Reviewerly/exHarmony huggingface dataset:
Machine Learning-as-a-Service Performance Evaluation on Multi-class Datasets

Machine learning technologies have invaded our daily lives with a wide range of applications such as fraud detection, product recommendations, data mining, and image recognition. Businesses that need to process and analyze huge amounts of data are competing to become the first to adopt these solutions. Nonetheless, developing classification algorithms using machine learning frameworks is time-consuming, costly, and requires a team with technical capabilities. To reduce these expenses, market leader cloud providers have started to offer the Machine Learning-as-a-service (MLaaS) cloud delivery model. However, businesses and users are still faced with the challenge of deciding on which platform to adopt. In this paper, we evaluate the machine learning classifiers and performance of BigML, Microsoft Azure ML Studio, IBM Watson ML Studio, and Google AutoML Table platforms on the classification of multi-class datasets based on the average-micro F-score, training time, and cost to enable users to make a more informed decision. Since the choice of classifiers can have a crucial impact on the average-micro F-score, we trained all pre-built algorithms offered by each platform on given multi-class datasets to conduct a comprehensive investigation. The results show that Google AutoML provides the user with the highest average-micro F-score, but it is costly and requires more training time. This research will enable the developers of intelligent edge computing services that rely on MLaaS to select the most optimal platform for their applications needs.
Incorporating heterogeneous information in deep learning with informative meta-paths for community recommendations

Communities of interest promote knowledge sharing and discovery in social network platforms. However, platform users face difficulties of finding suitable communities, given their increasing number. Although recommendations have been proposed to help users find communities of interest, these methods ignore or exclude heterogeneous interactions between users and communities. In addition, widely used meta-paths help capture the complex semantic relation among entities but heavily rely on domain knowledge. In this study, we propose a novel recommendation model based on informative meta-path discovery in heterogeneous information networks and deep learning. Users, communities, relevant items and their relations are considered as entities in a heterogeneous information network, from where informative meta-paths are extracted on the basis of information theory to measure user-community similarities. Finally, similarities are incorporated in a deep learning model to predict whether target users join candidate communities. The proposed recommendation model is evaluated and compared against baseline methods using two data sets. Results demonstrate the superior performance of the present model in terms of precision, recall and F score.
Blood Diseases Detection Using Data Mining Techniques

By Using data mining techniques, much study has been undertaken to derive information from medical data, which may enhance the curing ability. This study has been compared four automated methods for blood diseases detection using a different method for attributes reduction methods: PCA (Principal Component Analysis), ICA (Independent Component Analysis), original data set, and four algorithms, which are Naive Bayes, K-Nearest Neighbor, Decision Tree, and SVM (Support Vector Machine) for classification. The results show that after applying the PCA technique, the accuracy is: 0.98, 1.0, 0.79, and 0.78 for Naive Bayes, Decision Tree, SVM, and KNN and after applying the ICA technique, the accuracy is 0.76,.76, 0.76, and 0.74 for Naive Bayes, Decision Tree, SVM, and KNN, while the accuracy after applying on the original data set is: 0.98, 1.0, 0.78, and 0.75 for Naive Bayes, Decision Tree, SVM, and KNN. The results appeared that when applying decision tree on the original data set and after applying the PCA method, have been gotten the highest accuracy which is 100 %.
Towards a Clustering Guided Hierarchical Framework for Sensor-Based Activity Recognition

Human activity recognition plays a prominent role in numerous applications like smart homes, elderly healthcare and ambient intelligence. The complexity of human behavior leads to the difficulty of developing an accurate activity recognizer, especially in situations where different activities have similar sensor readings. Accordingly, how to measure the relationships among activities and construct an activity recognizer for better distinguishing the confusing activities remains critical. To this end, we in this study propose a clustering guided hierarchical framework to discriminate on-going human activities. Specifically, we first introduce a clustering-based activity confusion index and exploit it to automatically and quantitatively measure the confusion between activities in a data-driven way instead of relying on the prior domain knowledge. Afterwards, we design a hierarchical activity recognition framework under the guidance of the confusion relationships to reduce the recognition errors between similar activities. Finally, the simulations on the benchmark datasets are evaluated and results show the superiority of the proposed model over its competitors. In addition, we experimentally evaluate the key components of the framework comprehensively, which indicates its flexibility and stability.
Analysis of perspective air preparation network designs in launch complex temperature control systems

The purpose of the research is to perform a comparative analysis of compressed air preparation systems on the basis of moisture content. The article discusses conventional and perspective methods for drying compressed air using condensation, adsorption and membrane technologies. The article considers a temperature control system with the required dew point of minus 25°С at the exit under standard conditions, which corresponds to the dew point temperature plus 3 °С at a pressure of 1.0 MPa, or class 4 according to GOST R ISO 8573-1–2016. The main advantages of using advanced technological drying system designs are described, including the design of a modern drier with a rotary adsorber that can reduce the dew point temperature of compressed air to minus 25…30 °С at 1.0 MPa pressure without any losses on the adsorbent regeneration. This research is the first to analyze the applicability of modern adsorption and membrane modules to the launch complex functions. All the air drying designs were considered in this paper in relation to air temperature control systems based on air refrigerators using the I-d hygrometric chart for variable pressures.
Time and Energy Optimized Trajectory Generation for Multi-Agent Constellation Changes

Planning the simultaneous movement of multiple agents represents a challenging coordination problem, and ideally safety and efficiency are jointly addressed. This paper introduces a planning algorithm for fast and energy-efficient trajectories with reduced collision potential from a start to an end constellation. This new approach combines trajectory approximation based on model predictive control, collision avoidance with potential fields, and flight energy optimization with minimum snap trajectories. Our approach results in unprecedented transition times and success rates with less energy consumption, as shown in simulation and real experiments with 16 drones.
Injecting Text and Cross-Lingual Supervision in Few-Shot Learning from Self-Supervised Models

Self-supervised model pretraining has recently garnered significant interest. However, using additional resources in fine-tuning these models has received less attention. We demonstrate how universal phoneset acoustic models can leverage cross-lingual supervision to improve transfer of pretrained self-supervised representations to new languages. We also show how target-language text can be used to enable and improve fine-tuning with the lattice-free maximum mutual information (LF-MMI) objective. In three low-resource languages these techniques greatly improved few-shot learning performance.
Improving Effectiveness Information Retrieval System Using Pseudo Irrelevance Feedback

Pseudo relevance feedback (PRF) enhances the retrieval performance of the relevance feedback. Pseudo relevance feedback assumes that the k highest-ranking documents in the first retrieval are relevant and extract query expansion from them. Rocchio algorithm is a classical algorithm for implementing relevance feedback into vector space models. The Rocchio algorithm forms a new query moves toward the centroid of the relevant documents and keeps away from centroid of the irrelevant documents. However, in the relevance feedback method, irrelevant documents are ignored. In this paper, we conduct a method for pseudo irrelevance feedback (PIRF) documents components that effectively applied to the Rocchio algorithm. Documents with a high ranking outside of k relevant documents and those documents dissimilar to any k relevant documents can extract good query expansion if the documents are applied as irrelevant documents. The Rocchio algorithm uses PRF as a component of relevant documents and this research method for irrelevant documents as a component of irrelevant documents denoted by Roc PRF PIRF (filter). Experiment on CISI dataset show that Roc PRF PIRF (filter) improved performance by testing several variations the number of irrelevant documents compared to the standard Rocchio algorithm and Rocchio algorithm with irrelevant documents but without proposed method).
LSTM-Based Visibility Detection for Airport Images in Time Series

Atmospheric visibility detection is essential for assuring safety in traffic and aviation. There have been some methods that can indirectly detect visibility by analyzing the images. However, most of these methods ignore the continuous information in the video while getting independent images from the video. It’s also difficult work to defining image features that reflect visibility accurately and comprehensively. To address these issues, this paper proposes a visibility detection method based on image-feature information fusion LSTM (Long Short-Term Memory). For reducing the complexity of calculation and improve the effect of model training, the key image feature values were extracted into a vector, then the input matrix of the LSTM was composed by these vectors in time series. Experiments demonstrated that our method can focus on the key image features and make full use of continuous information in the video, and thus achieve a satisfying performance invisibility detection.
Few-Shot Learning for Chinese NLP Tasks

In the paper, we report the results for the NLPCC2021 shared-task of Few-shot Learning for Chinese NLP. This shared task is proposed in the context of pre-trained language models, where models only have access to limited human-labeled data. The goal of the task is to compare different learning schemes. The task includes nine sub-tasks and three task forms: single sentence classification, sentence pair classification, and machine reading comprehension. In order to accommodate the properties of few-shot learning, we sampled the examples using various sampling methods, some with 32 examples in total for one dataset, while others with 4 to 16 examples per class. Ninety teams registered for the shared task, employing a wide range of learning schemes, including data augmentation, utilizing multiple templates rather than a single template, using unlabeled data for pre-training or semi-supervised training. The best model achieved 65.3 in the mean accuracy, compared with the human score of 83.9. This result is 8 points higher than our baseline model (using the PET scheme). We believe our few-shot learning tasks and results demonstrate the potential of the recently introduced few-shot learning methods and provide guidance and important empirical evidence for future research.
Quadratic Secret Sharing and Conditional Disclosure of Secrets

There is a huge gap between the upper and lower bounds on the share size of secret-sharing schemes for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -party access structures; consistent with our current knowledge the optimal share size can be anywhere between polynomial and exponential in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> . For linear secret-sharing schemes, the share size for almost all <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -party access structures is exponential in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> . We would like to study larger classes of secret-sharing schemes with two goals: 1) prove lower bounds for larger classes of secret-sharing schemes; and 2) construct efficient secret-sharing schemes. Given this motivation, Paskin-Cherniavsky and Radune (ITC’20) introduced a new class of secret-sharing schemes in which the shares are generated by applying degree- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> polynomials to the secret and some random field elements. We define and study two additional classes of polynomial secret-sharing schemes: 1) schemes in which the reconstruction of the secret is done using polynomials; and 2) schemes in which both sharing and reconstruction are done by polynomials. Our main result is a construction of secret-sharing schemes and conditional disclosure of secrets protocols with quadratic sharing and reconstruction that are more efficient than linear secret-sharing schemes. To complement our results, we prove lower bounds on the share size for schemes with polynomial reconstruction. Finally, we give an evidence that schemes with polynomial sharing are probably stronger than schemes with polynomial reconstruction.
The library QR-projects in digital space

Abstract : The technological solutions in culture and arts increase accessibility of cultural goods and intensity of cultural environment. QR-technologies designed by Denso Wave Incorporated have become digitalization component increasingly popular in library and information services. The mobile access points call for new ways of library and information services. The concept, characteristics and advantages of QR technology are examined; options for QR code application in libraries are suggested. As the majority of library users owns mobile phones, QR technologies provide unique opportunities. MTS (Mobile Telecommunications System) was the first company to initiate the project of using mobile phones to expand library information space: QR-code linking to books were placed in the city public places. Library applications for QR-codes are outlined, e. g. library information advertisement, user services, social events to promote reading, monitoring in the pandemic situation, etc. The opportunities and prospects for this resource are investigated by the examples of RNPLS&amp;T, Belgorod State Universal Scientific Library, V. G. Korolenko Public Scientific Library (Glazov, Udmurt Republic), Centralized Library System (Georgievsk, Stavropol Territory). The author also suggests that QR codes may solve the problem of access to full and reliable information.
Semantic Representation of Sentences Employing an Automated Threshold

Semantic representations of sentences have become a challenging task for many fields of natural language processing. To represent the sentence, the semantic distributional representations of its words are being used. In this approach, all the features of the words are used to represent the sentence. The features of the words play a major role for all tasks including semantic representation of the sentence for the purpose of measuring semantic textual similarity (STS). In this paper, we study that all the features of the words are not necessary for semantic representation of the sentence to estimate the STS. Pre-trained word-embedding and BERT (Bidirectional Encoder Representation from Transformers) models are employed to get the semantic distribution of the words. An automated threshold is applied to the feature vectors of the words to get the sentence representation. Finally, the cosine similarity is employed on the sentence vectors to measure their STS. To validate the performance of our proposed approach, a wide range of experiments are carried out on four benchmark STS datasets. The results of the experiments concluded that our proposed approach is capable of semantic representation of the sentence and also outperforms some state-of-the-art methods.
IntoTheVideos: Exploration of Dynamic 3D Space Reconstruction From Single Sports Videos

We present IntoTheVideos, a novel system that takes a sports video and reconstructs the 3D space (e.g., basketball court, football field) along with the highlighted event (e.g., making a basket, scoring a touchdown) to enable viewers to experience the sports entertainment with more freedom. Users can move around in the reconstructed 3D space and explore the event freely from different angles and distances as well as view the players in 3D. With this system, we hope to offer users a new option to enjoy sports entertainment beyond passively watching video footage potentially leading to more active fan engagement and participation.
UC Secure Private Branching Program and Decision Tree Evaluation

Branching program (BP) is a DAG-based non-uniform computational model for L/poly class. It has been widely used in formal verification, logic synthesis, and data analysis. As a special BP, a decision tree is a popular machine learning classifier for its effectiveness and simplicity. In this work, we propose a UC-secure efficient 3-party computation platform for outsourced branching program and/or decision tree evaluation. We construct a constant-round protocol and a linear-round protocol. In particular, the overall (online + offline) communication cost of our linear-round protocol is <inline-formula><tex-math notation="LaTeX">$O(d(\ell + \log m+\log n))$</tex-math></inline-formula> and its round complexity is <inline-formula><tex-math notation="LaTeX">$2d-1$</tex-math></inline-formula> , where <inline-formula><tex-math notation="LaTeX">$m$</tex-math></inline-formula> is the DAG size, <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the number of features, <inline-formula><tex-math notation="LaTeX">$\ell$</tex-math></inline-formula> is the feature length, and <inline-formula><tex-math notation="LaTeX">$d$</tex-math></inline-formula> is the longest path length. To enable efficient oblivious hopping among the DAG nodes, we propose a lightweight 1-out-of- <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> shared OT protocol with logarithmic communication in both online and offline phase. This partial result may be of independent interest to some other cryptographic protocols. Our benchmark shows, compared with the state-of-the-arts, the proposed constant-round protocol is up to 10X faster in the WAN setting, while the proposed linear-round protocol is up to 15X faster in the LAN setting.
Min-Max Entropy Inverse RL of Multiple Tasks

Multi-task IRL recognizes that expert(s) could be switching between multiple ways of solving the same problem, or interleaving demonstrations of multiple tasks. The learner aims to learn the reward functions that individually guide these distinct ways. We present a new method for multi-task IRL that generalizes the well-known maximum entropy approach by combining it with a Dirichlet process based minimum entropy clustering of the observed data. This yields a single nonlinear optimization problem, called MinMaxEnt Multi-task IRL (MME-MTIRL), which can be solved using the Lagrangian relaxation and gradient descent methods. We evaluate MME-MTIRL on the robotic task of sorting onions on a processing line where the expert utilizes multiple ways of detecting and removing blemished onions. The method is able to learn the underlying reward functions to a high level of accuracy and it improves on the previous approaches.
FORTIS: Selfish Mining Mitigation by (FOR)geable (TI)me(S)tamps

The selfish mining (SM) attack of Eyal and Sirer allows a rational mining pool with a hash power (α) much less than 50% of the whole Bitcoin network to steal from the fair shares of honest miners. This attack has been studied extensively in various settings in order for its optimization and mitigation. In this context, Heilman proposes a defense “Freshness Preferred”, based on timestamps, which are issued routinely by a timestamp authority. In contrast, we consider the case where timestamps are generated by no authority; instead every miner includes the current time into a block freely. However, due to two attacks that we discover, this turns out to be a non-trivial task. These attacks are Oracle mining , which works by cleverly setting the timestamp to future, and Bold mining , which works by generating an alternative chain starting from a previous block. Unfortunately, these attacks are hard to analyze and optimize, and to our knowledge, the available tools fail to help us for this task. To ease this, we come up with generalized formulas for revenue and profitability of SM attacks. Our analyses show that the use of timestamps could be promising for selfish mining mitigation. Nevertheless, Freshness Preferred in its current form is quite vulnerable, as any rational miner with α &gt; 0 can directly benefit from our attacks. To cope with this problem, we propose a novel SM mitigation algorithm Fortis without an authority, which protects the honest miners’ shares against any attacker with α &lt; 27.0 against all the known SM-type attacks. By building upon the blockchain simulator BlockSim, we simulate our Oracle and Bold mining attacks against Freshness Preferred and Fortis . Simulation results also demonstrate the effectiveness of these attacks against the former and their ineffectiveness against the latter.
Research on the Technology of Information Fusion for the Redundancy Guidance for Aircraft Automatic Carrier Landing

Under the background of the application of the redundancy guidance for aircraft automatic carrier landing, aiming at the different degrees of transmission delay caused by the different working environments of multiple sensors, the current period data is predicted and estimated to achieve the time stamp calibration in this paper. The algorithm of Kalman sliding window filter fusion and the algorithm of covariance weighted fusion have been combined to unify the different processing methods in the different application scenarios in this paper. Simulation experiments prove that the algorithm of information fusion which is optimized and improved has the ability of high precision, stability and anti-interference, and can deal with emergencies caused by the external environment effectively.