentrations of values of sufficiently smooth functions of random variables around their mean values are applied. It is shown that the critical factor for suitability of a class of networks for computing randomly chosen classifiers is the maximum of sizes of the mean values of their correlations with network input–output functions. To include cases in which function values are not independent, the method of bounded differences is exploited.
A Tale of Two Efficient and Informative Negative Sampling Distributions

Softmax classifiers with a very large number of classes naturally occur in many applications such as natural language processing and information retrieval. The calculation of full softmax is costly from the computational and energy perspective. There have been various sampling approaches to overcome this challenge, popularly known as negative sampling (NS). Ideally, NS should sample negative classes from a distribution that is dependent on the input data, the current parameters, and the correct positive class. Unfortunately, due to the dynamically updated parameters and data samples, there is no sampling scheme that is provably adaptive and samples the negative classes efficiently. Therefore, alternative heuristics like random sampling, static frequency-based sampling, or learning-based biased sampling, which primarily trade either the sampling cost or the adaptivity of samples per iteration are adopted. In this paper, we show two classes of distributions where the sampling scheme is truly adaptive and provably generates negative samples in near-constant time. Our implementation in C++ on CPU is significantly superior, both in terms of wall-clock time and accuracy, compared to the most optimized TensorFlow implementations of other popular negative sampling approaches on powerful NVIDIA V100 GPU.
QAM: Question Answering System Based on Knowledge Graph in the Military

To automatically collect information from the Internet and provide it to users for query, much of the existing search engines have been focused on massive and sprawling information. However, we note that the search engines produce a host of answers to specific questions with little accuracy or intelligence. Therefore, we consider the approach of question answering (QA) systems based on the knowledge graph (KG). Resent works are widely shared that the KG can be deeper for the field of medicine and finance, etc. Nowadays, QA system in the military is sorely needed with the dramatic growth of the technology. In this paper, we propose a QA system based on the KG in the military (QAM). Which applies semantic Web technology, and allows a deep analysis of military questions and documents at both representation and interrogation levels. The experimental results reveal that KG applied in the military domain, can make up the inadequacy of the general search engine, return to refine and accurate results, and provide conveniently knowledge service in the military field.
Analyzing Student Reflection Sentiments and Problem-Solving Procedures in MOOCs

Student reflection is thought to be an important part of retaining and understanding knowledge gained in a course. Using natural language processing, we analyze and interpret student reflections from Massive Open Online Courses (MOOCs) to understand the students' sentiments and problem-solving procedures. The reflections are free text responses to questions from MIT 6.00.1x, an introductory programming MOOC. We compare different sentiment analysis methods, and conclude that the best-performing methods can robustly classify sentiment of student responses. In addition, we develop methods to analyze student problem-solving procedures using sentence parsing and topic modeling. We find our method can distinguish some common problem-solving procedures such as utilizing course resources.
HVAC: Evading Classifier-based Defenses in Hidden Voice Attacks

Recent years have witnessed the rapid development of automatic speech recognition (ASR) systems, providing a practical voice-user interface for widely deployed smart devices. With the ever-growing deployment of such an interface, several voice-based attack schemes have been proposed towards current ASR systems to exploit certain vulnerabilities. Posing one of the more serious threats,hidden voice attack uses the human-machine perception gap to generate obfuscated/hidden voice commands that are unintelligible to human listeners but can be interpreted as commands by machines. However, due to the nature of hidden voice commands (i.e., normal and obfuscated samples exhibit a significant difference in their acoustic features), recent studies show that they can be easily detected and defended by a pre-trained classifier, thereby making it less threatening. In this paper, we validate that such a defense strategy can be circumvented with a more advanced type of hidden voice attack calledHVAC. Our proposed HVAC attack can easily bypass the existing learning-based defense classifiers while preserving all the essential characteristics of hidden voice attacks (i.e., unintelligible to humans and recognizable to machines). Specifically, we find that all classifier-based defenses build on top of classification models that are trained with acoustic features extracted from the entire audio of normal and obfuscated samples. However, only speech parts (i.e., human voice parts) of these samples contain the useful linguistic information needed for machine transcription. We thus propose a fusion-based method to combine the normal sample and corresponding obfuscated sample as a hybrid HVAC command, which can effectively cheat the defense classifiers. Moreover, to make the command more unintelligible to humans, we tune the speed and pitch of the sample and make it even more distorted in the time domain while ensuring it can still be recognized by machines. Extensive physical over-the-air experiments demonstrate the robustness and generalizability of our HVAC attack under different realistic attack scenarios. Results show that our HVAC commands can achieve an average 94.1% success rate of bypassing machine-learning-based defense approaches under various realistic settings.
HELIPORT

Modern scientific collaborations and projects (MSCPs) employ various processing stages, starting with the proposal submission, continuing with data acquisition and concluding with final publications. The realization of such MSCPs poses a huge challenge due to (1) the complexity and diversity of the tools, (2) the heterogeneity of various involved computing and experimental platforms, (3) flexibility of analysis targets towards data acquisition and (4) data throughput. Another challenge for MSCPs is to provide additional metadata according to the FAIR principles for all processing stages for internal and external use. Consequently, the demand for a system, that assists the scientist in all project stages and archives all processes on the basis of metadata standards like DataCite to make really everything transparent, understandable and citable, has risen considerably. The aim of this project is the development of the HELmholtz ScIentific Project WORkflow PlaTform (HELIPORT), which ensures data provenance by accommodating the complete life cycle of a scientific project and linking all employed programs and systems. The modular structure of HELIPORT enables the deployment of the core applications to different Helmholtz centers (HZs) and can be adapted to center-specific needs simply by adding or replacing individual components. HELIPORT is based on modern web technologies and can be used on different platforms.
Peer-Grading at Scale with Rank Aggregation

Thanks to the wide availability of the internet and personal computing devices, online teaching methods like Massive Open Online Courses (MOOC) are becoming an essential part of modern education. While these methods enable educators to reach much more students, the massive volume of assignments to grade places a heavy burden on the instructors. Most online courses remedy this by restricting the question types to simple forms or performing naive peer-grading. These approaches are either too restricted to capture students' learning level, or require heavy supervision from the instructors to ensure the grades are fair. In this paper, we propose a rank-aggregation-based peer-grading method that estimates the quality of each assignment and the probability that each student is grading unbiasedly. The estimation errors have theoretical upper-bounds, and the bounds can be proved to tighten when the problem size increases, which is confirmed by our numerical experiment.
Edge Guided Low-Light Image Enhancement

Images captured under poorly lit environments often suffer from poor brightness, poor contrast, unwanted noise and color distortion. To enhance these images, there is a need to provide additional information while also reducing the burden of generalization on a single network. Therefore, this paper has proposed a novel fully convolutional two-step enhancement process consisting of EdgeNet and EnhanceNet. Edge-Net takes an under-exposed image as an input and predicts the edges in it's well-exposed image. The output from Edge-Net coupled with the low-light image is provided as input to EnhanceNet. EnhanceNet incorporates repeated use of Enhancement Blocks to eliminate noise, extract features from the low-light images and enhance these features to produce an output image with good visual characters. A major problem for low-light image enhancement is unavailability of paired data. To overcome this limitation, realistic low-light images are synthetically generated from well-exposed images. Extensive experiments show the efficacy of our method over the existing ones in qualitative as well as quantitative ways.
An Automated System for Recognizing Isolated Handwritten Bangla Characters using Deep Convolutional Neural Network

Among various handwritten character recognition of different languages, Bangla stands as one of the most challenging tasks. Because of its unique texture and morphologically complex structure often classification models do not provide the expected result as one hopes to have. In this research, a deep novel Convolutional Neural Network (CNN) of 11 layers is proposed. Among the layers, 6 are convolutional layers. This deep CNN model was trained to classify Bangla basic isolated characters of 50 classes (each representing a character). The research utilized a publicly available dataset, CMATERdb 3.1.2, for its classification purpose. The model performed relatively better than the current research on this field and wielded a 98.03% accuracy on the test dataset. This improvement leads us to believe that more accurate results can be achieved in the future by working with other such kinds of datasets or by tweaking the existing model.
Faster Stackelberg Planning via Symbolic Search and Information Sharing

Stackelberg planning is a recent framework where a leader and a follower each choose a plan in the same planning task, the leader's objective being to maximize plan cost for the follower. This formulation naturally captures security-related (leader=defender, follower=attacker) as well as robustness-related (leader=adversarial event, follower=agent) scenarios. Solving Stackelberg planning tasks requires solving many related planning tasks at the follower level (in the worst case, one for every possible leader plan). Here we introduce new methods to tackle this source of complexity, through sharing information across follower tasks. Our evaluation shows that these methods can significantly reduce both the time needed to solve follower tasks and the number of follower tasks that need to be solved in the first place.
LEADERSHIP STYLES, CRISIS MANAGEMENT, AND HOTEL PERFORMANCE: A CONCEPTUAL PERSPECTIVE OF THE JORDANIAN HOTEL INDUSTRY

Since December 2010, revolutionary waves have occurred in many Arab countries and described in the media as the Arab Spring. However, the situation has become even more serious due to the global Covid-19 pandemic which has severely impacted economies all around the world. Jordan has been severely affected by this situation and many visitors from Europe and America have cancelled their visits, thus reducing tourist numbers and package tours. Tourism in Jordan is reputed to be the principal export sector, a major employer in the private sector and directly linked to significant foreign exchange earnings. Hotels are key players in Jordanian tourism and hence, major players in the economy of Jordan. Therefore, crisis management is vital for hotels to survive in this present turbulent business climate. The main purpose of this paper is to review literature in order to develop a conceptual framework addressing the role of crisis management in leadership and hotel performance. Any inquiry into this subject could be hugely significant for Jordan. In view of this, conceptualizing the mediating role of crisis management, in leadership styles and hotel performance, will contribute greatly to the development of improved theoretical methodological, as well as a conceptual rationale for existing linkages between leadership styles, crisis management, and hotel performance. This will help to strengthen and broaden the discussion in the extant literature.
Enhanced metrics for temporal dimensions toward assessing Linked Data: A case study of Wikidata

The development of Linked Open Data (LOD) has transformed the web into a decentralized extensive database. With this dataflow, ensuring quality level became very quickly a crucial issue. Many approaches were proposed using metrics and dimensions inherited from traditional relational databases to assess LOD quality. However, classic metrics used to measure dimensions like Currency, Volatility and Timeliness are unreliable knowing they use exclusively temporal metadata. We believe that assessing data “freshness” could emphasize their validity, considering the assumption that current data is tightly correlated with its credibility. In this paper, we propose an upgraded version for time-related metrics to make them more efficient and more suitable for assessing linked data. Using Wikidata as study case and Wikipedia as reference to “current” data, we implement our approach and compare it to the most relevant approach to find out the crucial part played by a reliable Currency and Volatility to determine data up-to-dateness.
Construction of Intelligent Service System of University Library Based on Internet of Things in Artificial Intelligence Environment

Construction of the intelligent service system of university library based on the Internet of Things in artificial intelligence environment based on the theoretical analysis is conducted in this research. In our computation part, artificial intelligence [AI] is used for the data analysis. Individuals in the dominant population have a better population evolution mechanism. Compared with the disadvantaged populations, they have a greater probability of evolution, which can promote better evolution of individuals in the dominant population and speed up the convergence of the algorithm. Based on this, book retrieval system and IoT is used to construct the proposed model. The proposed system is compared with the other traditional library systems. It has reflected the strong evidence that, the proposed model outperforms the other existing models.