Reviewerly/exHarmony huggingface dataset:
APPLICATION OF HYBRID SFLA AND ACO ALGORITHM TO OMEGA PLATE FOR DRILLING PROCESS PLANNING AND COST MANAGEMENT

&amp;lt;p&amp;gt;Tool traveling and tool switching time constitute almost seventy percent (70%) of the total time&amp;lt;br /&amp;gt;consumed in drilling process. This fact that 70% of the total time is nonproductive and does not add any value to the job, grabs attention of the researchers and the industrialist for optimization. A literature on drilling process revealed that very few studies have been done on hybridization of metaheuristics for optimization of tool travel time. This research gap is the motivation of the present study. In this study, two metaheuristic approaches &amp;amp;ndash; the shuffled frog leaping algorithm (SFLA) and ant colony optimization (ACO) were hybridized. With respect to hybridization of SFLA and ACO, this study signifies its originality and novelty in which main objective is to minimize the tool travel time. The literature review also revealed that the shortest path generated through commercially available software is not optimal all the time. This aspect emphasizes the application of metaheuristic algorithms on the real-world industrial problems. In this study, the proposed hybrid algorithm was applied to drilling of omega plate which is used in automobile manufacturing industry. The results of the proposed hybrid algorithm were compared with those of manual drilling path and software generated path. The results obtained through proposed hybrid algorithm were improved by 11.1% when compared to results of manual drilling path. The results of proposed algorithm were also better than results of commercial software Creo 6.0 and Siemens NX by 5.9% each. This showed that hybrid algorithm outperformed the commercially available software. This not only validates the efficacy of proposed hybrid algorithm, but also indicates the significance of the metaheuristic algorithm applications in industrial optimization problems.&amp;lt;/p&amp;gt;
Review on the Application of Artificial Intelligence-Based Chatbots in Public Administration

Customer service continues to grow exponentially and is becoming more important every day, mainly because many companies, public and private organizations, are increasing the number of services they offer and the number of people who use them is also increasing. Given the above, business-to-customer service is an important strategy to ensure customer satisfaction. Artificial intelligence allows us to develop different tools, such as chatbots that can simulate human conversations providing customer service immediately. Unfortunately, according to the present research, the authors have realized that the architectures of existing chatbots, mainly those used in the public sector, are not fully efficient because they lack the use of artificial intelligence techniques and basically only consist of menus that provide information based on the selected option. Therefore, this chapter presents some improvements for the development and implementation of chatbots in public administration.
Power-Controlled Job Slowdown in Data Centers

As compute demands soar upwards, it is essential that systems not only scale throughput and latency, but also improve energy efficiency and alleviate power bottlenecks. A common quality of service metric for user-facing data center services is slowdown, the ratio of the actual response time of a computational job to the expected response time under no delay. In this work, we expand the definition of slowdown to the power-controlled, rate-optimizing setting. We take the initial steps to investigate and analyze the key tradeoffs between power and slowdown costs. Formulating the problem in a dynamic programming framework, we provide analytic solutions in the single class job setting, and formulate the multi-class job setting. Numerical solutions to the dynamic programming equation are also produced, and confirm the analytical results and parameter tradeoffs. In particular, we demonstrate both analytically and numerically that reasonable tradeoffs can be made between power and slowdown by optimizing the server processing rate. Optimizing for power consumption is increasingly important as data centers’ tackle the transition to renewable energy sources.
A Feasibility Study of Using Code Clone Detection for Secure Programming Education

Secure library reuse is critical for modern ap-plications to protect private information in software security engineering. Teaching secure programming is also more critical to tackle the challenges of new and evolving threats. However, novice students often make mistakes by API misuses due to a lack of understanding of secure libraries or a false sense of security. In this paper, we study the feasibility of applying code clone detection (CCD) for finding relevant examples to effectively teach secure programming to computer science students. CCD is an emerging new technology that extracts syntactically or semantically similar code fragments to support many software engineering tasks, such as program understanding, code quality analysis, software evolution analysis, and bug detection. We have developed a prototype implementation ExTUTOR that allows students to search for relevant examples as feedback when they want to fix their programming issues or vulnerabilities. In our evaluation, we applied ExTUTOR to open source subject applications in the security domain. Our approach should help novice students gain benefits from feedback and identify how to effectively make use of APIs, encouraging students to fix their own security violations in their own applications.
Certificate complexity of elementary symmetric Boolean functions

Boolean functions have important applications in information technology and computer science. Certificate complexity is an important combinatorial measure of Boolean function complexity. In this paper, we first introduce many concise and efficient notations then we study the elementary symmetric Boolean functions and obtain their certificate complexities when their degrees are odd or powers of 2. We show that both upper and lower bounds of certificate complexities can be attained.
Reinforcement learning as a basis for cross domain fusion of heterogeneous data

Abstract We propose to establish a research direction based on Reinforcement Learning in the scope of Cross Domain Fusion. More precisely, we combine the algorithmic approach of evolutionary rule-based Reinforcement Learning with the efficiency and performance of Deep Reinforcement Learning, while simultaneously developing a sound mathematical foundation. A possible scenario is traffic control in urban regions.
A distributed recovery algorithm for two-dimensional graph signals

In this paper, a novel distributed algorithm is proposed to recover two-dimensional graph signals. This work exploits a Cartesian product graph as a graph model to present complex datasets. Furthermore, incorporating the piecewise smooth assumption, a LASSO optimization formulation is tailored to the recovery problem. To solve the optimization formulation in a distributed manner, the Cartesian product graph is decomposed into a family of overlapping subgraphs, and then a series of localized subproblems resided on the subgraphs are introduced. The subproblems are solved via an alternating direction method of multipliers (ADMM). Experiments on three real-world datasets demonstrate the excellent performance of the proposed algorithm.
Capital Inflows, Macroeconomic Conditions and Growth Convergence in The West African Monetary Zone (WAMZ)

Capital inflows have been pinpointed as an important pathway through which the low-income, poor savings and investment as well as widening resource-gap, characterised within the West African sub-region can be filled. Besides the above, the convergence hypothesis has been identified as an important model with which to confirm evidence of club-convergence among countries within the sub-region aimed to supporting or refuting the call for a single-currency or monetary union recently agreed upon by the individual Heads of State. To achieve the above, this study employed Panel Autoregressive Distributed Lagged (PARDL) Technique to examine the threshold effects of capital inflows and macroeconomic conditions, as well as their interactive effects, necessary to spur growth and facilitate convergence among WAMZ countries from 1980 to 2017. The findings revealed that index of macroeconomic conditions and capital inflows are substitutes. Also, thresholds of capital inflows and macroeconomic conditions equivalent to 2.2 percent and 6 percent of GDP respectively are required within each member countries in order to enhance growth and accelerate growth convergence. The study recommends that macroeconomic stability should be ensured, in order to maintain inflows of adequate dosage. In addition, governments of member countries should maintain a threshold of 2.17% and 5.99% of GDP for index of capital inflows (CAPIN) and macroeconomic conditions (MACRO) in order to attract and retain investible funds, reduce external shocks arising from volatility, which in turn enhance growth and convergence. Most significantly, policy-makers should not rely on capital inflows in the short run because it transmits negative signals into the WAMZ economies which militates against growth. Rather, deliberate efforts should be channelled at advancing human capital and trade facilitation through qualitative education and infrastructural financing. In order to extend this study, researchers interested in international macroeconomics can re-examine the capital inflows-macroeconomic conditions relation by adding institutional quality into the models.
Color Image Encryption Algorithm Based on DNA Encoding And Memristor Chaotic System

In image encryption, in order to help the image resist the known plaintext attack, selective plaintext attack and differential attack, and improve the singularity of DNA coding algorithm, the association of plaintext, ciphertext and chaotic initial value is proposed, as well as the iterative operation combining plaintext, ciphertext and chaotic sequence in DNA operation. After encryption, through the analysis of histogram, correlation coefficient, information entropy, plaintext sensitivity and other aspects, it is proved that the algorithm can well resist differential attacks and select plaintext attacks, and image encryption is more secure.
Hybrid Feature Selection using Shapley Value and ReliefF for Medical Datasets

The medical databases are composed of vast amount of data. Increment in data volume has led to a massive amount of high-dimensional medical data made available to the public on the Internet. These large amounts of medical data can be put into good use through knowledge discovery by identifying knowledge that is useful via data mining. These high-dimensional data are often associated with redundant features removal. A range of information theoretic methods have been deployed in selecting the most viable and relevant feature sets, which have led to reduction in the size of data. Nonetheless, these methods have mostly failed in identifying the significance of each feature derived from the sets of features. An exceptional feature set not only decreases computational time and cost, but also enhances classifier accuracy in classification. As such, this study proposes a feature selection technique based on filter-wrapper technique using the ReliefF-Shapley Value hybrid. The ReliefF filter method was applied in the early stage stage to determine the accuracy of a feature in discriminating among classes. Next, the reduced set of features yielded from ReliefF was passed to the wrapper-based Shapley Value. In the wrapper method, Shapley Value was employed to add weight, and later, to assess each attribute based on the assessment standards. The outcomes were assessed using UCI-derived five medical datasets. The proposed method was able to yield competitive outcomes for most datasets.
External Service Sensing in an Uncertain World

The flourish of web-based service has led to an increasing number of user requirements that can be satisfied by an increasing variety of ways. To study how services can better satisfy user requirements, a research area called service computing was born. An obvious fact is that in an uncertain world, most services evolve constantly in order to adapt to various types of internal and external changes in a timely manner. However, traditional services computing techniques do not systematically consider the significant implications of service changes and many widely-accepted models/approaches do not work well in uncertain world. Sensing various types of service changes is of great significance to the practicability and rationality of services computing research. In this paper, a new research problem External Service Sensing (ESS) is defined to cope with various changes in services, and a research framework of ESS is presented to elaborate the scope and boundary of ESS. This framework is composed of four orthogonal dimensions: sensing objects, sensing contents, sensing channels, and sensing techniques. Each concrete ESS problem is defined by combining different values in these dimensions, and existing research work related to service changes can be well adapted to this framework. Real-world case studies demonstrate the soundness of ESS and its framework. Finally, some challenges and opportunities in ESS research are listed for researchers in the services computing community. To the best of our knowledge, this is the first time to systematically define service change-related research as a standard services computing problem, and thus broadening the research scope of services computing.
Know Your Victim: Tor Browser Setting Identification via Network Traffic Analysis

Network traffic analysis (NTA) is widely researched to fingerprint users’ behavior by analyzing network traffic with machine learning algorithms. It has introduced new lines of de-anonymizing attacks [1] in the Tor network, inclusive of Website Fingerprinting (WF) and Hidden Service Fingerprinting (HSF). Previous work [4] observed that the Tor browser version may affect network traffic and claimed that having identical browsing settings between the users and adversaries is one of the challenges in WF and HSF. Based on this observation, we propose a NTA method to identify users’ browser settings in the Tor network. We confirm that browser settings have notable impacts on network traffic and create a classifier to identify the browser settings. The classifier can establish over 99% accuracy under the closed-world assumption. The open-world assumption results indicate classification success except for one security setting option. Last, we provide our observations and insights through feature analysis and changelog inspection.
BitBooster: Effective Approximation of Distance Metrics via Binary Operations

The Euclidean distance is one of the most commonly used distance metrics. Several approximations have been pro-posed in the literature to reduce the complexity of this metric for high-dimensional or large datasets. In this paper, we propose BitBooster, an approximation to the Euclidean distance that can be efficiently computed using binary operations and which can also be applied to the Manhattan distance. The introduced approximation error is shown to be negligible when BitBooster is used for both convex- and density-based clustering. While obtaining clusters of almost the same quality as those obtained with the exact computation, we require only a fraction of the computation time. We demonstrate the superiority of our method to alternative approximations on 960 synthetic and 13 real-world datasets of varying sizes, dimensions and clusters.
High-quality Audio Network Transmissions with Raspberry PI

The Raspberry PI with its expansion possibilities allows the creation of low-cost solutions for many computing and network applications. We compare several options for bidirectional network audio transmissions using the Raspberry PI. We aimed to find the best option for high-quality and low-latency audio transmissions for demanding applications such as distance master classes in music education or music rehearsals and production.
Identity crisis and social dissociation in control societies

The article is devoted to the problem of the naming crisis of modern society. The sequences by which the social and cultural history of the West is ordered, represented by the evolution of economics, technology, religion, forms of capital and wealth, communications, following the technological acceleration of time, coexist with each other, compete for primacy, creating a society of atomized subjects who have ceased to understand their place in the history of society. This situation is described in the article as an identity crisis. The purpose of this work is to demonstrate the gaps that have been discovered in attempts to identify society, which are the object of criticism. The article refers to the tradition of explanatory modeling of social interactions in terms of psychopathology and psychiatry. The crisis of the identity of society is illustrated through the images of madness in modern art culture. The article hypothesizes that there is an unspoken ban on naming a society associated with the crisis of capitalism in the transition from disciplinary societies to control societies. Culture's interest in insanity serves as an indicator of a social crisis. The evolution of cultural interest in the figure of a madman from a frightening "outcast" to an attractive "rebel" is shown. The article suggests the tasks of critical philosophy that coincide with the tasks of caring for social health. The idea of the need for the deprivation of mental illness is expressed. Mental health care is considered as a social task.
Pure State-Based Computational Models

AbstractWe have selected the chapter’s epigram to suggest that an algorithmic model based entirely on Pillar S: STATE can capture valuable aspects of the phenomenon we call computation. Our mission is to verify this suggestion.
Discriminative Projection Learning With Adaptive Reversed Graph Embedding for Supervised and Semi-Supervised Dimensionality Reduction

The graph embedding based dimensionality reduction (DR) methods have been widely used to overcome the curse of dimensionality problem in high-dimensional data. Especially in pattern recognition and data analysis, they are frequently used to extract features. However, there are two problems in the graph embedding based supervised DR methods: 1) the original data will inevitably be corrupted by noise or outliers, which makes the fixed graph constructed from these data unreliable; and 2) determining how to model the projection for enhancing the discrimination of projected samples. To overcome the above two challenges, this paper proposes a new supervised DR method called discriminative projection learning with adaptive reversed graph embedding (DP-ARGE). Specifically, DP-ARGE learns the graph dynamically in a low-dimensional subspace by using the low-dimensional embedding rather than projected samples for improving its robustness. In addition, a novel discriminative regularization term is designed to enhance the discrimination of the projection samples by using a few pairwise marginal heterogeneous samples rather than the all. To tackle the problem of estimating the neighborhood size parameter of the graph, a simple yet effective automatic parameter estimation strategy based on the density and similarity of data set is proposed. Furthermore, for better handling the data set with a few labels, DP-ARGE is extend to a semi-supervised method (SDP-ARGE) by a neighborhood preserving regularization term. SDP-ARGE can be used to preserve the local manifold structure of the data in the semi-supervised scenario. Comprehensive experiments show the superiority of the proposed methods in several real-world data sets.
Towards a Digitized Workplace

The quote, illustrating how remote communications had been envisioned before World War II, is part of the verbiage on the back of collectable cards and stickers published by the German company Echte Wagner back in 1930. The quoted verse is part of a card that vividly portrays two women sitting outdoors while chatting to presumably their family members using portable monitors (Mann, 2021). Shortly after the first publicly demonstrated one-way videophone call in 1927 by then US Secretary of Commerce, Herbert Hoover (Schnaars & Wymbs, 2004), the Echte Wagner’s futuristic projection of videotelephony illustrated how individuals would be able to converse and exchange information with the use of a live video. Inspired primarily by these ideas, a closed-circuit videophone experiment took place in 1936, 6 years after the release of Echte Wagner’s cards, by a German visual telephone system called Gegensehn-Fernsprechanlagen. That video call connected the Minister of Post, Paul von Eltz-Rübenach, who was stationed in Berlin, and Leipzig’s mayor, Carl Friedrich Goerdeler. By mid-1938, said videophone system had expanded from Berlin and Leipzig to the cities of Nuremberg and Munich and was made available to the German public through specially designed videophone booths that enabled rudimental videocalls between the four cities (Burns, 1995). Lewis and Cosier (1997) see this historical development as a major milestone for kindling society’s interest in developing telepresence systems – and subsequently, virtual-world multimedia environments – that enable individuals to interact remotely.
Topic Detection of Multi-feature Fusion in the Financial Field

Financial-related texts represent relevant trends in the financial field and influence investors' decision-making and market trends. In order to efficiently and quickly obtain topic information from the massive financial texts, topic detection technology is used to obtain the topic information of financial texts. Considering the characteristics of financial texts, this paper extracts the features of the text from the three perspectives of subject, keyword, and entity, and the time attenuation factor is used to fuse the features. Then the text is clustered based on the combined single-pass clustering and neural network clustering algorithms to obtain the subject information of the text. The experimental results show that the above method can effectively mine the subject information of financial texts.
Optimization of Safety Critical IFR Helicopter Trajectories in Alpine Areas using MILP

Currently, helicopter operations according to Instrument Flight Rules (IFR) consist of high-altitude trajectories where the probability of collision with terrain is assumed to be very low. In such high altitudes, the risk of icing has to be considered, additionally, the flight time is unnecessarily long. Therefore, this paper elaborates on an optimization approach to find helicopter trajectories minimizing both flight time and height above terrain. To ensure safe operations, an iso-probability surface is calculated using collision probabilities, representing a surface above which the probability of collision with terrain is smaller than a chosen safety level. The optimization problem is solved using Mixed Integer Linear Programming (MILP). To evaluate the resulting trajectories the time until collision is evaluated for each waypoint, using a time-to-go approach based on Monte-Carlo simulations. The optimization with MILP yields feasible trajectories fulfilling a chosen safety level. Although the approach is computationally expensive, it serves as a basis for further research on these kinds of trajectory optimization applications.