Reviewerly/exHarmony huggingface dataset:
Survey on the Convergence of Machine Learning and Blockchain

Machine learning (ML) has been pervasively researched nowadays and it has been applied in many aspects of real life. Nevertheless, issues of model and data still accompany the development of ML. For instance, training of traditional ML models is limited to the access of data sets, which are generally proprietary; published ML models may soon be out of date without an update of new data and continuous training; malicious data contributors may upload wrongly labeled data that leads to undesirable training results; and the abuse of private data and data leakage also exist. With the utilization of blockchain, an emerging and swiftly developing technology, these problems can be efficiently solved. In this paper, we survey the convergence of collaborative ML and blockchain. Different ways of the combination of these two technologies are investigated and their fields of application are examined. Discussion on the limitations of current research and their future directions are also included.
VADOI: Voice-Activity-Detection Overlapping Inference for End-To-End Long-Form Speech Recognition

While end-to-end models have shown great success on the Automatic Speech Recognition task, performance degrades severely when target sentences are long-form. The previous proposed methods, (partial) overlapping inference are shown to be effective on long-form decoding. For both methods, word error rate (WER) decreases monotonically when over-lapping percentage decreases. Setting aside computational cost, the setup with 50% overlapping during inference can achieve the best performance. However, a lower overlapping percentage has an advantage of fast inference speed. In this paper, we first conduct comprehensive experiments comparing overlapping inference and partial overlapping inference with various configurations. We then propose Voice-Activity-Detection Overlapping Inference to provide a trade-off between WER and computation cost. Results show that the pro-posed method can achieve a 20% relative computation cost reduction on Librispeech and Microsoft Speech Language Translation long-form corpus while maintaining the WER performance when comparing to the best performing over-lapping inference algorithm. We also propose Soft-Match to compensate for similar words misaligned problem.
Who to Watch Next: Two-side Interactive Networks for Live Broadcast Recommendation

With the prevalence of live broadcast business nowadays, a new type of recommendation service, called live broadcast recommendation, is widely used in many mobile e-commerce Apps. Different from classical item recommendation, live broadcast recommendation is to automatically recommend user anchors instead of items considering the interactions among triple-objects (i.e., users, anchors, items) rather than binary interactions between users and items. Existing methods based on binary objects, ranging from early matrix factorization to recently emerged deep learning, obtain objects’ embeddings by mapping from pre-existing features. Directly applying these techniques would lead to limited performance, as they are failing to encode collaborative signals among triple-objects. In this paper, we propose a novel TWo-side Interactive NetworkS (TWINS) for live broadcast recommendation. In order to fully use both static and dynamic information on user and anchor sides, we combine a product-based neural network with a recurrent neural network to learn the embedding of each object. In addition, instead of directly measuring the similarity, TWINS effectively injects the collaborative effects into the embedding process in an explicit manner by modeling interactive patterns between the user’s browsing history and the anchor’s broadcast history in both item and anchor aspects. Furthermore, we design a novel co-retrieval technique to select key items among massive historic records efficiently. Offline experiments on real large-scale data show the superior performance of the proposed TWINS, compared to representative methods; and further results of online experiments on Diantao App show that TWINS gains average performance improvement of around 8% on ACTR metric, 3% on UCTR metric, 3.5% on UCVR metric.
Fine-tuned SPIHT Algorithm to Improve Compression Efficiency

This study introduces a method of applying parameters appropriately to image specifications to improve the compression efficiency of discrete wavelet transform (DWT) and set partitioning in hierarchical tree (SPIHT) algorithms, which are widely used in frame memory compression (FMC). The proposed method has the advantage of being very easy to be applied because the proposed method does not require any structural change and does not affect the application of transform techniques such as DWT. It also solves the problem that fundamentally hinders compression efficiency by making more magnitude bits compressible by reducing unnecessarily allocated bits. Experimental results show that the proposed method can achieve an average PSNR improvement of 0.92 dB.
Embedded Visual Object Tracking System Based on CSRT Tracker

Nowadays, the importance given to object detection and tracking studies is increasing. With the developing technol-ogy, the IoT systems that have gained an irresistible place in our lives are started to be used in these studies too. In this paper, we aim to measure the performance analysis of object tracking process on the videos via embedded systems. We used Jetson NANO and Raspberry Pi development boards for this purpose. The CSRT tracker was used for object tracking. The system was tested by two video datasets which are OTH-15 and LaSOT. Experimental results shows that Jetson NANO development kit is promising for object tracking on the videos with the average accuracy rate of 0.57 for OTH-15 dataset and 0.30 for LaSOT dataset.
Multivariate Quadratic Solver in GF(31) with Grover's Algorithm

The development of quantum computers has led to the possibility of solving problems intractable to deal with in clas-sical computers. That threatens the current standard of public-key cryptography based on factoring or discrete-log problem. As a result, it is necessary to use a secure cryptosystem even in a quantum computing environment. NIST has been conducting a Post-Quantum Cryptography (PQC) standardization process. Recently, there have been attempting to design binary MQ solver with the Grover's algorithm to solve the binary Multivariate Quadratic (MQ) problem. However, most PQC candidates based on the MQ problem do not select a small security parameter and are over GF(16) and above. In this paper, we design the Grover's oracle circuit to solve the MQ problem over GF(31) that can be used to analyze the quantum security of PQC candidates such as Rainbow and MQDSS.
The Influence of Intelligent Visual Sensing Technology on Online English Teaching in Wireless Network Environment

The study is aimed at improving the quality of online English teaching and enhancing students’ English learning ability and effect. First, the current situation of online English teaching is analyzed. Second, the image recognition algorithm in artificial intelligence (AI) visual sensing technology is used to process the image, and the effects of illumination and image acquisition angle are analyzed. Finally, AI image recognition technology is applied to online English teaching, and its impact on online English teaching is analyzed. The results show that AI visual sensing technology can accurately recognize students’ facial information. Therefore, it can confirm the identity of students, help to count the number of students in online English teaching, and improve students’ autonomous learning ability. In addition, it can also record students’ facial information in real time and analyze students’ emotional changes in the learning process to adjust the teaching process and improve the teaching quality in an all-round way. The results provide technical support for improving online English teaching and contribute to English teaching reform.
Image enhancement method for visual localization and mapping in complex light scenarios

Abstract Visual localization and mapping have received considerable attention in the fields of computer vision, photogrammetry, and remote sensing. Image matching is key to visual localization and mapping. However, light is complex and uneven in real scenarios, creating difficulties in feature extraction and matching. Hence, feature mismatch and loss reduce the efficiency, accuracy, and robustness of visual localization and mapping. We developed a visual localization and mapping method for complex light scenarios based on image enhancement. Starting with initial images, the irradiance and reflectance components were separated based on logarithmic transformation. Our method strengthened high-frequency components and restrained low-frequency components with improved homomorphic filtering, restraining the light component and enhancing the important reflection component. The SIFT algorithm was used for feature detection and matching. The proposed method was tested on images with uneven light captured using a stereo vision camera in an indoor environment, focusing on visual localization and mapping. The experimental results emphasized that the method improved the rate of image localization and number of point clouds, as well as the reprojection error, which ranged from 0.85 to 0.82 on average. Thus, the proposed method is robust rather than probabilistic for improving visual localization and mapping under complex light conditions.
Sentiment lexicon construction for Chinese book reviews based on ultrashort reviews

Purpose Sentiment lexicon is an essential resource for sentiment analysis of user reviews. By far, there is still a lack of domain sentiment lexicon with large scale and high accuracy for Chinese book reviews. This paper aims to construct a large-scale sentiment lexicon based on the ultrashort reviews of Chinese books. Design/methodology/approach First, large-scale ultrashort reviews of Chinese books, whose length is no more than six Chinese characters, are collected and preprocessed as candidate sentiment words. Second, non-sentiment words are filtered out through certain rules, such as part of speech rules, context rules, feature word rules and user behaviour rules. Third, the relative frequency is used to select and judge the polarity of sentiment words. Finally, the performance of the sentiment lexicon is evaluated through experiments. Findings This paper proposes a method of sentiment lexicon construction based on ultrashort reviews and successfully builds one for Chinese books with nearly 40,000 words based on the Douban book. Originality/value Compared with the idea of constructing a sentiment lexicon based on a small number of reviews, the proposed method can give full play to the advantages of data scale to build a corpus. Moreover, different from the computer segmentation method, this method helps to avoid the problems caused by immature segmentation technology and an imperfect N-gram language model.
Editorial for the Special Issue “Advances in Object and Activity Detection in Remote Sensing Imagery”

Advances in data collection and accessibility, such as unmanned aerial vehicle (UAV) technology, the availability of satellite imagery, and the increasing performance of deep learning models, have had significant impacts on solving various remote sensing problems and proposing new applications ranging from vegetation and wildlife monitoring to crowd monitoring [...]
The principle of least sensing

With the worldwide emergence of data protection regulations, how to conduct law-regulated big data analytics becomes a challenging and fundamental problem. This article introduces the principle of least sensing, a promising sensing paradigm toward legally regulated big data analytics.
State history, governance and financial institutions' efficiency: Evidence from African countries

Abstract This study investigates how statehood experience influences financial institutions' efficiency through the quality of governance. To this effect, the study employs a Tobit instrumental variables technique with data from 22 African countries for the period 2002–2018. The state antiquity index, recently provided by Ang and Fredriksson (2018), is used to capture statehood experience whereas rule of law, voice and accountability, regulatory quality, political stability and absence of violence, government effectiveness, and control of corruption are the indicators of the governance quality. Our results show that statehood experience, directly and indirectly, influences financial institutions' efficiency. The nature of the direct effect is negative, while that of the indirect effect depends on the governance indicator considered. Indeed, the negative effect of statehood experience is moderated by the strict application of the rule of law and amplified by control of corruption, political stability and absence of violence, and excessive regulation. Thus, to reduce the negative effect of statehood experience on the efficiency of financial institutions, older African states should: i) promote strict enforcement of the rule of law and alternation of power, and ii) relax regulation and government intervention.
Non-myopic Trajectory Planning for Autonomous Driving Combining Single-Query and Multi-Query Methods

Motion planning for autonomous driving in a dynamic environment is challenging in considering safety, efficiency, and passenger comfort. We clarify some scenarios in which previous multi-query motion planning methods demonstrate inefficient results, such as unnecessarily returning to the reference path and time delays due to myopic behavior. We propose a novel two-step method to address the problems. The myopic trajectory planning step uses an optimization method with the Frenet frame to generate exhaustive trajectory candidates on a very limited time horizon, which considers vehicle kinematics and dynamic safety. The non-myopic trajectory planning step uses variation in the rapidly exploring random tree to find the expected fastest trajectory to reach a local goal position among the results of the previous step. In 16.2% of entire random scenarios, our proposed method reduces the time to reach a destination by more than a 5% performance gap compared to other state-of-the-art motion planning methods.
Translating Sign Language to English Text in Real time using Deep Learning Models

Communication is the best means to express ourselves to others. There are various ways to communicate with others like writing, vocally and signing with hands. Generally normal people prefer to communicate vocally. Deaf and dumb people use the sign language for interaction. Sign language is a language expressed with hands and facial expression without speaking. Communication between normal and impaired people is difficult as there is no common language. To establish communication, a human sign language interpreter is required. To build a communication bridge between normal and impaired people, a sign language interpretation system can be used. Using this system one can translate sign words to text. In this paper real time conversion of word level sign language to English is presented. For the training of models, a user defined dataset is created and labeled. Three different models of Tensorflow are used for Sign Language interpretation SSD 320, SSD 640 and Centernet Resnet50. SSD 640 is more preferable as compared to others as it can detect sign words in less time and accurately.
Minimizing the monetary penalty and energy cost of server migration service

Abstract Current IaaS (infrastructure as a service) cloud service may not satisfy communication QoS (quality of service) requirements of delay‐sensitive network applications, if there is a significant physical distance between a server of the network application (NetApp server) at a data center and its network application clients (NetApp clients). In order to improve communication QoS of NetApp clients, we propose server migration service (SMS) in this article. SMS allows NetApp servers to migrate among different locations in the network (1) to optimally locate themselves in relation to NetApp clients and mitigate the QoS degradation caused by location‐related factors (ie, propagation delays on network links) and (2) to optimally distribute traffic load over routers and processing load over (physical) computers and decrease the energy consumption. We develop a mixed‐integer programming model that determines when and to which locations NetApp servers migrate to minimize the total operating cost of SMS, that is, the sum of the monetary penalty incurred due to QoS violation and energy cost incurred due to energy consumption, while preventing NetApp servers from excessively migrating and adversely impacting QoS of the non‐SMS service that share the resource of the substrate network with SMS. Simulation results show that the model developed in this article achieves up to 42% lower total operating cost of SMS compared to the model that only minimizes the monetary penalty of SMS without considering the energy cost of SMS.
Generating Goal-conditioned Sub-goals for Hierarchical Learning

Reinforcement learning (RL) has been actively re-searched and has shown good performance in various tasks. However, most RL agents solve their tasks by exploring the environment with only primitive actions, which is an inefficient and difficult way to solve complex tasks. A method of decomposing complex tasks into sub-tasks and solving them step-by-step has been studied as a solution, but most of these works learn sub-tasks that do not consider the goals of downstream tasks, making it challenging to find the optimal sub-task combination. In this paper, we propose a framework for solving complex tasks by learning to generate goal-conditioned sub-goals that leads to reaching a goal state. First, we introduce a deep latent variant model that learns the set of goal-conditioned sub-goals from datasets. We then propose an algorithm with a hierarchical structure to solve complex problems using learned sub-goals. The proposed model consists of two level policies, where the higher-level policy selects the optimal sub-task to solve the problem, and the lower-level policy outputs primitive actions to reach each sub-goal. To verify the effectiveness of our method, we compared our framework with other baselines in a long-horizon maze environment.
Patterns of distribution and peculiarities of formation of gold ore mineralization in metamorphic and black shale strata of Azerbaijan

&#x0D; &#x0D; &#x0D; &#x0D; The article discusses the patterns of distribution and the peculiarities of the formation of gold mineralization in the metamorphic and black shale strata of Azerbaijan in order to determine metallogenic specialization. Mineragenic analysis of gold-bearing territories was carried out using numerous data of quantitative studies of the substance of the studied objects: ore-bearing sedimentary and metamorphic strata, ore bodies and processes ore genesis. The spatio-temporal relationships of gold-bearing mineralization of various material-morphological types, conditioned by lithological-geochemical conditions and time of manifestation of lithogenetic processes and metamorphism, have been determined. In the petrographic description of the studied rocks, two groups were distinguished: Early Paleozoic crystalline shales and Upper Paleozoic sedimentary rocks. It has been determined that the shales were initially enriched with gold during sedimentogenesis. In subsequent processes of metamorphism, part of the gold was removed from the shale and redeposited, and thus spread unevenly. Favourable zones of ore deposition could lead to the concentration of gold in shales in industrial conditions. In the studied crystalline shales, attention is drawn to the contents of Pt and Pd, the commonest elements of the platinum group. The Pt content in shales is 0.08 g / T on average, while the clarke for clayey shales is 0.03 g / t, i. e. almost three times higher than the clarke. Even more contrasting is the content of the platinum group element, palladium. In shales, the Pd content is 0.046 g / t. These data, together with the high gold content in the shales, can be considered as a typomorphic geochemical feature of the crystalline shales of the Asrikçay massif of the pre-Jurassic basement of the Lesser Caucasus. The studies have shown that during the formation of gold mineralization in metamorphic and black shale strata, an important role was played by the features of the host carbonaceous sedimentary complexes, which served as additional sources of noble and associated metals. Some of the ore elements in unaltered sediments have their own mineral forms.&#x0D; &#x0D; &#x0D; &#x0D;
Clustering Algorithm for Big Datasets with Mixed Attribute Features under Spark

This paper presents an in-depth study and analysis of large datasets of mixed and attribute features under Spark using a large dataset clustering algorithm. The classical algorithm K-means based on division and the density-based clustering algorithm DPC, which has become more popular in recent years, are selected as the research objects of this paper. Secondly, the original K-means algorithm is improved by combining holdout validation and K-means++ method to address the shortcomings of the K-means algorithm that the number of class clusters K needs to be set in advance and the initial class cluster centers are chosen randomly, which leads to unstable iterations and slow convergence of clustering results. The similarity matrix will be continuously updated during the iterative process. It mainly refers to the process of dividing objects into multiple classes according to the degree of similarity between objects. After the division, the objects within the class are like each other, while the objects between the classes are different from each other. The comparison experiments of the improved algorithm before and after the MovieLens dataset are conducted to verify that the new algorithm has better performance in terms of clustering accuracy and efficiency. Again, to address the drawback that the clustering results in the DPC algorithm rely heavily on the subjective selection of the truncation distance parameter cd, and it is difficult to handle datasets with complex distribution and large density variation, the algorithm can generate the optimal cd adaptively by combining K-nearest neighbors and introducing the distance comparison quantity, which has a better performance by considering the overall and local distribution of the data. The feasibility of the improved method is verified by validating the algorithm with artificial datasets and UCI datasets as well as separation tests. Finally, the parallelized design and implementation of the improved K-means algorithm and CDPC-KNN algorithm are completed by building a Spark clustering environment, and the parallelized algorithm is verified to have much better data processing capability and be more adaptable to the clustering analysis of large-scale data by comparing algorithm string parallelism experiments.
Algorithm for operating an ordinary engineering system as a quantum bit

We show an algorithm for the previously proposed quantum computer using classical apparatuses. The target system is not limited to mass points but is a general engineering system that has a more manageable form in equipping terminal edge. After we define such system as “ordinary engineering system,” the method of quantum mechanical optimum feedback control of the nonlinear system is applied. The method makes an ordinary engineering system into a quantum one. As an example that is as stale as possible, a temperature control system is taken. Simulation studies show that our system works as a 1-qubit quantum computer without the special large-scale peripheral equipment required to maintain quantumness. The proposed system is a classic system that is everywhere, so it is easy for anyone to understand. Another advantage is that easy-to-use one-dimensional system can be applied.