Reviewerly/exHarmony huggingface dataset:
Counterfactual Explanations for Optimization-Based Decisions in the Context of the GDPR

The General Data Protection Regulations (GDPR) entitle individuals to explanations for automated decisions. The form, comprehensibility, and even existence of such explanations remain open problems, investigated as part of explainable AI. We adopt the approach of counterfactual explanations and apply it to decisions made by declarative optimization models. We argue that inverse combinatorial optimization is particularly suited for counterfactual explanations but that the computational difficulties and relatively nascent literature make its application a challenge. To make progress, we address the case of counterfactual explanations that isolate the minimal differences for an individual. We show that under two common optimization functions, full inverse optimization is unnecessary. In particular, we show that for functions of the form of the sum of weighted binary variables, which includes frameworks such as weighted MaxSAT, a solution can be found by solving a slightly modified version of the original optimization model. In contrast, the sum of weighted integer variables can be solved with a binary search over a series of modifications to the original model.
Image Representation Using Stacked Colour Histogram

Image representation plays a vital role in the realisation of Content-Based Image Retrieval (CBIR) system. The representation is performed because pixel-by-pixel matching for image retrieval is impracticable as a result of the rigid nature of such an approach. In CBIR therefore, colour, shape and texture and other visual features are used to represent images for effective retrieval task. Among these visual features, the colour and texture are pretty remarkable in defining the content of the image. However, combining these features does not necessarily guarantee better retrieval accuracy due to image transformations such rotation, scaling, and translation that an image would have gone through. More so, concerns about feature vector representation taking ample memory space affect the running time of the retrieval task. To address these problems, we propose a new colour scheme called Stack Colour Histogram (SCH) which inherently extracts colour and neighbourhood information into a descriptor for indexing images. SCH performs recurrent mean filtering of the image to be indexed. The recurrent blurring in this proposed method works by repeatedly filtering (transforming) the image. The output of a transformation serves as the input for the next transformation, and in each case a histogram is generated. The histograms are summed up bin-by-bin and the resulted vector used to index the image. The image blurring process uses pixel’s neighbourhood information, making the proposed SCH exhibit the inherent textural information of the image that has been indexed. The SCH was extensively tested on the Coil100, Outext, Batik and Corel10K datasets. The Coil100, Outext, and Batik datasets are generally used to assess image texture descriptors, while Corel10K is used for heterogeneous descriptors. The experimental results show that our proposed descriptor significantly improves retrieval and classification rate when compared with (CMTH, MTH, TCM, CTM and NRFUCTM) which are the start-of-the-art descriptors for images with textural features.
Military scientific libraries at the First All-Russia Congress of Libraries (On 110-th anniversary of the Congress)

For the first time, the findings of the comprehensive study of the contribution of military scientific libraries to the First All-Russia Congress of Libraries held on June 1-7, 1911, are published. The study was based on archival and prerevolutionary published sources. Four participation forms are distinguished: 1) preparation for the congress (leading role in various commissions organizational, preparatory commission for academic and special libraries, presidium of the section of public, academic and special libraries, as well as in the development of questionnaires for academic libraries); 2) work at the congress (speeches delivered by A. R. Voynich-Syanozhentsky and S. D. Maslovsky, discussions, chairing sections, secretarial responsibilities at the sections); 3) activity at the exhibition (presentations); 4) analysis and evaluation of the congress and its results. The study enabled to specify, systematize and significantly expand the knowledge of the role of military scientific libraries in preparation, organization and work of the First All-Russia Congress of Libraries. The historical experience of joint efforts of military and other library types evidences on the efficiency of such cooperation and the need to use it at the present stage. The findings will be useful for the professionals at military scientific and scientific libraries developing cooperation, as well as for researchers investigating into the history of military libraries, and organizers of library events, e.g. congresses, forums, congresses, symposia, assemblies.
AlloyMax: bringing maximum satisfaction to relational specifications

Alloy is a declarative modeling language based on a first-order relational logic. Its constraint-based analysis has enabled a wide range of applications in software engineering, including configuration synthesis, bug finding, test-case generation, and security analysis. Certain types of analysis tasks in these domains involve finding an optimal solution. For example, in a network configuration problem, instead of finding any valid configuration, it may be desirable to find one that is most permissive (i.e., it permits a maximum number of packets). Due to its dependence on SAT, however, Alloy cannot be used to specify and analyze these types of problems.
Event-Tree Based Sequence Mining Using LSTM Deep-Learning Model

During the operation of modern technical systems, the use of the LSTM model for the prediction of process variable values and system states is commonly widespread. The goal of this paper is to expand the application of the LSTM-based models upon obtaining information based on prediction. In this method, by predicting transition probabilities, the output layer is interpreted as a probability model by creating a prediction tree of sequences instead of just a single sequence. By further analyzing the prediction tree, we can take risk considerations into account, extract more complex prediction, and analyze what event trees are yielded from different input sequences, that is, with a given state or input sequence, the upcoming events and the probability of their occurrence are considered. In the case of online application, by utilizing a series of input events and the probability trees, it is possible to predetermine subsequent event sequences. The applicability and performance of the approach are demonstrated via a dataset in which the occurrence of events is predetermined, and further datasets are generated with a higher-order decision tree-based model. The case studies simply and effectively validate the performance of the created tool as the structure of the generated tree, and the determined probabilities reflect the original dataset.
Spider Monkey Optimization Algorithm for Load Balancing in Cloud Computing Environments

Scheduling of tasks is one of the main concerns in the Cloud Computing environment. The whole system performance depends on the used scheduling algorithm. The scheduling objective is to distribute tasks between the Virtual Machines and balance the load to prevent any virtual machine from being overloaded while other is underloaded. The problem of scheduling is considered an NP-hard optimization problem. Therefore, many heuristics have been proposed to solve this problem up to now. In this paper, we propose a new Spider Monkeys algorithm for load balancing called Spider Monkey Optimization Inspired Load Balancing (SMO-LB) based on mimicking the foraging behavior of Spider Monkeys. It aims to balance the load among virtual machines to increase the performance by reducing makespan and response time. Experimental results show that our proposed method reduces tasks' average response time to 10.7 seconds compared to 24.6 and 30.8 seconds for Round Robin and Throttled methods respectively. Also, the makespan was reduced to 21.5 seconds compared to 35.5 and 53.0 seconds for Round Robin and Throttled methods respectively.
An existential crisis resolved: type inference for first-class existential types

Despite the great success of inferring and programming with universal types, their dual—existential types—are much harder to work with. Existential types are useful in building abstract types, working with indexed types, and providing first-class support for refinement types. This paper, set in the context of Haskell, presents a bidirectional type-inference algorithm that infers where to introduce and eliminate existentials without any annotations in terms, along with an explicitly typed, type-safe core language usable as a compilation target. This approach is backward compatible. The key ingredient is to use strong existentials, which support (lazily) projecting out the encapsulated data, not weak existentials accessible only by pattern-matching.
I Told You Tomorrow: Practical Time-Locked Secrets using Smart Contracts

A Time-Lock enables the release of a secret at a future point in time. Many approaches implement Time-Locks as cryptographic puzzles, binding the recovery of the secret to the solution of the puzzle. Since the time required to find the puzzle’s solution may vary due to a multitude of factors, including the computational effort spent, these solutions may not suit all scenarios.
Machine Learning Approach for Credit Card Fraud Detection (KNN &amp;amp; Naïve Bayes)

The extraction of the useful information from the raw data is done a technique known as data mining. The prediction of new things from the current data has been done using the prediction analysis which is the application of data mining. Classifications techniques are most commonly used which are implemented for the prediction analysis. Hence, prediction of the credit card fraud detection is the main objective of this work. Author proposed various credit card fraud detection mechanisms and techniques to prevent and detect fraud timely. The fundamental of the proposed technique in the base paper is based on the conventional neural networks. This system drives the new values and learns from the previous experiences. For the detection of the credit card fraud, hybrid of KNN and naive bayes classifier is proposed in this research work using which input data is classified into normal and fraud transactions. Test and training sets are the two sub-parts of the input data. In terms of precision and recall, the normal and fraud transactions have been predicted on the basis of test and training sets.
Adaptive Modulation for Continuous-Variable Quantum Key Distribution With Real Local Oscillators Under Phase Attack

Continuous-variable quantum key distribution (CV-QKD) with a real local oscillator (LO) experiences a phase attack security challenge due to the reference pulses transmitted along with quantum signals over an unsecure quantum channel. In this paper, a phase attack on reference pulses of CV-QKD with a real LO is investigated. The performance of the discrete-modulated (DM) CV-QKD under this attack is analyzed analytically and by simulation, and compared with the Gaussian-modulated (GM) CV-QKD. It is found that such an attack by an eavesdropper's, significantly reduce the transmission distance for the GM based CV-QKD, whereas, the DM based CV-QKD shows a high tolerance to high phase noise. Based on phase attack monitoring, a new adaptive modulation scheme is proposed for CV-QKD to adaptively switch between GM and DM so as to maintain the optimum secret key rate.
Fear might motivate secure password choices in the short term, but at what cost?

Fear has been used to convince people to behave securely in a variety of cyber security domains. In this study, we tested the use of fear appeals, with the threat appraisal and coping appraisal components separately and together, on password hygiene behaviors. Fear did indeed elicit the anticipated response - people had higher levels of behavioral intention to have better password hygiene. Unfortunately, we also detected a largely negative affective response to the appeals. Fear, as a short lived emotion, can indeed be effective in the short term and snapshot-like studies like the one reported here might lead us to conclude that fear is indeed indicated and efficacious. Yet, it might well backfire in the long term, due to the negative affects that it triggers.
Elliptic Curve Digital Signature Algorithm Challenges and Development Stages

This paper try to introduce the advantages and disadvantages of the Elliptical Curve Digital Signature Algorithm that could be put in consideration for future usage through applications and future scalability depending on improvement levels of elliptical curve Digital Signature Algorithm starting from The elliptical Curve Cryptography that it was extracted from till showing, elliptical curve Digital Signature Algorithm's security power, performance quality, memory usage rate, applications, challenges, and last improvement. To help in the future in evading some problems like forgery, worse usage of memory because of big sized keys that cause the need for high processing power that reduces the performance quality, and considering it as a level in a new algorithm to reduce the tries of attacking and hacking specific messages between specific parties, after explaining the Elliptical Curve Cryptography and how the scientists reached from the Elliptical Curve Cryptography to the Elliptical Curve Digital Signature Algorithm and why they developed Elliptical Curve Digital Signature Algorithm, the paper will explain its advantages and disadvantages of the Elliptical Curve Digital Signature Algorithm, then the paper will explain what is the differences happen in a new type of the Elliptical Curve Digital Signature Algorithm called Multiple Elliptical Curve Digital Signature Algorithm, trying to show what is the improvement stages was happened in the Elliptical Curve Digital Signature Algorithm, how that affected the nowadays applications, and how that participated in improving the security performance and increasing the security strength, there fore what was included in the paper could be used as a nucleus for more and more improving of the Elliptical Curve Digital Signature Algorithm, and evading its defects.
Impact of Corporate Culture on the Universities’ Achievements in the “5-100” Project

The project called “5-100” has been established in Russia in 2013 for the period of time from 2013 up to 2020. It was supposed that this project would be resulted in entry of 5 Russian universities into 100 highest positions of the world university ratings. 21 Russian universities were chosen as the participants of the project. To achieve the purpose of the project, every chosen university received considerable financial support from the Russian federal government. Among others, two universities from the city of Tomsk – Tomsk State University (TSU) and Tomsk Polytechnic University (TPU) were the participants of this project. In spite of the identical starting points, the universities have demonstrated substantially different results by the end of the project implementation. In this article, we attempted to explain the difference of the results through difference of the corporate cultures of both universities. The corporate culture study has been done using OCAI method. The study showed that TGU’s corporate culture looks like more competitive than TPU’s one. It was proved by the comparison of the TPU and TGU cultures with cultures of the foreign universities.
Dynamic Multi-Scale Convolution for Dialect Identification

Time Delay Neural Networks (TDNN)-based methods are widely used in dialect identification. However, in previous work with TDNN application, subtle variant is being neglected in different feature scales. To address this issue, we propose a new architecture, named dynamic multi-scale convolution, which consists of dynamic kernel convolution, local multi-scale learning, and global multi-scale pooling. Dynamic kernel convolution captures features between short-term and long-term context adaptively. Local multi-scale learning, which represents multi-scale features at a granular level, is able to increase the range of receptive fields for convolution operation. Besides, global multi-scale pooling is applied to aggregate features from different bottleneck layers in order to collect information from multiple aspects. The proposed architecture significantly outperforms state-of-the-art system on the AP20-OLR-dialect-task of oriental language recognition (OLR) challenge 2020, with the best average cost performance (Cavg) of 0.067 and the best equal error rate (EER) of 6.52%. Compared with the known best results, our method achieves 9% of Cavg and 45% of EER relative improvement, respectively. Furthermore, the parameters of proposed model are 91% fewer than the best known model.
Communication-Efficient Agnostic Federated Averaging

In distributed learning settings such as federated learning, the training algorithm can be potentially biased towards different clients. Mohri et al. (2019) proposed a domain-agnostic learning algorithm, where the model is optimized for any target distribution formed by a mixture of the client distributions in order to overcome this bias. They further proposed an algorithm for the cross-silo federated learning setting, where the number of clients is small. We consider this problem in the cross-device setting, where the number of clients is much larger. We propose a communication-efficient distributed algorithm called Agnostic Federated Averaging (or AgnosticFedAvg) to minimize the domain-agnostic objective proposed in Mohri et al. (2019), which is amenable to other private mechanisms such as secure aggregation. We highlight two types of naturally occurring domains in federated learning and argue that AgnosticFedAvg performs well on both. To demonstrate the practical effectiveness of AgnosticFedAvg, we report positive results for large-scale language modeling tasks in both simulation and live experiments, where the latter involves training language models for Spanish virtual keyboard for millions of user devices.
Nonlinear Causal Discovery for High-Dimensional Deterministic Data

Nonlinear causal discovery with high-dimensional data where each variable is multidimensional plays a significant role in many scientific disciplines, such as social network analysis. Previous work majorly focuses on exploiting asymmetry in the causal and anticausal directions between two high-dimensional variables (a cause-effect pair). Although there exist some works that concentrate on the causal order identification between multiple variables, i.e., more than two high-dimensional variables, they do not validate the consistency of methods through theoretical analysis on multiple-variable data. In particular, based on the asymmetry for the cause-effect pair, if model assumptions for any pair of the data are violated, the asymmetry condition will not hold, resulting in the deduction of incorrect order identification. Thus, in this article, we propose a causal functional model, namely high-dimensional deterministic model (HDDM), to identify the causal orderings among multiple high-dimensional variables. We derive two candidates' selection rules to alleviate the inconvenient effects resulted from the violated-assumption pairs. The corresponding theoretical justification is provided as well. With these theoretical results, we develop a method to infer causal orderings for nonlinear multiple-variable data. Simulations on synthetic data and real-world data are conducted to verify the efficacy of our proposed method. Since we focus on deterministic relations in our method, we also verify the robustness of the noises in simulations.
Securing Interruptible Enclaved Execution on Small Microprocessors

Computer systems often provide hardware support for isolation mechanisms such as privilege levels, virtual memory, or enclaved execution. Over the past years, several successful software-based side-channel attacks have been developed that break, or at least significantly weaken, the isolation that these mechanisms offer. Extending a processor with new architectural or micro-architectural features brings a risk of introducing new software-based side-channel attacks. This article studies the problem of extending a processor with new features without weakening the security of the isolation mechanisms that the processor offers. Our solution is heavily based on techniques from research on programming languages. More specifically, we propose to use the programming language concept of full abstraction as a general formal criterion for the security of a processor extension. We instantiate the proposed criterion to the concrete case of extending a microprocessor that supports enclaved execution with secure interruptibility. This is a very relevant instantiation, as several recent papers have shown that interruptibility of enclaves leads to a variety of software-based side-channel attacks. We propose a design for interruptible enclaves and prove that it satisfies our security criterion. We also implement the design on an open-source enclave-enabled microprocessor and evaluate the cost of our design in terms of performance and hardware size.
Aligning Visual Prototypes with BERT Embeddings for Few-Shot Learning

Few-shot learning (FSL) is the task of learning to recognize previously unseen categories of images from a small number of training examples. This is a challenging task, as the available examples may not be enough to unambiguously determine which visual features are most characteristic of the considered categories. To alleviate this issue, we propose a method that additionally takes into account the names of the image classes. While the use of class names has already been explored in previous work, our approach differs in two key aspects. First, while previous work has aimed to directly predict visual prototypes from word embeddings, we found that better results can be obtained by treating visual and text-based prototypes separately. Second, we propose a simple strategy for learning class name embeddings using the BERT language model, which we found to substantially outperform the GloVe vectors that were used in previous work. We furthermore propose a strategy for dealing with the high dimensionality of these vectors, inspired by models for aligning cross-lingual word embeddings. We provide experiments on miniImageNet, CUB and tieredImageNet, showing that our approach consistently improves the state-of-the-art in metric-based FSL.