Reviewerly/exHarmony huggingface dataset:
A robust hybrid methodology between applied linear regression model (alrm) and multilayer perceptron (mlp)

Background: The goal of this study is to illustrate an optimum variable selection method using established Multiple Linear Regression (MLR) models and to validate the variable using Multilayer Perceptron Neural Network (MLP) models. Initially, all selected variables will be passed through the bootstrap methodology, and they were screened for significant relationships. Objective: The goal of this work is to analyze and construct a model for the factor linked with total crime cases by combining an Applied Linear Regression Model (ALRM) and a Multilayer Perceptron (MLP). Material and Methods: Around 200 data was simulated to build the methodology. Advanced computational statistical modeling methodologies were used to evaluate data descriptions of several variables in this retrospective study, including the total victim, gender, age, marital status, social class, adult in the household, children in household, burglary’s victim, sexual’s victim, victim’s report, and household location. The case study was developed and implemented using the R-Studio program and syntax. Results: The statistical method demonstrated that regression modeling surpasses R-squared and mean square error test in most situations. Researchers observed that when data is divided into two datasets for training and testing, the hybrid model approach performs significantly better at predicting the experiment’s outcome. When it came time to determine variable validity, the well-established bootstrap-integrated MLR approach was applied. Ten characteristics are taken into consideration in this case: Gender (: -0.4369700; p&lt; 0.25), age (: -0.0086757; p&lt; 0.25), marital status (: 0.2646097; p&lt; 0.25), social class ( : 0.0602540; p&lt; 0.25), adult in household (: -0.0211293; p&gt; 0.25), children in household (: -0.0025346; p&gt; 0.25), burglary’s victim (: 1.3473593; p&lt; 0.25), sexual’s victim (: 1.0382444; p&lt; 0.25), victim’s report (: -0.3176104; p&lt; 0.25), and location of household (: -0.1355046; p&lt; 0.25).There is a 0.07745823 MSE for the linear model in this scenario. Conclusion: The neural network’s Predicted Mean Square Error (PMSE) was used to assess MLP’s performance (MSE-forecasts the Network). PMSE is used to determine how far our projections are from the actual data, and the lowest MSE from the MLP indicates the best achievement. The R syntax for MLR and MLP is also included in this research article.As a result, the study’s conclusion establishes the superiority of the hybrid model technique. Bangladesh Journal of Medical Science Vol. 22 No. 01 January’23 Page : 38-46
Infrastructure as Code (IaC): Insights on Various Platforms

In the present-day tech-stack, cloud computing is evolving as a successful and one of the popular fields of technology where the new businesses are achieving success by deploying their functionalities, products, data, and services on cloud instead of on-premises system and that also without depending on any physical component. Infrastructure as code (IaC) is a set of methodologies which uses code to set up the install packages, virtual machines and networks, and configure environments. A successful IaC implementation and adoption by developers requires a broad set of skills and knowledge. It is DevelopmentOperations’ tactic of provisioning an application’s infrastructure and managing it through binary readable configuration files, instead of any hardware configuration.
Differential Cryptanalysis of Lightweight Block Ciphers SLIM and LCB

In this paper, we analyze the security of two recently proposed ultra-lightweight block ciphers, SLIM and LCB. SLIM is designed based on the Feistel paradigm, operating on 32-bit blocks and has an 80-bit key. The designers claim that SLIM is immune to differential cryptanalysis after they were only able to find a trail of up to 7 rounds by using a heuristic method. LCB is another ultra-lightweight block cipher with a 32-bit block and instead uses a 64-bit secret key. It was designed based on a hybrid of Feistel and substitution-permutation network structures. Although no concrete security analyses were performed, the designers claim that 10 rounds of the cipher is secure enough against various attacks including differential cryptanalysis. We verify these claims by proposing differential attacks on both ciphers. For SLIM, we first report optimal (i.e., having the best differential probability) trails for up to 32 rounds found using an SMT solver. We then propose practical key recovery attacks on up to 14 rounds that recover the final round key with time complexity $$2^{32}$$ . Next, a close inspection of LCB’s design revealed a lack of nonlinearity, whereby its S-box could be modelled as a permutation. As such, differential trails that hold with probability 1 can be trivially derived for any number of rounds of the cipher. A trivial distinguishing attack can be performed with just one known-ciphertext. We fix this flaw and go on to show that LCB is actually more secure (against differential cryptanalysis) than SLIM given the same number of rounds. To the best of our knowledge, these are the first third-party cryptanalysis attacks against both ciphers.
Complete trajectory extraction for moving targets in traffic scenes that considers multi-level semantic features

Complex traffic scenes are susceptible to the occlusion of moving targets and blurred target images, resulting in incomplete extraction of target trajectories within the field of view (FOV). To address this issue, we proposed a complete trajectory extraction algorithm for moving targets (TraEA) that considers the multi-level semantic features of traffic scenes following the FOV of single-camera surveillance videos. TraEA utilizes semantic information from the multi-level scene structure, such as image pixel features, geometric features, as well as temporal and spatial relationships between targets within each keyframe image. The semantic information helps extract and optimize the complete trajectory of a target, to assemble the target’s moving trajectory with improved integrity. An experiment with real-world traffic videos collected in Beijing, China validated the proposed algorithm and showed that TraEA improved the completeness, accuracy and robustness of target trajectory extraction. Traffic trajectories from the experiment can facilitate traffic supervision by analyzing the moving behaviors of individual targets. While the proposed TraEA algorithm is applicable to extract trajectories from videos in other domains, such as animal movements, potential privacy infringement and abusive applications warrant cautions of ethical considerations.
Cryptocurrency Exchanges and the Future of Cryptoassets

This chapter differentiates between centralized and decentralized exchanges (DEXs), emphasizing the importance of regulations and compliance related to the market’s development and expansion for cryptoasset trading and investment. It also explains the pros and cons of using different methods to trade cryptocurrencies or virtual currencies and their tradeoffs. The chapter discusses how centralized and DEXs emerged, their history and potential future, and the possible role of future regulations and regulatory clarity around how they may operate. Additionally, it compares cryptoasset markets to other more traditional markets such as equities, real estate, and foreign exchange.
Stablecoins

This chapter introduces the concept of stablecoins such as Tether, DAI, or Ampleforth. It also provides a taxonomy of the different types of stablecoins consisting of (1) traditional asset-backed stablecoins, (2) crypto-collateralized stablecoins, and (3) algorithmic stablecoins and seigniorage shares. The chapter continues with a brief history of stablecoins, starting from BitShares as the first stablecoin implementation over tether and market-wide stablecoin adoption to Facebook-initiated Diem. Next, the chapter explains the impact of stablecoins on cryptocurrencies and other markets and discusses trends and challenges facing stablecoins. The chapter provides a basic understanding of stablecoins, their defining characteristics, challenges, and markets.
Decentralized Finance

One aspect of the opportunities and challenges for cryptoassets concerns decentralized finance (DeFi). DeFi is a growing area of cryptoassets that couples blockchain technology, digital assets, and financial services. DeFi is a publicly available system on a decentralized blockchain network, offering financial products and applications. This chapter provides an overview of the DeFi universe that has enormous potential in various industries in the global market. It also discusses the implications of DeFi’s new wave and its applications involving initial coin offerings and stablecoins and specific challenges like the scalability trilemma in DeFi and financial markets.
Artificial Intelligence and Human Rights: Corporate Responsibility in AI Governance Initiatives

Private businesses are central actors in the development of artificial intelligence (AI), meaning they have a key role in ensuring that AI respects human rights. Meanwhile, international human rights law (IHRL) has been scrambling to catch up with technological developments that have occurred since the establishment of its state-centric framework that were not envisaged by its drafters. Despite progress in the development of international legal standards on business and human rights, uncertainties regarding the role and responsibilities of AI businesses remain. This article addresses these uncertainties from a governance perspective and against the backdrop of the public/private divide; it views laws as instruments of governance, which comprises activities by many public and private actors. Section 2 briefly assesses the current framework of IHRL regarding AI and businesses, focusing on the lack of legal certainty. Section 3 critically analyses AI initiatives beyond IHRL that have been adopted at international, regional, and national levels to gain insight into specific standards of behaviour expected of AI businesses, as well as to challenge a dichotomous public/private divide in this context. Section 4 provides conclusions and recommendations.
Slepian Scale-Discretised Wavelets on the Sphere

This work presents the construction of a novel spherical wavelet basis designed for incomplete spherical datasets, i.e. datasets which are missing in a particular region of the sphere. The eigenfunctions of the Slepian spatial-spectral concentration problem (the Slepian functions) are a set of orthogonal basis functions which are more concentrated within a defined region. Slepian functions allow one to compute a convolution on the incomplete sphere by leveraging the recently proposed sifting convolution and extending it to any set of basis functions. Through a tiling of the Slepian harmonic line, one may construct scale-discretised wavelets. An illustration is presented based on an example region on the sphere defined by the topographic map of the Earth. The Slepian wavelets and corresponding wavelet coefficients are constructed from this region and are used in a straightforward denoising example.
A Control Approach for Affine T-S Fuzzy Models with Uncertainty

While the affine T-S fuzzy model offers additional accuracy in terms of describing system dynamics compared to the regular T-S fuzzy model, the problem is that the resulting LMIs for the controller design are structurally infeasible without introducing additional inequalities. In this paper, first, we partition the state space into some cells and use the properties of the cells away from the origin to obtain favourable quadratic inequalities. Then two fuzzy controllers are given based on the partition. In addition, uncertainty in the system is also considered in the affine T-S fuzzy model. Finally, simulation results are provided for the verification of the proposed controllers.
Predictive keywords: Using machine learning to explain document characteristics

When exploring the characteristics of a discourse domain associated with texts, keyword analysis is widely used in corpus linguistics. However, one of the challenges facing this method is the evaluation of the quality of the keywords. Here, we propose casting keyword analysis as a prediction problem with the goal of discriminating the texts associated with the target corpus from the reference corpus. We demonstrate that, when using linear support vector machines, this approach can be used not only to quantify the discrimination between the two corpora, but also extract keywords. To evaluate the keywords, we develop a systematic and rigorous approach anchored to the concepts of usefulness and relevance used in machine learning. The extracted keywords are compared with the recently proposed text dispersion keyness measure. We demonstrate that that our approach extracts keywords that are highly useful and linguistically relevant, capturing the characteristics of their discourse domain.
Lessons learnt running distributed and remote mixed reality experiments

One traditional model of research on mixed-reality systems, is the laboratory-based experiment where a number of small variants of a user experience are presented to participants under the guidance of an experimenter. This type of experiment can give reliable and generalisable results, but there are arguments for running experiments that are distributed and remote from the laboratory. These include, expanding the participant pool, reaching specific classes of user, access to a variety of equipment, and simply because laboratories might be inaccessible. However, running experiments out of the laboratory brings a different set of issues into consideration. Here, we present some lessons learnt in running eleven distributed and remote mixed-reality experiments. We describe opportunities and challenges of this type of experiment as well as some technical lessons learnt.
Phase error model and compensation method for reflectivity and distance discontinuities in fringe projection profilometry

Phase shifting fringe projection profilometry is a widely used optical 3D surface measurement method due to its high resolution, high speed, and full-field inspection. However, the measurement accuracy decreases in regions with a reflectivity or distance discontinuity. To this end, first, a general continuous quasi-one-dimensional phase error model is proposed for discontinuity representation. Second, the discontinuities are further divided into degenerate and nondegenerate discontinuities to improve the computational speed. Third, a phase error compensation algorithm is proposed with a parameter estimation method to improve the measurement accuracy. Simulations and experiments demonstrate that the proposed methods are effective.
A supervised multinomial classification framework for emotion recognition in textual social data

The task of emotion recognition from text has received much attention since the proliferation of online social networking which has woven itself into the fabric of lives of people world-over. This study is aimed at extracting the lexical and contextual information from the text and combining it with semantic information for the detection of the emotional state of a sentence. We propose a supervised framework for recognition of emotions from text in this work. Our framework utilises word embeddings from Word2Vec to extract the set of words which fall in semantic proximity of an affect-bearing word and also takes into account the context in which the words are used. We incorporate class-specific emoticon features in all our experiments as emoticons are commonly used on social media platforms. As the nature of social media text is generally very informal and has an irregular structure, our framework encompasses an appropriate mechanism to handle it. We evaluate our support vector machine-based framework on stance sentiment emotion corpus (SSEC) and Aman's dataset. The classification results achieved are better than state of art techniques currently available.
Pruning Redundancy in Answer Set Optimization Applied to Preventive Maintenance Scheduling

Multi-component machines deployed, e.g., in paper and steel industries, have complex physical and functional dependencies between their components. This profoundly affects how they are maintained and motivates the use of logic-based optimization methods for scheduling preventive maintenance actions. Recently, an abstraction of maintenance costs, called miscoverage, has been proposed as an objective function for the preventive maintenance scheduling (PMS) of such machines. Since the minimization of miscoverage has turned out to be a computationally demanding task, the current paper studies ways to improve its efficiency. Given different answer set optimization encodings of the PMS problem, we motivate constraints that prune away some sub-optimal and otherwise redundant or invalid schedules from the search space. Our experimental results show that these constraints may enable up to ten-fold speed-ups in scheduling times, thus pushing the frontier of practically solvable PMS problem instances to longer timelines and larger machines.
Photon number resolution without optical mode multiplication

Abstract Common methods to achieve photon number resolution rely on fast on-off single-photon detectors in conjunction with temporal or spatial mode multiplexing. Yet, these methods suffer from an inherent trade-off between the efficiency of photon number discrimination and photon detection rate. Here, we introduce a method of photon number resolving detection that overcomes these limitations by replacing mode multiplexing with coherent absorption of a single optical mode in a distributed detector array. Distributed coherent absorption ensures complete and uniform absorption of light among the constituent detectors, enabling fast and efficient photon number resolution. As a proof-of-concept, we consider the case of a distributed array of superconducting nanowire single-photon detectors with realistic parameters and show that deterministic absorption and arbitrarily high photon number discrimination efficiency can be achieved by increasing the number of detectors in the array. Photon number resolution without optical mode multiplication provides a simple yet effective method to discriminate an arbitrary number of photons in large arrays of on-off detectors or in smaller arrays of mode multiplexed detectors.
Blind Image Despeckling Using Multi-Scale Attention-Guided Neural Network

Coherent imaging systems have been applied in the detection of target of interest, natural resource exploration, ailment diagnosis, etc. However, it is easy to generate speckle-degraded images due to the coherent interference of reflected echoes, restricting these practical applications. Speckle noise is a granular interference that affects the observed reflectivity. It is often modeled as multiplicative noise with a negative exponential distribution. This non-linear property makes despeckling of imaging data an intractable problem. To enhance despeckling performance, we propose to blindly remove speckle noise using an intelligent computing-enabled multi-scale attention-guided neural network (termed MSANN). In particular, we first introduce the logarithmic transformation to convert the multiplicative speckle noise model to an additive version. Our MSANN, essentially a feature pyramid network (FPN), is then exploited to restore degraded images in the logarithmic domain. To enhance the generalization ability of MSANN, a multi-scale feature enhancement attention module (FEAM) is incorporated into MSANN to extract multi-scale features for improving imaging quality. A multi-scale mixed loss function is further presented to increase network robustness during training. The final despeckled images are naturally equivalent to the exponential versions of the output of MSANN. Experimental results have shown that MSANN has the capacity of effectively removing speckle noise while preserving essential structures. It can achieve superior despeckling results in terms of visual quality and quantitative measures.
Adaptive Sampling-based Motion Planning with Control Barrier Functions

Sampling-based algorithms, such as Rapidly Exploring Random Trees (RRT) and its variants, have been used extensively for motion planning. Control barrier functions (CBFs) have been recently proposed to synthesize controllers for safety-critical systems. In this paper, we combine the effectiveness of RRT-based algorithms with the safety guarantees provided by CBFs in a method called CBF-RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∗</sup> . CBFs are used for local trajectory planning for RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∗</sup> , avoiding explicit collision checking of the extended paths. We prove that CBF-RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∗</sup> preserves the probabilistic completeness of RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∗</sup> . Furthermore, in order to improve the sampling efficiency of the algorithm, we equip the algorithm with an adaptive sampling procedure, which is based on the cross-entropy method (CEM) for importance sampling (IS). The procedure exploits the tree of samples to focus the sampling in promising regions of the configuration space. We demonstrate the efficacy of the proposed algorithms through simulation examples.
Trust in Robot–Robot Scaffolding

The study of robot trust in humans and other agents is not explored widely despite its importance for the near future human-robot symbiotic societies.Here we propose that robots should trust partners that tend to reduce their computational load, which is analogous to human cognitive load.We test this idea by adopting an interactive visual recalling task.In the first set of experiments, the robot can get help from online instructors with different guiding strategies to decide which one it should trust based on the computational load it experiences during the experiments.The second set of experiments involves robot-robot interactions.Akin to the robot-online instructor case, the Pepper robot is asked to scaffold the learning of a less capable 'infant' robot (Nao) with or without being equipped with the cognitive abilities of theory of mind and task experience memory to assess the contribution of these cognitive abilities to scaffolding performance.Overall, the results show that robot trust based on computational/cognitive load within a sequential decisionmaking framework leads to effective partner selection and robot-robot scaffolding.Thus, using the computational load incurred by the cognitive processing of a robot may serve as an internal signal for assessing the trustworthiness of interaction partners.
Quantitative Inhabitation for Different Lambda Calculi in a Unifying Framework

We solve the inhabitation problem for a language called λ!, a subsuming paradigm (inspired by call-by-push-value) being able to encode, among others, call-by-name and call-by-value strategies of functional programming. The type specification uses a non-idempotent intersection type system, which is able to capture quantitative properties about the dynamics of programs. As an application, we show how our general methodology can be used to derive inhabitation algorithms for different lambda-calculi that are encodable into λ!.