nils-herrmann/clean_scirepeval huggingface dataset:
Logics for resource-bounded agents

In this thesis, I criticise traditional epistemic logics based on possible worlds semantics, inspired by Hintikka, as a framework for representing the beliefs and knowledge of agents. The traditional approach suffers from the logical omniscience problem: agents are modelled as knowing all consequences of their knowledge, which is not an admissible assumption when modelling real-world reasoning agents. My thesis proposes a new logical framework for representing the knowledge and beliefs of multiple resource-bounded agents. I begin by arguing that amendments to the possible worlds framework for modelling knowledge and belief cannot successfully overcome the logical omniscience problem in all its guises, and conclude that a so-called sentential account of belief and knowledge is to be preferred. Sentential accounts do receive support from the recent literature, but tend to conflate belief with explicit assent. In response to this problem, I consider Dennett’s intentional stance to belief ascription, holding that beliefs can only be ascribed to an agent from the point of view of a particular predictive strategy. However, Dennett’s account itself suffers from logical omniscience. I offer a sentential account of belief based on Dennett’s that avoids logical omniscience. Briefly, we should only ascribe those sentences to agents as beliefs that the agent could explicitly assent to, given its situation and an appropriate bound on the agent’s cognitive resources. In the latter half of the thesis, I concentrate on developing a logical framework that respects this philosophical account of belief. In order to capture resource-bounded reasoning, a fine-grained approach capable of modelling individual acts of inference is required. An agent’s reasoning process is modelled as a non-deterministic succession of belief states, where each new belief state differs from the previous one by a single act of inference. The logic I develop is a modal logic with many interesting model-theoretic properties and a simple, yet complete and decidable proof theory. I focus on the rule-based agent paradigm from contemporary AI, as well as investigating classical propositional reasoning-by-assumption. I investigate the complexity of the resulting logics and conclude by discussing various extensions to the framework, including more expressive languages and the handling of non-monotonic reasoning.
Attributes2Classname: A Discriminative Model for Attribute-Based Unsupervised Zero-Shot Learning

We propose a novel approach for unsupervised zero-shot learning (ZSL) of classes based on their names. Most existing unsupervised ZSL methods aim to learn a model for directly comparing image features and class names. However, this proves to be a difficult task due to dominance of non-visual semantics in underlying vector-space embeddings of class names. To address this issue, we discriminatively learn a word representation such that the similarities between class and combination of attribute names fall in line with the visual similarity. Contrary to the traditional zero-shot learning approaches that are built upon attribute presence, our approach bypasses the laborious attributeclass relation annotations for unseen classes. In addition, our proposed approach renders text-only training possible, hence, the training can be augmented without the need to collect additional image data. The experimental results show that our method yields state-of-the-art results for unsupervised ZSL in three benchmark datasets.
Algoritmic music composition based on artificial intelligence: A survey

We present a taxonomy of the Artificial Intelligence (AI) methods currently applied for algorithmic music composition. The area known as algorithmic music composition concerns the research on processes of composing music pieces automatically by a computer system. The use of AI for algorithmic music consists on the application of AI techniques as the main tools for the composition generation. There are several models of AI used in music composition such as: heuristics in evolutionary algorithms, neural networks, stochastic methods, generative models, agents, decision trees, declarative programming and grammatical representation. In this survey we present the trending in techniques for automatic music composition. We summarized several research projects of the last seven years and highlight the directions of music composition based on AI techniques.
Detecting protected layer-3 rogue APs

Unauthorized rogue access points (APs), such as those brought into a corporate campus by employees, pose a security threat as they may be poorly managed or insufficiently secured. Any attacker in the vicinity can easily get onto the internal network through a rogue AP, bypassing all perimeter security measures. Existing detection solutions work well for detecting layer-2 rogue APs. It is a challenge, however, to accurately detect a layer-3 rogue AP that is protected by WEP or other security measures. In this paper, we describe a new rogue AP detection method to address this problem. Our solution uses a verifier on the internal wired network to send test traffic towards wireless edge, and uses wireless sniffers to identify rouge APs that relay the test packets. To quickly sweep all possible rogue APs, the verifier uses a greedy algorithm to schedule the channels for the sniffers to listen to. To work with the encrypted AP traffic, the sniffers use a probabilistic algorithm that only relies on observed packet size. Using extensive experiments, we show that the proposed approach can robustly detect rogue APs with moderate network overhead.
Compositional-ly Derived Representations of Morphologically Complex Words in Distributional Semantics

Speakers of a language can construct an unlimited number of new words through morphological derivation. This is a major cause of data sparseness for corpus-based approaches to lexical semantics, such as distributional semantic models of word meaning. We adapt compositional methods originally developed for phrases to the task of deriving the distributional meaning of morphologically complex words from their parts. Semantic representations constructed in this way beat a strong baseline and can be of higher quality than representations directly constructed from corpus data. Our results constitute a novel evaluation of the proposed composition methods, in which the full additive model achieves the best performance, and demonstrate the usefulness of a compositional morphology component in distributional semantics.
Bioinformatics methods in drug repurposing for Alzheimer's disease

Alarming epidemiological features of Alzheimer's disease impose curative treatment rather than symptomatic relief. Drug repurposing, that is reappraisal of a substance's indications against other diseases, offers time, cost and efficiency benefits in drug development, especially when in silico techniques are used. In this study, we have used gene signatures, where up- and down-regulated gene lists summarize a cell's gene expression perturbation from a drug or disease. To cope with the inherent biological and computational noise, we used an integrative approach on five disease-related microarray data sets of hippocampal origin with three different methods of evaluating differential gene expression and four drug repurposing tools. We found a list of 27 potential anti-Alzheimer agents that were additionally processed with regard to molecular similarity, pathway/ontology enrichment and network analysis. Protein kinase C, histone deacetylase, glycogen synthase kinase 3 and arginase inhibitors appear consistently in the resultant drug list and may exert their pharmacologic action in an epidermal growth factor receptor-mediated subpathway of Alzheimer's disease.
Ingested protein dose response of muscle and albumin protein synthesis after resistance exercise in young men.

The anabolic effect of resistance exercise is enhanced by the provision of dietary protein.



We aimed to determine the ingested protein dose response of muscle (MPS) and albumin protein synthesis (APS) after resistance exercise. In addition, we measured the phosphorylation of candidate signaling proteins thought to regulate acute changes in MPS.



Six healthy young men reported to the laboratory on 5 separate occasions to perform an intense bout of leg-based resistance exercise. After exercise, participants consumed, in a randomized order, drinks containing 0, 5, 10, 20, or 40 g whole egg protein. Protein synthesis and whole-body leucine oxidation were measured over 4 h after exercise by a primed constant infusion of [1-(13)C]leucine.



MPS displayed a dose response to dietary protein ingestion and was maximally stimulated at 20 g. The phosphorylation of ribosomal protein S6 kinase (Thr(389)), ribosomal protein S6 (Ser(240/244)), and the epsilon-subunit of eukaryotic initiation factor 2B (Ser(539)) were unaffected by protein ingestion. APS increased in a dose-dependent manner and also reached a plateau at 20 g ingested protein. Leucine oxidation was significantly increased after 20 and 40 g protein were ingested.



Ingestion of 20 g intact protein is sufficient to maximally stimulate MPS and APS after resistance exercise. Phosphorylation of candidate signaling proteins was not enhanced with any dose of protein ingested, which suggested that the stimulation of MPS after resistance exercise may be related to amino acid availability. Finally, dietary protein consumed after exercise in excess of the rate at which it can be incorporated into tissue protein stimulates irreversible oxidation.
Closed-Form Expressions for the Equivalent Circuit Model of Square-Waveguide T-Junctions and Its Application in Ortho-Mode Transducer Design

In this paper, closed-form expressions for the equivalent circuit model of square-waveguide T-junctions are presented. The square-waveguide T-junction is first modeled as a generalized admittance matrix and then reduced to a general five-port network. It is theoretically seen that the general five-port network can be simplified as a combination of a two-port subnetwork and a three-port subnetwork through the mode-matching analysis. Accurate closed-form expressions are derived with proper approximations to calculated the admittance parameters of these two subnetworks. A short-circuited branch ortho-mode transducer is then designed in a circuit simulator using the proposed equivalent circuit model. Measured results exhibit an excellent agreement with those from circuit simulation.
ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing

With the aim of developing a fast yet accurate algorithm for compressive sensing (CS) reconstruction of natural images, we combine in this paper the merits of two existing categories of CS methods: the structure insights of traditional optimization-based methods and the speed of recent network-based ones. Specifically, we propose a novel structured deep network, dubbed ISTA-Net, which is inspired by the Iterative Shrinkage-Thresholding Algorithm (ISTA) for optimizing a general $$ norm CS reconstruction model. To cast ISTA into deep network form, we develop an effective strategy to solve the proximal mapping associated with the sparsity-inducing regularizer using nonlinear transforms. All the parameters in ISTA-Net (e.g. nonlinear transforms, shrinkage thresholds, step sizes, etc.) are learned end-to-end, rather than being hand-crafted. Moreover, considering that the residuals of natural images are more compressible, an enhanced version of ISTA-Net in the residual domain, dubbed ISTA-Net+, is derived to further improve CS reconstruction. Extensive CS experiments demonstrate that the proposed ISTA-Nets outperform existing state-of-the-art optimization-based and network-based CS methods by large margins, while maintaining fast computational speed. Our source codes are available: http://jianzhang.tech/projects/ISTA-Net.
A Novel Hypergraph-Based Genetic Algorithm (HGGA) Built on Unimodular and Anti-homomorphism Properties for DNA Sequencing by Hybridization

The sequencing by hybridization (SBH) of determining the order in which nucleotides should occur on a DNA string is still under discussion for enhancements on computational intelligence although the next generation of DNA sequencing has come into existence. In the last decade, many works related to graph theory-based DNA sequencing have been carried out in the literature. This paper proposes a method for SBH by integrating hypergraph with genetic algorithm (HGGA) for designing a novel analytic technique to obtain DNA sequence from its spectrum. The paper represents elements of the spectrum and its relation as hypergraph and applies the unimodular property to ensure the compatibility of relations between l-mers. The hypergraph representation and unimodular property are bound with the genetic algorithm that has been customized with a novel selection and crossover operator reducing the computational complexity with accelerated convergence. Subsequently, upon determining the primary strand, an anti-homomorphism is invoked to find the reverse complement of the sequence. The proposed algorithm is implemented in the GenBank BioServer datasets, and the results are found to prove the efficiency of the algorithm. The HGGA is a non-classical algorithm with significant advantages and computationally attractive complexity reductions ranging to $$O(n^{2} )$$ O ( n 2 ) with improved accuracy that makes it prominent for applications other than DNA sequencing like image processing, task scheduling and big data processing.
HUMB: Automatic Key Term Extraction from Scientific Articles in GROBID

The Semeval task 5 was an opportunity for experimenting with the key term extraction module of GROBID, a system for extracting and generating bibliographical information from technical and scientific documents. The tool first uses GROBID’s facilities for analyzing the structure of scientific articles, resulting in a first set of structural features. A second set of features captures content properties based on phraseness, informativeness and keywordness measures. Two knowledge bases, GRISP and Wikipedia, are then exploited for producing a last set of lexical/semantic features. Bagged decision trees appeared to be the most efficient machine learning algorithm for generating a list of ranked key term candidates. Finally a post ranking was realized based on statistics of cousage of keywords in HAL, a large Open Access publication repository.
PSUN: An OFDM scheme for coexistence with pulsed radar

We propose Precoded SUbcarrier Nulling (PSUN), a transmission strategy for OFDM-based wireless communication networks (SCN, Secondary Communication Networks) that need to coexist with pulsed radar systems. It is a novel null-tone allocation method that effectively mitigates inter-carrier interference (ICI) remaining after pulse blanking (PB). When the power from the radar's pulse interference is high, the SCN Rx needs to employ PB to mitigate the interference power. Although PB is known to be an effective technique for suppressing pulsed interference, it magnifies the effect of ICI in OFDM waveforms, and thus degrades bit error rate (BER) performance. For more reliable performance evaluation, we take into account two characteristics of the incumbent radar significantly affect the performance of SCN: (i) antenna sidelobe and (ii) out-of-band emission. Our results show that PSUN effectively mitigates the impact of ICI remaining after PB.
Innovative moments and poor outcome in narrative therapy

To analyse a poor outcome case of narrative therapy with a woman victim of intimate violence.  The Innovative Moments Coding System: version 1 was applied to all sessions to track the innovative moments (i-moments) in the therapeutic process. I moments are the narrative details that occur in psychotherapeutic conversations that are outside the influence of the problematic narrative. This research aims to describe the processes involved in the stability of meanings in psychotherapy through a dialogical approach to meaning making.  Contrarily to what usually occurs in good outcome cases, re-conceptualization i-moments are absent. Moreover, two specific types of i-moments emerged with higher duration: reflection and protest. Qualitative analysis showed that the potential meanings of these i-moments were surpassed by a return to the problematic narrative.  The therapeutic stability seems to be maintained by a systematic return to the problematic narrative after the emergence of novelties. This process was referred from a dialogical perspective as a mutual in-feeding of voices, one that emerges in the i-moment and another one that supports the problematic narrative, which is maintained by an oscillation between these two types of voices during therapy.
Hyper-Spherical Search (HSS) algorithm: a novel meta-heuristic algorithm to optimize nonlinear functions

This paper proposes a novel optimization algorithm called Hyper-Spherical Search (HSS) algorithm. Like other evolutionary algorithms, the proposed algorithm starts with an initial population. Population individuals are of two types: particles and hyper-sphere centers that all together form particle sets. Searching the hyper-sphere inner space made by the hyper-sphere center and its particle is the basis of the proposed evolutionary algorithm. The HSS algorithm hopefully converges to a state at which there exists only one hyper-sphere center, and its particles are at the same position and have the same cost function value as the hyper-sphere center. Applying the proposed algorithm to some benchmark cost functions shows its ability in dealing with different types of optimization problems. The proposed method is compared with the genetic algorithm (GA), particle swarm optimization (PSO) and harmony search algorithm (HSA). The results show that the HSS algorithm has faster convergence and results in better solutions than GA, PSO and HSA.
Neuro-linguistic programming : Its potential for learning and teaching in formal education

In this paper we outline the nature of Neuro-linguistic Programming and explore its potential for learning and teaching. The paper draws on current research by Mathison (2003) to illustrate the role of language and internal imagery in teacherlearner interactions, and the way language influences beliefs about learning. Neuro-linguistic Programming (NLP) developed in the USA in the 1970's. It has achieved widespread popularity as a method for communication and personal development. The title, coined by the founders, Bandler and Grinder (1975a), refers to purported systematic, cybernetic links between a person's internal experience (neuro), their language (linguistic) and their patterns of behaviour (programming). In essence NLP is a form of modelling that offers potential for systematic and detailed understanding of people's subjective experience. NLP is eclectic, drawing on models and strategies from a wide range of sources. We outline NLP's approach to teaching and learning, and explore applications through illustrative data from Mathison's study. A particular implication for the training of educators is that of attention to communication skills. Finally we summarise criticisms of NLP that may represent obstacles to its acceptance by academe.
Uptraining for Accurate Deterministic Question Parsing

It is well known that parsing accuracies drop significantly on out-of-domain data. What is less known is that some parsers suffer more from domain shifts than others. We show that dependency parsers have more difficulty parsing questions than constituency parsers. In particular, deterministic shift-reduce dependency parsers, which are of highest interest for practical applications because of their linear running time, drop to 60% labeled accuracy on a question test set. We propose an uptraining procedure in which a deterministic parser is trained on the output of a more accurate, but slower, latent variable constituency parser (converted to dependencies). Uptraining with 100K unlabeled questions achieves results comparable to having 2K labeled questions for training. With 100K unlabeled and 2K labeled questions, uptraining is able to improve parsing accuracy to 84%, closing the gap between in-domain and out-of-domain performance.
The file drawer problem and tolerance for null results

For any given research area, one cannot tell how many studies have been conducted but never reported. The extreme view of the "file drawer problem" is that journals are filled with the 5% of the studies that show Type I errors, while the file drawers are filled with the 95% of the studies that show nonsignificant results. Quantitative procedures for computing the tolerance for filed and future null results are reported and illustrated, and the implications are discussed.
L-carnitine supplementation for the management of fatigue in patients with hypothyroidism on levothyroxine treatment: a randomized, double-blind, placebo-controlled trial.

Hypothyroid patients experience fatigue-related symptoms despite adequate thyroid hormone replacement. Thyroid hormone plays an essential role in carnitine-dependent fatty acid import and oxidation. We investigated the effects of L-carnitine supplementation on fatigue in patients with hypothyroidism. In total, 60 patients (age 50.0 ± 9.2 years, 3 males, 57 females) who still experienced fatigue (fatigue severity scale [FSS] score ≥ 36) were given L-carnitine (n = 30, 990 mg L-carnitine twice daily) or placebo (n = 30) for 12 weeks. After 12 weeks, although neither the FSS score nor the physical fatigue score (PFS) changed significantly, the mental fatigue score (MFS) was significantly decreased by treatment with L-carnitine compared with placebo (from 4.5 ± 1.9 to 3.9 ± 1.5 vs. from 4.2 ± 1.8 to 4.6 ± 1.6, respectively; P < 0.01). In the L-carnitine group, 75.0%, 53.6%, and 50.0% of patients showed improvement in the FSS score, PFS, and MFS, respectively, but only 20.0%, 24.0%, and 24.0%, respectively, did so in the placebo group (all P < 0.05). Both the PFS and MFS were significantly improved in patients younger than 50 years and those with free T3 ≥ 4.0 pg/mL by treatment with L-carnitine compared with placebo. Additionally, the MFS was significantly improved in patients taking thyroid hormone after thyroid cancer surgery. These results suggest that L-carnitine supplementation may be useful in alleviating fatigue symptoms in hypothyroid patients, especially in those younger than 50 years and those who have hypothyroidism after thyroidectomy for thyroid cancer (ClinicalTrials.gov: NCT01769157).
Neural Structural Correspondence Learning for Domain Adaptation

We introduce a neural network model that marries together ideas from two prominent strands of research on domain adaptation through representation learning: structural correspondence learning (SCL, (Blitzer et al., 2006)) and autoencoder neural networks (NNs). Our model is a three-layer NN that learns to encode the non-pivot features of an input example into a lowdimensional representation, so that the existence of pivot features (features that are prominent in both domains and convey useful information for the NLP task) in the example can be decoded from that representation. The low-dimensional representation is then employed in a learning algorithm for the task. Moreover, we show how to inject pre-trained word embeddings into our model in order to improve generalization across examples with similar pivot features. We experiment with the task of cross-domain sentiment classification on 16 domain pairs and show substantial improvements over strong