Reviewerly/exHarmony huggingface dataset:
14th International Workshop on Personalized Access to Cultural Heritage (PATCH 2023)

Following the successful series of PATCH workshops, PATCH 2023 will again be the meeting point between state-of-the-art cultural heritage (CH) research and personalization research, focused on those using different types of technology, with emphasis on ubiquitous and adaptive scenarios, to enhance the personal experience in CH sites. The workshop is aimed at bringing together researchers and practitioners who are working on various aspects of cultural heritage and are interested in exploring the potential of state-of-the-art mobile and personalized technology (onsite as well as online) to enhance the CH visiting experience. The expected result of the workshop is a multidisciplinary research agenda that will inform future research directions and, hopefully, forge some research collaborations. This summary provides an overview of the papers that have been accepted for presentation at the workshop and for publication in its proceedings.
Understanding User Perspectives on Sustainability and Fairness in Tourism Recommender Systems

Recommender Systems (RS) are widely used in various domains, including travel and tourism, to provide personalized recommendations for accommodations, activities, and destinations. However, the evaluation of RS has traditionally focused on satisfying the needs of end users, item providers, or the recommendation platform itself without considering the impact on society. Sustainable tourism practices are becoming increasingly important, and a Tourism Recommendation System (TRS) can play a crucial role in promoting sustainability by suggesting sustainable activities and less popular destinations to users.
Multi-Criteria Ranking by Using Relaxed Pareto Ranking Methods

Multi-criteria recommender systems can improve the quality of recommendations by considering user preferences on multiple criteria. One promising approach proposed recently is multi-criteria ranking, which uses Pareto ranking to assign a ranking score based on the dominance relationship between predicted ratings across criteria. However, applying Pareto ranking to all criteria may result in non-differentiable ranking scores. To alleviate this issue, we conducted a study on three relaxed Pareto ranking methods for multi-criteria ranking. We evaluated these methods on three real-world datasets and found that the k-dominance ranking approach, which is one of the relaxed Pareto ranking methods, was able to further enhance the ranking performance.
Human Activity Role Identification using Feature Vector and Encoding Techniques on Natural Language Sentences

Role Identification has the potential to enhance activity recognition applications since it adds more information. Most of the works in the field of activity recognition and role identification are mainly dominated by models that use images and videos. The existing datasets of human activity are not capable of role identification. In this view, this work attempt to develop a novel Human Activity Role Identification Dataset and a novel Computational Recurrent Model that takes textual data as input. Additionally, various feature vector generation methods like N-Grams extraction, Unique word extraction, and Word2Vec are used to encode the input data into feature vectors that describe the relationship between sequences of words. To determine the fundamental roles, these feature vectors are trained on various types of Recurrent Neural Networks (i.e. RNN, LSTM, GRU). The proposed model is validated on evaluation metrics such as Precision, Recall, F1 Score, etc., using Recurrent Neural Networks like RNN, LSTM, and GRU. Hence, the combination of LSTM with unique word extraction methods outperforms with an F1 Score, precision and recall by 0.44, 0.36 and 0.58 respectively. So this role identification work may help to bind roles with entity and objects in human activity recognition.
Access Control, Biometrics, and the Future

Access control is the process of protecting resources by allowing or rejecting access to particular resources. It is an integral part of physical security systems and protects resources from unauthorized use. Over the past decade, there has been a huge demand and need for more sophisticated and secure access technologies. Scientists are developing cutting edge technologies to fulfill the requirements for secure access control experiences. Access control systems, using the latest biometric technologies such as fingerprint, facial recognition, iris recognition, DNA, and signature recognition have offered a higher level of security than the conventional systems. Biometrics plays a vital role in the identification and authentication phase of access control. Every biometric system has weaknesses and benefits. The system success depends on selecting the proper type of biometric modality to use and understanding how it affects system performance, cost, and limitations. Fingerprint holds the first place according to its popularity but facial recognition and iris recognition continue to grow for its accuracy and are considered as the future of biometric recognition. Besides biometric technology, other future possibilities for secure access control are- mobile access control, cloud-based security, and multi-factor authentication. This paper presents access control components, functions, and types. The aim of the paper is to discuss and analyze the latest biometric access control to secure sensitive information. This paper also provides an overview of access control, discusses the latest biometric technology, and presents the future trend of access control.
All Your PC Are Belong to Us: Exploiting Non-control-Transfer Instruction BTB Updates for Dynamic PC Extraction

Leaking a program's instruction address (PC) pattern, completely and precisely, has long been a sought-after capability for microarchitectural side-channel attackers. Case in point, such a primitive would be sufficient to construct powerful control-flow leakage attacks (inferring program secrets impacting control flow) that defeat existing control-flow leakage mitigations, or even reverse-engineer private binaries through PC-trace granular fingerprinting. However, current side-channel attack techniques only capture PCs at a coarse granularity or for only specific instruction types.
Hardware Acceleration of Neural Graphics

Rendering and inverse rendering techniques have recently attained powerful new capabilities and building blocks in the form of neural representations (NR), with derived rendering techniques quickly becoming indispensable tools next to classic computer graphics algorithms, covering a wide range of functions throughout the full pipeline from sensing to pixels. NRs have recently been used to directly learn the geometric and appearance properties of scenes that were previously hard to capture, and to re-synthesize photo realistic imagery based on this information, thereby promising simplifications and replacements for several complex traditional computer graphics problems and algorithms with scalable quality and predictable performance. In this work we ask the question: Does neural graphics (graphics based on NRs) need hardware support? We studied four representative neural graphics applications (NeRF, NSDF, NVR, and GIA) showing that, if we want to render 4k resolution frames at 60 frames per second (FPS) there is a gap of ~ 1.51× to 55.50× in the desired performance on current GPUs. For AR and VR applications, there is an even larger gap of ~ 2--4 orders of magnitude (OOM) between the desired performance and the required system power. We identify that the input encoding and the multi-layer perceptron kernels are the performance bottlenecks, consuming 72.37%, 60.0% and 59.96% of application time for multi resolution hashgrid encoding, multi resolution densegrid encoding and low resolution densegrid encoding, respectively. We propose a neural graphics processing cluster (NGPC) - a scalable and flexible hardware architecture that directly accelerates the input encoding and multi-layer perceptron kernels through dedicated engines and supports a wide range of neural graphics applications. To achieve good overall application level performance improvements, we also accelerate the rest of the kernels by fusion into a single kernel, leading to a ~ 9.94× speedup compared to previous optimized implementations [17] which is sufficient to remove this performance bottleneck. Our results show that, NGPC gives up to 58.36× end-to-end application-level performance improvement, for multi resolution hashgrid encoding on average across the four neural graphics applications, the performance benefits are 12.94×, 20.85×, 33.73× and 39.04× for the hardware scaling factor of 8, 16, 32 and 64, respectively. Our results show that with multi resolution hashgrid encoding, NGPC enables the rendering of 4k Ultra HD resolution frames at 30 FPS for NeRF and 8k Ultra HD resolution frames at 120 FPS for all our other neural graphics applications.
Metior: A Comprehensive Model to Evaluate Obfuscating Side-Channel Defense Schemes

Microarchitectural side-channels enable an attacker to exfiltrate information via the observable side-effects of a victim's execution. Obfuscating mitigation schemes have recently gained in popularity for their appealing performance characteristics. These schemes, including randomized caches and DRAM traffic shapers, limit, but do not completely eliminate, side-channel leakage. An important (yet under-explored) research challenge is the quantitative study of the security effectiveness of these schemes, identifying whether these obfuscating schemes help increase the security level of a system, and if so, by how much.
Pensieve: Microarchitectural Modeling for Security Evaluation

Traditional modeling approaches in computer architecture aim to obtain an accurate estimation of performance, area, and energy of a processor design. With the advent of speculative execution attacks and their security concerns, these traditional modeling techniques fall short when used for security evaluation of defenses against these attacks.
Accelerating Personalized Recommendation with Cross-level Near-Memory Processing

The memory-intensive embedding layers of the personalized recommendation systems are the performance bottleneck as they demand large memory bandwidth and exhibit irregular and sparse memory access patterns. Recent studies propose near memory processing (NMP) to accelerate memory-bound embedding operations. However, due to the load imbalance caused by the skewed access frequency of the embedding data, existing NMP solutions that exploit fine-grained memory parallelism fail to translate the increasingly massive internal bandwidth to performance improvements, leading to resource underutilization and hardware overhead.
Teaching Blue Elephants the Maths for Machine Learning

Code-generation suits well for reverse mode automatic differentiation as it stores each partial derivative as a virtual register. Since the introduction of just-in-time compilation in PostgreSQL, an efficient operator for automatic differentiation seems feasible. Such an operator would allow for in-database machine learning and thus eliminate the need for data extraction.
Design and implementation of agricultural product traceability platform based on blockchain technology

The data information of the traditional traceability platform is stored in the central database, which is prone to data loss and tampering. However, the decentralization, non-tampering, transparency, and traceability of the blockchain technology can solve the traditional precise control and credible traceability that can guarantee the quality and safety of agricultural products, this paper applies blockchain technology to the traceability system of agricultural products and conducts an overall design to ensure the realization of quality traceability in terms of architecture.
Latent space knowledge representation enhancement for low resource machine translation

Low-resource machine translation usually uses data augmentation or constrained hidden space representations to improve translation quality, which ignores the knowledge representation divergence between different languages in latent space. We propose a latent space knowledge representation enhancement to improve the translation effect by reducing the divergence of knowledge representation between the source language and target language. Firstly, this method splits knowledge and feature representation from text representation in each language. Then, it promotes the knowledge representation of mutual learning between the two languages to reduce divergence. Next, the optimized knowledge representation and feature representation are re-combined to obtain enhanced text representation. Finally, the enhanced text representation is translated and reconstructed to reduce the differences between knowledge representations further. Through extensive experiments on public low-resource datasets 'English-German' and 'English-Turkish,' The method can achieve better performance on the test set. The results show that the method can effectively improve the ability of low-resource machine translation by reducing the divergence in knowledge representation between languages.
Differential adaptive SA-DEPSO algorithm based on particle swarm

The differential evolution algorithm is a random search algorithm. Aiming at the problems of premature convergence and slow optimization in differential evolution algorithm, a differential adaptive SA-DEPSO algorithm based on particle swarm optimization is proposed. First, the positioning problem is transformed into a function iteration optimization problem by using the least square method. Then the adaptive differential evolution strategy is fused on the basis of the particle swarm optimization algorithm. This algorithm can not only avoid the problem of premature convergence, but also improve the optimization speed and reduce the positioning error. Simulation analysis shows that when the number of iterations reaches 40, the algorithm in this paper reaches the optimal value and converges, saving the optimization time. Compared with DEPSO, SA-MCDE and literature 11, the average number of optimization runs is reduced by 75, 55 and 25 times; the average positioning error of the algorithm in this paper is reduced by 17.3%, 13.1% and 7.5% respectively.
Reinforcement Learning Agents Playing Ticket to Ride–A Complex Imperfect Information Board Game With Delayed Rewards

Board games are extensively studied in the AI community because of their ability to reflect/represent real-world problems with a high-level of abstraction, and their irreplaceable role as testbeds of state-of-the-art AI algorithms. Modern board games are commonly featured with partially observable state spaces and imperfect information. Despite some recent successes in AI tackling perfect information board games like chess and Go, most imperfect information games are still challenging and have yet to be solved. This paper empirically explores the capabilities of a state-of-the-art Reinforcement Learning (RL) algorithm – Proximal Policy Optimization (PPO) in playing Ticket to Ride, a popular board game with features of imperfect information, large state-action space, and delayed rewards. This paper explores the feasibility of the proposed generalizable modelling and training schemes using a general-purpose RL algorithm with no domain knowledge-based heuristics beyond game rules, game states and scores to tackle this complex imperfect information game. The performance of the proposed methodology is demonstrated in a scaled-down version of Ticket to Ride with a range of RL agents obtained with different training schemes. All RL agents achieve clear advantages over a set of well-designed heuristic agents. The agent constructed through a self-play training scheme outperforms the other RL agents in a Round Robin tournament. The high performance and versality of this self-play agent provide a solid demonstration of the capabilities of this framework.
Design and construction of medicinal plant planting management information system based on WebGIS

In order to promote the implementation of the national technical system of Chinese herbal medicine industry and accelerate the industrialization, scale and modernization of regional medicinal plants, this paper will take medicinal plants as the research object, innovatively introduce WebGIS technology into the production division and planting management of medicinal plants, and combine ArcGIS Server application platform and Ajax technology to complete the construction of medicinal plant planting management information system in Java language environment. The whole system adopts B/S architecture, and the Web Server is built by Spring framework, which can support users to log in and use the system by using the client browser, and can also control MySQL database to complete the storage and call of various data by JDBC. With the help of JSP page of the system and Openlayers framework, the prepared electronic map will be displayed, and the map will be visually integrated with the data of various elements in the process of planting medicinal plants, thus realizing the integration of information resources. Through systematic analysis, it provides necessary decision-making assistance for planting management, effectively improves the efficiency of medicinal plant planting management, and is conducive to the promotion and implementation of ecological planting mode.
The Translingual Literacy Movement: Optimizing Multilingual Skills through Literature

This study addresses the underutilization of multilingual capabilities in Indonesia's educational system by introducing a translingual literacy movement, with literature serving as a principal tool. We utilized a systematic literature review (SLR) methodology, scrutinizing relevant publications from 2012-2022. The investigation drew from 15 sorted articles from international journals focusing on 'sastra', 'literasi', and 'translingual'. With Indonesia's rich cultural diversity, the potential for multilingual learning is significant, yet educational institutions have inadequately integrated language skills, often creating separate barriers for different languages. The translingual approach, rooted in progressivism, was explored to leverage language diversity as a resource rather than an obstacle, enabling students to better communicate and acquire useful knowledge for their self-development. Literature's role in facilitating this multilingual proficiency was emphasized, promoting imaginative and creative learning against traditional and conservative approaches. The implementation of this translingual literacy movement is expected to empower students to effectively use their multilingual abilities, fostering competitiveness in a global context. The study advocates for a reformed language learning approach, highlighting the need for proper literary resources and a conducive ecosystem to realize students' multilingual potential.Highlights:&#x0D; &#x0D; The importance of the translingual literacy movement in leveraging language diversity and optimizing multilingual abilities in Indonesia.&#x0D; The critical role of literature in fostering imaginative and creative learning, thus facilitating the translingual approach rooted in progressivism.&#x0D; The potential global competitiveness of students enhanced by effective utilization of their multilingual skills via the proposed translingual literacy movement.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; Keywords: Translingual Literacy, Literature, Multilingualism, Progressive Education, Language Diversity.&#x0D; &#x0D; &#x0D;
An Argumentative Framework for Generating Explainable Group Recommendations

In the context of group recommender systems, explanations strategies have been proposed to improve recommendations perceived fairness, consensus, satisfaction, and to help the group members in the decision-making process. In general, such explanations try to clarify the underlying social chioce-based aggregation strategies used to generate the recommendations. However, results in the literature are conflicting, and the real benefit of such explanations seem to be limited. In this work, we propose a novel approach, which makes use of an argumentative framework built using information about the aspects that are connected to the recommended items. Such framework is used to generate recommendations, and related explanations. We provide a proof of concept on how to generate explanations for the group, as well as specific explanations for the group members, which use the information in the argumentative frameworks to enrich the explanations. Furthermore, we propose privacy-preserving versions for the explanations, as well as a graphical approach based on tag clouds. In future works, we plan to evaluate the quality of the provided recommendations in offline settings, as well as the impact of the proposed explanations in a series of user studies.
Sign Language Interpreter for Law Enforcement

Software that helps Law Enforcement to keep track of statements in sign language is currently not available in the market. Although other available programs are incredibly efficient, they might be very challenging to determine whether your hand movements are correct.Those programs are less user-friendly because it takes a lot of time to learn how to use it. Apart from that some of the software need hardware support to make the detection accurate. This paper, describe how to employ this model/project effectively while putting together a course on interactive sign language instruction.This project will make it easier to record statements from a deaf or dumb person more accurately and consistently. Additionally, A text-to-speech converter would allow for both listening and seeing the text in sign language. Sign language products such as this appear to be a very helpful tool for law enforcement as well as the deaf persons.Such a translator might be useful for individuals who have been victims of incorrect sign language translation, such as deaf and stupid people.
Prediction of Tuberculosis Patients’ Treatment Outcomes Using Multinomial Naive Bayes Algorithm and Class-Imbalanced Data

Tuberculosis (TB) is a severe and highly contagious disease that affects millions of people worldwide. The current TB treatment programs are challenging to complete for many patients due to numerous factors, including limited human resources and financial resources. To address these challenges, a solution is needed to aid in resource allocation strategies. This study suggests a machine learning methodology for predicting the treatment outcomes of TB patients. This will enable healthcare facilities to optimize resource allocation based on the prediction made. A large multi-variate TB patient dataset from the Brazilian Information System for Notifiable Disease (SINAN) was used in this study, containing attributes related to patient characteristics, clinical information, and laboratory data. The proposed model used the Naive Bayes algorithm due to its simplicity and efficiency in predicting treatment outcomes. The dataset was pre-processed, and the Synthetic Minority Oversampling Technique (SMOTE) was applied to overcome the class imbalance issues in the dataset. The combination of the borderline SMOTE and Naive Bayes algorithms on the preprocessed dataset was found to have achieved the highest levels of accuracy among the combinations sampled. This demonstrated the capability of the new algorithm in predicting the treatment outcome of TB patients. The proposed model could assist healthcare providers in implementing more targeted follow-ups and more appropriate resource allocation strategies to improve the overall treatment outcome of TB patients.