Reviewerly/exHarmony huggingface dataset:
Visualized performance evaluation of 3D CAD software based on the characterization of test data by cloud model

The performance of 3D CAD software is usually evaluated based on a lot of test data. However, the complex uncertainties intrinsic in the test data has not been investigated so far, the influence of which on the reliability of performance evaluation result has not been considered either. To address the problem, this paper analyses the complex uncertainties in test data and proposes a visualized evaluating method for the performance of 3D CAD software based on the characterization of test data by cloud model. First, the initial weight of each performance index is determined by analytic hierarchy process (AHP) and the test data are standardized. Then the standardized test data are input into the backward cloud generator to calculate the digital characteristics of the cloud model for each performance index, based on which the reliability of each performance index is calculated and the corresponding weight is adjusted accordingly. Finally, the overall performance of 3D CAD software is scored by AHP and the reliability of the evaluation result is also rated. An analytic hierarchy diagram is produced for illustrating the whole evaluating process and the cloud map of the overall performance is generated by the forward cloud generator to provide the intuitive and reliable evaluation process and result for users.
Adaptive Consensus-Based Unscented Information Filter for Distributed Space Target Tracking

The target tracking algorithm plays a key role in space situation awareness. The traditional target tracking filters are usually designed when only the maneuver or outlier is considered. In this paper, an adaptive consensus-based unscented information filter is proposed to deal with the dynamics model error caused by orbital maneuver and the measurement outlier during target tracking. The proposed method can handle the above two unfavorable factors by adopting the innovation anomaly detection for each sensor. Based on the detection result, the probability of outlier and the probability of maneuver under different sum of alarm are compared to determine which situation occurs. If the outlier occurs, it will be replaced by the predicted value of measurement. If the target performs maneuver, the predicted information state and matrix will be shrunken through a fading factor to suppress the impact of dynamics model error. The superiority of the proposed method is verified by simulations that a space target is tracked by a satellite swarm with ranging radars. Simulation results show that the proposed method can provide higher tracking accuracy than the traditional methods.
Error-free interconversion of nonlocal boxes

Understanding the structure of nonlocal correlations is important in many fields ranging from fundamental questions of physics to device-independent cryptography. We present a protocol that can convert extremal two-party--two-input nonlocal no-signaling boxes of any type into any other extremal two-party--two-input nonlocal no-signaling box perfectly. Our results are exact, and even though the number of required boxes cannot be determined in advance, their expected number is finite. Our protocol is adaptive and demonstrates for the first time the usefulness of using no-signaling boxes in different causal orders by the parties.
Identification of Counterfeit Drugs Using Decentralized Supply Chain

This research attempts to overcome the problems faced by the medical healthcare system by using the advancing technology of blockchains. Pharma companies that manufacture and sell medicines face difficulties in tracking drugs, which in turn allows the counterfeiters to exploit the system. The decentralized abilities of blockchain technology enable us to counter the problems in existing centralized systems. The blockchain helps us to make sure that the quality is maintained throughout the decentralized supply chain. The use of blockchains in the medicine supply chain solution entails tracking the validity of the medicine from the producer to the distributor to the pharmacy. It assures that the pharmacist receives the original medication and does not reach the grey market. In this paper, the decentralized application which we created works using Ethereum and is based on blockchain technology. The medicine discovered by a pharmaceutical company is to be validated by an officer in a decentralized manner using smart contracts over Ethereum transactions. The validated drugs can then be produced and sold on the platform, where the entire data and the stages of the drug/medicine are tracked and stored. Research and development of such a system are necessary to facilitate the proper supply and tracking of medicines and to avoid counterfeiting.
Crow Search Algorithm: A Systematic Review

Cognitive computing and Artificial Intelligence (AI) are Computer Science branches which aim to create machines and ingenious technologies that are capable of working and thinking like humans. Evolutionary computing is a subfield of AI that uses nature-inspired mechanisms (algorithms) and solves problems through processes that mimic the behavior of living organisms. Researchers have focused on several meta-heuristic algorithms, and the Crow Search Algorithm (CSA) is one of the recently developed algorithms dependent on the astute conduct of crows. CSA is a populace based methodology. It works by storing excess food in hiding places and extracting the food when necessary. This algorithm has been used in different fields such as medical diagnoses, fractional optimization problems, and energy problems. Several modifications have been made to this algorithm, and the current research focuses on a systematic review of the applications of the crow search algorithm in the medical domain and the variants of CSA and its application in different engineering fields.
Forms and Results of Zhang Neuronet of Reciprocal Kind Dealing with Time-Variant Overdetermined System of Linear Equations

In order to deal with time-variant overdetermined system of linear equations (TVOSLE), a new approach termed Zhang neuronet of reciprocal kind (ZNRK) is proposed and reformulated. As developed from the continuous-time Zhang neuronet (CTZN), the ZNRK model is, however, quite different from existing CTZN models. That is, a conventional CTZN model needs to compute the inverse of the coefficient matrix. When the dimension of the coefficient matrix is large, the inverse of the coefficient matrix is difficult to compute. Hence, we propose the ZNRK model that does not need to compute the inverse of the coefficient matrix, only needing to compute the reciprocal of a scalar, which greatly reduces the computation complexity. In this paper, three computer simulations are used to test the validity of the ZNRK model, and the results substantiate the effectiveness of the ZNRK model for dealing with TVOSLE. Investigating the convergence-rate effect of the ZNRK model, we find that the convergence time decreases with the value of the convergence parameter increasing.
Study About Navigation Systems for Autonomous Robots

Every year, new devices are developed and inte-grated into automotive vehicles in order to make them increasingly intelligent and independent of human drivers. Therefore, the article presents a study on the low-cost navigation system for autonomous vehicles using GPS and MPU6050 module. Tests were carried out and analyzed to evaluate the study in question.
Data Feedback Mining of College English Multimodal Online Guidance System based on the Fusion of TCP and UDP Protocols

Data feedback mining of the college English multimodal online guidance system based on the fusion of TCP and UDP protocols is discussed in the paper. The multimodal guiding resource environment is a fluid environment based on modern information technology and social media. Based on the flipped classroom teaching model, this research constructs a smart platform composed of multiple modal resources and a social platform. The fusion of TCP and UDP protocols is the essential part of the designed method. Combined with characteristics of UDP data transmission and FPGA, this paper designs a high-speed data transmission system based on FPGA hardware platform using UDP data transmission technology. The platform is then integrated with the data feedback mining algorithm to show the robustness of the model. The test on the performance has proven the effectiveness.
URL Protection and Bookmark Hiding using AES Algorithm

In recent years, with the growing number of social networks and technologies, phishing has become a significant threat. Phishing is the fraudulent collection and exploitation of private data by the intended receivers. The phisher preys on internet users by duping them into disclosing the personal information in order to steal it and exploit fraudulently. This paper will be discussing and listing a few of the techniques that will help us to prevent the data from the cyber attackers. The paper concentrates on protecting URLs and hide bookmarks securely by using encryption and decryption techniques. This task is attained by the Advanced Encryption Standard (AES) algorithm to encrypt and decrypt URLs for the authentication technique. Encryption is one of the most important and popular techniques to protect data from attackers using two vital processes namely encryption and decryption. When a user visits an encrypted URL, the user will be prompted for a password. A knocking sequence is used for the purpose of bookmark hiding. By using these methods, the users could progress towards preventing the data from such attacks.
A Secured GUI based Flexible Automated System for Face Recognition and Suspicious Behaviour Prediction

Protection of each organization or campus is vital in recent times, as the crime rate in modern days is growing every day. Unauthorized entry of people will affect the secrecy of the data along with adverse economic effects. As the world is transforming towards a complicated technological style, computerized systems have been designed and implemented in many sectors. The rise in the crime rate necessitated the development of automated systems that can replace manual involvement which is tedious and time-consuming. However, these systems should be accurate unless of which the complete objective will go in vain. Much work has been carried out that makes use of biometric strategies and fingerprint-based for getting admission to structures. But these approaches have the constraints of getting mimicked or affecting the decision accuracy of decision making. In this pandemic situation, contactless design development is getting important. As the people are becoming negligible in following Covid precautions affecting the surrounding community, face mask detection systems are introduced that can yield a better solution. The current work proposes a novel system that serves the multiple functionalities of automated security with face recognition for attendance monitoring and secured entry as well as mask detection under Covid pandemic prevailing situations. The novelty of the current work is that it can also be used for identifying emotions and for automated attendance monitoring.
A Smart Hand Glove that Converts Gesture into Text &amp; Speech to Assist the Handicapped-(Handtalk)

Human being wants to convey their feelings by interacting with people surrounding them. But this is not a case of speech-hearing impaired people whom we call deaf-dumb. This community face lots of helplessness &#x0026; discrimination. According to World Federation of the Deaf (WFD), there are approximately 70 million deaf &#x0026; mute people. Previous research works focused on building a communication system for this community but most are prone to inconveniency, for example, heavyweight, expensive, and user-unfriendly. Our paper focused on introducing a system with simplicity, ease of operability, portability. Overcoming the helplessness, we developed a smart hand talk system through which, hand gestures provided by the user will give text &#x0026; speech output and send the output to a server for emergency response of doctors or family members. The proposed system can control appliances. It sheds light on measuring actions of deaf-dumb people using flex sensors attached to the glove. The glove is placed in hands, whenever a specific sign/gesture will be commanded for action, the analog value will be obtained and the corresponding function will be identified by the arduino board. A xampp server connected to the system will ensure emergency care by doctors or family members.
Gaze Behavior as an Objective Measure to Assess Social Presence During Immersive Mediated Communication

Immersive communication systems provide increasingly realistic virtual environments, which may afford immersive social interactions that approach the quality of face-to-face (F2F) meetings by eliciting a sense of social presence; the feeling of being physically together with another person and having an affective and intellectual connection. To optimize a system’s ability to convey social presence, there is a need for tools that efficiently and reliably measure the degree to which users experience social presence. Currently, the most widely used tools to measure (social) presence are questionnaires. As their ecological validity is questionable, there is a need for objective and non-intrusive measures to measure social presence during naturalistic social interactions. In our study, we aimed to identify a set of determinants of social presence that enable the assessment of a system’s ability to convey social presence, preferably using easy to use, off-the-shelf tools. Considering eye gaze behavior is modulated by social presence and can be measured with relative ease for both F2F and mediated communication, we propose to use three eye gaze measures as an accessible means to assess the level of social presence a system can elicit.
Contributing to Chronemics - Multievents for Haptic Communication

Haptic communication offers many interesting opportunities such as an unparalleled feel of presence, opening up to emotions and assuring oneself in reality - except for adding to perception of sizes, distances, weight, hardness, softness, warmth, and coldness – all fundamental aspects of the world. Haptic senses (cutaneous, kinaesthesis etc.) are found all over the body which also means that any technological means, such as wearables have/should have the same potential of full body coverage. For haptics, proximal stimulus could be located at two (or more) spatial separated anatomical locations simultaneously. As any proximal stimulus is an event and events have a duration i.e. existing in temporal space, it is of interest to see how events relate to each other. We do this first for two events, where we get an exact number, and then try to generalize this to three and more events. We observe that natural languages typically lack the fine distinctions in their vocabulary, relaying on vague (as we show) expressions like “before” and “after”.
Projectivities of Informationally Complete Measurements

The physical problem behind informationally complete (IC) measurements is determining an unknown state statistically by measurement outcomes, known as state tomography. It is of central importance in quantum information processing such as channel estimating, device testing, quantum key distribution, etc. However, constructing such measurements with good properties is a long-standing problem. In this work, we investigate projective realizations of IC measurements. Conditions of informational completeness are presented with proofs first. Then the projective realizations of IC measurements, including proposing the first general construction of minimal projective IC measurements (MPICM) in no prime power dimensional systems, as well as determining an unknown state in $C^{n}$ via a single projective measurement with some kinds of optimalities in a larger system, are investigated. Finally, The results can be extended to local state tomography. Some discussions on employing several kinds of optimalities are also provided.
Safety Validation of Neural Networks

The safety-critical nature of autonomous vehicles means that safety is a critical objective in the design of the system and must be ensured to a high assurance level. In typical automotive applications this is done through functional safety validation, where the system is proven to be safe to a certifiable level, following the guidelines of a standard such as ISO 26262 [161]. The lack of adequate functional safety validation techniques which can ensure safety to a certifiable level is currently a significant obstacle for the implementation of deep learning systems in autonomous vehicles. Deep neural networks introduce a number of challenges which hinder the application of traditional verification and validation techniques. First, the opaque nature of deep neural networks limits their interpretability. With the field moving to more complex and deeper models, interpreting what the model has learned and how it makes its decisions is no longer feasible [159]. Second, online learning systems have the capability to self-optimize their performance by changing their internal structure during operation. This leads to further difficulty for functional safety validation when online learning is utilized, as these systems can no longer be fully validated offline [162–166]. Therefore, methods for run-time validation must also be considered for adaptive systems. Third, the complex nature of the operational environment for autonomous vehicles means that it is impossible to test the system for all possible eventualities [140, 141]. Therefore, the validation process must provide proof that the system has learned rules which ensure safe behavior is maintained even in previously unseen scenarios. Finally, deep neural networks introduce new vulnerabilities, such as malicious inputs through adversarial examples. Adversarial examples add small perturbations to images, which cause DNNs to misclassify them with high confidence [167–171]. These factors make the functional safety validation of deep learning systems significantly more difficult when compared to traditional software systems. New functional safety validation approaches must be found, or existing methods adapted, to move the field toward an adequate safety validation process for deep learning systems. For these reasons, this chapter is dedicated to reviewing a variety of safety validation approaches and analyzing their applicability to deep learning-based systems for autonomous vehicles.
Closing Thoughts

We are in the middle of a transition from a memory industry singularly focused on density to a memory industry up against the scaling wall and looking for value additions.
Other Approaches and Applications Using Emerging Patterns

There are many interesting approaches and applications that involve the use of emerging/contrast patterns, and they are often quite different from each other. This chapter gives an overview of representatives of these approaches and applications; the presentation contains some details, but it is not very detailed due to space limitations. From a technique-focused perspective, the sections belong to six groups: (1) Sections 11.1–11.7 discuss representative approaches that use emerging/ contrast patterns as group signatures/characterizations. They use such patterns to analyze given data groups, and perhaps also to solve other domain-specific problems. (2) Sections 11.8–11.14 are concerned with representative applications that use emerging patterns as features. (3) Sections 11.15–11.17 are about using CAEP to solve a range of classification/recognition problems. (4) Section 11.18 is about various ways to use emerging patterns to build classifiers. (5) Section 11.19 is about using emerging patterns for classification over streaming data. (6) Section 11.20 is about other applications. Section 11.21 provides a high-level view, from a “discipline and direction” focused perspective.
Understanding Structured Exception Handling

In this chapter, you will learn how to handle runtime anomalies in your C# code through the use of structured exception handling. Not only will you examine the C# keywords that allow you to handle such matters (try, catch, throw, finally, when), but you will also come to understand the distinction between application-level and system-level exceptions, as well as the role of the System.Exception base class. This discussion will lead into the topic of building custom exceptions and, finally, to a quick look at some exception-centric debugging tools of Visual Studio.
Unifying Example-Based Approaches

The approaches presented so far are tailored to the three main data models, from fully structured data (relational), to semi-structured (graphs) and unstructured (textual) data. In the implementation of an exploratory system, the natural choice of the specific example-based technique to employ depends on the data model and the search task. However, in the case in which we need to query heterogeneous datasets, the choice of the right algorithm is harder. Appropriate cross-models techniques can sooth this issue. Unfortunately, only a few works favor a model-agnostic approach to the problem.
Finite Sample Forecast Properties and Window Length Under Breaks in Cointegrated Systems

We show that extending the estimation window prior to structural breaks in cointegrated systems can be beneficial for forecasting performance and highlight under which conditions. In doing so, we generalize the Pesaran & Timmermann (2005)’s forecast error decomposition and show that it depends on four terms: 1) a period ahead risk; 2) a bias due to a conditional mean shift; 3) a bias due to a variance mismatch; 4) a gap term valid only conditionally. We also derive new expressionsfor the estimators of the adjustment matrix and a constant, which are auxiliary to the decomposition. Finally, we introduce new simulationbased estimators for the finite sample forecast properties which are based on the derived decomposition. Our finding points out that, insome cases, we can neglect parameter instability by extending the window backward and be insured against higher forecast risk under this model class as well, generalizing Pesaran & Timmermann (2005)’s result. Our result gives renewed importance to break tests, in order todistinguish cases when break-neglection is (not) appropriate.
Foundations

Ontologies have become increasingly important as the use of knowledge graphs, machine learning, and natural language processing (NLP), and the amount of data generated on a daily basis has exploded. Many ontology projects have failed, however, due at least in part to a lack of discipline in the development process. This book is designed to address that, by outlining a proven methodology for the work of ontology engineering based on the combined experience of the authors, our colleagues, and students. Our intent for this chapter is to provide a very basic introduction to knowledge representation and a bit of context for the material that follows in subsequent chapters.
Tree-Clustering Schemes

In this chapter, we take the bucket elimination algorithm a step further. We will show that bucket elimination can be viewed as an algorithm that sends messages along a tree (the bucket tree). The algorithm can then be augmented with a second set of messages passed from bottom to top, yielding a message-passing schemes that belongs to the class of cluster tree elimination algorithms.