Reviewerly/exHarmony huggingface dataset:
ChatGPT, Microsoft and Competition Law – Nemesis or Fresh Chance for Digital Markets Enforcement?

ChatGPT is one of the current mega topics, on users’ computer screens, in the public debate, but also already in the courtrooms. At the same time, Microsoft is pushing ChatGPT to a new level by integrating it into its Copilot software. The resulting “Chatpilot” may become a very important driver of innovation and efficiency, but it should also, early on, be a monitoring object for competition law enforcers and digital regulators. In case competition risks materialize, Chatpilot could also become a laboratory for the combined application of the EU’s new Digital Markets Act (DMA) and general competition law, for their respective adaptability to novel technologies, and for the design of appropriate remedies. Especially regarding the latter aspect, EU competition law has already gathered some thorny experiences, not least in cases concerning Microsoft. History doesn’t repeat itself, but it rhymes, and Chatpilot may show whether competition law has learned something from these experiences. This contribution explores these topics by sketching the workings of ChatGPT and Chatpilot, by discussing potential theories of harm to competition, by assessing whether the DMA may be applicable to Chatpilot and – if so – whether it contains helpful rules of conduct, by pointing out general competition law doctrines potentially helpful at least in a gap-filling function, and by addressing, as an important remedies aspect, the preferability of unbundled alternatives or selection mechanisms. A concluding section will look at the comparative relevance of general competition law and the DMA in a potential Chatpilot case, including their interim measure provisions.
Data Homogenization Method for Heterogeneous Sensors Applied to Reinforcement Learning

In autonomous navigation and route planning, the data obtained by the different sensors play a significant role. On the one hand, more data will lead to faster learning of the behavioral policy. On the other hand, agents equipped with different sensors will need more computing power to process the data, thus requiring more robust equipment and increasing the cost of implementation. In addition, the complexity of the algorithms increases as different types of data, i.e., data with different structures, have to be synchronized. Therefore, this paper addresses the problem of homogenization and synchronization of data provided by heterogeneous sensors. Furthermore, it presents a novel method of estimating data in order to provide the agent with a 360-degree view of the environment, similar to that provided by a laser. The method’s performance compares the different behavioral policies obtained by different viewing angles of a camera with the policy obtained by a laser. The data obtained from the different viewing angles of each sensor are used in a path planning algorithm, which was designed to use a single 24-scan laser as an input source. The results show that the proposed method is robust since the behavior policies can be reused regardless of the viewing angle with which the sensor (camera) is provided. Furthermore, the proposed novel algorithm achieves an average efficiency of 68% and 94% using a 90 and 360-degree camera, respectively.
Semantic Loop Closure Detection for Intelligent Vehicles Using Panoramas

Loop Closure Detection is of great significance in the field of intelligent driving systems, as it reduces the cumulative error of the estimated position of the system and assists in generating a consistent global map. Existing methods differ in frame representation methods and the corresponding frame-matching strategy. Traditionally, local feature points and descriptors are studied extensively while recently global descriptors and semantic information extracted from deep learning methods are considered superior in terms of promoting a high-level understanding of the surrounding environments of robots. However, one of the most challenging problems of using semantic information for loop detection is how to deal with inconsistent visual contents from different viewpoints in the same place. In this article, a semantic loop closure detection method using panoramas is proposed to address this issue. We design a pipeline for efficiently extracting and matching semantic information between frames to identify loops. Most importantly we propose a novel polar coordinate-based panorama representation to address the inconsistent visual appearance problem caused by viewpoint differences. Experiment results show that our proposed method can significantly increase the accuracy of loop closure detection tasks in challenging scenarios where traditional methods may fail.
Evaluating User Interactions in Wearable Extended Reality: Modeling, Online Remote Survey, and In-Lab Experimental Methods

The benefits of interaction techniques in extended reality (XR) have been investigated over the years. However, less attention has been given to comparing different methods to evaluate XR interactions. This study aims to (1) implement multimodal gaze-based and gesture-based interactions in a wearable XR environment and (2) evaluate the user interactions comprehensively using three methods – GOMS (Goals, Operators, Methods, and Selection rules)-based task analysis (modeling), a video-based online remote survey, and an in-lab user experiment. We used a pair of smart glasses (i.e., HoloLens 2) to implement three interaction modalities: two multimodal gaze-based interactions and a unimodal hand-based interaction. The first evaluation was performed by analyzing the task for the three interaction modes to understand human behavior in XR and predict task completion times using a GOMS model. For the second evaluation, an online survey was administered using a series of first-person point-of-view videos where a user performs a task using the three interactions. A total of 118 adults participated in this remote evaluation and compared the perceived workload of the interactions. The third evaluation was an in-lab experiment with 24 human subjects aiming to provide in-lab evidence for the former evaluations and compare their results. The results of the GOMS analysis showed that the task completion time was shorter in gaze-based modes than in the baseline. Moreover, the gaze-based interactions outperformed the baseline regarding physical demand and effort in the remote approach, but the baseline mode was preferred for mental demand and frustration. In addition, comparing the results of the GOMS and the remote experiment methods with an in-lab experiment for each interaction mode showed that the values are more consistent for the baseline mode since most of the users are generally familiar with hand interaction and prior knowledge helps them perceive this interaction more like the reality. This study paves the way for a deeper analysis of different evaluation methods in scrutinizing the gaze-based and gesture-based interactions in wearable XR environments.
Blockchain and Performance: A Literature Review in Business and Management Fields

During recent years, the blockchain technology has been widely studied and analysed in different domains. This research explores the application and discusses the importance of this innovative technology in four different sectors: Financial, industrial, healthcare and supply chain. Based on real world applications extracted from different databases like webpages, interviews and annual reports, the result of this chapter indicates that the employment of blockchain technology in business activities can enhance the managerial, operational, financial and commercial performance. In addition, the result shows that the employment of this new technology in financial sector can decrease the risk of money laundering.
Fuzzy-Based Ensemble Feature Selection for Automated Estimation of Speaker Height and Age Using Vocal Characteristics

Estimating speaker attributes from vocal characteristics is an important research area with applications in forensic science, biometric identification, and human-computer interaction. The accurate estimation of these attributes requires the effective extraction of relevant audio features from the audio signal. This work proposes a new approach for automatic speaker height and age estimation using fuzzy-based ensemble feature selection with speech parameters. This approach derives the initial feature importance from different feature selection (FS) methods. The obtained feature importance values are then sorted into a matrix, which is converted to ranks and passed to the fuzzy c-means (FCM) algorithm to produce the final feature ranking and identify the most distinctive features for estimation. The proposed approach can combine the results of multiple feature importance methods into an ensemble approach or choose the best features based on the feature importance from a single method without requiring a predefined number of top features. Several experiments were performed to evaluate the proposed approach on acoustic features obtained using the OpenSMILE toolkit from the TIMIT dataset. The results show that the proposed approach can effectively select the most informative features, and it outperforms similar studies on the same dataset, with promising results of 5.4, 4.71 mean absolute error (MAE) in height estimation and 5.38, 5.24 MAE for age estimation for males and females, respectively.
Who Wants Cryptocurrency in the Future and Why?

This study investigates the factors that make people want to hold cryptocurrency. We find that survey respondents from the U.S. will more likely hold cryptocurrency in the future the more they expect cryptocurrency to replace government money, to increase transparency in monetary affairs, and to yield high profits. Further, demand is driven by the anticipation that non-monetary uses of the blockchain technology will have a spillover effect on the Bitcoin price. By contrast, subjective expectations of a crypto-induced financial crisis dampen demand. Econometric analyses show that differences in the future demand among people with and without prior holdings of cryptocurrency largely stem from differences in their expectations.
The Necessity and Limitations of the Criminalization of Incitement to Terrorism

This chapter considers, on the one hand, the current need for and, on the other, essential limitations on criminal measures against incitement to terrorism in accordance with the principles of necessity and proportionality. Concerning the necessity to criminalize incitement to terrorism, this chapter, based on not only traditional criminal law theories but also empirical information, focuses on the risks of terrorism and of direct or indirect incitement in the current Internet age. Regarding limitations on the criminalization of incitement to terrorism, the protection of freedom of expression under international human rights law, which constitutes the basic reason for limiting the criminalization of incitement to terrorism, is firstly introduced. Subsequent discussions are centered on how to draw the balancing line between the criminalization of incitement to terrorism and the protection of freedom of expression. On this crucial issue, primary references worldwide include the European Court of Human Rights (ECtHR)’s and the US Supreme Court’s relevant case laws, which are studied from a comparative perspective.
Future Food: Unveiling Key Trends and Technological Innovations through a Conversation-based Survey with ChatGPT and DALL-E

This article presents a conversation-based survey conducted between ChatGPT, DALL-E, and a human expert, exploring the future of the food industry. It examines key topics and technologies shaping the industry and provides insights into future focus areas. Sustainability and climate change mitigation drive discussions on food waste reduction and regenerative agriculture, while blockchain technology and real-time monitoring systems ensure food safety and quality. The emergence of personalized nutrition and functional foods transforms health and wellness approaches, and clean label initiatives revolutionize food processing and packaging. Digitalization and automation optimize operations, enabling e-commerce and digital supply chain management. Additionally, alternative food sources like plant-based and cultured meat gain momentum for their environmental benefits.The conversation extends to the integration of artificial intelligence (AI), exemplified by systems such as ChatGPT and DALL-E, which enhance decision-making processes, quality control, product development, and personalized recommendations. AI technologies provide predictive analytics, resource management optimization, and improved traceability, bolstering consumer confidence and operational efficiency. Moreover, DALL-E, with its image generation capabilities, showcases visually captivating representations of future foods, fueling creativity and envisioning innovative possibilities.Looking ahead, advancements in AI algorithms, machine learning, and computer vision will continue to shape the industry, driving innovation and automation. However, a balanced approach that leverages human expertise alongside AI capabilities remains crucial for ethical considerations and complex decision-making. By embracing sustainable practices, cutting-edge technologies, and AI-driven solutions, the food industry can create a resilient, efficient, and consumer-centric food system. This article underscores the potential of AI in driving transformative changes, while highlighting the unique perspectives gained from the conversation between ChatGPT, DALL-E, and the human expert.
Conjunctive Query Based Constraint Solving for Feature Model Configuration

Feature model configuration can be supported on the basis of various types of reasoning approaches. Examples thereof are SAT solving, constraint solving, and answer set programming (ASP). Using these approaches requires technical expertise of how to define and solve the underlying configuration problem. In this paper, we show how to apply conjunctive queries typically supported by today's relational database systems to solve constraint satisfaction problems (CSP) and -- more specifically -- feature model configuration tasks. This approach allows the application of a wide-spread database technology to solve configuration tasks and also allows for new algorithmic approaches when it comes to the identification and resolution of inconsistencies.
Approaching Computer Vision And Image Processing Technique For Optical Mark Recognition Sheet Scan And Verification

Optical mark recognition (OMR) is an accumulation technique that substantially repossessed for examinations comprising multiple choice questions (MCQ) in educational institutes. For estimating the OMR sheets, current techniques needs steadfast scanner, high quality paper for OMR sheet, OMR software and high fidelity layout of OMR sheet. These techniques reduce cost but in a very accurate way. In most of the countries, this techniques are repossessed to manage many competitive entrance examinations. On account of high expense, small institutes, individual teachers and tutors cannot use this technique. By dint of the truancy of any precision, fast, powerful and low-cost OMR software, they utilize to manually grading the answer sheets. In this paper, we are used the low-quality images captured using mobile phone camera for OMR detection and gives less computation time by this steadfast technique. The purpose which is exploited that the computer vision basis associates the edition of extremity contradiction of data, that designs the template signifying precipitous strut without compromising the look of OMR answer sheet in the OMR sheet.
Automatic Bangla Signboard and Region of Text Interests Detection from Natural Scene

The majority of city establishments in developing cities are unlabeled due to the time-consuming process of manual annotation. As a result, location and trajectory services in such places remain underutilized. Accurate signboard localization in natural scene photos is critical for retrieving information such as cities, streets, etc. This information can be used for several purposes, including autonomous robot navigation, translators, assisting foreigners and visually impaired people, company listings, etc. In this research, we propose a two-step detection model that detects the Bangla Signboard from the natural scene images followed by the Region of Text Interest (RTI) detection from the signboard. The RTI is the shop/organization name and information consisting of address and contact number. For both steps, we used YOLOv4 with tuned hyperparameters. The first model achieved a very high accuracy of 98.6% and the second model achieved 97.4% accuracy. In addition, to our knowledge, we have created the largest Bangla signboard image dataset, consisting of precisely 17,308 images from all over Bangladesh.
An Image Restoration Technique Using Patch-Based Reference Frame for Through-the-Air Imaging

Removing geometric distortions from a sequence of atmospheric turbulence-degraded video is a challenging task. An improved method for geometrical corrections of a video sequence of a far-flung stationary scene is introduced in this paper. A patch-based technique is used to obtain a reference frame from a video sequence, in which the reference frame is built using patches of the best quality from different frames of the video sequence. A window-based image registration is used to estimate the geometrical shifts of the pixels, which is utilized later to restore a high-quality frame. The performance of the proposed method is compared with two earlier similar image restoration methods. Experimental findings show that the proposed patch-based image restoration method provides higher accuracy than other methods.
Automatic Text Summarization Using Term Frequency, Luhn's Heuristic, and Cosine Similarity Approaches

People these days are quite busy with their work and other activities. They do not have enough time to fully read newspapers. But they need to keep themselves informed about current events. As a result, text summarization can help us learn about events taking place throughout the world quickly and conserve our precious time. Hence, automatic text summarization is a mechanism for condensing textual information into a brief and understandable piece of knowledge that gives readers an idea of gist from a large passage within a very short time because literature and texts written in regional languages are frequently much long and usually there are no summaries that correlate to them that explain the main point. Although there are a number of research works by many authors, but in our paper, we have attempted to find the summary of paragraphs by three extractive text summarization techniques, namely, term frequency based algorithm, Luhn's algorithm and cosine similarity method. We have implemented our methods using python, and show the sample output for sample input. Moreover, we see that the cosine similarity approach shows lower execution time, and as a human reader, we notice that cosine similarity is the good one to summarize a paragraph.
Enhanced Optimum Design and Performance Evaluation for Grid-Connected Solar PV Rooftop Systems: A Case Study for Bangladesh

Load-shedding has been rising because of a vast gas shortage that has led to Bangladesh's significant fall in power generation. Renewable energy sources could help Bangladesh's electricity production overcome these problems. The roof-mounted solar PV system is a desirable alternative energy source. Location, design, appropriate installation, and solar module type all affect the performance and efficiency of a PV system. The PV simulation tool computes how much electricity will be produced by the PV array setup. The energy efficiency of rooftop PV systems is significantly impacted by the design and orientation of roofs. By increasing the energy production of rooftop PV systems through improved roof design, this study seeks to close a knowledge gap. In this study, we used SketchUp and PVsyst to design and simulate a grid-tied rooftop solar photovoltaic system for Bangladeshi educational-type consumer loads. This project also attempts to lower grid outages close to the building, energy costs (COE), grid dependence, and CO2 emissions. The suggested system has been accurately modeled, considering factors such as choosing the most suitable PV panel rating, inverter, tilt angle, sun azimuth, shading calculation, loss calculation, performance, and technical assessment. The electric grid receives extra energy. The suggested system will generate electricity efficiently, minimizing reliance on the grid, as shown by the real-time simulation.
Cost Effective Alternative of Pyranometer: Solar Radiation Prediction Using Artificial Intelligence

It is solar radiation which is the Earth's one of the major sources of energy. It has a monumental role in the Earth surface radiation symmetry, hydrological cycles, flora photosynthesis, and weather and climate events. So, the proper prediction of this solar radiation is therefore considered to be very important in all the fields like solar power industry and climatological research. Without measuring solar irradiance in a particular area and at the specific period of time of the year, we can't assume the proper amount of energy to be achieved from the sun using solar panel. Solar radiation is generally obtained with pyranometer sensor devices. But the fact is that the initializing and maintaining cost of these devices are very high. So, these are not widely used in the field. Furthermore, it can provide only confined coverage as a source of data for solar radiation. So various types of data acquired from meteorological stations of sensor are used to predict solar radiation with no use of pyranometer. Again, pyranometer can't give accurate result in winter due to the domes are covered with fogs. So, manual monitoring is required while using these sensor devices by an expert monitor. Yet, remote accessing of pyranometer data is though possible but highly expensive. In this paper, a low-cost system has been developed using artificial Intelligence (AI) and Internet of Things (IoT), which predicted solar irradiance efficiently. Linear Regression as Machine Learning model has been developed for predicting solar radiation taking temperature and relative humidity as input variables. The accuracy score of LR model has been recorded 0.7429 for daily solar radiation data. Predicted solar radiation has been achieved in a text file in the same folder of python file. The ultimate moto of this study is to develop a cost-effective solar irradiance measuring system which will not need manual monitoring. Remote data receiving problem has also been solved.
Neural TV program recommendation based on dynamic long-short term interest

TV program recommendation can help user find interested programs and improve user experience. The heterogeneous information of programs is important for alleviating the problem of data sparsity. In addition, the existing TV program recommendation methods are lacking in dynamics. This paper proposes a neural TV program recommendation based on dynamic long-short term interest (NPR-DLSTI), which mainly includes two modules: program and user encoder. In the program encoder module, we use convolutional neural network and attention mechanism to learn the heterogeneous information of the program and realize program representation. In the user encoder module, we use gated recurrent unit and personalized attention to learn the dynamic change law of users’ interests. Experiments on real data sets show that our method can effectively improve the effectiveness and dynamics of TV program recommendation than other existing models.