nils-herrmann/clean_scirepeval huggingface dataset:
The Cryptanalysis of the Enigma Cipher. The Plugboard and the Cryptologic Bomb

We study the problem of decoding secret messages encrypted by the German Army with the M3 Enigma machine after September 15, 1938. We focused our attention on the algorithmization and programming of this problem. A completion and optimization of Zygalski’s sheets method were presented previously. We describe below the missing algorithm solving the problem of the plugboard settings with an algebraic justification. This method is the original idea of the authors, and we can use it for cryptanalysis together with both Zygalski’s sheets method and Rejewski’s bomb method. Next, we present a reconstruction of the cryptologic bomb. We enclose an implementation of both algorithms in Cpp language.
Dynamic simultaneous fare proration for large-scale network revenue management

Network revenue management is concerned with managing demand for products that require inventory from one or several resources by controlling product availability and/or prices in order to maximize expected revenues subject to the available resource capacities. One can tackle this problem by decomposing it into resource-level subproblems that can be solved efficiently, e.g. by dynamic programming (DP). We propose a new dynamic fare proration method specifically having large-scale applications in mind. It decomposes the network problem by fare proration and solves the resource-level dynamic programs simultaneously using simple, endogenously obtained dynamic marginal capacity value estimates to update fare prorations over time. An extensive numerical simulation study demonstrates that the method results in tightened upper bounds on the optimal expected revenue, and that the obtained policies are very effective with regard to achieved revenues and required runtime.
Local-Gravity-Face (LG-face) for Illumination-Invariant and Heterogeneous Face Recognition

This paper proposes a novel method called local-gravity-face (LG-face) for illumination-invariant and heterogeneous face recognition (HFR). LG-face employs a concept called the local gravitational force angle (LGFA). The LGFA is the direction of the gravitational force that the center pixel exerts on the other pixels within a local neighborhood. A theoretical analysis shows that the LGFA is an illumination-invariant feature, considering only the reflectance part of the local texture effect of the neighboring pixels. It also preserves edge information. Rank 1 recognition rates of 97.78% on the CMU-PIE database and 97.31% on the Extended Yale B database are achieved under varying illumination, demonstrating that LG-face is an effective method of illumination-invariant face recognition. For HFR, when faces appear in different modalities, LG-face produces a common feature representation. Rank 1 recognition rates of 99.96% on the CUFS database, 98.67% on the CUFSF database, and 99.78% on the CASIA-HFB database show that the LG-face is also an effective method for HFR. The proposed method also performs consistently in the presence of complicated variations and noise.
Sentribute: image sentiment analysis from a mid-level perspective

Visual content analysis has always been important yet challenging. Thanks to the popularity of social networks, images become an convenient carrier for information diffusion among online users. To understand the diffusion patterns and different aspects of the social images, we need to interpret the images first. Similar to textual content, images also carry different levels of sentiment to their viewers. However, different from text, where sentiment analysis can use easily accessible semantic and context information, how to extract and interpret the sentiment of an image remains quite challenging. In this paper, we propose an image sentiment prediction framework, which leverages the mid-level attributes of an image to predict its sentiment. This makes the sentiment classification results more interpretable than directly using the low-level features of an image. To obtain a better performance on images containing faces, we introduce eigenface-based facial expression detection as an additional mid-level attributes. An empirical study of the proposed framework shows improved performance in terms of prediction accuracy. More importantly, by inspecting the prediction results, we are able to discover interesting relationships between mid-level attribute and image sentiment.
Environmental influences on eating and physical activity.

Obesity has increased dramatically over the past two decades and currently about 50% of US adults and 25% of US children are overweight. The current epidemic of obesity is caused largely by an environment that promotes excessive food intake and discourages physical activity. This chapter reviews what is known about environmental influences on physical activity and eating behaviors. Recent trends in food supply, eating out, physical activity, and inactivity are reviewed, as are the effects of advertising, promotion, and pricing on eating and physical activity. Public health interventions, opportunities, and potential strategies to combat the obesity epidemic by promoting an environment that supports healthy eating and physical activity are discussed.
Key technology and application of millimeter wave communications for 5G: a survey

As mobile communication technology continues to develop, millimeter-wave communication for 5G mobile networks is attracting widespread public attention. Many studies have appeared in the literatures related to millimeter wave mobile broadband communication systems. This paper briefly introduces the development of 5G and millimeter wave communications, analyses several practical designs and implementations of millimeter wave communications for 5G, and proposes some future applications and challenges for 5G.
SemEval-2017 Task 7: Detection and Interpretation of English Puns

Apun is a form of wordplay in which aword suggests two or more meanings by exploiting polysemy, homonymy, or phonological similarity to another word, for an intended humorous or rhetorical effect. Though a recurrent and expected feature in many discourse types, puns stymie traditional approaches to computational lexical semantics because they violate their one-sense-percontext assumption. This paper describes the first competitive evaluation for the automatic detection, location, and interpretation of puns. We describe the motivation for these tasks, the evaluation methods, and the manually annotated data set. Finally, we present an overview and discussion of the participating systems’ methodologies, resources, and results.
Control of a center-out reaching task using a reinforcement learning Brain-Machine Interface

In this work, we develop an experimental primate test bed for a center-out reaching task to test the performance of reinforcement learning based decoders for Brain-Machine Interfaces. Neural recordings obtained from the primary motor cortex were used to adapt a decoder using only sequences of neuronal activation and reinforced interaction with the environment. From a naïve state, the system was able to achieve 100% of the targets without any a priori knowledge of the correct neural-to-motor mapping. Results show that the coupling of motor and reward information in an adaptive BMI decoder has the potential to create more realistic and functional models necessary for future BMI control.
LTR-Retrotransposon Control by tRNA-Derived Small RNAs

Transposon reactivation is an inherent danger in cells that lose epigenetic silencing during developmental reprogramming. In the mouse, long terminal repeat (LTR)-retrotransposons, or endogenous retroviruses (ERV), account for most novel insertions and are expressed in the absence of histone H3 lysine 9 trimethylation in preimplantation stem cells. We found abundant 18 nt tRNA-derived small RNA (tRF) in these cells and ubiquitously expressed 22 nt tRFs that include the 3' terminal CCA of mature tRNAs and target the tRNA primer binding site (PBS) essential for ERV reverse transcription. We show that the two most active ERV families, IAP and MusD/ETn, are major targets and are strongly inhibited by tRFs in retrotransposition assays. 22 nt tRFs post-transcriptionally silence coding-competent ERVs, while 18 nt tRFs specifically interfere with reverse transcription and retrotransposon mobility. The PBS offers a unique target to specifically inhibit LTR-retrotransposons, and tRF-targeting is a potentially highly conserved mechanism of small RNA-mediated transposon control.
Automatic Conversational Helpdesk Solution using Seq2Seq and Slot-filling Models

Helpdesk is a key component of any large IT organization, where users can log a ticket about any issue they face related to IT infrastructure, administrative services, human resource services, etc. Normally, users have to assign appropriate set of labels to a ticket so that it could be routed to right domain expert who can help resolve the issue. In practice, the number of labels are very large and organized in form of a tree. It is non-trivial to describe the issue completely and attach appropriate labels unless one knows the cause of the problem and the related labels. Sometimes domain experts discuss the issue with the users and change the ticket labels accordingly, without modifying the ticket description. This results in inconsistent and badly labeled data, making it hard for supervised algorithms to learn from. In this paper, we propose a novel approach of creating a conversational helpdesk system, which will ask relevant questions to the user, for identification of the right category and will then raise a ticket on users' behalf. We use attention based seq2seq model to assign the hierarchical categories to tickets. We use a slot filling model to help us decide what questions to ask to the user, if the top-k model predictions are not consistent. We also present a novel approach to generate training data for the slot filling model automatically based on attention in the hierarchical classification model. We demonstrate via a simulated user that the proposed approach can give us a significant gain in accuracy on ticket-data without asking too many questions to users. Finally, we also show that our seq2seq model is as versatile as other approaches on publicly available datasets, as state of the art approaches.
Top-N Recommender System via Matrix Completion

Top-N recommender systems have been investigated widely both in industry and academia. However, the recommendation quality is far from satisfactory. In this paper, we propose a simple yet promising algorithm. We fill the user-item matrix based on a low-rank assumption and simultaneously keep the original information. To do that, a nonconvex rank relaxation rather than the nuclear norm is adopted to provide a better rank approximation and an efficient optimization strategy is designed. A comprehensive set of experiments on real datasets demonstrates that our method pushes the accuracy of Top-N recommendation to a new level.
Recognition of Indian Sign Language using feature fusion

Sign Language is the most natural and expressive way for the hearing impaired. This paper presents a method for automatic recognition of two handed signs of Indian Sign Language (ISL). The method consists of three phases: Segmentation, Feature Extraction and Recognition. The segmentation is done through Otsu's algorithm. In the feature extraction phase, shape descriptors, HOG descriptors (Histogram of Oriented Gradient) and SIFT (Scale Invariant Feature Transform) feature have been fused to compute a feature vector. In the recognition phase, a multi-class Support Vector Machine (MSVM) is used for training and classifying signs of ISL. The experimental results provide evidence of the effectiveness of the proposed approach with 93% recognition rate.
Crowdsourcing and the crisis-affected community

This article reports on Mission 4636, a real-time humanitarian crowdsourcing initiative that processed 80,000 text messages (SMS) sent from within Haiti following the 2010 earthquake. It was the first time that crowdsourcing (microtasking) had been used for international relief efforts, and is the largest deployment of its kind to date. This article presents the first full report and analysis of the initiative looking at the accuracy and timeliness in creating structured data from the messages and the collaborative nature of the process. Contrary to all previous papers, studies and media reports about Mission 4636, which have typically chosen to exclude empirical analyses and the involvement of the Haitian population, it is found that the greatest volume, speed and accuracy in information processing was by Haitian nationals, the Haitian diaspora, and those working closest with them, and that no new technologies played a significant role. It is concluded that international humanitarian organizations have been wrongly credited for large-scale information processing initiatives (here and elsewhere) and that for the most part they were largely just witnesses to crisis-affected communities bootstrapping their own recovery through communications technologies. The particular focus is on the role of the diaspora, an important population that are increasingly able to contribute to response efforts thanks to their increased communication potential. It is recommended that future humanitarian deployments of crowdsourcing focus on information processing within the populations they serve, engaging those with crucial local knowledge wherever they happen to be in the world.
BLEU Evaluation of Machine-Translated English-Croatian Legislation

This paper presents work on the evaluation of online available machine translation (MT) service, i.e. Google Translate, for English-Croatian language pair in the domain of legislation. The total set of 200 sentences, for which three reference translations are provided, is divided into short and long sentences. Human evaluation is performed by native speakers, using the criteria of adequacy and fluency. For measuring the reliability of agreement among raters, Fleiss' kappa metric is used. Human evaluation is enriched by error analysis, in order to examine the influence of error types on fluency and adequacy, and to use it in further research. Translation errors are divided into several categories: non-translated words, word omissions, unnecessarily translated words, morphological errors, lexical errors, syntactic errors and incorrect punctuation. The automatic evaluation metric BLEU is calculated with regard to a single and multiple reference translations. System level Pearson’s correlation between BLEU scores based on a single and multiple reference translations is given, as well as correlation between short and long sentences BLEU scores, and correlation between the criteria of fluency and adequacy and each error category.
Word2vec applied to recommendation: hyperparameters matter

Skip-gram with negative sampling, a popular variant of Word2vec originally designed and tuned to create word embeddings for Natural Language Processing, has been used to create item embeddings with successful applications in recommendation. While these fields do not share the same type of data, neither evaluate on the same tasks, recommendation applications tend to use the same already tuned hyperparameters values, even if optimal hyperparameters values are often known to be data and task dependent. We thus investigate the marginal importance of each hyperparameter in a recommendation setting through large hyperparameter grid searches on various datasets. Results reveal that optimizing neglected hyperparameters, namely negative sampling distribution, number of epochs, subsampling parameter and window-size, significantly improves performance on a recommendation task, and can increase it by an order of magnitude. Importantly, we find that optimal hyper-parameters configurations for Natural Language Processing tasks and Recommendation tasks are noticeably different.
HyperMinHash: Jaccard index sketching in LogLog space

In this extended abstract, we describe and analyse a streaming probabilistic sketch, HYPERMINHASH, to estimate the Jaccard index (or Jaccard similarity coefficient) over two sets A and B. HyperMinHash can be thought of as a compression of standard MinHash by building off of a HyperLogLog count-distinct sketch. Given Jaccard index δ , using k buckets of sizeO(log(l) + log log(|A∪ B |)) (in practice, typically 2 bytes) per set, HyperMinHash streams over A and B and generates an estimate of the Jaccard index δ with error O(1/l + √ k/δ ). This improves on the best previously known sketch, MinHash, which requires the same number of storage units (buckets), but using O(log(|A ∪ B |)) bit per bucket. For instance, our new algorithm allows estimating Jaccard indices of 0.01 for set cardinalities on the order of 1019 with relative error of around 5% using 64KiB of memory; the previous state-of-the-art MinHash can only estimate Jaccard indices for cardinalities of 1010 with the same memory consumption. Alternately, one can think of HyperMinHash as an augmentation of b-bit MinHash that enables streaming updates, unions, and cardinality estimation (and thus intersection cardinality by way of Jaccard), while using log log extra bits. CCS CONCEPTS • Theory of computation→ Sketching and sampling;
Compaction Management in Distributed Key-Value Datastores

Compactions are a vital maintenance mechanism used by datastores based on the log-structured merge-tree to counter the continuous buildup of data files under update-intensive workloads. While compactions help keep read latencies in check over the long run, this comes at the cost of significantly degraded read performance over the course of the compaction itself. In this paper, we offer an in-depth analysis of compaction-related performance overheads and propose techniques for their mitigation. We offload large, expensive compactions to a dedicated compaction server to allow the datastore server to better utilize its resources towards serving the actual workload. Moreover, since the newly compacted data is already cached in the compaction server’s main memory, we fetch this data over the network directly into the datastore server’s local cache, thereby avoiding the performance penalty of reading it back from the filesystem. In fact, pre-fetching the compacted data from the remote cache prior to switching the workload over to it can eliminate local cache misses altogether. Therefore, we implement a smarter warmup algorithm that ensures that all incoming read requests are served from the datastore server’s local cache even as it is warming up. We have integrated our solution into HBase, and using the YCSB and TPC-C benchmarks, we show that our approach significantly mitigates compaction-related performance problems. We also demonstrate the scalability of our solution by distributing compactions across multiple compaction servers.
Encoder Semantic Decoder Input Image MultiTask Loss Instance Decoder Depth Decoder Semantic Task Uncertainty Instance Task Uncertainty Depth Task Uncertainty Σ

Numerous deep learning applications benefit from multi-task learning with multiple regression and classification objectives. In this paper we make the observation that the performance of such systems is strongly dependent on the relative weighting between each task’s loss. Tuning these weights by hand is a difficult and expensive process, making multi-task learning prohibitive in practice. We propose a principled approach to multi-task deep learning which weighs multiple loss functions by considering the homoscedastic uncertainty of each task. This allows us to simultaneously learn various quantities with different units or scales in both classification and regression settings. We demonstrate our model learning per-pixel depth regression, semantic and instance segmentation from a monocular input image. Perhaps surprisingly, we show our model can learn multi-task weightings and outperform separate models trained individually on each task.
Aspect extraction for opinion mining with a deep convolutional neural network

In this paper, we present the first deep learning approach to aspect extraction in opinion mining. Aspect extraction is a subtask of sentiment analysis that consists in identifying opinion targets in opinionated text, i.e., in detecting the specific aspects of a product or service the opinion holder is either praising or complaining about. We used a 7-layer deep convolutional neural network to tag each word in opinionated sentences as either aspect or non-aspect word. We also developed a set of linguistic patterns for the same purpose and combined them with the neural network. The resulting ensemble classifier, coupled with a word-embedding model for sentiment analysis, allowed our approach to obtain significantly better accuracy than state-of-the-art methods.
On drone ground penetrating radar for landmine detection

Ground Penetrating Radar (GPR) system mounted on drone for landmine detection is presented. Classical methods for landmine detections are not time efficient and dangerous. With proposed solution a drone could autonomously scan desired area without contacting the investigated ground. For this purpose, a sub-nanosecond monocyclic pulse generator and Ultra-Wideband (UWB) antenna that is suitable for air coupled GPR system was developed. The preliminary laboratory experimental results show first success by object detection and are good basic for further development.
ENN: Extended Nearest Neighbor Method for Pattern Recognition [Research Frontier]

This article introduces a new supervised classification method - the extended nearest neighbor (ENN) - that predicts input patterns according to the maximum gain of intra-class coherence. Unlike the classic k-nearest neighbor (KNN) method, in which only the nearest neighbors of a test sample are used to estimate a group membership, the ENN method makes a prediction in a "two-way communication" style: it considers not only who are the nearest neighbors of the test sample, but also who consider the test sample as their nearest neighbors. By exploiting the generalized class-wise statistics from all training data by iteratively assuming all the possible class memberships of a test sample, the ENN is able to learn from the global distribution, therefore improving pattern recognition performance and providing a powerful technique for a wide range of data analysis applications.
Low-Temperature Sintering of Nanoscale Silver Paste for Attaching Large-Area $({>}100~{\rm mm}^{2})$ Chips

A low-temperature sintering technique enabled by a nanoscale silver paste has been developed for attaching large-area (>100 mm2) semiconductor chips. This development addresses the need of power device or module manufacturers who face the challenge of replacing lead-based or lead-free solders for high-temperature applications. The solder-reflow technique for attaching large chips in power electronics poses serious concern on reliability at higher junction temperatures above 125°C. Unlike the soldering process that relies on melting and solidification of solder alloys, the low-temperature sintering technique forms the joints by solid-state atomic diffusion at processing temperatures below 275°C with the sintered joints having the melting temperature of silver at 961°C. Recently, we showed that a nanoscale silver paste could be used to bond small chips at temperatures similar to soldering temperatures without any externally applied pressure. In this paper, we extend the use of the nanomaterial to attach large chips by introducing a low pressure up to 5 MPa during the densification stage. Attachment of large chips to substrates with silver, gold, and copper metallization is demonstrated. Analyses of the sintered joints by scanning acoustic imaging and electron microscopy showed that the attachment layer had a uniform microstructure with micrometer-sized porosity with the potential for high reliability under high-temperature applications.