Reviewerly/exHarmony huggingface dataset:
Transfer and Alignment Network for Generalized Category Discovery

Generalized Category Discovery is a crucial real-world task. Despite the improved performance on known categories, current methods perform poorly on novel categories. We attribute the poor performance to two reasons: biased knowledge transfer between labeled and unlabeled data and noisy representation learning on the unlabeled data. To mitigate these two issues, we propose a Transfer and Alignment Network (TAN), which incorporates two knowledge transfer mechanisms to calibrate the biased knowledge and two feature alignment mechanisms to learn discriminative features. Specifically, we model different categories with prototypes and transfer the prototypes in labeled data to correct model bias towards known categories. On the one hand, we pull instances with known categories in unlabeled data closer to these prototypes to form more compact clusters and avoid boundary overlap between known and novel categories. On the other hand, we use these prototypes to calibrate noisy prototypes estimated from unlabeled data based on category similarities, which allows for more accurate estimation of prototypes for novel categories that can be used as reliable learning targets later. After knowledge transfer, we further propose two feature alignment mechanisms to acquire both instance- and category-level knowledge from unlabeled data by aligning instance features with both augmented features and the calibrated prototypes, which can boost model performance on both known and novel categories with less noise. Experiments on three benchmark datasets show that our model outperforms SOTA methods, especially on novel categories. Theoretical analysis is provided for an in-depth understanding of our model in general. Our code and data are available at https://github.com/Lackel/TAN.
Source Code is a Graph, Not a Sequence: A Cross-Lingual Perspective on Code Clone Detection

Source code clone detection is the task of finding code fragments that have the same or similar functionality, but may differ in syntax or structure. This task is important for software maintenance, reuse, and quality assurance (Roy et al. 2009). However, code clone detection is challenging, as source code can be written in different languages, domains, and styles. In this paper, we argue that source code is inherently a graph, not a sequence, and that graph-based methods are more suitable for code clone detection than sequence-based methods. We compare the performance of two state-of-the-art models: CodeBERT (Feng et al. 2020), a sequence-based model, and CodeGraph (Yu et al. 2023), a graph-based model, on two benchmark data-sets: BCB (Svajlenko et al. 2014) and PoolC (PoolC no date). We show that CodeGraph outperforms CodeBERT on both data-sets, especially on cross-lingual code clones. To the best of our knowledge, this is the first work to demonstrate the superiority of graph-based methods over sequence-based methods on cross-lingual code clone detection.
FENet: Focusing Enhanced Network for Lane Detection

Inspired by human driving focus, this research pioneers networks augmented with Focusing Sampling, Partial Field of View Evaluation, Enhanced FPN architecture and Directional IoU Loss - targeted innovations addressing obstacles to precise lane detection for autonomous driving. Experiments demonstrate our Focusing Sampling strategy, emphasizing vital distant details unlike uniform approaches, significantly boosts both benchmark and practical curved/distant lane recognition accuracy essential for safety. While FENetV1 achieves state-of-the-art conventional metric performance via enhancements isolating perspective-aware contexts mimicking driver vision, FENetV2 proves most reliable on the proposed Partial Field analysis. Hence we specifically recommend V2 for practical lane navigation despite fractional degradation on standard entire-image measures. Future directions include collecting on-road data and integrating complementary dual frameworks to further breakthroughs guided by human perception principles. The Code is available at https://github.com/HanyangZhong/FENet.
Simple client-side encryption of personal information with Web Assembly

The HTTPS protocol has enforced a higher level of robustness to several attacks; however, it is not easy to set up the required certificates on intranets, nor is it effective in the case the server confidentiality is not reliable, as in the case of cloud services, or it could be compromised. A simple method is proposed to encrypt the data on the client side, using Web Assembly. It never transfers data to the server as clear text. Searching fields in the server is made possible by an encoding scheme that ensures a stable prefix correspondence between ciphertext and plaintext. The method has been developed for a semantic medical database, and allows accessing personal data using an additional password while maintaining non-sensitive information in clear form. Web Assembly has been chosen to guarantee the fast and efficient execution of encrypting/decrypting operations and because of its characteristic of producing modules that are very robust against reverse engineering. The code is available at https://github.com/mfalda/client-encdec.
K-PERM: Personalized Response Generation Using Dynamic Knowledge Retrieval and Persona-Adaptive Queries

Personalizing conversational agents can enhance the quality of conversations and increase user engagement. However, they often lack external knowledge to appropriately tend to a user's persona. This is particularly crucial for practical applications like mental health support, nutrition planning, culturally sensitive conversations, or reducing toxic behavior in conversational agents. To enhance the relevance and comprehensiveness of personalized responses, we propose using a two-step approach that involves (1) selectively integrating user personas and (2) contextualizing the response with supplementing information from a background knowledge source. We develop K-PERM (Knowledge-guided PErsonalization with Reward Modulation), a dynamic conversational agent that combines these elements. K-PERM achieves state-of-the-art performance on the popular FoCus dataset, containing real-world personalized conversations concerning global landmarks. We show that using responses from K-PERM can improve performance in state-of-the-art LLMs (GPT 3.5) by 10.5%, highlighting the impact of K-PERM for personalizing chatbots.
Empowering Working Memory for Large Language Model Agents

Large language models (LLMs) have achieved impressive linguistic capabilities. However, a key limitation persists in their lack of human-like memory faculties. LLMs exhibit constrained memory retention across sequential interactions, hindering complex reasoning. This paper explores the potential of applying cognitive psychology's working memory frameworks, to enhance LLM architecture. The limitations of traditional LLM memory designs are analyzed, including their isolation of distinct dialog episodes and lack of persistent memory links. To address this, an innovative model is proposed incorporating a centralized Working Memory Hub and Episodic Buffer access to retain memories across episodes. This architecture aims to provide greater continuity for nuanced contextual reasoning during intricate tasks and collaborative scenarios. While promising, further research is required into optimizing episodic memory encoding, storage, prioritization, retrieval, and security. Overall, this paper provides a strategic blueprint for developing LLM agents with more sophisticated, human-like memory capabilities, highlighting memory mechanisms as a vital frontier in artificial general intelligence.
Blockchain Encryption Algorithm Based on Aggregated Signature

As the concept of blockchain continues to the hype, an increasing number of industries join the ranks of using blockchain, which also leads to privacy leaks becoming a potential hidden danger. Therefore, more secure and private algorithms should be adopted to protect the privacy of users. The issue of network security will be placed at a strategic level. For this reason, a blockchain-based aggregation signature algorithm is proposed. Compared with other signature algorithms, this algorithm reduces the computational overhead in transaction and verification and does not require storage. No need to forget every time you use it, can the user verify by using the public key, which can ensure the fairness and privacy of the user's use. The final results show that the algorithm not only greatly saves the overhead but also improves the communication efficiency between nodes. The most important thing is to solve the problem of personal privacy leakage that users have been worried about. The algorithm can solve the problem of transaction privacy in the blockchain. It can trace the token to each account corresponding to each person, and with the aggregation encryption algorithm, each transaction of each user can achieve the purpose of mixed currency transaction.
Weight bounds for $(3,γ)$-hyperelliptic curves

{\it $(N,\gamma)$-hyperelliptic} semigroups were introduced by Fernando Torres to encapsulate the most salient properties of Weierstrass semigroups associated to totally-ramified points of $N$-fold covers of curves of genus $\gamma$. Torres characterized $(2,\gamma)$-hyperelliptic semigroups of maximal weight whenever their genus is large relative to $\gamma$. Here we do the same for $(3,\gamma)$-hyperelliptic semigroups, and we formulate a conjecture about the general case whenever $N \geq 3$ is prime.
The Perceived Effectiveness of WhatsApp as a Supplementary Tool for Enhancing English Language Skills among Master's Level EFL Learners

The world is dominated by technological advancement and information technology. Different fields of daily life are overpowered by such advancements. The world of education has also been dominated by technology. Use of different apps have made it to the classrooms. The study taken is to analyse the perceptions of the learners in the use of one such App known as WhatsApp in English Language Classes. The method used here is Qualitative in approach, where the selected participants were interviewed to see what they perceive of WhatsApp in their classes. Theoretical lens taken up by the researcher is Connectivism by Siemens (2008), which purports the knowledge is generated by interacting in the society. Population of the study were all the students of English language in universities. The sample selected were students of NUML University studying English language. The participants were selected randomly. It was analyzed through the study that WhatsApp is considered a very useful App in language learning. It not only enhances the four communication skills but also the vocabulary and Grammar. The role of teacher in using WhatsApp in classrooms was also considered important by the learners. However, it is also suggested in the study that more organized methods of using such apps must be introduced in the classrooms to ensure maximum productivity among students.
The Effect Of Using Interview Steps Techniques To Improve Students Speaking Skills At Tenth Grade of SMA Negeri 2 Rantau Utara

Interview Step is collaborative learning technique. Interview Step proves the students’ participation. The speaking skills of the students are not good. The students still have difficulty in aspect of fluency and pronouncition in speaking. The research method is quantitative experimental. Next, this research is tested as instrument, test was divided into pre-test and post-test. To analysis the data, the writer used formulation of analysis product moment. After calculating the data, there is an effect Of Using Three Steps Interview Techniques To Improve Students Speaking Skills At the Tenth Grade of SMA Negeri 2 Rantau Utara. It can be seen from t count > t table (2,96> 1,67) with level  0,05. So, the hypothesis was received.
Education Infrastructure, Expenditure, Enrollment &amp; Economic Development in Odisha, India

A key part of human capital is education. The development of educational infrastructure and economic growth are closely linked. Realizing the value of infrastructure, both the central and the state government have implemented a number of programmes to upgrade the physical facilities at public schools. Infrastructure is a key component of enabling high-quality education. This study intends to explore the linkage between economic development and spending on education for infrastructure development and quality of education. In 2019–20, the ratio of education department spending (revenue and capital account) to the gross state product (GSDP) is 2.84%. In 2020–21, the gross enrollment ratios (GER) for primary and secondary education are 97.4% and 84.5% respectively. As part of the study's methodology, analytical and empirical data from secondary sources were compiled. The multiple regression analysis demonstrates that the GER of primary and secondary education, as well as the expenditure (Revenue and Capital) on education by education departments to GSDP is significant to economic growth. Here, the economic growth of Odisha as a whole is significantly influenced by the state's educational system. The study's conclusions show that public spending on infrastructure and education has a big impact on economic growth. It is recommended that the infrastructure in schools should be well-equipped in order to meet the needs of the current generation. The government should collaborate with private corporate sector, industrial unit in constructing the infrastructural facility such as smart class room, computer and internet facility, laboratory etc in schools.
Photonic convolutional processor for network edge computing (Conference Presentation)

Performing feature extractions in convolution neural networks for deep-learning tasks is computational expensive in electronics. Fourier optics allows convolutional filtering via dot-product multiplication in the Fourier domain similar to the distributive law in mathematics. Here we experimentally demonstrate convolutional filtering exploiting massive parallelism (10^6 channels, 8-bit at 1kHz) of digital mirror display technology, thus enabling 250 TMAC/s. An FPGA-PCIe board controls the ‘weights’ and handles the data I/O, whereas a high-speed camera detects the inverse-Fourier transformed (2nd lens) data. Gen-1 processes with a total delay (including I/O) of ~1ms, while Gen-2 at 1-10ns leveraging integrated photonics at 10GHz and changing the front-end I/O to a joint-transform-correlator (JTC). These processors are suited for image/pattern recognition, super resolution for geolocalization, or real-time processing in autonomous vehicles or military decision making.
IvaNet: Learning to jointly detect and segment objets with the help of Local Top-Down Modules.

Driven by Convolutional Neural Networks, object detection and semantic segmentation have gained significant improvements. However, existing methods on the basis of a full top-down module have limited robustness in handling those two tasks simultaneously. To this end, we present a joint multi-task framework, termed IvaNet. Different from existing methods, our IvaNet backwards abstract semantic information from higher layers to augment lower layers using local top-down modules. The comparisons against some counterparts on the PASCAL VOC and MS COCO datasets demonstrate the functionality of IvaNet.
Investigating EFL students’ language attitude on speaking(A case study at fourth semester of State Islamic University Sultan Maulana Hasanudin Banten Majoring English Department)

ABSTRACT Anggi Budi Kusuma. 152301878. 2019. Investigating EFL students’ language attitude on speaking(A case study at fourth semester of State Islamic University Sultan Maulana Hasanudin Banten Majoring English Department). This research investigates the students’ language attitude on speaking. It is aimed to describe student’s attitude towards speaking and to know inn what extent the students find difficulties in speaking skill at fourth D semester students majoring English Education Department Faculty of Education and Teacher Training State Islamic University Sultan Maulana Hasanuddin Banten. The method used in this research is qualitative by using observation and questionnaire to collect the data from research subject. This research involves 27 students. The yield indicates this research that positive attitude is shown the students have high willingness of communication, high self-confidence and low anxiety. Furthermore, the negative attitude if it is contrary of contention above. The most students found difficulties in speaking is anxiety extent. So conclusion from twenty seven students of fourth D on academic year 2018-2019. There were 67%/eighteen students held positive attitude towards speaking English. Meanwhile, the rest of all, 33%/ nine students held negative attitude.Keywords: Attitude, Speaking English, Self Confidence, Willingness to Communicate, Language Anxiety
Calibrated Prediction with Covariate Shift via Unsupervised Domain Adaptation

Reliable uncertainty estimates are an important tool for helping autonomous agents or human decision makers understand and leverage predictive models. However, existing approaches to estimating uncertainty largely ignore the possibility of covariate shift--i.e., where the real-world data distribution may differ from the training distribution. As a consequence, existing algorithms can overestimate certainty, possibly yielding a false sense of confidence in the predictive model. We propose an algorithm for calibrating predictions that accounts for the possibility of covariate shift, given labeled examples from the training distribution and unlabeled examples from the real-world distribution. Our algorithm uses importance weighting to correct for the shift from the training to the real-world distribution. However, importance weighting relies on the training and real-world distributions to be sufficiently close. Building on ideas from domain adaptation, we additionally learn a feature map that tries to equalize these two distributions. In an empirical evaluation, we show that our proposed approach outperforms existing approaches to calibrated prediction when there is covariate shift.
Optimal Upward Scaling of Minimum-TSC Binary Signature Sets

We consider upward scaling an overloaded min-TSC binary signature set and propose an optimal solution based on improved sphere decoding algorithm. Instead of previous suboptimal approach, we are guaranteed to find the optimal maximum-likelihood (ML) searching result with low complexity.
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The attitude towards board game usage in interactive learning / Adibah Hussin, Nor Aziah Abd. Kadir, Azniza Zaini and et.al

Today, interactive learning has slowly replaced the traditional method of learning in classroom, which relies on listening to teacher’s lectures, using textbook, memorization of information, figures and equations. Student claims that using traditional method of learning, makes them difficult to understand the content particularly for economics subject. Keeping students motivated and interested during the lesson is a remarkable challenge for the lecturers. Therefore, developed an interactive economics game board named “Catur Ekonomi” to enable students give full attention and interest in learning economics subject. It provides an attractive way of learning economics subjects as the students’ understanding towards this subject could be enhanced and their academic performance could be improved. However, how far this board game really achieved its objective? Hence, this paper investigated the perception and attitude of students on the application of “Catur Ekonomi”. Besides, this paper also identifies the difference of students’ attitude on interactive learning across two demographic factors; gender and academic achievement. The students were given a set of questionnaires after playing the “Catur Ekonomi” and the data were analyzed. The result showed that students enjoy learning using the “Catur Ekonomi” and showed positive attitude towards its applications. On the other hand, both male and female students enjoy using the board game and high achiever students enjoyed more the board game than low achiever students.
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Hacking Quantum Networks: Extraction and Installation of Quantum Data

We study the problem of quantum hacking, which is the procedure of quantum-information extraction from and installation on a quantum network given only partial access. This problem generalizes a central topic in contemporary physics -- information recovery from systems undergoing scrambling dynamics, such as the Hayden--Preskill protocol in black-hole studies. We show that a properly prepared partially entangled probe state can generally outperform a maximally entangled one in quantum hacking. Moreover, we prove that finding an optimal decoder for this stronger task is equivalent to that for Hayden--Preskill-type protocols, and supply analytical formulas for the optimal hacking fidelity of large networks. In the two-user scenario where Bob attempts to hack Alice's data, we find that the optimal fidelity increases with Bob's hacking space relative to Alice's user space. However, if a third neutral party, Charlie, is accessing the computer concurrently, the optimal hacking fidelity against Alice drops with Charlie's user-space dimension, rendering targeted quantum hacking futile in high-dimensional multi-user scenarios without classical attacks. When applied to the black-hole information problem, the limited hacking fidelity implies a reflectivity decay of a black hole as an information mirror.
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Coherent frequency-conversion of quantum dot photons to the telecommunication C-band for quantum communication applications

We demonstrate frequency-conversion of quantum dot photons to the telecom C-band enabled by a custom-built, tunable mid-infrared laser. We achieve high brightness while preserving the photons’ coherence properties, outperforming state-of-the-art solid-state emitters at telecom wavelengths.
GPU-Accelerated Parallel Gene-pool Optimal Mixing in a Gray-Box  Optimization Setting

In a Gray-Box Optimization (GBO) setting that allows for partial evaluations, the fitness of an individual can be updated efficiently after a subset of its variables has been modified. This enables more efficient evolutionary optimization with the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) due to its key strength: Gene-pool Optimal Mixing (GOM). For each solution, GOM performs variation for many (small) sets of variables. To improve efficiency even further, parallel computing can be leveraged. For EAs, typically, this comprises population-wise parallelization. However, unless population sizes are large, this offers limited gains. For large GBO problems, parallelizing GOM-based variation holds greater speed-up potential, regardless of population size. However, this potential cannot be directly exploited because of dependencies between variables. We show how graph coloring can be used to group sets of variables that can undergo variation in parallel without violating dependencies. We test the performance of a CUDA implementation of parallel GOM on a Graphics Processing Unit (GPU) for the Max-Cut problem, a well-known problem for which the dependency structure can be controlled. We find that, for sufficiently large graphs with limited connectivity, finding high-quality solutions can be achieved up to 100 times faster, showcasing the great potential of our approach.