我们在学习和记录一些自己的练手项目时，一般使用远程仓库来进行存储，以防电脑坏了，自己辛辛苦苦敲的代码都没了！小编也是在最近使用IDEA提交代码到我的Gitee仓...
2.2 智能合约漏洞一览 https://dasp.co/ 接着讲的是智能合约的漏洞。...上面的DASP网站里有去中心化应用安全问题的TOP10，其实大家在近期了解到的智能合约问题上，比较多的是溢出和逻辑设计方面的缺陷。...DASP的可重入漏洞（ Reentrancy Vulnerabilities ）也是The DAO攻击的根源，还有的就是权限控制缺失以及拒绝服务、短地址攻击。...DASP在我们慢雾区也有中文版文档，大家可以去搜索一下。由于时间关系，在这次分享中不能与大家展开说明。
本文是第一版的 DASP Top10 2018 内容，详细描述包括递归调用漏洞、权限控制漏洞、算数问题、返回值问题、拒绝服务、伪随机在内的智能合约威胁。...*参考来源：dasp，本文编译整理Elaine，转载请注明FreeBuf.COM
org.springframework.boot.SpringApplication.run(SpringApplication.java:1151) [spring-boot-1.5.1.RELEASE.jar:1.5.1.RELEASE] at com.suning.dasp.Application.main...org.springframework.boot.SpringApplication.run(SpringApplication.java:1151) [spring-boot-1.5.1.RELEASE.jar:1.5.1.RELEASE] at com.suning.dasp.Application.main
对于深度模型，有几种特定的方法：DeepLIFT、DeepSHAP、Deep Approximate Shapley Propagation（DASP）和Shapley Explanation Networks...DASP利用不确定性传播和对Shapley值的表征，对每个博弈大小的预期边际贡献进行平均，以估计基线Shapley值。...DASP是确定性的，需要O(d^2)次模型评估，其中d是特征的数量，但它也可以以较少的评估次数以随机方式使用。...就其假设而言，ShapNets是最具限制性的，因为它们无法解释其他深度模型，而DASP也是具有限制性的，因为它要求在深度模型的每一层中进行一阶和二阶中心矩匹配，这只适用于某些层。...DeepLIFT和DeepSHAP更灵活，因为它们的规则通常适用于许多层，但由于DeepLIFT的灵活性，与DASP或ShapNets相比其对基线Shapley值的估计偏差较高。
计算当月inv/(最近p个月inv的最小值)。...#计算(当月inv)-(最近p个月inv的均值)。...(最近p个月inv的最大值))/(最近p个月inv的最大值)。...str(p), auto_value #计算(当月inv-最近p个月的inv均值)/inv均值。...+ str(p), auto_value #计算(当月inv-最近p个月的inv最小值)/inv最小值。
inv_r1t1<=0; inv_r1t2<=0; inv_r1t3<=0; end else begin inv_r1t1<=inv_r1...; inv_r1t2<=inv_r1t1; inv_r1t3<=inv_r1t2; end end reg [4:0] mt1,mt2,mt3,mt4,mt5...address2<=ampout_r-31;///inv_r2 diff <= inv_r1-inv_r2;...(inv_r1-inv_r2):'b0; //assign N=sel?(ampout-N1):0; //assign diff_r = en?...(address2), .inv_r1(inv_r1), .inv_r2(inv_r2)//, //.c(c) ); /
159,232 INV_00083127_18_5000005628_104616884_730902_001_145453.jpg 2017-06-19 11:07 159,232...INV_00083127_18_5000005628_104616884_730903_001_145451.jpg 2017-06-19 13:55 313,689 INV_00160210...-06-19 14:22 307,641 INV_00470110_1_2251011001_3170241511_730220_001_145329.jpg 2017-06-19...INV_00470148_1_2251011001_3070121522_730221_001_145301.jpg 2017-06-19 14:22 301,973 INV_00470167...2、for /f "delims==" %%i in ('dir %%s\inv*.jpg /b') %%i 前不能有变量。 3、do () ，do后面必须有一个空格。
Maintain the Printer Name in SPRO->Matl Mgmt->Inv Mgmt and Phy Inv->Print Control->Gen Settings->Printer...Ensure that in SPRO->Matl Mgmt->Inv Mgmt and Phy Inv->Print Control->Gen Settings->Item Print Indicator...In SPRO->Matl Mgmt->Inv Mgmt and Phy Inv->Print Control->Gen Settings->Print Version, maintain Print...In SPRO->Matl Mgmt->Inv Mgmt and Phy Inv->Print Control->Maintain Print Indicator for GI/Transfer Posting...In SPRO->Matl Mgmt->Inv Mgmt and Phy Inv->Output Determination->Maintain Output Types, for the Output
2.定义全局变量FAC和INV，分别表示阶乘表和阶乘结果的乘法逆元表。3.编写init函数，用于初始化FAC和INV数组。...// INV[i] -> i! 的逆元！// INV[n - k - i] -> (n - k - i)!...// INV[i] -> i! 的逆元！ // INV[n - k - i] -> (n - k - i)!...// INV[i] -> i! 的逆元！ // INV[n - k - i] -> (n - k - i)!...// INV[i] -> i! 的逆元！ // INV[n - k - i] -> (n - k - i)!
= scaler.inverse_transform(inv_y_train) inv_y = inv_y_train[:, -1] print('反归一化后的预测结果：', inv_y_predict...("c:\python\predict_result.csv") # 绘图 ''' inv_y=inv_y[delay:,] #inv_y=inv_y[:-delay,] for i in range(...delay): #inv_y=np.concatenate((inv_y,inv_y[-1:,]) , axis=0) inv_y = np.concatenate((inv_y[0:1..., ],inv_y), axis=0) ''' plt.plot(inv_y, color='red', label='Original') plt.plot(inv_y_predict, color=..., inv_y_predict) # calculate RMSE 均方根误差 rmse = sqrt(mean_squared_error(inv_y, inv_y_predict)) # calculate
(), nil) if async { if callBack, ok := inv.CallBack()....(), inv.Arguments(), inv.Attachments()), callBack, response) } else { result.Err...= di.client.CallOneway(NewRequest(url.Location, url, inv.MethodName(), inv.Arguments(), inv.Attachments...} else { result.Err = di.client.Call(NewRequest(url.Location, url, inv.MethodName(), inv.Arguments...(), inv.Attachments()), response) } } if result.Err == nil { result.Rest = inv.Reply
* 处理退货和非退货项目 clear: gt_ret,gt_ret[],gt_inv,gt_inv[]....gt_inv-ebeln = gs_invoice-ebeln."采购订单 gt_inv-ebelp = gs_invoice-ebelp."...行项目 gt_inv-lfbnr = gs_invoice-lfbnr. "参考凭证号 gt_inv-lfpos = gs_invoice-lfpos."...行项目 gt_inv-lfgja = gs_invoice-lfgja."年度 gt_inv-mwskz = gs_invoice-mwskz."...税码 gt_inv-dmbtr = gs_invoice-dmbtr. "金额 gt_inv-menge = gs_invoice-menge.
(a) = (p - p / a) * inv(p % a) % p 证明： 设x = p % a,y = p / a 于是有 x + y * a = p (x + y * a) % p = 0...移项得 x % p = (-y) * a % p x * inv(a) % p = (-y) % p inv(a) = (p - y) * inv(x) % p 于是 inv(a) = (p -...p / a) * inv(p % a) % p 然后一直递归到1为止，因为1的逆元就是1 1 #include 2 typedef long long LL; 3 LL inv(...int init() 6 { 7 inv[1] = 1; 8 for(int i = 2; i < N; i ++) 9 inv[i] = (MOD - MOD.../ i) * 1ll * inv[MOD % i] % MOD; 10 } 11 int main() 12 { 13 init(); 14 }
[1] = adjm[1] / det inv_m2[2] = -adjm[2] / det inv_m2[3] = -adjm[3] / det inv_m2...[4] = adjm[4] / det return inv_m2 end end -- inverse 3 function inv3(m3) local...[1] = adjm[1] / det inv_m3[2] = -adjm[2] / det inv_m3[3] = adjm[3] / det inv_m3...[4] = -adjm[4] / det inv_m3[5] = adjm[5] / det inv_m3[6] = -adjm[6] / det inv_m3...[7] = adjm[7] / det inv_m3[8] = -adjm[8] / det inv_m3[9] = adjm[9] / det
= 1, #seq - 1 do for j = i + 1, #seq do if seq[i] > seq[j] then inv_num...= inv_num + 1 end end end return inv_num end 上面的方法虽然简明,但是时间复杂度相对较高...[1] + 1 elseif seq[i] == 2 then inv_num = inv_num + count_buffer[3] + count_buffer...,但是通用性不高(限制了排列元素种类),我们可以简单扩展一下(Lua): function inverse_number(seq) local inv_num = 0 local...= inv_num + v end end count_buffer[seq[i]] = (count_buffer[seq[
lock : previousLock; } public void intercept(Invocation inv) { Controller controller...= inv.getController(); String cacheName = buildCacheName(inv, controller); String cacheKey...(Invocation inv, Controller controller) { StringBuilder sb = new StringBuilder(inv.getActionKey...) { inv.invoke(); Redis.use().del(buildCacheName(inv)); } private String buildCacheName...(Invocation inv) { CacheName cacheName = inv.getMethod().getAnnotation(CacheName.class);
getchar();} while(c>='0'&&c<='9'){x=x*10+c-'0';c=getchar();} return x*f; } int fac[MAXN],D[MAXN],inv...[MAXN]; void Pre() { fac[0]=fac[1]=inv[0]=inv[1]=D[0]=D[2]=1; for(int i=2;i<=1000001;i++) fac...[i]=(i*fac[i-1])%mod; for(int i=2;i<=1000001;i++) inv[i]=(mod-mod/i)*inv[mod%i]%mod; for(int...i=2;i<=1000001;i++) inv[i]=(inv[i]*inv[i-1])%mod; for(int i=3;i<=1000001;i++) D[i]=((i-1)*(D[i-1]...+D[i-2]))%mod; } int Query(int N,int M) { return fac[N] %mod * inv[ N-M ] %mod * inv[ M ] %mod *
= np.array(y_train_inv).flatten() preds_inv = [inverse_one_hot(label_encoder_train,np.array([list...'].inverse_transform([preds_inv]) preds_inv = np.array(preds_inv).flatten() correct = 0...over = 0 under = 0 errors = [] for i in range(len(preds_inv)): if preds_inv[i] ==...y_train_inv[i]: correct += 1 elif (preds_inv[i]) < (y_train_inv[i]):...(((preds_inv[i]) - (y_train_inv[i]))) print("correct: {}, over {}, under {}, accuracy {}, mse {}"
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