Miracle-dz/UniFAR-FTUs huggingface dataset:
An MTJ-based Flip-Flop Circuit to Improve Robustness in Store/Restore Operations

概要 MTJ(磁気トンネル接合)を使った従来の不揮 発性フリップフロップ(NVFF)回路における、 ストア時の動作不良について明らかにし、ロバス ト性を高めた省電力 NVFF を提案する。 1. はじめに センサーノードやウェアラブル端末向けデバ イスの省電力技術として、NVFF とパワーゲーテ ィングを組み合せる技術が報告されている[1]。 NVFF の回路方式として、通常動作時にデータを 常に MTJ に書込む方式[2]に比べ、図 1 に示す回 路(Pseudo-Spin-MOSFET (PSM) NVFF)[3]は、 電源遮断の直前だけ MTJ に書込むため遅延時間 や消費電力の増大を抑えられる。 一方、プロセス ばらつきの下での、MTJ へのストア動作の安定 性や電力増大については議論がなされていない。 本稿ではこの議論を行った上で、ロバスト性を高 めた低電力 NVFF 回路を提案し評価結果を示す。 2. ストア/リストア動作のロバスト性 PSM-NVFF 回路は、ストア時には SR信号を 0 にした上で CTRLを 1にし、例えばスレーブラッ チ内の記憶ノード N2 が Low なら、CTRL から N2 に電流が流れ MTJ2 は高抵抗状態にセットさ れる。 続いて CTRLを 0 にすると MTJ1は低抵抗 状態にセットされ、その後電源遮断を行う(nMOS パワースイッチ(PS)をオフ)。 リストア時に SRを 0、CTRLを1にしてPSをオンすると、MTJ1, MTJ2 の抵抗差および NAND-インバータループの増 幅作用により N1, N2がそれぞれ High, Lowに復 帰する。 このとき、高抵抗状態の MTJ 側(MTJ2) の N4は N3より電圧が低くなり pMOS(TR2)のソ ース電圧が下がってTR2のオン抵抗を増大させ、 N2 の電圧はさらに下がるため、ロバストなリス トア動作が実現できる。 これに対し、ストア動作 では、スレーブラッチのトランジスタを小さくす ると、ストア動作中にスレーブラッチの記憶デー タが反転してしまう(ラッチ破壊)不良があるこ とが判明した。 これを防ぐには MTJ1, 2 の抵抗、 TR1, 2 およびスレーブラッチ内トランジスタの オン抵抗の間に以下の式が成り立つ必要がある。 (1) (2) (3) (4) ここで、TR10, 11 はスレーブラッチ内のインバー タの nMOS と pMOS、TR20, 21, 22 は NAND の nMOS(2 個)と pMOS である。 プロセスばらつき の下で上記の条件を満たすには、スレーブラッチ のトランジスタサイズを大きくする必要がある が、面積増大と電力増加につながる。 3. 提案回路と評価結果 これを解決するため、図 2 に示す回路(Split Store/Restore (SSR) NVFF)を提案する。 ストアと リストアで、MTJ とスレーブラッチをつなぐ経 路を分け、ストア動作では新たに付加したインバ ータと nMOS トランジスタ(TR2, TR3)で MTJ に書込む。 これにより、スレーブラッチのトラン ジスタを大きくせずに、安定したストア動作が確 保でき、リストア時の安定性も維持できる。 65nm プロセスの MTJ を想定して、MOS トランジスタ のランダムばらつきに対する SPICE シミュレー ションを行った。 PSM-NVFF では、標準サイズ の 7倍のトランジスタでストア動作不良が 0にな るのに対し、SSR-NVFF では標準サイズでも動作 不良が生じなかった(図 3)。 ストア動作不良が 0 の条件の下で両者を回路設計し、レイアウトした ところ、SSR-NVFF は PSM-NVFF に比べ 55%の 面積で実現できることが分かった。 さらに、スト ア時(MTJの H→L遷移時)の書込み電流が 52% に減少し(図 4)、C-Q delay が 18%、ストア時の消 費エネルギーが 32%削減することが判明した。 参考文献 [1] N. Sakimura, et al, ISSCC2014. [2] Zhao, et al, IEEE Int. conf. on Nanotech. , 2007. [3] S. Yamamoto, Y. Shuto, S. Sugahara, Electronics Letters, Vol. 47, No. 18, Sept. 2011. 第63回応用物理学会春季学術講演会 講演予稿集 (2016 東京工業大学 大岡山キャンパス) 19p-P4-5
HCI Statistics without p-values

Statistics are tools to help end users (here,researchers) accomplish their task (advance scientific knowledge). Science is a collective and cumulative enterprise, so to be qualified as usable, statistical tools should support and promote clear thinking as well as clear and truthful communication. Yet areas such as human-computer interaction (HCI) have adopted tools – i.e., p-values and statistical significance testing – that have proven to be quite poor at supporting these tasks. The use and misuse of p-values and significance testing has been severely criticized in a range of disciplines for several decades, suggesting that tools should be blamed, not end users. This article explains why it would be beneficial for HCI to switch from statistical significance testing to estimation, i.e., reporting informative charts with effect sizes and confidence intervals, and offering nuanced interpretations of our results. Advice is offered on how to communicate our empirical results in a clear, accurate, and transparent way without using any p-value.
A survey of bibliometric tools and techniques and their applications for technology forecasting

i List of Figures 5 List of Tables 6 Abbrevations 7 CHAPTER ONE INTRODUCTION 8 Background 8 Issue 9 Aims and structure 9 Bibliometrics 10 Technology forecasting 11 CHAPTER 2 LITERATURE AND SOFTWARE REVIEW 12 Literature review 12 Introduction 12 Search details 12 Technology forecasting 13 Background 13 Development 13 Bibliometric techniques 14 Background 14 Limitations of the discipline 14 Objectivity of the discipline 15 Patents and patent citations 15 Bibliometric techniques for technology forecasting 16 Background 16 Indicators 16
Automatic Headline Generation for Tamil Scripts

A headline is recognized as a condensed summary of a document. The necessity for a computerized headline generation has been on the rise because of the need to deal with a wide number of documents, which really is a tedious and time-consuming process. Rather than in place of reading every document, the headline can be utilized to determine those that contain important and relevant information. In this paper, we generate headlines for the Tamil documents. However, the Tamil language includes a different statistical structure compared to the English language and requires special treatment to generate Tamil headlines, especially if we have no dedicated technique for the Tamil language. Headline words are selected using a statistical Translation algorithm which comes under statistical approach. Along the way, headline word selection, different preprocessing steps specific to the language are considered. Grammatical headline formation using a semantic understanding of the language can also be addressed using a clustering algorithm. The generated headlines were assessed using human judgements.
Transmisi Citra Menggunakan Joint LDPC Decoding

Metode transmisi citra menggunakan teknik Joint LDPC decoding merupakan teknik untuk mengirimkan citra menggunakan teknik diversitas dan pengkodean pengkoreksi kesalahan. Pada sisi pengirim, citra dikompresi menggunakan algoritma SPIHT. Data citra terkompresi kemudian dikirimkan melalui kanal paralel. Hal ini ditujukan bila pada salah satu kanal terjadi gangguan dan data tidak bisa didekodekan, maka data masih bisa diperoleh dari kanal lainnya. Hasil simulasi menunjukkan teknik joint LDPC decoding pada transmisi citra dapat meningkatkan kualitas citra rekonstruksi yang mampu memberikan coding gain sebesar 1,3 dB untuk BER 10-4 terhadap single LDPC decoding. Penggunaan teknik joint LDPC decoding secara ratarata memberikan peningkatan PSNR sebesar 4,44 dB. Hasil tersebut menunjukkan bahwa metode yang digunakan dapat meningkatkan performansi sistem transmisi citra. Kata Kunci: Joint LDPC decoding, kompresi citra SPIHT, kode LDPC, iterative decoding
Classification Algorithms And Analyzing TheFunctionality Of Protein Families

The rapid growth of bio-sequence data has resulted in an increasing demand for reliable algorithms that group proteins in a meaningful way. Many traditional classification and clustering algorithms have been adapted or directly applied to protein sequences or structures. To capture protein functionality, new algorithms have recently been proposed specifically aiming at incorporating protein functions. In this paper, we review some of the classification and clustering algorithms for proteins. We divide algorithms into four categories based on their use of dissimilarity measure, density characteristics, computational modeling and information of evidence. The algorithm based on information of evidence analyzes the biomolecular sequences as discrete-valued n-tuples such that discrete values rather than their variables are selected as evidence for the final groupings. The advantage of this approach is that the configuration of the final clusters does not depend on a reliable distance measure, a predefined computational model of the clusters, reliability of the adaptive learning method or a measure of the density function. Finally, the methods are reviewed with respect to the quality of reflecting functionality of the protein family.
From images to point clouds : practical considerations for three-dimensional computer vision

Three-dimensional scene reconstruction has been an important area of research for many decades. It has a myriad of applications ranging from entertainment to medicine. This thesis explores the 3D reconstruction pipeline and proposes novel methods to improve many of the steps necessary to achieve a high quality reconstruction. It proposes novel methods in the areas of depth sensor calibration, simultaneous localization and mapping, depth map inpainting, point cloud simplification, and free-viewpoint rendering. Geometric camera calibration is necessary in every 3D reconstruction pipeline. This thesis focuses on the calibration of depth sensors. It presents a review of sensors models and how they can be calibrated. It then examines the case of the well-known Kinect sensor and proposes a novel calibration method using only planar targets. Reconstructing a scene using only color cameras entails di_erent challenges than when using depth sensors. Moreover, online applications require real-time response and must update the model as new frames are received. The thesis looks at these challenges and presents a novel simultaneous localization and mapping system using only color cameras. It adaptively triangulates points based on the detected baseline while still utilizing non-triangulated features for pose estimation. The thesis addresses the extrapolating missing information in depth maps. It presents three novel methods for depth map inpainting. The first utilizes random sampling to fit planes in the missing regions. The second method utilizes a 2nd-order prior aligned with intensity edges. The third method learns natural filters to apply a Markov random field on a joint intensity and depth prior. This thesis also looks at the issue of reducing the quantity of 3D information to a manageable size. It looks at how to merge depth maps from multiple views without storing redundant information. It presents a method to discard this redundant information while still maintaining the naturally variable resolution. Finally, transparency estimation is examined in the context of free-viewpoint rendering. A procedure to estimate transparency maps for the foreground layers of a multi-view scene is presented. The results obtained reinforce the need for a high accuracy 3D reconstruction pipeline including all the previously presented steps.
Analyzing Behavioral Compatibility for Web Service Choreography Using Colored Petri Nets and ASK-CTL

—Web services have become the technology of choice for Service-Oriented Computing (SOC) implementation. Their composition is a recent ﬁeld that has seen a ﬂurry of different approaches proposed towards the goal of ﬂexible distributed heterogeneous inter-operation of software systems. These systems are usually derived from higher-level models rather than be coded at low level. In practice, achieving Web service compatibility nonetheless continues to require signiﬁcant efforts for modeling at multiple abstraction levels. Existing formal approaches typically require the analysis of the global space of joint executions of interacting Web services. We propose a formal approach where Web service choreography is represented with the high-level model of Colored Petri Nets (CPNs). ASK-Computational Tree Logic (ASK-CTL) is used to describe the behavioral compatibility of these services in terms of message order properties. Then, model checking is applied for the veriﬁcation of these properties. The effectiveness of our work has been validated with the recent version of CPN Tools.
Data Mining, Machine Learning and Big Data Analytics

This paper analyses deep learning and traditional data mining and machine learning methods; compares the advantages and disadvantage of the traditional methods; introduces enterprise needs, systems and data, IT challenges, and Big Data in an extended service infrastructure. The feasibility and challenges of the applications of deep learning and traditional data mining and machine learning methods in Big Data analytics are also analyzed and presented.
Digitalizácia matematických textov

Praca sa venuje rieseniu problemu OCR projektu digitalnej matematickej knižnice DML-CZ. Specificke požiadavky vyplývajuce zo spracovania za ucelom vytvorenia digitalnej matematickej knižnice si vyžiadali zvlastny pristup. Podobne aj spracovanie matematických textov si vyžaduje specialny pristup k niektorým problemom. Cieľom prace bolo umožniť automatizovane spracovanie OCR v ramci projektu. Množstvo problemov sa podarilo uspokojivo vyriesiť, niektore zostali ciastocne otvorene do buducna. Zrejme je, že procesy zabezpecujuce OCR je nutne pri automatizovanom davkovom spracovani objemov dat urovne DML-CZ priebežne vylepsovať a prisposobovať novým podmienkam. Implementacia rieseni jednotlivých problemov je popisana podrobnejsie.
Efficient colour image compression using fusion approach

The aim of image compression endeavour is to reduce the total data required to represent the image, which, in turn, decreases the demand of transmission bandwidth and storage space. In this work, we propose an image fusion based idea that can be exploited extensively to reduce the file size of JPEG compressed image further. Before performing the JPEG compression, we compute both intensity and a subsampled colour representation of the image undergoing compression. Then, similar to the JPEG compression, discrete cosine transformation, quantisation and entropy coding processes are applied on these images and stored in a single image file container. In the decoder, these two images are reconstructed and fused to obtain the resultant decoded image. Our experiments show that the proposed method does meet the lower storage and bandwidth requirement by reducing the average bits per pixel of the encoded image than that of the JPEG compressed image.
Proceedings of the Second Workshop on Statistical Machine Translation

The ACL 2007 Workshop on Statistical Machine Translation (WMT-07) took place on Saturday, June 23 in Prague, Czech Republic, immediately preceding the annual meeting of the Association for Computational Linguistics, which was hosted by Charles University. This was the second time this workshop had been held, following the first workshop at the 2006 HLT-NAACL conference. But its ancestry can be traced back farther to the ACL 2005 Workshop on Building and Using Parallel Texts (when we started our evaluation campaign on European languages), and even the ACL 2001 Workshop on Data-Driven Machine Translation (which was the first ACL workshop mostly directed at statistical machine translation). Over the last years, interest in statistical machine translation has been risen dramatically. We received an overwhelming number of full paper submission for a one-day workshop, 38 in total. Given our limited capacity, we were only able to accept 12 full papers for oral presentation and 9 papers for poster presentation, an acceptance rate of 55%. In a second poster session, 16 additional shared task papers were presented. The workshop also featured an invited talk by Jean Senellart of SYSTRAN Language Translation Technology, Paris. Prior to the workshop, in addition to soliciting relevant papers for review and possible presentation we conducted a shared task that brought together machine translation systems for an evaluation on previously unseen data. This year's task resembled the shared tasks of previous years in many ways. Its focus was again the translation of European languages, using a relatively large training corpus. This year, we included a variety of manual evaluations of the MT systems' outputs, and a variety of automated evaluation metrics. Also, as a special challenge this year, we posed the problem of domain adaptation. The results of the shared task were announced at the workshop, and these proceedings also include an overview paper for the shared task that summarizes the results, as well as provides information about the data used and any procedures that were followed in conducting or scoring the task. In addition, there are short papers from each participating team that describe their underlying system in some detail.
Enhancement of Text Retrieval Methods by using Hybrid Approach in Data Mining

Data mining, a branch of computer science is the process of extracting patterns from large data sets by combining methods from statistics and artificial intelligence with database management. Research papers text retrieval refers to text retrieval techniques applied to research articles and literature available of the different research papers. The volume of published different research papers in different areas especially in computers science, and therefore the underlying knowledge base, is expanding at an increasing rate. By discovering predictive relationships between different pieces of extracted data, data-mining algorithms can be used to improve the accuracy of information. This paper presents a technique for using soft clustering data mining algorithm to increase the accuracy of text extraction. In this proposed work, the work is done to increase the accuracy of text extraction by using KEA with the dictionary approach and by using Artificial Bees Colony algorithm.
Reweighted L1 Minimization for Compressed Sensing

Recent work in compressed sensing theory shows that m×n independent and identically distributed sensing matrices whose entries are drawn independently from certain probability distributions guarantee exact recovery of a sparse signal with high probability even if m≪n. In particular, it is well understood that the L1 minimization algorithm is able to recover sparse signals from incomplete measurements. In this paper, we propose a novel sparse signal reconstruction method that is based on the reweighted L1 minimization via support recovery.
Parallel Algorithm for Combinatorial Optimization Problem

Combinatorial optimization problems (COP) are difficult to solve by nature. One of the reasons is because the amount of neighborhood search required to generate high quality solutions based on sequential methods is intractable. In this paper, parallel algorithm for COP such as Knapsack Problem is presented. Knapsack problem arises in different types of resource allocation problems and has many applications in real-world problems. The proposed algorithm is based on MapReduce framework where the workload for neighborhood search is distributed across available computing nodes in the cluster. The design of Map and Reduce phases is proposed based on consecutive runs of MapReduce jobs. The computational results that shows the effect of degree of parallelism on the solution quality are provided.
n-gram and decision tree based language identification for written words

As the demand for multilingual speech recognizers increases, the development of systems which combine automatic language identification, language-specific pronunciation modeling and language-independent acoustic models becomes increasingly important. When the recognition grammar is dynamic and obtained directly from written text, the language associated with each grammar item has to be identified using that text. Many methods proposed in the literature require fairly large amounts of text, which may not always be available. This paper describes a text-based language identification system developed for the identification of the language of short words, e.g., proper names. Two different approaches are compared. The n-gram method commonly used in the literature is first reviewed and further enhanced. We also propose a simple method for language identification that is based on decision trees. The methods are first evaluated in a text-based language identification task. Both methods are also tested as preprocessors for a multilingual speech recognition task, where the language of each text item has to be determined, in order to choose the correct text-to-pronunciation mapping. The experimental results show that the proposed methods perform very well, and merit further development.
Accelerating data preparation fo Big Data analytics

We are living in a big data world, where data is being generated in high volume, high velocity and high variety. Big data brings enormous values and benefits, so that data analytics has become a critically important driver of business success across all sectors. However, if the data is not analyzed fast enough, the benefits of big data will be limited or even lost. Despite the existence of many modern large-scale data analysis systems, data preparation which is the most time-consuming process in data analytics has not received sufficient attention yet. In this thesis, we study the problem of how to accelerate data preparation for big data analytics. In particular, we focus on two major data preparation steps, data loading and data cleaning. As the first contribution of this thesis, we design DiNoDB, a SQL-on-Hadoop system which achieves interactive-speed query execution without requiring data loading. Modern applications involve heavy batch processing jobs over large volume of data and at the same time require efficient ad-hoc interactive analytics on temporary data generated in batch processing jobs. Existing solutions largely ignore the synergy between these two aspects, requiring to load the entire temporary dataset to achieve interactive queries. In contrast, DiNoDB avoids the expensive data loading and transformation phase. The key innovation of DiNoDB is to piggyback on the batch processing phase the creation of metadata, that DiNoDB exploits to expedite the interactive queries. The second contribution is a distributed stream data cleaning system, called Bleach. Existing scalable data cleaning approaches rely on batch processing to improve data quality, which are very time-consuming in nature. We target at stream data cleaning in which data is cleaned incrementally in real-time. Bleach is the first qualitative stream data cleaning system, which achieves both real-time violation detection and data repair on a dirty data stream. It relies on efficient, compact and distributed data structures to maintain the necessary state to clean data, and also supports rule dynamics. We demonstrate that the two resulting systems, DiNoDB and Bleach, both of which achieve excellent performance compared to state-of-the-art approaches in our experimental evaluations, and can help data scientists significantly reduce their time spent on data preparation.
Automatic Target Classification of SAR Images Using Global Scattering Center Model

Model based target classification method has many advantages over template based ones, such as smaller database, smaller computation load for search and match, flexibility in changed operation condition, etc. This paper introduces a SAR image classification method based on global scattering center model. We propose to extract regional feature from SAR images and match it with the scattering center templates. This region-to-point match scheme is easier to implement and is more robust to non-ideal factors than the point-to-point match scheme. Experiments using data computed by a high frequency electromagnetic code verify the validity of the method.
A digital alternative to a video decoder

New video transmission systems such as the satellite D2-MAC/packet system and second-generation local area networks for video-communications will use digital coding and decoding of video signals. To develop reliable and low-cost receivers, it appears necessary to implement the main functions using CMOS technology. Therefore many analog functions must be replaced by the equivalent digital ones before implementation. Digital circuits are described for the final video postprocessing, i.e. video postfiltering and dematrixing. An NMOS digital filter for Y, CR, and CB oversampling is presented. R, G, and B components are obtained using a CMOS matrix operator, also described. These two circuits are used in a 108-Mb/s experimental video decoder.