Reviewerly/exHarmony huggingface dataset:
Video-based Person Re-identification Algorithm Combining Spatial-temporal Self-attention Mechanism and Local Feature

Video-based person re-identification is susceptible to various interferences in video frames. This paper proposes a video-based person re-identification algorithm using the full-scale network (OSNet) that incorporates both spatio-temporal self-attention and local features to enhance the diversity of features. The proposed network has two branches, one for global feature representation and the other for local feature representation, which analyzes the features in the local branch using a blocking strategy and enhances the network's feature extraction capability by cascading the features of the two branches. Experimental results on the challenging video-based person re-identification dataset, MEVID, demonstrate that the proposed method outperforms most mainstream models and has a smaller model size than most existing methods.
An Image Captioning Model Based on SE-ResNest and EMSA

In recent years, with the development of technologies such as deep learning and attention mechanism, image captions have made great progress. In the traditional image caption model, there are problems of insufficient feature extraction and inaccurate information expression in the decoding process. In view of the above problems, this paper builds a model based on the framework of encoder-decoder, proposes an improvement based on ResNest network architecture in the encoder, and adds Squeeze-and-Excitation module to obtain image feature information. An improved two-layer long short-term memory (LSTM) image caption generation model is proposed on the decoder. Through more efficient multi-head attention, the model can more accurately understand the relationship between features, and generate more accurate and specific text description statements based on complete semantic information. In this paper, experiments are carried out on Flickr8k and Flickr30k datasets. Through the comparative analysis of the experimental results of the evaluation indicators, it is proved that the proposed model can effectively realize image caption and improve the accuracy of generating text description statements.
Transformer Speech Synthesis for Tibetan Lhasa Dialect Based on Multilevel Granular Unit

The application of Tacotron model in Tibetan end-to-end speech synthesis has achieved good results, however, this model which is based Recurrent Neural Network (RNN) suffers from low training and prediction efficiency and long-range information loss. To further improve the effect of Tibetan speech synthesis, an end-to-end speech synthesis model based on Transformer is proposed to realize the speech synthesis of multiple dialects of Tibetan. In the model, the hidden state in the Encoder and Decoder is constructed in parallel by using the multi-head attention mechanism, which effectively solves the problem of modeling long-distance information correlation and can take the advantage of multi-GPU parallel training. In this work, three different granular units (Tibetan characters, Latin letters, and Tibetan components) are selected as the input to the acoustic model for the purpose of selecting the best granular unit to improve synthesis result. The Transformer TTS network transforms the text sequences into Mel spectrograms, and the WaveNet vocoder converts the Mel spectrograms into final speech waveforms. This study conducts a series of comparative experiments. Firstly, the performances of three synthesis granular units based on our proposed method are compared, and then the effects of single-GPU training and multi-GPU parallel training are compared. In addition, a comparative experiment of Tacotron and Transformer applied to Tibetan Lhasa dialect speech synthesis is conducted. The experimental results show that the end-to-end speech synthesis model based on Transformer has better performance than Tacotron in Tibetan Lhasa dialect speech synthesis. The speech obtained by using Latin letters as the synthesis unit and parallel training with multi-GPU has better clarity and naturalness.
FlexiPCN: Flexible Payment Channel Network

Payment Channel Network (PCN) is a widely recognized and effective off-chain solution used to reduce on-chain operational costs. PCN is designed to address the scalability challenge and throughput issues in permissionless blockchains. Though transaction throughput is improved, many issues remain, like no flexibility, channel exhaustion, poor sustainability, etc. A separate deposit is required for each payment channel between two users, which locks a substantial amount of coins for a long period of time. Therefore, the flexibility to move these locked coins across channels is impossible through off-chain. Moreover, the channels get exhausted due to unbalanced (unidirectional) transfer. This causes the channel to become unsustainable (dead) until the PCN is rebalanced. This work presents a novel payment protocol called Flexible Payment Channel Networks (FlexiPCN), which allows users to deposit coins per user rather than per channel. So, users can move coins flexibly from one channel to another without the help of the blockchain or setting the cycle off-chain. FlexiPCN has been proven to be secure under the Universal Composability framework.
Solving Quadratic Assignment Problem by Using Meta-heuristic Search Method

While analytical solutions to Quadratic Assignment Problems (QAP) have indeed been since a long time, the expanding use of Evolutionary Algorithms (EAs) for similar issues gives a framework for dealing with QAP with an extraordinarily broad scope. The study's key contribution is that it normalizes all of the criteria into a single scale, regardless of their measurement systems or the requirements of minimum or maximum, relieving the researchers of the exhaustively quantifying the quality criteria. A tabu search algorithm for quadratic assignment problems (TSQAP) is proposed, which combines the limitations of tabu search with a discrete assignment problem. The effectiveness of the proposed technique has been compared to well-established alternatives, and its operating principle is illustrated with a numerical example. After repeating the solution of each issue (8) once and recording the algorithm results, it showed its agreement, once from a total (375) repetition of the experiment while the number of times the Artificial Bee Colony (ABC) arrived (2) as for the Firefly (FA) Algorithm giving (117), also Genetic (GA) and Particle Swarm (PSO) gives (120) and the Tabu Search algorithm (174). The proposed technique (TSQAP) is shown to yield a superior solution with low computing complexity. MATLAB was used to generate all of the findings (R2020b).
Vanishing Point Aided Hash-Frequency Encoding for Neural Radiance Fields (NeRF) from Sparse 360°Input

Neural Radiance Fields (NeRF) enable novel view synthesis of 3D scenes when trained with a set of 2D images. One of the key components of NeRF is the input encoding, i.e. mapping the coordinates to higher dimensions to learn high-frequency details, which has been proven to increase the quality. Among various input mappings, hash encoding is gaining increasing attention for its efficiency. However, its performance on sparse inputs is limited. To address this limitation, we propose a new input encoding scheme that improves hash-based NeRF for sparse inputs, i.e. few and distant cameras, specifically for 360° view synthesis. In this paper, we combine frequency encoding and hash encoding and show that this combination can increase dramatically the quality of hash-based NeRF for sparse inputs. Additionally, we explore scene geometry by estimating vanishing points in omnidirectional images (ODI) of indoor and city scenes in order to align frequency encoding with scene structures. We demonstrate that our vanishing point-aided scene alignment further improves deterministic and non-deterministic encodings on image regression and NeRF tasks where sharper textures and more accurate geometry of scene structures can be reconstructed.
Complete and Efficient Higher-Order Reasoning via Lambda-Superposition

Superposition is a highly successful proof calculus for reasoning about first-order logic with equality. We present λ-superposition, which extends superposition to higher-order logic. Its design goals include soundness, completeness, efficiency, and gracefulness with respect to standard first-order superposition. The calculus is implemented in two automatic theorem provers: E and Zipper position. These provers regularly win trophies at the CADE ATP System Competition, confirming the calculus's applicability. This paper is a summary of research that took place between 2017 and 2022.
Chatter Generation through Language Models

This work examines the feasibility of using Language Models (LMs) to generate chatter that stays in context based on persona descriptions. We clearly distinguish between chatter and dialogue, explain why we believe that chatter yields more promise for integration, and experimentally show that in 500 generated samples the majority (79%) of responses stayed in context. Additionally, we coarsely check that most (≈70%) consumer gaming hardware has enough random access memory (RAM) to store a small 7B 4-bit quantized LM model such as LLama.cpp on top of a demanding AAA game. Finally, we outline our vision for the future of games and language models and their potential synergies.
An Efficient Metamorphic Testing Leveraging Disentangled Transformation and Decision Oracle

Metamorphic testing (MT) is a commonly employed method to detect the defects of Deep Neural Network (DNN). Most frameworks utilizing metamorphic testing introduce semantic-preserving transformations to the input images to induce inconsistent predictions from the DNN. However, relying solely on prediction consistency may overlook some defects of DNNs. We highlight that decision consistency helps to more effectively expose the flaws within DNN, compared with prediction consistency alone. To better test decision consistency, we design a new mutation method, Disentangled Transformation, which mutates input images following human-decision. We name this new MT framework Decision-guided Metamorphic Testing with Disentangled Transformation (DMTDT). Experimental evaluations show that DMTDT outperforms existing frameworks on quantity and diversity of the generated test.
Survey of Embedded Neural Network Accelerator for Intelligent Aerospace Applications

With the increasing complexity of space missions, the demand for intelligent technologies in aerospace is rapidly growing. Neural network accelerators play an indispensable role in intelligent aerospace applications as dedicated hardware devices for accelerating neural network inference. In this paper, we have explored the challenges associated with selecting the appropriate Neural Processing Unit (NPU) for aerospace intelligent applications. We have shown that the peak specifications of an NPU may not accurately represent its actual performance. To overcome this challenge, we conducted a survey of neural network accelerators that have the potential to be used in our aerospace intelligent field. In addition, we conducted practical tests on specific algorithms to gain a deeper understanding of their performance. The results of our research provide valuable insights for the selection of embedded neural network accelerators for future aerospace intelligent applications. In conclusion, we summarized our findings and looked forward to the future development directions of neural network accelerators for space intelligent applications.
Quasi-Static Measurement Performances of Flex-Sensor-Based and Inertial-Measurement-Unit-Based Sensory Gloves

Sensory gloves convert hand postures and movements of fingers into electric signals. Different technologies can be adopted to achieve this conversion, and different approaches can be used to evaluate its effectiveness. In this study, we adopted two types of sensory gloves based on two types of sensors, namely the Resistive Flex Sensor (RFS) and the Inertial Measurement Unit (IMU). We evaluated the conversion effectiveness in terms of repeatability, reproducibility and reliability of quasi-static measurements. In particular, to take into account the nonlinear characteristics of sensors, we propose an improvement of the usually adopted measurement test protocol used for assessing the performance of sensory gloves. According to our results, the two sensory gloves have similar reliability. However, the IMU-based glove provides better repeatability, and the RFS-based glove provides better reproducibility. Overall, the average range ± standard deviation and intraclass correlation coefficient of the RFS-based glove were 5.66° ± 2.22° and 0.73 ± 0.17, respectively, and those of the IMU-based glove were 7.80° ± 2.47° and 0.76 ± 0.14, respectively. All in all, the novelty of this work concerns the comparison of two types of sensory gloves in terms of quasi-static measurement reproducibility and reliability and the improvement of an existing standard protocol for sensory glove assessment aimed at providing a more comprehensive analysis.
The Ethical Turn of Emerging Design Practices

Ignited by the transformative impact of technoscience, diverse alternative design practices have emerged, each distinct from prevailing norms. Within this heterogeneous landscape, a common theme emerges: an inclination toward ethical considerations, closely tied to or stemming from these novel approaches and practices, warranting thorough investigation. This article adopts a dual strategy. First, it introduces technoscience as the catalyst behind this transition, framing the contextual backdrop. Second, based on Jürgen Habermas’s knowledge-interest theory and its tripartite structure, this article focuses on those emerging design practices which deal with ethical and social design, a category that has become especially significant since 2000. The subsequent analysis explores the significant ethical turn within diverse disciplines and these emerging design practices, illustrated across domains. This transformation unfolds in ontological, epistemological, and methodological dimensions, signifying a renewal of design ethics. The article establishes a fundamental framework to comprehend a field without rigid boundaries, facilitating future critique and ongoing design research progress.
Librarians Gateway: Digital Library System

In an era marked by rapid technological advancements, libraries have transitioned from traditional repositories of physical books to multifaceted centers for digital information. This paper explores the development and deployment of a comprehensive Digital Library System (DLS), highlighting its pivotal role as a gateway empowering librarian to efficiently manage, organize, and distribute information within the digital landscape. This article delineates the objectives, methodology, results, and implications of integrating such a system into modern library practices, emphasizing enhanced accessibility, user experience, and streamlined resource management.
An Efficient Semi-Real-Time Algorithm for Path Planning in the Hamilton-Jacobi Formulation

We present a semi-real-time algorithm for minimal-time optimal path planning based on optimal control theory, dynamic programming, and Hamilton-Jacobi (HJ) equations. Partial differential equation (PDE) based optimal path planning methods are well-established in the literature, and provide an interpretable alternative to black-box machine learning algorithms. However, due to the computational burden of grid-based PDE solvers, many previous methods do not scale well to high dimensional problems and are not applicable in real-time scenarios even for low dimensional problems. We present a semi-real-time algorithm for optimal path planning in the HJ formulation, using grid-free numerical methods based on Hopf-Lax formulas. In doing so, we retain the intepretablity of PDE based path planning, but because the numerical method is grid-free, it is efficient and does not suffer from the curse of dimensionality, and thus can be applied in semi-real-time and account for realistic concerns like obstacle discovery. This represents a significant step in averting the tradeoff between interpretability and efficiency. We present the algorithm with application to synthetic examples of isotropic motion planning, though with slight adjustments, it could be applied to many other problems.
Comparative Analysis of Fuzzy Inference Systems Applications on Mobile Robot Navigation in Unknown Environments

Mobile robot navigation in unknown environments is a central problem in mobile robot control and several studies were carried out in the past decades. This paper compares three mobile robot navigation methods that use fuzzy inference systems (FIS) to develop a controller for the robot: Multiple Fuzzy Inference System (MFIS), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Fuzzy Artificial Potential Field (FAPF). The mobile robot navigation task was divided into two behaviors: go-to-target and obstacle avoidance. Four scenarios were used to validate the methods: a cluttered environment, a narrow path with the robot starting from the center point between walls, a narrow path with the robot starting from the left point misaligned with the center of the two walls, and an environment with an obstacle positioned to generate a point of local minima followed by a narrow gap. Simulation results were presented using MATLAB and CoppeliaSim software and the classical Artificial Potential Field (APF) method was used as the basis for the comparison. The FAPF method used a fuzzy inference system to weigh the value of the attraction force and the repulsion force of the artificial potential fields method ended up presenting the best general performance related to the distance traveled considering all four scenarios, being the method that the robot passes more centralized when between obstacles on both sides, while the MFIS method that used two fuzzy inference systems for each behavior ended up presenting the best performance related to the execution time of the tasks for all four scenarios.
Chatbots as Conversation Partners

This chapter discusses the use of chatbots as conversation partners for language learning and studies the impact of chatbot-based conversation practice and corrective feedback on language learners' writing proficiency and attitudes towards chatbots. The chapter describes a study where 34 English language learners at a Japanese university were offered text-based conversation practice with Replika, a generative AI chatbot. Half of the participants received corrective feedback, addressing vocabulary, grammar, and syntax errors in their messages, while the other half practiced without feedback. Pre-test and post-test surveys assessed improvement in perceived writing performance, and general feedback on chatbot-based practice was collected. Replika, despite its non-educational design, was effective at personalizing interactions and its use resulted in significant improvement in perceived writing proficiency. Despite minor issues during conversation practices, the general impression of the chatbot is positive suggesting its potential for an English language classroom use.
PENINGKATAN WAWASAN PETANI DENGAN INPUT TEKNOLOGI MELALUI ZPT (ZAT PENGATUR TUMBUH) ALAMI DARI KEONG MAS

Fertilizer is material that is put into the soil to provide nutrients for plant growth and development. Based on the source of the materials used, fertilizers can be separated into inorganic fertilizers and organic fertilizers. Furthermore, organic fertilizer is divided into two, namely liquid fertilizer and solid fertilizer. One of the raw materials for making natural growth regulators that comes from animals is the golden snail (Pomaceae canaliculata). Golden snails are animals that have a high protein content. The meat and shell of golden snails contain protein, fat, carbohydrates, Na, K, riboflavin, Niacin, Mn, C, Cu, Zn and Ca. Activities in the Gue village community, Kuta Baro District, Aceh Besar Regency are still very lacking, this is due to several problems, namely the lack of farmers attending training or seminars on agricultural cultivation, Still using synthetic Fertilizers and ZPT, Mas snails as pests on plants are only used as feed additional livestock while the rest are destroyed with synthetic pesticides. Never received counseling about the use of golden snails to make natural ZPT and liquid organic fertilizer. Low interest in the younger generation to take part in agriculture. Implementation of activities is carried out in 2 (two) activity stages. Stage one provides education and explanations about growth regulators and indoor golden snails. The second stage is to practice making growth regulators from golden snails in an open space. The location for this community service is in Gue village which will last for 6 days in December 2022. The people of Gue village are very enthusiastic about implementing science and technology activities for the community, already understand how to make liquid organic fertilizer from golden snails, and really understand the benefits of organic fertilizer liquid from golden snails on soil fertility and how to administer it to cultivated plants.
Media dan Peace Building: Media Literacy for Sekolah Wijaya Kusuma Students

Abstract&#x0D; &#x0D; Globalization changes the nature of media and information dissemination, where media plays an important part in enhancing peace. But on the other hand, media is not only a tool to promote harmony but conflict as well. However, peacebuilding depends largely on media literacy. Media literacy offers people potentialities to promote peace, especially at young ages. Therefore, this program aims to help students improve their media literacy competence, specifically regarding how to use media wisely. This program also collaborates with Jawara Internet Sehat to promote the internet for positive use to 100 Wijaya Kusuma School students. The implementations of this program are divided into 3 stages, preparation, delivery, and evaluation. The preparation stage focused on analyzing the problems faced by Sekolah Wijaya Kusuma to help us build suitable training modules. Then in the delivery stage, we conduct training through seminars and workshops that focus on using media wisely and avoiding hoaxes. And lastly, we evaluate the feedback given by the students and the result shows that this program helps the student to improve their media literacy and promote peacebuilding through media.
A Novel Mass-Energy Equation from Lorentz Factor and Energy of Motion

The relativistic momentum is concerned with the motion of a body whose velocity approaches the speed of light. Also, the velocity of a moving object with mass is less than the speed of light because light has no mass and the speed of light is the upper limit for the speeds of all objects in the universe. In this article, a novel mass-energy equation is derived using the Lorentz factor and the kinetic energy of classical mechanics.
Mathematical Aptitude Analysis of Prospective Teachers at the Indian Institute of Teacher Education

In this study, the researcher wants to find the mathematical aptitude of future teachers. The sample taken for the study is the teacher trainees of different colleges under the Indian Institute of Teacher Education, and the technique used is a random sampling technique. An investigation that covers the entire population is challenging to carry out. The population's representative subjects must, therefore, be chosen. An examined and observed sample is a subset of a larger population. DIET Gandhinagar, DIET Ahmedabad, Centre for Education, M. N. Shukla College, A.G. teacher's college. The sample is drawn from the 168 prospective teachers (teacher trainees) enrolled; 84 are from the District Institute of Education and Training, and the remaining 84 are from other colleges. The data is collected through a questionnaire as the research tool. Collected data were analyzed by t-test. The findings denoted that there are some differences between mathematical aptitude concerning gender and based on colleges.