nils-herrmann/clean_scirepeval huggingface dataset:
The SmartHand transradial prosthesis

Prosthetic components and control interfaces for upper limb amputees have barely changed in the past 40 years. Many transradial prostheses have been developed in the past, nonetheless most of them would be inappropriate if/when a large bandwidth human-machine interface for control and perception would be available, due to either their limited (or inexistent) sensorization or limited dexterity. SmartHand tackles this issue as is meant to be clinically experimented in amputees employing different neuro-interfaces, in order to investigate their effectiveness. This paper presents the design and on bench evaluation of the SmartHand. SmartHand design was bio-inspired in terms of its physical appearance, kinematics, sensorization, and its multilevel control system. Underactuated fingers and differential mechanisms were designed and exploited in order to fit all mechatronic components in the size and weight of a natural human hand. Its sensory system was designed with the aim of delivering significant afferent information to the user through adequate interfaces. SmartHand is a five fingered self-contained robotic hand, with 16 degrees of freedom, actuated by 4 motors. It integrates a bio-inspired sensory system composed of 40 proprioceptive and exteroceptive sensors and a customized embedded controller both employed for implementing automatic grasp control and for potentially delivering sensory feedback to the amputee. It is able to perform everyday grasps, count and independently point the index. The weight (530 g) and speed (closing time: 1.5 seconds) are comparable to actual commercial prostheses. It is able to lift a 10 kg suitcase; slippage tests showed that within particular friction and geometric conditions the hand is able to stably grasp up to 3.6 kg cylindrical objects. Due to its unique embedded features and human-size, the SmartHand holds the promise to be experimentally fitted on transradial amputees and employed as a bi-directional instrument for investigating -during realistic experiments- different interfaces, control and feedback strategies in neuro-engineering studies.
Towards a neural basis of music-evoked emotions

Music is capable of evoking exceptionally strong emotions and of reliably affecting the mood of individuals. Functional neuroimaging and lesion studies show that music-evoked emotions can modulate activity in virtually all limbic and paralimbic brain structures. These structures are crucially involved in the initiation, generation, detection, maintenance, regulation and termination of emotions that have survival value for the individual and the species. Therefore, at least some music-evoked emotions involve the very core of evolutionarily adaptive neuroaffective mechanisms. Because dysfunctions in these structures are related to emotional disorders, a better understanding of music-evoked emotions and their neural correlates can lead to a more systematic and effective use of music in therapy.
Merkelized Abstract Syntax Trees

In the context of modern cryptosystems, a common theme is the creation of distributed trust networks. In most of these designs, permanent storage of a contract is required. However, permanent storage can become a major performance and cost bottleneck. As a result, good code compression schemes are a key factor in scaling these contract based cryptosystems. For this project, we formalize and implement a data structure called the Merkelized Abstract Syntax Tree (MAST) to address both data integrity and compression. MASTs can be used to compactly represent contractual programs that will be executed remotely, and by using some of the properties of Merkle trees, they can also be used to verify the integrity of the code being executed. A concept by the same name has been discussed in the Bitcoin community for a while, the terminology originates from the work of Russel O’Connor and Pieter Wuille, however this discussion was limited to private correspondences. We present a formalization of it and provide an implementation.The project idea was developed with Bitcoin applications in mind, and the experiment we set up uses MASTs in a crypto currency network simulator. Using MASTs in the Bitcoin protocol  would increase the complexity (length) of contracts permitted on the network, while simultaneously maintaining the security of broadcasted data. Additionally, contracts may contain privileged, secret branches of execution.
IoMT: A Reliable Cross Layer Protocol for Internet of Multimedia Things

The futuristic trend is toward the merging of cyber world with physical world leading to the development of Internet of Things (IoT) framework. Current research is focused on the scalar data-based IoT applications thus leaving the gap between services and benefits of IoT objects and multimedia objects. Multimedia IoT (IoMT) applications require new protocols to be developed to cope up with heterogeneity among the various communicating objects. In this paper, we have presented a cross-layer protocol for IoMT. In proposed methodology, we have considered the cross communication of physical, data link, and routing layers for multimedia applications. Response time should be less, and communication among the devices must be energy efficient in multimedia applications. IoMT has considered both the issues and the comparative simulations in MATLAB have shown that it outperforms over the traditional protocols and presents the optimized solution for IoMT.
An Enhanced Method for Data Hiding using 2-Bit XOR in Image Steganography

As we all know security is needed when we want to send data over any medium so this requires a secure medium to send data. That’s why steganography comes in mind whose aim is to send data securely without knowing of any hacker. In this paper, a new technique is projected whose aim is to keep secrete communication intact. The proposed method blends the advantage of 2 bit LSB and XOR operation. In this, first we are XORing the 8th, 1st bit of data and 7th, 2nd bit of data after this two bit are obtained. These obtained bits are replaced at the LSB position. However, with some way, any person get know about hidden message and it takes the LSB position bit then there are no chances of getting message as it is not the actual message. An experiment was performed with different dataset of images. Furthermore, it was observed that the proposed method promises good result as the PSNR and MSE are good. When the method was compared with other existing methods, it shows enhancement in the imperceptibility and message capacity. Keyword Steganography, XOR, Information Hiding, LSB
Impact of Product Packaging on Consumer ’ s Buying Behavior

The objective of this study is to determine role of packaging on consumer’s buying behavior. The purpose of this research is to examine the essential factors, which are driving the success of a brand. This research also identified the relationship between the dependent and independent variables. This is the primary research and data has been collected through questionnaire and for analysis purpose SPSS software has been used. In this study samples of 150 respondents has been collected and tested the reliability of the model. According to the finding of the research study, it has been observed that the packaging is the most important factor. It is further concluded that the packaging elements like its Colour, Packaging material, Design of Wrapper and innovation are more important factors when consumers making any buying decision. Finally it has also been concluded that the Packaging is one of the most important and powerful factor, which influences consumer’s purchase decision.
Removal of heavy metal ions from wastewaters: a review.

Heavy metal pollution has become one of the most serious environmental problems today. The treatment of heavy metals is of special concern due to their recalcitrance and persistence in the environment. In recent years, various methods for heavy metal removal from wastewater have been extensively studied. This paper reviews the current methods that have been used to treat heavy metal wastewater and evaluates these techniques. These technologies include chemical precipitation, ion-exchange, adsorption, membrane filtration, coagulation-flocculation, flotation and electrochemical methods. About 185 published studies (1988-2010) are reviewed in this paper. It is evident from the literature survey articles that ion-exchange, adsorption and membrane filtration are the most frequently studied for the treatment of heavy metal wastewater.
LabelBank: Revisiting Global Perspectives for Semantic Segmentation

Semantic segmentation requires a detailed labeling of image pixels by object category. Information derived from local image patches is necessary to describe the detailed shape of individual objects. However, this information is ambiguous and can result in noisy labels. Global inference of image content can instead capture the general semantic concepts present. We advocate that holistic inference of image concepts provides valuable information for detailed pixel labeling. We propose a generic framework to leverage holistic information in the form of a LabelBank for pixellevel segmentation. We show the ability of our framework to improve semantic segmentation performance in a variety of settings. We learn models for extracting a holistic LabelBank from visual cues, attributes, and/or textual descriptions. We demonstrate improvements in semantic segmentation accuracy on standard datasets across a range of state-of-the-art segmentation architectures and holistic inference approaches.
Face Recognition with Support Vector Machines: Global versus Component-based Approach

We present a component-based method and two global methods for face recognition and evaluate them with respect to robustness against pose changes. In the component system we first locate facial components, extract them and combine them into a single feature vector which is classified by a Support Vector Machine (SVM). The two global systems recognize faces by classifying a single feature vector consisting of the gray values of the whole face image. In the first global system we trained a single SVM classifier for each person in the database. The second system consists of sets of viewpoint-specific SVM classifiers and involves clustering during training. We performed extensive tests on a database which included faces rotated up to about Æ in depth. The component system clearly outperformed both global systems on all tests.
Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures

State-of-the-art neural networks are getting deeper and wider. While their performance increases with the increasing number of layers and neurons, it is crucial to design an efficient deep architecture in order to reduce computational and memory costs. Designing an efficient neural network, however, is labor intensive requiring many experiments, and fine-tunings. In this paper, we introduce network trimming which iteratively optimizes the network by pruning unimportant neurons based on analysis of their outputs on a large dataset. Our algorithm is inspired by an observation that the outputs of a significant portion of neurons in a large network are mostly zero, regardless of what inputs the network received. These zero activation neurons are redundant, and can be removed without affecting the overall accuracy of the network. After pruning the zero activation neurons, we retrain the network using the weights before pruning as initialization. We alternate the pruning and retraining to further reduce zero activations in a network. Our experiments on the LeNet and VGG-16 show that we can achieve high compression ratio of parameters without losing or even achieving higher accuracy than the original network.
Early Word Learning 1 Early Word Learning and Conceptual Development: Everything Had a Name, and Each Name Gave Birth to a New Thought

Sandra R. Waxman & Erin M. Leddon Northwestern University Synopsis Perhaps more than any other developmental achievement, word-learning stands at the very intersection of language and cognition. Early word-learning represents infants’ entrance into a truly symbolic system and brings with it a means to establish reference. To succeed, infants must identify the relevant linguistic units, identify their corresponding concepts, and establish a mapping between the two. But how do infants begin to map words to concepts, and thus establish their meaning? How do they discover that different types of words (e.g., “dog” (noun), “fluffy” (adjective), “begging” (verb) refer to different aspects of the same scene (e.g, a standard poodle, seated on its hind legs and holding its front paws in the air)? We have proposed that infants begin the task of word-learning with a broad, universal expectation linking novel words to a broad range of commonalities, and that this initial expectation is subsequently fine-tuned on the basis of their experience with the objects and events they encounter and the native language under acquisition. In this chapter, we examine this proposal, in light of recent evidence with infants and young children.
Query Recommendation Using Query Logs in Search Engines

In this paper we propose a method that, given a query submitte d to a search engine, suggests a list of related queries. The rela t d queries are based in previously issued queries, and can be issued by the user to the search engine to tune or redirect the search process. The method proposed i s based on a query clustering process in which groups of semantically similar queries are identified. The clustering process uses the content of historical prefe renc s of users registered in the query log of the search engine. The method not onl y discovers the related queries, but also ranks them according to a relevanc criterion. Finally, we show with experiments over the query log of a search engine the ffectiveness of the method.
Natural Language Inference with Attentive Neural Networks

The task of recognizing textual entailment (RTE) is to infer whether a given premise sentence entails, contradicts, or stands neutral to a hypothesis sentence. In this paper, we implement and evaluate a number of state-of-the-art attention based sequence models. We observe that models using a matching-aggregation framework perform extremely well, but repeatedly make mistakes evaluating sentence pairs with ambiguity and irrelevant detail. We also experiment with stacked versions of these models. Finally, we perform an analysis of the Stanford Natural Language Inference (SNLI) dataset and note that we may be approaching an upper bound on model performance.
Finding temporal structure in music: blues improvisation with LSTM recurrent networks

Few types of signal streams are as ubiquitous as music. Here we consider the problem of extracting essential ingredients of music signals, such as well-defined global temporal structure in the form of nested periodicities (or meter). Can we construct an adaptive signal processing device that learns by example how to generate new instances of a given musical style? Because recurrent neural networks can in principle learn the temporal structure of a signal, they are good candidates for such a task. Unfortunately, music composed by standard recurrent neural networks (RNNs) often lacks global coherence. The reason for this failure seems to be that RNNs cannot keep track of temporally distant events that indicate global music structure. Long Short-Term Memory (LSTM) has succeeded in similar domains where other RNNs have failed, such as timing & counting and learning of context sensitive languages. In the current study we show that LSTM is also a good mechanism for learning to compose music. We present experimental results showing that LSTM successfully learns a form of blues music and is able to compose novel (and we believe pleasing) melodies in that style. Remarkably, once the network has found the relevant structure it does not drift from it: LSTM is able to play the blues with good timing and proper structure as long as one is willing to listen.
Why Cognitive Behavioral Therapy Is the Current Gold Standard of Psychotherapy

1 Department of Clinical Psychology and Psychotherapy, International Institute for Advanced Study in Psychotherapy and Applied Mental Health at Babes-Bolyai University, Cluj-Napoca, Romania, 2 Department of Population Health Sciences and Policy at Icahn School of Medicine at Mount Sinai, New York, NY, United States, 3 Department of Psychological and Brain Sciences, Boston University, Boston, MA, United States
Identify Crime Detection Using Data Mining Techniques

Credit-card-based purchases can be categorized into two types: 1) physical card and 2) virtual card. In a physical-card based purchase, the cardholder presents his card physically to a merchant for making a payment. To carry out fraudulent transactions in this kind of purchase, an attacker has to steal the credit card. If the cardholder does not realize the loss of card, it can lead to a substantial financial loss to the credit card company. In the second kind of purchase, only some important information about a card (card number, expiration date, secure code) is required to make the payment. Such purchases are normally done on the Internet or over the telephone. To commit fraud in these types of purchases, a fraudster simply needs to know the card details. Most of the time, the genuine cardholder is not aware that someone else has seen or stolen his card information.The only way to detect this kind of fraud is to analyze the spending patterns on every card and to figure out any inconsistency with respect to the “usual” spending patterns. Fraud detection based on the analysis of existing purchase data of cardholder is a promising way to reduce the rate of successful credit card frauds. The existing nondata mining detection system of business rules and scorecards, and known fraud matching have limitations. To address these limitations and combat identity crime in real time, this paper proposes a new multilayered detection system complemented withtwo additional layers: communal detection (CD) and spike detection (SD).CD finds realsocial relationships to reduce the suspicion score, and is tamper resistant to synthetic social relationships. It is the whitelist-oriented approach on a fixed set of attributes. SD finds spikes in duplicates to increase the suspicion score, and is probe-resistant for attributes. Key words— communal detection, spike detection, fraud detection, support vector machine
Probabilistic machine learning and artificial intelligence

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.
A Literature Review and Meta-analysis of Digital Games and Second Language Learning

Exposed to traditional language learning strategies, students will gradually lose interest in and motivation to not only learn English, but also any language or culture. Hence, researchers are seeking technology-based learning strategies, such as digital game-mediated language learning, to motivate students and improve learning performance. This paper synthesizes the findings of empirical studies focused on the effectiveness of digital games in language education published within the last five years. Nine qualitative, quantitative, and mixed-method studies are collected and analyzed in this paper. The review found that recent empirical research was conducted primarily to examine the effectiveness by measuring language learning outcomes, motivation, and interactions. Weak proficiency was found in vocabulary retention, but strong proficiency was present in communicative skills such as speaking. Furthermore, in general, students reported that they are motivated to engage in language learning when digital games are involved; however, the motivation is also observed to be weak due to the design of the game and/or individual differences. The most effective method used to stimulate interaction language learning process seems to be digital games, as empirical studies demonstrate that it effectively promotes language education. However, significant work is still required to provide clear answers with respect to innovative and effective learning practice.
Cloud Computing Research and Development Trend

With the development of parallel computing, distributed computing, grid computing, a new computing model appeared. The concept of computing comes from grid, public computing and SaaS. It is a new method that shares basic framework. The basic principles of cloud computing is to make the computing be assigned in a great number of distributed computers, rather then local computer or remoter server. The running of the enterprise’s data center is just like Internet. This makes the enterprise use the resource in the application that is needed, and access computer and storage system according to the requirement. This article introduces the background and principle of cloud computing, the character, style and actuality. This article also introduces the application field the merit of cloud computing, such as, it do not need user’s high level equipment, so it reduces the user’s cost. It provides secure and dependable data storage center, so user needn’t do the awful things such storing data and killing virus, this kind of task can be done by professionals. It can realize data share through different equipments. It analyses some questions and hidden troubles, and puts forward some solutions, and discusses the future of cloud computing. Cloud computing is a computing style that provide power referenced with IT as a service. Users can enjoy the service even he knows nothing about the technology of cloud computing and the professional knowledge in this field and the power to control it.
A simplified computer model of cardiovascular system with an arm branch.

Non-invasive pressure simulators that regenerate oscillometric waveforms promise an alternative to expensive clinical trials for validating oscillometric noninvasive blood pressure devices. However, existing simulators only provide oscillometric pressure in cuff and thus have a limited accuracy. It is promising to build a physical simulator that contains a synthetic arm with a built-in brachial artery and an affiliated hydraulic model of cardiovascular system. To guide the construction of this kind of simulator, this paper presents a computer model of cardiovascular system with a relatively simple structure, where the distribution of pressures and flows in aorta root and brachial artery can be simulated, and the produced waves are accordant with the physical data. This model can be used to provide the parameters and structure that will be needed to build the new simulator.
On Integrated Clustering and Outlier Detection

We model the joint clustering and outlier detection problem using an extension of the facility location formulation. The advantages of combining clustering and outlier selection include: (i) the resulting clusters tend to be compact and semantically coherent (ii) the clusters are more robust against data perturbations and (iii) the outliers are contextualised by the clusters and more interpretable. We provide a practical subgradient-based algorithm for the problem and also study the theoretical properties of algorithm in terms of approximation and convergence. Extensive evaluation on synthetic and real data sets attest to both the quality and scalability of our proposed method.