Reviewerly/exHarmony huggingface dataset:
Depth Estimation from Video using Computer Vision and Machine Learning with Hyperparameter Optimization

This research work focuses on a widespread interpretation for the operation of depth estimation using machine learning (ML) algorithms and computer vision. Depth estimation, also known as abstraction, refers to the processes and systems used to describe the geometric features of a situation. Computer vision is a branch of artificial intelligence that teaches advanced analytics and comprehends images. Based on the principle of disparity an algorithm was presented for the application of computer vision through which moving objects and human was detected using calibrated stereo camera. A correlation technique was used to identify the disparity as well as to detect, extract and match features of any given inputs. The experiment was performed on MATLAB environment by dint of a function of computer vision. Using this algorithm, detection and identification of a moving object was done and the depth of it was measured. The disparity map and reconstruction of the 3D image was presented as well as the distance of each person from the camera was calculated. Afterwards, a 3D mapping of that moving object was created using reconstruction. Moreover, monocular 3-D reconstruction was performed to provide nearly accurate measurement.
Mlphon: A Multifunctional Grapheme-Phoneme Conversion Tool Using Finite State Transducers

In this article we present the design and the development of a knowledge based computational linguistic tool, Mlphon [em.el.foːɳ] for Malayalam language. Mlphon computationally models linguistic rules using finite state transducers and performs multiple functions including grapheme to phoneme (g2p) and phoneme to grapheme (p2g) conversions, syllabification, phonetic feature analysis and script grammar check. This open source software tool, released under MIT license, is developed as a one-stop solution to handle different speech related text processing tasks for automatic speech recognition, text to speech synthesis and non-speech natural language processing tasks including syllable subword based language modeling, phoneme diversity analysis and text sanity check. The tool is evaluated on a manually crafted gold standard lexicon. Mlphon performs orthographic syllabification with 99% accuracy with a syllable error rate of 0.62% on the gold standard lexicon. For grapheme to phoneme conversion task, overall phoneme recognition accuracy of 99% with a phoneme error rate of 0.55% is obtained on gold standard lexicon. Additionally an extrinsic evaluation of Mlphon is performed by employing the pronunciation lexicon created using Mlphon, in Malayalam automatic speech recognition (ASR) task. Performance analysis in terms of the computation time of lexicon creation process and the word error rate (WER) on ASR task are presented along with a comparison over other automated tools for lexicon creation. Pronunciation lexicons with more than 100k commonly used Malayalam words in phonemised and syllabified forms is created and they are published as open language resources along with this work. We also demonstrate the usage of Mlphon on different natural language processing applications - syllable subword ASR, assisted pronunciation learning, phoneme diversity analysis and text sanity check. Being a knowledge based solution with open source code, Mlphon can be adapted to other languages of similar script nature.
Sampling Complexity of Path Integral Methods for Trajectory Optimization

The use of random sampling in decision-making and control has become popular with the ease of access to graphic processing units that can generate and calculate multiple random trajectories for real-time robotic applications. In contrast to sequential optimization, the sampling-based method can take advantage of parallel computing to maintain constant control loop frequencies. Inspired by its wide applicability in robotic applications, we calculate a sampling complexity result applicable to general nonlinear systems considered in the sampling based path integral method. The result determines the required number of samples to satisfy the given error bounds of the estimated control signal from the optimal value with the predefined risk probability. The sampling complexity result shows that the variance of the estimated control value is upper-bounded in terms of the expectation of the cost. Then, we apply the result to a linear time-varying dynamical system with quadratic cost with a quadratic plus indicator cost function.
Decoding McEliece with a Hint – Secret Goppa Key Parts Reveal Everything

We consider the McEliece cryptosystem with a binary Goppa code $$C \subset {{\mathbb F}}_2^n$$ specified by an irreducible Goppa polynomial $$g(x) \in {{\mathbb F}}_{2^m}[X]$$ and Goppa points $$(\alpha _1, \ldots , \alpha _n) \in {{\mathbb F}}_{2^m}^n$$ . Since g(x) together with the Goppa points allow for efficient decoding, these parameters form McEliece secret keys. Such a Goppa code C is an $$(n-tm)$$ -dimensional subspace of $${{\mathbb F}}_2^n$$ , and therefore C has co-dimension tm. For typical McEliece instantiations we have $$tm \approx \frac{n}{4}$$ . We show that given more than tm entries of the Goppa point vector $$(\alpha _1, \ldots , \alpha _n)$$ allows to recover the Goppa polynomial g(x) and the remaining entries in polynomial time. Hence, in case $$tm \approx \frac{n}{4}$$ roughly a fourth of a McEliece secret key is sufficient to recover the full key efficiently. Let us give some illustrative numerical examples. For ClassicMcEliece with $$(n,t,m)=(3488,64,12)$$ on input $$64\cdot 12+1=769$$ Goppa points, we recover the remaining $$3488-769=2719$$ Goppa points in $${{\mathbb F}}_{2^{12}}$$ and the degree-64 Goppa polynomial $$g(x) \in {{\mathbb F}}_{2^{12}}[x]$$ in 60 s. For ClassicMcEliece with $$(n,t,m)=(8192,128,13)$$ on input $$128\cdot 13+1=1665$$ Goppa points, we recover the remaining $$8192-1665=6529$$ Goppa points in $${{\mathbb F}}_{2^{13}}$$ and the degree-128 Goppa polynomial $$g(x) \in {{\mathbb F}}_{2^{13}}[x]$$ in 288 s. Our results also extend to the case of erroneous Goppa points, but in this case our algorithms are no longer polynomial time.
Multi-view clustering with graph regularized optimal transport

For a multi-view learning task, it is crucial to assign appropriate weights to each view in order to learn complementary and consistent information across different views. In the field of multi-view clustering, most existing methods have been able to handle the weights of different views. However, these algorithms face the problem of unacceptable time complexity when dealing with large-scale datasets, and the learned similarity matrix fails to satisfy the graph regularization. In this paper, we propose an auto-weight learning method called multi-view clustering with graph regularized optimal transport. First, an anchor-based method is employed to overcome the problem of heavy time complexity when processing large-scale datasets, and it is able to automatically learn an appropriate weight for each view. Second, by introducing optimal transport we learn a regularized doubly-stochastic similarity matrix applicable to multi-view clustering tasks. Third, the optimal regularized anchor graph can be classified into specific clusters by adding a rank constraint. Finally, an effective optimization method is designed to optimize the formulated problem. Comprehensive experiments on multiple real-world datasets demonstrate that the proposed algorithm achieves superior performance to other state-of-the-arts algorithms.
Proprioceptive art: How should it be defined, and why has it become so popular?

In recent years, there has been something of an explosion of interest in those artworks and installations that directly foreground the bodily senses. Often referred to as proprioceptive (or prop.) art, the question to be addressed in this narrative historical review is how it should be defined, and why has it become so popular? A contrast is drawn with examples of sculpture and/or tactile art. The entertainment/experiential element of such works cannot be denied, especially in an era where funding in the arts sector is so often linked to footfall. At the same time, however, a number of the works appear to be about little more than entertainment/amusement. One might wonder why such "edutainment" should be placed in the art gallery rather than, say, in a museum of science or illusion. Nevertheless, in the best cases, the foregrounding, or removal, of bodily sensations that proprioceptive artworks deliver can potentially help to connect people in an increasingly digital, online, mostly audiovisual, and hence in some sense disembodied contemporary existence. These issues are discussed in the context of the works of Carsten Höller, a prolific German installation and object artist.
ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN EDUCATION

The problem of implementing artificial intelligence (AI) technologies has become relevant in professional education, which performs the main function of training personnel for the economy. This is due to the growing needs on the side of economy for personnel able to create and use AI. The issues of using AI in various spheres of human life have been most studied in sufficient detail in Russian and foreign science. However, research on the technical side and software for the development and application of these technologies prevail over research related to the philosophical, social, educational and psychological aspects of the interaction between people and AI. In particular, the process of accumulating and describing the experience of using AI in the field of education is underway, however, the relevant information is not sufficiently systematized, and their humanistic and axiological aspects have not been studied enough. In this article, the author focuses on the use of AI technologies in education, i.e. in the social and humanitarian sphere, for which interpersonal interaction, self-identification and socialization of people, on the one hand, and improving the quality of education, on the other hand, are significant issues. The purpose of the article is to establish the signs of the current state of the use of AI technologies in education in accordance with the author's understanding of pedagogical guidelines in the implementation of relevant processes. The article defines the regulatory framework for the use of artificial intelligence technologies in the field of education; based on the study and systematization of the prerequisites of the study, the known and used expedient aspects of the use of AI in education were identified; substantiated from the author's positions scientific and pedagogical guidelines for the use of AI technologies in the field of education.
Integrated radioactive mineralization modeling using analytical hierarchy process for airborne radiometric and remote sensing data, East Wadi Qena (EWQ), Eastern Desert, Egypt

Considering the current economic growth that Egypt is going through, it is necessary to increase research about the exploration of mineral resources. The current study is one of those studies that aim to define highly radioactive zones at East Wadi Qena area (EWQ), Eastern Desert using airborne radiometric and remote sensing data. This area is characterized by a complex geological setting which encountered multi-phases of deformation and tectonic events. Airborne radiometric data are used for identifying the presence of uranium, thorium, and potassium in the present study. Ternary composite map is important in radioelement exploration, as they reveal the source of radioactivity. In addition, semi-quantitative statistical analysis is an effective way in calculating radiometric anomalies. Moreover, processing and classification of remote sensing data are applied for valuable mapping of alteration and mineralization zones in the granitic rocks in the considered area. In this regard, radioactive potential zones (RPZ) have been evaluated using the Analytical Hierarchy Process (AHP) through geospatial analysis tools in Geographic Information System (GIS). Five spatial maps are extracted from among the dominant and contributing factors to the concentration of radioactive elements for resolving a radioactive prediction model. The results show that EWQ area is promising for radioactive minerals exploration at diverse phases of granitic rocks, unexpectedly Dokhan volcanics and metavolcanic rocks. The integration between remote sensing and airborne radiometric data can effectively expect the radioactive favorable zones in the study site that can be help for future investigations in other difficult accessible areas.
1-to-1 or 1-to-n? Investigating the Effect of Function Inlining on Binary Similarity Analysis

Binary similarity analysis is critical to many code-reuse-related issues, where function matching is its fundamental task. “ 1-to-1 ” mechanism has been applied in most binary similarity analysis works, in which one function in a binary file is matched against one function in a source file or binary file. However, we discover that the function mapping is a more complex problem of “ 1-to-n ” (one binary function matches multiple source functions or binary functions) or even “ n-to-n ” (multiple binary functions match multiple binary functions) due to the existence of function inlining , different from traditional understanding. In this article, we investigate the effect of function inlining on binary similarity analysis. We carry out three studies to investigate the extent of function inlining, the performance of existing works under function inlining, and the effectiveness of existing inlining-simulation strategies. Firstly, a scalable and lightweight identification method is designed to recover function inlining in binaries. 88 projects (compiled in 288 versions and resulting in 32,460,156 binary functions) are collected and analyzed to construct four inlining-oriented datasets for four security tasks in the software supply chain, including code search, OSS (Open Source Software) reuse detection, vulnerability detection, and patch presence test. Datasets reveal that the proportion of function inlining ranges from 30–40% when using O3 and sometimes can reach nearly 70%. Then, we evaluate four existing works on our dataset. Results show most existing works neglect inlining and use the “1-to-1” mechanism. The mismatches cause a 30% loss in performance during code search and a 40% loss during vulnerability detection. Moreover, most inlined functions would be ignored during OSS reuse detection and patch presence test, thus leaving these functions risky. Finally, we analyze two inlining-simulation strategies on our dataset. It is shown that they miss nearly 40% of the inlined functions, and there is still a large space for promotion. By precisely recovering when function inlining happens, we discover that inlining is usually cumulative when optimization increases. Thus, conditional inlining and incremental inlining are recommended to design a low-cost and high-coverage inlining-simulation strategy.
Brakerski‐Gentry‐Vaikuntanathan fully homomorphic encryption cryptography for privacy preserved data access in cloud assisted Internet of Things services using glow‐worm swarm optimization

Abstract Health monitoring sensors are widely available to monitor patients' health remotely. The collected sensor data are sent to cloud storage and processed in a remote health monitoring system. The Internet of Things (IoT) with cloud support offers a promising answer to data exploding concerns for the ability to constrain specific gadgets. However, IoT faces many security issues when sharing data between two users because of the cloud's leverage nature. In the case of a private cloud, simple encryption techniques can ensure security. But, in the case of a public cloud, maintaining data privacy is a significant issue. To overcome this concern, an optimized Brakerski‐Gentry‐Vaikuntanathan fully homomorphic encryption (BGV‐FHE) encryption method for secured data mutuality is proposed in this article. In this method, IoT medical data are initially acquired and then encrypted using two key schemes, and the data are stored in cloud storage. The main objective of these key schemes is to select optimal key parameters. Key parameters are chosen using the glow‐worm swarm optimization and used for the encryption phase. Storing data in the cloud using a public key provides access to requested users. Meanwhile, sensitive data are encrypted using a private key and cannot be accessed unless authenticated. This proposed approach has two levels of encryption, and thus it is an efficient data secured protocol for cloud resources. Implementing the optimized BGV‐FHE scheme based encryption scheme in Python, two datasets result in an accuracy level of 77% and 91%, respectively.
Deductive Verification of Smart Contracts with Dafny

We present a methodology to develop verified smart contracts. We write smart contracts, their specifications and implementations in the verification-friendly language Dafny. In our methodology the ability to write specifications, implementations and to reason about correctness is a primary concern. We propose a simple, concise yet powerful solution to reasoning about contracts that have external calls. This includes arbitrary re-entrancy which is a major source of bugs and attacks in smart contracts. Although we do not yet have a compiler from Dafny to EVM bytecode, the results we obtain on the Dafny code can reasonably be assumed to hold on Solidity code: the translation of the Dafny code to Solidity is straightforward. As a result our approach can readily be used to develop and deploy safer contracts.
Flip-swap languages in binary reflected Gray code order

A flip-swap language is a set S of binary strings of length n such that S∪{0n} is closed under two operations (when applicable): (1) Flip the leftmost 1; and (2) Swap the leftmost 1 with the bit to its right. Flip-swap languages model many combinatorial objects including necklaces, Lyndon words, prefix normal words, left factors of k-ary Dyck words, lattice paths with at most k flaws, and feasible solutions to 0-1 knapsack problems. We prove that any flip-swap language forms a cyclic 2-Gray code when listed in binary reflected Gray code (BRGC) order. Furthermore, a generic successor rule computes the next string when provided with a membership tester. The rule generates each string in the aforementioned flip-swap languages in O(n)-amortized per string, except for prefix normal words of length n which require O(n1.864)-amortized per string. Our work generalizes results on necklaces and Lyndon words by Vajnovszki (2008) [35].
Enhancing the cloud security using side channel attack free QKD with entangled fuzzy logic

As technology advances, it becomes easier to share large amounts of data over the internet. Cloud computing is one of the technologies that allows for easy data sharing over the internet. It is critical to provide security for this data when they are being shared across the internet. The security of data saved in cloud storage, as well as data transport and transmitting a key required to encrypt data between two parties, has been a source of concern for the industry, as a result of the growing use of cloud services in recent years. Collective attacks are significantly more powerful than individual strikes, according to our research. Despite the fact that additional research works were studied in the previous literature review, there are some study concerns for not correcting third-party data hacking. Therefore, this paper focuses on the design of Secured Quantum Key Distribution (SQKD) with Fuzzy logic to improve the security of the shared key. Quantum Key Distribution, Post Quantum Key Distribution, and the EPR Proto-col are technologies that increase the security of data sharing. We have incorporated the Secured Quantum Key Distribution (SQKD) with Fuzzy logic in our proposed work to improve the security of the shared key. The proposed systems include some additional characteristics in addition to the existing approaches. The proposed model uses shifting algorithms and the fuzzification procedure to assure the security of the secret key in the Fuzzification of Quantum Key approach. The experimental results states that the mean value of security losses in SFQ is 1.8306051, and the mean value of QKD is 14.6448416, with standard deviations of 1.7329 and 13.863 for SFQ and QKD, respectively.
Robust backdoor injection with the capability of resisting network transfer

Deep neural networks have superior transferability between different tasks. However, attackers may inject a backdoor into the pre-trained network to indirectly inject the backdoor into the user’s new network which is transferred from the pre-trained network. The previous backdoor attack schemes cannot ensure that the backdoor in the pre-trained network will not be damaged during network transfer. To deal with this issue, we propose a universal robust backdoor attack scheme to injecting the backdoor into the feature maps of the pre-trained network. First, we show that abnormal values have favorable consistency and conductivity in the neural network. By using these properties, attackers can fine-tune the pre-trained network to create a path that can cause the feature maps of backdoor examples at the target layer to exhibit abnormal values. The backdoor injected with our scheme is robust and can cope with the following three challenging network transfer types: (a) the user fine-tunes all the layers. (b) the user retrains some layers of the network from scratch (c) the user adds the new layers for other categories of tasks. Finally, we experimentally verify that our scheme has a favorable attack success rate and is more robust than the state-of-the-art.
A survey on knowledge-aware news recommender systems

News consumption has shifted over time from traditional media to online platforms, which use recommendation algorithms to help users navigate through the large incoming streams of daily news by suggesting relevant articles based on their preferences and reading behavior. In comparison to domains such as movies or e-commerce, where recommender systems have proved highly successful, the characteristics of the news domain (e.g., high frequency of articles appearing and becoming outdated, greater dynamics of user interest, less explicit relations between articles, and lack of explicit user feedback) pose additional challenges for the recommendation models. While some of these can be overcome by conventional recommendation techniques, injecting external knowledge into news recommender systems has been proposed in order to enhance recommendations by capturing information and patterns not contained in the text and metadata of articles, and hence, tackle shortcomings of traditional models. This survey provides a comprehensive review of knowledge-aware news recommender systems. We propose a taxonomy that divides the models into three categories: neural methods, non-neural entity-centric methods, and non-neural path-based methods. Moreover, the underlying recommendation algorithms, as well as their evaluations are analyzed. Lastly, open issues in the domain of knowledge-aware news recommendations are identified and potential research directions are proposed.