nils-herrmann/clean_scirepeval huggingface dataset:
From Eliza to XiaoIce: challenges and opportunities with social chatbots

Conversational systems have come a long way since their inception in the 1960s. After decades of research and development, we have seen progress from Eliza and Parry in the 1960s and 1970s, to task-completion systems as in the Defense Advanced Research Projects Agency (DARPA) communicator program in the 2000s, to intelligent personal assistants such as Siri, in the 2010s, to today’s social chatbots like XiaoIce. Social chatbots’ appeal lies not only in their ability to respond to users’ diverse requests, but also in being able to establish an emotional connection with users. The latter is done by satisfying users’ need for communication, affection, as well as social belonging. To further the advancement and adoption of social chatbots, their design must focus on user engagement and take both intellectual quotient (IQ) and emotional quotient (EQ) into account. Users should want to engage with a social chatbot; as such, we define the success metric for social chatbots as conversation-turns per session (CPS). Using XiaoIce as an illustrative example, we discuss key technologies in building social chatbots from core chat to visual awareness to skills. We also show how XiaoIce can dynamically recognize emotion and engage the user throughout long conversations with appropriate interpersonal responses. As we become the first generation of humans ever living with artificial intelligenc (AI), we have a responsibility to design social chatbots to be both useful and empathetic, so they will become ubiquitous and help society as a whole.
Modelling internal logistics systems through ontologies

Industry is facing an era characterised by unpredictable market changes and by a turbulent competitive environment. The key to compete in such a context is to achieve high degrees of responsiveness by means of high flexibility and rapid reconfiguration capabilities. The deployment of modular solutions seems to be part of the answer to face these challenges. Semantic modelling and ontologies may represent the needed knowledge representation to support flexibility and modularity of production systems, when designing a new system or when reconfiguring an existing one. Although numerous ontologies for production systems have been developed in the past years, they mainly focus on discrete manufacturing, while logistics aspects, such as those related to internal logistics and warehousing, have not received the same attention. The paper aims at offering a representation of logistics aspects, reflecting what has become a de-facto standard terminology in industry and among researchers in the field. Such representation is to be used as an extension to the already-existing production systems ontologies that are more focused on manufacturing processes. The paper presents the structure of the hierarchical relations within the examined internal logistics elements, namely Storage and Transporters, structuring them in a series of classes and sub-classes, suggesting also the relationships and the attributes to be considered to complete the modelling. Finally, the paper proposes an industrial example with a miniload system to show how such a modelling of internal logistics elements could be instanced in the real world.
Query2Vec: An Evaluation of NLP Techniques for Generalized Workload Analytics

We consider methods for learning vector representations of SQL queries to support generalized workload analytics tasks, including workload summarization for index selection and predicting queries that will trigger memory errors. We consider vector representations of both raw SQL text and optimized query plans, and evaluate these methods on synthetic and real SQL workloads. We find that general algorithms based on vector representations can outperform existing approaches that rely on specialized features. For index recommendation, we cluster the vector representations to compress large workloads with no loss in performance from the recommended index. For error prediction, we train a classifier over learned vectors that can automatically relate subtle syntactic patterns with specific errors raised during query execution. Surprisingly, we also find that these methods enable transfer learning, where a model trained on one SQL corpus can be applied to an unrelated corpus and still enable good performance. We find that these general approaches, when trained on a large corpus of SQL queries, provides a robust foundation for a variety of workload analysis tasks and database features, without requiring application-specific feature engineering.  Shrainik Jain, Bill Howe, Jiaqi Yan, and Thierry Cruanes.  An Evaluation of NLP Techniques for Generalized Workload Analytics. PVLDB, 11 (5): xxxx-yyyy, 2018.
Data-to-Text Generation with Content Selection and Planning

Recent advances in data-to-text generation have led to the use of large-scale datasets and neural network models which are trained end-to-end, without explicitly modeling what to say and in what order. In this work, we present a neural network architecture which incorporates content selection and planning without sacrificing end-to-end training. We decompose the generation task into two stages. Given a corpus of data records (paired with descriptive documents), we first generate a content plan highlighting which information should be mentioned and in which order and then generate the document while taking the content plan into account. Automatic and human-based evaluation experiments show that our model outperforms strong baselines improving the state-of-the-art on the recently released ROTOWIRE dataset.
Exploring Optimizations to Paragraph Vectors

This paper replicates the results of Dai, Olah, and Le’s paper ”Document Embedding with Paragraph Vectors” and compares the performance of three unsupervised document modeling algorithms . We built and compared the results of Paragraph Vector, Latent Dirichlet Allocation, and traditional Word2Vec models on Wikipedia browsing. We then built three extensions to the original Paragraph Vector model, finding that combinations of paragraph structures assist in optimizing Paragraph Vector training.
Product Title Classification versus Text Classification

In most e-commerce platforms, product title classification is a crucial task. It can assist sellers listing an item in an appropriate category. At first glance, product title classification is merely an instance of text classification problems, which are well-studied in literature. However, product titles possess some properties very different from general documents. A title is usually a very short description, and an incomplete sentence. A product title classifier may need to be designed differently from a text classifier, although this issue has not been thoroughly studied. In this work, using a large-scale real-world data set, we examine conventional text-classification procedures on product title data. These procedures include word stemming, stop-word removal, feature representation and multi-class classification. Our major findings include that stemming and stop-word removal are harmful, and bigrams or degree-2 polynomial mappings are very effective. Further, if linear classifiers such as SVMs are applied, instance normalization does not downgrade the performance and binary/TF-IDF representations perform similarly. These results lead to a concrete guideline for practitioners on product title classification.
Lung Segmentation in Chest Radiographs Using Anatomical Atlases With Nonrigid Registration

The National Library of Medicine (NLM) is developing a digital chest X-ray (CXR) screening system for deployment in resource constrained communities and developing countries worldwide with a focus on early detection of tuberculosis. A critical component in the computer-aided diagnosis of digital CXRs is the automatic detection of the lung regions. In this paper, we present a nonrigid registration-driven robust lung segmentation method using image retrieval-based patient specific adaptive lung models that detects lung boundaries, surpassing state-of-the-art performance. The method consists of three main stages: 1) a content-based image retrieval approach for identifying training images (with masks) most similar to the patient CXR using a partial Radon transform and Bhattacharyya shape similarity measure, 2) creating the initial patient-specific anatomical model of lung shape using SIFT-flow for deformable registration of training masks to the patient CXR, and 3) extracting refined lung boundaries using a graph cuts optimization approach with a customized energy function. Our average accuracy of 95.4% on the public JSRT database is the highest among published results. A similar degree of accuracy of 94.1% and 91.7% on two new CXR datasets from Montgomery County, MD, USA, and India, respectively, demonstrates the robustness of our lung segmentation approach.
An FPGA Implementation of a Parallelized MT19937 Uniform Random Number Generator

Recent times have witnessed an increase in use of high-performance reconfigurable computing for accelerating large-scale simulations. A characteristic of such simulations, like infrared (IR) scene simulation, is the use of large quantities of uncorrelated random numbers. It is therefore of interest to have a fast uniform random number generator implemented in reconfigurable hardware. While there have been previous attempts to accelerate the MT19937 pseudouniform random number generator using FPGAs we believe that we can substantially improve the previous implementations to develop a higher throughput and more areatime efficient design. Due to the potential for parallel implementation of random numbers generators, designs that have both a small area footprint and high throughput are to be preferred to ones that have the high throughput but with significant extra area requirements. In this paper, we first present a single port design and then present an enhanced 624 port hardware implementation of the MT19937 algorithm. The 624 port hardware implementation when implemented on a Xilinx XC2VP70-6 FPGA chip has a throughput of 119.6 × 10 32 bit random numbers per second which is more than 17x that of the previously best published uniform random number generator. Furthermore it has the lowest area time metric of all the currently published FPGA-based pseudouniform random number generators.
Non-axiomatic reasoning system: exploring the essence of intelligence

Every arti cial-intelligence research project needs a working de nition of \intelligence", on which the deepest goals and assumptions of the research are based. In the project described in the following chapters, \intelligence" is de ned as the capacity to adapt under insu cient knowledge and resources. Concretely, an intelligent system should be nite and open, and should work in real time. If these criteria are used in the design of a reasoning system, the result is NARS, a non-axiomatic reasoning system. NARS uses a term-oriented formal language, characterized by the use of subject{ predicate sentences. The language has an experience-grounded semantics, according to which the truth value of a judgment is determined by previous experience, and the meaning of a term is determined by its relations with other terms. Several di erent types of uncertainty, such as randomness, fuzziness, and ignorance, can be represented in the language in a single way. The inference rules of NARS are based on three inheritance relations between terms. With di erent combinations of premises, revision, deduction, induction, abduction, exempli cation, comparison, and analogy can all be carried out in a uniform format, the major di erence between these types of inference being that di erent functions are used to calculate the truth value of the conclusion from the truth values of the premises. viii ix Since it has insu cient space{time resources, the system needs to distribute them among its tasks very carefully, and to dynamically adjust the distribution as the situation changes. This leads to a \controlled concurrency" control mechanism, and a \bag-based" memory organization. A recent implementation of the NARS model, with examples, is discussed. The system has many interesting properties that are shared by human cognition, but are absent from conventional computational models of reasoning. This research sheds light on several notions in arti cial intelligence and cognitive science, including symbol-grounding, induction, categorization, logic, and computation. These are discussed to show the implications of the new theory of intelligence. Finally, the major results of the research are summarized, a preliminary evaluation of the working de nition of intelligence is given, and the limitations and future extensions of the research are discussed.
Teaching Advanced Touch Sensing Technologies Using ARM Cortex-M-based Microcontroller

This paper presents our experiences of introducing in a senior level microprocessor course the latest touch sensing technologies, especially programming capacitive touch sensing devices and touchscreen. The emphasis is on the teaching practice details, including the enhanced course contents, outcomes and lecture and lab organization. By utilizing the software package provided by Atmel, students are taught to efficiently build MCU-based embedded applications which control various touch sensing devices. This work makes use of the 32-bit ARM Cortex-M4 microprocessor to control complex touch sensing devices (i.e., touch keys, touch slider and touchscreen). The Atmel SAM 4S-EK2 board is chosen as the main development board employed for practicing the touch devices programming. Multiple capstone projects have been developed, for example adaptive touch-based servo motor control, and calculator and games on the touchscreen. Our primary experiences indicate that the project-based learning approach with the utilization of the selected microcontroller board and software package is efficient and practical for teaching advanced touch sensing techniques. Students have shown the great interest and the capability in adopting touch devices into their senior design projects to improve human machine interface.
Bootstrapping the Blockchain - Directly

The Bitcoin backbone protocol [Eurocrypt 2015] extracts basic properties of Bitcoin's underlying blockchain data structure, such as common pre x and chain quality, and shows how fundamental applications including consensus and a robust public transaction ledger can be built on top of them. The underlying assumptions are proofs of work (POWs), adversarial hashing power strictly less than 1/2 and no adversarial pre-computation or, alternatively, the existence of an unpredictable genesis block. In this paper we show how to remove the latter assumption, presenting a bootstrapped Bitcoin-like blockchain protocol relying on POWs that builds genesis blocks from scratch in the presence of adversarial pre-computation. The only known previous result in the same setting (unauthenticated parties, no trusted setup) [Crypto 2015] is indirect in the sense of creating a PKI rst and then employing conventional PKI-based authenticated communication. With our construction we establish that consensus can be solved directly by a blockchain protocol without trusted setup assuming an honest majority (in terms of computational power). We also formalize miner unlinkability, a privacy property for blockchain protocols, and demonstrate that our protocol retains the same level of miner unlinkability as Bitcoin itself.
Decoupling Structure and Lexicon for Zero-Shot Semantic Parsing

Building a semantic parser quickly in a new domain is a fundamental challenge for conversational interfaces, as current semantic parsers require expensive supervision and lack the ability to generalize to new domains. In this paper, we introduce a zero-shot approach to semantic parsing that can parse utterances in unseen domains while only being trained on examples in other source domains. First, we map an utterance to an abstract, domainindependent, logical form that represents the structure of the logical form, but contains slots instead of KB constants. Then, we replace slots with KB constants via lexical alignment scores and global inference. Our model reaches an average accuracy of 53.4% on 7 domains in the OVERNIGHT dataset, substantially better than other zero-shot baselines, and performs as good as a parser trained on over 30% of the target domain examples.
The descended larynx is not uniquely human.

Morphological modifications of vocal anatomy are widespread among vertebrates, and the investigation of the physiological mechanisms and adaptive functions of such variants is an important focus of research into the evolution of communication. The "descended larynx" of adult humans has traditionally been considered unique to our species, representing an adaptation for articulate speech, and debate concerning the position of the larynx in extinct hominids assumes that a lowered larynx is diagnostic of speech and language. Here, we use bioacoustic analyses of vocalizing animals, together with anatomical analyses of functional morphology, to document descended larynges in red and fallow deer. The resting position of the larynx in males of these species is similar to that in humans, and, during roaring, red-deer stags lower the larynx even further, to the sternum. These findings indicate that laryngeal descent is not uniquely human and has evolved at least twice in independent lineages. We suggest that laryngeal descent serves to elongate the vocal tract, allowing callers to exaggerate their perceived body size by decreasing vocal-tract resonant frequencies. Vocal-tract elongation is common in birds and is probably present in additional mammals. Size exaggeration provides a non-linguistic alternative hypothesis for the descent of the larynx in human evolution.
Sensitivity of Normality Tests to Non-normal Data

In many statistical analyses, data need to be approximately normal or normally distributed. The Kolmogorov-Smirnov test, Anderson-Darling test, Cramer-von Mises test, and Shapiro-Wilk test are four statistical tests that are widely used for checking normality. One of the factors that influence these tests is the sample size. Given any test of normality mentioned, this study determined the sample sizes at which the tests would indicate that the data is not normal. The performance of the tests was evaluated under various spectrums of non-normal distributions and different sample sizes. The results showed that the Shapiro-Wilk test is the best normality test because this test rejects the null hypothesis of normality test at the smallest sample size compared to the other tests, for all levels of skewness and kurtosis of these distributions.
Investigating queries and search failures in academic search

Academic search concerns the retrieval and profiling of information objects in the domain of academic research. In this paper we reveal important observations of academic search queries, and provide an algorithmic solution to address a type of failure during search sessions: null queries. We start by providing a general characterization of academic search queries, by analyzing a large-scale transaction log of a leading academic search engine. Unlike previous small-scale analyses of academic search queries, we find important differences with query characteristics known from web search. E.g., in academic search there is a substantially bigger proportion of entity queries, and a heavier tail in query length distribution. We then focus on search failures and, in particular, on null queries that lead to an empty search engine result page, on null sessions that contain such null queries, and on users who are prone to issue null queries. In academic search approximately 1 in 10 queries is a null query, and 25% of the sessions contain a null query. They appear in different types of search sessions, and prevent users from achieving their search goal. To address the high rate of null queries in academic search, we consider the task of providing query suggestions. Specifically we focus on a highly frequent query type: non-boolean informational queries. To this end we need to overcome query sparsity and make effective use of session information. We find that using entities helps to surface more relevant query suggestions in the face of query sparsity. We also find that query suggestions should be conditioned on the type of session in which they are offered to be more effective. After casting the session classification problem as a multi-label classification problem, we generate session-conditional query suggestions based on predicted session type. We find that this session-conditional method leads to significant improvements over a generic query suggestion method. Personalization yields very little further improvements over session-conditional query suggestions.
SSD-Sface: Single shot multibox detector for small faces

In this thesis we present an approach to adapt the Single Shot multibox Detector (SSD) for face detection. Our experiments are performed on the WIDER dataset which contains a large amount of small faces (faces of 50 pixels or less). The results show that the SSD method performs poorly on the small/hard subset of this dataset. We analyze the influence of increasing the resolution during inference and training time. Building on this analysis we present two additions to the SSD method. The first addition is changing the SSD architecture to an image pyramid architecture. The second addition is creating a selection criteria on each of the different branches of the image pyramid architecture. The results show that increasing the resolution, even during inference, increases the performance for the small/hard subset. By combining resolutions in an image pyramid structure we observe that the performance keeps consistent across different sizes of faces. Finally, the results show that adding a selection criteria on each branch of the image pyramid further increases performance, because the selection criteria negates the competing behaviour of the image pyramid. We conclude that our approach not only increases performance on the small/hard subset of the WIDER dataset but keeps on performing well on the large subset.
Mobile Assistive Technologies for Visual Impaired Users: A Survey

In this article, we summarize some of the recent advancements in assistive technologies that are designed for people with visual impairments (VI) and blind people. Present technology enables applications to be actively disseminated and to efficiently operate on handheld mobile devices. These applications include also those that require high computational requirements. As a consequent, digital travel aids, visual sensing modules, text-to-speech applications, navigational assistance tools, and the combination with diverse assistive haptic devices are becoming consolidated with typical mobile devices. This direction has opened diversity of new perspectives for practical training and rehabilitation of people with VI. The aim of this article is to give an overview about the recent developments of assistive applications designed for people with VI. In conclusion, we recommend designing a unified robust system for people with VI which provides the support of the different kind of services.