Reviewerly/exHarmony huggingface dataset:
Professional self-determination of school graduates: social and personal factors

Given the negative demographic trends and the aging of labor resources in most regions of Russia, it is important to curb educational migration away from them. The study of the mechanisms of professional self-determination of schoolchildren goes beyond pedagogical tasks in the field of public administration and regional economy. Psychological mechanisms of career choice and educational strategy building were widely studied during the twentieth century, but the question of the influence of social conditions in a particular region on the professional self-determination of young people remains poorly understood. Solving the problems of professional self-determination of schoolchildren requires the synthesis of personality-oriented and socially-oriented pedagogical approaches. The article examines the factors that determine the professional self-determination of school graduates and analyzes the conditions and patterns of schoolchildren’s professional self�determination, together with the potential of their influence on educational strategy by regional universities. The research hypothesis is that, when providing pedagogical support for professional self-determination of pupils, it is necessary to take into account both their personal characteristics and individual motives, as well as socio-economic factors that determine the choice of educational strategy. The research used comparative analysis, classification, generalization, an inter-disciplinary review of literature and socio-economic statistics, a sociological survey, and expert assessments. The main results are the description of models of professional self-determination of school graduate based on their awareness, values, self-efficacy assessment, and the justification of the principles of career guidance. A survey of school graduates in the Ulyanovsk region in 2018-2019 (n=2075) showed that a significant number of graduates lacked professional plans and knowledge about regional universities and universities in other cities. The study revealed the professional interests of school graduates and their plans for educational migration from the region. School graduates did not justify their choice of a career and educational strategies by their professional interests, but rather on their desired standard of living upon graduation. It established that the quality of education at the university is not a determining factor for potential applicants. As actors in the regional educational space, professional education organizations can have a greater impact on the processes of professional self-determination of high school students. The results indicate the importance of regional socio-economic conditions for professional self�determination of school graduates, their educational strategy and choice of university. The study confirmed that their professional self-determination follows the principles of the Social Cognitive Career Theory.
Spoken Language Identification System for Kashmiri and Related Languages Using Mel-Spectrograms and Deep Learning Approach

Language identification, being the front-end for various natural language processing tasks, plays an important role in language translation. Owing to this, the focus has been on the field of speech recognition involving the identification & recognition of languages by a machine. Spoken language identification is the identification of language present in a speech segment despite its size (duration & speed), ambiance (topic & emotion), and moderator (gender, age, demographic region). In this model, six languages have been identified that include five officially spoken languages of Jammu and Kashmir (Kashmiri, Urdu, Dogri, Hindi, and English) along with Ladakhi language by applying Convolutional Neural Network (CNN) approach. The data is recorded from TV channels of languages (Kashmiri, Ladakhi, Urdu, and Dogri) and standard corpora IIIT-H and VoxForge for Hindi and English respectively. Data is pre-processed by removing background noise and segmented into segments of 5 seconds each. Each segment is converted into Mel-spectrograms by applying Fast Fourier Transformations and at last, CNN serves as a classifier. Experimental results showed that an average training and testing accuracies of 100% have been achieved by running the model at 100 epochs.
Signtalk: Sign Language to Text and Speech Conversion

People with hearing and speech impairments are facing lots of difficulties while communicating with the public. The proposed work provides a helping hand for hearing/speech-impaired and even blind people to communicate with others. For a sign input, the proposed model provides a speech/text output, thus providing a user-friendly platform for users. Being a minority, the sign language used by them is not known to most people. So, the idea proposed is a system which converts American Sign Language (ASL) to text and speech output. This work uses convolutional neural networks (CNN) to extract efficient hand features to identify the hand gestures according to ASL. The proposed model offers an accuracy of 88%. Hence, this system helps in recognizing the hand gestures of special people and converting them to text and speech to communicate more effectively with normal people.
Mitosis: Practically Scaling Permissioned Blockchains

Scalability remains one of the biggest challenges to the adoption of permissioned blockchain technologies for large-scale deployments. Namely, permissioned blockchains typically exhibit low latencies, compared to permissionless deployments—however at the cost of poor scalability. As a remedy, various solutions were proposed to capture “the best of both worlds”, targeting low latency and high scalability simultaneously. Among these, blockchain sharding emerges as the most prominent technique. Most existing sharding proposals exploit features of the permissionless model and are therefore restricted to cryptocurrency applications. A few permissioned sharding proposals exist, however, they either make strong trust assumptions on the number of faulty nodes or rely on trusted hardware or assume a static participation model where all nodes are expected to be available all the time. In practice, nodes may join and leave the system dynamically, which makes it challenging to establish how to shard and when.
LSFQ: A Low-Bit Full Integer Quantization for High-Performance FPGA-Based CNN Acceleration

The effective implementation of quantization depends not only on the specific task but also on the hardware resources. This article presents a hardware-aware customized quantization method for convolutional neural networks. We propose a learnable parameter soft clipping full integer quantization (LSFQ), which includes weight and activation quantization with the learnable clipping parameters. Moreover, the LSFQ accelerator architecture is customized on the field-programmable gate array (FPGA) platform to verify the hardware awareness of our method, in which DSP48E2 is designed to realize the parallel computation of six low-bit integer multiplications. The results showed that the accuracy loss of LSFQ is less than 1% compared with the full-precision models including VGG7, mobile-net v2 in CIFAR10, and CIFAR100. An LSFQ accelerator was demonstrated at the 57th IEEE/ACM Design Automation Conference System Design Contest (DAC-SDC) and won the championship at the FPGA track.
Why-So-Deep: Towards Boosting Previously Trained Models for Visual Place Recognition

Deep learning-based image retrieval techniques for the loop closure detection demonstrate satisfactory performance. However, it is still challenging to achieve high-level performance based on previously trained models in different geographical regions. This letter addresses the problem of their deployment with simultaneous localization and mapping (SLAM) systems in the new environment. The general baseline approach uses additional information, such as GPS, sequential keyframes tracking, and re-training the whole environment to enhance the recall rate. We propose a novel approach for improving image retrieval based on previously trained models. We present an intelligent method, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MAQBOOL</i> , to amplify the power of pre-trained models for better image recall and its application to real-time multiagent SLAM systems. We achieve comparable image retrieval results at a low descriptor dimension (512-D), compared to the high descriptor dimension (4096-D) of state-of-the-art methods. We use spatial information to improve the recall rate in image retrieval on pre-trained models. Material related to this work is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://usmanmaqbool.github.io/why-so-deep</uri> .
Performance Optimization of Allreduce Operation for Multi-GPU Systems

Allreduce is one of the important collective commu-nications used in distributed deep learning. We present a novel hybrid allreduce algorithm optimized for multi-GPU systems with NV-Link, which is current main computing platform for data-parallel distributed deep learning. The hybrid algorithm efficiently utilizes direct access to memory of peer GPU devices via the NV-Link. In addition to NV-Link, the hybrid algorithm employs PCI-Express network as extra bandwidth between GPU devices. To use the PCI-Express network we need to explicitly copy the data to host memory and these operations are software-pipelined. By selecting optimal parameters, the hybrid allreduce algorithm outperforms NVIDIA’s NCCL library.
Integration of Blockchain&amp; IoT in Precision Farming: Exploration, Scope and Security Challenges

Internet of things (IoT) plays a major role in every field. It helps in transforming the way with the use of sensors, edge devices and its infrastructure. As sensors are major source of information gathering which is stored on the cloud and relevant information is being retrieved. Information is passed at various levels of IoT ecosystem, which takes security into consideration. With the growth of interconnected devices, data security is becoming increasingly complex. The necessity arising for building a secure and smart structure leads in combining blockchain technology with IoT based system in precision agriculture. With the use of blockchain, traditional methods utilized for collecting and sharing agriculture data are replaced with transparent, reliable and decentralized data storage methods. This combined approach helps us in maintaining an autonomous precise agriculture system and managing in more secure and optimized ways. This paper presents a comprehensive approach on highlighting the importance of integrating blockchain technology with smart agriculture. This paper also proposes novel approach to blockchain models which are used as alternate solutions for challenges faced by IoT based smart agriculture. Various sub sectors like food supply chain, livestock grazing, and crops overseeing are also managed using blockchain platform. Later section of the paper focuses on open challenges and issues while developing blockchain based IoT system used in precision farming.
Sentiment Evolution Analysis and Association Rule Mining for COVID-19 Tweets

This article presents a data mining study carried out on social media users in the context of COVID-19 and offers four main contributions. The first one consists in the construction of a COVID-19 dataset composed of tweets posted by users during the first stages of the virus propagation. The second contribution offers a sample of the interactions between users on topics related to the pandemic. The third contribution is a sentiment analysis, which explores the evolution of emotions throughout time, while the fourth one is an association rule mining task. The indicators determined by statistics and the results obtained from sentiment analysis and association rule mining are eloquent. For instance, signs of an upcoming worldwide economic crisis were clearly detected at an early stage in this study. Overall results are promising and can be exploited in the prediction of the aftermath of COVID-19 and similar crisis in the future.
Lightweight Shape Analysis Based on Physical Types

To understand and detect possible errors in programs manipulating memory, static analyses of various levels of precision have been introduced, yet it remains hard to capture both information about the byte-level layout and precise global structural invariants. Classical pointer analyses struggle with the latter, whereas advanced shape analyses incur a higher computational cost. In this paper, we propose a new memory analysis by abstract interpretation that summarizes the heap by means of a type invariant, using a novel kind of physical types, which express the byte-level layout of values in memory. In terms of precision and expressiveness, our abstraction aims at a middle point between typical pointer analyses and shape analyses, hence the lightweight shape analysis name. We pair this summarizing abstraction with a retained and staged points-to predicates abstraction which refines information about the memory regions that are in use, hereby allowing strong updates without introducing disjunctions. We show that this combination of abstractions suffices to verify spatial memory safety and non-trivial structural invariants in the presence of low-level constructs such as pointer arithmetic and dynamic memory allocation, on both C and binary code.
Monetary policy effectiveness in Africa: the role of financial development and institutional quality

Purpose This study aims to provide empirical evidence of the pass-through effect of monetary policy on bank lending rates vis-à-vis the potential moderating effects of financial sector development and institutional quality in Africa. Design/methodology/approach The study uses robust fixed effects panel data estimation techniques and data from 1990 to 2017 across 37 countries in Africa. Findings The results show that financial development aids in the effectiveness of monetary policy transmission. A decomposition of financial development into financial institution development and financial market development shows that financial institutional development is more influential with regard to effectiveness of the interest rate pass-through compared to financial market development. This study again shows that improvements in the quality of institutions reduced lending rates in African economies. Practical implications The findings present relevant policy implications regarding effective transmission of monetary policy, by linking the pursuit of institutional quality, characterized by the control of corruption, political stability, regulatory quality, rule of law and the voice of accountability and development of financial institutions with lending rates and ultimately the demand for growth capital. Originality/value This study contributes to the literature on the factors influencing the effectiveness of monetary policy. This study considers financial sector development and institutional quality as conduits to monetary policy effectiveness in developing African countries.
Multi-resource collaborative optimization for adaptive virtual machine placement

The unbalanced resource utilization of physical machines (PMs) in cloud data centers could cause resource wasting, workload imbalance and even negatively impact quality of service (QoS). To address this problem, this paper proposes a multi-resource collaborative optimization control (MCOC) mechanism for virtual machine (VM) migration. It uses Gaussian model to adaptively estimate the probability that the running PMs are in the multi-resource utilization balance status. Given the estimated probability of the multi-resource utilization balance state, we propose effective selection algorithms for live VM migration between the source hosts and destination hosts, including adaptive Gaussian model-based VMs placement (AGM-VMP) algorithm and VMs consolidation (AGM-VMC) method. Experimental results show that the AGM-VMC method can effectively achieve load balance and significantly improve resource utilization, reduce data center energy consumption while guaranteeing QoS.
Short Quantum Accumulate Codes with High Rate and Multiple Error Corrections Capability

This paper proposes short but high rate quantum codes, derived from the classical accumulate codes, having capability of correcting multiple errors simultaneously. The high coding rate is obtained from k = 2 with minimum blocklength n, but still satisfying both Singletone and Hamming bounds to have error correction capability of t = 2 qubits, We perform an intensive computer search to find the best matrix of parity check that match with the accumulator matrix to provide zero simplectic inner product (SIP) mod 2. We found that it is difficult to obtain a perfect parity check matrix that provide zero diagonal element in SIP to guarantee completely unique syndromes. However, since it may be difficult, we also present in this paper a theoretical quantum word error rate (QWER), when some syndromes do not have unique values to identify the quantum errors of X, Z, and Y. The results in this paper are expected to provide groundbreaking foundation on the development of high rate quantum codes with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k\geq 2$</tex> , especially for short blocklength.
Hierarchical Composition Learning for Composed Query Image Retrieval

Composed query image retrieval is a growing research topic. The object is to retrieve images not only generally resemble the reference image, but differ according to the desired modification text. Existing methods mainly explore composing modification text with global feature or local entity descriptor of reference image. However, they ignore the fact that modification text is indeed diverse and arbitrary. It not only relates to abstractive global feature or concrete local entity transformation, but also often associates with the fine-grained structured visual adjustment. Thus, it is insufficient to emphasize the global or local entity visual for the query composition. In this work, we tackle this task by hierarchical composition learning. Specifically, the proposed method first encodes images into three representations consisting of global, entity and structure level representations. Structure level representation is richly explicable, which explicitly describes entities as well as attributes and relationships in the image with a directed graph. Based on these, we naturally perform hierarchical composition learning by fusing modification text and reference image in the global-entity-structure manner. It can transform the visual feature conditioned on modification text to target image in a coarse-to-fine manner, which takes advantage of the complementary information among three levels. Moreover, we introduce a hybrid space matching to explore global, entity and structure alignments which can get high performance and good interpretability.
The journey of Blockchain inclusion in Vehicular Networks: A Taxonomy

Vehicular Networks (VNs) improve the transportation systems with useful applications that enhance driver safety, minimize operating costs, and enable real-time infotainment services. These applications are based on a general scheme, the vehicular network architecture, which defines the role of each entity in the VN. Several architectures had been proposed to meet the security and performance requirements of the VN. This work presents a taxonomy of different architectures while focusing on blockchain technology inclusion in VNs. We describe each architecture and analyze its limitations and challenges. Furthermore, we emphasize the motivation behind blockchain technology adoption in VNs.
Smart Tourism Identity Authentication Service Based on BlockChain and Decentralized Identifier

Smart tourism has carried the hope of the tourism industry from the very beginning, and has provided new solutions for the improvement of efficiency, humanization of services and scientific management. However, as the continuous development of smart tourism industry, various types of dishonest even illegal behavior have gradually emerged. Due to the absence of an effective identity authentication mechanism for the travel services provider, there are illegal travel agencies, unqualified part-time tour guides, etc., thereby harming the rights and interests of tourist. Considering the above problems, this paper proposes a smart tourism identity authentication service based on blockchain and Decentralized Identifier (DID). The service requester can verify the attributes of the DID, and then check the certificate. By exploiting the collaborative management of on-chain and off-chain data, the entire process of smart tourism service identity authentication is safe, reliable and traceable. In order to alleviate the storage pressure of blockchain, the InterPlanetary File System (IPFS) is introduced to store DID document and Verifiable Credential certificate. The simulation experiments demonstrated that the proposed identity authentication system is both available and effective. The purpose of this paper is to help customers improve their identification ability, and promote the comprehensive and sustainable development of smart tourism.
Development and Subsea Testing of Polymetallic Nodule Crusher for Underwater Mining Machine

Abstract Polymetallic nodules found in the deep oceans are viewed as potential resources for meeting the world's demand of many metals in the near future. Polymetallic nodule mining systems require subsea crushing systems for reducing the size of nodules to facilitate energy-efficient and safe pumping through risers of optimum size. Polymetallic nodules are friable, and deep-sea crushing has to be done with care to minimize the formation of fines, while obtaining the required size reduction. The crusher could also encounter objects with greater hardness during operation like small rocks, splinters, long fish bones, and shark teeth. All components in the crusher should be capable of operating in the deep ocean environment, which is hyperbaric and sediment laden. The equipment should be compact with minimum weight. Reversal of direction and dumping arrangements in the event of stalling are other essential design requirements. An underwater crusher capable of crushing mined nodules from a maximum size of 100 mm to a crushed size of 30 mm was developed using principles of design synthesis. The crusher was tested in land and integrated into a remotely operated crawler-based underwater mining machine that could mine and pump nodules through a flexible riser. The system was tested using artificial nodules at 512-m water depth off the Malvan coast in the Arabian Sea. This paper describes developmental methodology, land-based performance tests, and sea trials conducted on the developed crusher.