Reviewerly/exHarmony huggingface dataset:
소비자의 수산물 안전 인식 영향요인 분석

This study aims to analyze factors that affect perception of seafood safety. In this study, we considered the population-social characteristics, subjective perception･experience and reliability for seafood production-distribution-consumption stages as factors influencing the safety perception of seafood products. The dependent variable is the reliability of seafood product safety, and the analysis used the partial proportional odds model because the parallel regresstion assumption of the ordered logit model result was violated. The main results are as follows; Female had less confidence in seafood safety than male, and consumers of high age class have high reliability in seafood safety; Consumers who highly value the health benefits of seafood products have high reliability in the safety of seafood products; The greatest influence in recognizing the safety of seafood products is the reliability of the production stage. Therefore, in order to improve perception of seafood safety, promotion and education in the safety of seafood products shall be conducted. And the reliability of the fisheries production stage should be strengthened. Finally, The government should provide safety information that meets consumer needs so that they can eat seafood with confidence.
SECURE BC- HRQLRL POL PROTOCOL FOR THE SMART HOME GATEWAY TO AVOID DATA FORGERY

The world is made smarter along with more receptive with the quick augmentation of the Internet of Things (IoT). Such transformation of IoT is utilized by Smart Home (SH), which appears to be the future wave. But, data security along with privacy problems for the centralized gateway data and data sharing have also increased with the augmenting broad adoption of IoT. Utilizing Blockchain (BC) technology, an approach for data privacy along with security is proposed by this paper in an SH for solving these challenges. A solution is generated by the works for overcoming the reported security drawback in the generally utilized permissioned SH gateways. For avoiding data forgery, a secure BC-based Highly Regularized Q-Learning reinforcement learning-based proof of learning (HRQLRL PoL) protocol for the SH gateway is proffered via the proposed work. Initially, the user details are registered by the work and it saves their personnel details in a security provision of the developed Cipolla’s Extended Euclidean Distance Algorithm Based Lattice Cryptosystem (CEED-LC) method. A small Key Generation (KG) with high security is offered via the proposed cryptosystem and the data’s confidentiality and integrity is maintained. Then, the blocks are produced with a unique gateway enrolment ID under the agreement of the HRQLRL PoL protocol that secures data with low computation time and memory. The chances of majority attacks like adversarial attacks are avoided and data’s decentralized distribution is offered. Experimental results illustrate that by attaining a higher throughput along with Packet delivery ratio (PDR) value, the proposed framework is very secure against vulnerabilities. It remains to be very scalable when analogized to the existent top-notch methods.
Neuro-Symbolic Approaches for Text-Based Policy Learning

Text-Based Games (TBGs) have emerged as important testbeds for reinforcement learning (RL) in the natural language domain. Previous methods using LSTM-based action policies are uninterpretable and often overfit the training games showing poor performance to unseen test games. We present SymboLic Action policy for Textual Environments (SLATE), that learns interpretable action policy rules from symbolic abstractions of textual observations for improved generalization. We outline a method for end-to-end differentiable symbolic rule learning and show that such symbolic policies outperform previous state-of-the-art methods in text-based RL for the coin collector environment from 5-10x fewer training games. Additionally, our method provides human-understandable policy rules that can be readily verified for their logical consistency and can be easily debugged.
Extractors: Low Entropy Requirements Colliding With Non-Malleability

The known constructions of negligible error (non-malleable) two-source extractors can be broadly classified in three categories: (1) Constructions where one source has min-entropy rate about $1/2$, the other source can have small min-entropy rate, but the extractor doesn't guarantee non-malleability. (2) Constructions where one source is uniform, and the other can have small min-entropy rate, and the extractor guarantees non-malleability when the uniform source is tampered. (3) Constructions where both sources have entropy rate very close to $1$ and the extractor guarantees non-malleability against the tampering of both sources. We introduce a new notion of collision resistant extractors and in using it we obtain a strong two source non-malleable extractor where we require the first source to have $0.8$ entropy rate and the other source can have min-entropy polylogarithmic in the length of the source. We show how the above extractor can be applied to obtain a non-malleable extractor with output rate $\frac 1 2$, which is optimal. We also show how, by using our extractor and extending the known protocol, one can obtain a privacy amplification secure against memory tampering where the size of the secret output is almost optimal.
Bilingual Alignment Pre-Training for Zero-Shot Cross-Lingual Transfer

Multilingual pre-trained models have achieved remarkable performance on cross-lingual transfer learning. Some multilingual models such as mBERT, have been pre-trained on unlabeled corpora, therefore the embeddings of different languages in the models may not be aligned very well. In this paper, we aim to improve the zero-shot cross-lingual transfer performance by proposing a pre-training task named Word-Exchange Aligning Model (WEAM), which uses the statistical alignment information as the prior knowledge to guide cross-lingual word prediction. We evaluate our model on multilingual machine reading comprehension task MLQA and natural language interface task XNLI. The results show that WEAM can significantly improve the zero-shot performance.
CPU Side-Channels: New Attacks and Applications

Author(s): Wang, Daimeng | Advisor(s): Qian, Zhiyun | Abstract: CPU micro-architectural side-channels, or CPU side-channels in short, have gained plenty of attention recently. Many existing works have proved that classical CPU side-channel attacks (e.g. Prime+probe and Flush+reload), as well as recently-discovered attacks (e.g. Spectre, Meltdown, Zombieload), are practical and effective against cryptographic libraries. However, it's in our belief that CPU side-channels have more potential and can be utilized in a wider variety of attacks and applications.In our work, we strive to push the capacity of existing CPU side-channel attacks and apply them for novel attacks and applications, and in the meanwhile discovering new research aspects. More specifically, 1) we propose the concept of a prime+probe attack to extract onscreen keyboard inputs on Android, 2) we design and implement an automated approach to augment prime+pro be attack in the environment of aggressive cache prefetching and demonstrate significant improvement over traditional prime+probe attack, 3) we design a machine-learning-based system to automatically discover execution timing side-channels in graphics rendering libraries and using flush+reload attack to exploit them on multiple platforms, evaluate using real-world applications and demonstrate its ability to infer sensitive user input with high accuracy, 4) we propose to use CPU side-channels as feedback to fuzzing when target binary cannot be modified and performed some initial evaluations, 5) we propose to use improve the coverage discovery rate of kernel fuzzing with reinforcement learning, implement it around Syzkaller and significantly improve its coverage growth fuzzing Linux kernel. Ultimately, we demonstrate that CPU side-channels have great potentials and can be practically applied in many attacks and applications. Moreover, researching CPU side-channel attacks and applications can sometimes lead to interesting new research aspects.
A Study in Developing an Educational Power Bank as Backup Source of Power in Using Technology and Instructional Material of Learning Process for Teachers

AbstractThe use of technology today is considered as phenomenon; for it is always associated with our daily lifestyles, hobbies and even in the aspect of learning process especially in using high-tech gadgets tools and other related devices as instructional material. It cannot be denied that all of these technologies consume power and run by it. Power on the other hand, should be available anywhere and anytime to make the said technology functional. But power outages can happen unexpectedly for a variety of reasons. It is always best to be prepared for a loss of power, especially if you live in a place that is prone to power interruptions, particularly during a stormy weather or far from the source of electricity. Eventually, in using instructional material inside the classroom like projector, blue tooth speaker with microphone and Wi-Fi modem needs electricity in order to be functional. Power bank is very important device as back up source of electricity. It can make teachers and learners continue to use their technology during power outages. The availability of power in the classroom as learning process takes place while using technology is an example of how power bank is necessary especially far from the source. It is for this reason that A Study in Developing an Educational Power Bank as Back up Source of Power in Using Technology and Instructional Material of Learning Process for School Teachers was conceptualized. This study will be conducted to develop an Educational Power Bank as an alternative source of electricity and instructional material of learning process.
Tracking Without Re-recognition in Humans and Machines.

Imagine trying to track one particular fruitfly in a swarm of hundreds. Higher biological visual systems have evolved to track moving objects by relying on both appearance and motion features. We investigate if state-of-the-art deep neural networks for visual tracking are capable of the same. For this, we introduce PathTracker, a synthetic visual challenge that asks human observers and machines to track a target object in the midst of identical-looking distractor objects. While humans effortlessly learn PathTracker and generalize to systematic variations in task design, state-of-the-art deep networks struggle. To address this limitation, we identify and model circuit mechanisms in biological brains that are implicated in tracking objects based on motion cues. When instantiated as a recurrent network, our circuit model learns to solve PathTracker with a robust visual strategy that rivals human performance and explains a significant proportion of their decision-making on the challenge. We also show that the success of this circuit model extends to object tracking in natural videos. Adding it to a transformer-based architecture for object tracking builds tolerance to visual nuisances that affect object appearance, resulting in a new state-of-the-art performance on the large-scale TrackingNet object tracking challenge. Our work highlights the importance of building artificial vision models that can help us better understand human vision and improve computer vision.
TAAC: Temporally Abstract Actor-Critic for Continuous Control

We propose temporally abstract actor-critic (TAAC), an off-policy RL algorithm that incorporates closed-loop temporal abstraction into the actor-critic framework in a simple manner. TAAC adds a second-stage binary policy to choose between the previous action and a new action output by an actor. Crucially, its act-or-repeat decision hinges on the actually sampled action instead of the expected behavior of the actor. This post-acting switching scheme let the overall policy make more informed decisions. TAAC has two important features: persistent exploration and a new compare-through Q operator for multi-step TD backup. We demonstrate TAAC's advantages over several strong baselines across 5 different categories of 14 continuous control tasks. Code is available at this https URL.
Learning to Combine Per-Example Solutions for Neural Program Synthesis

The goal of program synthesis from examples is to find a computer program that is consistent with a given set of input-output examples. Most learning-based approaches try to find a program that satisfies all examples at once. Our work, by contrast, considers an approach that breaks the problem into two stages: (a) find programs that satisfy only one example, and (b) leverage these per-example solutions to yield a program that satisfies all examples. We introduce the Cross Aggregator neural network module based on a multi-head attention mechanism that learns to combine the cues present in these per-example solutions to synthesize a global solution. Evaluation across programs of different lengths and under two different experimental settings reveal that when given the same time budget, our technique significantly improves the success rate over PCCoder arXiv:1809.04682v2 [cs.LG] and other ablation baselines. The code, data and trained models for our work can be found at this https URL.
Robustness of Graph Neural Networks at Scale

Graph Neural Networks (GNNs) are increasingly important given their popularity and the diversity of applications. Yet, existing studies of their vulnerability to adversarial attacks rely on relatively small graphs. We address this gap and study how to attack and defend GNNs at scale. We propose two sparsity-aware first-order optimization attacks that maintain an efficient representation despite optimizing over a number of parameters which is quadratic in the number of nodes. We show that common surrogate losses are not well-suited for global attacks on GNNs. Our alternatives can double the attack strength. Moreover, to improve GNNs' reliability we design a robust aggregation function, Soft Median, resulting in an effective defense at all scales. We evaluate our attacks and defense with standard GNNs on graphs more than 100 times larger compared to previous work. We even scale one order of magnitude further by extending our techniques to a scalable GNN.
Never Go Full Batch (in Stochastic Convex Optimization)

We study the generalization performance of $\text{full-batch}$ optimization algorithms for stochastic convex optimization: these are first-order methods that only access the exact gradient of the empirical risk (rather than gradients with respect to individual data points), that include a wide range of algorithms such as gradient descent, mirror descent, and their regularized and/or accelerated variants. We provide a new separation result showing that, while algorithms such as stochastic gradient descent can generalize and optimize the population risk to within $\epsilon$ after $O(1/\epsilon^2)$ iterations, full-batch methods either need at least $\Omega(1/\epsilon^4)$ iterations or exhibit a dimension-dependent sample complexity.
Modifikasi Algoritma XTEA dengan Pembangkitan Kunci Menggunakan Metode Linear Congruential Untuk Pengamanan File Dokumen

Confidential document files must have security both when the file process is sent or stored in the stroge media. This is done so that the confidential document file is not known by someone who does not have authority over the file, or to avoid loss if the file falls into the wrong hands. In order to achieve this, a document file security technique is needed that can convert document file data into unrecognized data. One of the techniques that can be used is cryptography. Cryptography makes use of algorithms to encode document files with an encryption process, and restore document files with a decryption process. One of the algorithms that can be used is the XTEA algorithm. XTEA divides the document file string into 2 parts, namely AL 32 bit and BL 32 bit. The weakness of the XTEA algorithm when carrying out the encryption or decryption process with repeated key scheduling so that it is not effective in optimizing the security of encrypted document files, therefore in this study the Linear Congruential Generator (LCG) method is added to generate XTEA keys. LCG has values ??a, c, m and X0 which triggers to get the XTEA key. So that in the process of distributing encrypted document files, the key sent is the LCG trigger value in the form of a, c, m and X0, if the value is known by the attacker, the encrypted document file cannot be returned. So that the results of the XTEA encryption algorithm with key optimizer based on the LCG method produce a document file cipher that is more optimal in security
Coverage of Information and Communication Technology News by Selected Newspapers in Tanzania

This study explored media coverage of Information and Communication Technology in Tanzania’s four print media published between 2009 and 2013. Specifically, the study aimed at; assessing the trend in covering ICTs information from 2009-2013, identifying ICTs themes covered and exploring the geographic coverage of ICTs information from 2009-2013. Data were collected using informetrics method. Microsoft Excel software 2007 and content analysis were used to analyse data. Results revealed 823 articles were published on ICTs in five years. Additionally, despite variation trends in coverage, the year 2013 had highest coverage. Results showed ICTs themes related to mobile technologies were highly reported compared to others. Results indicate international news on ICTs to be highly reported compared to local news. The study concludes that there is still low coverage of ICTs information in Tanzania newspapers. It recommends increased coverage of ICTs information in media by privately owned newspapers and reporting in local language.
Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark

Despite the recent popularity of neural network-based solvers for optimal transport (OT), there is no standard quantitative way to evaluate their performance. In this paper, we address this issue for quadratic-cost transport -- specifically, computation of the Wasserstein-2 distance, a commonly-used formulation of optimal transport in machine learning. To overcome the challenge of computing ground truth transport maps between continuous measures needed to assess these solvers, we use input-convex neural networks (ICNN) to construct pairs of measures whose ground truth OT maps can be obtained analytically. This strategy yields pairs of continuous benchmark measures in high-dimensional spaces such as spaces of images. We thoroughly evaluate existing optimal transport solvers using these benchmark measures. Even though these solvers perform well in downstream tasks, many do not faithfully recover optimal transport maps. To investigate the cause of this discrepancy, we further test the solvers in a setting of image generation. Our study reveals crucial limitations of existing solvers and shows that increased OT accuracy does not necessarily correlate to better results downstream.
Robust Predictable Control

Many of the challenges facing today's reinforcement learning (RL) algorithms, such as robustness, generalization, transfer, and computational efficiency are closely related to compression. Prior work has convincingly argued why minimizing information is useful in the supervised learning setting, but standard RL algorithms lack an explicit mechanism for compression. The RL setting is unique because (1) its sequential nature allows an agent to use past information to avoid looking at future observations and (2) the agent can optimize its behavior to prefer states where decision making requires few bits. We take advantage of these properties to propose a method (RPC) for learning simple policies. This method brings together ideas from information bottlenecks, model-based RL, and bits-back coding into a simple and theoretically-justified algorithm. Our method jointly optimizes a latent-space model and policy to be self-consistent, such that the policy avoids states where the model is inaccurate. We demonstrate that our method achieves much tighter compression than prior methods, achieving up to 5x higher reward than a standard information bottleneck. We also demonstrate that our method learns policies that are more robust and generalize better to new tasks.
Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameters

Theoretical analyses for graph learning methods often assume a complete observation of the input graph. Such an assumption might not be useful for handling any-size graphs due to the scalability issues in practice. In this work, we develop a theoretical framework for graph classification problems in the partial observation setting (i.e., subgraph samplings). Equipped with insights from graph limit theory, we propose a new graph classification model that works on a randomly sampled subgraph and a novel topology to characterize the representability of the model. Our theoretical framework contributes a theoretical validation of mini-batch learning on graphs and leads to new learning-theoretic results on generalization bounds as well as size-generalizability without assumptions on the input.
Dense Unsupervised Learning for Video Segmentation

We present a novel approach to unsupervised learning for video object segmentation (VOS). Unlike previous work, our formulation allows to learn dense feature representations directly in a fully convolutional regime. We rely on uniform grid sampling to extract a set of anchors and train our model to disambiguate between them on both inter- and intra-video levels. However, a naive scheme to train such a model results in a degenerate solution. We propose to prevent this with a simple regularisation scheme, accommodating the equivariance property of the segmentation task to similarity transformations. Our training objective admits efficient implementation and exhibits fast training convergence. On established VOS benchmarks, our approach exceeds the segmentation accuracy of previous work despite using significantly less training data and compute power.
Development of a Forecasting Model for Treasury Balance Using Machine Learning

The Government are always making various efforts to establish sound financial management and stable budget policies.. In particular, the work related to the fund plan and the operation plan of the treasury through manual work is subject to improvement, and a financial estimation system is needed to achieve it.. In this study, we wanted to analyze whether it is possible to predict the balance of the treasury by utilizing machine learning. First of all, we developed a forecasting model for treasury balances. In addition, to verify the applicability, the government balance predictability of machine learning techniques, performance verification, and major variable verification were performed. In detail, data collection and refinement, exploratory analysis, analytical model design and development, and analysis result review were studied. The analysis target data is 120 months of total data from 2010 to 2019, limited to income tax and value-added tax revenue forecasts, with monthly revenue volume and major taxes. This was predicted for 2019 tax revenue respectively. The application algorithms are Linear Regression, Random Forest, and Gradient Boosting. According to the analysis, Linear Regression was the best in revenue and income taxes, while Gradient Boosting was the best in VAT. In conclusion, it was analyzed that machine learning was applicable to treasury predictions. This will allow quick results, scientific analysis through the introduction of big data-based models, and the person in charge will be able to improve predictive power through new model tests. It is also expected that the government