nils-herrmann/clean_scirepeval huggingface dataset:
Cognitive Real-Time QoE Management in Video Streaming Services

Satisfying the needs of users of online video streaming services requires not only to manage the network Quality of Service (QoS), but also to address the user's Quality of Experience (QoE) expectations. While QoS factors reflect the status of individual networks, they do not comprehensively capture the end-to-end features affecting the quality delivered to the user. In this situation, QoE management is the better option. However, traditionally used QoE management models require human interaction and have stringent requirements in terms of time and complexity. Thus, they fail to achieve successful performance in terms of real-timeliness, accuracy, scalability and adaptability. This dissertation work investigates new methods to bring QoE management to the level required by the real-time management of video services. In this paper, we highlight our main contributions. First, with the aim to perform a combined network-service assessment, we designed an experimental methodology able to map network QoS onto service QoE. Our methodology is meant to provide service and network providers with the means to pinpoint the working boundaries of their video-sets and to predict the effect of network policies on perception. Second, we developed a generic machine learning framework that allows deriving accurate predictive No Reference (NR) assessment metrics, based on simplistic NR QoE methods, that are functionally and computationally viable for real-time QoE evaluation. The tools, methods and conclusions derived from this dissertation conform a solid contribution to QoE management of video streaming services, opening new venues for further research.
Reliability of copper wire bonding in humidity environment

Copper wire bonding is being developed rapidly in recent years to replace expensive gold wire for electronic packaging. However, one issue about reliability in humidity environment causes risk in its application. The copper wire-aluminum pad interface degradation usually leads to electrical open failure. Present paper studied all the 5 factors including pad finish of chip, molding compound, wire bonding parameters, copper wire type and protective gas type in wire bonding process that affect this issue. Through bond pull test at different stage of uHAST, the influence and significance degree of the factors were obtained. The pad finish was found has the most significant effect. No degradation occurred at copper-gold and copper-palladium interface. For aluminum finish, mold compound and wire bonding parameters have significant effect. Green EMC, high USG and low Search Force in wire bonding process is benefit to enhance reliability of copper wire bonding in humidity environment. Concerning the palladium coated copper wire and bare copper wire, forming protective gas of 5%H2+95%N2 and pure N2, there was no significant effect founded. So for the safe copper wire bonding application, noble metal such as gold and palladium pad finish is the best choice. If the aluminum pad is used, green EMC and optimized wire bonding parameters are necessary to guarantee the reliability.
UHF-RFID tag antenna with miniaturization techniques

This paper presents the design of a compact UHF-RFID tag antenna with several miniaturization techniques including meandering technique and capacitive tip-loading structure. Additionally, T-matching technique is also utilized in the antenna design for impedance matching. This antenna was designed on Rogers 5880 printed circuit board (PCB) with the dimension of 43 × 26 × 0.787 mm3 and relative permittivity, □r of 2.2. The performance of the proposed antenna was analyzed in terms of matched impedance, antenna gain, return loss and tag reading range through the simulation in CST Microwave Studio software. As a result, the proposed antenna obtained a gain of 0.97dB and a maximum reading range of 5.15 m at 921 MHz.
Speaker gender recognition using score level fusion by AdaBoost

We propose speaker gender recognition achieved by using score level fusion by AdaBoost. Soft biometrics has been focused on because recognition by fusing biométrie systems and soft biométrie traits may improve the accuracy of recognition and decrease the time for this. Gender recognition is important for speaker recognition and can provide important information to speaker recognition systems. Mel-frequency cepstral coefficient (MFCC) and pitch contain gender information. MFCCs and pitch are often used for gender recognition. Consequently, identification accuracy may be improved by using both MFCC and pitch. We focused on the score level fusion to accomplish speaker gender recognition. We propose speaker gender recognition based on the score level fusion using AdaBoost because it can control the recognition accuracy and recognition time. We experimentally demonstrate the proposed method's effectiveness through simulation results and show that it achieves greater accuracy than that obtained by using single information from voice.
It's Not the Way You Look, It's How You Move: Validating a General Scheme for Robot Affective Behaviour

In the emerging world of human-robot interaction, people and robots will work together to achieve joint objectives. This paper discusses the design and validation of a general scheme for creating emotionally expressive behaviours for robots, in order that people might better interpret how a robot collaborator is succeeding or failing in its work. It exemplifies a unified approach to creating robot behaviours for two very different robot forms, based on combinations of four groups of design parameters (approach/avoidance, energy, intensity and frequency). 59 people rated video clips of robots performing expressive behaviours both for emotional expressivity on Valence-Arousal-Dominance dimensions, and their judgement of the successfulness of the robots’ work. Results are discussed in terms of the utility of expressive behaviour for facilitating human understanding of robot intentions and the design of cues for basic emotional states.
A Similarity-Based Machine Learning Approach for Detecting Adversarial Android Malware

We introduce a similarity-based machine learning approach for detecting non-market, adversarial, malicious Android apps. By adversarial, we mean those apps designed to avoid detection. Our approach relies on identifying the Android applications that are similar to an adversarial known Android malware. In our approach, similarity is detected statically by computing the similarity score between two apps based on their methods similarity. The similarity between methods is computed using the normalized compression distance (NCD) in dependence of either zlib or bz2 compressors. The NCD calculates the semantic similarity between pair of methods in two compared apps. The first app is one of the sample apps in the input dataset, while the second app is one of malicious apps stored in a malware database. Later all the computed similarity scores are used as features for training a supervised learning classifier to detect suspicious apps with high similarity score to the malicious ones in the database.
Advanced System-on-Package ( SOP ) Multilayer Architectures for RF / Wireless Systems up to Millimeter-Wave Frequency Bands

This paper reviews the development of advanced System-on-Package (SOP) architectures for the compact and low cost wireless RF wireless systems. A compact stacked patch antenna adopting soft-and-hard surface structures and cavity resonator filters using inter-resonance coupling mechanism for V-band applications are presented. A novel ultra-compact 3D integration technology is proposed and utilized for the implementation of a Ku-band VCO module. The high Q-factor inductors fabricated on the Liquid Crystal Polymer based multilayer substrate demonstrate superior performance than conventional organic packages.
Biological activities of lavender essential oil.

Essential oils distilled from members of the genus Lavandula have been used both cosmetically and therapeutically for centuries with the most commonly used species being L. angustifolia, L. latifolia, L. stoechas and L. x intermedia. Although there is considerable anecdotal information about the biological activity of these oils much of this has not been substantiated by scientific or clinical evidence. Among the claims made for lavender oil are that is it antibacterial, antifungal, carminative (smooth muscle relaxing), sedative, antidepressive and effective for burns and insect bites. In this review we detail the current state of knowledge about the effect of lavender oils on psychological and physiological parameters and its use as an antimicrobial agent. Although the data are still inconclusive and often controversial, there does seem to be both scientific and clinical data that support the traditional uses of lavender. However, methodological and oil identification problems have severely hampered the evaluation of the therapeutic significance of much of the research on Lavandula spp. These issues need to be resolved before we have a true picture of the biological activities of lavender essential oil.
Synthetic apatite nanoparticles as a phosphorus fertilizer for soybean (Glycine max)

Some soluble phosphate salts, heavily used in agriculture as highly effective phosphorus (P) fertilizers, cause surface water eutrophication, while solid phosphates are less effective in supplying the nutrient P. In contrast, synthetic apatite nanoparticles could hypothetically supply sufficient P nutrients to crops but with less mobility in the environment and with less bioavailable P to algae in comparison to the soluble counterparts. Thus, a greenhouse experiment was conducted to assess the fertilizing effect of synthetic apatite nanoparticles on soybean (Glycine max). The particles, prepared using one-step wet chemical method, were spherical in shape with diameters of 15.8 ± 7.4 nm and the chemical composition was pure hydroxyapatite. The data show that application of the nanoparticles increased the growth rate and seed yield by 32.6% and 20.4%, respectively, compared to those of soybeans treated with a regular P fertilizer (Ca(H2PO4)2). Biomass productions were enhanced by 18.2% (above-ground) and 41.2% (below-ground). Using apatite nanoparticles as a new class of P fertilizer can potentially enhance agronomical yield and reduce risks of water eutrophication.
Natural mapping and intuitive interaction in videogames

Videogame control interfaces continue to evolve beyond their traditional roots, with devices encouraging more natural forms of interaction growing in number and pervasiveness. Yet little is known about their true potential for intuitive use. This paper proposes methods to leverage existing intuitive interaction theory for games research, specifically by examining different types of naturally mapped control interfaces for videogames using new measures for previous player experience. Three commercial control devices for a racing game were categorised using an existing typology, according to how the interface maps physical control inputs with the virtual gameplay actions. The devices were then used in a within-groups (n=64) experimental design aimed at measuring differences in intuitive use outcomes. Results from mixed design ANOVA are discussed, along with implications for the field.
On hesitant fuzzy sets and decision

Intuitionistic Fuzzy Sets (IFS) are a generalization of fuzzy sets where the membership is an interval. That is, membership, instead of being a single value, is an interval. A large number of operations have been defined for this type of fuzzy sets, and several applications have been developed in the last years. In this paper we describe hesitant fuzzy sets. They are another generalization of fuzzy sets. Although similar in intention to IFS, some basic differences on their interpretation and on their operators exist. In this paper we review their definition, the main results and we present an extension principle, which permits to generalize existing operations on fuzzy sets to this new type of fuzzy sets. We also discuss their use in decision making.
MalClassifier: Malware family classification using network flow sequence behaviour

Anti-malware vendors receive daily thousands of potentially malicious binaries to analyse and categorise before deploying the appropriate defence measure. Considering the limitations of existing malware analysis and classification methods, we present MalClassifier, a novel privacy-preserving system for the automatic analysis and classification of malware using network flow sequence mining. MalClassifier allows identifying the malware family behind detected malicious network activity without requiring access to the infected host or malicious executable reducing overall response time. MalClassifier abstracts the malware families' network flow sequence order and semantics behaviour as an n-flow. By mining and extracting the distinctive n-flows for each malware family, it automatically generates network flow sequence behaviour profiles. These profiles are used as features to build supervised machine learning classifiers (K-Nearest Neighbour and Random Forest) for malware family classification. We compute the degree of similarity between a flow sequence and the extracted profiles using a novel fuzzy similarity measure that computes the similarity between flows attributes and the similarity between the order of the flow sequences. For classifier performance evaluation, we use network traffic datasets of ransomware and botnets obtaining 96% F-measure for family classification. MalClassifier is resilient to malware evasion through flow sequence manipulation, maintaining the classifier's high accuracy. Our results demonstrate that this type of network flow-level sequence analysis is highly effective in malware family classification, providing insights on reoccurring malware network flow patterns.
12.1 A 0.518mm2 quasi-current-mode hysteretic buck DC-DC converter with 3μs load transient response in 0.35μm BCDMOS

As more features have been added to mobile devices, it has become necessary to integrate more DC-DC converters into the power-management IC. Consequently, there is a growing need for an area-efficient and simple controller design for DC-DC converters. A simple hysteretic control without any additional component for compensation is a very attractive solution because it is not only cost-effective, but also immediately responds to load change. However, a conventional current-mode hysteretic controller with low-ESR output capacitor, shown in Fig. 12.1.1, has an inherent trade-off between transient response and RC network time constant for emulating inductor current . For instance, at a switching frequency less than several MHz, which is widely used in industry, the RC network occupies a relatively large die area because the capacitance CSEN of the network can be up to 100pF at a switching frequency of 1 MHz. Another issue with the hysteretic converter is its variable switching frequency, which leads to difficulty in designing an EMI filter . To overcome these limitations, several state-of-the-art hysteretic converters have been reported that provide fast transient response and fixed switching frequency [2-5]. However, they suffer from noise due to the differentiator for amplifying ripple voltage , or need a large external or internal capacitor [3, 4], or generate high switching loss in the converter . In this paper, we present an area-efficient quasi-current-mode hysteretic buck converter with fixed switching frequency. By employing a quasi inductor current emulator (QICE) with reset operation, the controller only uses a total internal capacitance of 3pF and provides fast transient response.
Customer orientation and performance outcomes in supply chain management

Despite an extensive body of knowledge on the importance of customer orientation in the marketing and management literature, the impact of customer orientation and interactive system infrastructure throughout enterprise networks is not fully understood. The purpose of this paper is to present a model linking customer orientation, interactive system infrastructure, value chain practices, and network performance outcomes.  The prior literature on customer orientation and supply chains is reviewed and a framework is presented which shows the relationship between customer orientation and network performance outcomes, along with other variables.  The conclusion supports the importance of customer orientation in the context of the proposed value chain framework.  The framework introduced in this paper provides a review of customer orientation in the enterprise network and a basis for further empirical validation.  The research framework suggests that customer orientation practices may have a positive impact on network infrastructure design, practices, and performance outcomes. Implementation of customer orientation practices and outcomes within this research framework may allow management to meet customer requirements more effectively.  This paper expands the concept of customer-orientation in the extended enterprise network context.
An Approach for Parallel Genetic Algorithms in the Cloud using Software Containers

Genetic Algorithms (GAs) are a powerful technique to address hard optimisation problems. However, scalability issues might prevent them from being applied to real-world problems. Exploiting parallel GAs in the cloud might be an affordable approach to get time efficient solutions that benefit of the appealing features of the cloud, such as scalability, reliability, fault-tolerance and cost-effectiveness. Nevertheless, distributed computation is very prone to cause considerable overhead for communication and making GAs distributed in an on-demand fashion is not trivial. Aiming to keep under control the communication overhead and support GAs developers in the construction and deployment of parallel GAs in the cloud, in this paper we propose an approach to distribute GAs using the global parallelisation model, exploiting software containers and their cloud orchestration. We also devised a conceptual workflow covering each cloud GAs distribution phase, from resources allocation to actual deployment and execution, in a DevOps fashion.
Last Mile Distribution in Humanitarian Relief

Last mile distribution is the final stage of a humanitarian relief chain; it refers to delivery of relief supplies from local distribution centers (LDCs) to beneficiaries affected by disasters. In this study, we consider a vehicle-based last mile distribution system, in which an LDC stores and distributes emergency relief supplies to a number of demand locations. The main decisions are allocating the relief supplies at the LDCs among the demand locations and determining the delivery schedules/routes for each vehicle throughout the planning horizon. We propose a mixed integer programming model that determines delivery schedules for vehicles and equitably allocates resources, based on supply, vehicle capacity, and delivery time restrictions, with the objectives of minimizing transportation costs and maximizing benefits to aid recipients. We show how the proposed model optimizes resource allocation and routing decisions and discuss the tradeoffs between these decisions on a number of test problems. Finally, we identify opportunities for the use of intelligent transportation systems in last mile distribution.
Cities, productivity, and quality of life.

Technological changes and improved electronic communications seem, paradoxically, to be making cities more, rather than less, important. There is a strong correlation between urbanization and economic development across countries, and within-country evidence suggests that productivity rises in dense agglomerations. But urban economic advantages are often offset by the perennial urban curses of crime, congestion and contagious disease. The past history of the developed world suggests that these problems require more capable governments that use a combination of economic and engineering solutions. Though the scope of urban challenges can make remaining rural seem attractive, agrarian poverty has typically also been quite costly.
Towards semantic diff of XML documents

Applications are increasingly using XML to represent semi-structured data and, consequently, a large amount of XML documents is available worldwide. As XML documents evolve over time, comparing XML documents to understand their evolution becomes fundamental. The main focus of existing research for comparing XML documents resides in identifying syntactic changes. However, a deeper notion of the change meaning is usually desired. This paper presents an inference-based XML evolution approach using Prolog to deal with this problem. Differently from existing XML diff approaches, our approach composes multiple syntactic changes, which usually have a common purpose, to infer semantic changes. We evaluated our approach through ten versions of an employment XML document. In this evaluation, we could observe that each new version introduced syntactic changes that could be summarized into semantic changes.
Automatic Exam Seating & Teacher Duty Allocation System

Examinations are the most crucial section of any educational system. They are intended to measure student's knowledge, skills and aptitude. At any institute, a great deal of manual effort is required to plan and arrange examination. It includes making seating arrangement for students as well as supervision duty chart for invigilators. Many institutes performs this task manually using excel sheets. This results in excessive wastage of time and manpower. Automating the entire system can help solve the stated problem efficiently saving a lot of time. This paper presents the automatic exam seating allocation. It works in two modules First as, Students Seating Arrangement (SSA) and second as, Supervision Duties Allocation (SDA). It assigns the classrooms and the duties to the teachers in any institution. An input-output data is obtained from the real system which is found out manually by the organizers who set up the seating arrangement and chalk out the supervision duties. The results obtained using the real system and these two models are compared. The application shows that the modules are highly efficient, low-cost, and can be widely used in various colleges and universities.
uMine: A Blockchain Based on Human Miners

Blockchain technology like Bitcoin is a rapidly growing field of research which has found a wide array of applications. However, the power consumption of the mining process in the Bitcoin blockchain alone is estimated to be at least as high as the electricity consumption of Ireland which constitutes a serious liability to the widespread adoption of blockchain technology. We propose a novel instantiation of a proof of human-work which is a cryptographic proof that an amount of human work has been exercised, and show its use in the mining process of a blockchain. Next to our instantiation there is only one other instantiation known which relies on indistinguishability obfuscation, a cryptographic primitive whose existence is only conjectured. In contrast, our construction is based on the cryptographic principle of multiparty computation (which we use in a black box manner) and thus is the first known feasible proof of human-work scheme. Our blockchain mining algorithm called uMine, can be regarded as an alternative energy-efficient approach to mining.