nils-herrmann/clean_scirepeval huggingface dataset:
Classification in the Presence of Label Noise: A Survey

Label noise is an important issue in classification, with many potential negative consequences. For example, the accuracy of predictions may decrease, whereas the complexity of inferred models and the number of necessary training samples may increase. Many works in the literature have been devoted to the study of label noise and the development of techniques to deal with label noise. However, the field lacks a comprehensive survey on the different types of label noise, their consequences and the algorithms that consider label noise. This paper proposes to fill this gap. First, the definitions and sources of label noise are considered and a taxonomy of the types of label noise is proposed. Second, the potential consequences of label noise are discussed. Third, label noise-robust, label noise cleansing, and label noise-tolerant algorithms are reviewed. For each category of approaches, a short discussion is proposed to help the practitioner to choose the most suitable technique in its own particular field of application. Eventually, the design of experiments is also discussed, what may interest the researchers who would like to test their own algorithms. In this paper, label noise consists of mislabeled instances: no additional information is assumed to be available like e.g., confidences on labels.
Green Internet of Things for Smart World

Smart world is envisioned as an era in which objects (e.g., watches, mobile phones, computers, cars, buses, and trains) can automatically and intelligently serve people in a collaborative manner. Paving the way for smart world, Internet of Things (IoT) connects everything in the smart world. Motivated by achieving a sustainable smart world, this paper discusses various technologies and issues regarding green IoT, which further reduces the energy consumption of IoT. Particularly, an overview regarding IoT and green IoT is performed first. Then, the hot green information and communications technologies (ICTs) (e.g., green radio-frequency identification, green wireless sensor network, green cloud computing, green machine to machine, and green data center) enabling green IoT are studied, and general green ICT principles are summarized. Furthermore, the latest developments and future vision about sensor cloud, which is a novel paradigm in green IoT, are reviewed and introduced, respectively. Finally, future research directions and open problems about green IoT are presented. Our work targets to be an enlightening and latest guidance for research with respect to green IoT and smart world.
Pathophysiology of human visceral obesity: an update.

Excess intra-abdominal adipose tissue accumulation, often termed visceral obesity, is part of a phenotype including dysfunctional subcutaneous adipose tissue expansion and ectopic triglyceride storage closely related to clustering cardiometabolic risk factors. Hypertriglyceridemia; increased free fatty acid availability; adipose tissue release of proinflammatory cytokines; liver insulin resistance and inflammation; increased liver VLDL synthesis and secretion; reduced clearance of triglyceride-rich lipoproteins; presence of small, dense LDL particles; and reduced HDL cholesterol levels are among the many metabolic alterations closely related to this condition. Age, gender, genetics, and ethnicity are broad etiological factors contributing to variation in visceral adipose tissue accumulation. Specific mechanisms responsible for proportionally increased visceral fat storage when facing positive energy balance and weight gain may involve sex hormones, local cortisol production in abdominal adipose tissues, endocannabinoids, growth hormone, and dietary fructose. Physiological characteristics of abdominal adipose tissues such as adipocyte size and number, lipolytic responsiveness, lipid storage capacity, and inflammatory cytokine production are significant correlates and even possible determinants of the increased cardiometabolic risk associated with visceral obesity. Thiazolidinediones, estrogen replacement in postmenopausal women, and testosterone replacement in androgen-deficient men have been shown to favorably modulate body fat distribution and cardiometabolic risk to various degrees. However, some of these therapies must now be considered in the context of their serious side effects. Lifestyle interventions leading to weight loss generally induce preferential mobilization of visceral fat. In clinical practice, measuring waist circumference in addition to the body mass index could be helpful for the identification and management of a subgroup of overweight or obese patients at high cardiometabolic risk.
Helping Crisis Responders Find the Informative Needle in the Tweet Haystack

Crisis responders are increasingly using social media, data and other digital sources of information to build a situational understanding of a crisis situation in order to design an effective response. However with the increased availability of such data, the challenge of identifying relevant information from it also increases. This paper presents a successful automatic approach to handling this problem. Messages are filtered for informativeness based on a definition of the concept drawn from prior research and crisis response experts. Informative messages are tagged for actionable data – for example, people in need, threats to rescue efforts, changes in environment, and so on. In all, eight categories of actionability are identified. The two components – informativeness and actionability classification – are packaged together as an openly-available tool called Emina (
A term-based methodology for query reformulation understanding

Key to any research involving session search is the understanding of how a user’s queries evolve throughout the session. When a user creates a query reformulation, he or she is consciously retaining terms from their original query, removing others and adding new terms. By measuring the similarity between queries we can make inferences on the user’s information need and how successful their new query is likely to be. By identifying the origins of added terms we can infer the user’s motivations and gain an understanding of their interactions. In this paper we present a novel term-based methodology for understanding and interpreting query reformulation actions. We use TREC Session Track data to demonstrate how our technique is able to learn from query logs and we make use of click data to test user interaction behavior when reformulating queries. We identify and evaluate a range of term-based query reformulation strategies and show that our methods provide valuable insight into understanding query reformulation in session search.
Development of humanoid robot platform KHR-2 (KAIST humanoid robot-2)

We are presenting the mechanical and electrical system design and system integration of controllers including sensory devices of the humanoid, KHR-2 in this paper. The concept and the objective of the design will be described. We have developed KHR-2 since 2003 which has 41 DOF (degree of freedom). Each arm including a hand and a wrist has 11 DOF (5+2 DOF/hand (finger + wrist), 4 DOF/arm) and each leg has 6 DOF. Head and trunk have 6 DOF (2 DOF/eye and 2 DOF/neck) and 1 DOF respectively. The mechanical part of the robot is designed to have human friendly appearance and wide movable angle range. Joint actuators are designed to have negligible uncertainties such as backlash. To control all axes, distributed control architecture is adopted to reduce the computation burden of the main controller (PC) and to expand the devices easily. We developed a sub-controller which is a servo motor controller and sensor interfacing devices using microprocessor. The main controller (PC) attached on the back of the robot communicates with sub-controllers in real-time by using CAN (controller area network). Windows XP is used for OS (operating system) for fast development of main control program. RTX (real time extension) HAL extension software is used to realize the real-time control in Windows XP environment. KHR-2 has several sensor types, which are 3-axis F/T (force/torque) sensors at foot and wrist, inertia sensor system (accelerometer and rate gyro) and CCD camera. The F/T sensor at the foot is the most fundamental sensor for stable walking. The inertia sensor system is essential for determining the inclination between the ground and the robot. Finally, we used the CCD camera for navigation and stabilization of the robot in the future. We described the details of the KHR-2 in this paper.
Automatic ranking of swear words using word embeddings and pseudo-relevance feedback

This paper describes a method for automatically ranking a dictionary of swear words based on their level of rudeness. The final ranking is generated by combining two baseline rankings: 1) using the normalized accumulated cosine similarity between the word embeddings of the swear word and the n-best list of closest neighborhoods, and 2) using a pseudo-relevance feedback and bootstrapping algorithm. The proposed methods are trained using dialogues extracted from movies scripts and evaluated against a list of swear words ranked manually in 5 categories by four different annotators. The Spearman correlation coefficient between the rankings generated by the proposed system and a consolidated gold standard reaches a similar value to the ones obtained among the different human annotators, proving that the proposed method is a good alternative to the manual process.
Ensemble of exemplar-SVMs for object detection and beyond

This paper proposes a conceptually simple but surprisingly powerful method which combines the effectiveness of a discriminative object detector with the explicit correspondence offered by a nearest-neighbor approach. The method is based on training a separate linear SVM classifier for every exemplar in the training set. Each of these Exemplar-SVMs is thus defined by a single positive instance and millions of negatives. While each detector is quite specific to its exemplar, we empirically observe that an ensemble of such Exemplar-SVMs offers surprisingly good generalization. Our performance on the PASCAL VOC detection task is on par with the much more complex latent part-based model of Felzenszwalb et al., at only a modest computational cost increase. But the central benefit of our approach is that it creates an explicit association between each detection and a single training exemplar. Because most detections show good alignment to their associated exemplar, it is possible to transfer any available exemplar meta-data (segmentation, geometric structure, 3D model, etc.) directly onto the detections, which can then be used as part of overall scene understanding.
X-band antenna design for Indonesian ground surveillance man pack radar

In this paper, an X Band antenna for ground surveillance manpack radar was designed with 15 × 38 cm dimension using RT/Duroid 5880 substrate. The array consisted of 64 patches antenna with a carved slit to obtain a horizontal polarization. The gap between antenna feed were evaluated and the results are that that antenna gain and side lobe level will decrease as the gap became larger. This design has 200 MHz bandwidth with gain of 25.2 dB and side lobe level of −22.7 dB.
Testing Intrusion detection systems: a critique of the 1998 and 1999 DARPA intrusion detection system evaluations as performed by Lincoln Laboratory

In 1998 and again in 1999, the Lincoln Laboratory of MIT conducted a comparative evaluation of intrusion detection systems (IDSs) developed under DARPA funding. While this evaluation represents a significant and monumental undertaking, there are a number of issues associated with its design and execution that remain unsettled. Some methodologies used in the evaluation are questionable and may have biased its results. One problem is that the evaluators have published relatively little concerning some of the more critical aspects of their work, such as validation of their test data. The appropriateness of the evaluation techniques used needs further investigation. The purpose of this article is to attempt to identify the shortcomings of the Lincoln Lab effort in the hope that future efforts of this kind will be placed on a sounder footing. Some of the problems that the article points out might well be resolved if the evaluators were to publish a detailed description of their procedures and the rationale that led to their adoption, but other problems would clearly remain./par>
Data fusion schemes for cooperative spectrum sensing in cognitive radio networks

Cooperative spectrum sensing has proven its efficiency to detect spectrum holes in cognitive radio network (CRN) by combining sensing information of multiple cognitive radio users. In this paper, we study different fusion schemes that can be implemented in fusion center. Simulation comparison between these schemes based on hard, quantized and soft fusion rules are conducted. It is shown through computer simulation that the soft combination scheme outperforms the hard one at the cost of more complexity; the quantized combination scheme provides a good tradeoff between detection performance and complexity. In the paper, we also analyze a quantized combination scheme based on a tree-bit quantization and compare its performance with some hard and soft combination schemes.
A Compact Wideband Microstrip Crossover

In this letter, a planar microstrip crossover junction is presented. The conductor-backed coplanar waveguide (CB-CPW) structure with vias is employed as the core part of the crossover design. The dimensions of the CB-CPW crossover itself are 11.3 × 11.3 mm2. Two kinds of the transitions between the microstrip line (MSL) and the CB-CPW structure are merged into a double side print circuit board. This presented crossover junction has a bandwidth from 10 up to 6000 MHz for 20 dB return loss, 1 dB insertion loss and -20 dB isolation. Compared with the other designs, this planar crossover features wide bandwidth and compact configuration.
On the use of MapReduce for imbalanced big data using Random Forest

In this age, big data applications are increasingly becoming the main focus of attention because of the enormous increment of data generation and storage that has taken place in the last years. This situation becomes a challenge when huge amounts of data are processed to extract knowledge because the data mining techniques are not adapted to the new space and time requirements. Furthermore, real-world data applications usually present a class distribution where the samples that belong to one class, which is precisely the main interest, are hugely outnumbered by the samples of the other classes. This circumstance , known as the class imbalance problem, complicates the learning process as the standard learning techniques do not correctly address this situation. In this work, we analyse the performance of several techniques used to deal with imbal-anced datasets in the big data scenario using the Random Forest classifier. Specifically, oversampling, undersampling and cost-sensitive learning have been adapted to big data using MapReduce so that these techniques are able to manage datasets as large as needed providing the necessary support to correctly identify the underrepresented class. The Random Forest classifier provides a solid basis for the comparison because of its performance, robustness and versatility. An experimental study is carried out to evaluate the performance of the diverse algorithms considered. The results obtained show that there is not an approach to imbalanced big data classification that outperforms the others for all the data considered when using Random Forest. Moreover, even for the same type of problem, the best performing method is dependent on the number of mappers selected to run the experiments. In most of the cases, when the number of splits is increased, an improvement in the running times can be observed, however, this progress in times is obtained at the expense of a slight drop in the accuracy performance obtained. This decrement in the performance is related to the lack of density problem, which is evaluated in this work from the imbalanced data point of view, as this issue degrades the performance of classifiers in the imbalanced scenario more severely than in standard learning.
A 14mW 6.25Gb/s Transceiver in 90nm CMOS for Serial Chip-to-Chip Communications

A power-efficient 6.25Gb/s transceiver in 90nm CMOS for chip-to-chip communication is presented, it dissipates 2.2mW/Gb/s operating at a BER of <10-15 over a channel with -15dB attenuation at 3.125GHz. A shared LC-PLL, resonant clock distribution, a low-swing voltage-mode transmitter, a low-power phase rotator, and a software-based CDR and an adaptive equalizer are used to reduce power
Understanding the Influence of User Participation and Involvement on System Success – a Systematic Mapping Study

User participation and involvement in software development are considered to be essential for a successful software system. Three research areas, human aspects of software engineering, requirements engineering, and information systems, study these topics from various perspectives. We think it is important to analyze user participation and involvement in software engineering comprehensively to encourage further research in this area. We investigate the evidence on effects of user participation and involvement on system success and we explore which methods are available in literature. A systematic mapping study was conducted. The systematic search yielded 3,698 hits, from which we identified 289 unique papers. These papers were reviewed by the first author based on inclusion and exclusion criteria. The second author validated the selection of papers by reviewing the reasons for exclusion and inclusion and the corresponding papers on a sample base. 58 of the 289 papers were selected (22 statistical survey and meta-study papers and 36 methods papers). Based on the empirical evidence of the surveys and meta-studies, we developed a meta-analysis of structural equation models. This overview demonstrates that most papers showed positive correlations between aspects of development processes (including user participation) and human aspects (including user involvement) and system success. The analysis of the proposed solutions from the method papers revealed a wide variety of user participation and involvement practices for most activities within software development.
Autonomous robot path planning in dynamic environment using a new optimization technique inspired by Bacterial Foraging technique

Path planning is one of the basic and interesting functions for a mobile robot. This paper explores the application of Bacterial Foraging Optimization to the problem of mobile robot navigation to determine shortest feasible path to move from any current position to target position in unknown environment with moving obstacles. It develops a new algorithm based on Bacterial Foraging Optimization (BFO) technique. This algorithm finds a path towards the target and avoiding the obstacles using particles which are randomly distributed on a circle around a robot. The criterion on which it selects the best particle is the distance to target and the Gaussian cost function of the particle. Then, a high level decision strategy is used for the selection and thus proceeds for the result. It works on local environment by using a simple robot sensor. So, it is free from having generated additional map which adds cost. Furthermore, it can be implemented without requirement to tuning algorithm and complex calculation. To simulate the algorithm, the program is written in C language and the environment is created by OpenGL. To test the efficiency of proposed technique, results are compared with Basic Bacterial Foraging Optimization (BFO) and another well-known algorithm called Particle Swarm Optimization (PSO). From the experimental result it can be told that the proposed method gives better path or optimal path.
Attribute-Based Encryption for Circuits

In an attribute-based encryption (ABE) scheme, a ciphertext is associated with an ℓ-bit public index ind and a message m, and a secret key is associated with a Boolean predicate P. The secret key allows decrypting the ciphertext and learning m if and only if P(ind) = 1. Moreover, the scheme should be secure against collusions of users, namely, given secret keys for polynomially many predicates, an adversary learns nothing about the message if none of the secret keys can individually decrypt the ciphertext.
 We present attribute-based encryption schemes for circuits of any arbitrary polynomial size, where the public parameters and the ciphertext grow linearly with the depth of the circuit. Our construction is secure under the standard learning with errors (LWE) assumption. Previous constructions of attribute-based encryption were for Boolean formulas, captured by the complexity class NC1.
 In the course of our construction, we present a new framework for constructing ABE schemes. As a by-product of our framework, we obtain ABE schemes for polynomial-size branching programs, corresponding to the complexity class LOGSPACE, under quantitatively better assumptions.
Stock Prediction Using Functional Link Artificial Neural Network (FLANN)

Stock exchange that is, buying and selling of stock is considered to be an important factor in the economy sector. The Stockbrokers typically use time series or technical analysis in predicting the stock price. These techniques are based on trends and not the actual stock value. Therefore a method of prediction which takes into account the historical values of stock is desired. Neural Networks once again have become famous for prediction of stock. This is due to their ability to deal with non-linear data. The use of Artificial Neural Networks to for predicting the stock prices is proposed in this paper. The input features to the model sometimes can be non-related to the output. Hence,  Networks is used here to increase the number of related features in the form of inputs. The data is taken from NSE and is converted into a suitable form for FLANN and then prediction is carried out using Multi-layer feed forward Perceptron model.
Phoenix: A Self-Optimizing Chess Engine

Since the advent of computers, many tasks which required humans to spend a lot of time and energy have been trivialized by the computers' ability to perform repetitive tasks extremely quickly. However there are still many areas in which humans excel in comparison with the machines. One such area is chess. Even with great advances in the speed and computational power of modern machines, Grandmasters often beat the best chess programs in the world with relative ease1. This may be due to the fact that a game of chess cannot be won by pure calculation. There is more to the goodness of a chess position than some numerical value which apparently can be seen only by the human brain. Here an effort has been made to improve current chess engines by letting themselves evolve over a period of time. Firstly, the problem of learning is reduced into an optimization problem by defining Position Evaluation in terms of Positional Value Tables (PVTs). Next, the PVTs are optimized using Multi-Niche Crowding which successfully identifies the optima in a multimodal function, thereby arriving at distinctly different solutions which are close to the global optimum.