Reviewerly/exHarmony huggingface dataset:
Optimization of warehouse management based on artificial intelligence technology

This paper analyzes the artificial intelligence algorithms related to the storage path optimization problem and focuses on the ant colony algorithm and genetic algorithm with better applicability. The genetic algorithm is used to optimize the parameters of the ant colony algorithm, and the performance of the ant colony algorithm is improved. A typical route optimization problem model is taken as an example to prove the effectiveness of parameter optimization. This paper proposes a combined forecasting method through data preprocessing algorithm and artificial intelligence optimization. The combined prediction method first uses wavelet transform threshold processing to remove the noise data in the original data and then uses three separate methods to reduce noise. Forecast warehouse data and obtain intermediate forecast results. This article analyzes warehouse management and can solve the problems in the company’s warehouse management from the aspects of warehouse design and planning, warehouse design, and integrated warehouse management. After comparative analysis and selection, this paper uses the SLP method to rationally adjust and arrange the relative position and area of each functional area of the warehouse, and improve the evaluation index system. Experimental research shows that under the guidance of this article to optimize storage strategy, cargo location layout, and warehousing workflow, the employee reward mechanism mobilizes the enthusiasm of employees, improves work efficiency, and reduces storage costs. The above-mentioned various optimization and storage improvement measures finally reduced the total storage cost by 17%, effectively achieving the goal of cost control.
Innovative changes in the curriculum of information and communication technologies in the Czech Republic from the perspective of primary school teachers

The article focuses on issues related to the innovation of the curriculum of informatics and digital technologies in the Czech Republic. Within the research survey, special attention is paid to the opinions of secondary school teachers. It is found out how they view the ongoing curricular changes, which are characterized by strengthening the curriculum focused on algorithmization and programming.
Professional Perceptions of Students of the Polytechnic University

The paper is devoted to the results of research into professional perceptions of students of the Polytechnic university in the context of their professional formation and self-determination. The first- and second-year students of the bachelor’s degree program (60 people) took part in it. In the course of the research, the indicators of the level of formation of professional perceptions were studied. The most important factors that the respondents had in choosing a profession, the success of a future professional as well as their professional intentions were analyzed. At the initial stage of professional education, students’ perceptions of their chosen profession are not filled with the subject content, are fragmented and limited. There are differences in the level and structural indicators of professional perceptions between men and women. Men’s professional perceptions reflect a practically focused approach and an expressed desire to discover their own personality in their future professional activity. Women’s professional perceptions have a pronounced emotional coloring and are oriented to the communicative side of their professional activity and their contribution to the public interest. The results of the research confirm the need for a targeted job to develop professional self-consciousness of the would-be specialist. In the curriculum, subjects can be distinguished, in the process of mastering which there is a targeted work on restructuring the perceptions of the chosen field of the professional activity. Such subjects may become a platform for monitoring, making it possible to track transformation of the system of professional perceptions and development of professional self-determination of would-be specialists.
Colors Perception in Object Detectors

Detecting objects in computer vision have become very popular these days, especially in self-driving vehicles and autonomous cars. We are proposing a method to enhance this experience by detecting colors, which could also lead in the color perception by robotic systems in the future. First, there are technologies used described in the first section of the paper and then the algorithm itself is introduced with several conducted experiments.
PERLEX: A Bilingual Persian-English Gold Dataset for Relation Extraction

Relation extraction is the task of extracting semantic relations between entities in a sentence. It is an essential part of some natural language processing tasks such as information extraction, knowledge extraction, question answering, and knowledge base population. The main motivations of this research stem from a lack of a dataset for relation extraction in the Persian language as well as the necessity of extracting knowledge from the growing big data in the Persian language for different applications. In this paper, we present “PERLEX” as the first Persian dataset for relation extraction, which is an expert-translated version of the “SemEval-2010-Task-8” dataset. Moreover, this paper addresses Persian relation extraction utilizing state-of-the-art language-agnostic algorithms. We employ six different models for relation extraction on the proposed bilingual dataset, including a non-neural model (as the baseline), three neural models, and two deep learning models fed by multilingual BERT contextual word representations. The experiments result in the maximum F1-score of 77.66% (provided by BERTEM-MTB method) as the state of the art of relation extraction in the Persian language.
Enhancing Object Detection Using Synthetic Examples

Manual data annotation for training custom object detection can be a time-consuming and error-prone process. In this paper, we propose an automatic approach to generating synthetic, annotated images using differentiable neural rendering and 3D object models. We also investigate the possibility of using 3D adversarial object models to improve object detection accuracy. The experimental results show that the object detection models trained using both synthetic examples rendered from 3D object models and real data outperform the baseline model trained on only real data.
Beat Bad Microbes

Abstract Health-related information design has made a difference to people’s lives through clear explanation of procedures, processes, disease prevention and maintenance. This paper provides an example of user-centered design being applied to engage people with the prevention of drug-resistant infection. In particular, we focus on antibiotic resistance in the specific location of a community pharmacy in Rwanda. We describe an information campaign, Beat Bad Microbes, and summarize the challenges and opportunities of working in Rwanda on a cross-disciplinary project in which design research and practice are closely integrated.
Integral Image Generation Based on Improved BVH Ray Tracing

As the next generation of 3D display technology, integral imaging is widely concerned because of its wide applications, easy storage and high comfort. Nevertheless, its content generation process has always been balanced between rendering quality and operation efficiency. In this paper, a method of integral imaging element image (EI) array generation is proposed. By combining actual acquisition with computer virtual generation, an ideal camera array is established and ray tracing is applied to perform high quality image rendering for large real object models, and the bounding volume hierarchy (BVH) structure is improved for acceleration. The experimental results show that this method improves the rendering efficiency. Using a high-density small-pitch LED display with a high-transmittance lens array display platform to display stereo images, a continuous parallax effect can be seen, and there is no pseudoscopic problem. It provides a reference for the follow-up research work.
“The Mystery of the Raddlesham Mumps”: a Case Study for Combined Storytelling in a Theatre Play and Virtual Reality

“The Mystery of the Raddlesham Mumps” is a poem by Murray Lachlan Young, aimed at both children and adults. This poem has been adapted as a theatre play with a short prequel as a Virtual Reality (VR) / tablet app. We used this unique combination to explore the potential interaction between these different media elements for the level of “presence” and “immersion” in the story (i.e. the level to which one can imagine oneself within the story at the expense of the sense of physical time and space). The theatre audience had the opportunity to play the VR / tablet app in the foyer before the performance started. After the performance, a questionnaire measured participants' level of immersion and presence in the theatre play and their enjoyment of both play and app. The results showed that people of all ages interacted with and liked the app. Ratings for the play were also high and did not depend on prior engagement with the app. However, the play was liked more by adults than children, and the reverse was true for the app, suggesting a potential generation shift in multimedia story telling.
Effects of Individual Difference on User-Sketched Layouts of Vertex-Weighted Graphs

Recent empirical works on graph drawing have analyzed users' interpretation ability with user-sketched layouts. However, user-sketched layouts have not been studied with vertex-weighted graphs. We conducted a study that was to extend the previous work to conduct an empirical study with vertex-weighted graphs. In a four-stage experiment, we analyzed characteristics of the final graph drawings, participants' drawing processes and strategies, participants' drawing preferences through questionnaires, and the differences between two groups of participants divided according to different attributes. In this paper, we report on effects of individual difference in terms of user preference, gender and prior drawing experience on user-sketched layouts of vertex-weighted graphs.
Hybrid Research on Relevance Judgment and Eye Movement for Reverse Image Search

Relevance judgment has been studied in the information field for a long time. Eye movement data contains a large amount of user subjective information, and the way of its collection is becoming easier. With the rising penetration rate of mobile Internet, people are getting used to adopt the mobile search to solve problems. The higher the utilization rate of mobile search, the higher the user's requirements for the accuracy of mobile search results. In order to explore the user relevance judgment in the mobile reverse image search scenario, this paper combines eye movement data to figure out the relation between relevance judgment and users’ eye movement. With the help of the relation the user's relevance experience can be inferred through eye movement data, thereby optimizing the SERP page, so as to achieve the effect of reasonable search results ranking and recommendation accuracy.
Bias Detection and Prediction of Mapping Errors in Camera Calibration

Camera calibration is a prerequisite for many computer vision applications. While a good calibration can turn a camera into a measurement device, it can also deteriorate a system’s performance if not done correctly. In the recent past, there have been great efforts to simplify the calibration process. Yet, inspection and evaluation of calibration results typically still requires expert knowledge. In this work, we introduce two novel methods to capture the fundamental error sources in camera calibration: systematic errors (biases) and remaining uncertainty (variance). Importantly, the proposed methods do not require capturing additional images and are independent of the camera model. We evaluate the methods on simulated and real data and demonstrate how a state-of-the-art system for guided calibration can be improved. In combination, the methods allow novice users to perform camera calibration and verify both the accuracy and precision.
Hyperledger Architecture for Internet of Things and Supply Chain Management Services

The Internet of Things (IoT) has covered a huge area of the world in the last few decades. IoT has connected most of the areas of application like human-to-computer and computer-to-all environmental things. With the growth of IoT applications, several challenges are emerging with respect to time like lack of security of huge personal and professional data in real-time IoT applications under the third-party governance servers also known as centralized servers. Therefore, we would like to introduce a better way to move from centralized to decentralized networks, which is going to provide transparency in the work and decision services. Among the several popular decentralization techniques, Blockchain has risen to the top in recent years. Blockchain is the peer-to-peer transfer of digital liabilities in decentralized nature, and an immutable ledger which can solve numerous issues related to security and privacy in IoT networks and supply chain management services. Therefore, in this chapter, we would like to introduce a hyperledger-based blockchain architecture for IoT and supply chain management services to solve many challenges such as security, e-marketing, business model, and global marketing.
Probability Plot Result Comparison with Recurrent Neural Network Approach for Path Navigation of a Humanoid in Complex Terrain

This research work utilizes the concept of recurrent strategy of neurons, which performs sequential tasks where the output and input data are dependent with each other. The major advantage of using recurrent neural network (RNN) for humanoid motion planning lies in spending the previous used long sequence information through memory. RNNs form direct cycles having internal state and form prime candidate for handling learning procedure. In this paper, long short-term memory (LSTM) RNN is implemented in humanoid robot to test the motion planning analysis. In the neural network model, the obstacle distances from robot’s location are fed as input parameters, and moving angle (MA) is obtained as the output parameter from RNN to guide the humanoid to reach the target with LSTM. Both simulation and experimental navigations are carried out through the developed technique. Probability plot between the simulation and experimental results is performed with normal distribution with comparison analysis. It is found that the results are satisfactory for humanoid navigation. The percentage of deviation between simulation and experimental results in terms of navigation variable is below 6%, which is in acceptable limit range.
Generating Chinese Classical Poetry with Quatrain Generation Model (QGM) Using Encoder-Decoder LSTM

Chinese classical poetry has strict formats and complicated linguistic rules including harmonious rhyme, level and oblique tones (Pingze). Therefore, using NLP to generate classical poetry that meets the aforementioned constraints has always attracted much attention. Although many studies have shown that deep learning has good results in generating poetry, however, the use of unconstrained natural language may cause the generated poems to fail in conforming to the metric rules, and also make the expression of the meaning of the poems incomplete. In view of this, this study uses the quatrains of the Tang and Song Dynasties as the training sample, adopts Encoder Decoder LSTM as the quatrain generation model (QGM), and then uses Latent Dirichlet Allocation to expand the meaning of related words of each consecutive sentence of the poetry. The features and contributions of this study are as follows: (1) We propose a QGM which learns poetry knowledge from four consecutive sentences as corpus respectively in the poetry. (2) The QGM is leveraged to strengthen the meaningfulness, grammaticality, and poeticness of a poetry by extending the keywords to a collection of semantic related words. (3) We elaborate the evaluation of poetry generation with BLEU-2 metric, cooperating with human evaluation. This study shows that our model can effectively improve the coherence of the sentences with the requirement of the metric rules.
Human-Machine Teaming in Music: anchored narrative-graph Visualization and Machine Learning

During the traditional music analysis process, stylistic rules usually have to be deduced directly from examples of compositions or past performance. In such cases, musicians create external representations of a music style domain as source for reflection, inspiration and collaboration. However, due to the large number of music examples, creating such representations can be essential, but at the same time, slow and costly.In this paper, we show that interactive visualization and machine learning could aid in supporting and enhancing musician cognition and team-based collaboration. Specifically, we propose an approach to this problem which: (1) allows musicians to visually externalize their evolving mental models of a music domain, in the form of thematically organized anchored pairs. i.e., (narrative, graph), each one corresponding to a specific music pattern, and (2) uses such pairs to develop a music style classification system based on machine learning, as support for musicians during their activities (composition, performance). To this end, we introduce a novel graph representation of music stylistic patterns and discuss the advantages of linking such a representation to machine learning. Results of a preliminary study involving 10 musicians provided us with overall positive feedback about the effectiveness of our approach as well as further directions to explore.
Show, Price and Negotiate: A Negotiator With Online Value Look-Ahead

Negotiation, as an essential and complicated aspect of online shopping, is still challenging for an intelligent agent. To that end, we propose the Price Negotiator, a modular deep neural network that addresses the unsolved problems in recent studies by (1) considering images of the items as a crucial, though neglected, source of information in a negotiation, (2) heuristically finding the most similar items from an external online source to predict the potential value and an acceptable agreement price, (3) predicting a general price-based action at each turn which is fed into the language generator to output the supporting natural language, and (4) adjusting the prices based on the predicted actions. Empirically, we show that our model, that is trained in both supervised and reinforcement learning setting, significantly improves negotiation on the CraigslistBargain dataset, in terms of the agreement price, price consistency, and dialogue quality.
Visualisation of Jenkins Pipelines

Continuous Integration (CI) is an essential approach in modern software engineering. CI tools help merging the recent commits from the developers, thus the bugs can be realized in an early phase of development and integration hell can be avoided. Jenkins is the most well-known and most widely-used CI tool.&#x0D; Pipelines become first-class citizen in Jenkins 2. Pipelines consist of stages, such as compiling, building Docker image, integration testing, etc. However, comprehensive Jenkins pipelines are hard to see through and understand. In this paper, we argue for a modern visualisation of Jenkins pipelines. We present our solution for making Jenkins pipelines comprehensible on the dashboard.
Key‐dependent side‐channel cube attack on CRAFT

CRAFT is a tweakable block cipher introduced in 2019 that aims to provide strong protection against differential fault analysis. In this paper, we show that CRAFT is vulnerable to side-channel cube attacks. We apply side-channel cube attacks to CRAFT with the Hamming weight leakage assumption. We found that the first half of the secret key can be recovered from the Hamming weight leakage after the first round. Next, using the recovered key bits, we continue our attack to recover the second half of the secret key. We show that the set of equations that are solvable varies depending on the value of the key bits. Our result shows that 99.90% of the key space can be fully recovered within a practical time.
Probing Kinetic Inductance Pulses Below the Hotspot Activation Threshold of a Superconducting Nanowire

Superconducting Nanowire Single Photon Detectors (SNSPDs) have demonstrated timing and efficiency properties that have placed them at the frontier in single photon sensing applications (e.g. photon counting, deep space optical communications, quantum communication, and quantum encryption). Properties such as timing jitter as low as 3 ps, dark counts under 90 counts per day, high detection efficiency (>90%), short dead time and sensitivity over a wide range of photon energies (UV to IR). SNSPDs have a cutoff energy where the photon does not have enough energy to trigger the detection mechanism. In this paper, we lay out the framework and experimental setup to explore a modified approach in detecting photons below the cut off energy typical in an SNSPD. Leveraging the nanowire's change in kinetic inductance from photon absorption, the resulting voltage pulse is measured through a coupled quantum-noise-limited parametric amplifier. We consider nanowires with widths less than the vortex entry limit of w <; 4.4ξ, where ξ is the coherence length, limiting the sources of noise.
Identification of Challenges, Critical Success Factors, and Best Practices of Scrum Implementation: An Indonesia Telecommunication Company Case Study

Abstract The development of information technology makes companies now compete by increasing their digital capabilities as an added value to the products or services they offer. As a digital telco company in Indonesia, the company involved in this research has begun to transform using an agile approach to compete and continue to lead in today’s digital business. In meeting the fast and dynamic needs of the market, companies are using Scrum as their software development method. However, in its implementation, there are still some obstacles and challenges, including changing the stakeholder mindset from traditional to agile. Software development operational activities are still far from the expected expectations. Thus, causing some jobs to be hampered by the delivery process This paper aims at providing state-of-the-art insightful challenges, critical success factors, and best practices of implementing Scrum in a digital telecommunication company. The company previously using traditional approaches in developing its digital capability. The methods used were interviews and literature studies. It found that the problems experienced by the company included project team aspects, psychological and cultural aspects, processes and methods, and the environment. This paper is expected to provide an overview of recommended solutions for companies in increasing the success of implementing Scrum and as a basis for further studies to analyze the challenges of implementing Scrum.