Reviewerly/exHarmony huggingface dataset:
A New Hybrid Method for Global Optimization Based on the Bird Mating Optimizer and the Differential Evolution

Nature-inspired algorithms are often used by several diverse areas of engineering and science due to their easiness and versatility. Because metaheuristics operate by structurally changing and improving an established problem, they can often be extended to any optimization issues. The recent creation of metaheuristic algorithms has rendered them effective tools for solving NP problems. This paper presents a hybrid meta-heuristic method based on the Differential Evolution and Bird Mating Optimizer techniques to solve problems of global optimization. Bird Mating Optimizer is a novel method and is inspired by mating behavior of birds. Bird Mating Optimizer has some drawbacks such as producing poor results, trapping into local optima and slow convergence speed. Therefore, to conquer these insufficient it is hybridized with Differential Evolution approach. Differential Evolution technique is utilized to retain a preferable balance between both searches local and global. The performance and effectiveness of new Differential Evolution and Bird Mating Optimizer algorithm is tested and evaluated on 15 different functions of benchmark. The results of the experiment have shown the proposed technique possesses excellent performance in convergence speed, stability, and robustness, as compared to the well-known algorithms. It is proved that the Differential Evolution and Bird Mating Optimizer algorithm is very effective and superior to solve problems of global optimization. Experimental results indicate that the proposed hybrid Differential Evolution and Bird Mating Optimizer method is superior to previous existing state-of-the-art metaheuristic algorithms.
White Box Traitor Tracing

Traitor tracing aims to identify the source of leaked decryption keys. Since the "traitor" can try to hide their key within obfuscated code in order to evade tracing, the tracing algorithm should work for general, potentially obfuscated, decoder programs. In the setting of such general decoder programs, prior work uses black box tracing: the tracing algorithm ignores the implementation of the decoder, and instead traces just by making queries to the decoder and observing the outputs. We observe that, in some settings, such black box tracing leads to consistency and user privacy issues. On the other hand, these issues do not appear inherent to white box tracing, where the tracing algorithm actually inspects the decoder implementation. We therefore develop new white box traitor tracing schemes providing consistency and/or privacy. Our schemes can be instantiated under various assumptions ranging from public key encryption and NIZKs to indistinguishability obfuscation, with different trade-offs. To the best of our knowledge, ours is the first work to consider white box tracing in the general decoder setting.
Offline Character Recognition of Handwritten MODI Script Using Wavelet Transform and Decision Tree Classifier

MODI script is derived from the Nāgari family of scripts, and it was used for writing Marathi until twentieth century. Though currently not used as an official script, it has historical importance, as a large volume of manuscripts are preserved at various libraries across India. With the use of an appropriate recognition system, the handwritten documents can be transferred into digital media, so that it can be conveniently viewed, edited, or transliterated to other scripts. The research on MODI script is still in the initial stages, and there is a considerable demand for more research in this field. An implementation of wavelet transform-based feature extraction for MODI script’s character recognition is discussed in this paper. The experiment is performed using Daubechies, Haar, and Symlet wavelets, and performance comparison between these different mother wavelets is carried out. Decision tree classifier is used for the classification process, and the results indicate that the feature extraction using Daubechies wavelet yielded better character recognition result.
Spectral Differential Privacy: Application to Smart Meter Data

We present spectral differential privacy (SpDP), a novel form of differential privacy (DP) designed to protect the frequency content of time-series data that come from wide sense stationary (WSS) stochastic processes. This notion is motivated by privacy needs in applications with time-series data over unbounded time, such as smart meters. First, a notion of DP on the space of (discretized) spectral densities is introduced. A Gaussian-like mechanism for SpDP is then presented that provides DP to the spectral density. Next, a novel streaming implementation is developed to enable real-time use of the proposed mechanism. The privacy guarantee provided by SpDP is independent of the time duration over which data are collected or shared. In contrast, time-domain trajectory-level DP (TrDP) will require noise with large variance to provide privacy over an extended time duration. The technique is numerically evaluated using smart meter data from a single home to compare the utility of SpDP to that of time-domain TrDP. The noise added by SpDP is substantially smaller than that added by time-domain TrDP, particularly when privacy over long time horizons is sought by TrDP.
Failure prediction through a model-driven machine learning method

For the purpose of quality control in some production lines each product undergoes an end of line test. Early detection of products that will fail during or at the end of the test, can save time and material. Besides, it prevents hazards and damages to the test equipment and the product which can be caused by operating defective products. Utilizing data analysis for prediction of such anomalies can be challenging due to the non-stationary and multi-phased nature of the test/measurement process. This paper presents a model-driven machine learning method (using welding machine test data), where instead of fully data driven anomaly detection analysis, the analysis starts with approximate modeling of temperature evolution at the location of temperature sensors which is governed by physical process of heat transfer, originated from any unspecified heat source. This approach allows decision making about the test result just at the beginning of the test. In addition, since this method reduces the dimensionality of the input space, the machine learning model can be trained with much less number of samples, which is an enabler for many cases where access to training data is limited.
Probabilistic Planning with Preferences over Temporal Goals

We present a formal language for specifying qualitative preferences over temporal goals and a preference-based planning method in stochastic systems. Using automata-theoretic modeling, the proposed specification allows us to express preferences over different sets of outcomes, where each outcome describes a set of temporal sequences of subgoals. We define the value of preference satisfaction given a stochastic process over possible outcomes and develop an algorithm for time-constrained probabilistic planning in labeled Markov decision processes where an agent aims to maximally satisfy its preference formula within a pre-defined finite time duration. We present experimental results using a stochastic gridworld example and discuss possible extensions of the proposed preference model.
Traditional and Accelerated Gradient Descent for Neural Architecture Search

In this paper we introduce two algorithms for neural architecture search (NASGD and NASAGD) following the theoretical work by two of the authors [4] which used the geometric structure of optimal transport to introduce the conceptual basis for new notions of traditional and accelerated gradient descent algorithms for the optimization of a function on a semi-discrete space. Our algorithms, which use the network morphism framework introduced in [1] as a baseline, can analyze forty times as many architectures as the hill climbing methods [1, 10] while using the same computational resources and time and achieving comparable levels of accuracy. For example, using NASGD on CIFAR-10, our method designs and trains networks with an error rate of 4.06 in only 12 h on a single GPU.
An Effective Image Encryption Algorithm Using Bit Reversal Permutation and a New Chaotic Map

In this paper, a different approach to create a new chaotic model and an effective image encryption structure using Bit reversal permutation are proposed. Compared to most frequently used and well known chaotic maps, such as Logistic map, Sine map or Tent map, a new chaotic system based on Logistic map with Sine map is designed and used as an encryption key generator in the proposed algorithm. The new map has increased initial value sensitivity according to the results of Lyapunov analysis and shown better randomness output according to the chaotic trajectory analysis. In cryptography, a good key should be a stochastic and supposed to be sufficiently random and uniformly distributed with equal probability for an effective encryption. The designed chaotic map provides these properties very well. Before the basic encryption process, the Bit reversal method makes all pixel positions of input image rearranged in order to reduce the strong relation of adjacent pixels for higher encryption strength, which will enable strategic information sharing for production planning when this method analyzes a wind energy map in power plants. Other experimental results confirm that the proposed image encryption scheme has sufficient security, an effective encryption capability and can be transferred between power systems, keeping energy planning secret strategically.
Step Length Estimation Using Inertial Measurements Units

This paper focuses on step length estimation using either one inertial measurement unit (IMU) on each shank or one IMU each on the shank and thigh of a human subject. An observer design problem that requires estimating shank angle, thigh angle and bias parameters of the initial sensors in order to estimate step lengths is formulated. A nonlinear observer is designed using Lyapunov analysis to solve the formulated problem and utilizes an LMI to find a stabilizing observer gain. Experimental results are presented on the performance of the nonlinear observer and compared with reference measurements from an infrared camera capture system. The estimation system that utilizes only one inertial sensor on each shank is seen to provide an average step length accuracy of 91.21% while the estimation system that utilizes two sensors on each leg provides an average step length accuracy of 95.37%.
Smart question answering system using vectorization approach and statistical scoring method

In this digital era, internet users are increasing day by day to extract information related to various fields such as health, economic, educational, politics and daily needs from Information Retrieval Systems (IRS) such as Google, Ask, Yahoo etc. Information Retrieval (IR) is a conventional method which depends on metadata searching method that is searching of adocuments for information within documents and information about documents. Lot of research in the IR area improves accessing the quality of information. Most of the search engines such as Google, Yahoo, Ask Jeeves depends on keyword based query processing mechanism internally which can't handle natural language questions which results in returning long list of documents for a query. The task of users is to manually explore each document to retrieve correct answer which is a time consuming process. To overcome the problems of conventional search engines, smart question answering system comes to play. The objective of Smart Automated Answering system is to impart accurate and valid answers to user question in natural language rather than set of full documents. The Smart Question answering system which accepts user question in Natural language in specific domain and provide short and precise answer using Natural language processing technique, used for pre-processing, normalized term weighting-TF-IDF with cosine similarity approach for document retrieval and enhanced BM25 ranking function to extract answer. The main focus is on decreasing the response time, designing ranking function and giving the pertinent responses to the users' intent. The system achieved an overall Precision of 93.2%, Recall of 84.3% and F-measure of 88.5% with 80% of Accuracy.
A Suppositional Theory of Conditionals

Abstract Suppositional theories of conditionals take apparent similarities between supposition and conditionals as a starting point, appealing to features of the former to provide an account of the latter. This paper develops a novel form of suppositional theory, one which characterizes the relationship at the level of semantics rather than at the level of speech acts. In the course of doing so, it considers a range of novel data which shed additional light on how conditionals and supposition interact.
Response type selection for chat-like spoken dialog systems based on LSTM and multi-task learning

We propose a method of automatically selecting appropriate responses in conversational spoken dialog systems by explicitly determining the correct response type that is needed first, based on a comparison of the user's input utterance with many other utterances. Response utterances are then generated based on this response type designation (back channel, changing the topic, expanding the topic, etc.). This allows the generation of more appropriate responses than conventional end-to-end approaches, which only use the user's input to directly generate response utterances. As a response type selector, we propose an LSTM-based encoder–decoder framework utilizing acoustic and linguistic features extracted from input utterances. In order to extract these features more accurately, we utilize not only input utterances but also response utterances in the training corpus. To do so, multi-task learning using multiple decoders is also investigated. To evaluate our proposed method, we conducted experiments using a corpus of dialogs between elderly people and an interviewer. Our proposed method outperformed conventional methods using either a point-wise classifier based on Support Vector Machines, or a single-task learning LSTM. The best performance was achieved when our two response type selectors (one trained using acoustic features, and the other trained using linguistic features) were combined, and multi-task learning was also performed.
Recommendation System for Location-Based Services

Location-based services encompass a spectrum of services. Today, it is easier to locate or search for our favorite restaurant, shop, etc., under these services. It helps us get access to important and up-to-date information about their surroundings on a single tap. This research proposes two location-based recommendation systems by using the collaborative and content-based filtering recommendation techniques. The first one is a personalized location-based recommender that uses the content filtering technique. In this recommender, the behavioral patterns are extracted from the user’s location history and then provide personalized recommendations based on patterns. Apriori algorithm has been used to extract user-specific behavioral patterns based on time zone, weekday, and location type. The second one is a generalized location-based recommender that uses the collaborative filtering technique. It employs the K-means clustering algorithm and the silhouette metric and elbow method to find the optimal index K (clusters).
Multi-Layout Invoice Document Dataset (MIDD): A Dataset for Named Entity Recognition

The day-to-day working of an organization produces a massive volume of unstructured data in the form of invoices, legal contracts, mortgage processing forms, and many more. Organizations can utilize the insights concealed in such unstructured documents for their operational benefit. However, analyzing and extracting insights from such numerous and complex unstructured documents is a tedious task. Hence, the research in this area is encouraging the development of novel frameworks and tools that can automate the key information extraction from unstructured documents. However, the availability of standard, best-quality, and annotated unstructured document datasets is a serious challenge for accomplishing the goal of extracting key information from unstructured documents. This work expedites the researcher’s task by providing a high-quality, highly diverse, multi-layout, and annotated invoice documents dataset for extracting key information from unstructured documents. Researchers can use the proposed dataset for layout-independent unstructured invoice document processing and to develop an artificial intelligence (AI)-based tool to identify and extract named entities in the invoice documents. Our dataset includes 630 invoice document PDFs with four different layouts collected from diverse suppliers. As far as we know, our invoice dataset is the only openly available dataset comprising high-quality, highly diverse, multi-layout, and annotated invoice documents.
Variational Autoencoder based Novelty Detection for Real-World Time Series

There are numerous applications that deal with data captured over time making them potential subject to time series analysis. Detecting unknown events and anomalies in time series data is challenging due to the presence of noise, seasonalities and long-term trends. Data-driven methods applied to identify such patterns are called anomaly detection. Typically, the amount of available abnormal data, e.g. failure states in manufacturing plants, is not sufficient to construct an explicit model. Novelty detection is a special form of anomaly detection which detects when a data point differs from the majority of data. In this work, a novel approach to detect anomalous patterns and events in real-world time series data is proposed. The novelty detection approach is based on deep generative learning and utilizes natural properties of the variational autoencoder to create a novelty indicator. Therefore, a wide range of deterministic and stochastic novelty scores calculated in the latent and original data space are combined. The combination of these novelty scores leads to a novelty indicator that accurately detects novel events in real-world time series data. An experimental study evaluates the method on data collected at an electrocoating plant which was affected by internal and external disturbances. The proposed method is benchmarked against other state of the art methods and achieves highly competitive results.
Issues Related to the Use of Blockchains in IoT Applications

We are currently witnessing an impetuous development of blockchain technology in various fields like business, supply chains, or personal identity protection. Another technology that is becoming more widespread is the Internet of Things (IoT), which can be found in a lot of smart applications. In this paper, we aim to study how blockchain finds its utility in IoT applications. Finally, we analyze comparatively the reliability and availability of centralized and decentralized blockchain oracle mechanisms that provide data from IoT devices.
Assessing Connections and Tradeoffs Between Geospatial Data Ethics, Privacy, and the Effectiveness of Digital Contact Tracing Technologies

Contact tracing, a useful public health tool that aids in the identification of individuals who may have come into contact with a person known to be infected with a disease, has been identified as key to the mitigation and suppression of COVID-19. Effective contact tracing allows public health authorities to sever chains of transmission and shift policy to case-based interventions such as selective individual quarantines rather than population-wide interventions such as social distancing. While public health authorities have the ability to conduct manual contact tracing, many do not have the capacity to identify and trace infected individuals at the scale or speed needed to respond to the COVID-19 pandemic. To improve the reach and effectiveness of contact tracing, many are proposing to expand contact tracing capacity by introducing digital contact tracing technologies that use the geospatial tracking technologies (e.g., GPS, WiFi, Bluetooth) embedded in mobile devices to gather, store, transfer, and share the location and contact histories of individuals. This chapter examines contact tracing, its potential extension using geospatial technologies, and the tradeoffs between privacy and effectiveness that may arise as these systems are developed and deployed to address COVID-19. By identifying linkages between the potential capabilities of these technologies and ethical and privacy principles of geospatial data handling, we introduce a framework for assessing conflicts between privacy and effectiveness. This framework is needed if we are to hold an informed public discussion of two critical questions. First, how the potential spread of geospatial contact tracing technologies may impact the institutional structures of society. Second, how societal processes might change the form geospatial contact tracing technologies take and the role we intend for them to play.
Multi-agent Simulation of Agile Team Dynamics: An Investigation on Team Strategies Perspective

Scrum is an agile process that incrementally, iteratively and continuously delivers software based on time boxes (sprints). It comprises user stories stored in product backlogs and delivered through sprints by a Scrum teams consisting of team members, Scrum Master and Product Owner. The performance of a Scrum team is largely dependent on the team members and the technical practices that they adopt. One such practice, pair programming has been studied in a variety of contexts but not extensively in a Scrum context. Pair programming is a technique where two programmers work side by side at one computer to collaborate on the same design, algorithm, code, or test. The use of pair programming within Scrum has not been widely researched. A multi agent system is used to simulate the scrum environment where a team (with varying team members’ capability) work on delivering user stories (which consists of multiple tasks with varying complexities) in multiple sprints. Using this simulated environment, various strategies are investigated. Impact is measured based on the team’s work efficiency, completion time, effort time and idle time. Experiments were carried to test these strategies in varying environments and results showed that a intelligent pairing strategy performed the best in environments as it avoided any negative pairing impacts.
Smooth Projective Hash Function From Codes and its Applications

Nowadays, Smooth Projective Hash Functions (SPHFs) play an important role in constructing cryptographic tools such as secure Password-based Authenticated Key Exchange (PAKE) protocol in the standard model, oblivious transfer, and zero-knowledge proofs. Specifically, in this article, we focus on constructing PAKE protocol; that is, a kind of key exchange protocol which needs only a low entropy password to produce a cryptographically strong shared session key. In spite of relatively good progress of SPHFs in applications, it seems there has been little effort to build them upon quantum-resistant assumptions such as lattice-based cryptography and code-based cryptography to make them secure against quantum computer attacks. More precisely, there are two proposals based on lattice assumptions that utilize the SPHFs to construct PAKE secured in standard model. Considering quantum-resistant assumptions is less than straightforward and needs some relaxations. In this article, we introduce two new Approximate SPHF (ASPHFs) from error-correcting codes. Upon designing ASPHF, we can construct two efficient PAKE protocols. The security of our protocols could be proved based on the hardness of bounded decoding (BD) problem and learning with parity (LPN) problem in the standard model.