Reviewerly/exHarmony huggingface dataset:
Foreign investment and growth: A case of selected African economies

Objective: The purpose of the study is to indicate how foreign investment impact on economic growth in some selected African economies.Research Design &amp; Methods: The study employed the Panel ARDL (PMG) technique to estimate the impact of foreign investment proxy as FDI net inflows on Growth measured as GDP per capita.Findings: The results of Panel ARDL indicate that foreign investment has a positive impact on economic growth as well as a positive sign of trade openness, inflation, and labor. The study stresses that for increasing economic growth there is a need to seek more foreign investments, increase trade openness and inflation at the same time improve upon employment conditions in selected African developing countries.Contribution &amp; Value Added: The novelty of this research is in presenting and emphasizing the significance of FDI in some selected African countries on economic growth.
Learning Conceptual-Contextual Embeddings for Medical Text

External knowledge is often useful for natural language understanding tasks. We introduce a contextual text representation model called Conceptual-Contextual (CC) embeddings, which incorporates structured knowledge into text representations. Unlike entity embedding methods, our approach encodes a knowledge graph into a context model. CC embeddings can be easily reused for a wide range of tasks in a similar fashion to pre-trained language models. Our model effectively encodes the huge UMLS database by leveraging semantic generalizability. Experiments on electronic health records (EHRs) and medical text processing benchmarks showed our model gives a major boost to the performance of supervised medical NLP tasks.
Deep Time-Stream Framework for Click-through Rate Prediction by Tracking Interest Evolution

Click-through rate (CTR) prediction is an essential task in industrial applications such as video recommendation. Recently, deep learning models have been proposed to learn the representation of users' overall interests, while ignoring the fact that interests may dynamically change over time. We argue that it is necessary to consider the continuous-time information in CTR models to track user interest trend from rich historical behaviors. In this paper, we propose a novel Deep Time-Stream framework (DTS) which introduces the time information by an ordinary differential equations (ODE). DTS continuously models the evolution of interests using a neural network, and thus is able to tackle the challenge of dynamically representing users' interests based on their historical behaviors. In addition, our framework can be seamlessly applied to any existing deep CTR models by leveraging the additional Time-Stream Module, while no changes are made to the original CTR models. Experiments on public dataset as well as real industry dataset with billions of samples demonstrate the effectiveness of proposed approaches, which achieve superior performance compared with existing methods.
Pooch: A friend to fetch your data files

Scientific software is usually created to acquire, analyze, model, and visualize data.As such, many software libraries include sample datasets in their distributions for use in documentation, tests, benchmarks, and workshops.A common approach is to include smaller datasets in the GitHub repository directly and package them with the source and binary distributions (e.g., scikit-learn (Pedregosa et al., 2011) and scikit-image (Van der Walt et al., 2014) do this).As data files increase in size, it becomes unfeasible to store them in GitHub repositories.Thus, larger datasets require writing code to download the files from a remote server to the user's computer.The same problem is faced by scientists using version control to manage their research projects.While downloading a data file over HTTPS can be done easily with modern Python libraries, it is not trivial to manage a set of files, keep them updated, and check for corruption.For example, scikit-learn (Pedregosa et al., 2011), Cartopy (Met Office, n.d.), and PyVista (Sullivan & Kaszynski, 2019) all include code dedicated to this particular task.Instead of scientists and library authors recreating the same code, it would be best to have a minimalistic and easy to set up tool for fetching and maintaining data files.
Research on spatial positioning of online inspection robots for vertical storage tanks

Purpose This paper aims to introduce a new acoustic positioning method to solve the problem of space positioning for online inspection robots within the storage tank. Design/methodology/approach The proposed positioning system comprises two acoustic signal emitters and two receivers. Emitters are brought by the robot into the storage tank. Receivers are mounted on the external edge of the storage tank floor. The spatial coordinate values and motion directions of the robot in the storage tank are calculated by using the proposed acoustic positioning algorithm. Findings The experiment results and positioning error analysis indicate that the method can obtain the data of robotic space coordinates and motion orientation, while the positioning error of the method can be less than 20 cm. The accuracy reaches the positioning technology level of other tank online inspection robots. Originality/value This method not only expands the positioning of the inspection robots from 2D plane to 3D space but also significantly reduces the number of positioning sensors carried by a robot and improves the safety of a robot in the tank.
Trapped-ion Fock-state preparation by potential deformation

This paper proposes a protocol to shortcut to adiabaticity in order to control the motional state of an ion in a microfabricated trap. By deforming the trap potential it is possible to prepare states with highly excited levels, which are optimal for sensitive measurements.
Fidelity Preserved Data Hiding in Encrypted Images Based on Homomorphism and Matrix Embedding

This paper proposes a data hiding scheme for high quality stego-images in the encrypted images based on a homomorphic encryption algorithm and matrix embedding method. To achieve the message embedding in the encryption domain, we first present an image encryption algorithm satisfying additive homomorphism based on the stream cipher. Then, we offer a matrix embedding method using Golay code, which has good embedding efficiency. In the proposed scheme, we pre-code the least significant bit (LSB) vectors of the cover image into the codewords before image encryption, so that we can implement the matrix embedding in the encrypted image. The experimental results show that the proposed scheme has excellent security performance. We can get a high quality stego-image after the image decryption and recover the cover image into a fidelity image while providing considerable payload.
Project Westdrive: Unity City With Self-Driving Cars and Pedestrians for Virtual Reality Studies

Virtual environments will deeply alter the way we conduct scientific studies on human behavior. Possible applications range from spatial navigation over addressing moral dilemmas in a more natural manner to therapeutic applications for affective disorders. The decisive factor for this broad range of applications is that virtual reality (VR) is able to combine a well-controlled experimental environment together with the ecological validity of the immersion of test subjects. Until now, however, programming such an environment in Unity® requires profound knowledge of C# programming, 3D design and computer graphics. In order to give interested research groups access to a realistic VR environment which can easily adapt to the varying needs of experiments, we developed a large, open source, scriptable and modular VR city. It covers an area of 230 hectare, up to 150 self-driving vehicles and 655 active and passive pedestrians and thousands of nature assets to make it both highly dynamic and realistic. Furthermore, the repository presented here contains a stand-alone City AI toolkit for creating avatars and customizing cars. Finally, the package contains code to easily set up VR studies. All main functions are integrated into the graphical user interface of the Unity® Editor to ease the use of the embedded functionalities. In summary, the project named Westdrive is developed to enable research groups to access a state-of-the-art VR environment that is easily adapted to specific needs and allows focus on the respective research question.
Disorder-induced two-body localized state in interacting quantum walks

We observe the onset of non-ergodicity from ballistic propagation of a two-body bound state in an interacting discrete-time quantum walk (DTQW) due to time-dependent disorder in the interaction. The effect of the disorder on the two-body state can be interpreted as Anderson-type localisation in a projected DTQW, but without saturation of entanglement entropy. We characterise the two-body localisation in terms of the rate of growth of entanglement entropy and compute the localisation length. We find indications for two distinct growth laws for the entanglement: a logarithmic law in the delocalised phase and a double logarithmic law in the localised phase. We discuss similarities with many-body localisation (MBL).
Protocol Proxy: An FTE-based covert channel

In a hostile network environment, users must communicate without being detected. This involves blending in with the existing traffic. In some cases, a higher degree of secrecy is required. We present a proof-of-concept format transforming encryption (FTE)-based covert channel for tunneling TCP traffic through protected static protocols. Protected static protocols are UDP-based protocols with variable fields that cannot be blocked without collateral damage, such as power grid failures. We (1) convert TCP traffic to UDP traffic, (2) introduce observation-based FTE, and (3) model interpacket timing with a deterministic Hidden Markov Model (HMM). The resulting Protocol Proxy has a very low probability of detection and is an alternative to current covert channels. We tunnel a TCP session through a UDP protocol and guarantee delivery. Observation-based FTE ensures traffic cannot be detected by traditional rule-based analysis or DPI. A deterministic HMM ensures the Protocol Proxy accurately models interpacket timing to avoid detection by side-channel analysis. Finally, the choice of a protected static protocol foils stateful protocol analysis and causes collateral damage with false positives.
Joint effects of depth-aiding augmentations and viewing positions on the quality of experience in augmented telepresence

Abstract Virtual and augmented reality is increasingly prevalent in industrial applications, such as remote control of industrial machinery, due to recent advances in head-mounted display technologies and low-latency communications via 5G. However, the influence of augmentations and camera placement-based viewing positions on operator performance in telepresence systems remains unknown. In this paper, we investigate the joint effects of depth-aiding augmentations and viewing positions on the quality of experience for operators in augmented telepresence systems. A study was conducted with 27 non-expert participants using a real-time augmented telepresence system to perform a remote-controlled navigation and positioning task, with varied depth-aiding augmentations and viewing positions. The resulting quality of experience was analyzed via Likert opinion scales, task performance measurements, and simulator sickness evaluation. Results suggest that reducing the reliance on stereoscopic depth perception via camera placement has a significant benefit to operator performance and quality of experience. Conversely, the depth-aiding augmentations can partly mitigate the negative effects of inferior viewing positions. However the viewing-position based monoscopic and stereoscopic depth cues tend to dominate over cues based on augmentations. There is also a discrepancy between the participants’ subjective opinions on augmentation helpfulness, and its observed effects on positioning task performance.
SCDAE: Ethnicity and Gender Alteration on CLF and UTKFace Dataset

Global face attributes like Gender, Ethnicity, and Age are attracting attention due to their specific explanation of human faces. Mostly prior face attribute alteration works are on large-scale CelebA and LFW dataset. We address more challenging problem called global face attribute alteration on data sets like CLF and UTKFace. Our approach is based on sampling with global condition attribute. It consists of five components Encoder ($$E_{Z}$$), Encoder ($$E{_Y}$$), Sampling (S), Latent Space (ZL), and Decoder (D). The $$E{_Z}$$ with S component is responsible to generate structured latent vector Z and $$E_{Y}$$ produces condition vector L which we modify according to desired condition, latent vector Z and modified condition vector L are concatenated to make Latent Space ZL to help global face attribute alteration and Decoder D is used to generate modified images. We trained our SCDAE (Sampling and Condition based Deep AutoEncoder) model for gender and ethnicity alteration on CLF and UTKFace dataset. Both qualitative and quantitative experiments show that our approach can alter untouched global attributes and generates more realistic faces in term of person identity and age uniformity which is comparable to human observation.
On the Content of higher technical education curricula abroad: Current Trends (Review)

Researchers consider the discrepancy between university educational programs and engineering practice to be the common problem of higher technical education today. The changes taking place at higher technical school abroad demonstrate the search for solutions of engineering education problems in developed countries. Understanding current trends in the content of higher technical education curricula in those countries will help to identify the most effective ways to solve common problems of engineering education. The article presents the results of the analysis of innovative changes taking place in the higher technical education curricula content abroad nowadays. The research materials were articles in peer-reviewed journals included in the SCOPUS database published from 2009 to 2018, proceedings of conferences held by international engineering communities. The factual basis of the study was the curricula for training engineers at a number of foreign leading universities. As a result of the research, the following innovations in the content of higher technical education curricula were identified: 1) introduction of sustainable development principles into the engineering curricula; 2) the inclusion of modules on the basics of management, entrepreneurship and innovation into the curricula, the creation of interdisciplinary programs leading to double degrees in engineering and management; 3) cooperation of universities with partner enterprises in the development of curricula; 4) the internationalization of engineering curricula, the training of future engineers for global engineering activities. The CDIO approach is considered as an innovative basis for the design of educational programs for future engineers. Those changes in the engineering curricula are aimed at bridging the gap between the contents of engineering educational programs and industrial practice, the development of competencies necessary for successful professional engineering activities, and, as a result, they contribute to improving the quality of higher technical education.
Spell corrector for Bangla language using Norvig’s algorithm and Jaro-Winkler distance

In the online world, especially in the social media platform most of us write without much regard to correct spelling and grammar. The spelling mistakes are much larger in proportion when it comes to Bangla language. In our paper, we presented a method for error detection and correction in Bangla words' spellings. Our system could detect a misspelled Bangla word and provide two following services-suggesting correct spellings for the word and correcting the word. We had used Norvig's algorithm for the purpose but instead of using probabilities of the words to prepare the suggestions and corrections, we had used Jaro-Winkler distance. The previous works done in this field for Bangla language are either very slow or offers less accuracy. Our system successfully achieved a 97% accuracy when evaluated with 1000 Bangla words.
Invisible Poisoning: Highly Stealthy Targeted Poisoning Attack

Deep learning is widely applied to various areas for its great performance. However, it is vulnerable to adversarial attacks and poisoning attacks, which arouses a lot of concerns. A number of attack methods and defense strategies have been proposed, most of which focus on adversarial attacks that happen in the testing process. Poisoning attacks, using poisoned-training data to attack deep learning models, are more difficult to defend since the models heavily depend on the training data and strategies to guarantee their performances. Generally, poisoning attacks are conducted by leveraging benign examples with poisoned labels or poison-training examples with benign labels. Both cases are easy to detect. In this paper, we propose a novel poisoning attack named Invisible Poisoning Attack (IPA). In IPA, we use highly stealthy poison-training examples with benign labels, perceptually similar to their benign counterparts, to train the deep learning model. During the testing process, the poisoned model will handle the benign examples correctly, while output erroneous results when fed by the target benign examples (poisoning-trigger examples). We adopt the Non-dominated Sorting Genetic Algorithm (NSGA-II) as the optimizer for evolving the highly stealthy poison-training examples. The generated approximate optimal examples are promised to be both invisible and effective in attacking the target model. We verify the effectiveness of IPA against face recognition systems on different face datasets, including attack ability, stealthiness, and transferability performance.
Study to carry out the tutorial action through a learning management system at the secondary level

The educational level of secondary school is the final stage of basic education in our country, which integrates the subject of tutoring as a process of guidance, management and support under the coordination of a teacher-tutor. Said educational process is carried out in order to strengthen the integral formation of the students. In this sense, the present descriptive exploratory research was carried out, addressing as problematic the analysis of the aspects to design a learning management system with the use of the Moodle platform as an accompaniment tool in the tutorial action. Thus, the mixed court study was carried out inside the school of the center of the José Anastacio Díaz López school chain (JADILOP) in the city of Zacatecas. The survey applied to 120 students was used for the quantitative analysis, using the SPSS program for the processing of the obtained data. For the qualitative approach, the semi-structured interview applied to 12 teacher tutors and 3 specialists was used, using the specialized software MaxQDA for the analysis and processing of the data based on the grounded theory. As main findings, the Moodle platform was identified with functional characteristics to carry out the tutorial action in an efficient manner as well as an environment that favors the link with the other subjects of the second year of secondary school program; determining the aspects to integrate in its design and implementation.
Downsizing and enhancing broad learning systems by feature augmentation and residuals boosting

Abstract Recently, a broad learning system (BLS) has been theoretically and experimentally confirmed to be an efficient incremental learning system. To get rid of deep architecture, BLS shares the same architecture and learning mechanism of the well-known functional link neural networks (FLNN), but works in broad learning way on both the randomly mapped features of original features of data and their randomly generated enhancement nodes. As such, BLS often requires a huge heap of hidden nodes to achieve the prescribed or satisfactory performance, which may inevitably cause both overwhelming storage requirement and overfitting phenomenon. In this study, a stacked architecture of broad learning systems called D&amp;BLS is proposed to achieve enhanced performance and simultaneously downsize the system architecture. By boosting the residuals between previous and current layers and simultaneously augmenting the original input space with the outputs of the previous layer as the inputs of current layer, D&amp;BLS stacks several lightweight BLS sub-systems to guarantee stronger feature representation capability and better classification/regression performance. Three fast incremental learning algorithms of D&amp;BLS are also developed, without the need for the whole re-training. Experimental results on some popular datasets demonstrate the effectiveness of D&amp;BLS in the sense of both enhanced performance and reduced system architecture.
E-Commerce User Type Recognition Based on Access Sequence Similarity

In order to measure the similarity of non-equal length and non-numerical sequence effectively, in this paper, the access sequence similarity calculation method was proposed based on the characteristics of e-commerce user access sequence. The sliding window method was improved by increasing the similarity calculation of nodes and optimizing the sliding similarity calculation method. The key factor of Edit Distance on Real Sequences was optimized. It mainly includes the calculation method of increasing the similarity of nodes and optimizing the calculation method of sliding similarity; the calculation method of subcost in the editing distance of real sequences is optimized. Then, the optimized Edit Distance on Real Sequences was embedded into the improved sliding window method to replace the original distance calculation method. Based on the access sequence similarity calculation results, the clustering algorithm was used to get the e-commerce users type. The experimental results showed the following facts: The improved access sequence similarity algorithm can measure the similarity of non-numerical and non-equal length sequences more accurately; based on the similarity of access sequences, it is possible to divide the types of e-commerce users more effectively, besides the e-commerce users are mainly composed of young men, users’ online time shows obvious fragmentation characteristics, their online browsing behavior obeys long tail distribution, they still primarily buy hot items, and the e-commerce users can be divided into six categories.