Reviewerly/exHarmony huggingface dataset:
Reward tampering and evolutionary computation: a study of concrete AI-safety problems using evolutionary algorithms

Abstract Reward tampering is a problem that will impact the trustworthiness of the powerful AI systems of the future. Reward Tampering describes the problem where AI agents bypass their intended objective, enabling unintended and potentially harmful behaviours. This paper investigates whether the creative potential of evolutionary algorithms could help ensure trustworthy solutions when facing this problem. The reason why evolutionary algorithms may help combat reward tampering is that they are able to find a diverse collection of different solutions to a problem within a single run, aiding the search for desirable solutions. Four different evolutionary algorithms were deployed in tasks illustrating the problem of reward tampering. The algorithms were designed with varying degrees of human expertise, measuring how human guidance influences the ability to discover trustworthy solutions. The results indicate that the algorithms’ ability to find and preserve trustworthy solutions is very dependent on preserving diversity during the search. Algorithms searching for behavioural diversity showed to be the most effective against reward tampering. Human expertise also showed to improve the certainty and quality of safe solutions, but even with only a minimal degree of human expertise, domain-independent diversity management was found to discover safe solutions.
He Who Can Read and Write Has Four Eyes: Factors That Contribute to Gaps in the Entire Reading Ecosystem of South Africa’s Disadvantaged Rural Communities

Equitable and seamless access to information in diverse formats is indispensable for people of all ages to meet their dynamic and ever-evolving information needs for socioeconomic development. Reading is a skill that people need to acquire from an early age to obtain information essential to change their lives for the better. However, in South Africa, the entire reading ecosystem is characterised by huge gaps adversely affecting people of all age groups, particularly in disadvantaged rural communities, and militating against them acquiring reading habits, skills, a reading culture and essential information to meet their multiple daily needs. With low reading literacy in the country, the purpose of this study was to investigate factors that contribute to gaps in the entire reading ecosystem in South Africa’s disadvantaged rural communities. The study adopted a literature review to unearth factors contributing to gaps that deter people from reading and acquiring reading habits, skills and a reading culture. The study finds that there are numerous factors that contribute to gaps in the whole reading ecosystem in the country, thus hindering citizens from accessing information to quench their information needs. The study recommends that the government has a responsibility to bring information to the citizens by establishing adequate and responsive community libraries to encourage citizens to read for socioeconomic development. Robust digital infrastructure particularly in disadvantaged rural communities is essential for rural villagers to access electronic information globally to support and encourage the acquisition of reading skills and habits in all communities.
Social vulnerability and geographic access barriers to earthquake early warning education in museums and other free choice learning environments

Given the earthquake risk on the West Coast of the United States, individuals and communities require a basic understanding of ShakeAlert earthquake early warning technology, which may provide crucial seconds of warning. Free choice learning environments (FCLEs), such as museums, public libraries, and national parks, are uniquely positioned to expand the reach of earthquake early warning through educational initiatives and resources. Earthquake education in these spaces creates awareness of earthquake hazards and risk in areas where people live or visit and may also increase engagement in preparedness behavior as they are trusted sources of information in their communities. However, population demographics within the ShakeAlert states has yet to be examined; audience segmentation theories require a better understanding of the demographics of the people the system seeks to serve. Here we build upon previous typology research that examined over 150 earthquake displays around the United States and found that most did not include information about earthquake preparedness or associated protective actions. This new research shifts to a hierarchical clustering analysis that identifies seven main population groups within the ShakeAlert states. We also find that the cost of admission and the geographic distance away from FCLEs, including the potential cost and time of transportation, may lead to an urban-rural divide in visitor access. Using audience segmentation theories as related to earthquakes, we can understand critical barriers to FCLEs and opportunities for ShakeAlert education. As earthquake early warning systems expand internationally, thoughtful and equitable education initiatives are beneficial to reach critical audiences.
Deep-Learning Image Steganalysis Based on Generalized Gaussian Distribution Features Clustering

The difference in the statistical distribution between the training images and the test images reduces the performance of the steganalysis model, and this sample mismatch phenomenon makes it difficult to improve the accuracy of the detection model. To solve this problem, this paper proposed a deep-learning image steganalysis method based on generalized Gaussian distribution feature clustering. Firstly, a generalized Gaussian distribution is used to fit the coefficients of the image and extract statistical distribution features; Then, the extracted features are clustered to obtain corresponding category labels to achieve sample pre-classification; Finally, different subclasses of samples are used to train the network to achieve more reliable steganalysis. Experimental results show that this scheme can effectively divide images with similar statistical distributions into the same subcategory, reduce the impact of inconsistent statistical characteristics between the training image and the detection image on the steganalysis network, and improve the accuracy of steganalysis.
Multi-view Feature Complementary for Multi-query Vehicle Re-identification

Existing vehicle re-identification methods rely mainly on single query, which retrieves images of a given vehicle from other cameras based on the features extracted from a single image. However, the limited information contained in a single vehicle image significantly restricts the performance of vehicle re-identification in complex surveillance networks. In this paper, we propose a more practical and accessible task called multi-query vehicle re-identification, which utilizes multiple vehicle images from different viewpoint to overcome the viewpoint limitation of single query. Based on this task, we propose a novel multi-query feature complementary network (MFCNet), which fuses the information of vehicle images from multiple viewpoint during training to obtain the global features of vehicles. We then use these global features to optimize the local features extracted from a single viewpoint, enhancing the heterogeneity of vehicle features from different viewpoint. During testing, the complementary feature information from different vehicle perspectives is adaptively combined to reduce feature redundancy.
Approximation and Fixed Parameter Algorithms for the Approximate Cover Problem

Amir et al. (CPM 2017) introduce the approximate string cover problem (ACP) motivated by applications including molecular biology, coding, automata theory, formal language theory and combinatorics. A cover of a string T is a string C for which every letter of T lies within some occurrence of C. The input of the ACP consists of a string T and the goal is to find a string C of length less than the length of T that covers a string $$T'$$ , which is as close to T as possible (under some predefined distance). Amir et al. study this problem for the Hamming distance and show that it is NP-hard. In this paper we continue the work of Amir et al. and show the following results for the cover length relaxation of the ACP. After observing that the NP-hardness proof by Amir et al. (CPM 2017, TCS 2019) suffers from several lapses, we propose an amendment to the proof. We then introduce an approximation algorithm for a variant of the ACP, in which we aim to maximize the length of the input string minus the distance to the string covered by the approximate cover returned by the algorithm. This problem is naturally as hard as the ACP. We prove an asymptotic approximation ratio of $$\mathcal {O}(\sqrt{|T|})$$ , where |T| is the size of the input string. Finally, we present an FPT algorithm with respect to the alphabet size and the size of the cover based on a dynamic programming framework.
GEO-LOCATING AN IMAGE USING EXIF DATA

GPS (Global Positioning System) information is a type of EXIF (Exchangeable Image file Format) metadata that's implanted in a computerized photo record. The project aims to create a system that takes useruploaded pictures, extracts the metadata, shows the coordinates of the location information on a custom Google Maps embedded API, and displays the make and model of the gadget utilized to capture the picture. The system employs Python's exif and Pillow modules to extract the metadata from the uploaded image file. The metadata incorporates GPS information that contains the latitude and longitude coordinates of the area where the picture was taken. The extracted coordinates are then passed to the Google Maps API to show the area of the picture on map. This data is shown on the webpage, giving extra subtle elements almost the picture.
STEAM-EDUCATION AS A MEANS TO ADAPT RUSSIAN ECONOMY TO EXTERNAL SHOCKS

The field of STEAM (Science, Technology, Engineering, Arts and Mathematics) is one of the most promising in education, and these professionals are employed in high-tech and creative industries that determine the future of global economy. The paper presents a detailed analysis of this sphere based on original approaches, both in the Russian Federation at large and in its regions. In Russia more than half a million people are annually admitted to STEAM training courses for higher education programs. Contrary to the all-Russian trend of reducing the number of students, enrollment for these programs has been steadily growing. According to the latest data, about 36% of university graduates in Russia have a specialty within STEAM. For comparison, this share in South Korea, Singapore, and Germany is above 45%. The largest share of specialists is trained in Tomsk region, St. Petersburg, and Moscow. At the same time, the number of STEAM graduates decreased in 41 Russian regions. In 2022, therisks for STEAM industry in Russia increased markedly: there is a migration outflow of most qualified personnel, with declining domestic demand for them. Over six months from February to August 2022, the total number of vacancies in the Russian labor market decreased by 9%, while STEM - by 23% (IT - by 32.5%). This may affect the long-term scientific and technological development of the country: our calculations show the importance of STEAM for creating start-ups, increasing publication and innovative activity. The article concludeswith some measures for developing STEAM as a tool for long-term development and a way to adapt the country to external shocks.
Semantic segmentation of urban areas using relabeled heterogeneous unmanned aerial datasets and combined deep learning network

Unmanned aerial vehicles (UAVs) can overcome several limitations of satellite and aerial platforms using their multiple visit ability. However, UAVs usually collect images of small and simple regions from a large image scene and obtain high-resolution images from various viewing angles and altitudes. Multiple datasets created in various regions and conditions can be helpful considering data expansion to improve the usability of the UAV datasets with deep learning. The combined segmentation network (CSN), which can train two datasets simultaneously by sharing encoding blocks, was used to segment heterogeneous UAV datasets, such as UAVid and semantic drone dataset. CSN shared encoding blocks to learn general features from two datasets and decoding blocks trained separately on each dataset. For the preprocessing step, classes of each dataset were adjusted to minimize the difference between the two datasets. Experiment results show that CSN can segment more accurately for specific classes, such as background and vegetation, which have low ratios in the single dataset. This study presented the potential application of integrated heterogeneous UAV imagery datasets by learning shared layers. Thus, surface inspection would be effectively conducted using UAV datasets.
Pengaruh Penggunaan Media Pembelajaran Video Fragmen Terhadap Motivasi Belajar Pendidikan Agama Kristen Siswa Kelas VIII SMP Negeri 1 Onanrunggu Kabupaten Samosir Tahun Pembelajaran 2023/2024

This research aims to determine whether the learning motivation of students who are taught with fragment video media is higher than the learning motivation of students who are taught without fragment video media in Class VIII of SMP Negeri 1 Onanrunggu, Samosir Regency. The method used in this research is a quantitative method, a True-Experimental approach with a Posttest-Only Control Design form. The population was all 90 students in class VIII of SMP Negeri 1 Onanrunggu and the sample was determined to be 30 in the experimental class and 30 in the control class using purposive sampling techniques. The results of the analysis show that the average learning motivation of students who were taught using video fragment media (3.518) was higher than the learning motivation of students who were taught without video fragment media (3.345). Data analysis uses the variance pool t test formula, according to the test analysis requirements, namely n1 = n2 and the variance of the two sample groups is homogeneous. The results of data analysis show the value of tcount = 3.991 &gt; ttable (α = 0.01; dk = n1+n2-2 = 58) = 2.392, so H0 is rejected and H1 is accepted. Thus, the researchers concluded that the learning motivation of students who were taught with fragment video media was higher than the learning motivation of students who were taught without fragment video media in class VIII of SMP Negeri 1 Onanrunggu, Samosir Regency, Academic Year 2023/2024.
A Trust Paradox May Limit the Application of Al-Generated Knowledge

The extent of knowledge available to society may no longer be limited by human cognition or by refining algorithms. Instead, the interaction of the two may limit knowledge transfer and creation. We examine two behavioral paradoxes: Polanyi’s paradox, which has existed since 1966, and the Trust paradox, which we propose. We study four technology innovations—printing, digitalization, the Internet, and artificial intelligence (AI)—and assess how these paradoxes limit the use of knowledge created by each innovation. We found that while technology has largely overcome Polanyi’s paradox of limiting the transfer of tacit knowledge, the Trust paradox may limit the application of AI-generated knowledge. We identify five relevant drivers of trust—reliability, transparency, association, reciprocity, and accountability—and outline how each impacts and creates challenges for the application of AI.
A hardware-independent time estimation method for inference process of convolutional layers on GPU

Nowadays, various AI applications based on Convolutional Neural Networks (CNNs) are widely deployed on GPU-accelerated devices. However, due to the lack of visibility into GPU internal scheduling, accurately modeling the performance of CNN inference tasks or estimating the latency of CNN tasks that are executing or waiting on the GPU is challenging. This hurts the multi-model scheduling on multi-device and CNN real-time inference. Therefore, in this paper, we propose a time estimation method to estimate the forward execution time of a convolutional layer with an arbitrary shape on a GPU. The proposed method divides an explicit General Matrix Multiplication (GEMM) convolution operation into a series of estimatable GPU operations and constructs performance models at the level of sub-operations rather than hardware instructions or entire models. Also, the proposed method can be easily adapted to different hardware devices or underlying algorithm implementations, since it focuses on the variation of execution time relative to the input data scale, without focusing on specific instructions or hardware actions. According to the experiments on four typical CUDA compatible platforms, the proposed method has an average error rate of less than 5% for convolutional layers in some practical CNN models, and has about 8% error rate in estimating GEMM convolution implementations provided by cuDNN library. Experiments show that the proposed method can predict the forward execution time of convolutional layers of arbitrary size in CNN inference tasks on different GPU models.
Detection of fake customer reviews on e-commerce with supervised machine learning

According to consumer statistics, 74% of online consumers read reviews before purchasing a product. Consumers believe that the reviews are written by people who have purchased the same product. However, that is not the case sometimes. Many reviews are fake and generated by bots to enhance the brand value or demolish some product’s image by posting a negative review for it. Limited research has been done in this area and automatic detection systems show partial success in detecting fake reviews. In this project, we discuss the issue of fake reviews and methods to detect them. The project experiments with three models – Naïve Bayes, Support Vector Machine, and Random Forest for classification. By observing the results of these models, we can surely say that human eyes cannot detect computer-generated reviews as accurately as machine learning techniques can. This web application can be implemented in any e-commerce platform to train, and test based on their data, and it can provide consumer protection and increase the credibility of reviews.
Harmonic Data Recovery Method Based on Multivariate Norm Matrix

During state perception of a power system, fragments of harmonic data are inevitably lost owing to the loss of synchronization signals, transmission delays, instrument failures, or other factors. A harmonic data recovery method is proposed based on multivariate norm matrix in this paper. The proposed method involves dynamic time warping for correlation analysis of harmonic data, normalized cuts for correlation clustering of power-quality monitoring devices, and adaptive alternating direction method of multipliers for multivariable norm joint optimization. Compared with existing data recovery methods, our proposed method maintains excellent recovery accuracy without requiring prior information or optimization of the power-quality monitoring device. Simulation results on the IEEE 39-bus and IEEE 118-bus test systems demonstrate the low computational complexity of the proposed method and its robustness against noise. In addition, the application of the proposed method to field data from a real-world system provides consistent results with those obtained from simulations.
Coherent errors and readout errors in the surface code

We consider the combined effect of readout errors and coherent errors, i.e., deterministic phase rotations, on the surface code. We use a recently developed numerical approach, via a mapping of the physical qubits to Majorana fermions. We show how to use this approach in the presence of readout errors, treated on the phenomenological level: perfect projective measurements with potentially incorrectly recorded outcomes, and multiple repeated measurement rounds. We find a threshold for this combination of errors, with an error rate close to the threshold of the corresponding incoherent error channel (random Pauli-Z and readout errors). The value of the threshold error rate, using the worst case fidelity as the measure of logical errors, is 2.6%. Below the threshold, scaling up the code leads to the rapid loss of coherence in the logical-level errors, but error rates that are greater than those of the corresponding incoherent error channel. We also vary the coherent and readout error rates independently, and find that the surface code is more sensitive to coherent errors than to readout errors. Our work extends the recent results on coherent errors with perfect readout to the experimentally more realistic situation where readout errors also occur.
Understanding and Predicting User Satisfaction with Conversational Recommender Systems

User satisfaction depicts the effectiveness of a system from the user’s perspective. Understanding and predicting user satisfaction is vital for the design of user-oriented evaluation methods for conversational recommender systems (CRSs) . Current approaches rely on turn-level satisfaction ratings to predict a user’s overall satisfaction with CRS. These methods assume that all users perceive satisfaction similarly, failing to capture the broader dialogue aspects that influence overall user satisfaction. We investigate the effect of several dialogue aspects on user satisfaction when interacting with a CRS. To this end, we annotate dialogues based on six aspects (i.e., relevance , interestingness , understanding , task-completion , interest-arousal , and efficiency ) at the turn and dialogue levels. We find that the concept of satisfaction varies per user. At the turn level, a system’s ability to make relevant recommendations is a significant factor in satisfaction. We adopt these aspects as features for predicting response quality and user satisfaction. We achieve an F1-score of 0.80 in classifying dissatisfactory dialogues, and a Pearson’s r of 0.73 for turn-level response quality estimation, demonstrating the effectiveness of the proposed dialogue aspects in predicting user satisfaction and being able to identify dialogues where the system is failing. With this article, we release our annotated data. 1
Digital Libraries, Epigraphy and Paleography: Bring Records from the Distant Past to the Present: Part II

Abstract The two volumes of this Special Issue explore the intersections of digital libraries, epigraphy and paleography. Digital libraries research, practices and infrastructures have transformed the study of ancient inscriptions by providing organizing principles for collections building, defining interoperability requirements and developing innovative user tools and services. Yet linking collections and their contents to support advanced scholarly work in epigraphy and paleography tests the limits of current digital libraries applications. This is due, in part, to the magnitude and heterogeneity of works created over a time period of more than five millennia. The remarkable diversity ranges from the types of artifacts to the methods used in their production to the singularity of individual marks contained within them. Conversion of analogue collections to digital repositories is well underway—but most often not in a way that meets the basic requirements needed to support scholarly workflows. This is beginning to change as collections and content are being described more fully with rich annotations and metadata conforming to established standards. New use of imaging technologies and computational approaches are remediating damaged works and revealing text that has, over time, become illegible or hidden. Transcription of handwritten text to machine-readable form is still primarily a manual process, but research into automated transcription is moving forward. Progress in digital libraries research and practices coupled with collections development of ancient writtten works suggests that epigraphy and paleography will gain new prominence in the Academy.