Reviewerly/exHarmony huggingface dataset:
Generalized Neural Policies for Relational MDPs

A Relational Markov Decision Process (RMDP) is a first-order representation to express all instances of a single probabilistic planning domain with possibly unbounded number of objects. Early work in RMDPs outputs generalized (instance-independent) first-order policies or value functions as a means to solve all instances of a domain at once. Unfortunately, this line of work met with limited success due to inherent limitations of the representation space used in such policies or value functions. Can neural models provide the missing link by easily representing more complex generalized policies, thus making them effective on all instances of a given domain? We present SymNet, the first neural approach for solving RMDPs that are expressed in the probabilistic planning language of RDDL. SymNet trains a set of shared parameters for an RDDL domain using training instances from that domain. For each instance, SymNet first converts it to an instance graph and then uses relational neural models to compute node embeddings. It then scores each ground action as a function over the first-order action symbols and node embeddings related to the action. Given a new test instance from the same domain, SymNet architecture with pre-trained parameters scores each ground action and chooses the best action. This can be accomplished in a single forward pass without any retraining on the test instance, thus implicitly representing a neural generalized policy for the whole domain. Our experiments on nine RDDL domains from IPPC demonstrate that SymNet policies are significantly better than random and sometimes even more effective than training a state-of-the-art deep reactive policy from scratch.
Reducing Sampling Error in Batch Temporal Difference Learning

Temporal difference (TD) learning is one of the main foundations of modern reinforcement learning. This paper studies the use of TD(0), a canonical TD algorithm, to estimate the value function of a given policy from a batch of data. In this batch setting, we show that TD(0) may converge to an inaccurate value function because the update following an action is weighted according to the number of times that action occurred in the batch -- not the true probability of the action under the given policy. To address this limitation, we introduce \textit{policy sampling error corrected}-TD(0) (PSEC-TD(0)). PSEC-TD(0) first estimates the empirical distribution of actions in each state in the batch and then uses importance sampling to correct for the mismatch between the empirical weighting and the correct weighting for updates following each action. We refine the concept of a certainty-equivalence estimate and argue that PSEC-TD(0) is a more data efficient estimator than TD(0) for a fixed batch of data. Finally, we conduct an empirical evaluation of PSEC-TD(0) on three batch value function learning tasks, with a hyperparameter sensitivity analysis, and show that PSEC-TD(0) produces value function estimates with lower mean squared error than TD(0).
DeepEMD: Few-Shot Image Classification with Differentiable Earth Mover's Distance and Structured Classifiers

In this paper, we address the few-shot classification task from a newperspective of optimal matching between image regions. We adopt the EarthMover's Distance (EMD) as a metric to compute a structural distance betweendense image representations to determine image relevance. The EMD generates theoptimal matching flows between structural elements that have the minimummatching cost, which is used to represent the image distance forclassification. To generate the important weights of elements in the EMDformulation, we design a cross-reference mechanism, which can effectivelyminimize the impact caused by the cluttered background and large intra-classappearance variations. To handle k-shot classification, we propose to learn astructured fully connected layer that can directly classify dense imagerepresentations with the EMD. Based on the implicit function theorem, the EMDcan be inserted as a layer into the network for end-to-end training. We conductcomprehensive experiments to validate our algorithm and we set newstate-of-the-art performance on four popular few-shot classificationbenchmarks, namely miniImageNet, tieredImageNet, Fewshot-CIFAR100 (FC100) andCaltech-UCSD Birds-200-2011 (CUB).
More Information Supervised Probabilistic Deep Face Embedding Learning

Researches using margin based comparison loss demonstrate the effectiveness of penalizing the distance between face feature and their corresponding class centers. Despite their popularity and excellent performance, they do not explicitly encourage the generic embedding learning for an open set recognition problem. In this paper, we analyse margin based softmax loss in probability view. With this perspective, we propose two general principles: 1) monotonic decreasing and 2) margin probability penalty, for designing new margin loss functions. Unlike methods optimized with single comparison metric, we provide a new perspective to treat open set face recognition as a problem of information transmission. And the generalization capability for face embedding is gained with more clean information. An auto-encoder architecture called Linear-Auto-TS-Encoder(LATSE) is proposed to corroborate this finding. Extensive experiments on several benchmarks demonstrate that LATSE help face embedding to gain more generalization capability and it boosted the single model performance with open training dataset to more than $99\%$ on MegaFace test.
CANOA: CAN Origin Authentication Through Power Side-Channel Monitoring

The lack of any sender authentication mechanism in place makes Controller Area Network (CAN) vulnerable to security threats. For instance, an attacker can impersonate an Electronic Control Unit (ECU) on the bus and send spoofed messages unobtrusively with the identifier of the impersonated ECU. To address this problem, we propose a novel source authentication technique that uses power consumption measurements of the ECU to authenticate the source of a message. A transmission of an ECU affects the power consumption and a characteristic pattern will appear. Our technique exploits the power consumption of each ECU during the transmission of a message to determine whether the message actually originated from the purported sender. We evaluate our approach in both a lab setup and a real vehicle. We also evaluate our approach against factors that can impact the power consumption measurement of the ECU. The results of the evaluation show that the proposed technique is applicable in a broad range of operating conditions with reasonable computational power requirements and attaining good accuracy.
Secure Communication Using Cryptography and Covert Channel

The keys which are generated by cryptography algorithms have still been compromised by attackers.So, they extra efforts to enhance security, time consumption and communication overheads.Encryption can achieve confidentiality but cannot achieve integrity.Authentication is needed beside encryption technique to achieve integrity.The client can send data indirectly to the server through a covert channel.The covert channel needs pre-shared information between parties before using the channel.The main challenges of covert channel are security of pre-agreement information and detectability.In this chapter, merging between encryption, authentication, and covert channel leads to a new covert channel satisfying integrity and confidentiality of sending data.This channel is used for secure communication that enables parties to agree on keys that are used for future communication.
A Novel Cryptosystem Based on Steganography and Automata Technique for Searchable Encryption

In this paper we first propose a new cryptosystem based on our data hiding scheme (2,9,8) introduced in 2019 with high security, where encrypting and hiding are done at once, the ciphertext does not depend on the input image size as existing hybrid techniques of cryptography and steganography.We then exploit our automata approach presented in 2019 to design two algorithms for exact and approximate pattern matching on secret data encrypted by our cryptosystem.Theoretical analyses remark that these algorithms both have O(n) time complexity in the worst case, where for the approximate algorithm, we assume that it uses (1 ) ) m ε -    processors, where ε , m and n are the error of our string similarity measure and lengths of the pattern and secret data, respectively.In searchable encryption, our cryptosystem is used by users and our pattern matching algorithms are performed by cloud providers.
Enabling Interactive Answering of Procedural Questions

A mechanism to enable task oriented procedural question answering system for user assistance in English is presented in this paper. The primary aim is to create an answering “corpus” in a tree-form from unstructured document passages. This corpus is used to respond to the queries interactively to assist in completing a technical task. Reference manuals, documents or webpages are scraped to identify the sections depicting a “procedure” through machine learning techniques and then an integrated task tree with extracted procedural knowledge from text is generated. The automated mechanism breaks the procedural sections into steps, the appropriate “decision points” are identified, the interactive utterances are generated to gain user inputs and the alternative paths are created to complete the tree. Conventional tree traversal mechanism provides step by step guidance to complete a task. Efficacy of the proposed mechanism is tested on documents collected from three different domains and test results are presented.
Quality improvement through the implementation of structured reports

•Structured reports increase clinical impact in the staging and preoperative assessment of abdominal and pelvic carcinomas. •Structured reports offer opportunities for quality improvement and the incorporation of data from electronic health records. •Disease-specific, structured templates can help radiology practices embrace the future of personalized medicine. •The assessment of perihilar cholangiocarcinoma lends itself well to a template that can aid in studies of clinical outcomes.
La rivoluzione del linguaggio social-ista: umori, rumori, sparate e provocazioni / The revolution of social-ist language: moods, noises, shots, provocations

Contemporary populism could be read as a form of social-ism , i.e, a political discourse marked by the languages and the -isms of social networks. On the basis of this assumption, the aim of this paper is to explore and analyse three specific semiotic aspects of the social-ist language. First, the relationship between moods and noises that characterizes the enunciation in and of the social networks, in which screaming plays a fundamental role. Second, the prerequisite and consequences of shots/slams , whose roots are to be found in the correlation between the language of social media and that of neotelevision. Third, the forms of political provocation , rewritten, nowadays, by the communicative and interactional logic of digital media. The reasoning we develop is inspired by the analysis of texts – tweets, Facebook and Instagram posts, messages and conversations extracted from Whatsapp public groups – taken from both the Italian and Brazilian context. Nevertheless, we believe our findings are potentially generalizable, since they seem to resume how, on an international scale, the languages ​​of politics – and, vice versa, the politics of language – are nowadays constructed and articulated
Tenders 2.0 – A Stake-Based Blockchain Solution for Tender Procurement System

Tendering is a procedure taken up by organizations for procurement of goods and services. Tender process when followed properly allows bids from multiple vendors and thereby facilitates healthy competition and fair outcome. Almost all the countries follow this process in standard and similar fashion. Lack of transparency in the tendering process and assignment of contracts to preferred agencies (rather than to a deserving bidder) has led to unwise use of money and the spirit of speculation. Blockchain is a decentralized ledger that makes records immutable, facilitates the secure exchange of digital currency, and performs deals and transactions. Each member of the blockchain network has access to the latest copy of encrypted ledger so that they can validate a new transaction. In this paper, we propose a blockchain-based tender procurement system with secure, fair, and reliable bidding architecture that can transparently manage the whole bidding process and perform an unbiased evaluation of bids. This system allows citizens to evaluate the process with a single click through an auditing application, which accounts for some stake to be put in the system against a false claim of task completion and thus actively help in making the democratic system stronger.
On an Unknown Review of D. D. Ivanenko’s Article by S. I. Vavilov

This paper introduces for scientific use an earlier unknown Academician S. I. Vavilov’s review of the paper “Fundamentals of the theory of elementary particles” by a theoretical physicist, D. D. Ivanenko. This review was found by the author at a second-hand book auction in Moscow. This rather short hand-written text casts additional light on the picture of “struggle against idealism in physics”, waged in the USSR in the late 1940s, particularly on the internal opposition between Vavilov and Ivanenko concerning the ideological and philosophical judgment of the theory of relativity and quantum mechanics. So far this opposition remained practically unexamined although, as it happens, it had been quite dramatic, emotional, and full of overtones. Examining Vavilov’s review in the context of sociopolitical situation in the USSR allowed making several surmises. Thus, it is highly likely that Ivanenko’s article, written for a book of collected papers on the philosophy of science, was one of the elements of “theoretical”, “philosophical” justification for a campaign against the “reactionary role of idealism in physics” that was being prepared by the Soviet Union’s political authorities. Even though it had not been published as is, it provided the basis for several other publications by the same author.
Correction of English Translation Accuracy Based on Poisson Log-linear Model

Abstract In the conventional machine translation methods, pipelined sequential operation is used to perform part-of-speech identification and syntactic analysis on the raw corpus to obtain the syntactic structure of English language, thereby reducing the iterative transfer error between translation tasks and the accuracy of structured instances, resulting in reduced accuracy in English language and literature translation. In this paper, a Poisson log-linear model that saves the corresponding bilingual corpus by means of Chinese-English dependency-tree-to-string is designed to implement dependent structured processing on the source language end and ensure that the accurate translation of English language is further proofread through the data-oriented translation model in the Chinese-English bilingual correspondence. The experimental results show that translation with high accuracy can be obtained based on the proposed method, which is highly accurate and stable.
Research on Weather Condition Influence Factors on Intelligent Connected Vehicle

Abstract Now, there is no standard test method and flow for the performance of Intelligent Connected Vehicle under the complex weather conditions, which has greatly limited the development of Intelligent Connected Vehicle. This paper test and study the weather conditions that affect the performance of the on-board sensors, then analyse the weather conditions that affect Intelligent Connected Vehicle, the meteorological factors which affect Intelligent Connected Vehicle performance are obtained. It turns out that different conditions such as rain, fog, snow and light have different influence on the Intelligent Connected Vehicle, which provides the foundation for the performance test of Intelligent Connected Vehicle.
Prediction of Normal & Grades of Cancer on Colon Biopsy Images at Different Magnifications Using Minimal Robust Texture & Morphological Features

Classification of colon biopsy images to normal and various cancer grades is a pivotal task for histopathologists as it involves visual analysis under the microscope at different magnifications and hence may give rise to observational inconsistency. This paper emphasis on categorization of colon biopsy images into normal, well, moderate and poor classes thereby analyzing the best magnification and classifier suited for classification. A hybrid feature set consisting of morphological and texture features are obtained from images followed by class balancing to overcome imbalancing problem and then optimized feature selection. Classifiers such as SVM, Random Forest, Multilayer Perceptron and Naive Bayes are experimented for classification. The proposed model is evaluated with colon biopsy images acquired from Aster Medcity, Kochi, India at different magnifications 10X, 20X and 40X where all the magnifications performed well, but 20X gave an improved accuracy of 94.27% with the Random Forest classifier. Advance measures based on entropy triangle are used to rank classifiers apart from the standard performance measures, where Random Forest classifier is best for the proposed model for all magnifications.
The trace formula with respect to the Grover matrix of a graph

As a first step of the study of the relation between the distribution of prime, reduced cycles of a graph and the limit theorem for the probability of a quantum walk on a graph, we present a trace formula with respect to the Grover matrix that is the transition matrix of the Grover walk on a graph. We define a zeta function with respect to the Grover matrix of a graph, and differentiate the logarithm of its zeta function of a regular graph. Furthermore, we obtain a trace formula with respect to the Grover matrix by a suitable complex integral of its logarithm differential.
An Intelligent Artificial Bee Colony and Adaptive Bacterial Foraging Optimization Scheme for Reliable Breast Cancer Diagnosis

: Breast cancer is essential to be detected in primitive localized stage for enhancing the possibility of survival since it is considered as the major malediction to the women society around the globe. Most of the intelligent approaches devised for breast cancer necessitate expertise that results in reliable identification of patterns that conclude the presence of oncology cells and determine the possible treatment to breast cancer patients in order to enhance their survival feasibility. Moreover, the majority of the existing schemes of the literature incur intensive labor and time, inducing a predominant impact over the diagnosis time utilized for detecting breast cancer cells. An Intelligent Artificial Bee Colony and Adaptive Bacterial Foraging Optimization (IABC-ABFO) scheme is proposed for facilitating a better rate of local and global searching ability in selecting the optimal features subsets and optimal parameters of ANN considered for breast cancer diagnosis. In the proposed IABC-ABFO approach, the traditional ABC algorithm used for cancer detection is improved by integrating an adaptive bacterial foraging process in the onlooker bee and the employee bee phase that results in optimal exploitation and exploration. The investigation of results of the proposed IABC-ABFO approach facilitating the use of the Wisconsin breast cancer dataset showed a mean classification accuracy of 99.52% which is higher than the existing breast cancer detection schemes.
DS6: Deformation-aware learning for small vessel segmentation with small, imperfectly labeled dataset.

Originating from the initial segment of the middle cerebral artery of the human brain, Lenticulostriate Arteries (LSA) are a collection of perforating vessels that supply blood to the basal ganglia region. With the advancement of 7 Tesla scanner, we are able to detect these LSA which are linked to Small Vessel Diseases(SVD) and potentially a cause for neurodegenerative diseases. Segmentation of LSA with traditional approaches like Frangi or semi-automated/manual techniques can depict medium to large vessels but fail to depict the small vessels. Also, semi-automated/manual approaches are time-consuming. In this paper, we put forth a study that incorporates deep learning techniques to automatically segment these LSA using 3D 7 Tesla Time-of-fight Magnetic Resonance Angiogram images. The algorithm is trained and evaluated on a small dataset of 11 volumes. Deep learning models based on Multi-Scale Supervision U-Net accompanied by elastic deformations to give equivariance to the model, were utilized for the vessel segmentation using semi-automated labeled images. We make a qualitative analysis of the output with the original image and also on imperfect semi-manual labels to confirm the presence and continuity of small vessels.
Evaluation of four different virtual locomotion techniques in an interactive environment

Background: Virtual Reality (VR) devices are becoming more and more common as game systems. Even though modern VR Head Mounted Displays (HMD) allow the user to walk in real life, it still limits th ...
Algebraic Bayesian Networks: Parallel Algorithms for Maintaining Local Consistency

Algebraic Bayesian networks belong to the class of machine-learning probabilistic graphical models. One of the main tasks during researching machine learning models is the optimization of their time of work. This paper presents approaches to parallelizing algorithms for maintaining local consistency in algebraic Bayesian networks as one of the ways to optimize their time of work. An experiment provided to compare the time of parallel and nonparallel realizations of algorithms for maintaining local consistency.
Role-Based Architecture for Secure Management of Telepathology Sessions.

Digital pathology is the computer technology that allows the management of the information generated by the whole-slide scanners from a microscopic slide, encompassing the virtual microscopy. This paper proposes and describes the architecture of a secure collaborative platform that integrates a web pathology viewer with role-based access control. The proposed architecture is ensured by a shared medical repository that serves web pathology viewer with the medical images, using the DICOM standard. The system offers collaborative work session management tools as the managing of users, sessions, access control to sessions, and many others. Furthermore, the use cases related to telepathology and e-learning are presented.
Assessment of Energy Self-sufficiency Ratio Based on Renewable Market Potentials for Unit of Local Government

This study estimated the available renewable market potential based on Levelized Cost Of Electricity and then assessed the renewable derived energy self-sufficiency for the unit of local government in South Korea. To calculate energy self-sufficiency, 1 km gridded market renewable generation and local government scale of final energy consumption data were used based on the market costs and statistics for the recent three years. The results showed that the estimated renewable market potentials were 689 TWh (Install capacity 829 GW, 128 Mtoe), which can cover 120% of power consumption. 55% of municipalities can fully replace the existing energy consumption with renewable energy generation and the surplus generation can compensate for the rest area through electricity trade. However, it was confirmed that, currently, 47% of the local governments do not fully consider all renewable energy sources such as wind, hydro and geothermal in establishing 100% renewable energy. The results of this study suggest that energy planning is decentralized, and this will greatly contribute to the establishment of power planning of local governments and close the information gap between the central government, the local governments, and the public.