Reviewerly/exHarmony huggingface dataset:
Research on Human Action Recognition Algorithm Based on Video Image

Human Action recognition is one of the research hotspot in the field of computer vision and artificial intelligence. It can usually be applied in many fields such as virtual reality, intelligent pension, medical monitoring, child monitoring, public safety and business management. Due to the complexity and variability of human movements, it is difficult and challenging to use computers to accurately identify actions. Therefore, it is of great significance to study the algorithm of motion recognition for video images. In the study of motion recognition algorithm, RGB data are used as input, and Slowfast Network and I3D algorithm models are used for training respectively. Experimental results show that the training accuracy of SlowFast algorithm is 96.66%. The training accuracy of I3D algorithm is as high as 99.16%, and both algorithms can accurately identify human movements. This study confirms that the human motion recognition technology and algorithm will have a wide range of application scenarios in the future. After solving the technical problems of low recognition rate, the recognition algorithm and technology will be widely promoted and applied in various fields.
ANALISIS KINERJA KARYAWAN DIVISI IT BERDASARKAN KEPUASAN DAN FLEKSIBILITAS KERJA

The workload in technology and information is high, so demands for performance and productivity are inevitable. Especially in providing services, bringing quality is a must. The company, which is engaged in network and application development, is aware of this. In fact, this research was conducted to reveal information about the factors that influence the performance of employees in the field of technology and information. So that it can be stated the efforts to repair and increase it. To prove this, a quantitative approach was used, this was done so that it was easy to get data and could provide a picture that was easier to understand. The sample technique used is simple random, with the number of respondents 67. There is also a way to collect data, do the distribution of questionnaires. The data obtained, then analyzed with regression techniques. From the research results, it is known that an effective work model for improving the performance of IT employees is flexible working hours. This means that the type of IT work tends to spend a lot of time, so giving freedom in terms of working time is an effective way to increase morale and work productivity.
An Analysis of Success Factors for Industrial Robots Adoption in SMEs Manufacturers by Using AHP

The purpose of this study is to analyze the success factors for adoption of industrial robots in small and medium-sized enterprises(SMEs). We identified the perceptional differences of success factors between industrial robot suppliers and buyers. In this study we derived factors for adoption of industrial robots from literature review and developed questionnaire for the Analytic Hierarchy Process(AHP). Forty seven responses were collected and analyzed success factors. As a result of this study, technical factors were the most important factor as a critical success factor for adoption of industrial robots. Specifically, the competence of robot system integrators was analyzed as the most important factor, and robot product quality, top management support, automation level, and work standardization were important in order. The difference in perception of success factors between industrial robot suppliers and buyers was not statistically significant.
Users Experience On Airline Chatbots

A chatbot is an advancing technology that has taken the airline industry to a different level of service. The continuous pursuit drives this development to provide efficient and quality services; however, this technology is not void of challenges that may affect customers' experiences. This study utilizes a sequential mixed method approach that explored the Filipino experiences of using airline chatbots. The results revealed neutral users' experiences. The qualitative components revealed the following themes (1) Difficult experience; (2) Limitations of the chatbots; (3) Lack of human connections and; (4) Positive experiences. As a recommendation, further study may be required utilizing different indicators.
Traffic Cones Detection for Autonomous Ground Vehicle

Jelajah V-18 is an autonomous vehicle prototype where can navigate autonomously using Global Positioning System (GPS) for long-distance navigation and can find out the surrounding conditions to navigate short distances using cameras and proximity sensors. The vehicle also has a system that can search and recognize a static object using an image processing system. This research focuses on the detection of an objects in the form of traffic cones using the (Hue, Saturation, Value) HSV method to recognize objects by the color, then with the binary method the image of the object will be converted back to get the edges of the object shape and contour where the system can recognize the shape of the object. Based on the implementation and experiments conducted, the robot can detect traffic cone objects with a success rate of 100 percent at a distance of 1-6 meters and 35.67 percent at a distance of 7 meters.
Ablation Study On Feature Group Importance For Automated Essay Scoring

Grading of written academic essays by humans requires significant effort. It is a time-consuming task and is vulnerable to human biases. Ever since the introduction of modern computing, this has been one of the many automations being explored. Researches in automated essay scoring have been on-going, where the majority of the researches in recent years are based on extracting multiple linguistic features and using them to build a classification model for automated essay scoring. The 3 main types of features used are lexical, grammatical, and semantic. In our work, we conducted an ablation study to discover the engineered features that has the weakest influence. We did this using a generic feature engineering and classification approach that was used by the winners of the Automated Student Assessment Prize (ASAP). This is to mitigate biases that may have addressed specific feature engineering or models. Our results show that a semantic feature called the prompt has been the weakest feature in influencing the models. From further investigations, this was due to it being over-fitted in the classification model.
Video compression based on zig-zag 3D DCT and run-length encoding for multimedia communication systems

Purpose With the advent of technology, a huge amount of data is being transmitted and received through the internet. Large bandwidth and storage are required for the exchange of data and storage, respectively. Hence, compression of the data which is to be transmitted over the channel is unavoidable. The main purpose of the proposed system is to use the bandwidth effectively. The videos are compressed at the transmitter’s end and reconstructed at the receiver’s end. Compression techniques even help for smaller storage requirements. Design/methodology/approach The paper proposes a novel compression technique for three-dimensional (3D) videos using a zig-zag 3D discrete cosine transform. The method operates a 3D discrete cosine transform on the videos, followed by a zig-zag scanning process. Finally, to convert the data into a single bit stream for transmission, a run-length encoding technique is used. The videos are reconstructed by using the inverse 3D discrete cosine transform, inverse zig-zag scanning (quantization) and inverse run length coding techniques. The proposed method is simple and reduces the complexity of the convolutional techniques. Findings Coding reduction, code word reduction, peak signal to noise ratio (PSNR), mean square error, compression percent and compression ratio values are calculated, and the dominance of the proposed method over the convolutional methods is seen. Originality/value With zig-zag quantization and run length encoding using 3D discrete cosine transform for 3D video compression, gives compression up to 90% with a PSNR of 41.98 dB. The proposed method can be used in multimedia applications where bandwidth, storage and data expenses are the major issues.
Event‐based motion segmentation of small objects in the wild

Event-based cameras are sensitive to brightness changes and can capture rich temporal information with very high temporal resolution, which has great potential for motion segmentation of moving objects. Under static background, events are only triggered by motion of objects, thereby moving objects can be easily segmented. However, in many real-world applications, events are also triggered by the motion of camera or background and submerge the ones corresponding to moving objects. In this letter, an event-based motion segmentation method to segment moving small objects in events obtained from the wild is proposed. First, motion estimation is performed to align the events triggered by the background. Then, candidate events corresponding to moving objects or moving backgrounds are detected. Finally, motion information is adopted to segment the events of moving small objects from the ones triggered by the background. In addition, the first dataset for event-based motion segmentation of small objects is developed. Experimental results demonstrate the effectiveness of this method and show that this method can achieve robust motion segmentation of small moving objects in the wild.
Breast cancer: Three‐class masses classification in mammograms using Apriori dynamic selection

Summary Mammography is a commonly used screening technique for early diagnosis of breast cancer. However, the early detection of abnormalities remains challenging, particularly for dense breast categories. In this context, the automated classification of breast masses assists radiologists in their diagnosis and give them a second opinion. In this paper, we propose a machine learning‐based method for the classification of breast masses. First, the shape and texture features are extracted from the suspicious mammogram patches. These features are then fed to the Principal Component Analysis (PCA) to keep the relevant features only and are classified using the Support Vector Machine (SVM) and Random Forest (RF). Lastly, the Apriori dynamic selection method is applied for the final test predictions using the appropriate classifier for each test sample. The classification of breast masses patches into normal and abnormal attains accuracy of 96.43%, F 1‐score of 95.76%, precision of 96.29%, recall of 95.27%, specificity of 96.43%, and AUC of 0.963. Whereas the one‐stage multi‐classification of breast masses into normal, benign, and malignant achieves accuracy of 75.81%, F 1‐score of 76.47%, precision of 76.85%, recall of 78.77%, specificity of 87.91%, and AUC of 0.829.
The Interplay Amongst Internal Auditors’ Psychological Capital, Creative Performance, and Internal Control Efficiency

This study examined internal auditors’ psychological capital relationship with their creative performance. It also investigated the total effect of such a relationship on internal control efficiency related to targets’ achievement. The partial least squares structural equation modelling was employed to validate and test the study model, based on data collected from 118 internal auditors of companies listed on the Amman Stock Exchange. The main results indicate that internal auditors’ psychological capital positively influences their creative performance, indirectly enhancing the degree of internal control efficiency in achieving targets related to financial reporting, operations, and compliance. This study offers empirical evidence on the psychological capital value as an enabler to engage in creative performance and support the perpetual endeavours of those charged with governance to boost internal control efficiency. Also, the findings highlight the importance of providing internal auditors with the required resources and facilities to enhance their psychological capital and performance.
Sorting Minerals Differently Could Usher a New Era for Mineralogy

Grouping minerals by how they were formed yields insights into our planet’s evolution across billions of years.
Simplified Structured Analysis Dictionary Learning for Image Classification

In this paper, we focus on image classification based on dictionary learning. The structured analysis dictionary learning (SADL) algorithm introduces a mapping matrix to the representation coefficient matrix and imposes a structural constraint to enhance the capability of discrimination, and it achieves promising classification results. However, in the formulation of SADL, the discrimination of the model is mainly based on the transformed coefficient matrix rather than the original coefficients, and thus the sparsity of the original coefficients has little impact on classification. In addition, classification is based on a linear classifier learned simultaneously with the dictionary, which may also restrict the performance of the algorithm. To address these issues, we propose a simplified SADL (SSADL) algorithm by simplifying the original formulation of SADL and introducing a new classification approach based on support vector machine (SVM). Simulation results on widely used databases demonstrate that the proposed SSADL algorithm achieves better performance than several state-of-the-art classification algorithms based on dictionary learning.
Steganography Hiding Patterns: A Brief Review

“Hiding patterns” are an abstraction for describing various steganographic and information hiding approaches. Originally introduced in 2014, they have been subject to modifications and improvements through the years. In this paper, we review the development of the hiding patterns approach and propose a new definition for part of the terminology.
Databases of visual sources in the Internet: problems of heuristics (based on the example of studying the history of the Southern coast of Crimea in the early XX century)

The article analyzes the experience gained by the author in the course of working with three online databases of visual historical sources in the context of studying the transformation of the Southern Coast of Crimea in the late XIX - early XX centuries. They are: the electronic State Catalog of the Museum Fund of the Russian Federation, as well as two crowdsourcing platforms - Pastview and Retromap.ru . The author examines the origin, format, structure of these aggregators of visual historical sources, the information potential of the content, advantages and disadvantages of usage, examples of how visual information found using these databases contributed to solving specific historical and source problems. Different aspects of working with online databases of visual sources are considered in the context of historiographical discussions about the "digital turn" in humanitarian knowledge, the "visual turn" in the structure of the source base of historical research, disputes about the multimodality of the content of historical knowledge. It is suggested that soon online databases of visual sources will become as familiar an information search tool for a historian as electronic catalogues of libraries and archives. The emphasis is placed on the methodology of a systematic approach, methods of complex source studies that allow reconstructing historical reality by analyzing and comparing information extracted from sources of different types (written, pictorial and material) and types.
Architecting the Autocuckoo Filter to Defend Against Cross-Core Cache Attacks

Cross-core cache timing side-channel attacks, which observe cache access behavior of victims running on different physical cores to infer sensitive information, have become a significant threat. Although the attacks are covert, they cause the attacked cachelines to frequently migrate among cache hierarchies, rendering abnormal traffic. Based on this observation, the proposed scheme PiPoMonitor records cache-memory access traffic and prefetch suspicious lines under attack to interfere with adversaries’ probes. In pursuit of security and performance, PiPoMonitor exploits a Cuckoo filter as the recording structure and introduces two features to it: 1) autonomic deletion and 2) relocation accelerating. The former exponentially increases the uncertainty of record eviction against reverse engineering attacks, while the latter leverages a pipelined architecture to alleviate the impact of intensive filter queries on the memory critical path. PiPoMonitor is not only able to effectively mitigate cross-core cache attacks and defeat sophisticated defense-aware attackers but also induces a negligible performance penalty and acceptable hardware overhead.
Reliable, Robust, Accurate and Real-Time 2D LiDAR Human Tracking in Cluttered Environment: A Social Dynamic Filtering Approach

Reliable, robust, accurate, and real-time human tracking is essential for mobile robots and intelligent vehicles in real-life applications. 2D LiDAR is considered as the standard sensor for mobile robot navigation as well as human detection and tracking due to its low-cost and usability. However, 2D range limitation and occlusion caused by obstacles, especially dynamic human environments, make it less reliable, robust and accurate for human tracking. This letter introduces a new method for increasing the quality of 2D LiDAR human tracking in cluttered and crowded environments. We combined human content presented by Hall's Proxemics model with the global nearest neighbor to improve accuracy of scan-to-track data association of leg detection. Social dynamic confidence ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SDC</i> ) factor is generated based on features of human social norms, dynamic metrics and consistency developed in the detection stage. As a result, our proposed method improved multi-object tracking accuracy and runtime 24% and 45%, respectively, against the state-of-the-art <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">joint-leg-tracker</i> technique in crowded and cluttered environments.
Uncovering Cognitive Costs of Using Artificial Intelligence Tools at Work: A Daily Diary Study

Despite the growing implementation of artificial intelligence (AI) artifacts in the workplace, little is known about how utilizing AI in completing jobs may impact employees’ productivity and wellbeing. Extending prior research that assumed the potential positive impact of AI usage, we drew on the cognitive load theory and conducted a daily diary study to uncover the cognitive demands employees may encounter when utilizing AI-based information tools at work. We collected data over five workdays from 231 call center representatives working for a major bank located in South Korea. These representatives utilize an AI-based supporting system to address customers’ requests over phone calls on a daily basis. We found that on days of frequent AI usage, representatives were more likely to experience information overload, and thereby receiving poorer performance ratings from supervisors and having difficulty detaching from work in the evening. In addition, we found that the detrimental effect of using AI at work is exacerbated by job tenure and is mitigated by openness to experience. Our research has implications for understanding the AI-human collaboration dynamics as well as AI’s impacts on employees.
THE INFLUENCE OF NATIONAL CULTURE DIMENSIONS ON AGILE IMPLEMENTATIONS IN THE SOUTH AFRICAN SOFTWARE DEVELOPMENT CONTEXT

Culture influences how agile frameworks are implemented, and agility is said to be suitable in contexts where flexibility and spontaneity are emphasized.While past studies have investigated the influence of national culture on Agile implementations in Western and Eastern contexts, studies focusing on a South African software development context is limited.Furthermore, few studies have focused on the effect of cultural differences within software engineering in general.The purpose of this study is to describe how national culture influences Agile practices within the South African software development context.The study was interpretive and was executed using a qualitative, semi-structured interview research strategy directed at Agile practitioners in South African software development teams.The thematic analysis technique was used to analyze the data.Seven propositions have been formulated to highlight how national culture dimensions influence Agile practices.The findings reveal that various national culture dimensions influence the decision-making process, the degree of sprint interruptions, participation in Agile ceremonies, adherence to policies and prescribed Agile practices, how teams reach agreement, approaches to process improvement and sprint planning.
Rate Control for Entropy-Coded LPC: Application to Packet-Based Fronthauling

Linear prediction and entropy coding are widely used in speech, audio, video, and other coding systems. Rate-distortion optimization (RDO) is another relevant coding technique, which is especially useful when the encoder performance depends on tunable parameters from distinct coding subsystems. This work presents a RDO strategy for variable-rate encoders that use linear prediction and entropy coding. The method is suitable when the signal to be compressed is short-term stationary and the prediction order varies over signal segments. The second contribution of this paper is to customize the method to the compression of time-domain OFDM-based cellular radio signals. The method enables a 4G / 5G packet-based fronthaul to flexibly trade-off rate and distortion depending on the amount of information to be transmitted and, consequently, benefit from multiplexing gain. Simulation results with LTE signals show that the proposed method achieves improved performance with a relatively low computational cost, while operating with time-domain signals under different traffic scenarios.
Three Notes on Derivation and Demonstration

These are three unpublished notes from Popper’s Nachlass.
Does Exchange Rate Matters for Foreign Direct Investment Inflow to Ethiopia?

This study examines the effect of exchange rate on foreign direct investment inflows in Ethiopia.The aim of the study is to investigate how foreign investors through FDI respond to change in exchange rate level in Ethiopia.In line with the explanatory variable exchange rate; economic growth, inflation, trade openness, and external debt are added as a control variable in the study.The study uses explanatory research design and quantitative research approach with secondary time series data utilized over the study period 1992-2017(26 years).More specifically, the study adopts an autoregressive distributed lag (ARDL) model.Furthermore, the long run relationships of variables are quizzes through bound tests and confirm the existence of a long-run relationship among variables.So, in order to investigate the short run relationship among variables, the error correction model is employed in the study.The finding of the study reveals that; exchange rate level and foreign direct investment have a positive relationship in the short run as well as in the long run and statically significant at 1 percent significance level.So, devaluation of Ethiopian Birr against US dollar affects foreign direct investment positively in both cases.But, the last year effect (one period lag) of devaluation on current year foreign direct investment was found negative.On the other hand, variables like economic growth and inflation have a negative relationship with foreign direct investment in the long run as well as in the short run.But, except economic growth, inflation found insignificant in the long run.External debt found positive and insignificant in the long run.However, the relationship between trade openness and foreign direct investment were found positive and statically significant.This study suggests that the government shall ensure the stability of the exchange rate once devaluation is made.