Reviewerly/exHarmony huggingface dataset:
The Influence of Food Chain Puzzle Learning Media on Learning Outcomes in Class V Science Subjects at State Elementary School 024767 Binjai

Learning media functions as a tool to convey subject matter in a learning context. The aim of this research is to assess the impact of using learning media in the form of the Food Chain Puzzle on learning outcomes in science subjects, especially in Theme 5 Sub Theme 2 Learning 1 in class V of SD Negeri 024767 Binjai for the 2023/2024 academic year. This research is a quantitative type of research and uses an experimental method with a quasi-experimental design. The research results show that the T-test value for the experimental posttest is 0.000, and the control posttest value is also 0.000. Because these values ​​are less than or equal to 0.05, it can be concluded that the use of learning media in the form of the Food Chain Puzzle has a significant influence on learning outcomes in science subjects, especially in Theme 5 Sub Theme 2 Learning 1 in class V of State Elementary Schools. 024767 Binjai for the 2023/2024 academic year.
A Study on the Correlation between Local Government’s Integrity Policy and Integrity Level

The purpose of this study is to examine the relationship between integrity policies and integrity of local governments from the perspective of institutional isomorphism. Local governments are making efforts to improve the integrity through integrity policies. However, since the integrity policies of local governments are implemented according to the comprehensive integrity evaluation by the Anti-Corruption and Civil Rights Commission, it is judged that institutional isomorphism is occurring without reflecting the characteristics or organizational capabilities of the institutions. Institutional isomorphism means that policy activities of all organizations become similar while organizations accept them according to the framework required by the institutional environment. This institutional isomorphism makes it difficult for local governments to improve integrity and create a culture of integrity. Looking at the relationship between integrity policies and integrity of local governments, policies to improve corruption-vulnerable areas reflecting the characteristics of institutions appeared to be correlated, and other policies were found to be insignificant with integrity. It is judged that it will be difficult to expect improvement in the essential integrity of local governments unless they develop and implement integrity policies that can create an integrity climate by reflecting the characteristics of local government.
An Exploratory Research of Anti-Corruption Policy Using Blockchain

Research on the use of blockchain is increasing in various fields, and discussions are also starting in the anti-corruption field due to the impossibility of forgery and alteration of blockchain. The following research questions can be considered regarding anti-corruption policies using blockchain. Can blockchain be used as a new tool to prevent corruption? How is an anti-corruption policy using blockchain different from existing policies, and what are the advantages? What are the conditions necessary to introduce an anti-corruption policy using blockchain? Are there any negative aspects to anti-corruption policies using blockchain? This study sought to provide answers to these research questions. To find answers to the research questions, we selected studies that attempted case analysis targeting Indonesia, South Africa, and China from the existing literature and conducted an in-depth analysis. In addition, we evaluated the usability of anti-corruption policies using blockchain by dividing corruption into corruption involving financial transactions and corruption not involving financial transactions. The possibility of utilizing blockchain was examined by dividing anti-corruption policies into proactive and reactive policies. As a result of this work, the following arguments were presented. First, blockchain can be used as an anti-corruption tool in corruption involving financial transactions, so it is suitable for procurement administration and anti-corruption audits. Second, blockchain technology can be used not only as a proactive anti-corruption tool but also as a reactive anti-corruption tool. Third, blockchain-based anti-corruption policies are expected to reduce costs and have a higher deterrence against corruption compared to existing anti-corruption policies, so they are expected to be highly effective when introduced in developing countries. Fourth, in order for a blockchain-based anti-corruption policy to work properly, it must be introduced throughout the country's financial transaction system, and public officials must have an understanding of blockchain. Lastly, we must be wary of falling into technological omnipotence, knowing that blockchain can only be an anti-corruption policy but not a panacea that can completely eliminate corruption.
The hardest AI problem you’ve never heard of.

There’s a notion in artificial intelligence known as Moravec’s paradox: it’s relatively easy to teach a computer to play chess or checkers at a pretty decent level, but near impossible to teach it something as trivial as bipedal motion. (Hassabis 2017) The sensorimotor tasks that our truly wonderful brains have mastered by our second birthday are much harder to teach a computer than something arguably as ‘complex’ as beating a chess grandmaster.
Sintesis Nanopartikel Tembaga Untuk Pelapisan Kain Polipropilena Terhadap Karakterisasi

Abstract&#x0D; &#x0D; This journal discusses the distribution of nanoparticles on the surface of the sample cloth and its crystal structure based on XRD results. This study consists of experiments and analysis of results using XRD, SEM, and FTIR. The results of this study concluded that the distribution of nanoparticles was less than optimal, which might be due to not using surfactants to modify the surface of the fabric, making it difficult for the nanoparticle clumps to spread and stick more evenly. Then it can be concluded that the nanoparticles formed are copper oxide (Cu2O) nanoparticles and have a crystal lattice type of a Primitive Cubic or simple cubic crystal lattice type overall, and sizes in the range of 25-130 nm.&#x0D; &#x0D; Keywords: Characterization, Copper Nanoparticles, Polypropylene Fabric
My favourite Quora answers

I used to be on Quora. I’m not anymore. But I used to be. And I wrote some answers. Here are some of my favourites, categorised by field… sort of. &lt;strong&gt; AI &lt;/strong&gt; If I wrote an AI and trained it to score 200 on a standardized IQ test, what could a human of 100 IQ still do better? There’s something called Moravec’s paradox, which I’d sum up as follows: stuff that’s difficult for humans is not the same as what’s difficult for a computer.
High-speed photoelastic tomography for axisymmetric stress fields in a soft material: temporal evolution of all stress components

This study presents a novel approach for reconstructing all stress components of the dynamic axisymmetric fields of a soft material using photoelastic tomography (PT) and a high-speed polarization camera. This study focuses on static and dynamic Hertzian contact as an example of transient stress field reconstructions. For the static Hertzian contact (a solid sphere pressed against a gel block), all stress components in the urethane gel, which has an elastic modulus of 47.4 kPa, were reconstructed by PT using the measured photoelastic parameters. The results were compared with theoretical solutions and showed good agreement. For the dynamic Hertzian contact (a sphere impacting gel), a high-speed polarization camera was used to reconstruct the transient stress field within the gel. PT was used to quantitatively measure the shear and axial stress waves and showed different propagation speeds on the substrate. The technique allowed the simultaneous measurement of stress fields ranging from $O(10^{-1})$ to $O(10^1)$ kPa during large deformations, demonstrating its accuracy in capturing rapidly changing stress tensor components in dynamic scenarios. The scaling laws of the calculated impact force agreed with theoretical predictions, validating the accuracy of PT for measuring dynamic axisymmetric stress fields in soft materials.
Hypergraph-based Multi-robot Motion Planning with Topological Guidance

We present a multi-robot motion planning algorithm that efficiently finds paths for robot teams up to ten times larger than existing methods in congested settings with narrow passages in the environment. Narrow passages represent a source of difficulty for sampling-based motion planning algorithms. This problem is exacerbated for multi-robot systems where the planner must also avoid inter-robot collisions within these congested spaces, requiring coordination. Topological guidance, which leverages information about the robot's environment, has been shown to improve performance for mobile robot motion planning in single robot scenarios with narrow passages. Additionally, our prior work has explored using topological guidance in multi-robot settings where a high degree of coordination is required of the full robot group. This high level of coordination, however, is not always necessary and results in excessive computational overhead for large groups. Here, we propose a novel multi-robot motion planning method that leverages topological guidance to inform the planner when coordination between robots is necessary, leading to a significant improvement in scalability.
Stella Nera: Achieving 161 TOp/s/W with Multiplier-free DNN Acceleration based on Approximate Matrix Multiplication

From classical HPC to deep learning, MatMul is at the heart of today's computing. The recent Maddness method approximates MatMul without the need for multiplication by using a hash-based version of product quantization (PQ) indexing into a look-up table (LUT). Stella Nera is the first Maddness accelerator and it achieves 15x higher area efficiency (GMAC/s/mm^2) and more than 25x higher energy efficiency (TMAC/s/W) than direct MatMul accelerators implemented in the same technology. The hash function is a decision tree, which allows for an efficient hardware implementation as the multiply-accumulate operations are replaced by decision tree passes and LUT lookups. The entire Maddness MatMul can be broken down into parts that allow an effective implementation with small computing units and memories, allowing it to reach extreme efficiency while remaining generically applicable for MatMul tasks. In a commercial 14nm technology and scaled to 3nm, we achieve an energy efficiency of 161 TOp/s/W@0.55V with a Top-1 accuracy on CIFAR-10 of more than 92.5% using ResNet9.
Latent Feature-based Data Splits to Improve Generalisation Evaluation: A Hate Speech Detection Case Study

With the ever-growing presence of social media platforms comes the increased spread of harmful content and the need for robust hate speech detection systems. Such systems easily overfit to specific targets and keywords, and evaluating them without considering distribution shifts that might occur between train and test data overestimates their benefit. We challenge hate speech models via new train-test splits of existing datasets that rely on the clustering of models' hidden representations. We present two split variants (Subset-Sum-Split and Closest-Split) that, when applied to two datasets using four pretrained models, reveal how models catastrophically fail on blind spots in the latent space. This result generalises when developing a split with one model and evaluating it on another. Our analysis suggests that there is no clear surface-level property of the data split that correlates with the decreased performance, which underscores that task difficulty is not always humanly interpretable. We recommend incorporating latent feature-based splits in model development and release two splits via the GenBench benchmark.
FedTruth: Byzantine-Robust and Backdoor-Resilient Federated Learning Framework

Federated Learning (FL) enables collaborative machine learning model training across multiple parties without sharing raw data. However, FL's distributed nature allows malicious clients to impact model training through Byzantine or backdoor attacks, using erroneous model updates. Existing defenses measure the deviation of each update from a 'ground-truth model update.' They often rely on a benign root dataset on the server or use trimmed mean or median for clipping, both methods having limitations. We introduce FedTruth, a robust defense against model poisoning in FL. FedTruth doesn't assume specific data distributions nor requires a benign root dataset. It estimates a global model update with dynamic aggregation weights, considering contributions from all benign clients. Empirical studies demonstrate FedTruth's efficacy in mitigating the impacts of poisoned updates from both Byzantine and backdoor attacks.
Stable Differentiable Causal Discovery

Inferring causal relationships as directed acyclic graphs (DAGs) is an important but challenging problem. Differentiable Causal Discovery (DCD) is a promising approach to this problem, framing the search as a continuous optimization. But existing DCD methods are numerically unstable, with poor performance beyond tens of variables. In this paper, we propose Stable Differentiable Causal Discovery (SDCD), a new method that improves previous DCD methods in two ways: (1) It employs an alternative constraint for acyclicity; this constraint is more stable, both theoretically and empirically, and fast to compute. (2) It uses a training procedure tailored for sparse causal graphs, which are common in real-world scenarios. We first derive SDCD and prove its stability and correctness. We then evaluate it with both observational and interventional data and on both small-scale and large-scale settings. We find that SDCD outperforms existing methods in both convergence speed and accuracy and can scale to thousands of variables.
Virtual Heritage at iGrid 2000

As part of the iGrid Research Demonstration at INET 2000, we created two Virtual Cultural Heritage environments - "Virtual Harlem" and "Shared Miletus". The purpose of these applications was to explore possibilities in using the combination of high-speed international networks and virtual reality (VR) displays for cultural heritage education. Our ultimate goal is to enable the construction of tele-immersive museums and classes. In this paper we present an overview of the infrastructure used for these applications, and some details of their construction.
Towards Stronger Blockchains: Security Against Front-Running Attacks

Blockchains add transactions to a distributed shared ledger by arriving at consensus on sets of transactions contained in blocks. This provides a total ordering on a set of global transactions. However, total ordering is not enough to satisfy application semantics under the Byzantine fault model. This is due to the fact that malicious miners and clients can collaborate to add their own transactions ahead of correct clients' transactions in order to gain application level and financial advantages. These attacks fall under the umbrella of front-running attacks. Therefore, total ordering is not strong enough to preserve application semantics. In this paper, we propose causality preserving total order as a solution to this problem. The resulting Blockchains will be stronger than traditional consensus based blockchains and will provide enhanced security ensuring correct application semantics in a Byzantine setting.
Leveraging Function Space Aggregation for Federated Learning at Scale

The federated learning paradigm has motivated the development of methods for aggregating multiple client updates into a global server model, without sharing client data. Many federated learning algorithms, including the canonical Federated Averaging (FedAvg), take a direct (possibly weighted) average of the client parameter updates, motivated by results in distributed optimization. In this work, we adopt a function space perspective and propose a new algorithm, FedFish, that aggregates local approximations to the functions learned by clients, using an estimate based on their Fisher information. We evaluate FedFish on realistic, large-scale cross-device benchmarks. While the performance of FedAvg can suffer as client models drift further apart, we demonstrate that FedFish is more robust to longer local training. Our evaluation across several settings in image and language benchmarks shows that FedFish outperforms FedAvg as local training epochs increase. Further, FedFish results in global networks that are more amenable to efficient personalization via local fine-tuning on the same or shifted data distributions. For instance, federated pretraining on the C4 dataset, followed by few-shot personalization on Stack Overflow, results in a 7% improvement in next-token prediction by FedFish over FedAvg.
Secure Instruction and Data-Level Information Flow Tracking Model for RISC-V

Rising device use and third-party IP integration in semiconductors raise security concerns. Unauthorized access, fault injection, and privacy invasion are potential threats from untrusted actors. Different security techniques have been proposed to provide resilience to secure devices from potential vulnerabilities; however, no one technique can be applied as an overarching solution. We propose an integrated Information Flow Tracking (IFT) technique to enable runtime security to protect system integrity by tracking the flow of data from untrusted communication channels. Existing hardware-based IFT schemes are either fine-, which are resource-intensive, or coarse-grained models, which have minimal precision logic, providing either control flow or data-flow integrity. No current security model provides multi-granularity due to the difficulty in balancing both the flexibility and hardware overheads at the same time. This study proposes a multi-level granularity IFT model that integrates a hardware-based IFT technique with a gate-level-based IFT (GLIFT) technique, along with flexibility, for better precision and assessments. Translation from the instruction level to the data level is based on module instantiation with security-critical data for accurate information flow behaviors without any false conservative flows. A simulation-based IFT model is demonstrated, which translates the architecture-specific extensions into a compiler-specific simulation model with toolchain extensions for Reduced Instruction Set Architecture (RISC-V) to verify the security extensions. This approach provides better precision logic by enhancing the tagged mechanism with 1-bit tags and implementing an optimized shadow logic that eliminates the area overhead by tracking the data for only security-critical modules.
Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection

Graph anomaly detection plays a crucial role in identifying exceptional instances in graph data that deviate significantly from the majority. It has gained substantial attention in various domains of information security, including network intrusion, financial fraud, and malicious comments, et al. Existing methods are primarily developed in an unsupervised manner due to the challenge in obtaining labeled data. For lack of guidance from prior knowledge in unsupervised manner, the identified anomalies may prove to be data noise or individual data instances. In real-world scenarios, a limited batch of labeled anomalies can be captured, making it crucial to investigate the few-shot problem in graph anomaly detection. Taking advantage of this potential, we propose a novel few-shot Graph Anomaly Detection model called FMGAD (Few-shot Message-Enhanced Contrastive-based Graph Anomaly Detector). FMGAD leverages a self-supervised contrastive learning strategy within and across views to capture intrinsic and transferable structural representations. Furthermore, we propose the Deep-GNN message-enhanced reconstruction module, which extensively exploits the few-shot label information and enables long-range propagation to disseminate supervision signals to deeper unlabeled nodes. This module in turn assists in the training of self-supervised contrastive learning. Comprehensive experimental results on six real-world datasets demonstrate that FMGAD can achieve better performance than other state-of-the-art methods, regardless of artificially injected anomalies or domain-organic anomalies.
Exploring the Relationship between In-Context Learning and Instruction Tuning

In-Context Learning (ICL) and Instruction Tuning (IT) are two primary paradigms of adopting Large Language Models (LLMs) to downstream applications. However, they are significantly different. In ICL, a set of demonstrations are provided at inference time but the LLM's parameters are not updated. In IT, a set of demonstrations are used to tune LLM's parameters in training time but no demonstrations are used at inference time. Although a growing body of literature has explored ICL and IT, studies on these topics have largely been conducted in isolation, leading to a disconnect between these two paradigms. In this work, we explore the relationship between ICL and IT by examining how the hidden states of LLMs change in these two paradigms. Through carefully designed experiments conducted with LLaMA-2 (7B and 13B), we find that ICL is implicit IT. In other words, ICL changes an LLM's hidden states as if the demonstrations were used to instructionally tune the model. Furthermore, the convergence between ICL and IT is largely contingent upon several factors related to the provided demonstrations. Overall, this work offers a unique perspective to explore the connection between ICL and IT and sheds light on understanding the behaviors of LLM.
High-fidelity Person-centric Subject-to-Image Synthesis

Current subject-driven image generation methods encounter significant challenges in person-centric image generation. The reason is that they learn the semantic scene and person generation by fine-tuning a common pre-trained diffusion, which involves an irreconcilable training imbalance. Precisely, to generate realistic persons, they need to sufficiently tune the pre-trained model, which inevitably causes the model to forget the rich semantic scene prior and makes scene generation over-fit to the training data. Moreover, even with sufficient fine-tuning, these methods can still not generate high-fidelity persons since joint learning of the scene and person generation also lead to quality compromise. In this paper, we propose Face-diffuser, an effective collaborative generation pipeline to eliminate the above training imbalance and quality compromise. Specifically, we first develop two specialized pre-trained diffusion models, i.e., Text-driven Diffusion Model (TDM) and Subject-augmented Diffusion Model (SDM), for scene and person generation, respectively. The sampling process is divided into three sequential stages, i.e., semantic scene construction, subject-scene fusion, and subject enhancement. The first and last stages are performed by TDM and SDM respectively. The subject-scene fusion stage, that is the collaboration achieved through a novel and highly effective mechanism, Saliency-adaptive Noise Fusion (SNF). Specifically, it is based on our key observation that there exists a robust link between classifier-free guidance responses and the saliency of generated images. In each time step, SNF leverages the unique strengths of each model and allows for the spatial blending of predicted noises from both models automatically in a saliency-aware manner. Extensive experiments confirm the impressive effectiveness and robustness of the Face-diffuser.