nils-herrmann/clean_scirepeval huggingface dataset:
Path Planning of Mobile Robot Based on Modification of Vector Field Histogram using Neuro-Fuzzy Algorithm

In this paper, a neuro-fuzzy algorithm has been implemented to improve the path planning of a mobile robot based on modification of vector field histogram (VFH) approach using neuro-fuzzy algorithm. A back propagation neural network has been used for detection of unknown obstacle to avoid collision. A neural network model is used to learn (off line) many critical status of obstacle topology. Fuzzy rules were applied to steer the mobile robot to the target direction. The candidate valleys generated by neural network from local sensor data in the robot would be fuzzified to select a winning valley that is nearest to the target direction. The simulation result of the proposed algorithm shows good navigation properties and ability to overcome the limitation of traditional vector field histogram approach. This work was implemented using Matlab® and Mobotsim®
A Comparative Analysis of Selection Schemes Used in Genetic Algorithms

This paper considers a number of selection schemes commonly used in modern genetic algorithms. Specifically, proportionate reproduction, ranking selection, tournament selection, and Genitor (or «steady state") selection are compared on the basis of solutions to deterministic difference or differential equations, which are verified through computer simulations. The analysis provides convenient approximate or exact solutions as well as useful convergence time and growth ratio estimates. The paper recommends practical application of the analyses and suggests a number of paths for more detailed analytical investigation of selection techniques.
Energy Harvesting Chip and the Chip Based Power Supply Development for a Wireless Sensor Network

In this study, an energy harvesting chip was developed to scavenge energy from artificial light to charge a wireless sensor node. The chip core is a miniature transformer with a nano-ferrofluid magnetic core. The chip embedded transformer can convert harvested energy from its solar cell to variable voltage output for driving multiple loads. This chip system yields a simple, small, and more importantly, a battery-less power supply solution. The sensor node is equipped with multiple sensors that can be enabled by the energy harvesting power supply to collect information about the human body comfort degree. Compared with lab instruments, the nodes with temperature, humidity and photosensors driven by harvested energy had variation coefficient measurement precision of less than 6% deviation under low environmental light of 240 lux. The thermal comfort was affected by the air speed. A flow sensor equipped on the sensor node was used to detect airflow speed. Due to its high power consumption, this sensor node provided 15% less accuracy than the instruments, but it still can meet the requirement of analysis for predicted mean votes (PMV) measurement. The energy harvesting wireless sensor network (WSN) was deployed in a 24-hour convenience store to detect thermal comfort degree from the air conditioning control. During one year operation, the sensor network powered by the energy harvesting chip retained normal functions to collect the PMV index of the store. According to the one month statistics of communication status, the packet loss rate (PLR) is 2.3%, which is as good as the presented results of those WSNs powered by battery. Referring to the electric power records, almost 54% energy can be saved by the feedback control of an energy harvesting sensor network. These results illustrate that, scavenging energy not only creates a reliable power source for electronic devices, such as wireless sensor nodes, but can also be an energy source by building an energy efficient program.
Predicting latent attributes of Twitter user by employing lexical features

The rapid growth of social media, especially Twitter in Indonesia, has produced a large amount of user generated texts in the form of tweets. Since Twitter only provides the name and location of its users, we develop a classification system that predicts latent attributes of Twitter user based on his tweets. Latent attribute is an attribute that is not stated directly. Our system predicts age and job attributes of Twitter users that use Indonesian language. Classification model is developed by employing lexical features and three learning algorithms (Naïve Bayes, SVM, and Random Forest). Based on the experimental results, it can be concluded that the SVM method produces the best accuracy for balanced data.
Learning Cross-Modal Embeddings for Cooking Recipes and Food Images

In this paper, we introduce Recipe1M, a new large-scale, structured corpus of over 1m cooking recipes and 800k food images. As the largest publicly available collection of recipe data, Recipe1M affords the ability to train high-capacity models on aligned, multi-modal data. Using these data, we train a neural network to find a joint embedding of recipes and images that yields impressive results on an image-recipe retrieval task. Additionally, we demonstrate that regularization via the addition of a high-level classification objective both improves retrieval performance to rival that of humans and enables semantic vector arithmetic. We postulate that these embeddings will provide a basis for further exploration of the Recipe1M dataset and food and cooking in general. Code, data and models are publicly available
Challenges of Reengineering into Multi-Tenant SaaS Applications

Multi-tenancy is a relatively new software architecture principle in the realm of the Software as a Service (SaaS) business model. It allows to make full use of the economy of scale, as multiple customers – “tenants” – share the same application and database instance. All the while, the tenants enjoy a highly configurable application, making it appear that the application is deployed on a dedicated server. The major benefits of multi-tenancy are increased utilization of hardware resources and improved ease of maintenance, resulting in lower overall application costs, making the technology attractive for service providers targeting small and medium enterprises (SME). In our paper, we identify some of the core challenges of implementing multi-tenancy. Furthermore, we present a conceptual reengineering approach to support the migration of single-tenant applications into multi-tenant applications.
SCARFF: a Scalable Framework for Streaming Credit Card Fraud Detection with Spark

The expansion of the electronic commerce, together with an increasing confidence of customers in electronic payments, makes of fraud detection a critical factor. Detecting frauds in (nearly) real time setting demands the design and the implementation of scalable learning techniques able to ingest and analyse massive amounts of streaming data. Recent advances in analytics and the availability of open source solutions for Big Data storage and processing open new perspectives to the fraud detection field. In this paper we present a SCAlable Real-time Fraud Finder (SCARFF) which integrates Big Data tools (Kafka, Spark and Cassandra) with a machine learning approach which deals with imbalance, nonstationarity and feedback latency. Experimental results on a massive dataset of real credit card transactions show that this framework is scalable, efficient and accurate over a big stream of transactions.
DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning

Anomaly detection is a critical step towards building a secure and trustworthy system. The primary purpose of a system log is to record system states and significant events at various critical points to help debug system failures and perform root cause analysis. Such log data is universally available in nearly all computer systems. Log data is an important and valuable resource for understanding system status and performance issues; therefore, the various system logs are naturally excellent source of information for online monitoring and anomaly detection. We propose DeepLog, a deep neural network model utilizing Long Short-Term Memory (LSTM), to model a system log as a natural language sequence. This allows DeepLog to automatically learn log patterns from normal execution, and detect anomalies when log patterns deviate from the model trained from log data under normal execution. In addition, we demonstrate how to incrementally update the DeepLog model in an online fashion so that it can adapt to new log patterns over time. Furthermore, DeepLog constructs workflows from the underlying system log so that once an anomaly is detected, users can diagnose the detected anomaly and perform root cause analysis effectively. Extensive experimental evaluations over large log data have shown that DeepLog has outperformed other existing log-based anomaly detection methods based on traditional data mining methodologies.
Robust and fine-grained prosody control of end-to-end speech synthesis

We propose prosody embeddings for emotional and expressive speech synthesis networks. The proposed methods introduce temporal structures in the embedding networks, thus enabling fine-grained control of the speaking style of the synthesized speech. The temporal structures can be designed either on the speech side or the text side, leading to different control resolutions in time. The prosody embedding networks are plugged into end-to-end speech synthesis networks and trained without any other supervision except for the target speech for synthesizing. It is demonstrated that the prosody embedding networks learned to extract prosodic features. By adjusting the learned prosody features, we could change the pitch and amplitude of the synthesized speech both at the frame level and the phoneme level. We also introduce the temporal normalization of prosody embeddings, which shows better robustness against speaker perturbations during prosody transfer tasks.
Batch Kalman Normalization: Towards Training Deep Neural Networks with Micro-Batches

As an indispensable component, Batch Normalization (BN) has successfully improved the training of deep neural networks (DNNs) with mini-batches, by normalizing the distribution of the internal representation for each hidden layer. However, the effectiveness of BN would diminish with scenario of micro-batch (e.g. less than 10 samples in a mini-batch), since the estimated statistics in a mini-batch are not reliable with insufficient samples. In this paper, we present a novel normalization method, called Batch Kalman Normalization (BKN), for improving and accelerating the training of DNNs, particularly under the context of microbatches. Specifically, unlike the existing solutions treating each hidden layer as an isolated system, BKN treats all the layers in a network as a whole system, and estimates the statistics of a certain layer by considering the distributions of all its preceding layers, mimicking the merits of Kalman Filtering. BKN has two appealing properties. First, it enables more stable training and faster convergence compared to previous works. Second, training DNNs using BKN performs substantially better than those using BN and its variants, especially when very small mini-batches are presented. On the image classification benchmark of ImageNet, using BKN powered networks we improve upon the best-published model-zoo results: reaching 74.0% top-1 val accuracy for InceptionV2. More importantly, using BKN achieves the comparable accuracy with extremely smaller batch size, such as 64 times smaller on CIFAR-10/100 and 8 times smaller on ImageNet.
Recommendations for physical activity in older adults.

Over the past decade, research has increased our understanding of the effects of physical activity at opposite ends of the spectrum. Sedentary behaviour—too much sitting—has been shown to increase risk of chronic disease, particularly diabetes and cardiovascular disease. There is now a clear need to reduce prolonged sitting. Secondly, evidence on the potential of high intensity interval training inmanaging the same chronic diseases, as well as reducing indices of cardiometabolic risk in healthy adults, has emerged. This vigorous training typically comprises multiple 3-4 minute bouts of high intensity exercise interspersed with several minutes of low intensity recovery, three times a week. Between these two extremes of the activity spectrum is the mainstream public health recommendation for aerobic exercise, which is similar in many developed countries. The suggested target for older adults (≥65) is the same as for other adults (18-64): 150 minutes a week of moderate intensity activity in bouts of 10 minutes or more. It is often expressed as 30 minutes of brisk walking or equivalent activity five days a week, although 75 minutes of vigorous intensity activity spread across the week, or a combination of moderate and vigorous activity are sometimes suggested. Physical activity to improve strength should also be done at least two days a week. The 150 minute target is widely disseminated to health professionals and the public. However, many people, especially in older age groups, find it hard to achieve this level of activity. We argue that when advising patients on exercise doctors should encourage people to increase their level of activity by small amounts rather than focus on the recommended levels. The 150 minute target, although warranted, may overshadow other less concrete elements of guidelines. These include finding ways to do more lower intensity lifestyle activity. As people get older, activity may become more relevant for sustaining the strength, flexibility, and balance required for independent living in addition to the strong associations with hypertension, coronary heart disease, stroke, diabetes, breast cancer, and colon cancer. Observational data have confirmed associations between increased physical activity and reduction in musculoskeletal conditions such as arthritis, osteoporosis, and sarcopenia, and better cognitive acuity and mental health. Although these links may be modest and some lack evidence of causality, they may provide sufficient incentives for many people to be more active. Research into physical activity
Robust Tracking of Position and Velocity With Kalman Snakes

A new Kalman-filter based active contour model is proposed for tracking of nonrigid objects in combined spatio-velocity space. The model employs measurements of gradient-based image potential and of optical-flow along the contour as system measurements. In order to improve robustness to image clutter and to occlusions an optical-flow based detection mechanism is proposed. The method detects and rejects spurious measurements which are not consistent with previous estimation of image motion.
Will social media make or break the acceptance in new technology?

Parallels have been drawn between the rise of the internet in 1990s and the present rise of bitcoin (cryptocurrency) and underlying blockchain technology. This resulted in a widespread of media coverage due to extreme price fluctuations and increased supply and demand. Garcia et al. (2014) argues that this is driven by several social aspects including word-of-mouth communication on social media, indicating that this aspect of social media effects individual attitude formation and intention towards cryptocurrency. However, this combination of social media of antecedent of consumer acceptance is limited explored, especially in the context of technology acceptance.  The purpose of this thesis is to create further understanding in the Technology Acceptance Model with the additional construct: social influence, first suggested by Malhotra et al. (1999). Hereby, the additional construct of social media influence was added to advance the indirect effects of social media influence on attitude formation and behavioural intention towards cryptocurrency, through the processes of social influence (internalization; identification; compliance) by Kelman.  This study carries out a quantitative study where survey-research was used that included a total sample of 250 cases. This sample consists of individuals between 18-37 years old, where social media usage is part of the life. As a result of the data collection, analysis was conducted using multiple regression techniques.  Analysis of the findings established theoretical validation of the appliance of the Technology Acceptance Model on digital innovation, like cryptocurrency. By adding the construct of social media, further understanding is created in the behaviour of millennials towards cryptocurrency. The evidence suggests that there are clear indirect effects of social media on attitude formation and intention towards engaging in cryptocurrency through the processes of social influence. This study should be seen as preliminary, where future research could be built upon. More specifically, in terms of consumer acceptance of cryptocurrency and the extent of influence by social media.
8 Bidirectional DC-DC Converters for Energy Storage Systems

Bidirectional dc-dc converters (BDC) have recently received a lot of attention due to the increasing need to systems with the capability of bidirectional energy transfer between two dc buses. Apart from traditional application in dc motor drives, new applications of BDC include energy storage in renewable energy systems, fuel cell energy systems, hybrid electric vehicles (HEV) and uninterruptible power supplies (UPS). The fluctuation nature of most renewable energy resources, like wind and solar, makes them unsuitable for standalone operation as the sole source of power. A common solution to overcome this problem is to use an energy storage device besides the renewable energy resource to compensate for these fluctuations and maintain a smooth and continuous power flow to the load. As the most common and economical energy storage devices in medium-power range are batteries and super-capacitors, a dc-dc converter is always required to allow energy exchange between storage device and the rest of system. Such a converter must have bidirectional power flow capability with flexible control in all operating modes. In HEV applications, BDCs are required to link different dc voltage buses and transfer energy between them. For example, a BDC is used to exchange energy between main batteries (200-300V) and the drive motor with 500V dc link. High efficiency, lightweight, compact size and high reliability are some important requirements for the BDC used in such an application. BDCs also have applications in line-interactive UPS which do not use double conversion technology and thus can achieve higher efficiency. In a line-interactive UPS, the UPS output terminals are connected to the grid and therefore energy can be fed back to the inverter dc bus and charge the batteries via a BDC during normal mode. In backup mode, the battery feeds the inverter dc bus again via BDC but in reverse power flow direction. BDCs can be classified into non-isolated and isolated types. Non-isolated BDCs (NBDC) are simpler than isolated BDCs (IBDC) and can achieve better efficiency. However, galvanic isolation is required in many applications and mandated by different standards.
Megacities and atmospheric pollution.

The following is a discussion of the 2004 A&WMA Critical Review1,2,3 of megacities and their effects on air quality and pollution-control policies. In the Review, Drs. Mario J. Molina and Luisa T. Molina examined nine large metropolitan areas to identify similarities and differences that affect pollution and its adverse effects. They found that cities in developing nations have higher pollution levels than cities in wealthier nations, despite the greater use of public transportation by their residents. These cities do not have the infrastructure needed to deliver clean fuels to homes for cooking and heating, resulting in greater use of solid coal and biofuels. They also contain older vehicles with higher exhaust emissions and unpaved or poorly maintained roadways that suspend dust. Roadways and public transportation systems are congested and misuse of fuels is common. Cities in developing countries are concerned about loss of employment and economic benefits associated with polluting industries. The Review recommends the use of integrated assessment tools to evaluate emission reduction measures in the world’s megacities. These include accurate emission estimates, source and receptor models to identify pollution sources, and monitoring networks tailored to compliance, source assessment, forecasting, and tracking of reasonable progress. The Review concludes that pollution control efforts are often hindered by insufficient local expertise and resources, and by a lack of commitment to air quality management and control. Trained experts, working with local stakeholders, politicians, and the population at large, must take the initiative to institute changes. With appropriate planning, megacities can efficiently address their air pollution problems through integrated control strategies that are effectively implemented and embraced by the public. The discussions presented here agree and disagree with points made in the Review and add information relevant to the topic. Each discussion is self-contained, and joint authorship of this article does not imply that a discussant subscribes to the opinions expressed by others. This discussion was compiled from written submissions and presentation transcripts with revisions for conciseness and to minimize redundancy. Substantial deviations from the intent of a discussant are unintentional and can be addressed in a follow-up letter to the Journal. Invited discussants are: • Dr. Jitendra (Jitu) Shah, Lead Environmental Specialist for the World Bank in Washington, DC, CRITICAL REVIEW  ISSN 1047-3289
Automatic Segmentation of Femur Bones in Anterior-Posterior Pelvis X-Ray Images

Segmentation of femurs in Anterior-Posterior x-ray images is very important for fracture detection, computer-aided surgery and surgical planning. Existing methods do not perform well in segmenting bones in x-ray images due to the presence of large amount of spurious edges. This paper presents an atlas-based approach for automatic segmentation of femurs in x-ray images. A robust global alignment method based on consistent sets of edge segments registers the whole atlas to the image under joint constraints. After global alignment, the femur models undergo local refinement to extract detailed contours of the femurs. Test results show that the proposed algorithm is robust and accurate in segmenting the femur contours of different patients.
Cognitive adaptation training for outpatients with schizophrenia.

Schizophrenia is a complex neurodevelopmental disorder characterized by cognitive deficits. These deficits in cognitive functioning have been shown to relate to a variety of functional and treatment outcomes. Cognitive adaptation training (CAT) is a home-based, manual-driven treatment that utilizes environmental supports and compensatory strategies to bypass cognitive deficits and improve target behaviors and functional outcomes in individuals with schizophrenia. Unlike traditional case management, CAT provides environmental supports and compensatory strategies tailored to meet the behavioral style and neurocognitive deficits of each individual patient. The case of Ms. L. is presented to illustrate CAT treatment.