Reviewerly/exHarmony huggingface dataset:
A new perspective on probabilistic image modeling

We present the Deep Convolutional Gaussian Mixture Model (DCGMM), a new probabilistic approach for image modeling capable of density estimation, sampling and tractable inference. DCGMM instances exhibit a CNN-like layered structure, in which the principal building blocks are convolutional Gaussian Mixture (cGMM) layers. A key innovation w.r.t. related models like sum-product networks (SPNs) and probabilistic circuits (PCs) is that each cGMM layer optimizes an independent loss function and therefore has an independent probabilistic interpretation. This modular approach permits intervening transformation layers to harness the full spectrum of (potentially non-invertible) mappings available to CNNs, e.g., max-pooling or half-convolutions. DCGMM sampling and inference are realized by a deep chain of hierarchical priors, where a sample generated by a given cGMM layer defines the parameters of sampling in the next-lower cGMM layer. For sampling through non-invertible transformation layers, we introduce a new gradient-based sharpening technique that exploits redundancy (overlap) in, e.g., half-convolutions. DCGMMs can be trained end-to-end by SGD from random initial conditions, much like CNNs. We show that DCGMMs compare favorably to several recent PC and SPN models in terms of inference, classification and sampling, the latter particularly for challenging datasets such as SVHN. We provide a public TF2 implementation.
Improved Polynomial Multiplication Algorithms over Characteristic Three Fields and Applications to NTRU Prime

This paper introduces a new polynomial multiplication algorithm which decreases the arithmetic complexity and another modified algorithm that speeds up the implementation run-time over the characteristic three fields. We first introduce a new polynomial multiplication algorithm using a 4-way split approach and observe that its asymptotic arithmetic complexity is better than Bernstein’s 3-way method for characteristic three fields. We then define an unbalanced split version a 5-way split method which is faster than Bernstein’s 3-way method in terms of implementation speed. We observe that, compared to the most recent methods, for the input size 1280, the new 4-way method together with the unbalanced 5-way split one provide a $$48.6\%$$ decrease in arithmetic complexity for polynomial multiplication over $$\mathbb {F}_{9}$$ and a $$26.8\%$$ decrease for polynomial multiplication over $$\mathbb {F}_{3}$$ respectively. Moreover, from the implementation perspective, the unbalanced 5-way algorithm yields faster polynomial multiplication results. For application purposes, we pick the quantum-resistant key encapsulation protocol NTRU Prime, proposed by Bernstein et al., since it executes a characteristic three polynomial multiplication in the decapsulation stage. Implementing the proposed algorithms combining with the other known methods in C, on an Intel (R) Core (TM) i7-10510U architecture, we observe a $$29.85\%$$ speedup for sntrup653 and a $$35.52\%$$ speedup for sntrup761.
Data Encryption System for Smart Homes

With the improvement of people's living standards and the increasing maturity of wireless transmission technology, smart home is further promoted and applied, but due to the imperfect development of smart home bureau, there are still many security risks, mainly in the lack of effective authentication mechanism, the security of authentication is not high; smart home devices have loopholes, smart devices have memory loopholes, logic loopholes; data security is not high, in the transmission The data security is not high, and it is easy to be stolen and listened to during the transmission. In this paper, based on data security aspects, combining previous research experiences and combining asymmetric and symmetric encryption algorithms, we propose a smart home data encryption system based on RSA + AES hybrid encryption to improve the communication security of each smart home.
Comparison on Image to Image Translation Algorithms

Image to image translation(I2I) is one of the important parts in computer vision area. It includes lots of applications in this time. GAN provides basic theory for this problem which is an advanced method of machine learning area. The structure of GAN contains two main parts: Generator and Discriminator. This new method developed the performance of I2I problem. In this paper, five papers of I2I area using GAN method have been summarized including a paper about cGAN and a paper about cycle GAN. The rest three paper is about unsupervised learning. These five papers used different method based on GAN algorithm and can be used on different problems. Finally, there are still some problems cannot be solved in this area, this paper also discuss which problem could be development in the future.
Explainable Arguments

We introduce an intriguing new type of argument systems with the additional property of being explainable. Intuitively by explainable, we mean that given any argument under a statement, and any witness, we can produce the random coins for which the $$\textsf{Prove}$$ algorithm outputs the same bits of the argument. This work aims at introducing the foundations for the interactive as well as the non-interactive setting. We show how to build explainable arguments from witness encryption and indistinguishability obfuscation. Finally, we show applications of explainable arguments. Notably we construct deniable chosen-ciphertext secure encryption. Previous deniable encryption scheme achieved only chosen plaintext security.
A Method for Cross-receiver Specific Emitter Identification Based on CBAM-CNN-BDA

To solve the problem that recognition accuracy drops due to the receiver is replaced in the application of specific emitter identification (SEI) technology, a method for cross-receiver SEI based on CBAM-CNN-BDA is proposed. In this paper, transfer learning is used to match feature distributions, thereby reducing the influence of cross-receiver factors on recognition performance. Particularly, the Convolutional Block Attention Module (CBMA) is added into the Convolutional Neural Network (CNN) in order to extract deep features. Then, we map the features of different distributions to a high-dimensional Reproducing Kernel Hilbert Space (RKHS) with the method of Balanced Distribution Adaptive (BDA). Finally, K-Nearest Neighbor (KNN) is used to identify specific emitters and unsupervised transfer learning between different receivers is realized. The proposed framework in this paper has a prefect performance on the modeling simulation signals of cross-receiver SEI. Simultaneously, the experiment with real signals which are collected from Automatic Identification System (AIS) proves that CBAM-CNN-BDA can effectively improve the identification accuracy and outperform than the traditional algorithm.
Dynamic Collaboration Convolution for Robust RGBT Tracking

Learning powerful representation of individual modality is critical for RGBT tracking. Recent works mainly focus on utilizing multiple convolutions to model feature representations of each modality. However, they usually leverage static convolutions to extract features, which are hard to handle complex input data. To deal with this problem, we propose a dynamic collaboration convolution, named DC-Conv, including a set of static convolutions and a weight-router module, for robust RGBT tracking. In specific, we set four static convolutions to each modality in every layer to model each modality, and design a weight-router module to fuse these static convolutions using learned dynamic weights. Such a dynamic weighting scheme makes the convolutions can be adapted to the variations of input data, and thus greatly improves the tracking performance. In addition, we propose an effective progressive learning algorithm to maximize the role of each convolution to make it capture discriminative representations. We evaluate our method on two public RGBT tracking benchmarks, and the results demonstrate the effectiveness of our tracker against state-of-the-art methods.
IMPLEMENTATION OF COOPERATIVE LEARNING METHODS IN IMPROVING READING COMPREHENSION

The purpose of this study was to determine the implementation of the Cooperative Learning Method in teaching reading comprehension, especially reading Factual Report texts to Class X Catering (JB) students at SMK Negeri 1 Ciamis, Ciamis District, Ciamis Regency, 2017/2018 Academic Year. This type of research is Classroom Action Research (CAR) and the design of this research refers to the Kemmis and Mc.Taggart model which consists of one action one cycle, which begins with planning, action, observation and reflection. This research was conducted in the odd semester of the 2017/2018 academic year, namely in October 2017. The subjects of this research were students of class X Catering Services (JB) SMK Negeri 1 Ciamis, totaling 31 people. The object of research is students' reading comprehension. Research data obtained through observation, interviews, documentation and reading comprehension tests. This study uses qualitative data analysis and quantitative data analysis. Quantitative methods were used to analyze the data on students' reading test results (pre-test and post-test) which could be used to analyze statistical data on descriptive text learning materials. Meanwhile, qualitative methods were used to analyze data from observations, student questionnaires, and interviews. The data obtained from quantitative and qualitative methods are then compared and matched to obtain the final conclusions from the research. This research consists of two cycles of 4 actions. The average pre-test showed an achievement of 60.48. The average post-test results in the first cycle of meeting 1 increased by 6.81 to 68.06. Cycle I meeting 2 increased from the previous cycle of 4.04 to 72.10. The second cycle of the first meeting increased by 1.44 to 74.2 and in the second cycle of the second meeting it increased by 0.98 to 83.9. The results showed that the Cooperative Learning Method could improve students' reading skills, especially factual report texts.
Leveraging Network Similarity Measures for Recommendation Systems

With the growth of e-commerce websites, efficient recommendation systems are desired to reduce the turnaround time for servicing a customer. This study focuses on understanding the various techniques and algorithms that are used to model real-life recommendation systems. We present a recommendation engine for Amazon products that uses collaborative filtering (CF). Given a list of users and their reviews of Amazon products, our CF-based recommendation engine generates a ranked list of the top k products for individual users. The generated recommendations are based on the preferences of similar users and past purchases. We have created two such systems: a memory-based user-item CF system and a model-based CF system that utilizes matrix factorization techniques. Additionally, we also propose a graph-based recommendation technique that generates a list of the most similar products. This method relies on network-based local similarity metrics to generate product suggestions.
Application of innovative methods in the formation of intercultural competencies among students of higher educational institutions in the digital educational environment

At the present stage, the expansion of the interconnection and interdependence of different countries, peoples and cultures living in the global world space is becoming obvious. Managers of the 21st century are forced to work in the new economic, political, and cultural conditions of global business, which determine the process of forming a single financial and information space. At the same time, doing business is not related to a specific country and its culture and means that it defines business as international and intercultural. The Customs Service of the Russian Federation, performing the main functions of state management of foreign economic activity, acts as an independent socio-economic institution designed to create favorable conditions for the development of international cooperation. In this regard, there is a need to train qualified customs managers who speak foreign languages, are able to conduct effective professional activities in a foreign language environment, conduct intercultural dialogue, know the cultural realities of their native and foreign languages, be able to apply this knowledge in direct communication with participants in foreign economic activity, in other words, possess intercultural competencies. Many scientists in their research note the unsatisfactory preparation of students of modern higher educational institutions for an integrated world system. Given the growing cultural diversity of society, the need to possess intercultural competencies comes to the fore. In this regard, the article discusses the results of diagnostics of students' possession of intercultural competencies by future managers and the possibility of introducing innovative methods of teaching intercultural communication into the curriculum, which will allow them to form the ability to communicate orally and in writing in Russian and foreign languages to solve professional problems of interpersonal and intercultural interaction.
Towards a Polish Question Answering Dataset (PoQuAD)

This paper presents the efforts towards creating PoQuAD, a dataset for training automatic question answering models in Polish. It justifies why having native data is vital for training accurate Question Answering systems. PoQuAD broadly follows the methodology of SQuAD 2.0 (including impossible questions), but detracts from it in a few aspects. The first of these concerns reducing annotation density in order to broaden the range of topics included. The second is the inclusion of a generative answer layer to better suit the needs of a morphologically rich language. PoQuAD is a work in progress and so far consists of over 29000 question-answer pairs with contexts extracted from Polish Wikipedia. The planned size of the dataset is over 50 thousand such entries. The paper describes the annotation process and the guidelines which were given to annotators in order to ensure quality of the data. The collected data is subjected to analysis in order to shed some light on its linguistic properties and on the difficulty of the task.
Breaking Fault Attack Countermeasures With Side-Channel Information

In the persistent fault-based collision attack (PFCA) (Zheng et al. 2021), the adversary captures the information that the intermediate states have collided through identical correct/incorrect ciphertexts. However, fault countermeasures achieve suppression of incorrect ciphertexts and prevent the PFCA. In this paper, we measure the collision of internal states (or state bytes) using side-channel information. First, for round-level countermeasures, we identify state bytes hitting the same persistent fault during the first round of encryption by the shortest runtime. Additionally, we design sliding-window algorithms to automatically identify the runtime of one-round encryptions suitable for different execution environments. Second, for algorithm-level protections, we detect the collision of the internal states after the first round of encryption through the maximum similarity of power consumption traces. Meanwhile, to address the low success rate of key recovery caused by miss detection due to noise within runtime or power consumption, we further revise the original filtering algorithm in PFCA. Third, we implement round-level protected AES on PC to measure runtime, and both AES protected by round-level (or algorithm-level) countermeasures and SM4 (ISO/IEC 2021) protected by a round-level countermeasure on a smart card to collect power consumption. Finally, the experimental result proves that the revised PFCA successfully recovers the key.
A Generalized Transformed Density Rejection Algorithm

Transformed density rejection is a very flexible method for generating non-uniform random variates. It is based on the acceptance-rejection principle and utilizes a strictly monotone map that transforms the given density into a concave or convex function. Hat function and squeezes are then constructed by means of tangents and secant. We present a new method that works for arbitrary one time continuously differentiable densities. It requires together with the log-density and its derivative a partition of the domain into subdomains that contain at most one inflection point. This improves a previous method of the authors in which also the second derivative is required. We show how the algorithm can be applied to generate from the Generalized Inverse Gaussian distribution, from the Generalized Hyperbolic distribution and from the Watson distribution. The new algorithm can also generate random variates from truncated distributions without problems.
Dissecting Applications Uninstallers and Removers: Are They Effective?

Developing a safe application is so important as to properly install it in a system, and not an application’s tampered version. In a similar note, developers should properly care about applications’ uninstall process to avoid leaving traces of sensitive data behind in the system or interfere with the remaining applications. Until now, the academic literature has paid little attention to uninstall procedures so far. Moreover, a whole ecosystem of application uninstallers has been created, making multiple uninstallers available in software repositories. A key point is to understand how these applications work so as to develop stronger systems. To this end, we present a landscape work evaluating the operation of the 11 most downloaded uninstaller applications from the three most popular Internet software repositories. We discovered that most of these applications are not very different from the native Windows uninstaller. Although evaluated uninstallers present a more organized User Interface, thus enhancing usability, they are only able to find the same installed application as the native Windows uninstaller, but not broken installations. Few uninstallers apply heuristics to find broken application installations. However, we show that those heuristics can be abused by attackers to remove third applications. Finally, we also show that none of the removers is resistant to malicious uninstallers that terminate the remover process.
A Privacy-Preserving Distributed Machine Learning Protocol Based on Homomorphic Hash Authentication

Privacy-preserving machine learning is a hot topic in Artificial Intelligence (AI) area. However, there are also many security issues in all stages of privacy-oriented machine learning. This paper focuses on the dilemma that the privacy leakage of server-side parameter aggregation and external eavesdropper tampering during message transmission in the distributed machine learning framework. Combining with secret sharing techniques, we present a secure privacy-preserving distributed machine learning protocol under the double-server model based on homomorphic hash function, which enables our protocol verifiable. We also prove that our protocol can meet client semi-honest security requirements. Besides, we evaluate our protocol by comparing with other mainstream privacy preserving frameworks, in the aspects of computation, communication complexity analysis, in addition to a concrete implementation from the perspective of model convergence rate and execution time. Experimental results demonstrate that the local training model tends to converge at nearly 50 epochs where the convergence time is less than 400 s.
A Framework for Automated Worker Evaluation Based on Free-Text Responses with No Ground Truth

Evaluating workers according to their work quality is an important managerial task. However, such evaluation becomes highly challenging when no ground-truth information is available against which to compare workers’ output. Previous work has addressed this problem in settings in which workers produce binary, numerical or multi-categorical labels. Here, we consider the problem of automatically evaluating workers on the basis of their free-text responses to open-ended questions, without ground truth. To address this problem, we propose a new, unsupervised framework for automated worker evaluation. The framework is based on two main ideas: (a) framing the problem of textual response-based worker evaluation as a multidimensional voting problem; and (b) using an iterative reweighting algorithm that benefits from a holistic assessment of workers’ inherent capabilities. To evaluate the framework, we empirically test its performance in two separate studies: using a semi-synthetic-data-based evaluation; and using two datasets of real workers' textual responses. In a third study we use a pure numerical simulation to explore the method’s operating conditions. Overall, we find that, across multiple settings, the framework consistently obtains superior results compared with a baseline approach, and that its performance is robust in various challenging conditions. Thus, our framework can serve as a useful benchmark for future research on this problem. Additional benefits of our framework include scalability, modularity, and compatibility with existing, advanced, textual representations.
Temporal Alignment for History Representation in Reinforcement Learning

Environments in Reinforcement Learning are usually only partially observable. To address this problem, a possible solution is to provide the agent with information about the past. However, providing complete observations of numerous steps can be excessive. Inspired by human memory, we propose to represent history with only important changes in the environment and, in our approach, to obtain automatically this representation using self-supervision. Our method (TempAl) aligns temporally-close frames, revealing a general, slowly varying state of the environment. This procedure is based on contrastive loss, which pulls embeddings of nearby observations to each other while pushing away other samples from the batch. It can be interpreted as a metric that captures the temporal relations of observations. We propose to combine both common instantaneous and our history representation and we evaluate TempAl on all available Atari games from the Arcade Learning Environment. TempAl surpasses the instantaneous-only baseline in 35 environments out of 49. The source code of the method and of all the experiments is available at https://github.com/htdt/tempal.
Paths Through Spatial Networks

Spatial networks present unique challenges to understanding topo-logical structure. Each node occupies a location in physical space (e.g., longitude and latitude) and each link may either indicate a fixed and well-described path (e.g., along streets) or a logical connection only. Placing these elements in a map maintains these physical relationships while making it more difficult to identify topological features of interest such as clusters, cliques, and paths. While some systems provide coordinated representations of a spatial network, it is almost universally assumed that the network itself is the sole mechanism for movement across space. In this paper, I present a novel approach for exploring spatial networks by orienting them along a point or path in physical space that provides the guide for parameters in a force-directed layout. By specifying a path across topography, networks can be spatially filtered independently of the topology of the network. Initial case studies indicate promising results for exploring spatial networks in transportation and energy distribution.