nils-herrmann/clean_scirepeval huggingface dataset:
Examining Personality Differences in ChitChat Sequence to Sequence Conversational Agents

Personality inconsistency is one of the major problems for chit-chat sequence to sequence conversational agents. Works studying this problem have proposed models with the capability of generating personalized responses, but there is not an existing evaluation method for measuring the performance of these models on personality. This thesis develops a new evaluation method based on the psychological study of personality, in particular the Big Five personality traits. With the new evaluation method, the thesis examines if the responses generated by personalized chit-chat sequence to sequence conversational agents are distinguished for speakers with different personalities. The thesis also proposes a new model that generates distinguished responses based on given personalities. The results of our experiments in the thesis show that: for both the existing personalized model and the new model that we propose, the generated responses for speakers with different personalities are significantly more distinguished than a random baseline; specially for our new model, it has the capability of generating distinguished responses for different types of personalities measured by the Big Five personality traits.
Pareto Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II

Reconfiguration, by exchanging the functional links between the elements of the system, represents one of the most important measures which can improve the operational performance of a distribution system. The authors propose an original method, aiming at achieving such optimization through the reconfiguration of distribution systems taking into account various criteria in a flexible and robust approach. The novelty of the method consists in: the criteria for optimization are evaluated on active power distribution systems (containing distributed generators connected directly to the main distribution system and microgrids operated in grid-connected mode); the original formulation (Pareto optimality) of the optimization problem and an original genetic algorithm (based on NSGA-II) to solve the problem in a non-prohibitive execution time. The comparative tests performed on test systems have demonstrated the accuracy and promptness of the proposed algorithm.
Evaluating Human Cognition of Containing Relations with Physical Simulation

Containers are ubiquitous in daily life. By container, we consider any physical object that can contain other objects, such as bowls, bottles, baskets, trash cans, refrigerators, etc. In this paper, we are interested in following questions: What is a container? Will an object contain another object? How many objects will a container hold? We study those problems by evaluating human cognition of containers and containing relations with physical simulation. In the experiments, we analyze human judgments with respect to results of physical simulation under different scenarios. We conclude that the physical simulation is a good approximation to the human cognition of container and containing relations.
Database Meets Deep Learning: Challenges and Opportunities

Deep learning has recently become very popular on account of its incredible success in many complex datadriven applications, including image classification and speech recognition. The database community has worked on data-driven applications for many years, and therefore should be playing a lead role in supporting this new wave. However, databases and deep learning are different in terms of both techniques and applications. In this paper, we discuss research problems at the intersection of the two fields. In particular, we discuss possible improvements for deep learning systems from a database perspective, and analyze database applications that may benefit from deep learning techniques.
SMART POINT CLOUD: DEFINITION AND REMAINING CHALLENGES

Dealing with coloured point cloud acquired from terrestrial laser scanner, this paper identifies remaining challenges for a new data structure: the smart point cloud. This concept arises with the statement that massive and discretized spatial information from active remote sensing technology is often underused due to data mining limitations. The generalisation of point cloud data associated with the heterogeneity and temporality of such datasets is the main issue regarding structure, segmentation, classification, and interaction for an immediate understanding. We propose to use both point cloud properties and human knowledge through machine learning to rapidly extract pertinent information, using user-centered information (smart data) rather than raw data. A review of feature detection, machine learning frameworks and database systems indexed both for mining queries and data visualisation is studied. Based on existing approaches, we propose a new 3-block flexible framework around device expertise, analytic expertise and domain base reflexion. This contribution serves as the first step for the realisation of a comprehensive smart point cloud data structure.
Semantic Similarity of Short Texts

This paper presents a method for measuring the semantic similarity of texts using a corpus based measure of semantic word similarity and a normalized and modified versions of the Longest Common Subsequence (LCS) string matching algorithm. Existing methods for computing text similarity have focused mainly on either large documents or individual words. In this paper, we focus on computing the similarity between two sentence or between two short paragraphs. The proposed method can be exploited in a variety of applications involving textual knowledge representation and knowledge discovery. Evaluation results on two different data sets show that our method outperforms several competing methods.
An Overview to Customer Relationship Management

Marketing historically has undergone various shifts in emphasis from production through sales to marketing orientation. However, the various orientations have failed to engage customers in meaningful relationship mutually beneficial to organisations and customers, with all forms of the shift still exhibiting the transactional approach inherit in traditional marketing (Kubil & Doku, 2010). However, Coltman (2006) indicates that in strategy and marketing literature, scholars have long suggested that a customer centred strategy is fundamental to competitive advantage and that customer relationship management (CRM) programmes are increasingly being used by organisations to support the type of customer understanding and interdepartmental connectedness required to effectively execute a customer strategy.
Database Replication Techniques: A Three Parameter Classification

Data replication is an increasingly important topic as databases are more and more deployed over clusters of workstations. One of the challenges in database replicatio n is to introduce replication without severely affecting per formance. Because of this difficulty, current database product s use lazy replication, which is very efficient but can compromise consistency. As an alternative, eager replication guarantees consistency but most existing protocols have a prohibitive cost. In order to clarify the current state of th e art and open up new avenues for research, this paper analyses existing eager techniques using three key parameters. In our analysis, we distinguish eight classes of eager repli cation protocols and, for each category, discuss its requir ements, capabilities, and cost. The contribution lies in sho wing when eager replication is feasible and in spelling out the different aspects a database replication protocol must account for.
Extracting Structured Scholarly Information from the Machine Translation Literature

Current technologies for searching scientific litearture do not support answering many queries that could significantly improve the day-to-day activities of a researcher. For instance, a Machine Translation (MT) researcher would like to answer questions such as: • Which are the best published results reported on the NIST-09 Chinese dataset? • What are the most important methods for speeding up phrase-based decoding? • Are there papers showing that a neural translation model is better than a non-neural? Current methods cannot yet infer the main elements of experiments reported in papers; there is no consenus on what the elements and the relations between them should be.
A SDN-oriented DDoS blocking scheme for botnet-based attacks

DDoS attacks mounted by botnets today target a specific service, mobilizing only a small amount of legitimate-looking traffic to compromise the server. Detecting or blocking such clever attacks by only using anomalous traffic statistics has become difficult, and devising countermeasures has been mostly left to the victim server. In this paper, we investigate how a software-defined network (SDN) can be utilized to overcome the difficulty and effectively block legitimate looking DDoS attacks mounted by a larger number of bots. Specifically, we discuss a DDoS blocking application that runs over the SDN controller while using the standard OpenFlow interface.
Psychodynamic psychotherapy for children and adolescents: a meta-analysis of short-term psychodynamic models.

Psychodynamically based brief psychotherapy is frequently used in clinical practice for a range of common mental disorders in children and adolescents. To our knowledge, there have been no meta-analyses to evaluate the effectiveness of these therapies.



After a broad search, we meta-analyzed controlled outcome studies of short-term psychodynamic psychotherapies (STPP, 40 or fewer sessions). We also performed sensitivity analyses and evaluated the risk of bias in this body of studies.



We found 11 studies with a total of 655 patients covering a broad range of conditions including depression, anxiety disorders, anorexia nervosa, and borderline personality disorder. STPP did not separate from what were mostly robust treatment comparators, but there were some subgroup differences. Robust (g = 1.07, 95% CI = 0.80-1.34) within group effect sizes were observed suggesting the treatment may be effective. These effects increased in follow up compared to post treatment (overall, g = 0.24, 95% CI = 0.00-0.48), suggesting a tendency toward increased gains. Heterogeneity was high across most analyses, suggesting that these data need be interpreted with caution.



This review suggests that STPP may be effective in children and adolescents across a range of common mental disorders.
Neural Bag-of-Ngrams

Bag-of-ngrams (BoN) models are commonly used for representing text. One of the main drawbacks of traditional BoN is the ignorance of n-gram’s semantics. In this paper, we introduce the concept of Neural Bag-of-ngrams (Neural-BoN), which replaces sparse one-hot n-gram representation in traditional BoN with dense and rich-semantic n-gram representations. We first propose context guided n-gram representation by adding n-grams to word embeddings model. However, the context guided learning strategy of word embeddings is likely to miss some semantics for text-level tasks. Text guided ngram representation and label guided n-gram representation are proposed to capture more semantics like topic or sentiment tendencies. Neural-BoN with the latter two n-gram representations achieve state-of-the-art results on 4 documentlevel classification datasets and 6 semantic relatedness categories. They are also on par with some sophisticated DNNs on 3 sentence-level classification datasets. Similar to traditional BoN, Neural-BoN is efficient, robust and easy to implement. We expect it to be a strong baseline and be used in more real-world applications.
The use of fly larvae for organic waste treatment.

The idea of using fly larvae for processing of organic waste was proposed almost 100 years ago. Since then, numerous laboratory studies have shown that several fly species are well suited for biodegradation of organic waste, with the house fly (Musca domestica L.) and the black soldier fly (Hermetia illucens L.) being the most extensively studied insects for this purpose. House fly larvae develop well in manure of animals fed a mixed diet, while black soldier fly larvae accept a greater variety of decaying organic matter. Blow fly and flesh fly maggots are better suited for biodegradation of meat processing waste. The larvae of these insects have been successfully used to reduce mass of animal manure, fecal sludge, municipal waste, food scrapes, restaurant and market waste, as well as plant residues left after oil extraction. Higher yields of larvae are produced on nutrient-rich wastes (meat processing waste, food waste) than on manure or plant residues. Larvae may be used as animal feed or for production of secondary products (biodiesel, biologically active substances). Waste residue becomes valuable fertilizer. During biodegradation the temperature of the substrate rises, pH changes from neutral to alkaline, ammonia release increases, and moisture decreases. Microbial load of some pathogens can be substantially reduced. Both larvae and digested residue may require further treatment to eliminate pathogens. Facilities utilizing natural fly populations, as well as pilot and full-scale plants with laboratory-reared fly populations have been shown to be effective and economically feasible. The major obstacles associated with the production of fly larvae from organic waste on an industrial scale seem to be technological aspects of scaling-up the production capacity, insufficient knowledge of fly biology necessary to produce large amounts of eggs, and current legislation. Technological innovations could greatly improve performance of the biodegradation facilities and decrease production costs.
Holistic Scene Understanding for 3D Object Detection with RGBD Cameras

In this paper, we tackle the problem of indoor scene understanding using RGBD data. Towards this goal, we propose a holistic approach that exploits 2D segmentation, 3D geometry, as well as contextual relations between scenes and objects. Specifically, we extend the CPMC  framework to 3D in order to generate candidate cuboids, and develop a conditional random field to integrate information from different sources to classify the cuboids. With this formulation, scene classification and 3D object recognition are coupled and can be jointly solved through probabilistic inference. We test the effectiveness of our approach on the challenging NYU v2 dataset. The experimental results demonstrate that through effective evidence integration and holistic reasoning, our approach achieves substantial improvement over the state-of-the-art.
The History of Artificial Intelligence

Artificial Intelligence (AI) has been studied for decades and is still one of the most elusive subjects in Computer Science. This partly due to how large and nebulous the subject is. AI ranges from machines truly capable of thinking to search algorithms used to play board games. It has applications in nearly every way we use computers in society. This paper is about examining the history of artificial intelligence from theory to practice and from its rise to fall, highlighting a few major themes and advances. The term artificial intelligence was first coined by John McCarthy in 1956 when he held the first academic conference on the subject. But the journey to understand if machines can truly think began much before that. In Vannevar Bush's seminal work As We May Think [Bush45] he proposed a system which amplifies people's own knowledge and understanding. Five years later Alan Turing wrote a paper on the notion of machines being able to simulate human beings and the ability to do intelligent things, such as play Chess [Turing50]. No one can refute a computer's ability to process logic. But to many it is unknown if a machine can think. The precise definition of think is important because there has been some strong opposition as to whether or not this notion is even possible. For example, there is the so-called 'Chinese room' argument [Searle80]. Imagine someone is locked in a room, where they were passed notes in Chinese. Using an entire library of rules and look-up tables they would be able to produce valid responses in Chinese, but would they really 'understand' the language? The argument is that since computers would always be applying rote fact lookup they could never 'understand' a subject. This argument has been refuted in numerous ways by researchers, but it does undermine people's faith in machines and so-called expert systems in life-critical applications. The main advances over the past sixty years have been advances in search algorithms, machine learning algorithms, and integrating statistical analysis into understanding the world at large. However most of the breakthroughs in AI aren't noticeable to most people. Rather than talking machines used to pilot space ships to Jupiter, AI is used in more subtle ways such as examining purchase histories and influence marketing decisions [Shaw01]. What most people think of as 'true AI' hasn't experienced rapid progress over the decades. A common theme in the …
Development and Validation of an Instrument to Measure Maturity of IT Business Strategic Alignment Mechanisms

Achieving IT-business alignment has been a long-standing, critical, information management issue. A theoretical framework of the maturity levels of management practices and strategic IT choices that facilitate alignment was empirically tested and validated. Confirmatory factor analysis (CFA) validated 6 factors and identified 22 indices to measure strategic alignment maturity. A mixed model repeated measure analysis of variance (ANOVA) obtained significant results for both the main effect and interaction effect of differences for the 6 maturity factors across the 11 business units. Regression analysis found a positive association between overall strategic alignment maturity and respondents' self-rated maturity. These exploratory findings show promise for the assessment instrument to be used as a diagnostic tool for organizations to improve their IT-business alignment maturity levels.
Hardware Trojan Attacks: Threat Analysis and Countermeasures

Security of a computer system has been traditionally related to the security of the software or the information being processed. The underlying hardware used for information processing has been considered trusted. The emergence of hardware Trojan attacks violates this root of trust. These attacks, in the form of malicious modifications of electronic hardware at different stages of its life cycle, pose major security concerns in the electronics industry. An adversary can mount such an attack with an objective to cause operational failure or to leak secret information from inside a chip-e.g., the key in a cryptographic chip, during field operation. Global economic trend that encourages increased reliance on untrusted entities in the hardware design and fabrication process is rapidly enhancing the vulnerability to such attacks. In this paper, we analyze the threat of hardware Trojan attacks; present attack models, types, and scenarios; discuss different forms of protection approaches, both proactive and reactive; and describe emerging attack modes, defenses, and future research pathways.
On the Complexity of Neural Network Classifiers: A Comparison Between Shallow and Deep Architectures

Recently, researchers in the artificial neural network field have focused their attention on connectionist models composed by several hidden layers. In fact, experimental results and heuristic considerations suggest that deep architectures are more suitable than shallow ones for modern applications, facing very complex problems, e.g., vision and human language understanding. However, the actual theoretical results supporting such a claim are still few and incomplete. In this paper, we propose a new approach to study how the depth of feedforward neural networks impacts on their ability in implementing high complexity functions. First, a new measure based on topological concepts is introduced, aimed at evaluating the complexity of the function implemented by a neural network, used for classification purposes. Then, deep and shallow neural architectures with common sigmoidal activation functions are compared, by deriving upper and lower bounds on their complexity, and studying how the complexity depends on the number of hidden units and the used activation function. The obtained results seem to support the idea that deep networks actually implements functions of higher complexity, so that they are able, with the same number of resources, to address more difficult problems.
Predicting Phishing Websites using Neural Network trained with Back-Propagation

Phishing is increasing dramatically with the development of modern technologies and the global worldwide computer networks. This results in the loss of customer’s confidence in e-commerce and online banking, financial damages, and identity theft. Phishing is fraudulent effort aims to acquire sensitive information from users such as credit card credentials, and social security number. In this article, we propose a model for predicting phishing attacks based on Artificial Neural Network (ANN). A Feed Forward Neural Network trained by Back Propagation algorithm is developed to classify websites as phishing or legitimate. The suggested model shows high acceptance ability for noisy data, fault tolerance and high prediction accuracy with respect to false positive and false negative rates.
3 Understanding and Using the Implicit Association Test : IV What We Know ( So Far ) about the Method

Each time a latency in responding to a stimulus is measured, we owe a debt to F. C. Donders, who in the mid-19th century made the fundamental discovery that the time required to perform a mental computation reveals something fundamental about how the mind works. Donders expressed the idea in the following simple and optimistic statement about the feasibility of measuring the mind: “Will all quantitative treatment of mental processes be out of the question then? By no means! An important factor seemed to be susceptible to measurement: I refer to the time required for simple mental processes” (Donders, 1868/1969, pp. 413–414). With particular variations of simple stimuli and subjects’ choices, Donders demonstrated that it is possible to bring order to understanding invisible thought processes by computing the time that elapses between stimulus presentation and response production. A more specific observation he offered lies at the center of our own modern understanding of mental operations:
Generating Images from Captions with Attention

Motivated by the recent progress in generative models, we introduce a model that generates images from natural language descriptions. The proposed model iteratively draws patches on a canvas, while attending to the relevant words in the description. After training on MS COCO, we compare our models with several baseline generative models on image generation and retrieval tasks. We demonstrate our model produces higher quality samples than other approaches and generates images with novel scene compositions corresponding to previously unseen captions in the dataset. For more details, visit .