nils-herrmann/clean_scirepeval huggingface dataset:
The Security of the Cipher Block Chaining Message Authentication Code

Let F be some block cipher (eg., DES) with block length l. The Cipher Block Chaining Message Authentication Code (CBC MAC) speci es that an m-block message x = x1 xm be authenticated among parties who share a secret key a for the block cipher by tagging x with a pre x of ym, where y0 = 0 l and yi = Fa(mi yi 1) for i = 1; 2; : : : ;m. This method is a pervasively used international and U.S. standard. We provide its rst formal justi cation, showing the following general lemma: cipher block chaining a pseudorandom function yields a pseudorandom function. Underlying our results is a technical lemma of independent interest, bounding the success probability of a computationally unbounded adversary in distinguishing between a randomml-bit to l-bit function and the CBC MAC of a random l-bit to l-bit function. Department of Computer Science & Engineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA.   joe@research.nj.nec.com. z Department of Computer Science, University of California at Davis, Davis, CA 95616, USA.  http://wwwcsif.cs.ucdavis.edu/~rogaway. Supported by NSF CAREER Award CCR-9624560.
Building A Training Corpus For Word Sense Disambiguation In English-To-Vietnamese Machine Translation

The most difficult task in machine translation is the elimination of ambiguity in human languages. A certain word in English as well as Vietnamese often has different meanings which depend on their syntactical position in the sentence and the actual context. In order to solve this ambiguation, formerly, people used to resort to many hand-coded rules. Nevertheless, manually building these rules is a time-consuming and exhausting task. So, we suggest an automatic method to solve the above-mentioned problem by using semantically tagged corpus. In this paper, we mainly present building a semantically tagged bilingual corpus to word sense disambiguation (WSD) in English texts. To assign semantic tags, we have taken advantage of bilingual texts via word alignments with semantic class names of LLOCE (Longman Lexicon of Contemporary English). So far, we have built 5,000,000-word bilingual corpus in which 1,000,000 words have been semantically annotated with the accuracy of 70%. We have evaluated our result of semantic tagging by comparing with SEMCOR on SUSANNE part of our corpus. This semantically annotated corpus will be used to extract disambiguation rules automatically by TBL (Transformationbased Learning) method. These rules will be manually revised before being applied to the WSD module in the English-to-Vietnamese Translation (EVT) system.
Application of deep learning and geo-knowledge bases to scene understanding

Humans can easily perform tasks that use vision and language jointly, such as describing a scene and answering questions about objects in the scene and how they are related. Image captioning and visual question & answer are two popular research tasks that have emerged from advances in deep learning and the availability of datasets that specifically address these problems. However recent work has shown that deep learning based solutions to these tasks are just as brittle as solutions for only vision or only natural language tasks. Image captioning is vulnerable to adversarial perturbations; novel objects, which are not described in training data, and contextual biases in training data can degrade performance in surprising ways. For these reasons, it is important to find ways in which general-purpose knowledge can guide connectionist models. We investigate challenges to integrate existing ontologies and knowledge bases with deep learning solutions, and possible approaches for overcoming such challenges. We focus on geo-referenced data such as geo-tagged images and videos that capture outdoor scenery. Geo-knowledge bases are domain specific knowledge bases that contain concepts and relations that describe geographic objects. This work proposes to increase the robustness of automatic scene description and inference by leveraging geo-knowledge bases along with the strengths of deep learning for visual object detection and classification.
Peripheral and central mechanisms of pain generation.

Pain research has uncovered important neuronal mechanisms that underlie clinically relevant pain states such as inflammatory and neuropathic pain. Importantly, both the peripheral and the central nociceptive system contribute significantly to the generation of pain upon inflammation and nerve injury. Peripheral nociceptors are sensitized during inflammation, and peripheral nerve fibres develop ectopic discharges upon nerve injury or disease. As a consequence a complex neuronal response is evoked in the spinal cord where neurons become hyperexcitable, and a new balance is set between excitation and inhibition. The spinal processes are significantly influenced by brain stem circuits that inhibit or facilitate spinal nociceptive processing. Numerous mechanisms are involved in peripheral and central nociceptive processes including rapid functional changes of signalling and long-term regulatory changes such as up-regulation of mediator/receptor systems. Conscious pain is generated by thalamocortical networks that produce both sensory discriminative and affective components of the pain response.
Predicting ICT integration into classroom teaching in Chinese primary schools: exploring the complex interplay of teacher-related variables

Available research has explored a wide variety of factors influencing information and communication technologies (ICT) adoption and integration in classroom teaching; however, existing research seldom centre on the combined impact of these variables. In addition, the little research available is set up in the Chinese context. The latter is important given that the different cultural context in which the interplay between teacher beliefs and educational practices has only been documented quiet little. The present study centres on the complex interplay of a number of internal teacher variables to explain ICT classroom integration. These variables comprise ‘teachers’ constructivist teaching beliefs’, ‘teacher attitudes towards computers in education’, ‘teachers’ computer motivation’, ‘teacher perception of ICT-related policy’. A survey was set up, involving 820 Chinese primary school teachers. Path modeling was used to explore the direct and indirect effects of the teacher-related variables on their level of ICT classroom integration. Firstly, two distinctive types of ICT use can be distinguished in the Chinese context: (a) teacher supportive use of ICT that refers to the use of ICT for e.g. student administration, preparing worksheets, developing evaluation activities; and (b) classroom use of ICT to support and enhance the actual teaching and learning process. The results show that classroom use of ICT directly depends on teachers’ computer motivation and the supportive use of ICT. Teachers’ constructivist beliefs, their attitudes towards computers in education and perceptions about the ICT-related school policy influence ICT integration in an indirect way. The results demonstrate how the complex interplay between teacher-related variables and ICT integration in the classroom is partly in line with findings in non-Asian contexts. A number of differences can be explained by the particular Chinese context. In particular an indirect relationship was found between teachers’ constructivist beliefs and their level of ICT integration.
Creativity: Generating Diverse Questions Using Variational Autoencoders

Generating diverse questions for given images is an important task for computational education, entertainment and AI assistants. Different from many conventional prediction techniques is the need for algorithms to generate a diverse set of plausible questions, which we refer to as creativity. In this paper we propose a creative algorithm for visual question generation which combines the advantages of variational autoencoders with long short-term memory networks. We demonstrate that our framework is able to generate a large set of varying questions given a single input image.
Batteryless, wireless sensor powered by a sediment microbial fuel cell.

Sediment microbial fuel cells (SMFCs) are considered to be an alternative renewable power source for remote monitoring. There are two main challenges to using SMFCs as power sources: 1) a SMFC produces a low potential at which most sensor electronics do not operate, and 2) a SMFC cannot provide continuous power, so energy from the SMFC must be stored and then used to repower sensor electronics intermittently. In this study, we developed a SMFC and a power management system (PMS) to power a batteryless, wireless sensor. A SMFC operating with a microbial anode and cathode, located in the Palouse River, Pullman, Washington, U.S.A., was used to demonstrate the utility of the developed system. The designed PMS stored microbial energy and then started powering the wireless sensor when the SMFC potential reached 320 mV. It continued powering until the SMFC potential dropped below 52 mV. The system was repowered when the SMFC potential increased to 320 mV, and this repowering continued as long as microbial reactions continued. We demonstrated that a microbial fuel cell with a microbial anode and cathode can be used as an effective renewable power source for remote monitoring using custom-designed electronics.
Attention Neural Network-Based Abstractive Summarization and Headline Generation

With the ever-increasing frequency of daily content creation, the need for content curators or summarizers is only at its beginning. Manually performing these tasks drive massive costs, thus the increasing demand for efficient automatic summarization systems. Sequence-to-sequence learning has attracted intense interest over the past years. The success of end-to-end training of encoder-decoder neural networks in tasks like machine translation has sprouted active research using similar architectures in other transduction tasks such as paraphrase generation or abstractive summarization. In this thesis, the recent advances in neural network-based abstractive summarization are reviewed, and various attention neural network-based headline generation models are proposed. One of the most significant limitations of existing solutions is limiting the input to the first one or two sentences of the article. This thesis investigates new methods trying to add to the output the important information beyond what is included in the first sentence. Methods to represent sentences and words are used in combination with single attention or dualattention mechanisms to design new abstractive summarizers. The new models are compared experimentally to the state-of-the-art of abstractive headline generation based on various standard automatic and human evaluation measures and different tasks.
Fretting corrosion of tin-plated contacts

The susceptibility of tin-plated contacts to fretting corrosion is a major limitation for its use in electrical connectors. The present paper evaluates the influence of a variety of factors, such as, fretting amplitude (track length), frequency, temperature, humidity, normal load and current load on the fretting corrosion behaviour of tin-plated contacts. This paper also addresses the development of fretting corrosion maps and lubrication as a preventive strategy to increase the life-time of tin-plated contacts. The fretting corrosion tests were carried out using a fretting apparatus in which a hemispherical rider and flat contacts (tin-plated copper alloy) were mated in sphere plane geometry and subjected to fretting under gross-slip conditions. The variation in contact resistance as a function of fretting cycles and the time to reach a threshold value (100mO) of contact resistance enables a better understanding of the influence of various factors on the fretting corrosion behaviour of tin-plated contacts. Based on the change in surface profile and nature of changes in the contact zone assessed by laser scanning microscope (LSM) and surface analytical techniques, the mechanism of fretting corrosion of tin-plated contacts and fretting corrosion maps are proposed. Lubrication increases the life-time of tin-plated contacts by several folds and proved to be a useful preventive strategy.
Acoustic group feature selection using wrapper method for automatic eating condition recognition

In this paper, we present a wrapper-based acoustic group feature selection system for the INTERSPEECH 2015 Computational Paralinguistics Challenge (ComParE) 2015, Eating Condition (EC) Sub-challenge. The wrapper-based method has two components: the feature subset evaluation and the feature space search. The feature subset evaluation is performed using Support Vector Machine (SVM) classifiers. The wrapper method combined with complex algorithms such as SVM is computationally intensive. To address this, the feature space search uses Best Incremental Ranked Subset (BIRS), a fast and efficient algorithm. Moreover, we investigate considering the feature space in meaningful groups rather than individually. The acoustic feature space is partitioned into groups with each group representing a Low Level Descriptor (LLD). This partitioning reduces the time complexity of the search algorithm and makes the problem more tractable while attempting to gain insight into the relevant acoustic feature groups. Our wrapper-based system achieves improvement over the challenge baseline on the EC Sub-challenge test set using a variant of BIRS algorithm and LLD groups.
Convolutional Neural Tensor Network Architecture for Community-Based Question Answering

Retrieving similar questions is very important in community-based question answering. A major challenge is the lexical gap in sentence matching. In this paper, we propose a convolutional neural tensor network architecture to encode the sentences in semantic space and model their interactions with a tensor layer. Our model integrates sentence modeling and semantic matching into a single model, which can not only capture the useful information with convolutional and pooling layers, but also learn the matching metrics between the question and its answer. Besides, our model is a general architecture, with no need for the other knowledge such as lexical or syntactic analysis. The experimental results shows that our method outperforms the other methods on two matching tasks.
A 77-GHz CMOS Automotive Radar Transceiver With Anti-Interference Function

This paper presents a 77-GHz long-range automotive radar transceiver with the function of reducing mutual interference. The proposed frequency-hopping random chirp FMCW technique reconfigures the chirp sweep frequency and time every cycle to result in noise-like frequency response for mutual interference after the received signal is down-converted and demodulated. Thus, the false alarm rate can be reduced significantly. The transceiver IC is fully integrated in TSMC 1P9M 65-nm digital CMOS technology. The chip including pads occupies a silicon area of 1.03 mm × 0.94 mm. The transceiver consumes totally 275 mW of power, and the measured transmitting power and receiver noise figure are 6.4 dBm and 14.8 dB, respectively. To the authors' knowledge, this is the first integrated 77-GHz automotive radar transceiver with the feature of anti-interference.
Using Design Thinking to Improve Psychological Interventions: The Case of the Growth Mindset During the Transition to High School.

There are many promising psychological interventions on the horizon, but there is no clear methodology for preparing them to be scaled up. Drawing on design thinking, the present research formalizes a methodology for redesigning and tailoring initial interventions. We test the methodology using the case of fixed versus growth mindsets during the transition to high school. Qualitative inquiry and rapid, iterative, randomized "A/B" experiments were conducted with ~3,000 participants to inform intervention revisions for this population. Next, two experimental evaluations showed that the revised growth mindset intervention was an improvement over previous versions in terms of short-term proxy outcomes (Study 1, N=7,501), and it improved 9th grade core-course GPA and reduced D/F GPAs for lower achieving students when delivered via the Internet under routine conditions with ~95% of students at 10 schools (Study 2, N=3,676). Although the intervention could still be improved even further, the current research provides a model for how to improve and scale interventions that begin to address pressing educational problems. It also provides insight into how to teach a growth mindset more effectively.
Multi-task learning for face identification and attribute estimation

Convolution neural network (CNN) has been shown as one of state-of-the-art approaches for learning face representations. However, previous works only utilized identity information instead of leveraging human attributes (e.g., gender and age) which contain high-level semantic meaning. In this work, we aim to incorporate identity and human attributes in learning discriminative face representations through multi-task learning. In our experiments, we learn face representation by using the largest publicly face dataset CASIA-WebFace with gender and age labels, and then evaluate learned features on widely-used LFW benchmark for face verification and identification. We also compare the effectiveness of different attributes for improving face identification. The results show that the proposed model outperforms the baseline CNN method without using multi-task learning and hand-crafted features such as high-dimensional LBP. We also do experiments on gender and age estimation on Adience benchmark to demonstrate that human attribute prediction can also benefit from the proposed multi-task representation learning.
A Hierarchical Approach to Neural Context-Aware Modeling

We present a new recurrent neural network topology to enhance state-of-the-art machine learning systems by incorporating a broader context. Our approach overcomes recent limitations with extended narratives through a multi-layered computational approach to generate an abstract context representation. Therefore, the developed system captures the narrative on word-level, sentence-level, and contextlevel. Through the hierarchical set-up, our proposed model summarizes the most salient information on each level and creates an abstract representation of the extended context. We subsequently use this representation to enhance neural language processing systems on the task of semantic error detection. To show the potential of the newly introduced topology, we compare the approach against a context-agnostic set-up including a standard neural language model and a supervised binary classification network. The performance measures on the error detection task show the advantage of the hierarchical context-aware topologies, improving the baseline by 12.75% relative for unsupervised models and 20.37% relative for supervised models.
A survey on privacy issues in digital forensics

Privacy issues have always been a major concern in computer forensics and security and in case of any investigation whether it is pertaining to computer or not always privacy issues appear. To enable privacy’s protection in the physical world we need the law that should be legislated, but in a digital world by rapidly growing of technology and using the digital devices more and more that generate a huge amount of private data it is impossible to provide fully protected space in cyber world during the transfer, store and collect data. Since its introduction to the field, forensics investigators, and developers have faced challenges in finding the balance between retrieving key evidences and infringing user privacy. This paper looks into developmental trends in computer forensics and security in various aspects in achieving such a balance. In addition, the paper analyses each scenario to determine the trend of solutions in these aspects and evaluate their effectiveness in resolving the aforementioned issues.
A review of recent advances in wind turbine condition monitoring and fault diagnosis

The state-of-the-art advancement in wind turbine condition monitoring and fault diagnosis for the recent several years is reviewed. Since the existing surveys on wind turbine condition monitoring cover the literatures up to 2006, this review aims to report the most recent advances in the past three years, with primary focus on gearbox and bearing, rotor and blades, generator and power electronics, as well as system-wise turbine diagnosis. There are several major trends observed through the survey. Due to the variable-speed nature of wind turbine operation and the unsteady load involved, time-frequency analysis tools such as wavelets have been accepted as a key signal processing tool for such application. Acoustic emission has lately gained much more attention in order to detect incipient failures because of the low-speed operation for wind turbines. There has been an increasing trend of developing model based reasoning algorithms for fault detection and isolation as cost-effective approach for wind turbines as relatively complicated system. The impact of unsteady aerodynamic load on the robustness of diagnostic signatures has been notified. Decoupling the wind load from condition monitoring decision making will reduce the associated down-time cost.
Unleashing the Potential of Machine Learning and IoT in Cyber Physical Farming

In this work, we propose CyberKrisi, a machine learning based framework for cyber physical farming. IT based farming is very young and emerging with numerous IoT devices such as wireless sensors, surveillance cameras, drones and weather stations. These devices produce large amounts of data about crop, soil, fertilization, irrigation as well as environment. We exploit this data to assess crop performance and compute crop forecasts. We envision an IoT gateway and machine learning gateway in the vicinity of farm land which performs predictions and recommendations as well as relays this data to cloud. Our contribution are twofold: first, we show an application framework for farmers to provide an interface in understanding Farm data. Second, we built a prototype to provide illiterate Farmers an interactive experience with Farm land.
Predicting Target Language CCG Supertags Improves Neural Machine Translation

Neural machine translation (NMT) models are able to partially learn syntactic information from sequential lexical information. Still, some complex syntactic phenomena such as prepositional phrase attachment are poorly modeled. This work aims to answer two questions: 1) Does explicitly modeling target language syntax help NMT? 2) Is tight integration of words and syntax better than multitask training? We introduce syntactic information in the form of CCG supertags in the decoder, by interleaving the target supertags with the word sequence. Our results on WMT data show that explicitly modeling targetsyntax improves machine translation quality for German→English, a high-resource pair, and for Romanian→English, a lowresource pair and also several syntactic phenomena including prepositional phrase attachment. Furthermore, a tight coupling of words and syntax improves translation quality more than multitask training. By combining target-syntax with adding source-side dependency labels in the embedding layer, we obtain a total improvement of 0.9 BLEU for German→English and 1.2 BLEU for Romanian→English.