Reviewerly/exHarmony huggingface dataset:
Interactive methods of training for enhancing communication of IT-professionals

The paper deals with the issue of enhancing communication of IT-professionals using interactive training methods. It is emphasized that this problem is urgent for IT-education, which has the quickest rate of the content change, formation of new knowledge fields, shift in types and volumes of communication and interaction. Regulations, guides and qualifications frameworks of European countries and Ukraine in terms of requirements to communication of specialists in different fields have been analysed. The concepts of interactive teaching, interactive teaching methods and activities, and their advantages have been studied. It has been noted that interactive methods can be successfully used in teaching general and special subjects for future IT-professionals. Main peculiarities of certain interactive teaching methods have been described. Some practical ideas of the project method, brainstorming, the gamification method, the debate use in the educational process with the focus on the English language at higher educational institutions for enhancing better communication of IT-professionals have been presented. It is emphasized that the meticulous combination of interactive methods and preparation fosters comprehension at the sufficient level both in use of language aspects and in live communication.
Multilingual and Interlingual Semantic Representations for Natural Language Processing: A Brief Introduction

We introduce the Computational Linguistics special issue on Multilingual and Interlingual Semantic Representations for Natural Language Processing. We situate the special issue’s five articles in the context of our fast-changing field, explaining our motivation for this project. We offer a brief summary of the work in the issue, which includes developments on lexical and sentential semantic representations, from symbolic and neural perspectives.
Technology assisted analysis of timeline and connections in digital forensic investigations

This article describes ongoing research on the application of AI techniques such as Graph Neural Networks to assist investigators with the discovery of relations and patterns in digital forensic evidence. Digital forensic analysis of smartphones and computers reveals forensic artifacts that are extracted from structured databases maintained by the operating system and applications. Such forensic artifacts are part of a forensic ontology which can be used to build a relational graph of identifiers (e.g. users, documents) and a timeline of events. This information can assist with answering key investigation questions such as who, when, where etc. We propose to use a graph database and query language to assist in this analysis. Further, using key identifiers and aliases we want to augment digital forensic artifacts with entities, relations and events by extraction from the full-text of unstructured electronic contents such as emails and documents.
Cartooning and Mind Mapping in the Doctoral Journey

This article is a commentary of the author’s experiences with visual representation as a doctoral candidate. It is an autobiographical reflection of her doctoral journey towards a PhD in education, a mixed methods study which explored teaching for creativity and creative processes for music educators. The author reflects on the experience of engaging with her own work visually via cartooning and mind mapping, and what this meant for her studies and her experience as a research student. The author hopes that this can offer a scope for reflection to readers who are doing similar types of studies or intend to engage more creatively with their doctoral studies. The article aims to demonstrate the benefit of cartooning as a self-expressive tool for the PhD candidate and to show the value of visual representation through mind mapping in the process of data analysis.
Two-variable Dual Coordinate Descent Methods for Linear SVM with/without the Bias Term

Coordinate descent (CD) methods have been a stateof-the-art technique for training large-scale linear SVM.The most used setting is to solve the dual problem of an SVM formulation without the bias term (or an SVM formulation by embedding the bias term in the weight vector).The reason of omitting the bias term is that dual SVM no longer has a linear constraint and the CD procedure of updating one variable at a time is very simple.However, some have criticized the decision of not considering the bias term.To understand the role of the bias term in the design of CD methods for linear SVM, we give a thorough study on twovariable CD.First, if the bias term is not considered, we develop a two-variable CD that is competitive with the commonly used one-variable CD and is superior for difficult problems.The procedure is simple and has theoretical linear-rate convergence.Second, we investigate two-variable CD for linear SVM with the bias term.Analysis shows that CD is much less efficient for such a setting.Therefore, we conclude that in using CD for linear SVM, in general the bias term should not be considered.
Statistic Chinese New Word Recognition by Combing Supervised and Unsupervised Learning

This paper proposed a method using statistic methods with untagged and tagged data to detect Chinese new words. Chinese sentences have no blank to segment the characters to build words. So Chinese word segmentation has been a hot research field for a long time. Chinese word segmentation needs tagged data and dictionary. The dictionary should always be updated, thus ththereere will be less out of vocabulary words in the model. It will cost a lot of time to collect new words manually, and it has many limitations such as the mount of the words. Term frequency, pointwise mutual information and entropy are applied to detect new Chinese new words. They are based on the untagged data by statistic methods. Hidden markov model is used to segment Chinese sentences and detect new words. Hidden markov model need tagged data to train the model. Hidden markov model can learn the probability of a character's position of a word such as begin, middle, end or single. We combine these two methods: one method to use the untagged data and an other to use the tagged data. The untagged data has many resources and it does't cost any human operation, but it is not so accurate. The tagged data is tagged by human, so it is accurate and reliable, but it takes a lot time to tag it, only a few of data is free to use to train the method. In short, we try to use tagged data and untagged data at the same time by combing pointwise mutual information, entropy and hidden markov model. The experiment shows the new word candidates number is reduced from 306% to 173% compared to the training dictionary number, but the real words number is only reduced 4%.
Algorithm on Higher-Order Derivative Based on Ternary Optical Computer

As an important tool in the field of mathematics, higher-order derivation problems are widely used in differentials, quantum mechanics, and engineering applications.However, in the electronic computer (EC), due to the existence of the carry in the calculation, the computational efficiency is low when solving the higher-order derivation problem.In response to this problem, the ternary optical computer (TOC) has the advantages of no carry-in and the characteristics of numerous data bits, reconfigurable processors and parallel computing.Solve the higher-order derivation problems with complex operations by constructing multipliers and adders on the TOC platform, and copying multiple composite operator units (COUs).This article introduces the design of the higher-order derivative algorithm based on TOC in detail, the reconfiguration process of the multiplier and adder, and the number of bits of the multiplier and adder required in the implementation is given.Besides, the hardware resources and clock cycles in the operation are analyzed.The feasibility of the implementation scheme is verified by experiments.Compared with the traditional higherorder derivative, the higher-order derivative based on the TOC is superior in time performance, computational efficiency, and processing of complex operations.Due to the limitation of the research stage, the algorithm is only applicable to the function of polynomials, which lays a foundation for the further research of higherorder derivative algorithms, and has certain application significance.
Investigation of Different Observation and Action Spaces for Reinforcement Learning on Reaching Tasks

Deep reinforcement learning has been shown to be a potential alternative to a traditional controller for robotic manipulation tasks. Most of modern deep reinforcement learning methods that are used ...
Cryptographic Building Blocks

This chapter introduces basic cryptographic mechanisms that serve as foundational building blocks for computer security: symmetric-key and public-key encryption, public-key digital signatures, hash functions, and message authentication codes. Other mathematical and crypto background is deferred to specific chapters as warranted by context.
A Parameterized Intuitionistic Type-2 Fuzzy Inference System with Particle Swarm Optimization

The aim of this study was to develop a novel intuitionistic Type-2 fuzzy inference system (IT-2 FIS) which adopts a parameterized Yager-generating function and particle swarm optimization (PSO). In IT-2 FIS, the intuitionistic Type-2 is set as a fuzzy symmetrical triangular number in which the hesitation degree adopts the Yager-generating function, and the parameters of the proposed IT-2 FIS adopting the PSO are tuned. The intuitionistic and Type-2 fuzzy sets have been proven to be the most effective for handling more uncertainty. Therefore, this study proposes an intuitionistic Type-2 set with a Yager-generating function to enhance the conventional fuzzy inference system. Moreover, PSO can improve the fuzzy inference system by searching for the optimal parameters of IT-2 FIS. In this study, linguistic variables were represented by triangular fuzzy numbers (TFS). Two numerical examples were examined: capacity-planning and medical diagnosis problems. An approaching capacity-loadings example was used to verify that the proposed IT-2 FIS could effectively estimate the results of the capacity loadings. In the medical diagnosis problem, IT-2 FIS could obtain a higher correct rate by revealing experts’ knowledge. In both examples, the proposed IT-2 FIS provided more objective estimated values than traditional fuzzy inference systems (FIS) and Type-2 FIS.
Big Data Mining Optimization Algorithm Based on Machine Learning Model

In a big data mining optimization algorithm, the classification algorithm plays an important role.At present, there are many popular classification algorithms based on machine learning.Aiming at the problems of existing big data classification algorithms, two improved strategies and implementation methods are proposed in this paper.First of all, before the data training, the orders of magnitude of the original data are normalized to achieve better data pre-processing and classification.Then with the least mean square algorithm and the BP neural network classification algorithm as the basic algorithms, an improved batch learning BP algorithm is designed, based on the rules of batch learning.The experimental results indicate that the improved batch learning BP algorithm can better solve the imbalanced classification problem in big data.
Pengaruh Media Permainan Monopoli terhadap Hasil Belajar Siswa dalam Pembelajaran IPS Gugus 15 Kota Bengkulu

This study aimed to find the effect of using monopoli game media towards learning result on social science major at fourth grade of SDN Gugus 15 Kota Bengkulu. This research is quantitative, using experiment method, with The Matching Only Pretest-Posttest Control Group Design. The instrument used was test sheet in multiple choice form. The test was taken two times, before and after giving treatening to the sample class. The sample in this research was the students in IVB of SDN 76 Kota Bengkulu (control group) and the students in IV of SDN 104 Kota Bengkulu (experimental group). The t value gotten was (3,668) &gt; ttable (1,668). The result of t value was higher than t table, so it can be concluded that there are significant effect of using monopoli game media towards learning result on social science major at fourth grade SDN Gugus 15 Kota Bengkulu.
Analisis Jalur (Path Analysis) Pengaruh Kondisi Sosial Ekonomi dan Motivasi Belajar terhadap Prestasi Belajar Mahasiswa

Tujuan dari penelitian ini adalah untuk menguji pengaruh langsung kondisi sosial ekonomi dan motivasi belajar terhadap prestasi belajar mahasiswa, serta pengaruh langsung kondisi sosial ekonomi terhadap motivasi belajar mahasiswa. Penelitian dilakukan di Institut Sains dan Teknologi Al-Kamal Jakarta dengan populasi penelitian adalah seluruh mahasiswa Program Studi Teknik Informatika, Fakultas Teknologi Informasi &amp; Komunikasi Visual. Dari populasi tersebut, diambil sampel sebanyak 70 mahasiswa yang diperoleh dengan menggunakan teknik pengambilan sampel acak sederhana. Penelitian ini menggunakan metode survei kausal. Data penelitian dijaring melalui instrumen penelitian berupa kuesioner dengan skala Likert, kemudian diolah dan dianalisis dengan teknik analisis jalur. Hasil penelitian ini menunjukkan bahwa: (1) Tidak terdapat pengaruh langsung kondisi sosial ekonomi terhadap prestasi belajar mahasiswa; (2) Terdapat pengaruh langsung motivasi belajar terhadap prestasi belajar mahasiswa; dan (3) Terdapat pengaruh langsung kondisi sosial ekonomi terhadap motivasi belajar mahasiswa.&#x0D; The purpose of this research is to test the direct effect of socioeconomic conditions and learning motivation on learning achievement of college student, and the direct effect of socioeconomic conditions on learning motivation of college student. This research was conducted at the Al-Kamal, Institute of Science and Technology, in Jakarta, where all students of the Informatics Engineering Study Program, Faculty of Information &amp; Visual Communication Technology as the population. From this population, a sample of 70 students was taken using simple random sampling. This research uses a causal survey method. The research data were obtained through questionnaire instrument with a Likert scale, then processed and analyzed with path analysis. The results of this research indicate that: (1) There is no direct effect of socioeconomic conditions on learning achievement of college student; (2) There is a direct effect of learning motivation on learning achievement of college student; and (3) There is a direct effect of socioeconomic conditions on learning motivation of college student.
Pengendalian Suhu dan Kelembaban pada Budidaya Jamur Tiram Berbasis IoT

Oyster mushrooms (Pleurotus Ostreatus), such as white mushroom, chinese mushroom, and warm mushroom, is one type of edible mushrooms. Oyster mushroom cultivation is usually done at the mushroom house (kumbung) which is mostly made of bamboo. Oyster mushrooms can grow optimally by controlling the temperature and humidity in the kumbung. Kumbung environment conditioning is usually done manually by spraying water on the planting medium every 8 hours. But this is perceived as ineffective and requires high effort. For this reason, we need a technology that can monitor as well as control the environmental conditions inside the kumbung automatically and remotely. This paper describes the design of a system to monitor and control the temperature and humidity in the kumbung for oyster mushroom cultivation based on IoT (Internet of Things). This system is developed consisting of five parts, i.e sensor modules, microcontrollers, data loggers, actuators, and monitoring and control interfaces. The main result of this study is a remote monitoring and control of environmental conditions for oyster mushroom cultivation. The data of monitoring, in the form of temperature and humidity, are saved to the data logger and can be downloaded in the format (.csv). The system works functionally in accordance with the expected specifications, both in terms of sensor readings and actuator controlling.
A Lattice-Based Certificateless Public Key Encryption with Equality Test in Standard Model

Certificateless public key encryption ($$\mathsf {CL}\hbox {-}\mathsf {PKE}$$) solves the problems of establishing public-key infrastructure for traditional public key encryption and resolving key escrow for identity-based encryption. Equality test is an extremely useful property that enables the ability of checking whether two ciphertexts encrypting the same message. Qu et al. (Information Science 2019) introduced the notion of certificateless public key encryption with equality test ($$\mathsf {CL}\hbox {-}\mathsf {PKEET}$$), together with four types of adversaries, that solves certificate manangement and key escrow problems of public key encryption with equality test ($$\mathsf {PKEET}$$) and identity-based encryption with equality test ($$\mathsf {IBEET}$$), and proposed a first $$\mathsf {CL}\hbox {-}\mathsf {PKEET}$$ scheme based on Bilinear Diffie-Hellman assumption in random oracle model. In this paper, we propose the first lattice-based $$\mathsf {CL}\hbox {-}\mathsf {PKEET}$$ in standard model whose security is reduced to the hardness of the learning with errors problem. In particular, we prove that our schemes are secure against two types of selective-identity adversaries introduced by Qu et al.
Echo Chambers and Competition Law: Should Algorithmic Choices be Respected?

Algorithms are employed by a growing number of firms in order to make choices for users. One prominent example involves news and views consumption through media platforms, which is increasingly mediated by algorithmic personalization. Rather than engaging with the rich variety of ideas on the web, many online users are exposed primarily to content chosen by algorithms, which generally attempts to fit each user’s pre-existing views. This raises questions regarding competition law’s responsibility and ability to protect the free exchange of ideas in the marketplace. We argue that while competition law can be used to protect the diversity of content in the market, the protection of a diversity of exposure is much more challenging. An interference that is aimed at exposing users to diverse ideas need not conflict with the goals of antitrust. In particular, we argue that even if algorithmic choices attempt to cater to users’ preferences, they need not conflict with the ideals of consumer sovereignty, autonomy and choice on which competition law is based. Yet competition laws do not possesses the right tools to tackle this problem, and therefore doing so may best be left to other mechanisms. The discussion has implications for other algorithmic choices which may seem to cater to users’ preferences.
Does Financial inclusion affect the African banking stability

This paper offers an empirical investigation on the channels through which financial inclusion may influence the soundness of African banks. Using the dynamic panel approach, we construct a composite index of financial inclusion for 38 African countries over the period 2005 to 2014, and then investigate if the financial inclusion could influence directly or through the competition the banking stability. Our results indicate a strong relation between financial inclusion, and banking stability. In addition, the result suggests that the policy makers face tradeoffs between financial inclusion and bank competition to improve the financial stability.
The Influence of Learning Attitude on the Effect of Micro Class —A Case Study of College Students’ Mental Health Education

Learning attitude; College students’ mental health course; Effect of micro course
Slicenet: Slice-Wise 3D Shapes Reconstruction from Single Image

3D object reconstruction from a single image is a highly ill-posed problem, requiring strong prior knowledge of 3D shapes. Deep learning methods are popular for this task. Especially, most works utilized 3D deconvolution to generate 3D shapes. However, the resolution of results is limited by the high resource consumption of 3D deconvolution. In this paper, we propose SliceNet, sequentially generating 2D slices of 3D shapes with shared 2D deconvolution parameters. To capture relations between slices, the RNN is also introduced. Our model has three main advantages: First, the introduction of RNN allows the CNN to focus more on local geometry details,improving the results’ fine-grained plausibility. Second, replacing 3D deconvolution with 2D deconvolution reducs much consumption of memory, enabling higher resolution of final results. Third, an slice-aware attention mechanism is designed to provide dynamic information for each slice’s generation, which helps modeling the difference between multiple slices, making the learning process easier. Experiments on both synthesized data and real data illustrate the effectiveness of our method.
The efficiency of local-spatial filtration valid for noise-contaminated signals in identification problems

Volterra integral equation of the first kind
Exploring Entity-Level Spatial Relationships for Image-Text Matching

Exploring the entity-level (i.e., objects in an image, words in a text) spatial relationship contributes to understanding multimedia content precisely. The ignorance of spatial information in previous works probably leads to misunderstandings of image contents. For instance, sentences `Boats are on the water' and `Boats are under the water' describe the same objects, but correspond to different sceneries. To this end, we utilize the relative position of objects to capture entity-level spatial relationships for image-text matching. Specifically, we fuse semantic and spatial relationships of image objects in a visual intra-modal relation module. The module performs promisingly to understand image contents and improve object representation learning. It contributes to capturing entity-level latent correspondence of image-text pairs. Then the query (text) plays a role of textual context to refine the interpretable alignments of image-text pairs in the inter-modal relation module. Our proposed method achieves state-of-the-art results on MSCOCO and Flickr30K datasets.
Hierarchical Sequence Representation with Graph Network

Video classification problem is a challenging task in computer vision. The performance of this task is highly relied on the scale of training data and the effectiveness of video embedding via a robust embedding network. Unsupervised solutions such as feature average pooling technique, as a simple label-independent and parameter-free based method, cannot efficiently represent the video sequences. While supervised methods, such as RNN, can improve the recognition accuracy. The performance of RNN based methods, however, is decreased with the increasing length of the videos and the hierarchical relationships between frames across events in the video. In this paper, we propose a novel video classification method based on a deep convolutional graph neural network (DCGN). The proposed method utilizes the characteristics of the hierarchical structure of the video, and performed multi-level embedding feature extraction on the video frame sequence through the graph network, and obtained a video representation which reflects the event semantics hierarchically. Experiments on YouTube-8M Large-Scale Video Understanding dataset show that our proposed model outperforms the commonly used RNN based models, verifying its effectiveness for video classification.