nils-herrmann/clean_scirepeval huggingface dataset:
High-Speed and Energy-Efficient Carry Skip Adder Operating Under a Wide Range of Supply Voltage Levels

In this paper, we present a carry skip adder (CSKA) structure that has a higher speed yet lower energy consumption compared with the conventional one. The speed enhancement is achieved by applying concatenation and incrementation schemes to improve the efficiency of the conventional CSKA (Conv-CSKA) structure. In addition, instead of utilizing multiplexer logic, the proposed structure makes use of AND-OR-Invert (AOI) and OR-AND-Invert (OAI) compound gates for the skip logic. The structure may be realized with both fixed stage size and variable stage size styles, wherein the latter further improves the speed and energy parameters of the adder. Finally, a hybrid variable latency extension of the proposed structure, which lowers the power consumption without considerably impacting the speed, is presented. This extension utilizes a modified parallel structure for increasing the slack time, and hence, enabling further voltage reduction. The proposed structures are assessed by comparing their speed, power, and energy parameters with those of other adders using a 45-nm static CMOS technology for a wide range of supply voltages. The results that are obtained using HSPICE simulations reveal, on average, 44% and 38% improvements in the delay and energy, respectively, compared with those of the Conv-CSKA. In addition, the power-delay product was the lowest among the structures considered in this paper, while its energy-delay product was almost the same as that of the Kogge-Stone parallel prefix adder with considerably smaller area and power consumption. Simulations on the proposed hybrid variable latency CSKA reveal reduction in the power consumption compared with the latest works in this field while having a reasonably high speed.
Female genital mutilation (FGM): Australian midwives' knowledge and attitudes.

Female genital mutilation (FGM) is a women's health and human rights issue attracting global interest. My purpose in this qualitative study was to report the knowledge and attitudes of Australian midwives toward FGM. Verbatim transcription and thematic analysis of semistructured interviews with 11 midwives resulted in these themes: knowledge of female genital mutilation and attitude toward female genital mutilation. Significant gaps in knowledge about FGM featured prominently. The midwives expressed anger toward FGM and empathy for affected women. Recommendations include increased information on FGM and associated legislation among midwives and other health providers in countries where FGM may be encountered.
The Development of the Problematic Pornography Consumption Scale (PPCS).

To date, no short scale exists with strong psychometric properties that can assess problematic pornography consumption based on an overarching theoretical background. The goal of the present study was to develop a brief scale, the Problematic Pornography Consumption Scale (PPCS), based on Griffiths's (2005) six-component addiction model that can distinguish between nonproblematic and problematic pornography use. The PPCS was developed using an online sample of 772 respondents (390 females, 382 males; Mage = 22.56, SD = 4.98 years). Creation of items was based on previous problematic pornography use instruments and on the definitions of factors in Griffiths's model. A confirmatory factor analysis (CFA) was carried out-because the scale is based on a well-established theoretical model-leading to an 18-item second-order factor structure. The reliability of the PPCS was excellent, and measurement invariance was established. In the current sample, 3.6% of the users belonged to the at-risk group. Based on sensitivity and specificity analyses, we identified an optimal cutoff to distinguish between problematic and nonproblematic pornography users. The PPCS is a multidimensional scale of problematic pornography use with a strong theoretical basis that also has strong psychometric properties in terms of factor structure and reliability.
Domain-Specific Principles Affect Learning and Transfer in Children

In this paper I discuss the curious lock of contact between developmental psychologists studying the principles of early learning and those concentrating on loter learning in children, where predispositions to learn certain types of concepts are less reodlly discussed. Instead, there is tacit agreement thot learning and tronsfer mechanisms ore content-independent and age-dependent. I argue here that one cannot study leornlng and transfer In a vacuum ond that children's ablllty to learn is lntimotely dependent on what they ore required to learn and the context in which they must learn it. Specifically, I orgue that children learn and transfer readily, even in traditlonol laboratory settings, if they are requlred ta extend their knowledge about causal mechanisms that they already understond. This point Is illustrated In o series of studies with children from 1 to 3 years of age leorning about simple mechanisms of physical causality (pushing-pulling, wetting, cutting, etc.). In addition, I document children's difficulty learning about causally lmpassi-ble events, such OS pulling with strings thot da not appear to make contact with the object they are pulling. Even young children transfer an the bosis of deep structural principles rather than perceptual features when they have access to the requisite domain-specific knowledge. I argue that a search far causal ex-plonatlons is the basis of broad understanding, of wide patterns of generalization , and of flexible transfer ond creative Inferential projections-in sum, the essential elements of meanlngful learning. In this paper I will consider the effects of principles that guide early learning, such as those described by Gelman (this issue), on later learning in children. This is not an easy task, as psychologists who have studied constraints, This paper is based on a talk given in the symposium, Structural Constraints on Cognitive Development, Psychonomics, 1986. Preparation of the manuscript was supported by NICHD Grant HD 06864. I wish to thank Anne Slattery for her patience and sensitivity with the toddlers in the string and tool studies. I thank Rita Gaskill for her word processing skills and patient work on the many versions of this manuscript, Usha Goswami and Mary Jo Kane for collaborating on studies, and Stephanie Lyons-Olsen and Alison McClain for helping collect data. I would also like to thank Rachel Gelman for her helpful comments, and Jim Greeno, Annette Karmiloff-Smith, and Doug Medin for their thoughtful reviews of this manuscript. Portions of the discussion are adapted from Brown (1989).
Matching demand with supply in the smart grid using agent-based multiunit auction

Recent work has suggested reducing electricity generation cost by cutting the peak to average ratio (PAR) without reducing the total amount of the loads. However, most of these proposals rely on consumer's willingness to act. In this paper, we propose an approach to cut PAR explicitly from the supply side. The resulting cut loads are then distributed among consumers by the means of a multiunit auction which is done by an intelligent agent on behalf of the consumer. This approach is also in line with the future vision of the smart grid to have the demand side matched with the supply side. Experiments suggest that our approach reduces overall system cost and gives benefit to both consumers and the energy provider.
Learning Stable Stochastic Nonlinear Dynamical Systems

A data-driven identification of dynamical systems requiring only minimal prior knowledge is promising whenever no analytically derived model structure is available, e.g., from first principles in physics. However, meta-knowledge on the system’s behavior is often given and should be exploited: Stability as fundamental property is essential when the model is used for controller design or movement generation. Therefore, this paper proposes a framework for learning stable stochastic systems from data. We focus on identifying a state-dependent coefficient form of the nonlinear stochastic model which is globally asymptotically stable according to probabilistic Lyapunov methods. We compare our approach to other state of the art methods on real-world datasets in terms of flexibility and stability.
Recognition of finger movements using EEG signals for control of upper limb prosthesis using logistic regression

Brain computer interface decodes signals that the human brain generates and uses them to control external devices. The signals that are acquired are classified into movements on the basis of feature vector after being extracted from raw signals. This paper presents a novel method of classification of four finger movements (thumb movement, index finger movement, middle and index finger combined movement and fist movement) of the right hand on the basis of EEG (Electroencephalogram) data of the movements. The data-set was obtained from a right-handed neurologically intact volunteer using a noninvasive BCI (Brain-Computer Interface) system. The signals were obtained using a 14 channel electrode headset. The EEG signals that are obtained are first filtered to retain alpha and beta band (8-30 Hz) as they contain the maximum information of movement. Power Spectral Density (PSD) is used for analysis of the filtered EEG data. Classification of the features is done using various classifiers. Various classifiers have been tested and compared on basis of the mean class accuracy achieved. The classifier chosen for the study is logistic regression, which gives an accuracy of 65%. Other classifiers that were tested were multi-layer perceptron, linear discriminant analysis, and quadratic discriminant analysis. The novelty of this research is the targeted finger movements. These movements were targeted so they can be further used for control of upper limb prosthesis.
Multidimensional molecular identification by laser control mass spectrometry

Controlled molecular photofragmentation and ionization achieved with shaped femtosecond laser pulses is coupled with mass spectrometry to achieve a powerful multidimensional tool for fast, accurate, reproducible and quantitative molecular identification. Specific pulse shaping functions are introduced to enhance structure-dependent differences in fragmentation fingerprints. Identification of geometric and structural isomer mixtures is demonstrated. Receiver operational (ROC) curves from our experimental data demonstrate the enhanced reliability that can be achieved by femtosecond laser control mass spectrometry. The potential use of this method for identification of chemicals and explosives with no false alarms is discussed.
Spermatozoon ultrastructure of the pseudophyllidean cestode Paraechinophallus japonicus, a parasite of deep-sea fish Psenopsis anomala (Perciformes, Centrolophidae)

This paper provides the first ultrastructural data on the spermatozoon of a pseudophyllidean cestode belonging to the family Echinophallidae, Paraechinophallus japonicus, parasitizing deep-sea fish Psenopsis anomala from Japan. The mature spermatozoon contains two axonemes of the 9+“1” trepaxonematan pattern, nucleus, cortical microtubules, and electron-dense granules. Its anterior extremity exhibits a single electron-dense crested body. One of the most interesting features of the mature spermatozoon is the presence of a ring of tubular structures encircling the axoneme. This character, previously reported in species of the families Bothriocephalidae and Triaenophoridae, may be unique to pseudophyllideans of the group “Bothriocephalidea”, thus supporting the assumption that the order Pseudophyllidea is formed in fact by two unrelated clades; “Bothriocephalidea” and “Diphyllobothriidea”. The posterior extremity contains the posterior part of the nucleus and the disorganized axoneme—a character previously not described in the Eucestoda.
A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation

In this paper, a fuzzy model is suggested for the prediction of wind speed and the produced electrical power at a wind park. The model is trained using a genetic algorithm-based learning scheme. The training set includes wind speed and direction data, measured at neighboring sites up to 30 km away from the wind turbine clusters. Extensive simulation results are shown for two application cases, providing wind speed forecasts from 30 min to 2 h ahead. It is demonstrated that the suggested model achieves an adequate understanding of the problem while it exhibits significant improvement compared to the persistent method.
Neutralizing Cross-Site Scripting Attacks Using Open Source Technologies

Exploiting the security vulnerabilities in web browsers, web applications and firewalls is a fundamental trait of cross-site scripting (XSS) attacks. Majority of web population with basic web awareness are vulnerable and even expert web users may not notice the attack to be able to respond in time to neutralize the ill effects of attack. Due to their subtle nature, a victimized server, a compromised browser, an impersonated email or a hacked web application tends to keep this form of attacks alive even in the present times. XSS attacks severely offset the benefits offered by Internet based services thereby impacting the global internet community. This paper focuses on defense, detection and prevention mechanisms to be adopted at various network doorways to neutralize XSS attacks using open source tools.
A Trust Based Framework for Secure Data Aggregation in Wireless Sensor Networks

In unattended and hostile environments, node compromise can become a disastrous threat to wireless sensor networks and introduce uncertainty in the aggregation results. A compromised node often tends to completely reveal its secrets to the adversary which in turn renders purely cryptography-based approaches vulnerable. How to secure the information aggregation process against compromised-node attacks and quantify the uncertainty existing in the aggregation results has become an important research issue. In this paper, we address this problem by proposing a trust based framework, which is rooted in sound statistics and some other distinct and yet closely coupled techniques. The trustworthiness (reputation) of each individual sensor node is evaluated by using an information theoretic concept, Kullback-Leibler (KL) distance, to identify the compromised nodes through an unsupervised learning algorithm. Upon aggregating, an opinion, a metric of the degree of belief, is generated to represent the uncertainty in the aggregation result. As the result is being disseminated and assembled through the routes to the sink, this opinion will be propagated and regulated by Josang's belief model. Following this model, the uncertainty within the data and aggregation results can be effectively quantified throughout the network. Simulation results demonstrate that our trust based framework provides a powerful mechanism for detecting compromised nodes and reasoning about the uncertainty in the network. It further can purge false data to accomplish robust aggregation in the presence of multiple compromised nodes
MalDozer: Automatic framework for android malware detection using deep learning

Android OS experiences a blazing popularity since the last few years. This predominant platform has established itself not only in the mobile world but also in the Internet of Things (IoT) devices. This popularity, however, comes at the expense of security, as it has become a tempting target of malicious apps. Hence, there is an increasing need for sophisticated, automatic, and portable malware detection solutions. In this paper, we propose MalDozer, an automatic Android malware detection and family attribution framework that relies on sequences classification using deep learning techniques. Starting from the raw sequence of the app's API method calls, MalDozer automatically extracts and learns the malicious and the benign patterns from the actual samples to detect Android malware. MalDozer can serve as a ubiquitous malware detection system that is not only deployed on servers, but also on mobile and even IoT devices. We evaluate MalDozer on multiple Android malware datasets ranging from 1 K to 33 K malware apps, and 38 K benign apps. The results show that MalDozer can correctly detect malware and attribute them to their actual families with an F1-Score of 96%e99% and a false positive rate of 0.06% e2%, under all tested datasets and settings.
Understanding the Dynamics of Engaging Interaction in Public Spaces

We present an analysis of three interactive installations in public spaces, in terms of their support of engagement as an evolving process. In particular, we focus on how engagement unfolds as a dynamic process that may be understood in terms of evolving relations between cultural, physical, content-related, and social elements of interactive environments. These elements are explored through the literature on engagement with interaction design, and it is argued that, although valuable contributions have been made towards understanding engagement with interactive environments, the ways in which engagement unfolds as a dynamic process remains relatively unexplored. We propose that we may understand engagement as a product of the four above-mentioned elements, and in our analysis we provide concrete examples of how engagement plays out in practice by analyzing the emergence, transformation and relations between these elements.
Leveraging the VTA-TVM Hardware-Software Stack for FPGA Acceleration of 8-bit ResNet-18 Inference

We present a full-stack design to accelerate deep learning inference with FPGAs. Our contribution is two-fold. At the software layer, we leverage and extend TVM, the end-to-end deep learning optimizing compiler, in order to harness FPGA-based acceleration. At the the hardware layer, we present the Versatile Tensor Accelerator (VTA) which presents a generic, modular, and customizable architecture for TPU-like accelerators. Our results take a ResNet-18 description in MxNet and compiles it down to perform 8-bit inference on a 256-PE accelerator implemented on a low-cost Xilinx Zynq FPGA, clocked at 100MHz. Our full hardware acceleration stack will be made available for the community to reproduce, and build upon at http://github.com/uwsaml/vta.
Installing dual axis solar tracker on rooftop to meet the soaring demand of energy for developing countries

Energy crisis is one of the biggest problems in the third world developing country like Bangladesh. There is a big gap between generation and demand of Electric energy. Almost 50% population of our country is very far away from this blessings. Renewable energy is the only solution of this problem to be an energy efficient developed country. Solar energy is one of the great resources of the renewable energy which can play a crucial role in developing a power deficient country like Bangladesh. This paper provides a proposal of using dual axis solar tracker instead of solar panel. This encompasses a design of ideal solar house model using azimuth-altitude dual axis solar tracker on rooftop. It has been proved through mathematical calculation that the solar energy increases up to 50-60% where dual axis solar tracker is used. Apart from the mentioned design, this paper presents a structure and application of a microcontroller based azimuth-altitude dual axis solar tracker which tracks the solar panel according to the direction of the solar radiation. A built-in ADC converter is used to drive motor circuit. To minimize the power consumption by dual axis solar tracker, we are in favor of using two stepper motor especially during the seasonal change. The proposed model demonstrates that we require a very small amount of power from national grid if we can install dual axis solar tracker on rooftops of our residence; this is how increasing energy demand can effectively be met.
Garlic and aging: new insights into an old remedy

There has been an impressive gain in individual life expectancy with parallel increases in age-related chronic diseases of the cardiovascular, brain and immune systems. These can cause loss of autonomy, dependence and high social costs for individuals and society. It is now accepted that aging and age-related diseases are in part caused by free radical reactions. The arrest of aging and stimulation of rejuvenation of the human body is also being sought. Over the last 20 years the use of herbs and natural products has gained popularity and these are being consumed backed by epidemiological evidence. One such herb is garlic, which has been used throughout the history of civilization for treating a wide variety of ailments associated with aging. The role of garlic in preventing age-related diseases has been investigated extensively over the last 10-15 years. Garlic has strong antioxidant properties and it has been suggested that garlic can prevent cardiovascular disease, inhibit platelet aggregation, thrombus formation, prevent cancer, diseases associated with cerebral aging, arthritis, cataract formation, and rejuvenate skin, improve blood circulation and energy levels. This review provides an insight in to garlic's antioxidant properties and presents evidence that it may either prevent or delay chronic diseases associated with aging.
Anterior guidance: its effect on electromyographic activity of the temporal and masseter muscles.

In his classic article D’Amico states: “When canines are in normal interlocking position, the lateral and forward movement is limited so that when an attempt is made to move the mandible laterally or forward, there is an involuntary reaction when the canines come in contact. The reaction is an immediate break in the tension of the temporal and masseter muscles, thus reducing the magnitude of the applied force. Regardless of how hard the individual tries to tense these muscles, as long as the canines are in contact, it is impossible for these muscles to assume full tension.” He continues: “The length of the roots of the canines and the anatomical structure of the supporting alveolar process gives testimony to nature’s intention as to the function intended. What may appear as trauma as they come in contact is not trauma at all, because when contact is made, muscular tension is involuntarily reduced, thus reducing the magnitude of applied force.”
Agent-based demand forecast in multi-echelon supply chain

Supply chain management (SCM) is an emerging field that has commanded attention and support from the industrial community. Demand forecast taking inventory into consideration is an important issue in SCM. There are many diverse inventory systems, in theory or practice, which are operated by entities (companies) in a supply chain. In order to increase supply chain effectiveness, minimize total cost, and reduce the bullwhip effect, integration and coordination of these different systems in the supply chain (SC) are required using information technology and effective communication. The paper develops a multi-agent system to simulate a supply chain, where agents operate these entities with different inventory systems. Agents are coordinated to control inventory and minimize the total cost of a SC by sharing information and forecasting knowledge. The demand is forecasted with a genetic algorithm (GA) and the ordering quantity is offered at each echelon incorporating the perspective of bsystems thinkingQ. By using this agent-based system, the results show that total cost decreases and the ordering variation curve becomes smooth. D 2005 Elsevier B.V. All rights reserved.